From 0fcee70833a9d058881711543d9c04a1a60c7d02 Mon Sep 17 00:00:00 2001 From: Jake Hillion Date: Wed, 17 Dec 2025 15:25:55 +0000 Subject: [PATCH] prep repo for v1 --- .circleci/config.yml | 376 ----- .gitattributes | 2 - .github/bench.py | 401 ------ .github/bootstrap.sh | 330 ----- .github/optimize_performance.sh | 95 -- .github/workflows/bench_job.yml | 207 --- .github/workflows/benchmarks.yml | 71 - .gitignore | 175 --- .style.yapf | 19 - LICENSE | 675 --------- README.md | 285 +--- configure_mlx.sh | 43 - docs/exo-logo-black-bg.jpg | Bin 8089 -> 0 bytes docs/exo-logo-transparent-black-text.png | 3 - docs/exo-logo-transparent.png | 3 - docs/exo-rounded.png | 3 - docs/exo-screenshot.jpg | Bin 302470 -> 0 bytes examples/astra/README.md | 3 - .../astra/astra.xcodeproj/project.pbxproj | 653 --------- .../contents.xcworkspacedata | 7 - .../xcshareddata/IDEWorkspaceChecks.plist | 8 - .../xcshareddata/swiftpm/Package.resolved | 33 - .../AccentColor.colorset/Contents.json | 11 - 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CLANG=1 - fi - - # Start first instance - EXO_HOME="$(pwd)/.exo_cache_node1" DEBUG_DISCOVERY=7 DEBUG=7 exo --inference-engine <> \ - --node-id "node1" --listen-port 5678 --broadcast-port 5679 --chatgpt-api-port 8000 \ - --chatgpt-api-response-timeout 900 --disable-tui > output1.log & - PID1=$! - tail -f output1.log & - TAIL1=$! - - # Start second instance - EXO_HOME="$(pwd)/.exo_cache_node2" DEBUG_DISCOVERY=7 DEBUG=7 exo --inference-engine <> \ - --node-id "node2" --listen-port 5679 --broadcast-port 5678 --chatgpt-api-port 8001 \ - --chatgpt-api-response-timeout 900 --disable-tui > output2.log & - PID2=$! - tail -f output2.log & - TAIL2=$! - - # Remember to kill the tail processes at the end - trap 'kill $TAIL1 $TAIL2' EXIT - - # Wait for discovery - sleep 10 - - # Function to check if processes are still running - check_processes() { - if ! kill -0 $PID1 2>/dev/null; then - echo "First instance (PID $PID1) died unexpectedly. Log output:" - cat output1.log - exit 1 - fi - if ! kill -0 $PID2 2>/dev/null; then - echo "Second instance (PID $PID2) died unexpectedly. Log output:" - cat output2.log - exit 1 - fi - } - - # Check processes before proceeding - check_processes - - echo "Sending request to first instance..." - response_1=$(curl -s http://localhost:8000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "<>", - "messages": [{"role": "user", "content": "<>"}], - "temperature": 0.7 - }') - echo "Response 1: $response_1" - - # Check processes after first response - check_processes - - echo "Sending request to second instance..." - response_2=$(curl -s http://localhost:8001/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "<>", - "messages": [{"role": "user", "content": "<>"}], - "temperature": 0.7 - }') - echo "Response 2: $response_2" - - # Check processes after second response - check_processes - - # Stop both instances - kill $PID1 $PID2 - - echo "" - # Extract content using jq and check if it contains expected output - content1=$(echo "$response_1" | jq -r '.choices[0].message.content') - content2=$(echo "$response_2" | jq -r '.choices[0].message.content') - - if [[ "$content1" != *"<>"* ]] || [[ "$content2" != *"<>"* ]]; then - echo "Test failed: Response does not match '<>'" - echo "Response 1 content: $content1" - echo "" - echo "Response 2 content: $content2" - echo "Output of first instance:" - cat output1.log - echo "Output of second instance:" - cat output2.log - exit 1 - else - echo "Test passed: Response from both nodes matches '<>'" - fi - -jobs: - unit_test: - macos: - xcode: "16.0.0" - resource_class: m2pro.large - steps: - - checkout - - run: - name: Set up Python - command: | - brew install python@3.12 - python3.12 -m venv env - source env/bin/activate - - run: - name: Install dependencies - command: | - source env/bin/activate - pip install --upgrade pip - pip install . - - run: - name: Run tests - command: | - source env/bin/activate - # set TEMPERATURE to 0 for deterministic sampling - echo "Running inference engine tests..." - METAL_DEVICE_WRAPPER_TYPE=1 METAL_DEBUG_ERROR_MODE=0 METAL_XCODE=1 TEMPERATURE=0 python3 -m exo.inference.test_inference_engine - echo "Running tokenizer tests..." - python3 ./test/test_tokenizers.py - python3 ./test/test_model_helpers.py - - discovery_integration_test: - macos: - xcode: "16.0.0" - steps: - - checkout - - run: - name: Set up Python - command: | - brew install python@3.12 - python3.12 -m venv env - source env/bin/activate - - run: - name: Install dependencies - command: | - source env/bin/activate - pip install --upgrade pip - pip install . - - run: - name: Run discovery integration test - command: | - source env/bin/activate - DEBUG_DISCOVERY=7 DEBUG=7 exo --node-id "node1" --listen-port 5678 --broadcast-port 5679 --chatgpt-api-port 8000 --disable-tui > output1.log 2>&1 & - PID1=$! - DEBUG_DISCOVERY=7 DEBUG=7 exo --node-id "node2" --listen-port 5679 --broadcast-port 5678 --chatgpt-api-port 8001 --disable-tui > output2.log 2>&1 & - PID2=$! - sleep 10 - kill $PID1 $PID2 - if grep -q "Peer statuses: {\\'node2\\': \\'is_connected=True, health_check=True" output1.log && ! grep -q "Failed to connect peers:" output1.log && grep -q "Peer statuses: {\\'node1\\': \\'is_connected=True, health_check=True" output2.log && ! grep -q "Failed to connect peers:" output2.log; then - echo "Test passed: Both instances discovered each other" - exit 0 - else - echo "Test failed: Devices did not discover each other" - echo "Output of first instance:" - cat output1.log - echo "Output of second instance:" - cat output2.log - exit 1 - fi - - chatgpt_api_integration_test_mlx: - macos: - xcode: "16.0.0" - resource_class: m2pro.large - steps: - - checkout - - run: - name: Set up Python - command: | - brew install python@3.12 - python3.12 -m venv env - source env/bin/activate - - run: - name: Install dependencies - command: | - source env/bin/activate - pip install --upgrade pip - pip install . - - run_chatgpt_api_test: - inference_engine: mlx - model_id: llama-3.2-1b - prompt: "Keep responses concise. Who was the king of pop?" - expected_output: "Michael Jackson" - - chatgpt_api_integration_test_dummy: - macos: - xcode: "16.0.0" - resource_class: m2pro.large - steps: - - checkout - - run: - name: Set up Python - command: | - brew install python@3.12 - python3.12 -m venv env - source env/bin/activate - - run: - name: Install dependencies - command: | - source env/bin/activate - pip install --upgrade pip - pip install . - - run_chatgpt_api_test: - inference_engine: dummy - model_id: dummy - prompt: "Dummy prompt." - expected_output: "dummy" - - chatgpt_api_integration_test_tinygrad: - macos: - xcode: "16.0.0" - resource_class: m2pro.large - steps: - - checkout - - run: - name: Set up Python - command: | - brew install python@3.12 - python3.12 -m venv env - source env/bin/activate - - run: - name: Install dependencies - command: | - source env/bin/activate - pip install --upgrade pip - pip install . - - run_chatgpt_api_test: - inference_engine: tinygrad - model_id: llama-3.2-1b - prompt: "Keep responses concise. Who was the king of pop?" - expected_output: "Michael Jackson" - - chatgpt_api_integration_test_tinygrad_linux: - machine: - image: ubuntu-2204:current - resource_class: xlarge - steps: - - checkout - - run: - name: Set up Python - command: | - export DEBIAN_FRONTEND=noninteractive - export DEBCONF_NONINTERACTIVE_SEEN=true - sudo apt-get update - sudo add-apt-repository -y ppa:deadsnakes/ppa - sudo apt-get update - sudo apt-get install -y python3.12 python3.12-venv clang - python3.12 -m venv env - source env/bin/activate - - run: - name: Install dependencies - command: | - source env/bin/activate - pip install --upgrade pip - pip install . - - run_chatgpt_api_test: - inference_engine: tinygrad - model_id: llama-3.2-1b - prompt: "Keep responses concise. Who was the king of pop?" - expected_output: "Michael Jackson" - - measure_pip_sizes: - macos: - xcode: "16.0.0" - steps: - - checkout - - run: - name: Set up Python - command: | - brew install python@3.12 - python3.12 -m venv env - source env/bin/activate - - run: - name: Install dependencies and measure sizes - command: | - source env/bin/activate - pip install --upgrade pip - pip install . - python ./extra/pipsize.py --json ./pipsize.json - - store_artifacts: - path: ./pipsize.json - destination: pip-sizes.json - - check_line_count: - docker: - - image: cimg/python:3.10 - steps: - - checkout - - - run: - name: Setup git for PR comparison - command: | - if [[ -n "$CIRCLE_PULL_REQUEST" ]]; then - PR_NUMBER=$(echo $CIRCLE_PULL_REQUEST | rev | cut -d'/' -f1 | rev) - BASE_BRANCH=$(curl -s -H "Circle-Token: $CIRCLE_TOKEN" \ - "https://circleci.com/api/v2/project/github/$CIRCLE_PROJECT_USERNAME/$CIRCLE_PROJECT_REPONAME/pipeline/$CIRCLE_WORKFLOW_ID" \ - | jq -r '.target_branch') - - git clone -b $BASE_BRANCH --single-branch \ - https://github.com/$CIRCLE_PROJECT_USERNAME/$CIRCLE_PROJECT_REPONAME.git \ - base_branch - fi - - - run: - name: Install dependencies - command: | - python -m pip install --upgrade pip - pip install tabulate - - - run: - name: Run line count check - command: | - if [[ -n "$CIRCLE_PULL_REQUEST" ]]; then - python extra/line_counter.py base_branch . - else - python extra/line_counter.py . - fi - - - store_artifacts: - path: line-count-snapshot.json - destination: line-count-snapshot.json - - - store_artifacts: - path: line-count-diff.json - destination: line-count-diff.json - - - run: - name: Create test results directory - command: | - mkdir -p test-results/line-count - cp line-count-*.json test-results/line-count/ - - - store_test_results: - path: test-results - -workflows: - version: 2 - build_and_test: - jobs: - - check_line_count: - filters: - branches: - only: /.*/ - tags: - only: /.*/ - - unit_test - - discovery_integration_test - - chatgpt_api_integration_test_mlx - - chatgpt_api_integration_test_tinygrad - - chatgpt_api_integration_test_tinygrad_linux - - chatgpt_api_integration_test_dummy - - measure_pip_sizes diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index ab26661d..00000000 --- a/.gitattributes +++ /dev/null @@ -1,2 +0,0 @@ -*.mp3 filter=lfs diff=lfs merge=lfs -text -*.png filter=lfs diff=lfs merge=lfs -text diff --git a/.github/bench.py b/.github/bench.py deleted file mode 100644 index 9bc52e89..00000000 --- a/.github/bench.py +++ /dev/null @@ -1,401 +0,0 @@ -import aiohttp -import asyncio -import time -import json -import os -import boto3 -from typing import Dict, Any -from datetime import datetime -import subprocess -import psutil -import platform -from pathlib import Path - - -def check_system_state(): - print("\n=== System State Check ===", flush=True) - - # Add macOS-specific checks - try: - # Check powermetrics with sudo - try: - power_metrics = subprocess.run( - ['sudo', 'powermetrics', '-n', '1', '-i', '1000', '--samplers', 'cpu_power'], - capture_output=True, text=True - ) - print("\nPower Metrics:", power_metrics.stdout, flush=True) - except Exception as e: - print(f"Error getting power metrics: {e}", flush=True) - - # Check thermal state - thermal_state = subprocess.run(['pmset', '-g', 'therm'], capture_output=True, text=True) - print("\nThermal State:", thermal_state.stdout, flush=True) - - # Check if running under Rosetta - arch = subprocess.run(['arch'], capture_output=True, text=True) - print("\nArchitecture:", arch.stdout, flush=True) - - # Check MLX compilation mode - only if mlx is available - try: - import mlx.core as mx - if hasattr(mx, 'build_info'): - print("\nMLX Build Info:", mx.build_info(), flush=True) - else: - print("\nMLX Build Info: Not available in this version", flush=True) - except ImportError: - print("\nMLX: Not installed", flush=True) - except Exception as e: - print(f"\nError checking MLX: {e}", flush=True) - - except Exception as e: - print(f"Error in macOS checks: {e}", flush=True) - - # CPU Info - print("\nCPU Information:", flush=True) - try: - if platform.system() == 'Darwin' and platform.processor() == 'arm': - # Use sysctl for Apple Silicon Macs - cpu_info = subprocess.run(['sysctl', 'machdep.cpu'], capture_output=True, text=True) - if cpu_info.returncode == 0: - print(f"CPU Info (Apple Silicon):", cpu_info.stdout, flush=True) - - # Parse powermetrics output for clearer CPU frequency display - try: - power_metrics = subprocess.run( - ['sudo', 'powermetrics', '-n', '1', '-i', '100', '--samplers', 'cpu_power'], - capture_output=True, text=True - ) - if power_metrics.returncode == 0: - output = power_metrics.stdout - print("\nDetailed CPU Frequency Information:") - - # Extract cluster frequencies and max frequencies - current_cluster = None - max_freqs = {'E': 0, 'P0': 0, 'P1': 0} - - for line in output.split('\n'): - # Track which cluster we're processing - if "E-Cluster" in line: - current_cluster = 'E' - elif "P0-Cluster" in line: - current_cluster = 'P0' - elif "P1-Cluster" in line: - current_cluster = 'P1' - - # Get current frequencies - if "HW active frequency:" in line: - freq = line.split(':')[1].strip() - if freq != "0 MHz": - print(f"Current {current_cluster}-Cluster Frequency: {freq}") - - # Get max frequencies from residency lines - if current_cluster and "active residency:" in line and "MHz:" in line: - try: - # Extract all frequency values - freqs = [] - parts = line.split('MHz:')[:-1] # Skip last part as it's not a frequency - for part in parts: - freq_str = part.split()[-1] - try: - freq = float(freq_str) - freqs.append(freq) - except ValueError: - continue - if freqs: - max_freqs[current_cluster] = max(max_freqs[current_cluster], max(freqs)) - except Exception: - continue - - # Print max frequencies - print("\nMaximum Available Frequencies:") - for cluster, max_freq in max_freqs.items(): - if max_freq > 0: - print(f"{cluster}-Cluster Max: {max_freq:.0f} MHz") - - except Exception as e: - print(f"Error parsing powermetrics: {e}", flush=True) - else: - # Use psutil for other systems - cpu_freq = psutil.cpu_freq() - print(f"CPU Frequency - Current: {cpu_freq.current:.2f}MHz, Min: {cpu_freq.min:.2f}MHz, Max: {cpu_freq.max:.2f}MHz", flush=True) - - print(f"\nCPU Usage per Core: {psutil.cpu_percent(percpu=True)}%", flush=True) - - # Check if running in low power mode - power_mode = subprocess.run(['pmset', '-g'], capture_output=True, text=True) - print("\nPower Settings:", power_mode.stdout, flush=True) - except Exception as e: - print(f"Error getting CPU info: {e}", flush=True) - - # Memory Info - print("\nMemory Information:", flush=True) - try: - mem = psutil.virtual_memory() - print(f"Total: {mem.total/1024/1024/1024:.2f}GB", flush=True) - print(f"Available: {mem.available/1024/1024/1024:.2f}GB", flush=True) - print(f"Used: {mem.used/1024/1024/1024:.2f}GB ({mem.percent}%)", flush=True) - - # Check swap - swap = psutil.swap_memory() - print(f"Swap Used: {swap.used/1024/1024/1024:.2f}GB of {swap.total/1024/1024/1024:.2f}GB", flush=True) - except Exception as e: - print(f"Error getting memory info: {e}", flush=True) - - # GPU Info - print("\nGPU Information:", flush=True) - try: - # Check MLX GPU settings - print("MLX Environment Variables:", flush=True) - mlx_vars = {k: v for k, v in os.environ.items() if k.startswith('MLX')} - print(json.dumps(mlx_vars, indent=2), flush=True) - - # Check Metal GPU memory allocation - gpu_mem = subprocess.run(['sysctl', 'iogpu'], capture_output=True, text=True) - print("GPU Memory Settings:", gpu_mem.stdout, flush=True) - except Exception as e: - print(f"Error getting GPU info: {e}", flush=True) - - # Process Priority - print("\nProcess Priority Information:", flush=True) - try: - current_process = psutil.Process() - print(f"Process Nice Value: {current_process.nice()}", flush=True) - # Only try to get ionice if the platform supports it - if hasattr(current_process, 'ionice'): - print(f"Process IO Nice Value: {current_process.ionice()}", flush=True) - except Exception as e: - print(f"Error getting process priority info: {e}", flush=True) - - # System Load - print("\nSystem Load:", flush=True) - try: - load_avg = psutil.getloadavg() - print(f"Load Average: {load_avg}", flush=True) - - # Get top processes by CPU and Memory - print("\nTop Processes by CPU Usage:", flush=True) - processes = [] - for proc in psutil.process_iter(['pid', 'name', 'cpu_percent', 'memory_percent']): - try: - pinfo = proc.info - if pinfo['cpu_percent'] is not None and pinfo['memory_percent'] is not None: - processes.append(pinfo) - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue - - # Sort and display top 5 CPU-consuming processes - sorted_by_cpu = sorted(processes, key=lambda x: x['cpu_percent'] or 0, reverse=True)[:5] - for proc in sorted_by_cpu: - print(f"PID: {proc['pid']}, Name: {proc['name']}, CPU: {proc['cpu_percent']}%, Memory: {proc['memory_percent']:.1f}%") - except Exception as e: - print(f"Error getting system load info: {e}", flush=True) - - print("\n=== End System State Check ===\n", flush=True) - - -def check_gpu_access(): - try: - # Check if MLX can see the GPU - import mlx.core as mx - print("MLX device info:", mx.default_device()) - - # Check Metal device availability - result = subprocess.run(['system_profiler', 'SPDisplaysDataType'], capture_output=True, text=True) - print("GPU Info:", result.stdout) - except Exception as e: - print(f"Failed to check GPU access: {e}") - - -async def measure_performance(api_endpoint: str, prompt: str, model: str) -> Dict[str, Any]: - """ - Measures the performance of an API endpoint by sending a prompt and recording metrics. - - Args: - api_endpoint (str): The API endpoint URL. - prompt (str): The prompt to send to the API. - - Returns: - Dict[str, Any]: A dictionary containing performance metrics or error information. - """ - - results = { - 'model': model, - 'run_id': os.environ.get('GITHUB_RUN_ID', 'unknown'), - 'branch': os.environ.get('GITHUB_REF_NAME', 'unknown'), - 'commit': os.environ.get('GITHUB_SHA', 'unknown'), - 'configuration': json.loads(os.environ.get('HARDWARE_CONFIG', '{}')) - } - - # Get token count - session = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=600, connect=10, sock_read=600, sock_connect=10)) - try: - response = await session.post( - "http://localhost:52415/v1/chat/token/encode", - json={ - "model": model, - "messages": [{"role": "user", "content": prompt}] - } - ) - response.raise_for_status() - token_data = await response.json() - results['prompt_len'] = token_data['num_tokens'] - except Exception as e: - await session.close() - raise RuntimeError(f"Failed to get token count: {str(e)}") - - # Measure completion performance - try: - start_time = time.time() - response = await session.post( - api_endpoint, - json={ - "model": model, - "messages": [{"role": "user", "content": prompt}], - "temperature": 0, - "stream": True - } - ) - response.raise_for_status() - - first_token_time = None - total_tokens = 0 - - async for line in response.content.iter_chunks(): - line = line[0].decode('utf-8').strip() - if not line.startswith('data: '): - continue - - data = json.loads(line[6:]) # Skip 'data: ' prefix - if content := data.get('choices', [{}])[0].get('delta', {}).get('content'): - print(f"Received content: {content}", flush=True) - if first_token_time is None: - first_token_time = time.time() - ttft = first_token_time - start_time - results.update({ - 'ttft': ttft, - 'prompt_tps': results['prompt_len'] / ttft - }) - total_tokens += 1 - - total_time = time.time() - start_time - results.update({ - 'generation_tps': total_tokens / total_time, - 'response_len': total_tokens, - 'total_time': total_time - }) - - except Exception as e: - raise RuntimeError(f"Performance measurement failed: {str(e)}") - finally: - await session.close() - - return results - - -async def main() -> None: - api_endpoint = "http://localhost:52415/v1/chat/completions" - - # Define prompts - prompt_warmup = "what is the capital of France?" - prompt_essay = "write an essay about cats" - - model = os.environ.get('model', 'llama-3.2-1b') - # Warmup request - print("\nPerforming warmup request...", flush=True) - try: - warmup_results = await measure_performance(api_endpoint, prompt_warmup, model) - print("Warmup completed successfully", flush=True) - except Exception as e: - print(f"Warmup request failed: {e}", flush=True) - - # Measure performance for the essay prompt - print("\nMeasuring performance for the essay prompt...", flush=True) - results = await measure_performance(api_endpoint, prompt_essay, model) - - try: - s3_client = boto3.client( - 's3', - aws_access_key_id=os.environ.get('aws_access_key_id'), - aws_secret_access_key=os.environ.get('aws_secret_key') - ) - job_name = os.environ.get('GITHUB_JOB') - - # Create S3 key with timestamp and commit info - now = datetime.utcnow() - timestamp = now.strftime('%H-%M-%S') - commit_sha = os.environ.get('GITHUB_SHA', 'unknown')[:7] - s3_key = f"{job_name}/{model}/{now.year}/{now.month}/{now.day}/{timestamp}_{commit_sha}.json" - - # Upload to S3 - s3_client.put_object( - Bucket='exo-benchmarks', - Key=s3_key, - Body=json.dumps(results), - ContentType='application/json' - ) - print(f"Performance metrics uploaded to S3: s3://exo-benchmarks/{s3_key}", flush=True) - except Exception as e: - print(f"Failed to upload metrics to S3: {e}", flush=True) - - # Optionally print the metrics for visibility - print("Performance metrics:", flush=True) - print(json.dumps(results, indent=4), flush=True) - - -def optimize_system_performance(): - """Set optimal system performance settings before running benchmark.""" - try: - # Try to set high performance power mode - subprocess.run(['sudo', 'pmset', '-a', 'powermode', '2'], check=False) - - # Ensure MLX uses performance cores and GPU - os.environ['MLX_FORCE_P_CORES'] = '1' - os.environ['MLX_METAL_PREWARM'] = '1' - os.environ['MLX_USE_GPU'] = '1' - - # Set process priority - current_process = psutil.Process() - try: - # Set highest priority - subprocess.run(['sudo', 'renice', '-n', '-20', '-p', str(current_process.pid)], check=False) - - # Print current process state - print("\nProcess State Before Benchmark:", flush=True) - proc_info = subprocess.run( - ['ps', '-o', 'pid,ppid,user,%cpu,%mem,nice,stat,pri,command', '-p', str(current_process.pid)], - capture_output=True, text=True - ) - print(proc_info.stdout, flush=True) - - # Verify power mode - power_info = subprocess.run(['pmset', '-g'], capture_output=True, text=True) - if 'powermode 0' in power_info.stdout: - print("\nWarning: System still in normal power mode. Trying to set high performance mode again...", flush=True) - subprocess.run(['sudo', 'pmset', '-a', 'powermode', '2'], check=False) - - except Exception as e: - print(f"Warning: Could not set process priority: {e}", flush=True) - - except Exception as e: - print(f"Warning: Could not optimize system performance: {e}", flush=True) - - # Print optimization status - print("\nOptimization Settings:", flush=True) - print("MLX Environment Variables:", flush=True) - for var in ['MLX_FORCE_P_CORES', 'MLX_METAL_PREWARM', 'MLX_USE_GPU']: - print(f"{var}: {os.environ.get(var, 'Not set')}", flush=True) - - try: - nice_value = psutil.Process().nice() - print(f"Process Nice Value: {nice_value}", flush=True) - if nice_value != -20: - print("Warning: Process not running at highest priority", flush=True) - except Exception: - pass - - -if __name__ == "__main__": - check_system_state() - check_gpu_access() - optimize_system_performance() - asyncio.run(main()) diff --git a/.github/bootstrap.sh b/.github/bootstrap.sh deleted file mode 100755 index 03b13079..00000000 --- a/.github/bootstrap.sh +++ /dev/null @@ -1,330 +0,0 @@ -#!/bin/bash -set -e - -command_exists() { - command -v "$1" >/dev/null 2>&1 -} - -log() { - echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" -} - -if [ "$EUID" -eq 0 ]; then - log "Please do not run as root. Run as regular user with sudo access." - exit 1 -fi - -# Check for required arguments -if [ -z "$1" ]; then - log "Error: Runner token is required" - log "Usage: $0 [tailscale-auth-key]" - exit 1 -fi - -RUNNER_TOKEN=$1 -TAILSCALE_AUTH_KEY=$2 -REPO="exo-explore/exo" - -# Add sudoers configuration -log "Configuring sudo access..." -SUDOERS_CONTENT="$(whoami) ALL=(ALL) NOPASSWD: ALL" -echo "$SUDOERS_CONTENT" | sudo tee /etc/sudoers.d/github-runner > /dev/null -sudo chmod 440 /etc/sudoers.d/github-runner - -log "Configuring privacy permissions..." -sudo tccutil reset All -sudo tccutil reset SystemPolicyAllFiles -sudo tccutil reset SystemPolicyNetworkVolumes - -# Configure power management for maximum performance -log "Configuring power management..." -sudo pmset -a powermode 2 # Force highest performance mode -sudo pmset -a gpuswitch 2 # Force discrete/high-performance GPU -sudo pmset -a lowpowermode 0 -sudo pmset -a lessbright 0 -sudo pmset -a disablesleep 1 -sudo pmset -a sleep 0 -sudo pmset -a hibernatemode 0 -sudo pmset -a autopoweroff 0 -sudo pmset -a standby 0 -sudo pmset -a powernap 0 - -# For Python specifically -PYTHON_PATH="/opt/homebrew/bin/python3.12" -sudo chmod 755 "$PYTHON_PATH" - -# Add to firewall -log "Configuring firewall access..." -sudo /usr/libexec/ApplicationFirewall/socketfilterfw --add "$PYTHON_PATH" -sudo /usr/libexec/ApplicationFirewall/socketfilterfw --unblock "$PYTHON_PATH" - -# Set Homebrew paths based on architecture -if [ "$(uname -p)" = "arm" ]; then - BREW_PREFIX="/opt/homebrew" -else - BREW_PREFIX="/usr/local" -fi - -# Install Homebrew if not present -if ! command_exists brew; then - log "Installing Homebrew..." - /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" - echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> ~/.zshrc - eval "$(/opt/homebrew/bin/brew shellenv)" -fi - -# Install required packages -log "Installing required packages..." -export HOMEBREW_NO_AUTO_UPDATE=1 -brew install python@3.12 coreutils - -# Optional Tailscale setup if auth key is provided -if [ -n "$TAILSCALE_AUTH_KEY" ]; then - log "Installing and configuring Tailscale..." - brew install --quiet tailscale - sudo brew services stop tailscale 2>/dev/null || true - sudo rm -f /var/db/tailscale/tailscaled.state 2>/dev/null || true - sudo brew services start tailscale - sleep 2 - sudo tailscale up --authkey=$TAILSCALE_AUTH_KEY - - # Enable SSH and Screen Sharing - log "Enabling remote access services..." - sudo launchctl load -w /System/Library/LaunchDaemons/ssh.plist - sudo /System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart \ - -activate \ - -configure -access -on \ - -configure -allowAccessFor -allUsers \ - -configure -restart -agent -privs -all - - # Create launch daemon for remote access - sudo bash -c 'cat > /Library/LaunchDaemons/com.remote.access.setup.plist' << 'EOL' - - - - - Label - com.remote.access.setup - ProgramArguments - - /bin/bash - -c - - launchctl load -w /System/Library/LaunchDaemons/ssh.plist; - /System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -activate -configure -access -on - - - RunAtLoad - - - -EOL - - sudo chmod 644 /Library/LaunchDaemons/com.remote.access.setup.plist - sudo launchctl load -w /Library/LaunchDaemons/com.remote.access.setup.plist -fi - -# Configure GitHub Actions Runner -log "Gathering system metadata..." -MACHINE_NAME=$(scutil --get ComputerName) -MACHINE_NAME="runner-$(echo -n "$MACHINE_NAME" | tr '[:upper:]' '[:lower:]' | tr -cd '[:alnum:]-')" - -# Enhanced Apple Silicon detection -MACHINE_INFO=$(system_profiler SPHardwareDataType) -CHIP_FULL=$(echo "$MACHINE_INFO" | grep "Chip" | cut -d: -f2 | xargs) -if [[ $CHIP_FULL =~ "Apple" ]]; then - CHIP_MODEL=$(echo "$CHIP_FULL" | sed 's/^Apple //' | tr -d ' ' | tr '[:lower:]' '[:upper:]') - GPU_CORES=$(ioreg -l | grep "gpu-core-count" | awk -F'= ' '{print $2}') - if [ -z "$GPU_CORES" ]; then - GPU_CORES="N/A" - fi -else - CHIP_MODEL="Intel" - GPU_CORES="N/A" -fi - -MEMORY=$(($(sysctl -n hw.memsize) / 1024 / 1024 / 1024)) - -# Set up GitHub Runner -RUNNER_DIR="$HOME/actions-runner" - -# Check if runner is already configured -if [ -f "$RUNNER_DIR/.runner" ]; then - log "Runner already configured. Stopping existing service..." - sudo launchctl unload /Library/LaunchDaemons/com.github.runner.plist 2>/dev/null || true -fi - -# Create runner directory if it doesn't exist -mkdir -p "$RUNNER_DIR" -cd "$RUNNER_DIR" - -CUSTOM_LABELS="self-hosted,macos,arm64,${CHIP_MODEL}_GPU${GPU_CORES}_${MEMORY}GB" - -# Only download and extract if not already present or if forced -if [ ! -f "$RUNNER_DIR/run.sh" ] || [ "${FORCE_SETUP:-false}" = "true" ]; then - log "Downloading GitHub Actions runner..." - RUNNER_VERSION=$(curl -s https://api.github.com/repos/actions/runner/releases/latest | grep '"tag_name":' | cut -d'"' -f4) - curl -o actions-runner.tar.gz -L "https://github.com/actions/runner/releases/download/${RUNNER_VERSION}/actions-runner-osx-arm64-${RUNNER_VERSION#v}.tar.gz" - tar xzf actions-runner.tar.gz - rm actions-runner.tar.gz -else - log "Runner already downloaded, skipping download step" -fi - -log "Configuring runner with labels: $CUSTOM_LABELS" -./config.sh --unattended \ - --url "https://github.com/${REPO}" \ - --token "${RUNNER_TOKEN}" \ - --name "${MACHINE_NAME}" \ - --labels "${CUSTOM_LABELS}" \ - --work "_work" - -# Set optimal performance settings -log "Configuring system for optimal performance..." - -# Configure CPU performance -log "Setting CPU performance controls..." -# Disable timer coalescing -sudo sysctl -w kern.timer.coalescing_enabled=0 -sudo sysctl -w kern.timer_coalesce_bg_scale=-5 -sudo sysctl -w kern.timer_resort_threshold_ns=0 -# Set minimum timer intervals -sudo sysctl -w kern.wq_max_timer_interval_usecs=1000 -sudo sysctl -w kern.timer_coalesce_bg_ns_max=1000 -# Set minimum timer coalescing for all tiers -sudo sysctl -w kern.timer_coalesce_tier0_scale=-5 -sudo sysctl -w kern.timer_coalesce_tier0_ns_max=1000 -sudo sysctl -w kern.timer_coalesce_tier1_scale=-5 -sudo sysctl -w kern.timer_coalesce_tier1_ns_max=1000 -sudo sysctl -w kern.timer_coalesce_tier2_scale=-5 -sudo sysctl -w kern.timer_coalesce_tier2_ns_max=1000 -sudo sysctl -w kern.timer_coalesce_tier3_scale=-5 -sudo sysctl -w kern.timer_coalesce_tier3_ns_max=1000 -sudo sysctl -w kern.timer_coalesce_tier4_scale=-5 -sudo sysctl -w kern.timer_coalesce_tier4_ns_max=1000 -# Disable QoS restrictions -sudo sysctl -w net.qos.policy.restricted=0 -sudo sysctl -w net.qos.policy.restrict_avapps=0 -sudo sysctl -w net.qos.policy.wifi_enabled=0 -sudo sysctl -w net.qos.policy.capable_enabled=0 -# Set scheduler parameters -sudo sysctl -w kern.sched_rt_avoid_cpu0=0 -sudo sysctl -w debug.sched=2 -sudo sysctl -w net.pktsched.netem.sched_output_ival_ms=1 - -# Clean up any existing runner services -log "Cleaning up existing runner services..." -for service in com.github.runner com.github.runner.monitor com.github.runner.cpuaffinity com.github.runner.affinity; do - sudo launchctl bootout system/$service 2>/dev/null || true - sudo rm -f /Library/LaunchDaemons/$service.plist -done - -# Create a simple runner service configuration -sudo tee /Library/LaunchDaemons/com.github.runner.plist > /dev/null << EOF - - - - - Label - com.github.runner - UserName - $(whoami) - GroupName - staff - WorkingDirectory - $RUNNER_DIR - ProgramArguments - - $RUNNER_DIR/run.sh - - RunAtLoad - - KeepAlive - - SuccessfulExit - - Crashed - - - ProcessType - Interactive - LowPriorityIO - - AbandonProcessGroup - - EnableTransactions - - ThrottleInterval - 0 - HardResourceLimits - - NumberOfFiles - 524288 - MemoryLock - -1 - - SoftResourceLimits - - NumberOfFiles - 524288 - MemoryLock - -1 - - QOSClass - User-Interactive - StandardOutPath - $RUNNER_DIR/_diag/runner.log - StandardErrorPath - $RUNNER_DIR/_diag/runner.err - EnvironmentVariables - - PATH - /usr/local/bin:/opt/homebrew/bin:/usr/bin:/bin:/usr/sbin:/sbin - - Nice - -20 - - -EOF - -# Set proper permissions for the LaunchDaemon -sudo chown root:wheel /Library/LaunchDaemons/com.github.runner.plist -sudo chmod 644 /Library/LaunchDaemons/com.github.runner.plist - -# Remove any existing service -sudo launchctl bootout system/com.github.runner 2>/dev/null || true - -# Load the new service using bootstrap -sudo launchctl bootstrap system /Library/LaunchDaemons/com.github.runner.plist - -# Add Runner.Listener permissions (after runner installation) -RUNNER_PATH="$RUNNER_DIR/bin/Runner.Listener" -sudo chmod 755 "$RUNNER_PATH" -sudo /usr/libexec/ApplicationFirewall/socketfilterfw --add "$RUNNER_PATH" -sudo /usr/libexec/ApplicationFirewall/socketfilterfw --unblock "$RUNNER_PATH" - -# Create connection info file if Tailscale is configured -if [ -n "$TAILSCALE_AUTH_KEY" ]; then - TAILSCALE_IP=$(tailscale ip) - cat > "$HOME/remote_access_info.txt" << EOL -Mac Remote Access Information -============================ -Computer Name: $MACHINE_NAME -Username: $USER -Tailscale IP: $TAILSCALE_IP - -SSH Command: ssh $USER@$TAILSCALE_IP -Screen Sharing: vnc://$TAILSCALE_IP -EOL - chmod 600 "$HOME/remote_access_info.txt" -fi - -log "Verifying runner service status..." -if sudo launchctl list | grep com.github.runner > /dev/null; then - log "GitHub Actions runner service is running successfully!" - log "Runner labels: $CUSTOM_LABELS" - [ -n "$TAILSCALE_AUTH_KEY" ] && log "Remote access details saved to: $HOME/remote_access_info.txt" -else - log "Error: Failed to start GitHub Actions runner service" - exit 1 -fi \ No newline at end of file diff --git a/.github/optimize_performance.sh b/.github/optimize_performance.sh deleted file mode 100755 index 7b5c77be..00000000 --- a/.github/optimize_performance.sh +++ /dev/null @@ -1,95 +0,0 @@ -#!/bin/bash -set -e - -# Function to log with timestamp -log() { - echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" -} - -log "Applying comprehensive performance optimizations..." - -# System-wide power management -log "Configuring power management..." -sudo pmset -a lessbright 0 -sudo pmset -a disablesleep 1 -sudo pmset -a sleep 0 -sudo pmset -a hibernatemode 0 -sudo pmset -a autopoweroff 0 -sudo pmset -a standby 0 -sudo pmset -a powernap 0 -sudo pmset -a proximitywake 0 -sudo pmset -a tcpkeepalive 1 -sudo pmset -a powermode 2 -sudo pmset -a gpuswitch 2 -sudo pmset -a displaysleep 0 -sudo pmset -a disksleep 0 - -# Memory and kernel optimizations -log "Configuring memory and kernel settings..." -sudo sysctl -w kern.memorystatus_purge_on_warning=0 -sudo sysctl -w kern.memorystatus_purge_on_critical=0 -sudo sysctl -w kern.timer.coalescing_enabled=0 - -# Metal and GPU optimizations -log "Configuring Metal and GPU settings..." -defaults write com.apple.CoreML MPSEnableGPUValidation -bool false -defaults write com.apple.CoreML MPSEnableMetalValidation -bool false -defaults write com.apple.CoreML MPSEnableGPUDebug -bool false -defaults write com.apple.Metal GPUDebug -bool false -defaults write com.apple.Metal GPUValidation -bool false -defaults write com.apple.Metal MetalValidation -bool false -defaults write com.apple.Metal MetalCaptureEnabled -bool false -defaults write com.apple.Metal MTLValidationBehavior -string "Disabled" -defaults write com.apple.Metal EnableMTLDebugLayer -bool false -defaults write com.apple.Metal MTLDebugLevel -int 0 -defaults write com.apple.Metal PreferIntegratedGPU -bool false -defaults write com.apple.Metal ForceMaximumPerformance -bool true -defaults write com.apple.Metal MTLPreferredDeviceGPUFrame -bool true - -# Create MPS cache directory with proper permissions -sudo mkdir -p /tmp/mps_cache -sudo chmod 777 /tmp/mps_cache - -# Process and resource limits -log "Configuring process limits..." -sudo launchctl limit maxfiles 524288 524288 -ulimit -n 524288 || log "Warning: Could not set file descriptor limit" -ulimit -c 0 -ulimit -l unlimited || log "Warning: Could not set memory lock limit" - -# Export performance-related environment variables -cat << 'EOF' > /tmp/performance_env.sh -# Metal optimizations -export MTL_DEBUG_LAYER=0 -export METAL_DEVICE_WRAPPER_TYPE=1 -export METAL_DEBUG_ERROR_MODE=0 -export METAL_FORCE_PERFORMANCE_MODE=1 -export METAL_DEVICE_PRIORITY=high -export METAL_MAX_COMMAND_QUEUES=1024 -export METAL_LOAD_LIMIT=0 -export METAL_VALIDATION_ENABLED=0 -export METAL_ENABLE_VALIDATION_LAYER=0 -export OBJC_DEBUG_MISSING_POOLS=NO -export MPS_CACHEDIR=/tmp/mps_cache - -# MLX optimizations -export MLX_USE_GPU=1 -export MLX_METAL_COMPILE_ASYNC=1 -export MLX_METAL_PREALLOCATE=1 -export MLX_METAL_MEMORY_GUARD=0 -export MLX_METAL_CACHE_KERNELS=1 -export MLX_PLACEMENT_POLICY=metal -export MLX_METAL_VALIDATION=0 -export MLX_METAL_DEBUG=0 -export MLX_FORCE_P_CORES=1 -export MLX_METAL_MEMORY_BUDGET=0 -export MLX_METAL_PREWARM=1 - -# Python optimizations -export PYTHONUNBUFFERED=1 -export PYTHONOPTIMIZE=2 -export PYTHONHASHSEED=0 -export PYTHONDONTWRITEBYTECODE=1 -EOF - -log "Performance optimizations completed. Environment variables written to /tmp/performance_env.sh" \ No newline at end of file diff --git a/.github/workflows/bench_job.yml b/.github/workflows/bench_job.yml deleted file mode 100644 index 5e089283..00000000 --- a/.github/workflows/bench_job.yml +++ /dev/null @@ -1,207 +0,0 @@ -# This is the reusable workflow file -name: Distributed Job Runner - -on: - workflow_call: - inputs: - config: - required: true - type: string - model: - required: true - type: string - calling_job_name: - required: true - type: string - network_interface: - required: true - type: string -jobs: - generate-matrix: - runs-on: ubuntu-latest - outputs: - matrix: ${{ steps.set-matrix.outputs.matrix }} - steps: - - id: set-matrix - env: - CONFIG: ${{ inputs.config }} - run: | - MATRIX=$(echo $CONFIG | jq -c '{cpu: [to_entries | .[] | .key as $k | range(.value) | $k]}') - echo "matrix=$MATRIX" >> $GITHUB_OUTPUT - - run-distributed-job: - needs: generate-matrix - strategy: - matrix: ${{fromJson(needs.generate-matrix.outputs.matrix)}} - runs-on: ['self-hosted', 'macOS', '${{ matrix.cpu }}'] - env: - HARDWARE_CONFIG: ${{ inputs.config }} - model: ${{ inputs.model }} - # Add performance-related environment variables - MTL_DEBUG_LAYER: 0 - METAL_VALIDATION_ENABLED: 0 - MLX_METAL_VALIDATION: 0 - MLX_METAL_DEBUG: 0 - MLX_FORCE_P_CORES: 1 - MLX_METAL_PREWARM: 1 - PYTHONOPTIMIZE: 2 - steps: - - name: Cleanup workspace - run: | - sudo rm -rf "$GITHUB_WORKSPACE" - sudo mkdir -p "$GITHUB_WORKSPACE" - sudo chown -R $(whoami):$(id -g) "$GITHUB_WORKSPACE" - - - uses: actions/checkout@v4 - - - name: Install dependencies - run: | - export PATH="/usr/local/bin:/opt/homebrew/bin:$PATH" - python3.12 -m venv .venv || { - echo "Failed to find python3.12. Checking installation locations:" - ls -l /usr/local/bin/python* /opt/homebrew/bin/python* 2>/dev/null || true - exit 1 - } - source .venv/bin/activate - pip install --upgrade pip - pip install -e . - pip install boto3==1.35.76 - - - name: Apply Performance Optimizations - run: | - # Export performance-related environment variables - cat << 'EOF' > /tmp/performance_env.sh - # MLX and Metal optimizations - export MTL_DEBUG_LAYER=0 - export METAL_VALIDATION_ENABLED=0 - export MLX_METAL_VALIDATION=0 - export MLX_METAL_DEBUG=0 - export MLX_FORCE_P_CORES=1 - export MLX_METAL_PREWARM=1 - export PYTHONOPTIMIZE=2 - EOF - - # Source the performance environment variables - source /tmp/performance_env.sh - - # MLX Memory Settings - ./configure_mlx.sh - - # Verify optimizations - echo "Verifying performance settings..." - env | grep -E "MLX_|METAL_|MTL_" - - - name: Run exo - env: - aws_access_key_id: ${{ secrets.S3_EXO_BENCHMARKS_AWS_ACCESS_KEY_ID }} - aws_secret_key: ${{ secrets.S3_EXO_BENCHMARKS_AWS_SECRET_ACCESS_KEY }} - run: | - # Source performance environment variables - source /tmp/performance_env.sh - - # Debug information - echo "Current commit SHA: $GITHUB_SHA" - git rev-parse HEAD - git status - - CALLING_JOB="${{ inputs.calling_job_name }}" - UNIQUE_JOB_ID="${CALLING_JOB}_${model}_${GITHUB_RUN_ID}" - ALL_NODE_IDS=$(for i in $(seq ${{ strategy.job-total }} -1 0); do echo -n "${UNIQUE_JOB_ID}_${i},"; done | sed 's/,$//') - MY_NODE_ID="${UNIQUE_JOB_ID}_${{ strategy.job-index }}" - - source .venv/bin/activate - export PATH="/usr/local/bin:/opt/homebrew/bin:$PATH" - - echo "=== Before starting exo ===" - ps -eo pid,ppid,user,%cpu,%mem,nice,state,pri,command | head -1 - ps -eo pid,ppid,user,%cpu,%mem,nice,state,pri,command | grep -i python - - echo "Starting exo daemon..." - - echo "Power mode settings:" - sudo pmset -g - - # Start exo with explicit process control - sudo taskpolicy -d default -g default -a -t 0 -l 0 .venv/bin/exo \ - --node-id="${MY_NODE_ID}" \ - --node-id-filter="${ALL_NODE_IDS}" \ - --interface-type-filter="${{ inputs.network_interface }}" \ - --disable-tui \ - --max-generate-tokens 250 \ - --chatgpt-api-response-timeout 900 \ - --chatgpt-api-port 52415 > output1.log 2>&1 & - PID1=$! - - echo "Exo process started with PID: $PID1" - tail -f output1.log & - TAIL1=$! - - # Give process time to start - sleep 2 - - # Set additional process priorities - sudo renice -n -20 -p $PID1 - sudo taskpolicy -t 4 -p $PID1 - - echo "=== After starting exo ===" - ps -eo pid,ppid,user,%cpu,%mem,nice,state,pri,command | head -1 - ps -eo pid,ppid,user,%cpu,%mem,nice,state,pri,command | grep $PID1 - - echo "Additional process details:" - sudo powermetrics -n 1 -i 1000 --show-process-energy | grep -A 5 $PID1 || true - - trap 'kill $TAIL1' EXIT - trap 'kill $PID1' EXIT - - echo "Waiting for all nodes to connect..." - for i in {1..20}; do - echo "Attempt $i: Checking node count..." - nodes=$(curl -s http://localhost:52415/topology | jq ".nodes | length") - echo "Current node count: $nodes" - if [ "$nodes" -eq "${{ strategy.job-total }}" ]; then - echo "All nodes connected successfully!" - break - fi - if [ $i -eq 20 ]; then - echo "ERROR: Failed to connect all nodes after 20 attempts. Expected ${{ strategy.job-total }} nodes, but got $nodes" - exit 1 - fi - sleep 5 - done - - if ! kill -0 $PID1 2>/dev/null; then - echo "ERROR: Instance (PID $PID1) died unexpectedly. Full log output:" - cat output1.log - exit 1 - fi - - if [ "${{ strategy.job-index }}" -eq "0" ]; then - sleep 10 - echo "This is the primary node (index 0). Running benchmark..." - GITHUB_JOB=$CALLING_JOB python .github/bench.py - else - echo "This is a secondary node (index ${{ strategy.job-index }}). Waiting for completion..." - sleep 10 - while true; do - echo "Checking if primary node is still running..." - nodes=$(curl -s http://localhost:52415/topology | jq ".nodes | length") - echo "Current node count: $nodes" - if [ "$nodes" -lt "${{ strategy.job-total }}" ]; then - echo "Primary node completed, exiting..." - break - fi - sleep 5 - done - fi - - - name: Check Final System State - if: always() - run: | - echo "=== Final System State ===" - sudo pmset -g - sudo powermetrics -n 1 -i 1000 --show-process-energy || true - system_profiler SPDisplaysDataType - sysctl iogpu - ps -eo pid,ppid,user,%cpu,%mem,nice,state,command | grep -i python - env | grep -E "MLX_|METAL_|MTL_" - echo "=== End Final System State ===" diff --git a/.github/workflows/benchmarks.yml b/.github/workflows/benchmarks.yml deleted file mode 100644 index 2750ac4a..00000000 --- a/.github/workflows/benchmarks.yml +++ /dev/null @@ -1,71 +0,0 @@ -name: Build and Test - -on: - push: - branches: [ '*' ] - tags: [ '*' ] - pull_request: - branches: [ '*' ] - -jobs: - single-m4-pro: - strategy: - matrix: - model: ['llama-3.2-1b', 'llama-3.2-3b', 'llama-3.1-8b'] - uses: ./.github/workflows/bench_job.yml - with: - config: '{"M4PRO_GPU16_24GB": 1}' - model: ${{ matrix.model }} - calling_job_name: 'single-m4-pro' - network_interface: 'Ethernet' - secrets: inherit - - two-m4-pro-cluster: - strategy: - matrix: - model: ['llama-3.2-1b', 'llama-3.2-3b', 'llama-3.1-8b'] - uses: ./.github/workflows/bench_job.yml - with: - config: '{"M4PRO_GPU16_24GB": 2}' - model: ${{ matrix.model }} - calling_job_name: 'two-m4-pro-cluster' - network_interface: 'Ethernet' - secrets: inherit - - # two-m4-pro-cluster-thunderbolt: - # strategy: - # matrix: - # model: ['llama-3.2-1b', 'llama-3.2-3b', 'llama-3.1-8b'] - # uses: ./.github/workflows/bench_job.yml - # with: - # config: '{"M4PRO_GPU16_24GB": 2}' - # model: ${{ matrix.model }} - # calling_job_name: 'two-m4-pro-cluster-thunderbolt' - # network_interface: 'Thunderbolt' - # secrets: inherit - - three-m4-pro-cluster: - strategy: - matrix: - model: ['llama-3.2-1b', 'llama-3.2-3b', 'llama-3.1-8b', 'llama-3.3-70b'] - fail-fast: false - uses: ./.github/workflows/bench_job.yml - with: - config: '{"M4PRO_GPU16_24GB": 3}' - model: ${{ matrix.model }} - calling_job_name: 'three-m4-pro-cluster' - network_interface: 'Ethernet' - secrets: inherit - - # test-m3-single-node: - # strategy: - # matrix: - # model: ['llama-3.2-1b'] - # fail-fast: false - # uses: ./.github/workflows/bench_job.yml - # with: - # config: '{"M3MAX_GPU40_128GB": 1}' - # model: ${{ matrix.model }} - # calling_job_name: 'test-m3-cluster' - # network_interface: 'Ethernet' - # secrets: inherit \ No newline at end of file diff --git a/.gitignore b/.gitignore deleted file mode 100644 index bc6a38d7..00000000 --- a/.gitignore +++ /dev/null @@ -1,175 +0,0 @@ -__pycache__/ -.venv* -test_weights.npz -.exo_used_ports -.exo_node_id -.idea -.DS_Store - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -/.Python -/develop-eggs/ -/dist/ -/downloads/ -/eggs/ -/.eggs/ -/lib/ -/lib64/ -/parts/ -/sdist/ -/var/ -/wheels/ -/share/python-wheels/ -/*.egg-info/ -/.installed.cfg -/*.egg -/MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints -Untitled.ipynb - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# poetry -# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. -# This is especially recommended for binary packages to ensure reproducibility, and is more -# commonly ignored for libraries. -# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock - -# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/latest/usage/project/#working-with-version-control -.pdm.toml -.pdm-python -.pdm-build/ - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm -# JetBrains specific template is maintained in a separate JetBrains.gitignore that can -# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore -# and can be added to the global gitignore or merged into this file. For a more nuclear -# option (not recommended) you can uncomment the following to ignore the entire idea folder. -#.idea/ - -**/*.xcodeproj/* -.aider* - -exo/tinychat/images/*.png diff --git a/.style.yapf b/.style.yapf deleted file mode 100644 index 7301910e..00000000 --- a/.style.yapf +++ /dev/null @@ -1,19 +0,0 @@ -[style] -based_on_style = pep8 -indent_width = 2 -column_limit = 200 -allow_split_before_dict_value = False -dedent_closing_brackets = True -split_before_first_argument = False -split_complex_comprehension = False -continuation_indent_width = 2 -indent_dictionary_value = True -allow_multiline_dictionary_keys = True -each_dict_entry_on_separate_line = False -allow_multiline_lambdas = True -blank_line_before_nested_class_or_def = False -arithmetic_precedence_indication = True -no_spaces_around_selected_binary_operators = "*,/" -coalesce_brackets = True -space_between_ending_comma_and_closing_bracket = False -split_before_expression_after_opening_paren = False \ No newline at end of file diff --git a/LICENSE b/LICENSE deleted file mode 100644 index 53d1f3d0..00000000 --- a/LICENSE +++ /dev/null @@ -1,675 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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If not, see . - -Also add information on how to contact you by electronic and paper mail. - - If the program does terminal interaction, make it output a short -notice like this when it starts in an interactive mode: - - Copyright (C) - This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. - This is free software, and you are welcome to redistribute it - under certain conditions; type `show c' for details. - -The hypothetical commands `show w' and `show c' should show the appropriate -parts of the General Public License. Of course, your program's commands -might be different; for a GUI interface, you would use an "about box". - - You should also get your employer (if you work as a programmer) or school, -if any, to sign a "copyright disclaimer" for the program, if necessary. -For more information on this, and how to apply and follow the GNU GPL, see -. - - The GNU General Public License does not permit incorporating your program -into proprietary programs. If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. - diff --git a/README.md b/README.md index 905bd88a..ef097f9b 100644 --- a/README.md +++ b/README.md @@ -15,8 +15,7 @@ exo: Run your own AI cluster at home with everyday devices. Maintained by [exo l [![GitHub Repo stars](https://img.shields.io/github/stars/exo-explore/exo)](https://github.com/exo-explore/exo/stargazers) -[![Tests](https://dl.circleci.com/status-badge/img/circleci/TrkofJDoGzdQAeL6yVHKsg/4i5hJuafuwZYZQxbRAWS71/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/circleci/TrkofJDoGzdQAeL6yVHKsg/4i5hJuafuwZYZQxbRAWS71/tree/main) -[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) +[![License: GPL v3](https://img.shields.io/badge/License-Apache2.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0.html) exo-explore%2Fexo | Trendshift @@ -26,286 +25,6 @@ exo: Run your own AI cluster at home with everyday devices. Maintained by [exo l > **EXO** > -> EXO started out of a desire to run research experiments on large language models using the hardware we already owned. -> -> What began here is becoming part of something much larger. -> -> soon™ -> -> \- The EXO Team +> Coming soon. For legacy exo, see this repo's history or [exo-explore/ex-exo](https://github.com/exo-explore/ex-exo) for a snapshot. --- - -Unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, NVIDIA, Raspberry Pi, pretty much any device! - -
-

Update: exo is hiring. See here for more details.

-
- -## Get Involved - -exo is **experimental** software. Expect bugs early on. Create issues so they can be fixed. The [exo labs](https://x.com/exolabs) team will strive to resolve issues quickly. - -We also welcome contributions from the community. We have a list of bounties in [this sheet](https://docs.google.com/spreadsheets/d/1cTCpTIp48UnnIvHeLEUNg1iMy_Q6lRybgECSFCoVJpE/edit?usp=sharing). - -## Features - -### Wide Model Support - -exo supports different models including LLaMA ([MLX](exo/inference/mlx/models/llama.py) and [tinygrad](exo/inference/tinygrad/models/llama.py)), Mistral, LlaVA, Qwen, and Deepseek. - -### Dynamic Model Partitioning - -exo [optimally splits up models](exo/topology/ring_memory_weighted_partitioning_strategy.py) based on the current network topology and device resources available. This enables you to run larger models than you would be able to on any single device. - -### Automatic Device Discovery - -exo will [automatically discover](https://github.com/exo-explore/exo/blob/945f90f676182a751d2ad7bcf20987ab7fe0181e/exo/orchestration/node.py#L154) other devices using the best method available. Zero manual configuration. - -### ChatGPT-compatible API - -exo provides a [ChatGPT-compatible API](exo/api/chatgpt_api.py) for running models. It's a [one-line change](examples/chatgpt_api.sh) in your application to run models on your own hardware using exo. - -### Device Equality - -Unlike other distributed inference frameworks, exo does not use a master-worker architecture. Instead, exo devices [connect p2p](https://github.com/exo-explore/exo/blob/945f90f676182a751d2ad7bcf20987ab7fe0181e/exo/orchestration/node.py#L161). As long as a device is connected somewhere in the network, it can be used to run models. - -Exo supports different [partitioning strategies](exo/topology/partitioning_strategy.py) to split up a model across devices. The default partitioning strategy is [ring memory weighted partitioning](exo/topology/ring_memory_weighted_partitioning_strategy.py). This runs an inference in a ring where each device runs a number of model layers proportional to the memory of the device. - -!["A screenshot of exo running 5 nodes](docs/exo-screenshot.jpg) - -## Installation - -The current recommended way to install exo is from source. - -### Prerequisites - -- Python>=3.12.0 is required because of [issues with asyncio](https://github.com/exo-explore/exo/issues/5) in previous versions. -- For Linux with NVIDIA GPU support (Linux-only, skip if not using Linux or NVIDIA): - - NVIDIA driver - verify with `nvidia-smi` - - CUDA toolkit - install from [NVIDIA CUDA guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#cuda-cross-platform-installation), verify with `nvcc --version` - - cuDNN library - download from [NVIDIA cuDNN page](https://developer.nvidia.com/cudnn-downloads), verify installation by following [these steps](https://docs.nvidia.com/deeplearning/cudnn/latest/installation/linux.html#verifying-the-install-on-linux:~:text=at%20a%20time.-,Verifying%20the%20Install%20on%20Linux,Test%20passed!,-Upgrading%20From%20Older) - -### Hardware Requirements - -- The only requirement to run exo is to have enough memory across all your devices to fit the entire model into memory. For example, if you are running llama 3.1 8B (fp16), you need 16GB of memory across all devices. Any of the following configurations would work since they each have more than 16GB of memory in total: - - 2 x 8GB M3 MacBook Airs - - 1 x 16GB NVIDIA RTX 4070 Ti Laptop - - 2 x Raspberry Pi 400 with 4GB of RAM each (running on CPU) + 1 x 8GB Mac Mini -- exo is designed to run on devices with heterogeneous capabilities. For example, you can have some devices with powerful GPUs and others with integrated GPUs or even CPUs. Adding less capable devices will slow down individual inference latency but will increase the overall throughput of the cluster. - -### From source - - -```sh -git clone https://github.com/exo-explore/exo.git -cd exo -pip install -e . -# alternatively, with venv -source install.sh -``` - - -### Troubleshooting - -- If running on Mac, MLX has an [install guide](https://ml-explore.github.io/mlx/build/html/install.html) with troubleshooting steps. - -### Performance - -- There are a number of things users have empirically found to improve performance on Apple Silicon Macs: - -1. Upgrade to the latest version of macOS Sequoia. -2. Run `./configure_mlx.sh`. This runs commands to optimize GPU memory allocation on Apple Silicon Macs. - - -## Documentation - -### Example Usage on Multiple macOS Devices - -#### Device 1: - -```sh -exo -``` - -#### Device 2: -```sh -exo -``` - -That's it! No configuration required - exo will automatically discover the other device(s). - -exo starts a ChatGPT-like WebUI (powered by [tinygrad tinychat](https://github.com/tinygrad/tinygrad/tree/master/examples/tinychat)) on http://localhost:52415 - -For developers, exo also starts a ChatGPT-compatible API endpoint on http://localhost:52415/v1/chat/completions. Examples with curl: - -#### Llama 3.2 3B: - -```sh -curl http://localhost:52415/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "llama-3.2-3b", - "messages": [{"role": "user", "content": "What is the meaning of exo?"}], - "temperature": 0.7 - }' -``` - -#### Llama 3.1 405B: - -```sh -curl http://localhost:52415/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "llama-3.1-405b", - "messages": [{"role": "user", "content": "What is the meaning of exo?"}], - "temperature": 0.7 - }' -``` - -#### DeepSeek R1 (full 671B): - -```sh -curl http://localhost:52415/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "deepseek-r1", - "messages": [{"role": "user", "content": "What is the meaning of exo?"}], - "temperature": 0.7 - }' -``` - -#### Llava 1.5 7B (Vision Language Model): - -```sh -curl http://localhost:52415/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "llava-1.5-7b-hf", - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "What are these?" - }, - { - "type": "image_url", - "image_url": { - "url": "http://images.cocodataset.org/val2017/000000039769.jpg" - } - } - ] - } - ], - "temperature": 0.0 - }' -``` - -### Example Usage on Multiple Heterogenous Devices (macOS + Linux) - -#### Device 1 (macOS): - -```sh -exo -``` - -Note: We don't need to explicitly tell exo to use the **tinygrad** inference engine. **MLX** and **tinygrad** are interoperable! - -#### Device 2 (Linux): -```sh -exo -``` - -Linux devices will automatically default to using the **tinygrad** inference engine. - -You can read about tinygrad-specific env vars [here](https://docs.tinygrad.org/env_vars/). For example, you can configure tinygrad to use the cpu by specifying `CLANG=1`. - -### Example Usage on a single device with "exo run" command - -```sh -exo run llama-3.2-3b -``` - -With a custom prompt: - -```sh -exo run llama-3.2-3b --prompt "What is the meaning of exo?" -``` - -### Model Storage - -Models by default are stored in `~/.cache/exo/downloads`. - -You can set a different model storage location by setting the `EXO_HOME` env var. - -## Model Downloading - -Models are downloaded from Hugging Face. If you are running exo in a country with strict internet censorship, you may need to download the models manually and put them in the `~/.cache/exo/downloads` directory. - -To download models from a proxy endpoint, set the `HF_ENDPOINT` environment variable. For example, to run exo with the huggingface mirror endpoint: - -```sh -HF_ENDPOINT=https://hf-mirror.com exo -``` - -## Debugging - -Enable debug logs with the DEBUG environment variable (0-9). - -```sh -DEBUG=9 exo -``` - -For the **tinygrad** inference engine specifically, there is a separate DEBUG flag `TINYGRAD_DEBUG` that can be used to enable debug logs (1-6). - -```sh -TINYGRAD_DEBUG=2 exo -``` - -## Formatting - -We use [yapf](https://github.com/google/yapf) to format the code. To format the code, first install the formatting requirements: - -```sh -pip3 install -e '.[formatting]' -``` - -Then run the formatting script: - -```sh -python3 format.py ./exo -``` - -## Known Issues - -- On certain versions of Python on macOS, certificates may not installed correctly, potentially causing SSL errors (e.g., when accessing huggingface.co). To resolve this, run the `Install Certificates` command, typicall as follows: - -```sh -/Applications/Python 3.x/Install Certificates.command -``` - -- 🚧 As the library is evolving so quickly, the iOS implementation has fallen behind Python. We have decided for now not to put out the buggy iOS version and receive a bunch of GitHub issues for outdated code. We are working on solving this properly and will make an announcement when it's ready. If you would like access to the iOS implementation now, please email alex@exolabs.net with your GitHub username explaining your use-case and you will be granted access on GitHub. - -## Inference Engines - -exo supports the following inference engines: - -- ✅ [MLX](exo/inference/mlx/sharded_inference_engine.py) -- ✅ [tinygrad](exo/inference/tinygrad/inference.py) -- 🚧 [PyTorch](https://github.com/exo-explore/exo/pull/139) -- 🚧 [llama.cpp](https://github.com/exo-explore/exo/issues/167) - -## Discovery Modules - -- ✅ [UDP](exo/networking/udp) -- ✅ [Manual](exo/networking/manual) -- ✅ [Tailscale](exo/networking/tailscale) -- 🚧 Radio -- 🚧 Bluetooth - -# Peer Networking Modules - -- ✅ [GRPC](exo/networking/grpc) -- 🚧 NCCL diff --git a/configure_mlx.sh b/configure_mlx.sh deleted file mode 100755 index f1cfe6e6..00000000 --- a/configure_mlx.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/usr/bin/env bash - -# Get the total memory in MB -TOTAL_MEM_MB=$(($(sysctl -n hw.memsize) / 1024 / 1024)) - -# Calculate 80% and TOTAL_MEM_GB-5GB in MB -EIGHTY_PERCENT=$(($TOTAL_MEM_MB * 80 / 100)) -MINUS_5GB=$((($TOTAL_MEM_MB - 5120))) - -# Calculate 70% and TOTAL_MEM_GB-8GB in MB -SEVENTY_PERCENT=$(($TOTAL_MEM_MB * 70 / 100)) -MINUS_8GB=$((($TOTAL_MEM_MB - 8192))) - -# Set WIRED_LIMIT_MB to higher value -if [ $EIGHTY_PERCENT -gt $MINUS_5GB ]; then - WIRED_LIMIT_MB=$EIGHTY_PERCENT -else - WIRED_LIMIT_MB=$MINUS_5GB -fi - -# Set WIRED_LWM_MB to higher value -if [ $SEVENTY_PERCENT -gt $MINUS_8GB ]; then - WIRED_LWM_MB=$SEVENTY_PERCENT -else - WIRED_LWM_MB=$MINUS_8GB -fi - -# Display the calculated values -echo "Total memory: $TOTAL_MEM_MB MB" -echo "Maximum limit (iogpu.wired_limit_mb): $WIRED_LIMIT_MB MB" -echo "Lower bound (iogpu.wired_lwm_mb): $WIRED_LWM_MB MB" - -# Apply the values with sysctl, but check if we're already root -if [ "$EUID" -eq 0 ]; then - sysctl -w iogpu.wired_limit_mb=$WIRED_LIMIT_MB - sysctl -w iogpu.wired_lwm_mb=$WIRED_LWM_MB -else - # Try without sudo first, fall back to 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- - - - IDEDidComputeMac32BitWarning - - - diff --git a/examples/astra/astra.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved b/examples/astra/astra.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved deleted file mode 100644 index edc15da2..00000000 --- a/examples/astra/astra.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved +++ /dev/null @@ -1,33 +0,0 @@ -{ - "originHash" : "8f61689e55c5551e76f2c686d145061dc1fa621a58cbca576565ebfabc15c894", - "pins" : [ - { - "identity" : "swift-argument-parser", - "kind" : "remoteSourceControl", - "location" : "https://github.com/apple/swift-argument-parser.git", - "state" : { - "revision" : "c8ed701b513cf5177118a175d85fbbbcd707ab41", - "version" : "1.3.0" - } - }, - { - "identity" : "swift-transformers", - "kind" : "remoteSourceControl", - "location" : "https://github.com/huggingface/swift-transformers.git", - "state" : { - "revision" : "74b94211bdc741694ed7e700a1104c72e5ba68fe", - "version" : "0.1.7" - } - }, - { - "identity" : "whisperkit", - "kind" : "remoteSourceControl", - "location" : "https://github.com/argmaxinc/whisperkit", - "state" : { - "branch" : "main", - "revision" : "59aaa4e5f211622f9a5e133440220d9974641d3b" - } - } - ], - "version" : 3 -} diff --git a/examples/astra/astra/Assets.xcassets/AccentColor.colorset/Contents.json b/examples/astra/astra/Assets.xcassets/AccentColor.colorset/Contents.json deleted file mode 100644 index eb878970..00000000 --- a/examples/astra/astra/Assets.xcassets/AccentColor.colorset/Contents.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "colors" : [ - { - "idiom" : "universal" - } - ], - "info" : { - "author" : "xcode", - "version" : 1 - } -} diff --git a/examples/astra/astra/Assets.xcassets/AppIcon.appiconset/Contents.json b/examples/astra/astra/Assets.xcassets/AppIcon.appiconset/Contents.json deleted file mode 100644 index 532cd729..00000000 --- a/examples/astra/astra/Assets.xcassets/AppIcon.appiconset/Contents.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "images" : [ - { - "idiom" : "universal", - "platform" : "ios", - "size" : "1024x1024" - }, - { - "idiom" : "mac", - "scale" : "1x", - "size" : "16x16" - }, - { - "idiom" : "mac", - "scale" : "2x", - "size" : "16x16" - }, - { - "idiom" : "mac", - "scale" : "1x", - "size" : "32x32" - }, - { - "idiom" : "mac", - "scale" : "2x", - "size" : "32x32" - }, - { - "idiom" : "mac", - "scale" : "1x", - "size" : "128x128" - }, - { - "idiom" : "mac", - "scale" : "2x", - "size" : "128x128" - }, - { - "idiom" : "mac", - "scale" : "1x", - "size" : "256x256" - }, - { - "idiom" : "mac", - "scale" : "2x", - "size" : "256x256" - }, - { - "idiom" : "mac", - "scale" : "1x", - "size" : "512x512" - }, - { - "idiom" : "mac", - "scale" : "2x", - "size" : "512x512" - } - ], - "info" : { - "author" : "xcode", - "version" : 1 - } -} diff --git a/examples/astra/astra/Assets.xcassets/Contents.json b/examples/astra/astra/Assets.xcassets/Contents.json deleted file mode 100644 index 73c00596..00000000 --- a/examples/astra/astra/Assets.xcassets/Contents.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "info" : { - "author" : "xcode", - "version" : 1 - } -} diff --git a/examples/astra/astra/ContentView.swift b/examples/astra/astra/ContentView.swift deleted file mode 100644 index 52327fc2..00000000 --- a/examples/astra/astra/ContentView.swift +++ /dev/null @@ -1,729 +0,0 @@ -import SwiftUI -import WhisperKit -import AVFoundation -import Foundation -import Combine -import Vision -import AVFAudio - -actor CameraActor { - let captureSession = AVCaptureSession() - private let photoOutput = AVCapturePhotoOutput() - private var isConfigured = false - private var currentPhotoCaptureDelegate: PhotoCaptureDelegate? - - func configure() throws { - guard !isConfigured else { - print("Camera already configured") - return - } - - print("Starting camera configuration") - - guard let camera = AVCaptureDevice.default(for: .video) else { - print("No camera device available") - throw CameraError.cameraUnavailable - } - - do { - let input = try AVCaptureDeviceInput(device: camera) - print("Camera input created successfully") - - guard captureSession.canAddInput(input) else { - print("Cannot add camera input to session") - throw CameraError.cannotAddInputOutput - } - - guard captureSession.canAddOutput(photoOutput) else { - print("Cannot add photo output to session") - throw CameraError.cannotAddInputOutput - } - - captureSession.beginConfiguration() - captureSession.addInput(input) - captureSession.addOutput(photoOutput) - captureSession.commitConfiguration() - - print("Camera session configured successfully") - - Task.detached { [weak self] in - self?.captureSession.startRunning() - print("Camera session started running") - } - - isConfigured = true - print("Camera fully configured and ready") - } catch { - print("Error during camera configuration: \(error)") - throw error - } - } - - func capturePhoto() async throws -> String { - guard isConfigured else { - throw CameraError.notConfigured - } - - return try await withCheckedThrowingContinuation { continuation in - let photoSettings = AVCapturePhotoSettings() - - let delegate = PhotoCaptureDelegate { result in - self.currentPhotoCaptureDelegate = nil - continuation.resume(with: result) - } - - self.currentPhotoCaptureDelegate = delegate - - Task { @MainActor in - self.photoOutput.capturePhoto(with: photoSettings, delegate: delegate) - } - } - } -} - -class PhotoCaptureDelegate: NSObject, AVCapturePhotoCaptureDelegate { - private let completionHandler: (Result) -> Void - - init(completionHandler: @escaping (Result) -> Void) { - self.completionHandler = completionHandler - } - - func photoOutput(_ output: AVCapturePhotoOutput, didFinishProcessingPhoto photo: AVCapturePhoto, error: Error?) { - if let error = error { - completionHandler(.failure(error)) - return - } - - guard let imageData = photo.fileDataRepresentation() else { - completionHandler(.failure(CameraError.imageProcessingFailed)) - return - } - - let base64String = imageData.base64EncodedString() - completionHandler(.success(base64String)) - } -} - -enum CameraError: Error { - case cameraUnavailable - case cannotAddInputOutput - case notConfigured - case imageProcessingFailed -} - -struct CameraPreview: UIViewControllerRepresentable { - let cameraActor: CameraActor - - func makeUIViewController(context: Context) -> UIViewController { - let viewController = UIViewController() - let previewLayer = AVCaptureVideoPreviewLayer(session: cameraActor.captureSession) - previewLayer.videoGravity = .resizeAspectFill - viewController.view.layer.addSublayer(previewLayer) - previewLayer.frame = viewController.view.bounds - return viewController - } - - func updateUIViewController(_ uiViewController: UIViewController, context: Context) { - if let previewLayer = uiViewController.view.layer.sublayers?.first as? AVCaptureVideoPreviewLayer { - previewLayer.frame = uiViewController.view.bounds - } - } -} - -struct ContentView: View { - @State private var whisperKit: WhisperKit? - @State private var isListening = false - @State private var currentText = "" - @State private var bufferSeconds: Double = 0.5 // or whatever the actual buffer size is - @State private var modelState: ModelState = .unloaded - - @AppStorage("selectedModel") private var selectedModel: String = "large-v3" - @AppStorage("selectedLanguage") private var selectedLanguage: String = "english" - @AppStorage("selectedTask") private var selectedTask: String = "transcribe" - - @State private var isRecordingMemo = false - @State private var currentMemo = "" - @State private var lastVoiceActivityTime = Date() - @State private var silenceTimer: Timer? - @State private var voiceActivityThreshold: Float = 0.40 - @State private var silenceTimeThreshold = 1.0 - @State private var debugText = "" - @State private var apiEndpoint = "http://192.168.212.74:52415/v1/chat/completions" - @State private var audioBuffer: [Float] = [] - @State private var bufferDuration: Double = 0.5 // 0.5 seconds buffer - @State private var isInitialTranscription = true - @State private var streamingResponse = "" - @State private var cancellables = Set() - - @State private var cameraActor: CameraActor? - @State private var showLiveCamera = false - @State private var capturedImageBase64: String? - @State private var errorMessage: String? - @State private var isCameraReady = false - - @State private var speechSynthesizer = AVSpeechSynthesizer() - @State private var speechBuffer = "" - @State private var wordCount = 0 - let maxWords = 12 - @State private var originalSilenceThreshold: Float = 0.40 - @State private var isTTSActive: Bool = false - @State private var canRecordAudio: Bool = true - @State private var ttsFinishTime: Date? - - @State private var isRequestInProgress = false - @State private var isFirst3WordsOfResponse = true - - var body: some View { - ZStack { - if showLiveCamera, isCameraReady, let actor = cameraActor { - CameraPreview(cameraActor: actor) - .edgesIgnoringSafeArea(.all) - } - - ScrollView { - VStack { - Text(currentText) - .padding() - - Text(isListening ? "Listening..." : "Not listening") - .foregroundColor(isListening ? .green : .red) - - if isRecordingMemo { - Text("Recording...") - .foregroundColor(.blue) - } - - Picker("Model", selection: $selectedModel) { - Text("large-v3").tag("large-v3") - Text("base").tag("base") - Text("small").tag("small") - } - .pickerStyle(SegmentedPickerStyle()) - .padding() - - Button("Load Model") { - loadModel(selectedModel) - } - .disabled(modelState == .loaded) - .padding() - - Text("Model State: \(modelState.description)") - - Text(debugText) - .font(.caption) - .foregroundColor(.gray) - - Text("TTS Active: \(isTTSActive ? "Yes" : "No")") - .font(.caption) - .foregroundColor(isTTSActive ? .green : .red) - - Text("Current Silence Threshold: \(voiceActivityThreshold, specifier: "%.2f")") - .font(.caption) - .foregroundColor(.blue) - - Text("Original Silence Threshold: \(originalSilenceThreshold, specifier: "%.2f")") - .font(.caption) - .foregroundColor(.orange) - - Slider(value: $voiceActivityThreshold, in: 0.01...1.0) { - Text("Voice Activity Threshold: \(voiceActivityThreshold, specifier: "%.2f")") - } - - Text("API Response:") - .font(.headline) - .padding(.top) - - ScrollView { - Text(streamingResponse) - .padding() - } - .frame(height: 200) - .border(Color.gray, width: 1) - - Toggle("Show Live Camera", isOn: $showLiveCamera) - .padding() - .onChange(of: showLiveCamera) { newValue in - if newValue { - Task { - await setupCamera() - } - } else { - cameraActor = nil - isCameraReady = false - print("Camera disabled") - } - } - - if !showLiveCamera { - Text("Camera Ready: \(isCameraReady ? "Yes" : "No")") - .padding() - - if let errorMessage = errorMessage { - Text("Error: \(errorMessage)") - .foregroundColor(.red) - .padding() - } - } - } - } - .opacity(showLiveCamera ? 0.7 : 1) - } - .onAppear { - setupWhisperKit() - startTTSMonitoring() - } - } - - private func setupWhisperKit() { - Task { - do { - whisperKit = try await WhisperKit(verbose: true) - print("WhisperKit initialized successfully") - startListening() - startAudioBuffering() - } catch { - print("Error initializing WhisperKit: \(error)") - } - } - } - - private func startTTSMonitoring() { - Timer.scheduledTimer(withTimeInterval: 0.1, repeats: true) { _ in - let newTTSActive = speechSynthesizer.isSpeaking - if newTTSActive != isTTSActive { - isTTSActive = newTTSActive - canRecordAudio = !newTTSActive - if isTTSActive { - voiceActivityThreshold = 1.0 // Set to max to prevent recording - whisperKit?.audioProcessor.purgeAudioSamples(keepingLast: 0) // Flush audio buffer - print("TTS Started - Audio recording paused") - } else { - ttsFinishTime = Date() - print("TTS Finished - Waiting 0.5 seconds before resuming audio recording") - } - updateDebugText() - } - - if !isTTSActive, let finishTime = ttsFinishTime, Date().timeIntervalSince(finishTime) >= 0.5 { - whisperKit?.audioProcessor.purgeAudioSamples(keepingLast: 0) // Flush audio buffer - voiceActivityThreshold = originalSilenceThreshold - canRecordAudio = true - ttsFinishTime = nil - print("Audio recording resumed after TTS delay") - updateDebugText() - } - } - } - - private func updateDebugText() { - debugText += "\nTTS Active: \(isTTSActive)" - debugText += "\nCurrent Silence Threshold: \(voiceActivityThreshold)" - debugText += "\nOriginal Silence Threshold: \(originalSilenceThreshold)" - debugText += "\n---" - } - - private func startAudioBuffering() { - Task { - while true { - if let samples = whisperKit?.audioProcessor.audioSamples { - let bufferSize = Int(Double(WhisperKit.sampleRate) * bufferDuration) - audioBuffer = Array(samples.suffix(bufferSize)) - } - try await Task.sleep(nanoseconds: 100_000_000) // Update every 0.1 seconds - } - } - } - - private func loadModel(_ model: String) { - Task { - let success = try await loadModel(selectedModel) - if success { - startListening() - } else { - print("Model failed to load, cannot start listening") - } - } - } - - private func startListening() { - guard let audioProcessor = whisperKit?.audioProcessor else { - print("AudioProcessor not available") - return - } - - do { - try audioProcessor.startRecordingLive { buffer in - DispatchQueue.main.async { - checkVoiceActivity() - } - } - isListening = true - } catch { - print("Error starting listening: \(error)") - } - } - - private func checkVoiceActivity() { - guard canRecordAudio, let audioProcessor = whisperKit?.audioProcessor else { return } - - let voiceDetected = AudioProcessor.isVoiceDetected( - in: audioProcessor.relativeEnergy, - nextBufferInSeconds: Float(bufferSeconds), - silenceThreshold: Float(voiceActivityThreshold) - ) - - let energyValuesToConsider = Int(Float(bufferSeconds) / 0.1) - let nextBufferEnergies = audioProcessor.relativeEnergy.suffix(energyValuesToConsider) - let numberOfValuesToCheck = max(10, nextBufferEnergies.count - 10) - let relevantEnergies = Array(nextBufferEnergies.prefix(numberOfValuesToCheck)) - - debugText = """ - Buffer seconds: \(bufferSeconds) - Energy values to consider: \(energyValuesToConsider) - Number of values to check: \(numberOfValuesToCheck) - Silence threshold: \(voiceActivityThreshold) - Relevant energies: \(relevantEnergies) - Max energy: \(relevantEnergies.max() ?? 0) - Voice detected: \(voiceDetected) - """ - - if voiceDetected { - lastVoiceActivityTime = Date() - if !isRecordingMemo { - startNewMemo() - } - } else { - checkSilence() - } - } - - private func checkSilence() { - let silenceDuration = Date().timeIntervalSince(lastVoiceActivityTime) - debugText += "\nSilence duration: \(silenceDuration)" - - if silenceDuration > silenceTimeThreshold { - endCurrentMemo() - } - } - - private func endCurrentMemo() { - if isRecordingMemo { - isRecordingMemo = false - silenceTimer?.invalidate() - silenceTimer = nil - if !currentMemo.isEmpty { - saveMemoToFile(currentMemo) - currentMemo = "" - } - currentText = "" - whisperKit?.audioProcessor.purgeAudioSamples(keepingLast: 0) - print("Ended memo") - debugText += "\nMemo ended" - } - } - - private func startNewMemo() { - isRecordingMemo = true - currentMemo = "" - isInitialTranscription = true - silenceTimer?.invalidate() - silenceTimer = Timer.scheduledTimer(withTimeInterval: 0.5, repeats: true) { _ in - checkSilence() - } - transcribeInRealTime() - print("Started new memo") - } - - private func transcribeInRealTime() { - Task { - while isRecordingMemo { - if canRecordAudio, let samples = whisperKit?.audioProcessor.audioSamples, samples.count > WhisperKit.sampleRate { - do { - let samplesToTranscribe: [Float] - if isInitialTranscription { - samplesToTranscribe = audioBuffer + samples - isInitialTranscription = false - } else { - samplesToTranscribe = Array(samples) - } - - let result = try await whisperKit?.transcribe(audioArray: samplesToTranscribe) - await MainActor.run { - let newText = result?.first?.text ?? "" - if !newText.isEmpty { - currentMemo = newText - currentText = newText - } - } - } catch { - print("Transcription error: \(error)") - } - } - try await Task.sleep(nanoseconds: 500_000_000) // Sleep for 0.5 seconds - } - } - } - - private func saveMemoToFile(_ memo: String) { - let dateFormatter = DateFormatter() - dateFormatter.dateFormat = "yyyy-MM-dd_HH-mm-ss" - let fileName = "memo_\(dateFormatter.string(from: Date())).txt" - - guard let documentsDirectory = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask).first else { - print("Unable to access documents directory") - return - } - - let fileURL = documentsDirectory.appendingPathComponent(fileName) - - do { - try memo.write(to: fileURL, atomically: true, encoding: .utf8) - print("Memo saved to: \(fileURL.path)") - } catch { - print("Error saving memo: \(error)") - } - - Task { - if !isCameraReady { - print("Camera not ready, initializing...") - await setupCamera() - } - - if let imageBase64 = await capturePhotoBase64() { - sendMemoToAPI(memo, imageBase64: imageBase64) - } else { - sendMemoToAPI(memo) - } - } - } - - private func setupCamera() async { - print("Setting up camera...") - do { - let actor = CameraActor() - print("CameraActor instance created") - try await actor.configure() - print("Camera configured successfully") - await MainActor.run { - self.cameraActor = actor - self.errorMessage = nil - self.isCameraReady = true - print("Camera setup complete, UI updated") - } - } catch { - print("Camera setup failed: \(error)") - await MainActor.run { - self.errorMessage = "Failed to initialize camera: \(error.localizedDescription)" - self.isCameraReady = false - print("Camera setup failure reflected in UI") - } - } - } - - private func capturePhotoBase64() async -> String? { - print("Attempting to capture photo...") - if !isCameraReady { - print("Camera not ready, attempting to initialize...") - await setupCamera() - } - - guard let actor = cameraActor, isCameraReady else { - print("Camera not initialized or not ready, cannot capture photo") - await MainActor.run { - self.errorMessage = "Camera not initialized or not ready" - } - return nil - } - - do { - let base64String = try await actor.capturePhoto() - print("Photo captured successfully") - await MainActor.run { - self.errorMessage = nil - } - return base64String - } catch { - print("Error capturing photo: \(error)") - await MainActor.run { - self.errorMessage = "Failed to capture photo: \(error.localizedDescription)" - } - return nil - } - } - - private func sendMemoToAPI(_ memo: String, imageBase64: String? = nil) { - Task { - guard !isRequestInProgress else { - print("A request is already in progress. Skipping this one.") - return - } - - isRequestInProgress = true - isFirst3WordsOfResponse = true // Reset for new request - defer { isRequestInProgress = false } - - do { - print("Starting API request for memo: \(memo.prefix(50))...") - - guard let url = URL(string: apiEndpoint) else { - print("Invalid API endpoint URL: \(apiEndpoint)") - return - } - - var payload: [String: Any] = [ - "model": "llava-1.5-7b-hf", - "messages": [ - ["role": "user", "content": [ - ["type": "text", "text": "You are a helpful conversational assistant chatting with a Gen Z user using their iPhone for voice transcription and sending images to you with their iPhone camera. Be conversational and concise, with a laid back attitude and be cheerful with humour. User said: " + memo], - ]] - ], - "temperature": 0.7, - "stream": true - ] - - if let imageBase64 = imageBase64 { - if var userMessage = (payload["messages"] as? [[String: Any]])?.last, - var content = userMessage["content"] as? [[String: Any]] { - content.append(["type": "image_url", "image_url": ["url": "data:image/jpeg;base64,\(imageBase64)"]]) - userMessage["content"] = content - payload["messages"] = [userMessage] - } - } - - guard let jsonData = try? JSONSerialization.data(withJSONObject: payload) else { - print("Failed to serialize JSON payload") - return - } - - var request = URLRequest(url: url) - request.httpMethod = "POST" - request.setValue("application/json", forHTTPHeaderField: "Content-Type") - request.httpBody = jsonData - - print("Sending request to \(url.absoluteString)") - - await MainActor.run { - self.streamingResponse = "" - } - - let (bytes, response) = try await URLSession.shared.bytes(for: request) - - guard let httpResponse = response as? HTTPURLResponse else { - print("Invalid response") - return - } - - print("Response status code: \(httpResponse.statusCode)") - - for try await line in bytes.lines { - print("Received line: \(line)") - await processStreamLine(line) - } - - print("Stream completed") - } catch { - print("Error: \(error.localizedDescription)") - } - } - } - - private func processStreamLine(_ line: String) async { - let jsonString: String - if line.hasPrefix("data: ") { - jsonString = String(line.dropFirst(6)) - } else { - jsonString = line - } - - if jsonString.trimmingCharacters(in: .whitespacesAndNewlines).isEmpty { - return - } - - if let jsonData = jsonString.data(using: .utf8), - let json = try? JSONSerialization.jsonObject(with: jsonData, options: []) as? [String: Any], - let choices = json["choices"] as? [[String: Any]], - let firstChoice = choices.first, - let delta = firstChoice["delta"] as? [String: String], - let content = delta["content"] { - print("Extracted content: \(content)") - await MainActor.run { - self.streamingResponse += content - bufferContent(content) - } - } - } - - private func bufferContent(_ content: String) { - speechBuffer += content - let words = speechBuffer.split(separator: " ") - wordCount = words.count - - if isFirst3WordsOfResponse && wordCount >= 3 { - isFirst3WordsOfResponse = false - speakBufferedContent() - } else if content.contains(".") || content.contains("!") || content.contains("?") || wordCount >= maxWords { - speakBufferedContent() - } - } - - private func speakBufferedContent() { - guard !speechBuffer.isEmpty else { return } - speakContent(speechBuffer) - speechBuffer = "" - wordCount = 0 - } - - private func speakContent(_ content: String) { - let utterance = AVSpeechUtterance(string: content) - utterance.voice = AVSpeechSynthesisVoice(language: "en-US") - utterance.rate = 0.5 - speechSynthesizer.speak(utterance) - } - - private func loadModel(_ model: String) async throws -> Bool { - guard let whisperKit = whisperKit else { - print("WhisperKit instance not initialized") - return false - } - modelState = .loading - do { - print("Starting to load model: \(model)") - try await whisperKit.loadModels() - await MainActor.run { - modelState = .loaded - print("Model loaded successfully: \(model)") - } - return true - } catch { - print("Error loading model: \(error)") - await MainActor.run { modelState = .unloaded } - return false - } - } - - private func capturePhoto() async { - print("Attempting to capture photo...") - print("Camera ready: \(isCameraReady), CameraActor exists: \(cameraActor != nil)") - guard let actor = cameraActor, isCameraReady else { - print("Camera not initialized or not ready, cannot capture photo") - await MainActor.run { - self.errorMessage = "Camera not initialized or not ready" - } - return - } - - do { - let base64String = try await actor.capturePhoto() - print("Photo captured successfully") - await MainActor.run { - self.capturedImageBase64 = base64String - self.errorMessage = nil - } - } catch { - print("Error capturing photo: \(error)") - await MainActor.run { - self.errorMessage = "Failed to capture photo: \(error.localizedDescription)" - } - } - } -} diff --git a/examples/astra/astra/Preview Content/Preview Assets.xcassets/Contents.json b/examples/astra/astra/Preview Content/Preview Assets.xcassets/Contents.json deleted file mode 100644 index 73c00596..00000000 --- a/examples/astra/astra/Preview Content/Preview Assets.xcassets/Contents.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "info" : { - "author" : "xcode", - "version" : 1 - } -} diff --git a/examples/astra/astra/astra.entitlements b/examples/astra/astra/astra.entitlements deleted file mode 100644 index ee23443d..00000000 --- a/examples/astra/astra/astra.entitlements +++ /dev/null @@ -1,22 +0,0 @@ - - - - - com.apple.developer.kernel.increased-memory-limit - - com.apple.security.app-sandbox - - com.apple.security.device.audio-input - - com.apple.security.files.downloads.read-only - - com.apple.security.files.user-selected.read-write - - com.apple.security.network.client - - com.apple.security.network.server - - com.apple.security.device.camera - - - diff --git a/examples/astra/astra/astraApp.swift b/examples/astra/astra/astraApp.swift deleted file mode 100644 index 79cfe707..00000000 --- a/examples/astra/astra/astraApp.swift +++ /dev/null @@ -1,17 +0,0 @@ -// -// astraApp.swift -// astra -// -// Created by Alex on 18/08/2024. -// - -import SwiftUI - -@main -struct astraApp: App { - var body: some Scene { - WindowGroup { - ContentView() - } - } -} diff --git a/examples/astra/astraTests/astraTests.swift b/examples/astra/astraTests/astraTests.swift deleted file mode 100644 index 982e0c91..00000000 --- a/examples/astra/astraTests/astraTests.swift +++ /dev/null @@ -1,35 +0,0 @@ -// -// astraTests.swift -// astraTests -// -// Created by Alex on 18/08/2024. -// - -import XCTest - -final class astraTests: XCTestCase { - - override func setUpWithError() throws { - // Put setup code here. This method is called before the invocation of each test method in the class. - } - - override func tearDownWithError() throws { - // Put teardown code here. This method is called after the invocation of each test method in the class. - } - - func testExample() throws { - // This is an example of a functional test case. - // Use XCTAssert and related functions to verify your tests produce the correct results. - // Any test you write for XCTest can be annotated as throws and async. - // Mark your test throws to produce an unexpected failure when your test encounters an uncaught error. - // Mark your test async to allow awaiting for asynchronous code to complete. Check the results with assertions afterwards. - } - - func testPerformanceExample() throws { - // This is an example of a performance test case. - measure { - // Put the code you want to measure the time of here. - } - } - -} diff --git a/examples/astra/astraUITests/astraUITests.swift b/examples/astra/astraUITests/astraUITests.swift deleted file mode 100644 index 97a293d9..00000000 --- a/examples/astra/astraUITests/astraUITests.swift +++ /dev/null @@ -1,41 +0,0 @@ -// -// astraUITests.swift -// astraUITests -// -// Created by Alex on 18/08/2024. -// - -import XCTest - -final class astraUITests: XCTestCase { - - override func setUpWithError() throws { - // Put setup code here. This method is called before the invocation of each test method in the class. - - // In UI tests it is usually best to stop immediately when a failure occurs. - continueAfterFailure = false - - // In UI tests it’s important to set the initial state - such as interface orientation - required for your tests before they run. The setUp method is a good place to do this. - } - - override func tearDownWithError() throws { - // Put teardown code here. This method is called after the invocation of each test method in the class. - } - - func testExample() throws { - // UI tests must launch the application that they test. - let app = XCUIApplication() - app.launch() - - // Use XCTAssert and related functions to verify your tests produce the correct results. - } - - func testLaunchPerformance() throws { - if #available(macOS 10.15, iOS 13.0, tvOS 13.0, watchOS 7.0, *) { - // This measures how long it takes to launch your application. - measure(metrics: [XCTApplicationLaunchMetric()]) { - XCUIApplication().launch() - } - } - } -} diff --git a/examples/astra/astraUITests/astraUITestsLaunchTests.swift b/examples/astra/astraUITests/astraUITestsLaunchTests.swift deleted file mode 100644 index d05a1484..00000000 --- a/examples/astra/astraUITests/astraUITestsLaunchTests.swift +++ /dev/null @@ -1,32 +0,0 @@ -// -// astraUITestsLaunchTests.swift -// astraUITests -// -// Created by Alex on 18/08/2024. -// - -import XCTest - -final class astraUITestsLaunchTests: XCTestCase { - - override class var runsForEachTargetApplicationUIConfiguration: Bool { - true - } - - override func setUpWithError() throws { - continueAfterFailure = false - } - - func testLaunch() throws { - let app = XCUIApplication() - app.launch() - - // Insert steps here to perform after app launch but before taking a screenshot, - // such as logging into a test account or navigating somewhere in the app - - let attachment = XCTAttachment(screenshot: app.screenshot()) - attachment.name = "Launch Screen" - attachment.lifetime = .keepAlways - add(attachment) - } -} diff --git a/examples/chatgpt_api.sh b/examples/chatgpt_api.sh deleted file mode 100755 index 44401836..00000000 --- a/examples/chatgpt_api.sh +++ /dev/null @@ -1,39 +0,0 @@ -# exo provides an API that aims to be a drop-in replacements for the ChatGPT-API. -# This example shows how you can use the API first without streaming and second with streaming. -# This works the same in a single-node set up and in a multi-node setup. -# You need to start exo before running this by running `python3 main.py`. - -API_ENDPOINT="http://${API_ENDPOINT:-$(ifconfig | grep 'inet ' | grep -v '127.0.0.1' | awk '{print $2}' | head -n 1):52415}" -MODEL="llama-3.1-8b" -PROMPT="What is the meaning of exo?" -TEMPERATURE=0.7 - -echo "" -echo "" -echo "--- Output without streaming:" -echo "" -curl "${API_ENDPOINT}/v1/chat/completions" --silent \ - -H "Content-Type: application/json" \ - -d '{ - "model": "'"${MODEL}"'", - "messages": [{"role": "user", "content": "'"${PROMPT}"'"}], - "temperature": '"${TEMPERATURE}"' - }' - -echo "" -echo "" -echo "--- Output with streaming:" -echo "" -curl "${API_ENDPOINT}/v1/chat/completions" --silent \ - -H "Content-Type: application/json" \ - -d '{ - "model": "'"${MODEL}"'", - "messages": [{"role": "user", "content": "'"${PROMPT}"'"}], - "temperature": '"${TEMPERATURE}"', - "stream": true - }' | while read -r line; do - if [[ $line == data:* ]]; then - content=$(echo "$line" | sed 's/^data: //') - echo "$content" | jq -r '.choices[].delta.content' --unbuffered | tr -d '\n' - fi - done \ No newline at end of file diff --git a/examples/function_calling.py b/examples/function_calling.py deleted file mode 100644 index dcab2a8d..00000000 --- a/examples/function_calling.py +++ /dev/null @@ -1,111 +0,0 @@ -import json -import re -import requests - -def get_current_weather(location: str, unit: str = "celsius"): - """Mock weather data function""" - # Hardcoded response for demo purposes - return { - "location": location, - "temperature": 22 if unit == "celsius" else 72, - "unit": unit, - "forecast": "Sunny with light clouds" - } - -def try_parse_tool_calls(content: str): - """Try parse the tool calls.""" - tool_calls = [] - offset = 0 - for i, m in enumerate(re.finditer(r"\n(.+)?\n", content)): - if i == 0: - offset = m.start() - try: - func = json.loads(m.group(1)) - tool_calls.append({"type": "function", "function": func}) - if isinstance(func["arguments"], str): - func["arguments"] = json.loads(func["arguments"]) - except json.JSONDecodeError as e: - print(f"Failed to parse tool calls: the content is {m.group(1)} and {e}") - pass - if tool_calls: - if offset > 0 and content[:offset].strip(): - c = content[:offset] - else: - c = "" - return {"role": "assistant", "content": c, "tool_calls": tool_calls} - return {"role": "assistant", "content": re.sub(r"<\|im_end\|>$", "", content)} - -def chat_completion(messages): - """Send chat completion request to local server""" - response = requests.post( - "http://localhost:52415/v1/chat/completions", - json={ - "model": "qwen-2.5-1.5b", - "messages": messages, - "tools": [{ - "type": "function", - "function": { - "name": "get_current_weather", - "description": "Get the current weather in a given location", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA" - }, - "unit": { - "type": "string", - "enum": ["celsius", "fahrenheit"] - } - }, - "required": ["location"] - } - } - }], - "tool_choice": "auto" - } - ) - return response.json() - -def main(): - # Initial conversation - messages = [{ - "role": "user", - "content": "Hi there, what's the weather in Boston?" - }] - - # Get initial response - response = chat_completion(messages) - print(f"First response: {response}") - assistant_message = try_parse_tool_calls(response["choices"][0]["message"]["content"]) - messages.append(assistant_message) - - # If there are tool calls, execute them and continue conversation - if "tool_calls" in assistant_message: - for tool_call in assistant_message["tool_calls"]: - if tool_call["function"]["name"] == "get_current_weather": - args = tool_call["function"]["arguments"] - weather_data = get_current_weather(**args) - - # Add tool response to messages - messages.append({ - "role": "tool", - "content": json.dumps(weather_data), - "name": tool_call["function"]["name"] - }) - - # Get final response with weather data - response = chat_completion(messages) - print(f"Final response: {response}") - messages.append({ - "role": "assistant", - "content": response["choices"][0]["message"]["content"] - }) - - # Print full conversation - for msg in messages: - print(f"\n{msg['role'].upper()}: {msg['content']}") - -if __name__ == "__main__": - main() diff --git a/exo/__init__.py b/exo/__init__.py deleted file mode 100644 index 596559f3..00000000 --- a/exo/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from exo.helpers import DEBUG as DEBUG, DEBUG_DISCOVERY as DEBUG_DISCOVERY, VERSION as VERSION \ No newline at end of file diff --git a/exo/api/__init__.py b/exo/api/__init__.py deleted file mode 100644 index 660e7507..00000000 --- a/exo/api/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from exo.api.chatgpt_api import ChatGPTAPI as ChatGPTAPI diff --git a/exo/api/chatgpt_api.py b/exo/api/chatgpt_api.py deleted file mode 100644 index 1020fdbc..00000000 --- a/exo/api/chatgpt_api.py +++ /dev/null @@ -1,645 +0,0 @@ -import uuid -import time -import asyncio -import json -import os -from pathlib import Path -from transformers import AutoTokenizer -from typing import List, Literal, Union, Dict, Optional -from aiohttp import web -import aiohttp_cors -import traceback -import signal -from exo import DEBUG, VERSION -from exo.helpers import PrefixDict, shutdown, get_exo_images_dir -from exo.inference.tokenizers import resolve_tokenizer -from exo.orchestration import Node -from exo.models import build_base_shard, build_full_shard, model_cards, get_repo, get_supported_models, get_pretty_name -from typing import Callable, Optional -from PIL import Image -import numpy as np -import base64 -from io import BytesIO -import platform -from exo.download.download_progress import RepoProgressEvent -from exo.download.new_shard_download import delete_model -import tempfile -from exo.apputil import create_animation_mp4 -from collections import defaultdict - -if platform.system().lower() == "darwin" and platform.machine().lower() == "arm64": - import mlx.core as mx -else: - import numpy as mx - - -class Message: - def __init__(self, role: str, content: Union[str, List[Dict[str, Union[str, Dict[str, str]]]]], tools: Optional[List[Dict]] = None): - self.role = role - self.content = content - self.tools = tools - - def to_dict(self): - data = {"role": self.role, "content": self.content} - if self.tools: - data["tools"] = self.tools - return data - - -class ChatCompletionRequest: - def __init__(self, model: str, messages: List[Message], temperature: float, tools: Optional[List[Dict]] = None): - self.model = model - self.messages = messages - self.temperature = temperature - self.tools = tools - - def to_dict(self): - return {"model": self.model, "messages": [message.to_dict() for message in self.messages], "temperature": self.temperature, "tools": self.tools} - - -def generate_completion( - chat_request: ChatCompletionRequest, - tokenizer, - prompt: str, - request_id: str, - tokens: List[int], - stream: bool, - finish_reason: Union[Literal["length", "stop"], None], - object_type: Literal["chat.completion", "text_completion"], -) -> dict: - completion = { - "id": f"chatcmpl-{request_id}", - "object": object_type, - "created": int(time.time()), - "model": chat_request.model, - "system_fingerprint": f"exo_{VERSION}", - "choices": [{ - "index": 0, - "message": {"role": "assistant", "content": tokenizer.decode(tokens)}, - "logprobs": None, - "finish_reason": finish_reason, - }], - } - - if not stream: - completion["usage"] = { - "prompt_tokens": len(tokenizer.encode(prompt)), - "completion_tokens": len(tokens), - "total_tokens": len(tokenizer.encode(prompt)) + len(tokens), - } - - choice = completion["choices"][0] - if object_type.startswith("chat.completion"): - key_name = "delta" if stream else "message" - choice[key_name] = {"role": "assistant", "content": tokenizer.decode(tokens)} - elif object_type == "text_completion": - choice["text"] = tokenizer.decode(tokens) - else: - ValueError(f"Unsupported response type: {object_type}") - - return completion - - -def remap_messages(messages: List[Message]) -> List[Message]: - remapped_messages = [] - last_image = None - for message in messages: - if not isinstance(message.content, list): - remapped_messages.append(message) - continue - - remapped_content = [] - for content in message.content: - if isinstance(content, dict): - if content.get("type") in ["image_url", "image"]: - image_url = content.get("image_url", {}).get("url") or content.get("image") - if image_url: - last_image = {"type": "image", "image": image_url} - remapped_content.append({"type": "text", "text": "[An image was uploaded but is not displayed here]"}) - else: - remapped_content.append(content) - else: - remapped_content.append(content) - remapped_messages.append(Message(role=message.role, content=remapped_content)) - - if last_image: - # Replace the last image placeholder with the actual image content - for message in reversed(remapped_messages): - for i, content in enumerate(message.content): - if isinstance(content, dict): - if content.get("type") == "text" and content.get("text") == "[An image was uploaded but is not displayed here]": - message.content[i] = last_image - return remapped_messages - - return remapped_messages - - -def build_prompt(tokenizer, _messages: List[Message], tools: Optional[List[Dict]] = None): - messages = remap_messages(_messages) - chat_template_args = {"conversation": [m.to_dict() for m in messages], "tokenize": False, "add_generation_prompt": True} - if tools: - chat_template_args["tools"] = tools - - try: - prompt = tokenizer.apply_chat_template(**chat_template_args) - if DEBUG >= 3: print(f"!!! Prompt: {prompt}") - return prompt - except UnicodeEncodeError: - # Handle Unicode encoding by ensuring everything is UTF-8 - chat_template_args["conversation"] = [ - {k: v.encode('utf-8').decode('utf-8') if isinstance(v, str) else v - for k, v in m.to_dict().items()} - for m in messages - ] - prompt = tokenizer.apply_chat_template(**chat_template_args) - if DEBUG >= 3: print(f"!!! Prompt (UTF-8 encoded): {prompt}") - return prompt - - -def parse_message(data: dict): - if "role" not in data or "content" not in data: - raise ValueError(f"Invalid message: {data}. Must have 'role' and 'content'") - return Message(data["role"], data["content"], data.get("tools")) - - -def parse_chat_request(data: dict, default_model: str): - return ChatCompletionRequest( - data.get("model", default_model), - [parse_message(msg) for msg in data["messages"]], - data.get("temperature", 0.0), - data.get("tools", None), - ) - - -class PromptSession: - def __init__(self, request_id: str, timestamp: int, prompt: str): - self.request_id = request_id - self.timestamp = timestamp - self.prompt = prompt - - -class ChatGPTAPI: - def __init__( - self, - node: Node, - inference_engine_classname: str, - response_timeout: int = 90, - on_chat_completion_request: Callable[[str, ChatCompletionRequest, str], None] = None, - default_model: Optional[str] = None, - system_prompt: Optional[str] = None - ): - self.node = node - self.inference_engine_classname = inference_engine_classname - self.response_timeout = response_timeout - self.on_chat_completion_request = on_chat_completion_request - self.app = web.Application(client_max_size=100*1024*1024) # 100MB to support image upload - self.prompts: PrefixDict[str, PromptSession] = PrefixDict() - self.prev_token_lens: Dict[str, int] = {} - self.stream_tasks: Dict[str, asyncio.Task] = {} - self.default_model = default_model or "llama-3.2-1b" - self.token_queues = defaultdict(asyncio.Queue) - - # Get the callback system and register our handler - self.token_callback = node.on_token.register("chatgpt-api-token-handler") - self.token_callback.on_next(lambda _request_id, tokens, is_finished: asyncio.create_task(self.handle_tokens(_request_id, tokens, is_finished))) - self.system_prompt = system_prompt - - cors = aiohttp_cors.setup(self.app) - cors_options = aiohttp_cors.ResourceOptions( - allow_credentials=True, - expose_headers="*", - allow_headers="*", - allow_methods="*", - ) - cors.add(self.app.router.add_get("/models", self.handle_get_models), {"*": cors_options}) - cors.add(self.app.router.add_get("/v1/models", self.handle_get_models), {"*": cors_options}) - cors.add(self.app.router.add_post("/chat/token/encode", self.handle_post_chat_token_encode), {"*": cors_options}) - cors.add(self.app.router.add_post("/v1/chat/token/encode", self.handle_post_chat_token_encode), {"*": cors_options}) - cors.add(self.app.router.add_post("/chat/completions", self.handle_post_chat_completions), {"*": cors_options}) - cors.add(self.app.router.add_post("/v1/chat/completions", self.handle_post_chat_completions), {"*": cors_options}) - cors.add(self.app.router.add_post("/v1/image/generations", self.handle_post_image_generations), {"*": cors_options}) - cors.add(self.app.router.add_get("/v1/download/progress", self.handle_get_download_progress), {"*": cors_options}) - cors.add(self.app.router.add_get("/modelpool", self.handle_model_support), {"*": cors_options}) - cors.add(self.app.router.add_get("/healthcheck", self.handle_healthcheck), {"*": cors_options}) - cors.add(self.app.router.add_post("/quit", self.handle_quit), {"*": cors_options}) - cors.add(self.app.router.add_delete("/models/{model_name}", self.handle_delete_model), {"*": cors_options}) - cors.add(self.app.router.add_get("/initial_models", self.handle_get_initial_models), {"*": cors_options}) - cors.add(self.app.router.add_post("/create_animation", self.handle_create_animation), {"*": cors_options}) - cors.add(self.app.router.add_post("/download", self.handle_post_download), {"*": cors_options}) - cors.add(self.app.router.add_get("/v1/topology", self.handle_get_topology), {"*": cors_options}) - cors.add(self.app.router.add_get("/topology", self.handle_get_topology), {"*": cors_options}) - - # Add static routes - if "__compiled__" not in globals(): - self.static_dir = Path(__file__).parent.parent/"tinychat" - self.app.router.add_get("/", self.handle_root) - self.app.router.add_static("/", self.static_dir, name="static") - - # Always add images route, regardless of compilation status - self.images_dir = get_exo_images_dir() - self.images_dir.mkdir(parents=True, exist_ok=True) - self.app.router.add_static('/images/', self.images_dir, name='static_images') - - self.app.middlewares.append(self.timeout_middleware) - self.app.middlewares.append(self.log_request) - - async def handle_quit(self, request): - if DEBUG >= 1: print("Received quit signal") - response = web.json_response({"detail": "Quit signal received"}, status=200) - await response.prepare(request) - await response.write_eof() - await shutdown(signal.SIGINT, asyncio.get_event_loop(), self.node.server) - - async def timeout_middleware(self, app, handler): - async def middleware(request): - try: - return await asyncio.wait_for(handler(request), timeout=self.response_timeout) - except asyncio.TimeoutError: - return web.json_response({"detail": "Request timed out"}, status=408) - - return middleware - - async def log_request(self, app, handler): - async def middleware(request): - if DEBUG >= 2: print(f"Received request: {request.method} {request.path}") - return await handler(request) - - return middleware - - async def handle_root(self, request): - return web.FileResponse(self.static_dir/"index.html") - - async def handle_healthcheck(self, request): - return web.json_response({"status": "ok"}) - - async def handle_model_support(self, request): - try: - response = web.StreamResponse(status=200, reason='OK', headers={ 'Content-Type': 'text/event-stream', 'Cache-Control': 'no-cache', 'Connection': 'keep-alive' }) - await response.prepare(request) - async for path, s in self.node.shard_downloader.get_shard_download_status(self.inference_engine_classname): - model_data = { s.shard.model_id: { "downloaded": s.downloaded_bytes == s.total_bytes, "download_percentage": 100 if s.downloaded_bytes == s.total_bytes else 100 * float(s.downloaded_bytes) / float(s.total_bytes), "total_size": s.total_bytes, "total_downloaded": s.downloaded_bytes } } - await response.write(f"data: {json.dumps(model_data)}\n\n".encode()) - await response.write(b"data: [DONE]\n\n") - return response - - except Exception as e: - print(f"Error in handle_model_support: {str(e)}") - traceback.print_exc() - return web.json_response({"detail": f"Server error: {str(e)}"}, status=500) - - async def handle_get_models(self, request): - models_list = [{"id": model_name, "object": "model", "owned_by": "exo", "ready": True} for model_name, _ in model_cards.items()] - return web.json_response({"object": "list", "data": models_list}) - - async def handle_post_chat_token_encode(self, request): - data = await request.json() - model = data.get("model", self.default_model) - if model and model.startswith("gpt-"): # Handle gpt- model requests - model = self.default_model - if not model or model not in model_cards: - if DEBUG >= 1: print(f"Invalid model: {model}. Supported: {list(model_cards.keys())}. Defaulting to {self.default_model}") - model = self.default_model - shard = build_base_shard(model, self.inference_engine_classname) - messages = [parse_message(msg) for msg in data.get("messages", [])] - tokenizer = await resolve_tokenizer(get_repo(shard.model_id, self.inference_engine_classname)) - prompt = build_prompt(tokenizer, messages, data.get("tools", None)) - tokens = tokenizer.encode(prompt) - return web.json_response({ - "length": len(prompt), - "num_tokens": len(tokens), - "encoded_tokens": tokens, - "encoded_prompt": prompt, - }) - - async def handle_get_download_progress(self, request): - progress_data = {} - for node_id, progress_event in self.node.node_download_progress.items(): - if isinstance(progress_event, RepoProgressEvent): - if progress_event.status != "in_progress": continue - progress_data[node_id] = progress_event.to_dict() - else: - print(f"Unknown progress event type: {type(progress_event)}. {progress_event}") - return web.json_response(progress_data) - - async def handle_post_chat_completions(self, request): - data = await request.json() - if DEBUG >= 2: print(f"[ChatGPTAPI] Handling chat completions request from {request.remote}: {data}") - stream = data.get("stream", False) - chat_request = parse_chat_request(data, self.default_model) - if chat_request.model and chat_request.model.startswith("gpt-"): # to be compatible with ChatGPT tools, point all gpt- model requests to default model - chat_request.model = self.default_model - if not chat_request.model or chat_request.model not in model_cards: - if DEBUG >= 1: print(f"[ChatGPTAPI] Invalid model: {chat_request.model}. Supported: {list(model_cards.keys())}. Defaulting to {self.default_model}") - chat_request.model = self.default_model - shard = build_base_shard(chat_request.model, self.inference_engine_classname) - if not shard: - supported_models = [model for model, info in model_cards.items() if self.inference_engine_classname in info.get("repo", {})] - return web.json_response( - {"detail": f"Unsupported model: {chat_request.model} with inference engine {self.inference_engine_classname}. Supported models for this engine: {supported_models}"}, - status=400, - ) - - tokenizer = await resolve_tokenizer(get_repo(shard.model_id, self.inference_engine_classname)) - if DEBUG >= 4: print(f"[ChatGPTAPI] Resolved tokenizer: {tokenizer}") - - # Add system prompt if set - if self.system_prompt and not any(msg.role == "system" for msg in chat_request.messages): - chat_request.messages.insert(0, Message("system", self.system_prompt)) - - prompt = build_prompt(tokenizer, chat_request.messages, chat_request.tools) - request_id = str(uuid.uuid4()) - if self.on_chat_completion_request: - try: - self.on_chat_completion_request(request_id, chat_request, prompt) - except Exception as e: - if DEBUG >= 2: traceback.print_exc() - - if DEBUG >= 2: print(f"[ChatGPTAPI] Processing prompt: {request_id=} {shard=} {prompt=}") - - try: - await asyncio.wait_for(asyncio.shield(asyncio.create_task(self.node.process_prompt(shard, prompt, request_id=request_id))), timeout=self.response_timeout) - - if DEBUG >= 2: print(f"[ChatGPTAPI] Waiting for response to finish. timeout={self.response_timeout}s") - - if stream: - response = web.StreamResponse( - status=200, - reason="OK", - headers={ - "Content-Type": "text/event-stream", - "Cache-Control": "no-cache", - }, - ) - await response.prepare(request) - - try: - # Stream tokens while waiting for inference to complete - while True: - if DEBUG >= 2: print(f"[ChatGPTAPI] Waiting for token from queue: {request_id=}") - tokens, is_finished = await asyncio.wait_for( - self.token_queues[request_id].get(), - timeout=self.response_timeout - ) - if DEBUG >= 2: print(f"[ChatGPTAPI] Got token from queue: {request_id=} {tokens=} {is_finished=}") - - eos_token_id = None - if not eos_token_id and hasattr(tokenizer, "eos_token_id"): eos_token_id = tokenizer.eos_token_id - if not eos_token_id and hasattr(tokenizer, "_tokenizer"): eos_token_id = tokenizer.special_tokens_map.get("eos_token_id") - - finish_reason = None - if is_finished: finish_reason = "stop" if tokens[-1] == eos_token_id else "length" - if DEBUG >= 2: print(f"{eos_token_id=} {tokens[-1]=} {finish_reason=}") - - completion = generate_completion( - chat_request, - tokenizer, - prompt, - request_id, - tokens, - stream, - finish_reason, - "chat.completion", - ) - - await response.write(f"data: {json.dumps(completion)}\n\n".encode()) - - if is_finished: - break - - await response.write_eof() - return response - - except asyncio.TimeoutError: - if DEBUG >= 2: print(f"[ChatGPTAPI] Timeout waiting for token: {request_id=}") - return web.json_response({"detail": "Response generation timed out"}, status=408) - - except Exception as e: - if DEBUG >= 2: - print(f"[ChatGPTAPI] Error processing prompt: {e}") - traceback.print_exc() - return web.json_response( - {"detail": f"Error processing prompt: {str(e)}"}, - status=500 - ) - - finally: - # Clean up the queue for this request - if request_id in self.token_queues: - if DEBUG >= 2: print(f"[ChatGPTAPI] Cleaning up token queue: {request_id=}") - del self.token_queues[request_id] - else: - tokens = [] - while True: - _tokens, is_finished = await asyncio.wait_for(self.token_queues[request_id].get(), timeout=self.response_timeout) - tokens.extend(_tokens) - if is_finished: - break - finish_reason = "length" - eos_token_id = None - if not eos_token_id and hasattr(tokenizer, "eos_token_id"): eos_token_id = tokenizer.eos_token_id - if not eos_token_id and hasattr(tokenizer, "_tokenizer"): eos_token_id = tokenizer.special_tokens_map.get("eos_token_id") - if DEBUG >= 2: print(f"Checking if end of tokens result {tokens[-1]=} is {eos_token_id=}") - if tokens[-1] == eos_token_id: - finish_reason = "stop" - - return web.json_response(generate_completion(chat_request, tokenizer, prompt, request_id, tokens, stream, finish_reason, "chat.completion")) - except asyncio.TimeoutError: - return web.json_response({"detail": "Response generation timed out"}, status=408) - except Exception as e: - if DEBUG >= 2: traceback.print_exc() - return web.json_response({"detail": f"Error processing prompt (see logs with DEBUG>=2): {str(e)}"}, status=500) - - async def handle_post_image_generations(self, request): - data = await request.json() - - if DEBUG >= 2: print(f"Handling chat completions request from {request.remote}: {data}") - stream = data.get("stream", False) - model = data.get("model", "") - prompt = data.get("prompt", "") - image_url = data.get("image_url", "") - if DEBUG >= 2: print(f"model: {model}, prompt: {prompt}, stream: {stream}") - shard = build_base_shard(model, self.inference_engine_classname) - if DEBUG >= 2: print(f"shard: {shard}") - if not shard: - return web.json_response({"error": f"Unsupported model: {model} with inference engine {self.inference_engine_classname}"}, status=400) - - request_id = str(uuid.uuid4()) - callback_id = f"chatgpt-api-wait-response-{request_id}" - callback = self.node.on_token.register(callback_id) - try: - if image_url != "" and image_url != None: - img = self.base64_decode(image_url) - else: - img = None - await asyncio.wait_for(asyncio.shield(asyncio.create_task(self.node.process_prompt(shard, prompt, request_id=request_id, inference_state={"image": img}))), timeout=self.response_timeout) - - response = web.StreamResponse(status=200, reason='OK', headers={ - 'Content-Type': 'application/octet-stream', - "Cache-Control": "no-cache", - }) - await response.prepare(request) - - def get_progress_bar(current_step, total_steps, bar_length=50): - # Calculate the percentage of completion - percent = float(current_step)/total_steps - # Calculate the number of hashes to display - arrow = '-'*int(round(percent*bar_length) - 1) + '>' - spaces = ' '*(bar_length - len(arrow)) - - # Create the progress bar string - progress_bar = f'Progress: [{arrow}{spaces}] {int(percent * 100)}% ({current_step}/{total_steps})' - return progress_bar - - async def stream_image(_request_id: str, result, is_finished: bool): - if isinstance(result, list): - await response.write(json.dumps({'progress': get_progress_bar((result[0]), (result[1]))}).encode('utf-8') + b'\n') - - elif isinstance(result, np.ndarray): - try: - im = Image.fromarray(np.array(result)) - # Save the image to a file - image_filename = f"{_request_id}.png" - image_path = self.images_dir/image_filename - im.save(image_path) - - # Get URL for the saved image - try: - image_url = request.app.router['static_images'].url_for(filename=image_filename) - base_url = f"{request.scheme}://{request.host}" - full_image_url = base_url + str(image_url) - - await response.write(json.dumps({'images': [{'url': str(full_image_url), 'content_type': 'image/png'}]}).encode('utf-8') + b'\n') - except KeyError as e: - if DEBUG >= 2: print(f"Error getting image URL: {e}") - # Fallback to direct file path if URL generation fails - await response.write(json.dumps({'images': [{'url': str(image_path), 'content_type': 'image/png'}]}).encode('utf-8') + b'\n') - - if is_finished: - await response.write_eof() - - except Exception as e: - if DEBUG >= 2: print(f"Error processing image: {e}") - if DEBUG >= 2: traceback.print_exc() - await response.write(json.dumps({'error': str(e)}).encode('utf-8') + b'\n') - - stream_task = None - - def on_result(_request_id: str, result, is_finished: bool): - nonlocal stream_task - stream_task = asyncio.create_task(stream_image(_request_id, result, is_finished)) - return _request_id == request_id and is_finished - - await callback.wait(on_result, timeout=self.response_timeout*10) - - if stream_task: - # Wait for the stream task to complete before returning - await stream_task - - return response - - except Exception as e: - if DEBUG >= 2: traceback.print_exc() - return web.json_response({"detail": f"Error processing prompt (see logs with DEBUG>=2): {str(e)}"}, status=500) - - async def handle_delete_model(self, request): - model_id = request.match_info.get('model_name') - try: - if await delete_model(model_id, self.inference_engine_classname): return web.json_response({"status": "success", "message": f"Model {model_id} deleted successfully"}) - else: return web.json_response({"detail": f"Model {model_id} files not found"}, status=404) - except Exception as e: - if DEBUG >= 2: traceback.print_exc() - return web.json_response({"detail": f"Error deleting model: {str(e)}"}, status=500) - - async def handle_get_initial_models(self, request): - model_data = {} - for model_id in get_supported_models([[self.inference_engine_classname]]): - model_data[model_id] = { - "name": get_pretty_name(model_id), - "downloaded": None, # Initially unknown - "download_percentage": None, # Change from 0 to null - "total_size": None, - "total_downloaded": None, - "loading": True # Add loading state - } - return web.json_response(model_data) - - async def handle_create_animation(self, request): - try: - data = await request.json() - replacement_image_path = data.get("replacement_image_path") - device_name = data.get("device_name", "Local Device") - prompt_text = data.get("prompt", "") - - if DEBUG >= 2: print(f"Creating animation with params: replacement_image={replacement_image_path}, device={device_name}, prompt={prompt_text}") - - if not replacement_image_path: - return web.json_response({"error": "replacement_image_path is required"}, status=400) - - # Create temp directory if it doesn't exist - tmp_dir = Path(tempfile.gettempdir())/"exo_animations" - tmp_dir.mkdir(parents=True, exist_ok=True) - - # Generate unique output filename in temp directory - output_filename = f"animation_{uuid.uuid4()}.mp4" - output_path = str(tmp_dir/output_filename) - - if DEBUG >= 2: print(f"Animation temp directory: {tmp_dir}, output file: {output_path}, directory exists: {tmp_dir.exists()}, directory permissions: {oct(tmp_dir.stat().st_mode)[-3:]}") - - # Create the animation - create_animation_mp4(replacement_image_path, output_path, device_name, prompt_text) - - return web.json_response({"status": "success", "output_path": output_path}) - - except Exception as e: - if DEBUG >= 2: traceback.print_exc() - return web.json_response({"error": str(e)}, status=500) - - async def handle_post_download(self, request): - try: - data = await request.json() - model_name = data.get("model") - if not model_name: return web.json_response({"error": "model parameter is required"}, status=400) - if model_name not in model_cards: return web.json_response({"error": f"Invalid model: {model_name}. Supported models: {list(model_cards.keys())}"}, status=400) - shard = build_full_shard(model_name, self.inference_engine_classname) - if not shard: return web.json_response({"error": f"Could not build shard for model {model_name}"}, status=400) - asyncio.create_task(self.node.inference_engine.shard_downloader.ensure_shard(shard, self.inference_engine_classname)) - - return web.json_response({"status": "success", "message": f"Download started for model: {model_name}"}) - except Exception as e: - if DEBUG >= 2: traceback.print_exc() - return web.json_response({"error": str(e)}, status=500) - - async def handle_get_topology(self, request): - try: - topology = self.node.current_topology - if topology: - return web.json_response(topology.to_json()) - else: - return web.json_response({}) - except Exception as e: - if DEBUG >= 2: traceback.print_exc() - return web.json_response({"detail": f"Error getting topology: {str(e)}"}, status=500) - - async def handle_tokens(self, request_id: str, tokens: List[int], is_finished: bool): - await self.token_queues[request_id].put((tokens, is_finished)) - - async def run(self, host: str = "0.0.0.0", port: int = 52415): - runner = web.AppRunner(self.app) - await runner.setup() - site = web.TCPSite(runner, host, port) - await site.start() - - def base64_decode(self, base64_string): - #decode and reshape image - if base64_string.startswith('data:image'): - base64_string = base64_string.split(',')[1] - image_data = base64.b64decode(base64_string) - img = Image.open(BytesIO(image_data)) - W, H = (dim - dim%64 for dim in (img.width, img.height)) - if W != img.width or H != img.height: - if DEBUG >= 2: print(f"Warning: image shape is not divisible by 64, downsampling to {W}x{H}") - img = img.resize((W, H), Image.NEAREST) # use desired downsampling filter - img = mx.array(np.array(img)) - img = (img[:, :, :3].astype(mx.float32)/255)*2 - 1 - img = img[None] - return img diff --git a/exo/apputil/__init__.py b/exo/apputil/__init__.py deleted file mode 100644 index b6ac6e25..00000000 --- a/exo/apputil/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from exo.apputil.anim import create_animation_mp4 \ No newline at end of file diff --git a/exo/apputil/anim.py b/exo/apputil/anim.py deleted file mode 100644 index b64aace3..00000000 --- a/exo/apputil/anim.py +++ /dev/null @@ -1,168 +0,0 @@ -from PIL import Image, ImageDraw, ImageFont, ImageFilter -import os -import numpy as np -import cv2 -import sys - -def draw_rounded_rectangle(draw, coords, radius, fill): - left, top, right, bottom = coords - diameter = radius * 2 - draw.rectangle([left + radius, top, right - radius, bottom], fill=fill) - draw.rectangle([left, top + radius, right, bottom - radius], fill=fill) - draw.pieslice([left, top, left + diameter, top + diameter], 180, 270, fill=fill) - draw.pieslice([right - diameter, top, right, top + diameter], 270, 360, fill=fill) - draw.pieslice([left, bottom - diameter, left + diameter, bottom], 90, 180, fill=fill) - draw.pieslice([right - diameter, bottom - diameter, right, bottom], 0, 90, fill=fill) - -def draw_centered_text_rounded(draw, text, font, rect_coords, radius=10, text_color="yellow", bg_color=(43,33,44)): - bbox = font.getbbox(text) - text_width = bbox[2] - bbox[0] - text_height = bbox[3] - bbox[1] - rect_left, rect_top, rect_right, rect_bottom = rect_coords - rect_width = rect_right - rect_left - rect_height = rect_bottom - rect_top - text_x = rect_left + (rect_width - text_width) // 2 - text_y = rect_top + (rect_height - text_height) // 2 - draw_rounded_rectangle(draw, rect_coords, radius, bg_color) - draw.text((text_x, text_y), text, fill=text_color, font=font) - -def draw_left_aligned_text_rounded(draw, text, font, rect_coords, padding_left=20, radius=10, text_color="yellow", bg_color=(43,33,44)): - bbox = font.getbbox(text) - text_height = bbox[3] - bbox[1] - rect_left, rect_top, rect_right, rect_bottom = rect_coords - rect_height = rect_bottom - rect_top - text_y = rect_top + (rect_height - text_height) // 2 - text_x = rect_left + padding_left - draw_rounded_rectangle(draw, rect_coords, radius, bg_color) - draw.text((text_x, text_y), text, fill=text_color, font=font) - -def draw_right_text_dynamic_width_rounded(draw, text, font, base_coords, padding=20, radius=10, text_color="yellow", bg_color=(43,33,44)): - bbox = font.getbbox(text) - text_width = bbox[2] - bbox[0] - text_height = bbox[3] - bbox[1] - _, rect_top, rect_right, rect_bottom = base_coords - rect_height = rect_bottom - rect_top - new_rect_left = rect_right - (text_width + (padding * 2)) - text_y = rect_top + (rect_height - text_height) // 2 - text_x = new_rect_left + padding - draw_rounded_rectangle(draw, (new_rect_left, rect_top, rect_right, rect_bottom), radius, bg_color) - draw.text((text_x, text_y), text, fill=text_color, font=font) - return new_rect_left - -def draw_progress_bar(draw, progress, coords, color="yellow", bg_color=(70, 70, 70)): - left, top, right, bottom = coords - total_width = right - left - draw.rectangle(coords, fill=bg_color) - progress_width = int(total_width * progress) - if progress_width > 0: - draw.rectangle((left, top, left + progress_width, bottom), fill=color) - -def crop_image(image, top_crop=70): - width, height = image.size - return image.crop((0, top_crop, width, height)) - -def create_animation_mp4( - replacement_image_path, - output_path, - device_name, - prompt_text, - fps=30, - target_size=(512, 512), - target_position=(139, 755), - progress_coords=(139, 1285, 655, 1295), - device_coords=(1240, 370, 1640, 416), - prompt_coords=(332, 1702, 2662, 1745) -): - frames = [] - try: - font = ImageFont.truetype("/System/Library/Fonts/SFNSMono.ttf", 20) - promptfont = ImageFont.truetype("/System/Library/Fonts/SFNSMono.ttf", 24) - except: - font = ImageFont.load_default() - promptfont = ImageFont.load_default() - - # Get the base directory for images when running as a bundled app - if hasattr(sys, '_MEIPASS'): - base_dir = os.path.join(sys._MEIPASS, "exo", "apputil", "baseimages") - else: - base_dir = os.path.join(os.path.dirname(__file__), "baseimages") - - # Process first frame - base_img = Image.open(os.path.join(base_dir, "image1.png")) - draw = ImageDraw.Draw(base_img) - draw_centered_text_rounded(draw, device_name, font, device_coords) - frames.extend([crop_image(base_img)] * 30) # 1 second at 30fps - - # Process second frame with typing animation - base_img2 = Image.open(os.path.join(base_dir, "image2.png")) - for i in range(len(prompt_text) + 1): - current_frame = base_img2.copy() - draw = ImageDraw.Draw(current_frame) - draw_centered_text_rounded(draw, device_name, font, device_coords) - if i > 0: # Only draw if we have at least one character - draw_left_aligned_text_rounded(draw, prompt_text[:i], promptfont, prompt_coords) - frames.extend([crop_image(current_frame)] * 2) # 2 frames per character for smooth typing - - # Hold the complete prompt for a moment - frames.extend([frames[-1]] * 30) # Hold for 1 second - - # Create blur sequence - replacement_img = Image.open(replacement_image_path) - base_img = Image.open(os.path.join(base_dir, "image3.png")) - blur_steps = [int(80 * (1 - i/8)) for i in range(9)] - - for i, blur_amount in enumerate(blur_steps): - new_frame = base_img.copy() - draw = ImageDraw.Draw(new_frame) - - replacement_copy = replacement_img.copy() - replacement_copy.thumbnail(target_size, Image.Resampling.LANCZOS) - if blur_amount > 0: - replacement_copy = replacement_copy.filter(ImageFilter.GaussianBlur(radius=blur_amount)) - - mask = replacement_copy.split()[-1] if replacement_copy.mode in ('RGBA', 'LA') else None - new_frame.paste(replacement_copy, target_position, mask) - - draw_progress_bar(draw, (i + 1) / 9, progress_coords) - draw_centered_text_rounded(draw, device_name, font, device_coords) - draw_right_text_dynamic_width_rounded(draw, prompt_text, promptfont, (None, 590, 2850, 685), padding=30) - - frames.extend([crop_image(new_frame)] * 15) # 0.5 seconds at 30fps - - # Create and add final frame (image4) - final_base = Image.open(os.path.join(base_dir, "image4.png")) - draw = ImageDraw.Draw(final_base) - - draw_centered_text_rounded(draw, device_name, font, device_coords) - draw_right_text_dynamic_width_rounded(draw, prompt_text, promptfont, (None, 590, 2850, 685), padding=30) - - replacement_copy = replacement_img.copy() - replacement_copy.thumbnail(target_size, Image.Resampling.LANCZOS) - mask = replacement_copy.split()[-1] if replacement_copy.mode in ('RGBA', 'LA') else None - final_base.paste(replacement_copy, target_position, mask) - - frames.extend([crop_image(final_base)] * 30) # 1 second at 30fps - - # Convert frames to video using H.264 codec - if frames: - first_frame = np.array(frames[0]) - height, width = first_frame.shape[:2] - fourcc = cv2.VideoWriter_fourcc(*'avc1') - out = cv2.VideoWriter( - output_path, - fourcc, - fps, - (width, height), - isColor=True - ) - - if not out.isOpened(): - print("Error: VideoWriter failed to open") - return - - for frame in frames: - frame_array = cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR) - out.write(frame_array) - - out.release() - print(f"Video saved successfully to {output_path}") diff --git a/exo/apputil/baseimages/image1.png b/exo/apputil/baseimages/image1.png deleted file mode 100644 index b8375e7e..00000000 --- a/exo/apputil/baseimages/image1.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:361fdadd67c277d45cd18b0bfc8c5ceea5fd89f2d65aef157fd915ce9cbb8599 -size 814460 diff --git a/exo/apputil/baseimages/image2.png b/exo/apputil/baseimages/image2.png deleted file mode 100644 index b2d4f31c..00000000 --- a/exo/apputil/baseimages/image2.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f0e3891bc6b4f4dfa7444af53fcaa4b3ba06b0549546202be3243f08a0e6bd7e -size 814235 diff --git a/exo/apputil/baseimages/image3.png b/exo/apputil/baseimages/image3.png deleted file mode 100644 index 9ee38a57..00000000 --- a/exo/apputil/baseimages/image3.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a2dc5b3378aef397d60fd1252da8a1c578ad97e202a859590ffa416b49551d19 -size 146633 diff --git a/exo/apputil/baseimages/image4.png b/exo/apputil/baseimages/image4.png deleted file mode 100644 index e0959d7b..00000000 --- a/exo/apputil/baseimages/image4.png +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:dbc6883e2a3c5233ec7b844c98646922bdc4f5e42e1f424857eaff56f785dbcd -size 668550 diff --git a/exo/download/__init__.py b/exo/download/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/download/download_progress.py b/exo/download/download_progress.py deleted file mode 100644 index c02733a3..00000000 --- a/exo/download/download_progress.py +++ /dev/null @@ -1,65 +0,0 @@ -from typing import Dict, Callable, Coroutine, Any, Literal -from exo.inference.shard import Shard -from dataclasses import dataclass -from datetime import timedelta - - -@dataclass -class RepoFileProgressEvent: - repo_id: str - repo_revision: str - file_path: str - downloaded: int - downloaded_this_session: int - total: int - speed: int - eta: timedelta - status: Literal["not_started", "in_progress", "complete"] - start_time: float - - def to_dict(self): - return { - "repo_id": self.repo_id, "repo_revision": self.repo_revision, "file_path": self.file_path, "downloaded": self.downloaded, "downloaded_this_session": self.downloaded_this_session, - "total": self.total, "speed": self.speed, "eta": self.eta.total_seconds(), "status": self.status, "start_time": self.start_time - } - - @classmethod - def from_dict(cls, data): - if 'eta' in data: data['eta'] = timedelta(seconds=data['eta']) - return cls(**data) - - -@dataclass -class RepoProgressEvent: - shard: Shard - repo_id: str - repo_revision: str - completed_files: int - total_files: int - downloaded_bytes: int - downloaded_bytes_this_session: int - total_bytes: int - overall_speed: int - overall_eta: timedelta - file_progress: Dict[str, RepoFileProgressEvent] - status: Literal["not_started", "in_progress", "complete"] - - def to_dict(self): - return { - "shard": self.shard.to_dict(), "repo_id": self.repo_id, "repo_revision": self.repo_revision, "completed_files": self.completed_files, "total_files": self.total_files, "downloaded_bytes": self.downloaded_bytes, - "downloaded_bytes_this_session": self.downloaded_bytes_this_session, "total_bytes": self.total_bytes, "overall_speed": self.overall_speed, "overall_eta": self.overall_eta.total_seconds(), - "file_progress": {k: v.to_dict() - for k, v in self.file_progress.items()}, "status": self.status - } - - @classmethod - def from_dict(cls, data): - if 'overall_eta' in data: data['overall_eta'] = timedelta(seconds=data['overall_eta']) - if 'file_progress' in data: data['file_progress'] = {k: RepoFileProgressEvent.from_dict(v) for k, v in data['file_progress'].items()} - if 'shard' in data: data['shard'] = Shard.from_dict(data['shard']) - - return cls(**data) - - -RepoFileProgressCallback = Callable[[RepoFileProgressEvent], Coroutine[Any, Any, None]] -RepoProgressCallback = Callable[[RepoProgressEvent], Coroutine[Any, Any, None]] diff --git a/exo/download/hf/__init__.py b/exo/download/hf/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/download/hf/hf_helpers.py b/exo/download/hf/hf_helpers.py deleted file mode 100644 index 01cd8650..00000000 --- a/exo/download/hf/hf_helpers.py +++ /dev/null @@ -1,98 +0,0 @@ -import aiofiles.os as aios -from typing import Union -import os -from typing import Callable, Optional, Dict, List, Union -from fnmatch import fnmatch -from pathlib import Path -from typing import Generator, Iterable, TypeVar -from exo.helpers import DEBUG -from exo.inference.shard import Shard -import aiofiles - -T = TypeVar("T") - -def filter_repo_objects( - items: Iterable[T], - *, - allow_patterns: Optional[Union[List[str], str]] = None, - ignore_patterns: Optional[Union[List[str], str]] = None, - key: Optional[Callable[[T], str]] = None, -) -> Generator[T, None, None]: - if isinstance(allow_patterns, str): - allow_patterns = [allow_patterns] - if isinstance(ignore_patterns, str): - ignore_patterns = [ignore_patterns] - if allow_patterns is not None: - allow_patterns = [_add_wildcard_to_directories(p) for p in allow_patterns] - if ignore_patterns is not None: - ignore_patterns = [_add_wildcard_to_directories(p) for p in ignore_patterns] - - if key is None: - def _identity(item: T) -> str: - if isinstance(item, str): - return item - if isinstance(item, Path): - return str(item) - raise ValueError(f"Please provide `key` argument in `filter_repo_objects`: `{item}` is not a string.") - key = _identity - - for item in items: - path = key(item) - if allow_patterns is not None and not any(fnmatch(path, r) for r in allow_patterns): - continue - if ignore_patterns is not None and any(fnmatch(path, r) for r in ignore_patterns): - continue - yield item - -def _add_wildcard_to_directories(pattern: str) -> str: - if pattern[-1] == "/": - return pattern + "*" - return pattern - -def get_hf_endpoint() -> str: - return os.environ.get('HF_ENDPOINT', "https://huggingface.co") - -def get_hf_home() -> Path: - """Get the Hugging Face home directory.""" - return Path(os.environ.get("HF_HOME", Path.home()/".cache"/"huggingface")) - -async def get_hf_token(): - """Retrieve the Hugging Face token from the user's HF_HOME directory.""" - token_path = get_hf_home()/"token" - if await aios.path.exists(token_path): - async with aiofiles.open(token_path, 'r') as f: - return (await f.read()).strip() - return None - -async def get_auth_headers(): - """Get authentication headers if a token is available.""" - token = await get_hf_token() - if token: - return {"Authorization": f"Bearer {token}"} - return {} - -def extract_layer_num(tensor_name: str) -> Optional[int]: - # This is a simple example and might need to be adjusted based on the actual naming convention - parts = tensor_name.split('.') - for part in parts: - if part.isdigit(): - return int(part) - return None - -def get_allow_patterns(weight_map: Dict[str, str], shard: Shard) -> List[str]: - default_patterns = set(["*.json", "*.py", "tokenizer.model", "*.tiktoken", "*.txt"]) - shard_specific_patterns = set() - if weight_map: - for tensor_name, filename in weight_map.items(): - layer_num = extract_layer_num(tensor_name) - if layer_num is not None and shard.start_layer <= layer_num <= shard.end_layer: - shard_specific_patterns.add(filename) - sorted_file_names = sorted(weight_map.values()) - if shard.is_first_layer(): - shard_specific_patterns.add(sorted_file_names[0]) - elif shard.is_last_layer(): - shard_specific_patterns.add(sorted_file_names[-1]) - else: - shard_specific_patterns = set(["*.safetensors"]) - if DEBUG >= 3: print(f"get_allow_patterns {weight_map=} {shard=} {shard_specific_patterns=}") - return list(default_patterns | shard_specific_patterns) diff --git a/exo/download/new_shard_download.py b/exo/download/new_shard_download.py deleted file mode 100644 index 02e9f7bf..00000000 --- a/exo/download/new_shard_download.py +++ /dev/null @@ -1,307 +0,0 @@ -from exo.inference.shard import Shard -from exo.models import get_repo -from pathlib import Path -from exo.download.hf.hf_helpers import get_hf_endpoint, get_auth_headers, filter_repo_objects, get_allow_patterns -from exo.download.shard_download import ShardDownloader -from exo.download.download_progress import RepoProgressEvent, RepoFileProgressEvent -from exo.helpers import AsyncCallbackSystem, DEBUG -from exo.models import get_supported_models, build_full_shard -import os -import aiofiles.os as aios -import aiohttp -import aiofiles -from urllib.parse import urljoin -from typing import Callable, Union, Tuple, Dict, List, Optional, Literal, AsyncIterator -import time -from datetime import timedelta -import asyncio -import json -import traceback -import shutil -import tempfile -import hashlib - -def exo_home() -> Path: - return Path(os.environ.get("EXO_HOME", Path.home()/".cache"/"exo")) - -def exo_tmp() -> Path: - return Path(tempfile.gettempdir())/"exo" - -async def ensure_exo_home() -> Path: - await aios.makedirs(exo_home(), exist_ok=True) - return exo_home() - -async def ensure_exo_tmp() -> Path: - await aios.makedirs(exo_tmp(), exist_ok=True) - return exo_tmp() - -async def has_exo_home_read_access() -> bool: - try: return await aios.access(exo_home(), os.R_OK) - except OSError: return False - -async def has_exo_home_write_access() -> bool: - try: return await aios.access(exo_home(), os.W_OK) - except OSError: return False - -async def ensure_downloads_dir() -> Path: - downloads_dir = exo_home()/"downloads" - await aios.makedirs(downloads_dir, exist_ok=True) - return downloads_dir - -async def delete_model(model_id: str, inference_engine_name: str) -> bool: - repo_id = get_repo(model_id, inference_engine_name) - model_dir = await ensure_downloads_dir()/repo_id.replace("/", "--") - if not await aios.path.exists(model_dir): return False - await asyncio.to_thread(shutil.rmtree, model_dir, ignore_errors=False) - return True - -async def seed_models(seed_dir: Union[str, Path]): - """Move model in resources folder of app to .cache/huggingface/hub""" - source_dir = Path(seed_dir) - dest_dir = await ensure_downloads_dir() - for path in source_dir.iterdir(): - if path.is_dir() and path.name.startswith("models--"): - dest_path = dest_dir/path.name - if await aios.path.exists(dest_path): print('Skipping moving model to .cache directory') - else: - try: await aios.rename(str(path), str(dest_path)) - except: - print(f"Error seeding model {path} to {dest_path}") - traceback.print_exc() - -async def fetch_file_list_with_cache(repo_id: str, revision: str = "main") -> List[Dict[str, Union[str, int]]]: - cache_file = (await ensure_exo_tmp())/f"{repo_id.replace('/', '--')}--{revision}--file_list.json" - if await aios.path.exists(cache_file): - async with aiofiles.open(cache_file, 'r') as f: return json.loads(await f.read()) - file_list = await fetch_file_list_with_retry(repo_id, revision) - await aios.makedirs(cache_file.parent, exist_ok=True) - async with aiofiles.open(cache_file, 'w') as f: await f.write(json.dumps(file_list)) - return file_list - -async def fetch_file_list_with_retry(repo_id: str, revision: str = "main", path: str = "") -> List[Dict[str, Union[str, int]]]: - n_attempts = 30 - for attempt in range(n_attempts): - try: return await _fetch_file_list(repo_id, revision, path) - except Exception as e: - if attempt == n_attempts - 1: raise e - await asyncio.sleep(min(8, 0.1 * (2 ** attempt))) - -async def _fetch_file_list(repo_id: str, revision: str = "main", path: str = "") -> List[Dict[str, Union[str, int]]]: - api_url = f"{get_hf_endpoint()}/api/models/{repo_id}/tree/{revision}" - url = f"{api_url}/{path}" if path else api_url - - headers = await get_auth_headers() - async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=30, connect=10, sock_read=30, sock_connect=10)) as session: - async with session.get(url, headers=headers) as response: - if response.status == 200: - data = await response.json() - files = [] - for item in data: - if item["type"] == "file": - files.append({"path": item["path"], "size": item["size"]}) - elif item["type"] == "directory": - subfiles = await _fetch_file_list(repo_id, revision, item["path"]) - files.extend(subfiles) - return files - else: - raise Exception(f"Failed to fetch file list: {response.status}") - -async def calc_hash(path: Path, type: Literal["sha1", "sha256"] = "sha1") -> str: - hash = hashlib.sha1() if type == "sha1" else hashlib.sha256() - if type == "sha1": - header = f"blob {(await aios.stat(path)).st_size}\0".encode() - hash.update(header) - async with aiofiles.open(path, 'rb') as f: - while chunk := await f.read(8 * 1024 * 1024): - hash.update(chunk) - return hash.hexdigest() - -async def file_meta(repo_id: str, revision: str, path: str) -> Tuple[int, str]: - url = urljoin(f"{get_hf_endpoint()}/{repo_id}/resolve/{revision}/", path) - headers = await get_auth_headers() - async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=1800, connect=60, sock_read=1800, sock_connect=60)) as session: - async with session.head(url, headers=headers) as r: - content_length = int(r.headers.get('x-linked-size') or r.headers.get('content-length') or 0) - etag = r.headers.get('X-Linked-ETag') or r.headers.get('ETag') or r.headers.get('Etag') - assert content_length > 0, f"No content length for {url}" - assert etag is not None, f"No remote hash for {url}" - if (etag[0] == '"' and etag[-1] == '"') or (etag[0] == "'" and etag[-1] == "'"): etag = etag[1:-1] - return content_length, etag - -async def download_file_with_retry(repo_id: str, revision: str, path: str, target_dir: Path, on_progress: Callable[[int, int], None] = lambda _, __: None) -> Path: - n_attempts = 30 - for attempt in range(n_attempts): - try: return await _download_file(repo_id, revision, path, target_dir, on_progress) - except Exception as e: - if isinstance(e, FileNotFoundError) or attempt == n_attempts - 1: raise e - print(f"Download error on attempt {attempt}/{n_attempts} for {repo_id=} {revision=} {path=} {target_dir=}") - traceback.print_exc() - await asyncio.sleep(min(8, 0.1 * (2 ** attempt))) - -async def _download_file(repo_id: str, revision: str, path: str, target_dir: Path, on_progress: Callable[[int, int], None] = lambda _, __: None) -> Path: - if await aios.path.exists(target_dir/path): return target_dir/path - await aios.makedirs((target_dir/path).parent, exist_ok=True) - length, etag = await file_meta(repo_id, revision, path) - remote_hash = etag[:-5] if etag.endswith("-gzip") else etag - partial_path = target_dir/f"{path}.partial" - resume_byte_pos = (await aios.stat(partial_path)).st_size if (await aios.path.exists(partial_path)) else None - if resume_byte_pos != length: - url = urljoin(f"{get_hf_endpoint()}/{repo_id}/resolve/{revision}/", path) - headers = await get_auth_headers() - if resume_byte_pos: headers['Range'] = f'bytes={resume_byte_pos}-' - n_read = resume_byte_pos or 0 - async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=1800, connect=60, sock_read=1800, sock_connect=60)) as session: - async with session.get(url, headers=headers, timeout=aiohttp.ClientTimeout(total=1800, connect=60, sock_read=1800, sock_connect=60)) as r: - if r.status == 404: raise FileNotFoundError(f"File not found: {url}") - assert r.status in [200, 206], f"Failed to download {path} from {url}: {r.status}" - async with aiofiles.open(partial_path, 'ab' if resume_byte_pos else 'wb') as f: - while chunk := await r.content.read(8 * 1024 * 1024): on_progress(n_read := n_read + await f.write(chunk), length) - - final_hash = await calc_hash(partial_path, type="sha256" if len(remote_hash) == 64 else "sha1") - integrity = final_hash == remote_hash - if not integrity: - try: await aios.remove(partial_path) - except Exception as e: print(f"Error removing partial file {partial_path}: {e}") - raise Exception(f"Downloaded file {target_dir/path} has hash {final_hash} but remote hash is {remote_hash}") - await aios.rename(partial_path, target_dir/path) - return target_dir/path - - -def calculate_repo_progress(shard: Shard, repo_id: str, revision: str, file_progress: Dict[str, RepoFileProgressEvent], all_start_time: float) -> RepoProgressEvent: - all_total_bytes = sum([p.total for p in file_progress.values()]) - all_downloaded_bytes = sum([p.downloaded for p in file_progress.values()]) - all_downloaded_bytes_this_session = sum([p.downloaded_this_session for p in file_progress.values()]) - elapsed_time = time.time() - all_start_time - all_speed = all_downloaded_bytes_this_session / elapsed_time if elapsed_time > 0 else 0 - all_eta = timedelta(seconds=(all_total_bytes - all_downloaded_bytes) / all_speed) if all_speed > 0 else timedelta(seconds=0) - status = "complete" if all(p.status == "complete" for p in file_progress.values()) else "in_progress" if any(p.status == "in_progress" for p in file_progress.values()) else "not_started" - return RepoProgressEvent(shard, repo_id, revision, len([p for p in file_progress.values() if p.downloaded == p.total]), len(file_progress), all_downloaded_bytes, all_downloaded_bytes_this_session, all_total_bytes, all_speed, all_eta, file_progress, status) - -async def get_weight_map(repo_id: str, revision: str = "main") -> Dict[str, str]: - target_dir = (await ensure_exo_tmp())/repo_id.replace("/", "--") - index_file = await download_file_with_retry(repo_id, revision, "model.safetensors.index.json", target_dir) - async with aiofiles.open(index_file, 'r') as f: index_data = json.loads(await f.read()) - return index_data.get("weight_map") - -async def resolve_allow_patterns(shard: Shard, inference_engine_classname: str) -> List[str]: - try: - weight_map = await get_weight_map(get_repo(shard.model_id, inference_engine_classname)) - return get_allow_patterns(weight_map, shard) - except: - if DEBUG >= 1: print(f"Error getting weight map for {shard.model_id=} and inference engine {inference_engine_classname}") - if DEBUG >= 1: traceback.print_exc() - return ["*"] - -async def get_downloaded_size(path: Path) -> int: - partial_path = path.with_suffix(path.suffix + ".partial") - if await aios.path.exists(path): return (await aios.stat(path)).st_size - if await aios.path.exists(partial_path): return (await aios.stat(partial_path)).st_size - return 0 - -async def download_shard(shard: Shard, inference_engine_classname: str, on_progress: AsyncCallbackSystem[str, Tuple[Shard, RepoProgressEvent]], max_parallel_downloads: int = 8, skip_download: bool = False) -> tuple[Path, RepoProgressEvent]: - if DEBUG >= 2 and not skip_download: print(f"Downloading {shard.model_id=} for {inference_engine_classname}") - repo_id = get_repo(shard.model_id, inference_engine_classname) - revision = "main" - target_dir = await ensure_downloads_dir()/repo_id.replace("/", "--") - if not skip_download: await aios.makedirs(target_dir, exist_ok=True) - - if repo_id is None: - raise ValueError(f"No repo found for {shard.model_id=} and inference engine {inference_engine_classname}") - - allow_patterns = await resolve_allow_patterns(shard, inference_engine_classname) - if DEBUG >= 2: print(f"Downloading {shard.model_id=} with {allow_patterns=}") - - all_start_time = time.time() - file_list = await fetch_file_list_with_cache(repo_id, revision) - filtered_file_list = list(filter_repo_objects(file_list, allow_patterns=allow_patterns, key=lambda x: x["path"])) - file_progress: Dict[str, RepoFileProgressEvent] = {} - def on_progress_wrapper(file: dict, curr_bytes: int, total_bytes: int): - start_time = file_progress[file["path"]].start_time if file["path"] in file_progress else time.time() - downloaded_this_session = file_progress[file["path"]].downloaded_this_session + (curr_bytes - file_progress[file["path"]].downloaded) if file["path"] in file_progress else curr_bytes - speed = downloaded_this_session / (time.time() - start_time) if time.time() - start_time > 0 else 0 - eta = timedelta(seconds=(total_bytes - curr_bytes) / speed) if speed > 0 else timedelta(seconds=0) - file_progress[file["path"]] = RepoFileProgressEvent(repo_id, revision, file["path"], curr_bytes, downloaded_this_session, total_bytes, speed, eta, "complete" if curr_bytes == total_bytes else "in_progress", start_time) - on_progress.trigger_all(shard, calculate_repo_progress(shard, repo_id, revision, file_progress, all_start_time)) - if DEBUG >= 6: print(f"Downloading {file['path']} {curr_bytes}/{total_bytes} {speed} {eta}") - for file in filtered_file_list: - downloaded_bytes = await get_downloaded_size(target_dir/file["path"]) - file_progress[file["path"]] = RepoFileProgressEvent(repo_id, revision, file["path"], downloaded_bytes, 0, file["size"], 0, timedelta(0), "complete" if downloaded_bytes == file["size"] else "not_started", time.time()) - - semaphore = asyncio.Semaphore(max_parallel_downloads) - async def download_with_semaphore(file): - async with semaphore: - await download_file_with_retry(repo_id, revision, file["path"], target_dir, lambda curr_bytes, total_bytes: on_progress_wrapper(file, curr_bytes, total_bytes)) - if not skip_download: await asyncio.gather(*[download_with_semaphore(file) for file in filtered_file_list]) - final_repo_progress = calculate_repo_progress(shard, repo_id, revision, file_progress, all_start_time) - on_progress.trigger_all(shard, final_repo_progress) - if gguf := next((f for f in filtered_file_list if f["path"].endswith(".gguf")), None): - return target_dir/gguf["path"], final_repo_progress - else: - return target_dir, final_repo_progress - -def new_shard_downloader(max_parallel_downloads: int = 8) -> ShardDownloader: - return SingletonShardDownloader(CachedShardDownloader(NewShardDownloader(max_parallel_downloads))) - -class SingletonShardDownloader(ShardDownloader): - def __init__(self, shard_downloader: ShardDownloader): - self.shard_downloader = shard_downloader - self.active_downloads: Dict[Shard, asyncio.Task] = {} - - @property - def on_progress(self) -> AsyncCallbackSystem[str, Tuple[Shard, RepoProgressEvent]]: - return self.shard_downloader.on_progress - - async def ensure_shard(self, shard: Shard, inference_engine_name: str) -> Path: - if shard not in self.active_downloads: self.active_downloads[shard] = asyncio.create_task(self.shard_downloader.ensure_shard(shard, inference_engine_name)) - try: return await self.active_downloads[shard] - finally: - if shard in self.active_downloads and self.active_downloads[shard].done(): del self.active_downloads[shard] - - async def get_shard_download_status(self, inference_engine_name: str) -> AsyncIterator[tuple[Path, RepoProgressEvent]]: - async for path, status in self.shard_downloader.get_shard_download_status(inference_engine_name): - yield path, status - -class CachedShardDownloader(ShardDownloader): - def __init__(self, shard_downloader: ShardDownloader): - self.shard_downloader = shard_downloader - self.cache: Dict[tuple[str, Shard], Path] = {} - - @property - def on_progress(self) -> AsyncCallbackSystem[str, Tuple[Shard, RepoProgressEvent]]: - return self.shard_downloader.on_progress - - async def ensure_shard(self, shard: Shard, inference_engine_name: str) -> Path: - if (inference_engine_name, shard) in self.cache: - if DEBUG >= 2: print(f"ensure_shard cache hit {shard=} for {inference_engine_name}") - return self.cache[(inference_engine_name, shard)] - if DEBUG >= 2: print(f"ensure_shard cache miss {shard=} for {inference_engine_name}") - target_dir = await self.shard_downloader.ensure_shard(shard, inference_engine_name) - self.cache[(inference_engine_name, shard)] = target_dir - return target_dir - - async def get_shard_download_status(self, inference_engine_name: str) -> AsyncIterator[tuple[Path, RepoProgressEvent]]: - async for path, status in self.shard_downloader.get_shard_download_status(inference_engine_name): - yield path, status - -class NewShardDownloader(ShardDownloader): - def __init__(self, max_parallel_downloads: int = 8): - self.max_parallel_downloads = max_parallel_downloads - self._on_progress = AsyncCallbackSystem[str, Tuple[Shard, RepoProgressEvent]]() - - @property - def on_progress(self) -> AsyncCallbackSystem[str, Tuple[Shard, RepoProgressEvent]]: - return self._on_progress - - async def ensure_shard(self, shard: Shard, inference_engine_name: str) -> Path: - target_dir, _ = await download_shard(shard, inference_engine_name, self.on_progress, max_parallel_downloads=self.max_parallel_downloads) - return target_dir - - async def get_shard_download_status(self, inference_engine_name: str) -> AsyncIterator[tuple[Path, RepoProgressEvent]]: - if DEBUG >= 2: print("Getting shard download status for", inference_engine_name) - tasks = [download_shard(build_full_shard(model_id, inference_engine_name), inference_engine_name, self.on_progress, skip_download=True) for model_id in get_supported_models([[inference_engine_name]])] - for task in asyncio.as_completed(tasks): - try: - path, progress = await task - yield (path, progress) - except Exception as e: - print("Error downloading shard:", e) diff --git a/exo/download/shard_download.py b/exo/download/shard_download.py deleted file mode 100644 index 9e06e40a..00000000 --- a/exo/download/shard_download.py +++ /dev/null @@ -1,49 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Optional, Tuple, Dict, AsyncIterator -from pathlib import Path -from exo.inference.shard import Shard -from exo.download.download_progress import RepoProgressEvent -from exo.helpers import AsyncCallbackSystem - - -class ShardDownloader(ABC): - @abstractmethod - async def ensure_shard(self, shard: Shard, inference_engine_name: str) -> Path: - """ - Ensures that the shard is downloaded. - Does not allow multiple overlapping downloads at once. - If you try to download a Shard which overlaps a Shard that is already being downloaded, - the download will be cancelled and a new download will start. - - Args: - shard (Shard): The shard to download. - inference_engine_name (str): The inference engine used on the node hosting the shard - """ - pass - - @property - @abstractmethod - def on_progress(self) -> AsyncCallbackSystem[str, Tuple[Shard, RepoProgressEvent]]: - pass - - @abstractmethod - async def get_shard_download_status(self, inference_engine_name: str) -> AsyncIterator[tuple[Path, RepoProgressEvent]]: - """Get the download status of shards. - - Returns: - Optional[Dict[str, float]]: A dictionary mapping shard IDs to their download percentage (0-100), - or None if status cannot be determined - """ - pass - - -class NoopShardDownloader(ShardDownloader): - async def ensure_shard(self, shard: Shard, inference_engine_name: str) -> Path: - return Path("/tmp/noop_shard") - - @property - def on_progress(self) -> AsyncCallbackSystem[str, Tuple[Shard, RepoProgressEvent]]: - return AsyncCallbackSystem() - - async def get_shard_download_status(self, inference_engine_name: str) -> AsyncIterator[tuple[Path, RepoProgressEvent]]: - if False: yield diff --git a/exo/download/test_new_shard_download.py b/exo/download/test_new_shard_download.py deleted file mode 100644 index 57b33c65..00000000 --- a/exo/download/test_new_shard_download.py +++ /dev/null @@ -1,14 +0,0 @@ -from exo.download.new_shard_download import NewShardDownloader -from exo.inference.shard import Shard -import asyncio - -async def test_new_shard_download(): - shard_downloader = NewShardDownloader() - shard_downloader.on_progress.register("test").on_next(lambda shard, event: print(shard, event)) - await shard_downloader.ensure_shard(Shard(model_id="llama-3.2-1b", start_layer=0, end_layer=0, n_layers=16), "MLXDynamicShardInferenceEngine") - async for path, shard_status in shard_downloader.get_shard_download_status("MLXDynamicShardInferenceEngine"): - print("Shard download status:", path, shard_status) - -if __name__ == "__main__": - asyncio.run(test_new_shard_download()) - diff --git a/exo/helpers.py b/exo/helpers.py deleted file mode 100644 index ff0205f0..00000000 --- a/exo/helpers.py +++ /dev/null @@ -1,372 +0,0 @@ -import os -import sys -import asyncio -from typing import Callable, TypeVar, Optional, Dict, Generic, Tuple, List -import socket -import random -import platform -import psutil -import uuid -from scapy.all import get_if_addr, get_if_list -import re -import subprocess -from pathlib import Path -import tempfile -import json -from concurrent.futures import ThreadPoolExecutor -import traceback - -DEBUG = int(os.getenv("DEBUG", default="0")) -DEBUG_DISCOVERY = int(os.getenv("DEBUG_DISCOVERY", default="0")) -VERSION = "0.0.1" - -exo_text = r""" - _____ _____ - / _ \ \/ / _ \ -| __/> < (_) | - \___/_/\_\___/ - """ - -# Single shared thread pool for subprocess operations -subprocess_pool = ThreadPoolExecutor(max_workers=4, thread_name_prefix="subprocess_worker") - - -def get_system_info(): - if psutil.MACOS: - if platform.machine() == "arm64": - return "Apple Silicon Mac" - if platform.machine() in ["x86_64", "i386"]: - return "Intel Mac" - return "Unknown Mac architecture" - if psutil.LINUX: - return "Linux" - return "Non-Mac, non-Linux system" - - -def find_available_port(host: str = "", min_port: int = 49152, max_port: int = 65535) -> int: - used_ports_file = os.path.join(tempfile.gettempdir(), "exo_used_ports") - - def read_used_ports(): - if os.path.exists(used_ports_file): - with open(used_ports_file, "r") as f: - return [int(line.strip()) for line in f if line.strip().isdigit()] - return [] - - def write_used_port(port, used_ports): - with open(used_ports_file, "w") as f: - print(used_ports[-19:]) - for p in used_ports[-19:] + [port]: - f.write(f"{p}\n") - - used_ports = read_used_ports() - available_ports = set(range(min_port, max_port + 1)) - set(used_ports) - - while available_ports: - port = random.choice(list(available_ports)) - if DEBUG >= 2: print(f"Trying to find available port {port=}") - try: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind((host, port)) - write_used_port(port, used_ports) - return port - except socket.error: - available_ports.remove(port) - - raise RuntimeError("No available ports in the specified range") - - -def print_exo(): - print(exo_text) - - -def print_yellow_exo(): - yellow = "\033[93m" # ANSI escape code for yellow - reset = "\033[0m" # ANSI escape code to reset color - print(f"{yellow}{exo_text}{reset}") - - -def terminal_link(uri, label=None): - if label is None: - label = uri - parameters = "" - - # OSC 8 ; params ; URI ST OSC 8 ;; ST - escape_mask = "\033]8;{};{}\033\\{}\033]8;;\033\\" - - return escape_mask.format(parameters, uri, label) - - -T = TypeVar("T") -K = TypeVar("K") - - -class AsyncCallback(Generic[T]): - def __init__(self) -> None: - self.condition: asyncio.Condition = asyncio.Condition() - self.result: Optional[Tuple[T, ...]] = None - self.observers: list[Callable[..., None]] = [] - - async def wait(self, check_condition: Callable[..., bool], timeout: Optional[float] = None) -> Tuple[T, ...]: - async with self.condition: - await asyncio.wait_for(self.condition.wait_for(lambda: self.result is not None and check_condition(*self.result)), timeout) - assert self.result is not None # for type checking - return self.result - - def on_next(self, callback: Callable[..., None]) -> None: - self.observers.append(callback) - - def set(self, *args: T) -> None: - self.result = args - for observer in self.observers: - observer(*args) - asyncio.create_task(self.notify()) - - async def notify(self) -> None: - async with self.condition: - self.condition.notify_all() - - -class AsyncCallbackSystem(Generic[K, T]): - def __init__(self) -> None: - self.callbacks: Dict[K, AsyncCallback[T]] = {} - - def register(self, name: K) -> AsyncCallback[T]: - if name not in self.callbacks: - self.callbacks[name] = AsyncCallback[T]() - return self.callbacks[name] - - def deregister(self, name: K) -> None: - if name in self.callbacks: - del self.callbacks[name] - - def trigger(self, name: K, *args: T) -> None: - if name in self.callbacks: - self.callbacks[name].set(*args) - - def trigger_all(self, *args: T) -> None: - for callback in self.callbacks.values(): - callback.set(*args) - - -K = TypeVar('K', bound=str) -V = TypeVar('V') - - -class PrefixDict(Generic[K, V]): - def __init__(self): - self.items: Dict[K, V] = {} - - def add(self, key: K, value: V) -> None: - self.items[key] = value - - def find_prefix(self, argument: str) -> List[Tuple[K, V]]: - return [(key, value) for key, value in self.items.items() if argument.startswith(key)] - - def find_longest_prefix(self, argument: str) -> Optional[Tuple[K, V]]: - matches = self.find_prefix(argument) - if len(matches) == 0: - return None - - return max(matches, key=lambda x: len(x[0])) - - -def is_valid_uuid(val): - try: - uuid.UUID(str(val)) - return True - except ValueError: - return False - - -def get_or_create_node_id(): - NODE_ID_FILE = Path(tempfile.gettempdir())/".exo_node_id" - try: - if NODE_ID_FILE.is_file(): - with open(NODE_ID_FILE, "r") as f: - stored_id = f.read().strip() - if is_valid_uuid(stored_id): - if DEBUG >= 2: print(f"Retrieved existing node ID: {stored_id}") - return stored_id - else: - if DEBUG >= 2: print("Stored ID is not a valid UUID. Generating a new one.") - - new_id = str(uuid.uuid4()) - with open(NODE_ID_FILE, "w") as f: - f.write(new_id) - - if DEBUG >= 2: print(f"Generated and stored new node ID: {new_id}") - return new_id - except IOError as e: - if DEBUG >= 2: print(f"IO error creating node_id: {e}") - return str(uuid.uuid4()) - except Exception as e: - if DEBUG >= 2: print(f"Unexpected error creating node_id: {e}") - return str(uuid.uuid4()) - - -def pretty_print_bytes(size_in_bytes: int) -> str: - if size_in_bytes < 1024: - return f"{size_in_bytes} B" - elif size_in_bytes < 1024**2: - return f"{size_in_bytes / 1024:.2f} KB" - elif size_in_bytes < 1024**3: - return f"{size_in_bytes / (1024 ** 2):.2f} MB" - elif size_in_bytes < 1024**4: - return f"{size_in_bytes / (1024 ** 3):.2f} GB" - else: - return f"{size_in_bytes / (1024 ** 4):.2f} TB" - - -def pretty_print_bytes_per_second(bytes_per_second: int) -> str: - if bytes_per_second < 1024: - return f"{bytes_per_second} B/s" - elif bytes_per_second < 1024**2: - return f"{bytes_per_second / 1024:.2f} KB/s" - elif bytes_per_second < 1024**3: - return f"{bytes_per_second / (1024 ** 2):.2f} MB/s" - elif bytes_per_second < 1024**4: - return f"{bytes_per_second / (1024 ** 3):.2f} GB/s" - else: - return f"{bytes_per_second / (1024 ** 4):.2f} TB/s" - - -def get_all_ip_addresses_and_interfaces(): - ip_addresses = [] - for interface in get_if_list(): - try: - ip = get_if_addr(interface) - if ip.startswith("0.0."): continue - simplified_interface = re.sub(r'^\\Device\\NPF_', '', interface) - ip_addresses.append((ip, simplified_interface)) - except: - if DEBUG >= 1: print(f"Failed to get IP address for interface {interface}") - if DEBUG >= 1: traceback.print_exc() - if not ip_addresses: - if DEBUG >= 1: print("Failed to get any IP addresses. Defaulting to localhost.") - return [("localhost", "lo")] - return list(set(ip_addresses)) - - - -async def get_macos_interface_type(ifname: str) -> Optional[Tuple[int, str]]: - try: - # Use the shared subprocess_pool - output = await asyncio.get_running_loop().run_in_executor( - subprocess_pool, lambda: subprocess.run(['system_profiler', 'SPNetworkDataType', '-json'], capture_output=True, text=True, close_fds=True).stdout - ) - - data = json.loads(output) - - for interface in data.get('SPNetworkDataType', []): - if interface.get('interface') == ifname: - hardware = interface.get('hardware', '').lower() - type_name = interface.get('type', '').lower() - name = interface.get('_name', '').lower() - - if 'thunderbolt' in name: - return (5, "Thunderbolt") - if hardware == 'ethernet' or type_name == 'ethernet': - if 'usb' in name: - return (4, "Ethernet [USB]") - return (4, "Ethernet") - if hardware == 'airport' or type_name == 'airport' or 'wi-fi' in name: - return (3, "WiFi") - if type_name == 'vpn': - return (1, "External Virtual") - - except Exception as e: - if DEBUG >= 2: print(f"Error detecting macOS interface type: {e}") - - return None - - -async def get_interface_priority_and_type(ifname: str) -> Tuple[int, str]: - # On macOS, try to get interface type using networksetup - if psutil.MACOS: - macos_type = await get_macos_interface_type(ifname) - if macos_type is not None: return macos_type - - # Local container/virtual interfaces - if (ifname.startswith(('docker', 'br-', 'veth', 'cni', 'flannel', 'calico', 'weave')) or 'bridge' in ifname): - return (7, "Container Virtual") - - # Loopback interface - if ifname.startswith('lo'): - return (6, "Loopback") - - # Traditional detection for non-macOS systems or fallback - if ifname.startswith(('tb', 'nx', 'ten')): - return (5, "Thunderbolt") - - # Regular ethernet detection - if ifname.startswith(('eth', 'en')) and not ifname.startswith(('en1', 'en0')): - return (4, "Ethernet") - - # WiFi detection - if ifname.startswith(('wlan', 'wifi', 'wl')) or ifname in ['en0', 'en1']: - return (3, "WiFi") - - # Non-local virtual interfaces (VPNs, tunnels) - if ifname.startswith(('tun', 'tap', 'vtun', 'utun', 'gif', 'stf', 'awdl', 'llw')): - return (1, "External Virtual") - - # Other physical interfaces - return (2, "Other") - - -async def shutdown(signal, loop, server): - """Gracefully shutdown the server and close the asyncio loop.""" - print(f"Received exit signal {signal.name}...") - print("Thank you for using exo.") - print_yellow_exo() - server_tasks = [t for t in asyncio.all_tasks() if t is not asyncio.current_task()] - [task.cancel() for task in server_tasks] - print(f"Cancelling {len(server_tasks)} outstanding tasks") - await asyncio.gather(*server_tasks, return_exceptions=True) - await server.stop() - - -def is_frozen(): - return getattr(sys, 'frozen', False) or os.path.basename(sys.executable) == "exo" \ - or ('Contents/MacOS' in str(os.path.dirname(sys.executable))) \ - or '__nuitka__' in globals() or getattr(sys, '__compiled__', False) - -async def get_mac_system_info() -> Tuple[str, str, int]: - """Get Mac system information using system_profiler.""" - try: - output = await asyncio.get_running_loop().run_in_executor( - subprocess_pool, - lambda: subprocess.check_output(["system_profiler", "SPHardwareDataType"]).decode("utf-8") - ) - - model_line = next((line for line in output.split("\n") if "Model Name" in line), None) - model_id = model_line.split(": ")[1] if model_line else "Unknown Model" - - chip_line = next((line for line in output.split("\n") if "Chip" in line), None) - chip_id = chip_line.split(": ")[1] if chip_line else "Unknown Chip" - - memory_line = next((line for line in output.split("\n") if "Memory" in line), None) - memory_str = memory_line.split(": ")[1] if memory_line else "Unknown Memory" - memory_units = memory_str.split() - memory_value = int(memory_units[0]) - memory = memory_value * 1024 if memory_units[1] == "GB" else memory_value - - return model_id, chip_id, memory - except Exception as e: - if DEBUG >= 2: print(f"Error getting Mac system info: {e}") - return "Unknown Model", "Unknown Chip", 0 - -def get_exo_home() -> Path: - if psutil.WINDOWS: docs_folder = Path(os.environ["USERPROFILE"])/"Documents" - else: docs_folder = Path.home()/"Documents" - if not docs_folder.exists(): docs_folder.mkdir(exist_ok=True) - exo_folder = docs_folder/"Exo" - if not exo_folder.exists(): exo_folder.mkdir(exist_ok=True) - return exo_folder - - -def get_exo_images_dir() -> Path: - exo_home = get_exo_home() - images_dir = exo_home/"Images" - if not images_dir.exists(): images_dir.mkdir(exist_ok=True) - return images_dir diff --git a/exo/inference/__init__.py b/exo/inference/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/inference/debug_inference_engine.py b/exo/inference/debug_inference_engine.py deleted file mode 100644 index d81ac023..00000000 --- a/exo/inference/debug_inference_engine.py +++ /dev/null @@ -1,58 +0,0 @@ -from exo.inference.inference_engine import InferenceEngine -from exo.inference.shard import Shard -from exo.inference.tinygrad.inference import TinygradDynamicShardInferenceEngine -import asyncio -import numpy as np - - -# An inference engine should work the same for any number of Shards, as long as the Shards are continuous. -async def test_inference_engine(inference_engine_1: InferenceEngine, inference_engine_2: InferenceEngine, model_id: str): - from exo.inference.tinygrad.inference import Tokenizer - from pathlib import Path - - _tokenizer = Tokenizer(str(Path(model_id)/"tokenizer.model")) - - prompt = "In a single word only, what is the last name of the president of the United States? " - resp_full = await inference_engine_1.infer_prompt("A", shard=Shard(model_id=model_id, start_layer=0, end_layer=31, n_layers=32), prompt=prompt) - token_full = await inference_engine_1.sample(resp_full) - - next_resp_full, _ = await inference_engine_1.infer_tensor( - "A", - shard=Shard(model_id=model_id, start_layer=0, end_layer=31, n_layers=32), - input_data=token_full, - ) - - resp1, _ = await inference_engine_1.infer_prompt("B", shard=Shard(model_id=model_id, start_layer=0, end_layer=30, n_layers=32), prompt=prompt) - resp2, _ = await inference_engine_2.infer_tensor( - "B", - shard=Shard(model_id=model_id, start_layer=31, end_layer=31, n_layers=32), - input_data=resp1, - ) - token2 = await inference_engine_2.sample(resp2) - resp3, _ = await inference_engine_1.infer_tensor( - "B", - shard=Shard(model_id=model_id, start_layer=0, end_layer=30, n_layers=32), - input_data=token2, - ) - resp4, _ = await inference_engine_2.infer_tensor( - "B", - shard=Shard(model_id=model_id, start_layer=31, end_layer=31, n_layers=32), - input_data=resp3, - ) - - print(f"{resp2=}") - print(f"full: {_tokenizer.decode(resp_full)}") - print(f"next full: {_tokenizer.decode(next_resp_full)}") - print(f"resp2: {_tokenizer.decode(resp2)}") - print(f"{resp4=}") - print(f"resp4: {_tokenizer.decode(resp4)}") - - assert np.array_equal(resp_full, resp2) - assert np.array_equal(next_resp_full, resp4) - - -asyncio.run(test_inference_engine( - TinygradDynamicShardInferenceEngine(), - TinygradDynamicShardInferenceEngine(), - "llama3-8b-sfr", -)) diff --git a/exo/inference/dummy_inference_engine.py b/exo/inference/dummy_inference_engine.py deleted file mode 100644 index 98cf8894..00000000 --- a/exo/inference/dummy_inference_engine.py +++ /dev/null @@ -1,37 +0,0 @@ -from typing import Optional, Tuple, TYPE_CHECKING -import numpy as np -from exo.inference.inference_engine import InferenceEngine -from exo.inference.shard import Shard -from exo.inference.tokenizers import DummyTokenizer - -class DummyInferenceEngine(InferenceEngine): - def __init__(self): - self.shard = None - self.vocab_size = 1000 - self.hidden_size = 256 - self.eos_token_id = 0 - self.latency_mean = 0.1 - self.latency_stddev = 0.02 - self.num_generate_dummy_tokens = 10 - self.tokenizer = DummyTokenizer() - - async def encode(self, shard: Shard, prompt: str) -> np.ndarray: - return np.array(self.tokenizer.encode(prompt)) - - async def sample(self, x: np.ndarray, temp: float = 0.0, top_p: float = 1.0) -> np.ndarray: - if x[0] > self.num_generate_dummy_tokens: return np.array([self.tokenizer.eos_token_id]) - return x - - async def decode(self, shard: Shard, tokens: np.ndarray) -> str: - return self.tokenizer.decode(tokens) - - async def infer_tensor(self, request_id: str, shard: Shard, input_data: np.ndarray, inference_state: Optional[dict] = None) -> tuple[np.ndarray, Optional[dict]]: - await self.ensure_shard(shard) - return input_data + 1 if self.shard.is_last_layer() else input_data, None - - async def ensure_shard(self, shard: Shard): - if self.shard == shard: return - self.shard = shard - - async def load_checkpoint(self, shard: Shard, path: str): - await self.ensure_shard(shard) diff --git a/exo/inference/inference_engine.py b/exo/inference/inference_engine.py deleted file mode 100644 index b6200037..00000000 --- a/exo/inference/inference_engine.py +++ /dev/null @@ -1,77 +0,0 @@ -import numpy as np -import os -from exo.helpers import DEBUG # Make sure to import DEBUG - -from typing import Tuple, Optional -from abc import ABC, abstractmethod -from .shard import Shard -from exo.download.shard_download import ShardDownloader - - -class InferenceEngine(ABC): - session = {} - - @abstractmethod - async def encode(self, shard: Shard, prompt: str) -> np.ndarray: - pass - - @abstractmethod - async def sample(self, x: np.ndarray) -> np.ndarray: - pass - - @abstractmethod - async def decode(self, shard: Shard, tokens: np.ndarray) -> str: - pass - - @abstractmethod - async def infer_tensor(self, request_id: str, shard: Shard, input_data: np.ndarray, inference_state: Optional[dict] = None) -> tuple[np.ndarray, Optional[dict]]: - pass - - @abstractmethod - async def load_checkpoint(self, shard: Shard, path: str): - pass - - async def save_checkpoint(self, shard: Shard, path: str): - pass - - async def save_session(self, key, value): - self.session[key] = value - - async def clear_session(self): - self.session.empty() - - async def infer_prompt(self, request_id: str, shard: Shard, prompt: str, inference_state: Optional[dict] = None) -> tuple[np.ndarray, Optional[dict]]: - tokens = await self.encode(shard, prompt) - if shard.model_id != 'stable-diffusion-2-1-base': - x = tokens.reshape(1, -1) - else: - x = tokens - output_data, inference_state = await self.infer_tensor(request_id, shard, x, inference_state) - - return output_data, inference_state - - -inference_engine_classes = { - "mlx": "MLXDynamicShardInferenceEngine", - "tinygrad": "TinygradDynamicShardInferenceEngine", - "dummy": "DummyInferenceEngine", -} - - -def get_inference_engine(inference_engine_name: str, shard_downloader: ShardDownloader): - if DEBUG >= 2: - print(f"get_inference_engine called with: {inference_engine_name}") - if inference_engine_name == "mlx": - from exo.inference.mlx.sharded_inference_engine import MLXDynamicShardInferenceEngine - - return MLXDynamicShardInferenceEngine(shard_downloader) - elif inference_engine_name == "tinygrad": - from exo.inference.tinygrad.inference import TinygradDynamicShardInferenceEngine - import tinygrad.helpers - tinygrad.helpers.DEBUG.value = int(os.getenv("TINYGRAD_DEBUG", default="0")) - - return TinygradDynamicShardInferenceEngine(shard_downloader) - elif inference_engine_name == "dummy": - from exo.inference.dummy_inference_engine import DummyInferenceEngine - return DummyInferenceEngine() - raise ValueError(f"Unsupported inference engine: {inference_engine_name}") diff --git a/exo/inference/mlx/__init__.py b/exo/inference/mlx/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/inference/mlx/losses.py b/exo/inference/mlx/losses.py deleted file mode 100644 index 1052caa7..00000000 --- a/exo/inference/mlx/losses.py +++ /dev/null @@ -1,37 +0,0 @@ -import mlx.core as mx -import mlx.nn as nn -def length_masked_ce_loss(model, inputs, targets, lengths): - # Run model on inputs - logits = model(inputs).astype(mx.float32) - - # Mask padding tokens - length_mask = mx.arange(inputs.shape[1])[None, :] < lengths[:, None] - - # Calculate the loss - ce = nn.losses.cross_entropy(logits, targets) * length_mask - loss = ce.sum() / length_mask.sum() -# print(f"| {inputs=}\n| ==>{logits=}\n| ~^~{ce=}\n| == {loss=}") - return loss - -#Naive intermediate layer loss, where we replace the targets with gradients and just multiply the output by the gradients to derive the loss. This is naive and may warrant some further iteration, but will do the job for now -def back_gradient_loss(model, inputs, gradients, lengths): - out = model(inputs).astype(mx.float32) - grad = gradients.astype(mx.float32) - - # Mask padding tokens - length_mask = mx.repeat(mx.arange(inputs.shape[1])[None, :] < lengths[:, None], out.shape[-1]).reshape(out.shape) - - masked_sum = (out * length_mask).sum(axis=1) - gradient_lens = mx.abs(grad * masked_sum) - loss = gradient_lens.sum() / length_mask.sum() -# print(f"| {inputs=}\n" -# + f"| ==>{out=}\n" -# + f"| ~^~{masked_sum=}\n" -# + f"| <~>{gradient_lens=}\n" -# + f"| == {loss=}") - return loss - -loss_fns = { - "back_gradient": back_gradient_loss, - "length_masked_ce": length_masked_ce_loss, -} diff --git a/exo/inference/mlx/models/StableDiffusionPipeline.py b/exo/inference/mlx/models/StableDiffusionPipeline.py deleted file mode 100644 index f8e7c054..00000000 --- a/exo/inference/mlx/models/StableDiffusionPipeline.py +++ /dev/null @@ -1,307 +0,0 @@ -# Adapted from https://github.com/ml-explore/mlx-examples/blob/main/stable_diffusion/stable_diffusion/__init__.py - -import time -from typing import Optional, Tuple -import inspect - -import mlx.core as mx -import mlx.nn as nn -from pathlib import Path - -from tqdm import tqdm - -from .sd_models.vae import ModelArgs as VAEArgs -from .sd_models.vae import Autoencoder -from .sd_models.tokenizer import load_tokenizer -from .sd_models.clip import CLIPTextModel -from .sd_models.clip import ModelArgs as CLIPArgs -from .sd_models.unet import UNetConfig, UNetModel - -from dataclasses import dataclass, field -from exo.inference.shard import Shard - -@dataclass -class DiffusionConfig: - beta_schedule: str = "scaled_linear" - beta_start: float = 0.00085 - beta_end: float = 0.012 - num_train_steps: int = 1000 - - @classmethod - def from_dict(cls, params): - return cls(**{k: v for k, v in params.items() if k in inspect.signature(cls).parameters}) - - -#Sampler -def _linspace(a, b, num): - x = mx.arange(0, num) / (num - 1) - return (b - a) * x + a - - -def _interp(y, x_new): - """Interpolate the function defined by (arange(0, len(y)), y) at positions x_new.""" - x_low = x_new.astype(mx.int32) - x_high = mx.minimum(x_low + 1, len(y) - 1) - - y_low = y[x_low] - y_high = y[x_high] - delta_x = x_new - x_low - y_new = y_low * (1 - delta_x) + delta_x * y_high - - return y_new - -class SimpleEulerSampler: - """A simple Euler integrator that can be used to sample from our diffusion models. - - The method ``step()`` performs one Euler step from x_t to x_t_prev. - """ - - def __init__(self, config: DiffusionConfig): - # Compute the noise schedule - if config.beta_schedule == "linear": - betas = _linspace( - config.beta_start, config.beta_end, config.num_train_steps - ) - elif config.beta_schedule == "scaled_linear": - betas = _linspace( - config.beta_start**0.5, config.beta_end**0.5, config.num_train_steps - ).square() - else: - raise NotImplementedError(f"{config.beta_schedule} is not implemented.") - - alphas = 1 - betas - alphas_cumprod = mx.cumprod(alphas) - - self._sigmas = mx.concatenate( - [mx.zeros(1), ((1 - alphas_cumprod) / alphas_cumprod).sqrt()] - ) - - @property - def max_time(self): - return len(self._sigmas) - 1 - - def sample_prior(self, shape, dtype=mx.float32, key=None): - noise = mx.random.normal(shape, key=key) - return ( - noise * self._sigmas[-1] * (self._sigmas[-1].square() + 1).rsqrt() - ).astype(dtype) - - def add_noise(self, x, t, key=None): - noise = mx.random.normal(x.shape, key=key) - s = self.sigmas(t) - return (x + noise * s) * (s.square() + 1).rsqrt() - - def sigmas(self, t): - return _interp(self._sigmas, t) - - def timesteps(self, num_steps: int, start_time=None, dtype=mx.float32): - start_time = start_time or (len(self._sigmas) - 1) - assert 0 < start_time <= (len(self._sigmas) - 1) - steps = _linspace(start_time, 0, num_steps + 1).astype(dtype) - return list(zip(steps, steps[1:])) - - def current_timestep(self, step, total_steps, start_time=None): - if step < total_steps: - steps = self.timesteps(total_steps, start_time) - return steps[step] - else: - return mx.array(0),mx.array(0) - - def step(self, eps_pred, x_t, t, t_prev): - sigma = self.sigmas(t).astype(eps_pred.dtype) - sigma_prev = self.sigmas(t_prev).astype(eps_pred.dtype) - - dt = sigma_prev - sigma - x_t_prev = (sigma.square() + 1).sqrt() * x_t + eps_pred * dt - - x_t_prev = x_t_prev * (sigma_prev.square() + 1).rsqrt() - - return x_t_prev - -@dataclass -class ShardConfig: - model_id:str - start_layer:int - end_layer:int - n_layers:int - -@dataclass -class StableDiffusionConfig: - model_type:str - vae:VAEArgs - text_encoder:CLIPArgs - scheduler:DiffusionConfig - unet:UNetConfig - shard:ShardConfig - - @classmethod - def from_dict(cls, params): - return cls(**{k: v for k, v in params.items() if k in inspect.signature(cls).parameters}) - -@dataclass -class ModelArgs(StableDiffusionConfig): - shard:Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - if isinstance(self.shard, dict): - self.shard = Shard(**self.shard) - - if not isinstance(self.shard, Shard): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - -class Model(nn.Module): - def __init__(self, config): - super().__init__() - self.model_type = config.model_type - self.config = config - self.model_path = config.vae['path'].split('/vae')[0] - self.shard = config.shard - self.shard_clip, self.shard_encoder, self.shard_unet, self.shard_decoder = model_shards(config.shard) - self.config_clip=CLIPArgs.from_dict(config.text_encoder['config']) - if self.shard_clip.start_layer != -1: - self.text_encoder = CLIPTextModel(self.config_clip, shard=self.shard_clip) - else: - self.text_encoder = nn.Identity() - self.tokenizer = load_tokenizer(Path(self.model_path), "vocab.json", "merges.txt") - self.diffusion_config = DiffusionConfig.from_dict(config.scheduler['config']) - self.sampler = SimpleEulerSampler(self.diffusion_config) - if self.shard_unet.start_layer!=-1: - self.config_unet = UNetConfig.from_dict(config.unet['config']) - self.unet = UNetModel(self.config_unet, self.shard_unet) - else: - self.unet = nn.Identity() - self.config_vae=VAEArgs.from_dict(config.vae['config']) - if self.shard_encoder.start_layer != -1: - self.encoder=Autoencoder(self.config_vae, self.shard_encoder, "vae_encoder") - else: - self.encoder = nn.Identity() - if self.shard_decoder.start_layer != -1: - self.decoder=Autoencoder(self.config_vae, self.shard_decoder, "vae_decoder") - else: - self.decoder = nn.Identity() - - def __call__(self,x, step= 0, cfg_weight: float = 7.5,total_steps=50,conditioning=None,mask=None,residual=None,x_t_prev=None,is_finished=False,is_step_finished=False, image=None, strength=0.65, start_step=None): - t, t_prev = self.sampler.current_timestep(step=step, total_steps=total_steps, start_time=start_step) - is_finished = False - is_step_finished = False - if t.item()==1000: - if self.shard_clip.start_layer == 0: - conditioning = x - if self.shard_clip.start_layer != -1: - conditioning, mask= self.text_encoder(conditioning,mask) - seed = int(time.time()) - mx.random.seed(seed) - if image is None: - if self.shard_encoder.is_last_layer(): - x = self.sampler.sample_prior((1, *(64, 64), self.config_vae.latent_channels_in), dtype=mx.float32) - x_t_prev=x - start_step = self.sampler.max_time - else: - if self.shard_encoder.start_layer != -1: - image= self.encoder.encode(image) - if self.shard_encoder.is_last_layer(): - start_step = self.sampler.max_time*strength - total_steps = int(total_steps*strength) - image = mx.broadcast_to(image, (1,) + image.shape[1:]) - x_t_prev=self.sampler.add_noise(image, mx.array(start_step)) - image = None - t, t_prev = self.sampler.current_timestep(step=step, total_steps=total_steps, start_time=start_step) - # Perform the denoising loop - if self.shard_unet.start_layer != -1: - with tqdm(total=total_steps,initial=step+1) as pbar: - if step 1 else x - else: - x_t_unet = x - t_unet = mx.broadcast_to(t, [len(x_t_unet)]) - x, residual= self.unet(x_t_unet, t_unet, encoder_x=conditioning, residuals=residual) - if self.shard_unet.is_last_layer(): - if cfg_weight > 1: - eps_text, eps_neg = x.split(2) - eps_pred = eps_neg + cfg_weight * (eps_text - eps_neg) - x = self.sampler.step(eps_pred, x_t_prev, t, t_prev) - x_t_prev=x - mx.eval(x) - - if self.shard_decoder.is_last_layer(): - is_step_finished=True - if self.shard_decoder.start_layer != -1: - x=self.decoder.decode(x) - if self.shard_decoder.is_last_layer(): - x = mx.clip(x / 2 + 0.5, 0, 1) - B, H, W, C = x.shape - x = x.reshape(1, B // 1, H, W, C).transpose(0, 2, 1, 3, 4) - x = x.reshape(1 * H, B // 1 * W, C) - x = (x * 255).astype(mx.uint8) - if t_prev.item() ==0: - is_finished=True - mx.eval(x) - - return x, {'conditioning':conditioning, 'mask':mask,'residual':residual,'x_t_prev':x_t_prev,'is_finished':is_finished,'is_step_finished':is_step_finished, 'step':step, 'total_steps':total_steps, 'start_step':start_step, 'image':image} - - - def load(self): - if self.shard_encoder.start_layer != -1: - vae_weights = mx.load(self.config_vae.weight_files[0]) - vae_weights = self.encoder.sanitize(vae_weights) - self.encoder.load_weights(list(vae_weights.items()), strict=True) - if self.shard_decoder.start_layer != -1: - vae_weights = mx.load(self.config_vae.weight_files[0]) - vae_weights = self.decoder.sanitize(vae_weights) - self.decoder.load_weights(list(vae_weights.items()), strict=True) - if self.shard_clip.start_layer != -1: - clip_weights = mx.load(self.config_clip.weight_files[0]) - clip_weights = self.text_encoder.sanitize(clip_weights) - self.text_encoder.load_weights(list(clip_weights.items()), strict=True) - if self.shard_unet.start_layer !=-1: - unet_weights = mx.load(self.config_unet.weight_files[0]) - unet_weights = self.unet.sanitize(unet_weights) - self.unet.load_weights(list(unet_weights.items()), strict=True) - -def model_shards(shard:ShardConfig): - def create_shard(shard, model_ranges): - start_layer = shard.start_layer - end_layer = shard.end_layer - - shards = {} - - for model_name, (range_start, range_end) in model_ranges.items(): - if start_layer < range_end and end_layer >= range_start: - # Calculate the overlap with the model range - overlap_start = max(start_layer, range_start) - overlap_end = min(end_layer, range_end - 1) - - # Adjust the layers relative to the model's range - relative_start = overlap_start - range_start - relative_end = overlap_end - range_start - shards[model_name] = Shard(model_name, relative_start, relative_end, range_end - range_start) - else: - # If no overlap, create a zero-layer shard - shards[model_name] = Shard(model_name, -1, -1, range_end - range_start) - - return shards - - # Define the ranges for different models - model_ranges = { - 'clip': (0, 12), - 'vae_encoder':(12,17), - 'unet':(17,26), - 'vae_decoder': (26, 31) # Example range for unet - } - - # Call the function and get the shards for all models - shards = create_shard(shard, model_ranges) - - # Access individual shards - shard_clip = shards['clip'] - shard_encoder = shards['vae_encoder'] - shard_unet = shards['unet'] - shard_decoder = shards['vae_decoder'] - - return shard_clip, shard_encoder, shard_unet, shard_decoder - - - diff --git a/exo/inference/mlx/models/__init__.py b/exo/inference/mlx/models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/inference/mlx/models/base.py b/exo/inference/mlx/models/base.py deleted file mode 100644 index 5181adfd..00000000 --- a/exo/inference/mlx/models/base.py +++ /dev/null @@ -1,9 +0,0 @@ -from typing import Optional -import mlx.core as mx -import mlx.nn as nn -from mlx_lm.models.cache import KVCache - - -class IdentityBlock(nn.Module): - def __call__(self, x: mx.array, mask: Optional[mx.array] = None, cache: Optional[KVCache] = None) -> mx.array: - return x diff --git a/exo/inference/mlx/models/deepseek_v2.py b/exo/inference/mlx/models/deepseek_v2.py deleted file mode 100644 index 8952e9e6..00000000 --- a/exo/inference/mlx/models/deepseek_v2.py +++ /dev/null @@ -1,127 +0,0 @@ -from dataclasses import dataclass, field -from typing import Optional - -import mlx.core as mx -import mlx.nn as nn - -from mlx_lm.models.cache import KVCache -from mlx_lm.models.deepseek_v2 import ModelArgs, DeepseekV2DecoderLayer -from .base import IdentityBlock -from exo.inference.shard import Shard - - -@dataclass -class ModelArgs(ModelArgs): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - if isinstance(self.shard, Shard): - return - if not isinstance(self.shard, dict): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - self.shard = Shard(**self.shard) - - -class DeepseekV2Model(nn.Module): - def __init__(self, config: ModelArgs): - super().__init__() - self.args = config - self.num_hidden_layers = config.num_hidden_layers - self.vocab_size = config.vocab_size - if self.args.shard.is_first_layer(): - self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) - - self.layers = [] - for i in range(self.num_hidden_layers): - if self.args.shard.start_layer <= i <= self.args.shard.end_layer: - self.layers.append(DeepseekV2DecoderLayer(config, i)) - else: - self.layers.append(IdentityBlock()) - - if self.args.shard.is_last_layer(): - self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) - - def __call__( - self, - x: mx.array, - cache: Optional[KVCache] = None, - ) -> mx.array: - if self.args.shard.is_first_layer(): - h = self.embed_tokens(x) - else: - h = x - - mask = None - T = h.shape[1] - if T > 1: - mask = nn.MultiHeadAttention.create_additive_causal_mask(T) - mask = mask.astype(h.dtype) - - if cache is None: - cache = [None]*len(self.layers) - - for layer, c in zip(self.layers, cache): - h = layer(h, mask, c) - - if self.args.shard.is_last_layer(): - h = self.norm(h) - return h - - -class Model(nn.Module): - def __init__(self, config: ModelArgs): - super().__init__() - self.args = config - self.model_type = config.model_type - self.model = DeepseekV2Model(config) - if self.args.shard.is_last_layer(): - self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - def __call__( - self, - inputs: mx.array, - cache: Optional[KVCache] = None, - ): - out = self.model(inputs, cache) - if self.args.shard.is_last_layer(): - return self.lm_head(out) - return out - - def sanitize(self, weights): - shard_state_dict = {} - - for key, value in weights.items(): - if key.startswith('model.layers.'): - layer_num = int(key.split('.')[2]) - if self.args.shard.start_layer <= layer_num <= self.args.shard.end_layer: - shard_state_dict[key] = value - elif self.args.shard.is_first_layer() and key.startswith('model.embed_tokens'): - shard_state_dict[key] = value - elif self.args.shard.is_last_layer() and (key.startswith('model.norm') or key.startswith('lm_head')): - shard_state_dict[key] = value - - for l in range(self.args.num_hidden_layers): - prefix = f"model.layers.{l}" - for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]: - for k in ["weight", "scales", "biases"]: - if f"{prefix}.mlp.experts.0.{m}.{k}" in shard_state_dict: - to_join = [shard_state_dict.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}") for e in range(self.args.n_routed_experts)] - shard_state_dict[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join) - - return shard_state_dict - - @property - def layers(self): - return self.model.layers - - @property - def head_dim(self): - return ( - self.args.qk_nope_head_dim + self.args.qk_rope_head_dim, - self.args.v_head_dim, - ) - - @property - def n_kv_heads(self): - return self.args.num_key_value_heads diff --git a/exo/inference/mlx/models/deepseek_v3.py b/exo/inference/mlx/models/deepseek_v3.py deleted file mode 100644 index bf36a86b..00000000 --- a/exo/inference/mlx/models/deepseek_v3.py +++ /dev/null @@ -1,134 +0,0 @@ -from dataclasses import dataclass, field -from typing import Optional - -import mlx.core as mx -import mlx.nn as nn - -from mlx_lm.models.cache import KVCache -from mlx_lm.models.deepseek_v3 import ( - ModelArgs as V3ModelArgs, - DeepseekV3DecoderLayer, -) -from .base import IdentityBlock -from exo.inference.shard import Shard - - -@dataclass -class ModelArgs(V3ModelArgs): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - if isinstance(self.shard, Shard): - return - if not isinstance(self.shard, dict): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - self.shard = Shard(**self.shard) - - -class DeepseekV3Model(nn.Module): - def __init__(self, config: ModelArgs): - super().__init__() - self.args = config - self.num_hidden_layers = config.num_hidden_layers - self.vocab_size = config.vocab_size - if self.args.shard.is_first_layer(): - self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) - - self.layers = [] - for i in range(self.num_hidden_layers): - if self.args.shard.start_layer <= i <= self.args.shard.end_layer: - self.layers.append(DeepseekV3DecoderLayer(config, i)) - else: - self.layers.append(IdentityBlock()) - - if self.args.shard.is_last_layer(): - self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) - - def __call__( - self, - x: mx.array, - cache: Optional[KVCache] = None, - ) -> mx.array: - if self.args.shard.is_first_layer(): - h = self.embed_tokens(x) - else: - h = x - - mask = None - T = h.shape[1] - if T > 1: - mask = nn.MultiHeadAttention.create_additive_causal_mask(T) - mask = mask.astype(h.dtype) - - if cache is None: - cache = [None]*len(self.layers) - - for layer, c in zip(self.layers, cache): - h = layer(h, mask, c) - - if self.args.shard.is_last_layer(): - h = self.norm(h) - return h - - -class Model(nn.Module): - def __init__(self, config: ModelArgs): - super().__init__() - self.args = config - self.model_type = config.model_type - self.model = DeepseekV3Model(config) - if self.args.shard.is_last_layer(): - self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - def __call__( - self, - inputs: mx.array, - cache: Optional[KVCache] = None, - ): - out = self.model(inputs, cache) - if self.args.shard.is_last_layer(): - return self.lm_head(out) - return out - - def sanitize(self, weights): - shard_state_dict = {} - - for key, value in weights.items(): - if key.startswith('model.layers.'): - layer_num = int(key.split('.')[2]) - if self.args.shard.start_layer <= layer_num <= self.args.shard.end_layer: - shard_state_dict[key] = value - elif self.args.shard.is_first_layer() and key.startswith('model.embed_tokens'): - shard_state_dict[key] = value - elif self.args.shard.is_last_layer() and (key.startswith('model.norm') or key.startswith('lm_head')): - shard_state_dict[key] = value - - for l in range(self.args.num_hidden_layers): - prefix = f"model.layers.{l}" - for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]: - for k in ["weight", "scales", "biases"]: - expert_key = f"{prefix}.mlp.experts.0.{m}.{k}" - if expert_key in shard_state_dict: - to_join = [ - shard_state_dict.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}") - for e in range(self.args.n_routed_experts) - ] - shard_state_dict[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join) - - return shard_state_dict - - @property - def layers(self): - return self.model.layers - - @property - def head_dim(self): - return ( - self.args.qk_nope_head_dim + self.args.qk_rope_head_dim, - self.args.v_head_dim, - ) - - @property - def n_kv_heads(self): - return self.args.num_key_value_heads diff --git a/exo/inference/mlx/models/gemma2.py b/exo/inference/mlx/models/gemma2.py deleted file mode 100644 index 88957fd9..00000000 --- a/exo/inference/mlx/models/gemma2.py +++ /dev/null @@ -1,118 +0,0 @@ -from dataclasses import dataclass, field - -import mlx.core as mx -import mlx.nn as nn - -from mlx_lm.models.base import create_attention_mask -from mlx_lm.models.gemma2 import TransformerBlock, ModelArgs, RMSNorm - -from ...shard import Shard -from .base import IdentityBlock - - -@dataclass -class ModelArgs(ModelArgs): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - if isinstance(self.shard, Shard): - return - if not isinstance(self.shard, dict): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - self.shard = Shard(**self.shard) - - -class GemmaModel(nn.Module): - def __init__(self, args: ModelArgs): - super().__init__() - self.args = args - self.vocab_size = args.vocab_size - self.num_hidden_layers = args.num_hidden_layers - assert self.vocab_size > 0 - if args.shard.is_first_layer() or args.shard.is_last_layer(): - self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) - self.layers = [] - for i in range(self.num_hidden_layers): - if args.shard.start_layer <= i <= args.shard.end_layer: - self.layers.append(TransformerBlock(args=args)) - else: - self.layers.append(IdentityBlock()) - if args.shard.is_last_layer(): - self.norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps) - - def __call__( - self, - inputs: mx.array, - cache=None, - ): - if self.args.shard.is_first_layer(): - h = self.embed_tokens(inputs) - h = h * (self.args.hidden_size**0.5) - else: - h = inputs - - mask = None - if h.ndim > 1 and h.shape[1] > 1: - mask = create_attention_mask(h, cache) - - if cache is None: - cache = [None]*len(self.layers) - - for layer, c in zip(self.layers, cache): - h = layer(h, mask, cache=c) - - if self.args.shard.is_last_layer(): - h = self.norm(h) - return h - - -class Model(nn.Module): - def __init__(self, args: ModelArgs): - super().__init__() - self.args = args - self.model_type = args.model_type - self.model = GemmaModel(args) - if args.shard.is_last_layer(): - self.final_logit_softcapping = args.final_logit_softcapping - - def __call__( - self, - inputs: mx.array, - cache=None, - ): - out = self.model(inputs, cache) - if self.args.shard.is_last_layer(): - out = self.model.embed_tokens.as_linear(out) - out = mx.tanh(out / self.final_logit_softcapping) - out = out * self.final_logit_softcapping - return out - - def sanitize(self, weights): - shard_state_dict = {} - - for key, value in weights.items(): - if "self_attn.rotary_emb.inv_freq" in key: - continue - if key.startswith('model.layers.'): - layer_num = int(key.split('.')[2]) - if self.args.shard.start_layer <= layer_num <= self.args.shard.end_layer: - shard_state_dict[key] = value - elif (self.args.shard.is_first_layer() or self.args.shard.is_last_layer()) and key.startswith('model.embed_tokens'): - shard_state_dict[key] = value - elif self.args.shard.is_last_layer() and (key.startswith('model.norm')): - shard_state_dict[key] = value - - return shard_state_dict - - @property - def layers(self): - return self.model.layers - - @property - def head_dim(self): - return self.args.head_dim - - @property - def n_kv_heads(self): - return self.args.num_key_value_heads diff --git a/exo/inference/mlx/models/llama.py b/exo/inference/mlx/models/llama.py deleted file mode 100644 index 8b069ecc..00000000 --- a/exo/inference/mlx/models/llama.py +++ /dev/null @@ -1,125 +0,0 @@ -from dataclasses import dataclass, field - -import mlx.core as mx -import mlx.nn as nn - -from mlx_lm.models.base import create_attention_mask -from mlx_lm.models.llama import TransformerBlock, ModelArgs - -from ...shard import Shard -from .base import IdentityBlock - - -@dataclass -class ModelArgs(ModelArgs): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - super().__post_init__() # Ensure parent initializations are respected - - if isinstance(self.shard, Shard): - return - if not isinstance(self.shard, dict): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - self.shard = Shard(**self.shard) - - -class LlamaModel(nn.Module): - def __init__(self, args: ModelArgs): - super().__init__() - self.args = args - self.vocab_size = args.vocab_size - self.num_hidden_layers = args.num_hidden_layers - assert self.vocab_size > 0 - if args.shard.is_first_layer() or (args.shard.is_last_layer() and args.tie_word_embeddings): - self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) - self.layers = [] - for i in range(self.num_hidden_layers): - if args.shard.start_layer <= i <= args.shard.end_layer: - self.layers.append(TransformerBlock(args=args)) - else: - self.layers.append(IdentityBlock()) - if args.shard.is_last_layer(): - self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) - - def __call__( - self, - inputs: mx.array, - cache=None, - ): - if self.args.shard.is_first_layer(): - h = self.embed_tokens(inputs) - else: - h = inputs - - mask = None - if h.ndim > 1 and h.shape[1] > 1: - mask = create_attention_mask(h, cache) - - if cache is None: - cache = [None]*len(self.layers) - - for layer, c in zip(self.layers, cache): - h = layer(h, mask, cache=c) - - if self.args.shard.is_last_layer(): - h = self.norm(h) - return h - - -class Model(nn.Module): - def __init__(self, args: ModelArgs): - super().__init__() - self.args = args - self.model_type = args.model_type - self.model = LlamaModel(args) - if args.shard.is_last_layer(): - if not args.tie_word_embeddings: - self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False) - - def __call__( - self, - inputs: mx.array, - cache=None, - ): - out = self.model(inputs, cache) - if self.args.shard.is_last_layer(): - if self.args.tie_word_embeddings: - out = self.model.embed_tokens.as_linear(out) - else: - out = self.lm_head(out) - return out - - def sanitize(self, weights): - shard_state_dict = {} - - for key, value in weights.items(): - if "self_attn.rotary_emb.inv_freq" in key: - continue - if key.startswith('model.layers.'): - layer_num = int(key.split('.')[2]) - if self.args.shard.start_layer <= layer_num <= self.args.shard.end_layer: - shard_state_dict[key] = value - elif self.args.shard.is_first_layer() and key.startswith('model.embed_tokens'): - shard_state_dict[key] = value - elif (self.args.shard.is_last_layer() and self.args.tie_word_embeddings) and key.startswith('model.embed_tokens'): - shard_state_dict[key] = value - elif (self.args.shard.is_last_layer() and not self.args.tie_word_embeddings) and key.startswith('lm_head'): - shard_state_dict[key] = value - elif self.args.shard.is_last_layer() and (key.startswith('model.norm')): - shard_state_dict[key] = value - - return shard_state_dict - - @property - def layers(self): - return self.model.layers - - @property - def head_dim(self): - return (self.args.head_dim or self.args.hidden_size // self.args.num_attention_heads) - - @property - def n_kv_heads(self): - return self.args.num_key_value_heads diff --git a/exo/inference/mlx/models/llava.py b/exo/inference/mlx/models/llava.py deleted file mode 100644 index b734b09b..00000000 --- a/exo/inference/mlx/models/llava.py +++ /dev/null @@ -1,585 +0,0 @@ -# Copyright © 2024 Apple Inc. - -import math -import inspect -from dataclasses import dataclass, field -from typing import Optional, Dict, Union - -import mlx.core as mx -import mlx.nn as nn -from mlx_lm.models.base import BaseModelArgs, KVCache -from exo.inference.shard import Shard -from .base import IdentityBlock -import numpy as np - - -@dataclass -class VisionConfig: - model_type: str - num_hidden_layers: int = 24 - hidden_size: int = 1024 - intermediate_size: int = 4096 - num_attention_heads: int = 16 - image_size: int = 336 - patch_size: int = 14 - projection_dim: int = 768 - vocab_size: int = 32000 - num_channels: int = 3 - layer_norm_eps: float = 1e-5 - - @classmethod - def from_dict(cls, params): - return cls(**{k: v for k, v in params.items() if k in inspect.signature(cls).parameters}) - - -class VisionAttention(nn.Module): - def __init__( - self, - dims: int, - num_heads: int, - query_input_dims: Optional[int] = None, - key_input_dims: Optional[int] = None, - value_input_dims: Optional[int] = None, - value_dims: Optional[int] = None, - value_output_dims: Optional[int] = None, - bias: bool = False, - ): - super().__init__() - - if (dims % num_heads) != 0: - raise ValueError("The input feature dimensions should be divisible by the " - f"number of heads ({dims} % {num_heads}) != 0") - - query_input_dims = query_input_dims or dims - key_input_dims = key_input_dims or dims - value_input_dims = value_input_dims or key_input_dims - value_dims = value_dims or dims - value_output_dims = value_output_dims or dims - - self.num_heads = num_heads - self.q_proj = nn.Linear(query_input_dims, dims, bias=bias) - self.k_proj = nn.Linear(key_input_dims, dims, bias=bias) - self.v_proj = nn.Linear(value_input_dims, value_dims, bias=bias) - self.out_proj = nn.Linear(value_dims, value_output_dims, bias=bias) - - def __call__(self, queries, keys, values, mask=None): - queries = self.q_proj(queries) - keys = self.k_proj(keys) - values = self.v_proj(values) - - num_heads = self.num_heads - B, L, D = queries.shape - _, S, _ = keys.shape - queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3) - keys = keys.reshape(B, S, num_heads, -1).transpose(0, 2, 3, 1) - values = values.reshape(B, S, num_heads, -1).transpose(0, 2, 1, 3) - - scale = math.sqrt(1/queries.shape[-1]) - scores = (queries*scale) @ keys - if mask is not None: - scores = scores + mask.astype(scores.dtype) - scores = mx.softmax(scores, axis=-1) - values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1) - - return self.out_proj(values_hat) - - -class VisionMLP(nn.Module): - def __init__(self, config: VisionConfig): - super().__init__() - self.activation_fn = nn.GELU(approx="fast") - self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) - self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) - - def __call__(self, x: mx.array) -> mx.array: - x = self.activation_fn(self.fc1(x)) - x = self.fc2(x) - return x - - -class VisionEncoderLayer(nn.Module): - def __init__(self, config: VisionConfig): - super().__init__() - self.embed_dim = config.hidden_size - self.self_attn = VisionAttention(config.hidden_size, config.num_attention_heads, bias=True) - self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) - self.mlp = VisionMLP(config) - self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) - - def __call__(self, x: mx.array, mask: Optional[mx.array] = None) -> mx.array: - y = self.layer_norm1(x) - y = self.self_attn(y, y, y, mask) - x = x + y - y = self.layer_norm2(x) - y = self.mlp(y) - return x + y - - -class VisionEncoder(nn.Module): - def __init__(self, config: VisionConfig): - super().__init__() - self.layers = [VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)] - - -class VisionEmbeddings(nn.Module): - def __init__(self, config: VisionConfig): - super().__init__() - self.config = config - self.embed_dim = config.hidden_size - self.image_size = config.image_size - self.patch_size = config.patch_size - - self.class_embedding = mx.zeros((config.hidden_size,)) - - self.patch_embedding = nn.Conv2d( - in_channels=config.num_channels, - out_channels=self.embed_dim, - kernel_size=self.patch_size, - stride=self.patch_size, - bias=False, - ) - - self.num_patches = (self.image_size // self.patch_size)**2 - self.num_positions = self.num_patches + 1 - self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) - - def __call__(self, x: mx.array) -> mx.array: - batch_size = x.shape[0] - patch_embeddings = self.patch_embedding(x) - patch_embeddings = mx.flatten(patch_embeddings, start_axis=1, end_axis=2) - embed_dim = patch_embeddings.shape[-1] - cls_embeddings = mx.broadcast_to(self.class_embedding, (batch_size, 1, embed_dim)) - embeddings = mx.concatenate((cls_embeddings, patch_embeddings), axis=1) - embeddings += self.position_embedding.weight - return embeddings - - -class ClipVisionModel(nn.Module): - def __init__(self, config: VisionConfig): - super().__init__() - self.embeddings = VisionEmbeddings(config) - self.pre_layrnorm = nn.LayerNorm(config.hidden_size) - self.encoder = VisionEncoder(config) - self.post_layernorm = nn.LayerNorm(config.hidden_size) - - def __call__( - self, - x: mx.array, - output_hidden_states: Optional[bool] = None, - ) -> mx.array: - x = self.embeddings(x) - x = self.pre_layrnorm(x) - - encoder_states = (x,) if output_hidden_states else None - - for l in self.encoder.layers: - x = l(x, mask=None) - if output_hidden_states: - encoder_states = encoder_states + (x,) - - pooler_output = self.post_layernorm(x[:, 0, :]) - return pooler_output, x, encoder_states - - -class VisionModel(nn.Module): - def __init__(self, config: VisionConfig): - super().__init__() - - self.model_type = config.model_type - if self.model_type != "clip_vision_model": - raise ValueError(f"Unsupported model type: {self.model_type}") - - self.vision_model = ClipVisionModel(config) - - def __call__(self, x: mx.array, output_hidden_states: Optional[bool] = None) -> mx.array: - return self.vision_model(x, output_hidden_states) - - def sanitize(self, weights): - sanitized_weights = {} - for k, v in weights.items(): - if "position_ids" in k: - # Remove unused position_ids - continue - elif "patch_embedding.weight" in k: - # PyTorch conv2d weight tensors have shape: - # [out_channels, in_channels, kH, KW] - # MLX conv2d expects the weight be of shape: - # [out_channels, kH, KW, in_channels] - sanitized_weights[k] = v.transpose(0, 2, 3, 1) - else: - sanitized_weights[k] = v - - return sanitized_weights - - -@dataclass -class TextConfig: - model_type: str - hidden_size: int = 4096 - num_hidden_layers: int = 32 - intermediate_size: int = 11008 - num_attention_heads: int = 32 - head_dim: int = None - rms_norm_eps: float = 1e-6 - vocab_size: int = 32000 - num_key_value_heads: int = None - rope_theta: float = 10000 - rope_traditional: bool = False - rope_scaling: Optional[Dict[str, Union[float, str]]] = None - - @classmethod - def from_dict(cls, params): - return cls(**{k: v for k, v in params.items() if k in inspect.signature(cls).parameters}) - - def __post_init__(self): - if self.num_key_value_heads is None: - self.num_key_value_heads = self.num_attention_heads - - if self.head_dim is None: - self.head_dim = self.hidden_size // self.num_attention_heads - - if self.model_type is None: - self.model_type = "llama" - - if self.rope_scaling: - required_keys = {"factor", "type"} - if not all(key in self.rope_scaling for key in required_keys): - raise ValueError(f"rope_scaling must contain keys {required_keys}") - - if self.rope_scaling["type"] != "linear": - raise ValueError("rope_scaling 'type' currently only supports 'linear'") - - -class TextAttention(nn.Module): - def __init__(self, config: TextConfig): - super().__init__() - - dim = config.hidden_size - self.n_heads = n_heads = config.num_attention_heads - self.n_kv_heads = n_kv_heads = config.num_key_value_heads - - self.repeats = n_heads // n_kv_heads - - head_dim = config.hidden_size // n_heads - self.scale = head_dim**-0.5 - - self.q_proj = nn.Linear(dim, n_heads*head_dim, bias=False) - self.k_proj = nn.Linear(dim, n_kv_heads*head_dim, bias=False) - self.v_proj = nn.Linear(dim, n_kv_heads*head_dim, bias=False) - self.o_proj = nn.Linear(n_heads*head_dim, dim, bias=False) - - rope_scale = (1/config.rope_scaling["factor"] if config.rope_scaling is not None and config.rope_scaling["type"] == "linear" else 1) - self.rope = nn.RoPE( - head_dim, - traditional=config.rope_traditional, - base=config.rope_theta, - scale=rope_scale, - ) - - def __call__( - self, - x: mx.array, - mask: Optional[mx.array] = None, - cache: Optional[KVCache] = None, - ) -> mx.array: - B, L, D = x.shape - - queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x) - - # Prepare the queries, keys and values for the attention computation - queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3) - keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3) - values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3) - - if cache is not None: - queries = self.rope(queries, offset=cache.offset) - keys = self.rope(keys, offset=cache.offset) - keys, values = cache.update_and_fetch(keys, values) - else: - queries = self.rope(queries) - keys = self.rope(keys) - - output = mx.fast.scaled_dot_product_attention(queries, keys, values, scale=self.scale, mask=mask) - output = output.transpose(0, 2, 1, 3).reshape(B, L, -1) - return self.o_proj(output) - - -class TextMLP(nn.Module): - def __init__(self, dim, hidden_dim): - super().__init__() - self.gate_proj = nn.Linear(dim, hidden_dim, bias=False) - self.down_proj = nn.Linear(hidden_dim, dim, bias=False) - self.up_proj = nn.Linear(dim, hidden_dim, bias=False) - - def __call__(self, x) -> mx.array: - return self.down_proj(nn.silu(self.gate_proj(x))*self.up_proj(x)) - - -class TransformerBlock(nn.Module): - def __init__(self, config: TextConfig): - super().__init__() - self.num_attention_heads = config.num_attention_heads - self.hidden_size = config.hidden_size - self.self_attn = TextAttention(config) - self.mlp = TextMLP(config.hidden_size, config.intermediate_size) - self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) - self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) - self.config = config - - def __call__( - self, - x: mx.array, - mask: Optional[mx.array] = None, - cache: Optional[KVCache] = None, - ) -> mx.array: - r = self.self_attn(self.input_layernorm(x), mask, cache) - h = x + r - r = self.mlp(self.post_attention_layernorm(h)) - out = h + r - return out - - -class Llama(nn.Module): - def __init__(self, config: TextConfig, shard: Shard): - super().__init__() - self.config = config - self.shard = shard - self.vocab_size = config.vocab_size - self.model_type = config.model_type - self.num_hidden_layers = config.num_hidden_layers - self.num_key_value_heads = config.num_key_value_heads - self.head_dim = config.head_dim - assert self.vocab_size > 0 - if self.shard.is_first_layer(): - self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) - self.layers = [] - for i in range(self.num_hidden_layers): - if self.shard.start_layer <= i <= self.shard.end_layer: - self.layers.append(TransformerBlock(config=config)) - else: - self.layers.append(IdentityBlock()) - if self.shard.is_last_layer(): - self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps) - - def __call__( - self, - inputs: mx.array, - cache=None, - inputs_embeds=None, - ): - # for passing merged input embeddings - if inputs_embeds is None: - if self.shard.is_first_layer(): - h = self.embed_tokens(inputs) - else: - h = inputs - else: - h = inputs_embeds - - mask = None - if h.shape[1] > 1: - mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1]) - mask = mask.astype(h.dtype) - - if cache is None: - cache = [None]*len(self.layers) - - for layer, c in zip(self.layers, cache): - h = layer(h, mask, c) - - if self.shard.is_last_layer(): - h = self.norm(h) - return h - - -class LanguageModel(nn.Module): - def __init__(self, config: TextConfig, shard: Shard): - super().__init__() - self.model_type = config.model_type - if self.model_type != "llama": - raise ValueError(f"Model type {self.model_type} not supported. Currently only 'llama' is supported") - self.shard = shard - self.model = Llama(config, shard) - if self.shard.is_last_layer(): - self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - def __call__( - self, - inputs: mx.array, - cache=None, - inputs_embeds=None, - ): - out = self.model(inputs, cache, inputs_embeds) - if self.shard.is_last_layer(): - out = self.lm_head(out) - return out - - def sanitize(self, weights): - shard_state_dict = {} - for key, value in weights.items(): - if "self_attn.rotary_emb.inv_freq" in key: - continue - - if key.startswith('language_model.model.layers.'): - layer_num = int(key.split('.')[3]) - if layer_num < self.shard.start_layer or layer_num > self.shard.end_layer: - continue - if not self.shard.is_first_layer() and key.startswith('language_model.model.embed_tokens'): - continue - elif not self.shard.is_last_layer() and (key.startswith('language_model.model.norm') or key.startswith('language_model.lm_head')): - continue - - shard_state_dict[key] = value - - return shard_state_dict - - -@dataclass -class LlaVAConfig(BaseModelArgs): - text_config: TextConfig - vision_config: VisionConfig = None - model_type: str = "llava" - ignore_index: int = -100 - image_token_index: int = 32000 - vision_feature_select_strategy: str = "default" - vision_feature_layer: int = -2 - vocab_size: int = 32000 - - @classmethod - def from_dict(cls, params): - updated_params = {} - class_params = inspect.signature(cls).parameters - for k, v in params.items(): - if k in class_params: - if k in ["text_config", "vision_config"]: - v = class_params[k].annotation.from_dict(v) - updated_params.update({k: v}) - - return cls(**updated_params) - - -@dataclass -class ModelArgs(LlaVAConfig): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - if isinstance(self.shard, dict): - self.shard = Shard(**self.shard) - - if not isinstance(self.shard, Shard): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - if not self.shard.is_first_layer(): - self.vision_config = None - - -class LlavaMultiModalProjector(nn.Module): - def __init__(self, config: LlaVAConfig): - super().__init__() - self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True) - self.gelu = nn.GELU() - self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True) - - def __call__(self, x: mx.array) -> mx.array: - x = self.linear_1(x) - x = self.gelu(x) - x = self.linear_2(x) - return x - - -class Model(nn.Module): - def __init__(self, config: ModelArgs): - super().__init__() - self.config = config - self.model_type = config.model_type - if config.vision_config: - self.vision_tower = VisionModel(config.vision_config) - self.multi_modal_projector = LlavaMultiModalProjector(config) - self.vision_feature_layer = config.vision_feature_layer - self.vision_feature_select_strategy = config.vision_feature_select_strategy - self.language_model = LanguageModel(config.text_config, config.shard) - - def get_input_embeddings( - self, - input_ids: Optional[mx.array] = None, - pixel_values: Optional[mx.array] = None, - ): - if pixel_values is None: - return self.language_model(input_ids) - - # Get the input embeddings from the language model - inputs_embeds = self.language_model.model.embed_tokens(input_ids) - - # Get the ouptut hidden states from the vision model - *_, hidden_states = self.vision_tower(pixel_values.transpose(0, 2, 3, 1), output_hidden_states=True) - - # Select the hidden states from the desired layer - selected_image_feature = hidden_states[self.vision_feature_layer] - - if self.vision_feature_select_strategy == "default": - selected_image_feature = selected_image_feature[:, 1:] - elif self.vision_feature_select_strategy == "full": - selected_image_feature = selected_image_feature - else: - raise ValueError("Unexpected feature selection strategy: " - f"{self.vision_feature_select_strategy}") - - # Pass image features through the multi-modal projector - image_features = self.multi_modal_projector(selected_image_feature) - - # Insert special image tokens in the input_ids - final_inputs_embeds = self._merge_input_ids_with_image_features(image_features, inputs_embeds, input_ids) - return final_inputs_embeds - - def _merge_input_ids_with_image_features(self, image_features, inputs_embeds, input_ids): - image_token_index = self.config.image_token_index - num_images, num_image_patches, embed_dim = image_features.shape - - # Positions of tokens in input_ids, assuming batch size is 1 - image_positions = np.where(input_ids[0] == image_token_index)[0].tolist() - - if len(image_positions) != num_images: - raise ValueError(f"The number of image tokens ({len(image_positions)}) does not " - f" match the number of image inputs ({num_images}).") - - text_segments = [] - start_idx = 0 - - for position in image_positions: - text_segments.append(inputs_embeds[:, start_idx:position]) - start_idx = position + 1 - - image_embeddings = mx.split(image_features, image_features.shape[0]) - final_embeddings = [v for p in zip(text_segments, image_embeddings) for v in p] - final_embeddings += [inputs_embeds[:, start_idx:]] - - # Create a final embedding of shape - # (1, num_image_patches*num_images + sequence_len, embed_dim) - return mx.concatenate(final_embeddings, axis=1) - - def __call__(self, input_ids: mx.array, pixel_values: mx.array = None, cache=None): - input_embddings = None - if pixel_values is not None: - input_embddings = self.get_input_embeddings(input_ids, pixel_values) - logits = self.language_model(input_ids, cache=cache, inputs_embeds=input_embddings) - return logits - - def sanitize(self, weights): - if self.config.vision_config: - weights = self.vision_tower.sanitize(weights) - else: - weights = {k: v for k, v in weights.items() if not k.startswith(('vision_tower', 'multi_modal_projector', 'vision_feature_layer', 'vision_feature_select_strategy'))} - weights = self.language_model.sanitize(weights) - return weights - - @property - def layers(self): - return self.language_model.model.layers - - @property - def head_dim(self): - return (self.language_model.model.head_dim or self.language_model.model.hidden_size // self.language_model.model.num_attention_heads) - - @property - def n_kv_heads(self): - return self.language_model.model.num_key_value_heads diff --git a/exo/inference/mlx/models/phi3.py b/exo/inference/mlx/models/phi3.py deleted file mode 100644 index acd4114a..00000000 --- a/exo/inference/mlx/models/phi3.py +++ /dev/null @@ -1,117 +0,0 @@ -from dataclasses import dataclass, field - -import mlx.core as mx -import mlx.nn as nn - -from mlx_lm.models.base import create_attention_mask -from mlx_lm.models.phi3 import TransformerBlock, ModelArgs - -from ...shard import Shard -from .base import IdentityBlock - -@dataclass -class ModelArgs(ModelArgs): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - super().__post_init__() - - if isinstance(self.shard, Shard): - return - if not isinstance(self.shard, dict): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - self.shard = Shard(**self.shard) - -class Phi3Model(nn.Module): - def __init__(self, args: ModelArgs): - super().__init__() - self.args = args - self.vocab_size = args.vocab_size - self.num_hidden_layers = args.num_hidden_layers - assert self.vocab_size > 0 - - if self.args.shard.is_first_layer(): - self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) - - self.layers = [] - for i in range(self.num_hidden_layers): - if self.args.shard.start_layer <= i <= self.args.shard.end_layer: - self.layers.append(TransformerBlock(args=args)) - else: - self.layers.append(IdentityBlock()) - - if self.args.shard.is_last_layer(): - self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) - - def __call__( - self, - inputs: mx.array, - cache=None, - ): - if self.args.shard.is_first_layer(): - h = self.embed_tokens(inputs) - else: - h = inputs - - mask = None - if h.shape[1] > 1: - mask = create_attention_mask(h, cache) - - if cache is None: - cache = [None] * len(self.layers) - - for layer, c in zip(self.layers, cache): - h = layer(h, mask, c) - - if self.args.shard.is_last_layer(): - h = self.norm(h) - return h - -class Model(nn.Module): - def __init__(self, args: ModelArgs): - super().__init__() - self.args = args - self.model_type = args.model_type - self.model = Phi3Model(args) - if self.args.shard.is_last_layer(): - self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False) - - def __call__( - self, - inputs: mx.array, - cache=None, - ): - out = self.model(inputs, cache) - if self.args.shard.is_last_layer(): - out = self.lm_head(out) - return out - - def sanitize(self, weights): - shard_state_dict = {} - - for key, value in weights.items(): - if "self_attn.rope.inv_freq" in key: - continue - if key.startswith('model.layers.'): - layer_num = int(key.split('.')[2]) - if self.args.shard.start_layer <= layer_num <= self.args.shard.end_layer: - shard_state_dict[key] = value - elif self.args.shard.is_first_layer() and key.startswith('model.embed_tokens'): - shard_state_dict[key] = value - elif self.args.shard.is_last_layer() and (key.startswith('lm_head') or key.startswith('model.norm')): - shard_state_dict[key] = value - - return shard_state_dict - - @property - def layers(self): - return self.model.layers - - @property - def head_dim(self): - return self.args.hidden_size // self.args.num_attention_heads - - @property - def n_kv_heads(self): - return self.args.num_key_value_heads diff --git a/exo/inference/mlx/models/qwen2.py b/exo/inference/mlx/models/qwen2.py deleted file mode 100644 index 492fbb27..00000000 --- a/exo/inference/mlx/models/qwen2.py +++ /dev/null @@ -1,129 +0,0 @@ -from dataclasses import dataclass, field - -import mlx.core as mx -import mlx.nn as nn - -from mlx_lm.models.base import create_attention_mask -from mlx_lm.models.qwen2 import TransformerBlock, ModelArgs - -from ...shard import Shard -from .base import IdentityBlock - -@dataclass -class ModelArgs(ModelArgs): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - super().__post_init__() - - if isinstance(self.shard, Shard): - return - if not isinstance(self.shard, dict): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - self.shard = Shard(**self.shard) - -class Qwen2Model(nn.Module): - def __init__(self, args: ModelArgs): - super().__init__() - self.args = args - self.vocab_size = args.vocab_size - self.num_hidden_layers = args.num_hidden_layers - assert self.vocab_size > 0 - - if self.args.shard.is_first_layer() or (self.args.shard.is_last_layer() and args.tie_word_embeddings): - self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) - - self.layers = [] - for i in range(self.num_hidden_layers): - if self.args.shard.start_layer <= i <= self.args.shard.end_layer: - self.layers.append(TransformerBlock(args=args)) - else: - self.layers.append(IdentityBlock()) - - if self.args.shard.is_last_layer(): - self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps) - - def __call__( - self, - inputs: mx.array, - cache=None, - ): - if self.args.shard.is_first_layer(): - h = self.embed_tokens(inputs) - else: - h = inputs - - mask = None - if h.shape[1] > 1: - mask = create_attention_mask(h, cache) - - if cache is None: - cache = [None]*len(self.layers) - - for layer, c in zip(self.layers, cache): - h = layer(h, mask, c) - - if self.args.shard.is_last_layer(): - h = self.norm(h) - return h - - -class Model(nn.Module): - def __init__(self, args: ModelArgs): - super().__init__() - self.args = args - self.model_type = args.model_type - self.model = Qwen2Model(args) - if self.args.shard.is_last_layer(): - if not args.tie_word_embeddings: - self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False) - - def __call__( - self, - inputs: mx.array, - cache=None, - ): - out = self.model(inputs, cache) - if self.args.shard.is_last_layer(): - if self.args.tie_word_embeddings: - out = self.model.embed_tokens.as_linear(out) - else: - out = self.lm_head(out) - return out - - def sanitize(self, weights): - shard_state_dict = {} - - for key, value in weights.items(): - if "self_attn.rotary_emb.inv_freq" in key: - continue - if key.startswith('model.layers.'): - layer_num = int(key.split('.')[2]) - if self.args.shard.start_layer <= layer_num <= self.args.shard.end_layer: - shard_state_dict[key] = value - elif self.args.shard.is_first_layer() and key.startswith('model.embed_tokens'): - shard_state_dict[key] = value - elif (self.args.shard.is_last_layer() and self.args.tie_word_embeddings) and key.startswith('model.embed_tokens'): - shard_state_dict[key] = value - elif (self.args.shard.is_last_layer() and not self.args.tie_word_embeddings) and key.startswith('lm_head'): - shard_state_dict[key] = value - elif self.args.shard.is_last_layer() and (key.startswith('model.norm')): - shard_state_dict[key] = value - - if self.args.tie_word_embeddings: - shard_state_dict.pop("lm_head.weight", None) - - return shard_state_dict - - @property - def layers(self): - return self.model.layers - - @property - def head_dim(self): - return self.args.hidden_size // self.args.num_attention_heads - - @property - def n_kv_heads(self): - return self.args.num_key_value_heads diff --git a/exo/inference/mlx/models/sd_models/clip.py b/exo/inference/mlx/models/sd_models/clip.py deleted file mode 100644 index 849460f4..00000000 --- a/exo/inference/mlx/models/sd_models/clip.py +++ /dev/null @@ -1,191 +0,0 @@ -# Adapted from https://github.com/ml-explore/mlx-examples/blob/main/stable_diffusion/stable_diffusion/clip.py - -import math -from dataclasses import dataclass -from typing import List, Optional - -import mlx.core as mx -import mlx.nn as nn -from dataclasses import field, dataclass -from exo.inference.shard import Shard -from exo.inference.mlx.models.base import IdentityBlock - -_ACTIVATIONS = {"quick_gelu": nn.gelu_fast_approx, "gelu": nn.gelu} - - - -@dataclass -class CLIPTextModelConfig: - num_layers: int = 23 - model_dims: int = 1024 - num_heads: int = 16 - max_length: int = 77 - vocab_size: int = 49408 - projection_dim: Optional[int] = None - hidden_act: str = "quick_gelu" - - @classmethod - def from_dict(cls, config): - return ModelArgs( - num_layers=config["num_hidden_layers"], - model_dims=config["hidden_size"], - num_heads=config["num_attention_heads"], - max_length=config["max_position_embeddings"], - vocab_size=config["vocab_size"], - projection_dim=config["projection_dim"] if "WithProjection" in config['architectures'][0] else None, - hidden_act=config.get("hidden_act", "quick_gelu"), - weight_files=config.get("weight_files", []) - ) - -@dataclass -class ModelArgs(CLIPTextModelConfig): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - weight_files: List[str] = field(default_factory=lambda: []) - def __post_init__(self): - if isinstance(self.shard, dict): - self.shard = Shard(**self.shard) - - if not isinstance(self.shard, Shard): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - if not self.shard.is_first_layer(): - self.vision_config = None - - -@dataclass -class CLIPOutput: - pooled_output: Optional[mx.array] = None - last_hidden_state: Optional[mx.array] = None - hidden_states: Optional[List[mx.array]] = None - - -class CLIPEncoderLayer(nn.Module): - """The transformer encoder layer from CLIP.""" - - def __init__(self, model_dims: int, num_heads: int, activation: str): - super().__init__() - - self.layer_norm1 = nn.LayerNorm(model_dims) - self.layer_norm2 = nn.LayerNorm(model_dims) - - self.attention = nn.MultiHeadAttention(model_dims, num_heads) - self.attention.query_proj.bias = mx.zeros(model_dims) - self.attention.key_proj.bias = mx.zeros(model_dims) - self.attention.value_proj.bias = mx.zeros(model_dims) - self.attention.out_proj.bias = mx.zeros(model_dims) - - self.linear1 = nn.Linear(model_dims, 4 * model_dims) - self.linear2 = nn.Linear(4 * model_dims, model_dims) - - self.act = _ACTIVATIONS[activation] - - def __call__(self, x, attn_mask=None): - - y = self.layer_norm1(x) - y = self.attention(y, y, y, attn_mask) - x = y + x - - y = self.layer_norm2(x) - y = self.linear1(y) - y = self.act(y) - y = self.linear2(y) - x = y + x - return x - - -class CLIPTextModel(nn.Module): - """Implements the text encoder transformer from CLIP.""" - - def __init__(self, config: CLIPTextModelConfig, shard: Shard): - super().__init__() - - self.shard = shard - self.layers_range = range(self.shard.start_layer*2, self.shard.end_layer*2+2) - if self.shard.is_first_layer(): - self.token_embedding = nn.Embedding(config.vocab_size, config.model_dims) - self.position_embedding = nn.Embedding(config.max_length, config.model_dims) - self.layers = [] - for i in range(math.ceil(config.num_layers/2)): - if 2*i in self.layers_range: - self.layers.append(CLIPEncoderLayer(config.model_dims, config.num_heads, config.hidden_act)) - if 2*i+1 in self.layers_range and 2*i+1 < config.num_layers: - self.layers.append(CLIPEncoderLayer(config.model_dims, config.num_heads, config.hidden_act)) - else: - self.layers.append(IdentityBlock()) - if self.shard.is_last_layer(): - self.final_layer_norm = nn.LayerNorm(config.model_dims) - - if config.projection_dim is not None: - self.text_projection = nn.Linear( - config.model_dims, config.projection_dim, bias=False - ) - - def _get_mask(self, N, dtype): - indices = mx.arange(N) - mask = indices[:, None] < indices[None] - mask = mask.astype(dtype) * (-6e4 if dtype == mx.float16 else -1e9) - return mask - - def __call__(self, x, mask=None): - # Extract some shapes - if self.shard.is_first_layer(): - B, N = x.shape - eos_tokens = x.argmax(-1) - - # Compute the embeddings - x = self.token_embedding(x) - - x = x + self.position_embedding.weight[:N] - # Compute the features from the transformer - mask = self._get_mask(N, x.dtype) - - for l in self.layers: - x = l(x, mask) - # Apply the final layernorm and return - - if self.shard.is_last_layer(): - x = self.final_layer_norm(x) - - - - return x, mask - def sanitize(self, weights): - sanitized_weights = {} - for key, value in weights.items(): - if "position_ids" in key: - continue - if key.startswith("text_model."): - key = key[11:] - if key.startswith("embeddings."): - key = key[11:] - if key.startswith("encoder."): - key = key[8:] - - # Map attention layers - if "self_attn." in key: - key = key.replace("self_attn.", "attention.") - if "q_proj." in key: - key = key.replace("q_proj.", "query_proj.") - if "k_proj." in key: - key = key.replace("k_proj.", "key_proj.") - if "v_proj." in key: - key = key.replace("v_proj.", "value_proj.") - - # Map ffn layers - if "mlp.fc1" in key: - key = key.replace("mlp.fc1", "linear1") - if "mlp.fc2" in key: - key = key.replace("mlp.fc2", "linear2") - - if key.startswith("layers."): - layer_num = int(key.split(".")[1]) - if layer_num not in self.layers_range: - continue - if not self.shard.is_first_layer() and "embedding" in key: - continue - if not self.shard.is_last_layer() and key.startswith("final_layer_norm"): - continue - if not self.shard.is_last_layer() and key.startswith("text_projection"): - continue - sanitized_weights[key] = value - return sanitized_weights diff --git a/exo/inference/mlx/models/sd_models/tokenizer.py b/exo/inference/mlx/models/sd_models/tokenizer.py deleted file mode 100644 index 4987bb90..00000000 --- a/exo/inference/mlx/models/sd_models/tokenizer.py +++ /dev/null @@ -1,131 +0,0 @@ -# adapted from https://github.com/ml-explore/mlx-examples/blob/main/stable_diffusion/stable_diffusion/tokenizer.py - -import regex -import json -import glob - - -class Tokenizer: - """A simple port of CLIPTokenizer from https://github.com/huggingface/transformers/ .""" - - def __init__(self, bpe_ranks, vocab): - self.bpe_ranks = bpe_ranks - self.vocab = vocab - self.pat = regex.compile( - r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", - regex.IGNORECASE, - ) - - self._cache = {self.bos: self.bos, self.eos: self.eos} - - @property - def bos(self): - return "<|startoftext|>" - - @property - def bos_token(self): - return self.vocab[self.bos] - - @property - def eos(self): - return "<|endoftext|>" - - @property - def eos_token(self): - return self.vocab[self.eos] - - def bpe(self, text): - if text in self._cache: - return self._cache[text] - - unigrams = list(text[:-1]) + [text[-1] + ""] - unique_bigrams = set(zip(unigrams, unigrams[1:])) - - if not unique_bigrams: - return unigrams - - # In every iteration try to merge the two most likely bigrams. If none - # was merged we are done. - # - # Ported from https://github.com/huggingface/transformers/blob/main/src/transformers/models/clip/tokenization_clip.py - while unique_bigrams: - bigram = min( - unique_bigrams, key=lambda pair: self.bpe_ranks.get(pair, float("inf")) - ) - if bigram not in self.bpe_ranks: - break - - new_unigrams = [] - skip = False - for a, b in zip(unigrams, unigrams[1:]): - if skip: - skip = False - continue - - if (a, b) == bigram: - new_unigrams.append(a + b) - skip = True - - else: - new_unigrams.append(a) - - if not skip: - new_unigrams.append(b) - - unigrams = new_unigrams - unique_bigrams = set(zip(unigrams, unigrams[1:])) - - self._cache[text] = unigrams - - return unigrams - - def tokenize(self, text, prepend_bos=True, append_eos=True): - if isinstance(text, list): - return [self.tokenize(t, prepend_bos, append_eos) for t in text] - - # Lower case cleanup and split according to self.pat. Hugging Face does - # a much more thorough job here but this should suffice for 95% of - # cases. - clean_text = regex.sub(r"\s+", " ", text.lower()) - tokens = regex.findall(self.pat, clean_text) - - # Split the tokens according to the byte-pair merge file - bpe_tokens = [ti for t in tokens for ti in self.bpe(t)] - - # Map to token ids and return - tokens = [self.vocab[t] for t in bpe_tokens] - if prepend_bos: - tokens = [self.bos_token] + tokens - if append_eos: - tokens.append(self.eos_token) - - return tokens - - def encode(self, prompt): - tokens = [self.tokenize(prompt)] - negative_text = "" - if negative_text is not None: - tokens += [self.tokenize(negative_text)] - lengths = [len(t) for t in tokens] - N = max(lengths) - tokens = [t + [0] * (N - len(t)) for t in tokens] - return tokens - -def load_tokenizer( - model_path: str, - vocab_key: str = "tokenizer_vocab", - merges_key: str = "tokenizer_merges", -): - - vocab_file = glob.glob(str(model_path/"tokenizer"/vocab_key))[0] - with open(vocab_file, encoding="utf-8") as f: - vocab = json.load(f) - - merges_file = glob.glob(str(model_path/"tokenizer"/merges_key))[0] - with open(merges_file, encoding="utf-8") as f: - bpe_merges = f.read().strip().split("\n")[1 : 49152 - 256 - 2 + 1] - bpe_merges = [tuple(m.split()) for m in bpe_merges] - bpe_ranks = dict(map(reversed, enumerate(bpe_merges))) - - return Tokenizer(bpe_ranks, vocab) - diff --git a/exo/inference/mlx/models/sd_models/unet.py b/exo/inference/mlx/models/sd_models/unet.py deleted file mode 100644 index 3fe44b86..00000000 --- a/exo/inference/mlx/models/sd_models/unet.py +++ /dev/null @@ -1,629 +0,0 @@ -# Adapted from https://github.com/ml-explore/mlx-examples/blob/main/stable_diffusion/stable_diffusion/unet.py - -import math -from typing import Optional - -import mlx.core as mx -import mlx.nn as nn - -from dataclasses import dataclass, field -from typing import Tuple, Optional, List -from exo.inference.shard import Shard - -@dataclass -class UNetConfig: - in_channels: int = 4 - out_channels: int = 4 - conv_in_kernel: int = 3 - conv_out_kernel: int = 3 - block_out_channels: Tuple[int] = (320, 640, 1280, 1280) - layers_per_block: Tuple[int] = (2, 2, 2, 2) - mid_block_layers: int = 2 - transformer_layers_per_block: Tuple[int] = (1, 1, 1, 1) - num_attention_heads: Tuple[int] = (5, 10, 20, 20) - cross_attention_dim: Tuple[int] = (1024,) * 4 - norm_num_groups: int = 32 - down_block_types: Tuple[str] = ( - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D", - "DownBlock2D", - ) - up_block_types: Tuple[str] = ( - "UpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - "CrossAttnUpBlock2D", - ) - addition_embed_type: Optional[str] = None - addition_time_embed_dim: Optional[int] = None - projection_class_embeddings_input_dim: Optional[int] = None - weight_files: List[str] = field(default_factory=lambda: []) - - - - @classmethod - def from_dict(cls,config): - n_blocks = len(config['block_out_channels']) - return UNetConfig( - in_channels=config["in_channels"], - out_channels=config["out_channels"], - block_out_channels=config["block_out_channels"], - layers_per_block=[config["layers_per_block"]] * n_blocks, - transformer_layers_per_block=config.get( - "transformer_layers_per_block", (1,) * 4 - ), - num_attention_heads=( - [config["attention_head_dim"]] * n_blocks - if isinstance(config["attention_head_dim"], int) - else config["attention_head_dim"] - ), - cross_attention_dim=[config["cross_attention_dim"]] * n_blocks, - norm_num_groups=config["norm_num_groups"], - down_block_types=config["down_block_types"], - up_block_types=config["up_block_types"][::-1], - addition_embed_type=config.get("addition_embed_type", None), - addition_time_embed_dim=config.get("addition_time_embed_dim", None), - projection_class_embeddings_input_dim=config.get( - "projection_class_embeddings_input_dim", None - ), - weight_files=config.get("weight_files", []) - - ) - - -def upsample_nearest(x, scale: int = 2): - B, H, W, C = x.shape - x = mx.broadcast_to(x[:, :, None, :, None, :], (B, H, scale, W, scale, C)) - x = x.reshape(B, H * scale, W * scale, C) - - return x - - -class TimestepEmbedding(nn.Module): - def __init__(self, in_channels: int, time_embed_dim: int): - super().__init__() - - self.linear_1 = nn.Linear(in_channels, time_embed_dim) - self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim) - - def __call__(self, x): - x = self.linear_1(x) - x = nn.silu(x) - x = self.linear_2(x) - - return x - - -class TransformerBlock(nn.Module): - def __init__( - self, - model_dims: int, - num_heads: int, - hidden_dims: Optional[int] = None, - memory_dims: Optional[int] = None, - ): - super().__init__() - - self.norm1 = nn.LayerNorm(model_dims) - self.attn1 = nn.MultiHeadAttention(model_dims, num_heads) - self.attn1.out_proj.bias = mx.zeros(model_dims) - - memory_dims = memory_dims or model_dims - self.norm2 = nn.LayerNorm(model_dims) - self.attn2 = nn.MultiHeadAttention( - model_dims, num_heads, key_input_dims=memory_dims - ) - self.attn2.out_proj.bias = mx.zeros(model_dims) - - hidden_dims = hidden_dims or 4 * model_dims - self.norm3 = nn.LayerNorm(model_dims) - self.linear1 = nn.Linear(model_dims, hidden_dims) - self.linear2 = nn.Linear(model_dims, hidden_dims) - self.linear3 = nn.Linear(hidden_dims, model_dims) - - def __call__(self, x, memory, attn_mask, memory_mask): - # Self attention - y = self.norm1(x) - y = self.attn1(y, y, y, attn_mask) - x = x + y - - # Cross attention - y = self.norm2(x) - y = self.attn2(y, memory, memory, memory_mask) - x = x + y - - # FFN - y = self.norm3(x) - y_a = self.linear1(y) - y_b = self.linear2(y) - y = y_a * nn.gelu(y_b) - y = self.linear3(y) - x = x + y - - return x - - -class Transformer2D(nn.Module): - """A transformer model for inputs with 2 spatial dimensions.""" - - def __init__( - self, - in_channels: int, - model_dims: int, - encoder_dims: int, - num_heads: int, - num_layers: int = 1, - norm_num_groups: int = 32, - ): - super().__init__() - - self.norm = nn.GroupNorm(norm_num_groups, in_channels, pytorch_compatible=True) - self.proj_in = nn.Linear(in_channels, model_dims) - self.transformer_blocks = [ - TransformerBlock(model_dims, num_heads, memory_dims=encoder_dims) - for i in range(num_layers) - ] - self.proj_out = nn.Linear(model_dims, in_channels) - - def __call__(self, x, encoder_x, attn_mask, encoder_attn_mask): - # Save the input to add to the output - input_x = x - dtype = x.dtype - - # Perform the input norm and projection - B, H, W, C = x.shape - x = self.norm(x.astype(mx.float32)).astype(dtype).reshape(B, -1, C) - x = self.proj_in(x) - - # Apply the transformer - for block in self.transformer_blocks: - x = block(x, encoder_x, attn_mask, encoder_attn_mask) - - # Apply the output projection and reshape - x = self.proj_out(x) - x = x.reshape(B, H, W, C) - - return x + input_x - - -class ResnetBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: Optional[int] = None, - groups: int = 32, - temb_channels: Optional[int] = None, - ): - super().__init__() - - out_channels = out_channels or in_channels - - self.norm1 = nn.GroupNorm(groups, in_channels, pytorch_compatible=True) - self.conv1 = nn.Conv2d( - in_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - if temb_channels is not None: - self.time_emb_proj = nn.Linear(temb_channels, out_channels) - self.norm2 = nn.GroupNorm(groups, out_channels, pytorch_compatible=True) - self.conv2 = nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - - if in_channels != out_channels: - self.conv_shortcut = nn.Linear(in_channels, out_channels) - - def __call__(self, x, temb=None): - dtype = x.dtype - - if temb is not None: - temb = self.time_emb_proj(nn.silu(temb)) - y = self.norm1(x.astype(mx.float32)).astype(dtype) - - y = nn.silu(y) - - y = self.conv1(y) - - - if temb is not None: - y = y + temb[:, None, None, :] - y = self.norm2(y.astype(mx.float32)).astype(dtype) - y = nn.silu(y) - y = self.conv2(y) - - x = y + (x if "conv_shortcut" not in self else self.conv_shortcut(x)) - return x - - -class UNetBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - prev_out_channels: Optional[int] = None, - num_layers: int = 1, - transformer_layers_per_block: int = 1, - num_attention_heads: int = 8, - cross_attention_dim=1280, - resnet_groups: int = 32, - add_downsample=True, - add_upsample=True, - add_cross_attention=True, - ): - super().__init__() - - # Prepare the in channels list for the resnets - if prev_out_channels is None: - in_channels_list = [in_channels] + [out_channels] * (num_layers - 1) - else: - in_channels_list = [prev_out_channels] + [out_channels] * (num_layers - 1) - res_channels_list = [out_channels] * (num_layers - 1) + [in_channels] - in_channels_list = [ - a + b for a, b in zip(in_channels_list, res_channels_list) - ] - - # Add resnet blocks that also process the time embedding - self.resnets = [ - ResnetBlock2D( - in_channels=ic, - out_channels=out_channels, - temb_channels=temb_channels, - groups=resnet_groups, - ) - for ic in in_channels_list - ] - - # Add optional cross attention layers - if add_cross_attention: - self.attentions = [ - Transformer2D( - in_channels=out_channels, - model_dims=out_channels, - num_heads=num_attention_heads, - num_layers=transformer_layers_per_block, - encoder_dims=cross_attention_dim, - ) - for i in range(num_layers) - ] - - # Add an optional downsampling layer - if add_downsample: - self.downsample = nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=2, padding=1 - ) - - # or upsampling layer - if add_upsample: - self.upsample = nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - - def __call__( - self, - x, - encoder_x=None, - temb=None, - attn_mask=None, - encoder_attn_mask=None, - residual_hidden_states=None, - ): - output_states = [] - - for i in range(len(self.resnets)): - if residual_hidden_states is not None: - x = mx.concatenate([x, residual_hidden_states.pop()], axis=-1) - - x = self.resnets[i](x, temb) - - if "attentions" in self: - x = self.attentions[i](x, encoder_x, attn_mask, encoder_attn_mask) - - output_states.append(x) - - if "downsample" in self: - x = self.downsample(x) - output_states.append(x) - - if "upsample" in self: - x = self.upsample(upsample_nearest(x)) - output_states.append(x) - - return x, output_states - - -class UNetModel(nn.Module): - """The conditional 2D UNet model that actually performs the denoising.""" - - def __init__(self, config: UNetConfig, shard: Shard): - super().__init__() - self.shard = shard - self.start_layer = shard.start_layer - self.end_layer = shard.end_layer - self.layers_range = list(range(self.start_layer, self.end_layer+1)) - if shard.is_first_layer(): - self.conv_in = nn.Conv2d( - config.in_channels, - config.block_out_channels[0], - config.conv_in_kernel, - padding=(config.conv_in_kernel - 1) // 2, - ) - - self.timesteps = nn.SinusoidalPositionalEncoding( - config.block_out_channels[0], - max_freq=1, - min_freq=math.exp( - -math.log(10000) + 2 * math.log(10000) / config.block_out_channels[0] - ), - scale=1.0, - cos_first=True, - full_turns=False, - ) - self.time_embedding = TimestepEmbedding( - config.block_out_channels[0], - config.block_out_channels[0] * 4, - ) - - if config.addition_embed_type == "text_time": - self.add_time_proj = nn.SinusoidalPositionalEncoding( - config.addition_time_embed_dim, - max_freq=1, - min_freq=math.exp( - -math.log(10000) - + 2 * math.log(10000) / config.addition_time_embed_dim - ), - scale=1.0, - cos_first=True, - full_turns=False, - ) - self.add_embedding = TimestepEmbedding( - config.projection_class_embeddings_input_dim, - config.block_out_channels[0] * 4, - ) - - # Make the downsampling blocks - block_channels = [config.block_out_channels[0]] + list( - config.block_out_channels - ) - self.down_blocks = [] - - for i, (in_channels, out_channels) in enumerate(zip(block_channels, block_channels[1:])): - if i in self.layers_range: - self.down_blocks.append( - UNetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=config.block_out_channels[0] * 4, - num_layers=config.layers_per_block[i], - transformer_layers_per_block=config.transformer_layers_per_block[i], - num_attention_heads=config.num_attention_heads[i], - cross_attention_dim=config.cross_attention_dim[i], - resnet_groups=config.norm_num_groups, - add_downsample=(i < len(config.block_out_channels) - 1), - add_upsample=False, - add_cross_attention="CrossAttn" in config.down_block_types[i], - ) - ) - else: - self.down_blocks.append(nn.Identity()) - - - # Make the middle block - if 4 in self.layers_range: - self.mid_blocks = [ - ResnetBlock2D( - in_channels=config.block_out_channels[-1], - out_channels=config.block_out_channels[-1], - temb_channels=config.block_out_channels[0] * 4, - groups=config.norm_num_groups, - ), - Transformer2D( - in_channels=config.block_out_channels[-1], - model_dims=config.block_out_channels[-1], - num_heads=config.num_attention_heads[-1], - num_layers=config.transformer_layers_per_block[-1], - encoder_dims=config.cross_attention_dim[-1], - ), - ResnetBlock2D( - in_channels=config.block_out_channels[-1], - out_channels=config.block_out_channels[-1], - temb_channels=config.block_out_channels[0] * 4, - groups=config.norm_num_groups, - ), - ] - - # Make the upsampling blocks - block_channels = ( - [config.block_out_channels[0]] - + list(config.block_out_channels) - + [config.block_out_channels[-1]] - ) - - total_items = len(block_channels) - 3 - reversed_channels = list(reversed(list(zip(block_channels, block_channels[1:], block_channels[2:])))) - - self.up_blocks = [] - for rev_i, (in_channels, out_channels, prev_out_channels) in enumerate(reversed_channels): - i = total_items - rev_i - if rev_i+5 in self.layers_range: - self.up_blocks.append( - UNetBlock2D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=config.block_out_channels[0] * 4, - prev_out_channels=prev_out_channels, - num_layers=config.layers_per_block[i] + 1, - transformer_layers_per_block=config.transformer_layers_per_block[i], - num_attention_heads=config.num_attention_heads[i], - cross_attention_dim=config.cross_attention_dim[i], - resnet_groups=config.norm_num_groups, - add_downsample=False, - add_upsample=(i > 0), - add_cross_attention="CrossAttn" in config.up_block_types[i], - ) - ) - else: - self.up_blocks.append(nn.Identity()) - - - if shard.is_last_layer(): - self.conv_norm_out = nn.GroupNorm( - config.norm_num_groups, - config.block_out_channels[0], - pytorch_compatible=True, - ) - self.conv_out = nn.Conv2d( - config.block_out_channels[0], - config.out_channels, - config.conv_out_kernel, - padding=(config.conv_out_kernel - 1) // 2, - ) - - def __call__( - self, - x, - timestep, - encoder_x, - attn_mask=None, - encoder_attn_mask=None, - text_time=None, - residuals=None, - ): - # Compute the time embeddings - - temb = self.timesteps(timestep).astype(x.dtype) - temb = self.time_embedding(temb) - - # Add the extra text_time conditioning - if text_time is not None: - text_emb, time_ids = text_time - emb = self.add_time_proj(time_ids).flatten(1).astype(x.dtype) - emb = mx.concatenate([text_emb, emb], axis=-1) - emb = self.add_embedding(emb) - temb = temb + emb - - if self.shard.is_first_layer(): - # Preprocess the input - x = self.conv_in(x) - residuals = [x] - # Run the downsampling part of the unet - - for i in range(len(self.down_blocks)): - if i in self.layers_range: - x, res = self.down_blocks[i]( - x, - encoder_x=encoder_x, - temb=temb, - attn_mask=attn_mask, - encoder_attn_mask=encoder_attn_mask, - ) - residuals.extend(res) - else: - x= self.down_blocks[i](x) - - if 4 in self.layers_range: - # Run the middle part of the unet - x = self.mid_blocks[0](x, temb) - x = self.mid_blocks[1](x, encoder_x, attn_mask, encoder_attn_mask) - x = self.mid_blocks[2](x, temb) - - # Run the upsampling part of the unet - for i in range(len(self.up_blocks)): - if i+5 in self.layers_range: - x, _ = self.up_blocks[i]( - x, - encoder_x=encoder_x, - temb=temb, - attn_mask=attn_mask, - encoder_attn_mask=encoder_attn_mask, - residual_hidden_states=residuals, - ) - else: - x= self.up_blocks[i](x) - - # Postprocess the output - if self.shard.is_last_layer(): - dtype = x.dtype - x = self.conv_norm_out(x.astype(mx.float32)).astype(dtype) - x = nn.silu(x) - x = self.conv_out(x) - - return x, residuals - def sanitize(self, weights): - sanitized_weights = {} - for key, value in weights.items(): - k1="" - k2="" - if "downsamplers" in key: - key = key.replace("downsamplers.0.conv", "downsample") - if "upsamplers" in key: - key = key.replace("upsamplers.0.conv", "upsample") - - # Map the mid block - if "mid_block.resnets.0" in key: - key = key.replace("mid_block.resnets.0", "mid_blocks.0") - if "mid_block.attentions.0" in key: - key = key.replace("mid_block.attentions.0", "mid_blocks.1") - if "mid_block.resnets.1" in key: - key = key.replace("mid_block.resnets.1", "mid_blocks.2") - - # Map attention layers - if "to_k" in key: - key = key.replace("to_k", "key_proj") - if "to_out.0" in key: - key = key.replace("to_out.0", "out_proj") - if "to_q" in key: - key = key.replace("to_q", "query_proj") - if "to_v" in key: - key = key.replace("to_v", "value_proj") - - # Map transformer ffn - if "ff.net.2" in key: - key = key.replace("ff.net.2", "linear3") - if "ff.net.0" in key: - k1 = key.replace("ff.net.0.proj", "linear1") - k2 = key.replace("ff.net.0.proj", "linear2") - v1, v2 = mx.split(value, 2) - - - if "conv_shortcut.weight" in key: - value = value.squeeze() - - # Transform the weights from 1x1 convs to linear - if len(value.shape) == 4 and ("proj_in" in key or "proj_out" in key): - value = value.squeeze() - - if len(value.shape) == 4: - value = value.transpose(0, 2, 3, 1) - value = value.reshape(-1).reshape(value.shape) - - if key.startswith("conv_in") : - if 0 not in self.layers_range: - continue - - if key.startswith("down_blocks"): - layer_num = int(key.split(".")[1]) - if layer_num not in self.layers_range: - continue - - if key.startswith("mid_block"): - if 4 not in self.layers_range: - continue - - if key.startswith("up_blocks"): - layer_num = int(key.split(".")[1]) - if (layer_num+5) not in self.layers_range: - continue - - if key.startswith("conv_out") or key.startswith("conv_norm_out"): - if 8 not in self.layers_range: - continue - - if len(k1)>0: - sanitized_weights[k1] = v1 - sanitized_weights[k2] = v2 - else: - sanitized_weights[key] = value - - - return sanitized_weights diff --git a/exo/inference/mlx/models/sd_models/vae.py b/exo/inference/mlx/models/sd_models/vae.py deleted file mode 100644 index 0f148517..00000000 --- a/exo/inference/mlx/models/sd_models/vae.py +++ /dev/null @@ -1,429 +0,0 @@ -# Adapted from https://github.com/ml-explore/mlx-examples/blob/main/stable_diffusion/stable_diffusion/vae.py - -import math -from typing import List - -import mlx.core as mx -import mlx.nn as nn - -from .unet import ResnetBlock2D, upsample_nearest -from dataclasses import dataclass, field -from exo.inference.shard import Shard -from typing import Tuple -import inspect -from ..base import IdentityBlock - -@dataclass -class AutoencoderConfig: - in_channels: int = 3 - out_channels: int = 3 - latent_channels_out: int = 8 - latent_channels_in: int = 4 - block_out_channels: Tuple[int] = (128, 256, 512, 512) - layers_per_block: int = 2 - norm_num_groups: int = 32 - scaling_factor: float = 0.18215 - weight_files: List[str] = field(default_factory=lambda: []) - @classmethod - def from_dict(cls, params): - return cls(**{k: v for k, v in params.items() if k in inspect.signature(cls).parameters}) - - -@dataclass -class ModelArgs(AutoencoderConfig): - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - - def __post_init__(self): - if isinstance(self.shard, dict): - self.shard = Shard(**self.shard) - - if not isinstance(self.shard, Shard): - raise TypeError(f"Expected shard to be a Shard instance or a dict, got {type(self.shard)} instead") - - if not self.shard.is_first_layer(): - self.vision_config = None - - -class Attention(nn.Module): - """A single head unmasked attention for use with the VAE.""" - - def __init__(self, dims: int, norm_groups: int = 32): - super().__init__() - - self.group_norm = nn.GroupNorm(norm_groups, dims, pytorch_compatible=True) - self.query_proj = nn.Linear(dims, dims) - self.key_proj = nn.Linear(dims, dims) - self.value_proj = nn.Linear(dims, dims) - self.out_proj = nn.Linear(dims, dims) - - def __call__(self, x): - B, H, W, C = x.shape - - y = self.group_norm(x) - - queries = self.query_proj(y).reshape(B, H * W, C) - keys = self.key_proj(y).reshape(B, H * W, C) - values = self.value_proj(y).reshape(B, H * W, C) - - scale = 1 / math.sqrt(queries.shape[-1]) - scores = (queries * scale) @ keys.transpose(0, 2, 1) - attn = mx.softmax(scores, axis=-1) - y = (attn @ values).reshape(B, H, W, C) - - y = self.out_proj(y) - x = x + y - - return x - - -class EncoderDecoderBlock2D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - num_layers: int = 1, - resnet_groups: int = 32, - add_downsample=True, - add_upsample=True, - ): - super().__init__() - - # Add the resnet blocks - self.resnets = [ - ResnetBlock2D( - in_channels=in_channels if i == 0 else out_channels, - out_channels=out_channels, - groups=resnet_groups, - ) - for i in range(num_layers) - ] - - # Add an optional downsampling layer - if add_downsample: - self.downsample = nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=2, padding=0 - ) - - # or upsampling layer - if add_upsample: - self.upsample = nn.Conv2d( - out_channels, out_channels, kernel_size=3, stride=1, padding=1 - ) - - def __call__(self, x): - for resnet in self.resnets: - x = resnet(x) - if "downsample" in self: - x = mx.pad(x, [(0, 0), (0, 1), (0, 1), (0, 0)]) - x = self.downsample(x) - - if "upsample" in self: - x = self.upsample(upsample_nearest(x)) - return x - - -class Encoder(nn.Module): - """Implements the encoder side of the Autoencoder.""" - - def __init__( - self, - in_channels: int, - latent_channels_out: int, - block_out_channels: List[int] = [64], - layers_per_block: int = 2, - resnet_groups: int = 32, - layers_range: List[int] = [], - shard: Shard = field(default_factory=lambda: Shard("", 0, 0, 0)) - ): - super().__init__() - self.layers_range = layers_range - self.shard = shard - if self.shard.is_first_layer(): - self.conv_in = nn.Conv2d( - in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1 - ) - - channels = [block_out_channels[0]] + list(block_out_channels) - self.down_blocks = [] - current_layer = 1 - for i, (in_channels, out_channels) in enumerate(zip(channels, channels[1:])): - if current_layer in self.layers_range: - self.down_blocks.append( - EncoderDecoderBlock2D( - in_channels, - out_channels, - num_layers=layers_per_block, - resnet_groups=resnet_groups, - add_downsample=i < len(block_out_channels) - 1, - add_upsample=False, - ) - ) - else: - self.down_blocks.append(IdentityBlock()) - current_layer += 1 - - if self.shard.is_last_layer(): - self.mid_blocks = [ - ResnetBlock2D( - in_channels=block_out_channels[-1], - out_channels=block_out_channels[-1], - groups=resnet_groups, - ), - Attention(block_out_channels[-1], resnet_groups), - ResnetBlock2D( - in_channels=block_out_channels[-1], - out_channels=block_out_channels[-1], - groups=resnet_groups, - ), - ] - - self.conv_norm_out = nn.GroupNorm( - resnet_groups, block_out_channels[-1], pytorch_compatible=True - ) - self.conv_out = nn.Conv2d(block_out_channels[-1], latent_channels_out, 3, padding=1) - - def __call__(self, x): - if self.shard.is_first_layer(): - x = self.conv_in(x) - - for l in self.down_blocks: - x = l(x) - - if self.shard.is_last_layer(): - x = self.mid_blocks[0](x) - x = self.mid_blocks[1](x) - x = self.mid_blocks[2](x) - - x = self.conv_norm_out(x) - x = nn.silu(x) - x = self.conv_out(x) - - return x - - -class Decoder(nn.Module): - """Implements the decoder side of the Autoencoder.""" - - def __init__( - self, - in_channels: int, - out_channels: int, - shard: Shard, - layer_range: List[int], - block_out_channels: List[int] = [64], - layers_per_block: int = 2, - resnet_groups: int = 32, - ): - super().__init__() - self.out_channels = out_channels - self.layers_range = layer_range - if 0 in layer_range: - self.conv_in = nn.Conv2d( - in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1 - ) - - if 0 in layer_range: - self.mid_blocks = [ - ResnetBlock2D( - in_channels=block_out_channels[-1], - out_channels=block_out_channels[-1], - groups=resnet_groups, - ), - Attention(block_out_channels[-1], resnet_groups), - ResnetBlock2D( - in_channels=block_out_channels[-1], - out_channels=block_out_channels[-1], - groups=resnet_groups, - ), - ] - - channels = list(reversed(block_out_channels)) - channels = [channels[0]] + channels - - self.up_blocks = [] - current_layer = 1 - - for i, (in_channels, out_channels) in enumerate(zip(channels, channels[1:])): - if current_layer in layer_range: - self.up_blocks.append( - EncoderDecoderBlock2D( - in_channels, - out_channels, - num_layers=layers_per_block, - resnet_groups=resnet_groups, - add_downsample=False, - add_upsample=i < len(block_out_channels) - 1, - ) - ) - else: - self.up_blocks.append(IdentityBlock()) - current_layer += 1 - if 4 in layer_range: - self.conv_norm_out = nn.GroupNorm( - resnet_groups, block_out_channels[0], pytorch_compatible=True - ) - self.conv_out = nn.Conv2d(block_out_channels[0], self.out_channels, 3, padding=1) - - - def __call__(self, x): - if 0 in self.layers_range: - x = self.conv_in(x) - x = self.mid_blocks[0](x) - x = self.mid_blocks[1](x) - x = self.mid_blocks[2](x) - - for l in self.up_blocks: - x = l(x) - if 4 in self.layers_range: - x = self.conv_norm_out(x) - x = nn.silu(x) - x = self.conv_out(x) - return x - - -class Autoencoder(nn.Module): - """The autoencoder that allows us to perform diffusion in the latent space.""" - - def __init__(self, config: AutoencoderConfig, shard: Shard, model_shard: str): - super().__init__() - self.shard = shard - self.start_layer = shard.start_layer - self.end_layer = shard.end_layer - self.layers_range = list(range(self.start_layer, self.end_layer+1)) - self.latent_channels = config.latent_channels_in - self.scaling_factor = config.scaling_factor - self.model_shard = model_shard - if self.model_shard == "vae_encoder": - self.encoder = Encoder( - config.in_channels, - config.latent_channels_out, - config.block_out_channels, - config.layers_per_block, - resnet_groups=config.norm_num_groups, - layers_range=self.layers_range, - shard=shard - ) - if self.shard.is_last_layer(): - self.quant_proj = nn.Linear( - config.latent_channels_out, config.latent_channels_out - ) - if self.model_shard == "vae_decoder": - self.decoder = Decoder( - config.latent_channels_in, - config.out_channels, - shard, - self.layers_range, - config.block_out_channels, - config.layers_per_block + 1, - resnet_groups=config.norm_num_groups, - ) - if self.shard.is_first_layer(): - self.post_quant_proj = nn.Linear( - config.latent_channels_in, config.latent_channels_in - ) - - def decode(self, z): - if self.shard.is_first_layer(): - z = z / self.scaling_factor - z=self.post_quant_proj(z) - return self.decoder(z) - - def encode(self, x): - x = self.encoder(x) - if self.shard.is_last_layer(): - x = self.quant_proj(x) - mean, logvar = x.split(2, axis=-1) - mean = mean * self.scaling_factor - logvar = logvar + 2 * math.log(self.scaling_factor) - x = mean - return x - - def __call__(self, x, key=None): - mean, logvar = self.encode(x) - z = mx.random.normal(mean.shape, key=key) * mx.exp(0.5 * logvar) + mean - x_hat = self.decode(z) - - return dict(x_hat=x_hat, z=z, mean=mean, logvar=logvar) - - def sanitize(self, weights): - shard = self.shard - layers = self.layers_range - sanitized_weights = {} - for key, value in weights.items(): - - if "downsamplers" in key: - key = key.replace("downsamplers.0.conv", "downsample") - if "upsamplers" in key: - key = key.replace("upsamplers.0.conv", "upsample") - - # Map attention layers - if "key" in key: - key = key.replace("key", "key_proj") - if "proj_attn" in key: - key = key.replace("proj_attn", "out_proj") - if "query" in key: - key = key.replace("query", "query_proj") - if "value" in key: - key = key.replace("value", "value_proj") - - # Map the mid block - if "mid_block.resnets.0" in key: - key = key.replace("mid_block.resnets.0", "mid_blocks.0") - if "mid_block.attentions.0" in key: - key = key.replace("mid_block.attentions.0", "mid_blocks.1") - if "mid_block.resnets.1" in key: - key = key.replace("mid_block.resnets.1", "mid_blocks.2") - - # Map the quant/post_quant layers - if "quant_conv" in key: - key = key.replace("quant_conv", "quant_proj") - value = value.squeeze() - - # Map the conv_shortcut to linear - if "conv_shortcut.weight" in key: - value = value.squeeze() - - if len(value.shape) == 4: - value = value.transpose(0, 2, 3, 1) - value = value.reshape(-1).reshape(value.shape) - - - if "post_quant_conv" in key : - key = key.replace("quant_conv", "quant_proj") - value = value.squeeze() - - if 'decoder' in key and self.model_shard == "vae_decoder": - if key.startswith("decoder.mid_blocks."): - if 0 in layers: - sanitized_weights[key] = value - if "conv_in" in key and 0 in layers: - sanitized_weights[key] = value - if key.startswith("decoder.up_blocks."): - layer_num = int(key.split(".")[2])+1 - if layer_num in layers: - sanitized_weights[key] = value - if key.startswith("decoder.conv_norm_out") and 4 in layers: - sanitized_weights[key] = value - if key.startswith("decoder.conv_out") and 4 in layers: - sanitized_weights[key] = value - if self.model_shard == "vae_decoder": - if key.startswith("post_quant_proj") and 0 in layers: - sanitized_weights[key] = value - if self.model_shard == "vae_encoder": - if key.startswith("encoder."): - if "conv_in" in key and shard.is_first_layer(): - sanitized_weights[key] = value - if key.startswith("encoder.down_blocks."): - layer_num = int(key.split(".")[2])+1 - if layer_num in layers: - sanitized_weights[key] = value - if key.startswith("encoder.mid_blocks.") and shard.is_last_layer(): - sanitized_weights[key] = value - if "conv_norm_out" in key and shard.is_last_layer(): - sanitized_weights[key] = value - if "conv_out" in key and shard.is_last_layer(): - sanitized_weights[key] = value - if key.startswith("quant_proj") and shard.is_last_layer(): - sanitized_weights[key] = value - return sanitized_weights - diff --git a/exo/inference/mlx/perf_improvements.md b/exo/inference/mlx/perf_improvements.md deleted file mode 100644 index aa9869c8..00000000 --- a/exo/inference/mlx/perf_improvements.md +++ /dev/null @@ -1,7 +0,0 @@ -# Perf improvements - -Target: 460 tok/sec -- removing sample goes from 369 -> 402 -- performance degrades as we generate more tokens -- make mlx inference engien synchronous, removing thread pool executor: 402 -> 413 -- remove self.on_opaque_status.trigger_all: 413 -> 418 diff --git a/exo/inference/mlx/sharded_inference_engine.py b/exo/inference/mlx/sharded_inference_engine.py deleted file mode 100644 index cb730321..00000000 --- a/exo/inference/mlx/sharded_inference_engine.py +++ /dev/null @@ -1,179 +0,0 @@ -import numpy as np -import mlx.core as mx -import mlx.nn as nn -from mlx_lm.sample_utils import make_sampler -import mlx.optimizers as optim -from ..inference_engine import InferenceEngine -from .sharded_utils import load_model_shard, resolve_tokenizer -from .losses import loss_fns -from ..shard import Shard -from typing import Dict, Optional, Tuple -from exo.download.shard_download import ShardDownloader -import asyncio -from collections import OrderedDict -from mlx_lm.models.cache import make_prompt_cache -from concurrent.futures import ThreadPoolExecutor - -class MLXDynamicShardInferenceEngine(InferenceEngine): - def __init__(self, shard_downloader: ShardDownloader): - self.shard = None - self.shard_downloader = shard_downloader - self.caches = OrderedDict() - self.sampler_params: tuple[float, float] = (0.0, 0.0, 0.0, 1) - self.sampler = make_sampler(*self.sampler_params) - self._mlx_thread = ThreadPoolExecutor(max_workers=1, thread_name_prefix="mlx") - self._tokenizer_thread = ThreadPoolExecutor(max_workers=1, thread_name_prefix="tokenizer") - self.session = {} - self._shard_lock = asyncio.Lock() - - async def _eval_mlx(self, *args): - await asyncio.get_running_loop().run_in_executor(self._mlx_thread, mx.eval, *args) - - async def poll_state(self, request_id: str, max_caches=2): - if request_id in self.caches: - self.caches.move_to_end(request_id) - else: - newcache = make_prompt_cache(self.model) - if len(self.caches) > max_caches: - self.caches.popitem(last=False) - self.caches[request_id] = newcache - return {"cache": self.caches[request_id]} - - async def sample(self, x: np.ndarray, temp: float = 0.0, top_p: float = 1.0) -> np.ndarray: - if (temp, top_p, 0.0, 1) != self.sampler_params: - self.sampler_params = (temp, top_p, 0.0, 1) - self.sampler = make_sampler(*self.sampler_params) - logits = mx.array(x) - logits = logits[:, -1, :] - logprobs = logits - mx.logsumexp(logits, keepdims=True) - result = self.sampler(logprobs) - await self._eval_mlx(result) - return np.asarray(result, dtype=int) - - async def encode(self, shard: Shard, prompt: str) -> np.ndarray: - await self.ensure_shard(shard) - return np.asarray( - await asyncio.get_running_loop().run_in_executor( - self._tokenizer_thread, - self.tokenizer.encode, - prompt - ) - ) - - async def decode(self, shard: Shard, tokens) -> str: - await self.ensure_shard(shard) - return await asyncio.get_running_loop().run_in_executor( - self._tokenizer_thread, - self.tokenizer.decode, - tokens - ) - - async def save_checkpoint(self, shard: Shard, path: str): - await self.ensure_shard(shard) - await asyncio.get_running_loop().run_in_executor(self._mlx_thread, lambda: self.model.save_weights(path)) - - async def load_checkpoint(self, shard: Shard, path: str): - await self.ensure_shard(shard) - await asyncio.get_running_loop().run_in_executor(self._mlx_thread, lambda: self.model.load_weights(path)) - - async def infer_tensor(self, request_id: str, shard: Shard, input_data: np.ndarray, inference_state: Optional[dict] = None) -> tuple[np.ndarray, Optional[dict]]: - await self.ensure_shard(shard) - state = await self.poll_state(request_id) if self.model.model_type != 'StableDiffusionPipeline' else {} - x = mx.array(input_data) - - if self.model.model_type != 'StableDiffusionPipeline': - output_data = await asyncio.get_running_loop().run_in_executor( - self._mlx_thread, - lambda: self.model(x, **state, **(inference_state or {})) - ) - inference_state = None - else: - result = await asyncio.get_running_loop().run_in_executor( - self._mlx_thread, - lambda: self.model(x, **state, **(inference_state or {})) - ) - output_data, inference_state = result - - await self._eval_mlx(output_data) - output_data = await asyncio.get_running_loop().run_in_executor( - self._mlx_thread, - lambda: np.array(output_data, copy=False) - ) - return output_data, inference_state - - async def evaluate(self, request_id: str, shard: Shard, inputs, targets, lengths, loss: str = "length_masked_ce"): - await self.ensure_shard(shard) - await self.save_session('loss', loss_fns[loss]) - x = mx.array(inputs) - y = mx.array(targets) - l = mx.array(lengths) - - score = await asyncio.get_running_loop().run_in_executor( - self._mlx_thread, - lambda: self.session['loss'](self.model, x, y, l) - ) - return score - - async def ensure_train(self, shard: Shard, loss: str, opt=optim.SGD, lr=1e-5, trainable_layers=['input_layernorm', 'gate_proj']): - await self.ensure_shard(shard) - - if 'train_layers' not in self.session or self.session['train_layers'] != trainable_layers: - await self.save_session('train_layers', trainable_layers) - def freeze_unfreeze(): - self.model.freeze() - self.model.apply_to_modules( - lambda k, v: v.unfreeze() if any(k.endswith(layer_name) for layer_name in trainable_layers) else None - ) - await asyncio.get_running_loop().run_in_executor(self._mlx_thread, freeze_unfreeze) - - if 'lossname' not in self.session or 'LVaG' not in self.session or self.session['lossname'] != loss: - await self.save_session('lossname', loss) - await self.save_session('LVaG', nn.value_and_grad(self.model, loss_fns[loss])) - - if 'opt' not in self.session: - await self.save_session('opt', opt(lr)) - return True - - async def train(self, request_id: str, shard: Shard, inputs, targets, lengths, loss: str = "length_masked_ce", opt=optim.SGD, lr=1e-5): - await self.ensure_train(shard, loss, opt, lr) - - def train_step(inp, tar, lng): - lval, grad = self.session['LVaG'](self.model, inp, tar, lng) - gradlayers = grad['model']['layers'] - self.session['opt'].update(self.model, grad) - return lval, gradlayers, (self.model.parameters(), self.session['opt'].state, lval) - - x = mx.array(inputs) - y = mx.array(targets) - l = mx.array(lengths) - score, gradients, eval_args = await asyncio.get_running_loop().run_in_executor( - self._mlx_thread, - lambda: train_step(x, y, l) - ) - await self._eval_mlx(*eval_args) - - layers = [{k: v["weight"] for k, v in layer.items() if 'weight' in v} for layer in gradients if layer] - first_layer = np.array(layers[0]['input_layernorm'], copy=False) - await self._eval_mlx(first_layer) - return score, first_layer - - async def ensure_shard(self, shard: Shard): - async with self._shard_lock: - if self.shard == shard: return - model_path = await self.shard_downloader.ensure_shard(shard, self.__class__.__name__) - if self.shard != shard: - model_shard = await asyncio.get_running_loop().run_in_executor( - self._mlx_thread, - lambda: load_model_shard(model_path, shard, lazy=False) - ) - if hasattr(model_shard, "tokenizer"): - self.tokenizer = model_shard.tokenizer - else: - self.tokenizer = await resolve_tokenizer(model_path) - self.shard = shard - self.model = model_shard - self.caches = OrderedDict() - self.session = {} - - async def cleanup(self): - self._mlx_thread.shutdown(wait=True) diff --git a/exo/inference/mlx/sharded_utils.py b/exo/inference/mlx/sharded_utils.py deleted file mode 100644 index 34f29604..00000000 --- a/exo/inference/mlx/sharded_utils.py +++ /dev/null @@ -1,257 +0,0 @@ -# Adapted from https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/utils.py - -import glob -import importlib -import json -import logging -import asyncio -import aiohttp -from functools import partial -from pathlib import Path -from typing import Optional, Tuple, Union, List, Callable -from PIL import Image -from io import BytesIO -import base64 -import traceback - -import mlx.core as mx -import mlx.nn as nn -from transformers import AutoProcessor - -from mlx_lm.tokenizer_utils import load_tokenizer, TokenizerWrapper - -from exo import DEBUG -from exo.inference.tokenizers import resolve_tokenizer -from ..shard import Shard - - -class ModelNotFoundError(Exception): - def __init__(self, message): - self.message = message - super().__init__(self.message) - - -MODEL_REMAPPING = { - "mistral": "llama", # mistral is compatible with llama - "phi-msft": "phixtral", -} - - -def _get_classes(config: dict): - """ - Retrieve the model and model args classes based on the configuration. - - Args: - config (dict): The model configuration. - - Returns: - A tuple containing the Model class and the ModelArgs class. - """ - model_type = config["model_type"] - model_type = MODEL_REMAPPING.get(model_type, model_type) - try: - arch = importlib.import_module(f"exo.inference.mlx.models.{model_type}") - except ImportError: - msg = f"Model type {model_type} not supported." - logging.error(msg) - traceback.print_exc() - raise ValueError(msg) - - return arch.Model, arch.ModelArgs - - -def load_config(model_path: Path) -> dict: - try: - config_path = model_path / "config.json" - if config_path.exists(): - with open(config_path, "r") as f: - config = json.load(f) - return config - - model_index_path = model_path / "model_index.json" - if model_index_path.exists(): - config = load_model_index(model_path, model_index_path) - return config - except FileNotFoundError: - logging.error(f"Config file not found in {model_path}") - raise - return config - -def load_model_shard( - model_path: Path, - shard: Shard, - lazy: bool = False, - model_config: dict = {}, -) -> nn.Module: - """ - Load and initialize the model from a given path. - - Args: - model_path (Path): The path to load the model from. - lazy (bool): If False eval the model parameters to make sure they are - loaded in memory before returning, otherwise they will be loaded - when needed. Default: ``False`` - model_config(dict, optional): Configuration parameters for the model. - Defaults to an empty dictionary. - - Returns: - nn.Module: The loaded and initialized model. - - Raises: - FileNotFoundError: If the weight files (.safetensors) are not found. - ValueError: If the model class or args class are not found or cannot be instantiated. - """ - config = load_config(model_path) - config.update(model_config) - - # TODO hack - config["shard"] = { - "model_id": model_path.name, - "start_layer": shard.start_layer, - "end_layer": shard.end_layer, - "n_layers": shard.n_layers, - } - - weight_files = glob.glob(str(model_path/"model*.safetensors")) - - if not weight_files: - # Try weight for back-compat - weight_files = glob.glob(str(model_path/"weight*.safetensors")) - - model_class, model_args_class = _get_classes(config=config) - - class ShardedModel(model_class): - def __init__(self, args): - super().__init__(args) - self.shard = Shard(args.shard.model_id, args.shard.start_layer, args.shard.end_layer, args.shard.n_layers) - - def __call__(self, x, *args, **kwargs): - y = super().__call__(x, *args, **kwargs) - return y - - model_args = model_args_class.from_dict(config) - model = ShardedModel(model_args) - - if config.get("model_index", False): - model.load() - return model - - if not weight_files: - logging.error(f"No safetensors found in {model_path}") - raise FileNotFoundError(f"No safetensors found in {model_path}") - - weights = {} - for wf in sorted(weight_files): - if DEBUG >= 8: - layer_nums = set() - for k in mx.load(wf): - if k.startswith("model.layers."): - layer_num = int(k.split(".")[2]) - layer_nums.add(layer_num) - if k.startswith("language_model.model.layers."): - layer_num = int(k.split(".")[3]) - layer_nums.add(layer_num) - print(f"\"{wf.split('/')[-1]}\": {sorted(layer_nums)},") - - weights.update(mx.load(wf)) - - - - if hasattr(model, "sanitize"): - weights = model.sanitize(weights) - if DEBUG >= 8: - print(f"\n|| {config=} ||\n") - - if (quantization := config.get("quantization", None)) is not None: - # Handle legacy models which may not have everything quantized - def class_predicate(p, m): - if not hasattr(m, "to_quantized"): - return False - return f"{p}.scales" in weights - - - nn.quantize( - model, - **quantization, - class_predicate=class_predicate, - ) - - model.load_weights(list(weights.items()), strict=True) - - if not lazy: - mx.eval(model.parameters()) - - model.eval() - return model - -async def load_shard( - model_path: str, - shard: Shard, - tokenizer_config={}, - model_config={}, - adapter_path: Optional[str] = None, - lazy: bool = False, -) -> Tuple[nn.Module, TokenizerWrapper]: - model = load_model_shard(model_path, shard, lazy, model_config) - - # TODO: figure out a generic solution - if model.model_type == "llava": - processor = AutoProcessor.from_pretrained(model_path) - processor.eos_token_id = processor.tokenizer.eos_token_id - processor.encode = processor.tokenizer.encode - return model, processor - elif hasattr(model, "tokenizer"): - tokenizer = model.tokenizer - return model, tokenizer - else: - tokenizer = await resolve_tokenizer(model_path) - return model, tokenizer - - -async def get_image_from_str(_image_str: str): - image_str = _image_str.strip() - - if image_str.startswith("http"): - async with aiohttp.ClientSession() as session: - async with session.get(image_str, timeout=10) as response: - content = await response.read() - return Image.open(BytesIO(content)).convert("RGB") - elif image_str.startswith("data:image/"): - # Extract the image format and base64 data - format_prefix, base64_data = image_str.split(";base64,") - image_format = format_prefix.split("/")[1].lower() - if DEBUG >= 2: print(f"{image_str=} {image_format=}") - imgdata = base64.b64decode(base64_data) - img = Image.open(BytesIO(imgdata)) - - # Convert to RGB if not already - if img.mode != "RGB": - img = img.convert("RGB") - - return img - else: - raise ValueError("Invalid image_str format. Must be a URL or a base64 encoded image.") - -# loading a combined config for all models in the index -def load_model_index(model_path: Path, model_index_path: Path): - models_config = {} - with open(model_index_path, "r") as f: - model_index = json.load(f) - models_config["model_index"] = True - models_config["model_type"] = model_index["_class_name"] - models_config["models"] = {} - for model in model_index.keys(): - model_config_path = glob.glob(str(model_path / model / "*config.json")) - if len(model_config_path)>0: - with open(model_config_path[0], "r") as f: - model_config = { } - model_config["model_type"] = model - model_config["config"] = json.load(f) - model_config["path"] = model_path / model - if model_config["path"]/"*model.safetensors": - model_config["config"].update({"weight_files": list(glob.glob(str(model_config["path"]/"*model.safetensors")))}) - model_config["path"] = str(model_path / model) - m = {} - m[model] = model_config - models_config.update(m) - return models_config diff --git a/exo/inference/mlx/test_non_blocking.py b/exo/inference/mlx/test_non_blocking.py deleted file mode 100644 index 4eb2e9fb..00000000 --- a/exo/inference/mlx/test_non_blocking.py +++ /dev/null @@ -1,81 +0,0 @@ -import asyncio -import time -import numpy as np -from exo.inference.mlx.sharded_inference_engine import MLXDynamicShardInferenceEngine -from exo.download.new_shard_download import NewShardDownloader -from exo.inference.shard import Shard -from exo.models import build_base_shard -from collections import deque -from statistics import mean, median - -async def test_non_blocking(): - # Setup - shard_downloader = NewShardDownloader() - engine = MLXDynamicShardInferenceEngine(shard_downloader) - _shard = build_base_shard("llama-3.1-8b", "MLXDynamicShardInferenceEngine") - shard = Shard(_shard.model_id, _shard.start_layer, _shard.n_layers - 1, _shard.n_layers) - await engine.ensure_shard(shard) - - queue = asyncio.Queue() - measurements = deque(maxlen=1000000) - running = True - - async def mlx_worker(): - try: - start_time = time.time() - count = 0 - while running and (time.time() - start_time) < 5: # Hard time limit - start = time.perf_counter_ns() - await engine.infer_prompt("req1", shard, "test prompt") - duration = (time.perf_counter_ns() - start) / 1_000_000 # Convert to ms - count += 1 - print(f"MLX operation {count} took: {duration:.3f}ms") - except asyncio.CancelledError: - pass - finally: - print(f"\nTotal MLX operations completed: {count}") - print(f"Average rate: {count/5:.1f} ops/second") - - async def latency_producer(): - try: - start_time = time.perf_counter_ns() - count = 0 - while running: - await queue.put(time.perf_counter_ns()) - count += 1 - await asyncio.sleep(0) # Yield to event loop without delay - duration = (time.perf_counter_ns() - start_time) / 1e9 # Convert to seconds - print(f"\nProducer iterations: {count}") - print(f"Producer rate: {count/duration:.1f} iterations/second") - except asyncio.CancelledError: - pass - - async def latency_consumer(): - try: - while running: - timestamp = await queue.get() - latency = (time.perf_counter_ns() - timestamp) / 1_000_000 # Convert to ms - measurements.append(latency) - queue.task_done() - except asyncio.CancelledError: - pass - - tasks = [ - asyncio.create_task(mlx_worker()), - asyncio.create_task(latency_producer()), - asyncio.create_task(latency_consumer()) - ] - - try: - await asyncio.wait_for(asyncio.gather(*tasks), timeout=6) - except asyncio.TimeoutError: - print("\nTest timed out") - finally: - running = False - for task in tasks: - task.cancel() - await asyncio.gather(*tasks, return_exceptions=True) - print(f"\nFinal measurement count: {len(measurements)}") - -if __name__ == "__main__": - asyncio.run(test_non_blocking()) diff --git a/exo/inference/mlx/test_sharded_model.py b/exo/inference/mlx/test_sharded_model.py deleted file mode 100644 index c9743d07..00000000 --- a/exo/inference/mlx/test_sharded_model.py +++ /dev/null @@ -1,52 +0,0 @@ -from exo.inference.shard import Shard -import mlx.core as mx -import mlx.nn as nn -from typing import Optional -import numpy as np - - -class DummyModel(nn.Module): - def __init__(self, shard: Optional[Shard] = None): - self.shard = shard - self.layers = [ - nn.Linear(8, 128), - nn.Linear(128, 128), - nn.Linear(128, 128), - nn.Linear(128, 128), - nn.Linear(128, 8), - ] - - self.n_kv_heads = 4 - self.head_dim = 4 - - def __call__(self, x, cache=None): - if self.shard: - for layer in self.layers[self.shard.start_layer:self.shard.end_layer + 1]: - x = layer(x) - if self.shard.is_last_layer(): - x = x.reshape((1, 2, 4)) - else: - for layer in self.layers: - x = layer(x) - x = x.reshape((1, 2, 4)) - - return x - - -model = DummyModel() -model.save_weights("./test_weights.npz") -n_layers = 5 -shard1 = Shard("test", 0, n_layers // 2, n_layers) -sharded_model1 = DummyModel(shard1) -shard2 = Shard("test", n_layers//2 + 1, n_layers - 1, n_layers) -sharded_model2 = DummyModel(shard2) - -model.load_weights("./test_weights.npz") -sharded_model1.load_weights("./test_weights.npz") -sharded_model2.load_weights("./test_weights.npz") - -fullresp = model(mx.array([1, 2, 3, 4, 5, 6, 7, 8])) -resp1 = sharded_model1(mx.array([1, 2, 3, 4, 5, 6, 7, 8])) -resp2 = sharded_model2(resp1) - -assert np.all(np.array(fullresp) == np.array(resp2)) diff --git a/exo/inference/shard.py b/exo/inference/shard.py deleted file mode 100644 index 21b662f6..00000000 --- a/exo/inference/shard.py +++ /dev/null @@ -1,39 +0,0 @@ -from dataclasses import dataclass, field - - -@dataclass(frozen=True) -class Shard: - model_id: str - start_layer: int - end_layer: int - n_layers: int - - def __hash__(self): - return hash((self.model_id, self.start_layer, self.end_layer, self.n_layers)) - - def is_first_layer(self) -> bool: - return self.start_layer == 0 - - def is_last_layer(self) -> bool: - return self.end_layer == self.n_layers - 1 - - def get_layer_count(self) -> int: - return self.end_layer - self.start_layer + 1 - - def to_dict(self) -> dict: - return { - "model_id": self.model_id, - "start_layer": self.start_layer, - "end_layer": self.end_layer, - "n_layers": self.n_layers, - } - - def from_dict(data: dict) -> 'Shard': - return Shard(**data) - - def overlaps(self, other: 'Shard') -> bool: - return shards_overlap(self, other) - - -def shards_overlap(shard1: Shard, shard2: Shard) -> bool: - return (shard1.model_id == shard2.model_id and max(shard1.start_layer, shard2.start_layer) <= min(shard1.end_layer, shard2.end_layer)) diff --git a/exo/inference/test_dummy_inference_engine.py b/exo/inference/test_dummy_inference_engine.py deleted file mode 100644 index fad5178e..00000000 --- a/exo/inference/test_dummy_inference_engine.py +++ /dev/null @@ -1,47 +0,0 @@ -import pytest -import numpy as np -from exo.inference.dummy_inference_engine import DummyInferenceEngine -from exo.inference.shard import Shard - - -@pytest.mark.asyncio -async def test_dummy_inference_specific(): - engine = DummyInferenceEngine() - test_shard = Shard(model_id="test_model", start_layer=0, end_layer=1, n_layers=1) - test_prompt = "This is a test prompt" - - result, _ = await engine.infer_prompt("test_request", test_shard, test_prompt) - - print(f"Inference result shape: {result.shape}") - - assert result.shape[0] == 1, "Result should be a 2D array with first dimension 1" - - -@pytest.mark.asyncio -async def test_dummy_inference_engine(): - # Initialize the DummyInferenceEngine - engine = DummyInferenceEngine() - - # Create a test shard - shard = Shard(model_id="test_model", start_layer=0, end_layer=1, n_layers=1) - - # Test infer_prompt - output, _ = await engine.infer_prompt("test_id", shard, "Test prompt") - - assert isinstance(output, np.ndarray), "Output should be a numpy array" - assert output.ndim == 2, "Output should be 2-dimensional" - - # Test infer_tensor - input_tensor = np.array([[1, 2, 3]]) - output, _ = await engine.infer_tensor("test_id", shard, input_tensor) - - assert isinstance(output, np.ndarray), "Output should be a numpy array" - assert output.ndim == 2, "Output should be 2-dimensional" - - print("All tests passed!") - - -if __name__ == "__main__": - import asyncio - asyncio.run(test_dummy_inference_engine()) - asyncio.run(test_dummy_inference_specific()) diff --git a/exo/inference/test_inference_engine.py b/exo/inference/test_inference_engine.py deleted file mode 100644 index 956a1162..00000000 --- a/exo/inference/test_inference_engine.py +++ /dev/null @@ -1,54 +0,0 @@ -from exo.inference.mlx.sharded_inference_engine import MLXDynamicShardInferenceEngine -from exo.inference.inference_engine import InferenceEngine -from exo.download.new_shard_download import NewShardDownloader -from exo.inference.shard import Shard -from exo.helpers import DEBUG -import os -import asyncio -import numpy as np - - -# An inference engine should work the same for any number of Shards, as long as the Shards are continuous. -async def test_inference_engine(inference_engine_1: InferenceEngine, inference_engine_2: InferenceEngine, model_id: str, n_layers: int): - prompt = "In a single word only, what is the last name of the current president of the USA?" - resp_full, _ = await inference_engine_1.infer_prompt("A", shard=Shard(model_id=model_id, start_layer=0, end_layer=n_layers - 1, n_layers=n_layers), prompt=prompt) - token_full = await inference_engine_1.sample(resp_full) - token_full = token_full.reshape(1, -1) - next_resp_full, _ = await inference_engine_1.infer_tensor( - "A", - shard=Shard(model_id=model_id, start_layer=0, end_layer=n_layers - 1, n_layers=n_layers), - input_data=token_full, - ) - - pp = n_layers // 2 - resp1, _ = await inference_engine_1.infer_prompt("B", shard=Shard(model_id=model_id, start_layer=0, end_layer=pp, n_layers=n_layers), prompt=prompt) - resp2, _ = await inference_engine_2.infer_tensor( - "B", - shard=Shard(model_id=model_id, start_layer=pp + 1, end_layer=n_layers - 1, n_layers=n_layers), - input_data=resp1, - ) - tokens2 = await inference_engine_1.sample(resp2) - tokens2 = tokens2.reshape(1, -1) - resp3, _ = await inference_engine_1.infer_tensor( - "B", - shard=Shard(model_id=model_id, start_layer=0, end_layer=pp, n_layers=n_layers), - input_data=tokens2, - ) - resp4, _ = await inference_engine_2.infer_tensor( - "B", - shard=Shard(model_id=model_id, start_layer=pp + 1, end_layer=n_layers - 1, n_layers=n_layers), - input_data=resp3, - ) - - assert np.array_equal(resp_full, resp2) - assert np.array_equal(next_resp_full, resp4) - - -asyncio.run(test_inference_engine(MLXDynamicShardInferenceEngine(NewShardDownloader()), MLXDynamicShardInferenceEngine(NewShardDownloader()), "llama-3.2-1b", 16)) - -if os.getenv("RUN_TINYGRAD", default="0") == "1": - import tinygrad - import os - from exo.inference.tinygrad.inference import TinygradDynamicShardInferenceEngine - tinygrad.helpers.DEBUG.value = int(os.getenv("TINYGRAD_DEBUG", default="0")) - asyncio.run(test_inference_engine(TinygradDynamicShardInferenceEngine(NewShardDownloader()), TinygradDynamicShardInferenceEngine(NewShardDownloader()), "llama-3.2-1b", 32)) diff --git a/exo/inference/tinygrad/__init__.py b/exo/inference/tinygrad/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/inference/tinygrad/inference.py b/exo/inference/tinygrad/inference.py deleted file mode 100644 index 8e336dce..00000000 --- a/exo/inference/tinygrad/inference.py +++ /dev/null @@ -1,157 +0,0 @@ -from pathlib import Path -import json -import os -from exo.inference.tinygrad.models.llama import Transformer, TransformerShard, convert_from_huggingface, fix_bf16, sample_logits -from exo.inference.shard import Shard -from exo.inference.tokenizers import resolve_tokenizer -from tinygrad.nn.state import safe_save, safe_load, get_state_dict, load_state_dict -from tinygrad import Tensor, nn, Context, TinyJit -from exo.inference.inference_engine import InferenceEngine -import numpy as np -from exo.inference.tinygrad.tinygrad_helpers import concat_weights, load -from exo.download.shard_download import ShardDownloader -from concurrent.futures import ThreadPoolExecutor -from .stateful_model import make_prompt_state -from .losses import length_masked_ce_loss -from collections import OrderedDict -import asyncio -from typing import Optional -Tensor.no_grad = True -# default settings -TEMPERATURE = int(os.getenv("TEMPERATURE", 0.85)) -TOP_K = 25 -TOP_P = 0.9 -ALPHA_F = 0.1 -ALPHA_P = 0.0 -MODEL_PARAMS = { - "1B": { - "args": { - "dim": 2048, "n_heads": 32, "n_kv_heads": 8, "n_layers": 16, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 8192, - "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "tie_word_embeddings": True - }, "files": 1 - }, "3B": { - "args": { - "dim": 3072, "n_heads": 24, "n_kv_heads": 8, "n_layers": 28, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 8192, - "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "tie_word_embeddings": True - }, "files": 1 - }, "8B": {"args": {"dim": 4096, "n_heads": 32, "n_kv_heads": 8, "n_layers": 32, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 14336}, "files": 1}, - "70B": {"args": {"dim": 8192, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 28672}, "files": 8} -} - - -def build_transformer(model_path: Path, shard: Shard, model_size="8B", device=None): - # build model - linear = nn.Linear - model = Transformer(**MODEL_PARAMS[model_size]["args"], linear=linear, max_context=8192, jit=True, shard=shard) - - # load weights - if model_path.is_dir(): - if (model_path/"model.safetensors.index.json").exists(): weights = load(str(model_path/"model.safetensors.index.json"), shard) - elif (model_path/"model.safetensors").exists(): weights = load(str(model_path/"model.safetensors"), shard) - else: weights = concat_weights([load(str(model_path/f"consolidated.{i:02d}.pth"), shard) for i in range(MODEL_PARAMS[model_size]["files"])], device[0] if isinstance(device, tuple) else device) - else: - weights = load(str(model_path), shard) - weights = convert_from_huggingface(weights, model, MODEL_PARAMS[model_size]["args"]["n_heads"], MODEL_PARAMS[model_size]["args"]["n_kv_heads"]) - weights = fix_bf16(weights) - - with Context(BEAM=0): - # replace weights in model - load_state_dict(model, weights, strict=False, consume=False) # consume=True - model = TransformerShard(shard, model) - - return model - -_executor = ThreadPoolExecutor(max_workers=1) # singleton so tinygrad always runs on the same thread -class TinygradDynamicShardInferenceEngine(InferenceEngine): - def __init__(self, shard_downloader: ShardDownloader): - self.shard = None - self.shard_downloader = shard_downloader - self.states = OrderedDict() - self.executor = _executor - - def poll_state(self, x, request_id: str, max_states=2): - if request_id not in self.states: - if len(self.states) >= max_states: - self.states.popitem(last=False) - self.states[request_id] = make_prompt_state(x, self.model) - else: - self.states.move_to_end(request_id) - state = self.states[request_id] - return {"start_pos": state.start, "cache": state.cache} - - async def sample(self, x: np.ndarray, temp=TEMPERATURE, top_p: float = 0.0) -> np.ndarray: - def sample_wrapper(): - logits = x[:, -1, :] - return sample_logits(Tensor(logits).flatten(), temp, 0, 0.8, top_p, 0.0).realize().numpy().astype(int) - return await asyncio.get_running_loop().run_in_executor(self.executor, sample_wrapper) - - async def encode(self, shard: Shard, prompt: str) -> np.ndarray: - await self.ensure_shard(shard) - tokens = await asyncio.get_running_loop().run_in_executor(self.executor, self.tokenizer.encode, prompt) - return await asyncio.get_running_loop().run_in_executor(self.executor, np.array, tokens) - - async def decode(self, shard: Shard, tokens) -> str: - await self.ensure_shard(shard) - tokens = await asyncio.get_running_loop().run_in_executor(self.executor, self.tokenizer.decode, tokens) - return tokens - - async def load_checkpoint(self, shard: Shard, path: str): - await self.ensure_shard(shard) - state_dict = safe_load(path) - await asyncio.get_running_loop().run_in_executor(self.executor, load_state_dict, self.model, state_dict) - - async def save_checkpoint(self, shard: Shard, path: str): - await self.ensure_shard(shard) - state_dict = await asyncio.get_running_loop().run_in_executor(self.executor, get_state_dict, self.model) - safe_save(state_dict, path) - - async def infer_tensor(self, request_id: str, shard: Shard, input_data: np.ndarray, inference_state: Optional[dict] = None) -> tuple[np.ndarray, Optional[dict]]: - await self.ensure_shard(shard) - def wrap_infer(): - x = Tensor(input_data) - h = self.model.embed(x) - state = self.poll_state(h, request_id) - out = self.model.forward(h, **state) - self.states[request_id].start += x.shape[1] - return out.numpy() - output_data = await asyncio.get_running_loop().run_in_executor(self.executor, wrap_infer) - return output_data, inference_state - - async def evaluate(self, request_id: str, shard: Shard, inputs, targets, lengths, loss=length_masked_ce_loss): - def step(x, y, l): - Tensor.training = False - return self.session['loss'](self.model, x, y, l) - await self.ensure_shard(shard) - score = await asyncio.get_running_loop().run_in_executor(self.executor, lambda: self.session['jit'](Tensor(inputs), targets, lengths)) - out = score.numpy() - return out - - async def train(self, request_id: str, shard: Shard, inputs, targets, lengths, loss=length_masked_ce_loss, opt=nn.optim.Adam, lr=1e-5): - def step(x, y, l): - Tensor.training = True - score = self.session['loss'](self.model, x, y, l) - self.session['opt'].zero_grad() - score.backward() - self.session['opt'].step() - return score - await self.ensure_shard(shard) - - score = await asyncio.get_running_loop().run_in_executor(self.executor, lambda: self.session['jit'](Tensor(inputs), targets, lengths).realize()) - - return loss.numpy(), loss.numpy() - - async def ensure_shard(self, shard: Shard): - if self.shard == shard: - return - - model_path = await self.shard_downloader.ensure_shard(shard, self.__class__.__name__) - - if self.shard != shard: - loop = asyncio.get_running_loop() - parameters = "1B" if "1b" in shard.model_id.lower() else "3B" if "3b" in shard.model_id.lower() else "8B" if "8b" in shard.model_id.lower() else "70B" - model_shard = await loop.run_in_executor(self.executor, build_transformer, model_path, shard, parameters) - - tokenizer_path = str((model_path if model_path.is_dir() else model_path.parent)) - self.tokenizer = await resolve_tokenizer(tokenizer_path) - self.shard = shard - self.model = model_shard diff --git a/exo/inference/tinygrad/losses.py b/exo/inference/tinygrad/losses.py deleted file mode 100644 index 02f54231..00000000 --- a/exo/inference/tinygrad/losses.py +++ /dev/null @@ -1,14 +0,0 @@ -from tinygrad import Tensor, dtypes -import numpy as np -def length_masked_ce_loss(model, inputs, targets, lengths): - # Run model on inputs - logits = model(inputs).cast(dtypes.float32).contiguous() - - # Mask padding tokens - length_mask = Tensor(np.arange(inputs.shape[1])[None, :] < lengths[:, None], requires_grad=False) - - # Calculate the loss - ce = logits.sparse_categorical_crossentropy(Tensor(targets, requires_grad=False)).mul(length_mask) - loss = ce.sum() / length_mask.sum() - return loss - diff --git a/exo/inference/tinygrad/models/__init__.py b/exo/inference/tinygrad/models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/inference/tinygrad/models/llama.py b/exo/inference/tinygrad/models/llama.py deleted file mode 100644 index fbe0d69f..00000000 --- a/exo/inference/tinygrad/models/llama.py +++ /dev/null @@ -1,327 +0,0 @@ -from typing import Tuple, Union, Optional, Dict, Any, List -from tinygrad import Tensor, Variable, TinyJit, dtypes, nn, Device -from tinygrad.helpers import getenv -from collections import OrderedDict - - -# https://github.com/facebookresearch/llama/blob/1076b9c51c77ad06e9d7ba8a4c6df775741732bd/llama/model.py#L47 -def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, dtype=dtypes.half, rope_scaling: Optional[Dict[str, float]] = None) -> Tensor: - freqs = 1.0/(theta**(Tensor.arange(0, dim, 2)[:(dim // 2)]/dim)) - - if rope_scaling: - factor = rope_scaling.get('factor', 1.0) - low_freq_factor = rope_scaling.get('low_freq_factor', 1.0) - high_freq_factor = rope_scaling.get('high_freq_factor', 1.0) - original_max_pos_emb = rope_scaling.get('original_max_position_embeddings', end) - - freqs[:dim // 4] *= low_freq_factor - freqs[dim // 4:] = freqs[dim // 4:].contiguous()*high_freq_factor - freqs *= (original_max_pos_emb/end)**(1.0/factor) - - freqs = Tensor.arange(end).unsqueeze(dim=1)*freqs.unsqueeze(dim=0) - # TODO: move dtype outside this - return Tensor.stack(freqs.cos().cast(dtype), freqs.sin().cast(dtype), dim=-1).reshape(1, end, 1, dim // 2, 2) - - -# (a+i*b) * (c+i*d) = (ac-bd) + i*(ad+bc) -def complex_mult(A, c, d): - a, b = A[..., 0:1], A[..., 1:2] - ro = a*c - b*d - co = a*d + b*c - return ro.cat(co, dim=-1) - - -def apply_rotary_emb(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> Tuple[Tensor, Tensor]: - assert freqs_cis.shape[1] == xq.shape[1] == xk.shape[1], f"freqs_cis shape mismatch {freqs_cis.shape} xq:{xq.shape} xk:{xk.shape}" - xq = xq.reshape(*xq.shape[0:-1], -1, 2) - xk = xk.reshape(*xk.shape[0:-1], -1, 2) - assert len(xq.shape) == len(xk.shape) == len(freqs_cis.shape) == 5 - c, d = freqs_cis[..., 0:1], freqs_cis[..., 1:2] - xq_out = complex_mult(xq, c, d) - xk_out = complex_mult(xk, c, d) - return xq_out.flatten(3), xk_out.flatten(3) - - -def repeat_kv(x: Tensor, n_rep: int) -> Tensor: - bs, seqlen, n_kv_heads, head_dim = x.shape - if n_rep == 1: return x - # NOTE: this is different from x.repeat((1, 1, n_rep, 1)) - return x.repeat((1, 1, 1, n_rep)).reshape(bs, seqlen, n_kv_heads*n_rep, head_dim) - -class Attention: - def __init__(self, dim, n_heads, n_kv_heads, max_context, linear=nn.Linear): - self.n_heads = n_heads - self.n_kv_heads = n_kv_heads if n_kv_heads is not None else n_heads # n_kv_heads != n_heads implies MQA [arxiv/2307.09288, A.2.1] - self.head_dim = dim // n_heads - self.n_rep = self.n_heads // self.n_kv_heads - self.max_context = max_context - - self.wq = linear(dim, self.n_heads*self.head_dim, bias=False) - self.wk = linear(dim, self.n_kv_heads*self.head_dim, bias=False) - self.wv = linear(dim, self.n_kv_heads*self.head_dim, bias=False) - self.wo = linear(self.n_heads*self.head_dim, dim, bias=False) - - def __call__(self, x: Tensor, start_pos: Union[Variable, int], freqs_cis: Tensor, mask: Optional[Tensor], cache: Optional[Tensor]=None) -> Tensor: - if getenv("WQKV"): - if not hasattr(self, 'wqkv'): self.wqkv = Tensor.cat(self.wq.weight, self.wk.weight, self.wv.weight) - xqkv = x @ self.wqkv.T - xq, xk, xv = xqkv.split([self.wq.weight.shape[0], self.wk.weight.shape[0], self.wv.weight.shape[0]], dim=2) - else: - xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) - - xq = xq.reshape(xq.shape[0], xq.shape[1], self.n_heads, self.head_dim) - xk = xk.reshape(xk.shape[0], xk.shape[1], self.n_kv_heads, self.head_dim) - xv = xv.reshape(xv.shape[0], xv.shape[1], self.n_kv_heads, self.head_dim) - - xq, xk = apply_rotary_emb(xq, xk, freqs_cis) - bsz, seqlen, _, _ = xq.shape - - if cache is not None: - # update the cache - assert xk.dtype == xv.dtype == cache.dtype, f"{xk.dtype=}, {xv.dtype=}, {cache.dtype=}" - cache.shrink((None, None, (start_pos, start_pos + seqlen), None, None)).assign(Tensor.stack(xk, xv)).realize() - - keys = cache[0].shrink((None, (0, start_pos + seqlen), None, None)) if start_pos > 0 else xk - values = cache[1].shrink((None, (0, start_pos + seqlen), None, None)) if start_pos > 0 else xv - else: - keys = xk - values = xv - - keys, values = repeat_kv(keys, self.n_rep), repeat_kv(values, self.n_rep) - xq, keys, values = xq.transpose(1, 2), keys.transpose(1, 2), values.transpose(1, 2) - attn = xq.scaled_dot_product_attention(keys, values, mask).transpose(1, 2) - attn = attn.reshape(bsz, seqlen, -1) - return self.wo(attn) - - -class FeedForward: - def __init__(self, dim: int, hidden_dim: int, linear=nn.Linear): - self.w1 = linear(dim, hidden_dim, bias=False) - self.w2 = linear(hidden_dim, dim, bias=False) - self.w3 = linear(dim, hidden_dim, bias=False) # the gate in Gated Linear Unit - - def __call__(self, x: Tensor) -> Tensor: - return self.w2(self.w1(x).silu()*self.w3(x)) # SwiGLU [arxiv/2002.05202, eq (5)] - - -class TransformerBlock: - def __init__(self, dim: int, hidden_dim: int, n_heads: int, n_kv_heads: int, norm_eps: float, max_context: int, linear=nn.Linear, feed_forward=FeedForward): - self.attention = Attention(dim, n_heads, n_kv_heads, max_context, linear) - self.feed_forward = feed_forward(dim, hidden_dim, linear) - self.attention_norm = nn.RMSNorm(dim, norm_eps) - self.ffn_norm = nn.RMSNorm(dim, norm_eps) - - def __call__(self, x: Tensor, start_pos: Union[Variable, int], freqs_cis: Tensor, mask: Optional[Tensor], cache: Optional[Tensor]=None): - h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask, cache=cache) - return (h + self.feed_forward(self.ffn_norm(h))).contiguous() - - -# standard openai sampling -def sample_logits(logits: Tensor, temp: float, k: int, p: float, af: float, ap: float): - assert logits.ndim == 1, "only works on 1d tensors" - assert 0 <= p <= 1, "p must be between 0 and 1" - assert 0 <= k <= logits.numel(), "k must be between 0 and numel" - - # if temperature is very low just use argmax - if temp < 1e-6: return logits.argmax().reshape(1) - - # alpha sampling - if af or ap: - if not hasattr(sample, "alpha_counter"): - setattr(sample, "alpha_counter", Tensor.zeros_like(logits, dtype=dtypes.int32).contiguous()) - logits = logits - (sample.alpha_counter*af + (sample.alpha_counter > 0)*ap) - - # replace NaNs with -inf - logits = (logits != logits).where(-float("inf"), logits) - - # softmax - t = (logits/temp).softmax() - - counter, counter2 = Tensor.arange(t.numel(), device=logits.device).contiguous(), Tensor.arange(t.numel() - 1, -1, -1, device=logits.device).contiguous() - # top k - if k: - output, output_indices = Tensor.zeros(k, device=logits.device).contiguous(), Tensor.zeros(k, device=logits.device, dtype=dtypes.int32).contiguous() - for i in range(k): - t_argmax = (t.numel() - ((t == (t_max := t.max()))*counter2).max() - 1).cast(dtypes.default_int) - output = output + t_max.unsqueeze(0).pad(((i, k - i - 1),)) - output_indices = output_indices + t_argmax.unsqueeze(0).pad(((i, k - i - 1),)) - t = (counter == t_argmax).where(0, t) - - # approximate top p - # because we are already limited to top k elements we can do top p "without sorting" - output_cumsum = output[::-1]._cumsum()[::-1] + t.sum() - output = (output_cumsum >= (1 - p))*output - output_indices = (output_cumsum >= (1 - p))*output_indices - - # sample - output_idx = output.multinomial() - output_token = output_indices[output_idx] - else: - output_token = t.multinomial() - - # increase alpha counter - if af or ap: - sample.alpha_counter = (counter == output_token).where(sample.alpha_counter + 1, sample.alpha_counter) - - return output_token - - -from exo.inference.shard import Shard - - -class Transformer: - def __init__( - self, - dim: int, - hidden_dim: int, - n_heads: int, - n_layers: int, - norm_eps: float, - vocab_size, - shard: Shard = None, - linear=nn.Linear, - n_kv_heads=None, - rope_theta=10000, - max_context=1024, - jit=True, - feed_forward=FeedForward, - rope_scaling: Optional[Dict[str, float]] = None, - tie_word_embeddings=False, - ): - self.layers = [TransformerBlock(dim, hidden_dim, n_heads, n_kv_heads, norm_eps, max_context, linear, feed_forward=feed_forward) for _ in range(n_layers)] - self.norm = nn.RMSNorm(dim, norm_eps) - self.tok_embeddings = nn.Embedding(vocab_size, dim) - self.output = nn.Linear(dim, vocab_size, bias=False) - if tie_word_embeddings: - self.output.weight = self.tok_embeddings.weight - self.max_context = max_context - self.freqs_cis = precompute_freqs_cis(dim // n_heads, self.max_context*2, rope_theta, rope_scaling=rope_scaling).contiguous() - self.forward_jit = TinyJit(self.forward_base) if jit else None - self.shard = shard - - def forward_base(self, x: Tensor, start_pos: Union[Variable, int], cache: Optional[List[Tensor]] = None): - seqlen = x.shape[1] - freqs_cis = self.freqs_cis.shrink((None, (start_pos, start_pos + seqlen), None, None, None)) - mask = Tensor.full((1, 1, seqlen, start_pos + seqlen), float("-100000000"), dtype=x.dtype, device=x.device).triu(start_pos + 1).realize() if seqlen > 1 else None - - h = x - - if cache is None: - cache = [None for _ in range(self.shard.start_layer, self.shard.end_layer + 1)] - for i, c in zip(range(self.shard.start_layer, self.shard.end_layer + 1), cache): - layer = self.layers[i] - h = layer(h, start_pos, freqs_cis, mask, cache=c) - - if self.shard.is_last_layer(): - logits = self.output(self.norm(h)).float().realize() - return logits - else: - return h - - def embed(self, inputs: Tensor): - if self.shard.is_first_layer(): - h = self.tok_embeddings(inputs) - else: - h = inputs - return h - - def forward(self, x: Tensor, start_pos: int, cache: Optional[List[Tensor]] = None): - if x.shape[0:2] == (1, 1) and self.forward_jit is not None and start_pos != 0: - return self.forward_jit(x, Variable("start_pos", 1, self.max_context).bind(start_pos), cache=cache) - return self.forward_base(x, start_pos, cache=cache) - - def __call__(self, x: Tensor, start_pos: Variable, cache: Optional[List[Tensor]] = None): - # TODO: better way to handle the first call v.s. the rest? - h = self.embed(x) - return self.forward(h, start_pos, cache=cache) - -class TransformerShard: - def __init__( - self, - shard: Shard, - base, - jit: bool = True, - ): - shardrange = range(shard.start_layer, shard.end_layer + 1) - self.layers = [layer for layer, n in zip(base.layers, range(shard.n_layers)) if n in shardrange] - self.norm = base.norm - self.tok_embeddings = base.tok_embeddings - self.embed = (lambda x: self.tok_embeddings(x)) if shard.is_first_layer() else (lambda x: x) - self.output = base.output - self.post = (lambda x: self.output(x)) if shard.is_last_layer() else (lambda x: x) - self.max_context = base.max_context - self.null_cache = [None for _ in shardrange] - self.freqs_cis = base.freqs_cis - self.forward_jit = TinyJit(self.forward_base) if jit else None - - def forward_base(self, x: Tensor, start_pos: Union[Variable, int], cache): - seqlen = x.shape[1] - freqs_cis = self.freqs_cis.shrink((None, (start_pos, start_pos + seqlen), None, None, None)) - mask = Tensor.full((1, 1, seqlen, start_pos + seqlen), float("-100000000"), dtype=x.dtype, device=x.device).triu(start_pos + 1).realize() if seqlen > 1 else None - - for layer, c in zip(self.layers, cache): - x = layer(x, start_pos, freqs_cis, mask, cache=c) - - out = self.post(x) - return out - - def forward(self, x: Tensor, start_pos: int, cache: Optional[List[Tensor]] = None): - if x.shape[0:2] == (1, 1) and self.forward_jit is not None and start_pos != 0: - return self.forward_jit(x, Variable("start_pos", 1, self.max_context).bind(start_pos), cache=cache) - return self.forward_base(x, start_pos, cache=cache) - - def __call__(self, x: Tensor, start_pos: Variable, cache: Optional[List[Tensor]] = None): - # TODO: better way to handle the first call v.s. the rest? - h = self.embed(x) - return self.forward(h, start_pos, cache=self.null_cache if cache is None else cache) - -# *** helpers *** - - -def convert_from_huggingface(weights: Dict[str, Tensor], model: Transformer, n_heads: int, n_kv_heads: int): - def permute(v: Tensor, n_heads: int): - return v.reshape(n_heads, 2, v.shape[0] // n_heads // 2, v.shape[1]).transpose(1, 2).reshape(*v.shape[:2]) - - keymap = { - "model.embed_tokens.weight": "tok_embeddings.weight", - **{f"model.layers.{l}.input_layernorm.weight": f"layers.{l}.attention_norm.weight" - for l in range(len(model.layers))}, - **{f"model.layers.{l}.self_attn.{x}_proj.weight": f"layers.{l}.attention.w{x}.weight" - for x in ["q", "k", "v", "o"] - for l in range(len(model.layers))}, - **{f"model.layers.{l}.post_attention_layernorm.weight": f"layers.{l}.ffn_norm.weight" - for l in range(len(model.layers))}, - **{f"model.layers.{l}.mlp.{x}_proj.weight": f"layers.{l}.feed_forward.w{y}.weight" - for x, y in {"gate": "1", "down": "2", "up": "3"}.items() - for l in range(len(model.layers))}, - "model.norm.weight": "norm.weight", - "lm_head.weight": "output.weight", - } - sd = {} - for k, v in weights.items(): - if ".rotary_emb." in k: continue - v = v.to(Device.DEFAULT) - if "model.layers" in k: - if "q_proj" in k: - v = permute(v, n_heads) - elif "k_proj" in k: - v = permute(v, n_kv_heads) - if k in keymap: - sd[keymap[k]] = v - else: - sd[k] = v - return sd - - -def fix_bf16(weights: Dict[Any, Tensor]): - if Device.DEFAULT == "CLANG": - # TODO: without casting to float16, 70B llama OOM on tinybox. - return { - k: (v.llvm_bf16_cast(dtypes.float32).to(v.device) if v.dtype == dtypes.bfloat16 else v) - for k, v in weights.items() - } - if getenv("SUPPORT_BF16", 1): - # TODO: without casting to float16, 70B llama OOM on tinybox. - return {k: v.cast(dtypes.float32).cast(dtypes.float16) if v.dtype == dtypes.bfloat16 else v for k, v in weights.items()} - # TODO: check if device supports bf16 - return {k: v.llvm_bf16_cast(dtypes.half).to(v.device) if v.dtype == dtypes.bfloat16 else v for k, v in weights.items()} diff --git a/exo/inference/tinygrad/stateful_model.py b/exo/inference/tinygrad/stateful_model.py deleted file mode 100644 index 1d9b605d..00000000 --- a/exo/inference/tinygrad/stateful_model.py +++ /dev/null @@ -1,22 +0,0 @@ -from tinygrad import Tensor, Variable -from collections import OrderedDict -from typing import List, Optional - -def create_kv_cache(x: Tensor, layer): - cache_kv = Tensor.zeros(2, x.shape[0], layer.max_context, layer.n_kv_heads, layer.head_dim, dtype=x.dtype).contiguous().realize() - if isinstance(x.device, tuple): - # TODO: instead of specifying how to shard, it can follow how xk and xv are being sharded - cache_kv.shard_((x.device), axis=3 if getenv("SHARD_KVCACHE") else None).realize() - return cache_kv.realize() - -class ModelState: - cache: List[Tensor] - start: int - def __init__(self, cache: List[Tensor], start: int = 0): - self.cache = cache - self.start = start - -def make_prompt_state(x: Tensor, model): - cache = [create_kv_cache(x, l.attention) for l in model.layers] - - return ModelState(cache) diff --git a/exo/inference/tinygrad/tinygrad_helpers.py b/exo/inference/tinygrad/tinygrad_helpers.py deleted file mode 100644 index a33b01ae..00000000 --- a/exo/inference/tinygrad/tinygrad_helpers.py +++ /dev/null @@ -1,52 +0,0 @@ -from tinygrad.nn.state import safe_load, torch_load -from tinygrad import Tensor -from pathlib import Path -import json -from typing import List -from exo.inference.shard import Shard -from exo.helpers import DEBUG -from exo.download.hf.hf_helpers import get_allow_patterns -from fnmatch import fnmatch -import re - - -# **** helper functions **** -def concat_weights(models, device=None): - def convert(name) -> Tensor: - disk_tensors: List[Tensor] = [model[name] for model in models] - if len(disk_tensors) == 1 or len(disk_tensors[0].shape) == 1: - return disk_tensors[0].to(device=device) - axis = 1 if name.endswith(".attention.wo.weight") or name.endswith(".feed_forward.w2.weight") else 0 - lazy_tensors = [data.to(device=device) for data in disk_tensors] - return lazy_tensors[0].cat(*lazy_tensors[1:], dim=axis) - - return {name: convert(name) for name in {name: None for model in models for name in model}} - - -def load(fn: str, shard: Shard): - if fn.endswith('.index.json'): - with open(fn) as fp: - weight_map = json.load(fp)['weight_map'] - parts = {} - filtered_weight_map = {} - allow_patterns = get_allow_patterns(weight_map, shard) - for k, n in weight_map.items(): - if allow_patterns is not None and not any(fnmatch(n, r) for r in allow_patterns): - continue - if k.startswith("model.layers."): - layer_num = int(k.split('.')[2]) - if layer_num < shard.start_layer or layer_num > shard.end_layer: - continue - - parts[n] = load(str(Path(fn).parent/Path(n).name), shard) - filtered_weight_map[k] = n - if DEBUG >= 2: print(f"Excluded model param keys for {shard=}: {sorted(set(weight_map.keys()) - set(filtered_weight_map.keys()))}") - return {k: parts[n][k] for k, n in filtered_weight_map.items()} - elif fn.endswith(".safetensors"): - weight_map = safe_load(fn) - for k in list(weight_map): - if (n := re.search(r"\.(\d+)\.", k)) and not (shard.start_layer <= int(n.group(1)) <= shard.end_layer): - del weight_map[k] - return weight_map - else: - return torch_load(fn) diff --git a/exo/inference/tokenizers.py b/exo/inference/tokenizers.py deleted file mode 100644 index 865765d5..00000000 --- a/exo/inference/tokenizers.py +++ /dev/null @@ -1,63 +0,0 @@ -import traceback -from os import PathLike -from aiofiles import os as aios -from typing import Union -from transformers import AutoTokenizer, AutoProcessor -import numpy as np -from exo.helpers import DEBUG -from exo.download.new_shard_download import ensure_downloads_dir - - -class DummyTokenizer: - def __init__(self): - self.eos_token_id = 69 - self.vocab_size = 1000 - - def apply_chat_template(self, conversation, tokenize=True, add_generation_prompt=True, tools=None, **kwargs): - return "dummy_tokenized_prompt" - - def encode(self, text): - return np.array([1]) - - def decode(self, tokens): - return "dummy" * len(tokens) - - -async def resolve_tokenizer(repo_id: Union[str, PathLike]): - if repo_id == "dummy": - return DummyTokenizer() - local_path = await ensure_downloads_dir()/str(repo_id).replace("/", "--") - if DEBUG >= 2: print(f"Checking if local path exists to load tokenizer from local {local_path=}") - try: - if local_path and await aios.path.exists(local_path): - if DEBUG >= 2: print(f"Resolving tokenizer for {repo_id=} from {local_path=}") - return await _resolve_tokenizer(local_path) - except: - if DEBUG >= 5: print(f"Local check for {local_path=} failed. Resolving tokenizer for {repo_id=} normally...") - if DEBUG >= 5: traceback.print_exc() - return await _resolve_tokenizer(repo_id) - - -async def _resolve_tokenizer(repo_id_or_local_path: Union[str, PathLike]): - try: - if DEBUG >= 4: print(f"Trying AutoProcessor for {repo_id_or_local_path}") - processor = AutoProcessor.from_pretrained(repo_id_or_local_path, use_fast=True if "Mistral-Large" in f"{repo_id_or_local_path}" else False, trust_remote_code=True) - if not hasattr(processor, 'eos_token_id'): - processor.eos_token_id = getattr(processor, 'tokenizer', getattr(processor, '_tokenizer', processor)).eos_token_id - if not hasattr(processor, 'encode'): - processor.encode = getattr(processor, 'tokenizer', getattr(processor, '_tokenizer', processor)).encode - if not hasattr(processor, 'decode'): - processor.decode = getattr(processor, 'tokenizer', getattr(processor, '_tokenizer', processor)).decode - return processor - except Exception as e: - if DEBUG >= 4: print(f"Failed to load processor for {repo_id_or_local_path}. Error: {e}") - if DEBUG >= 4: print(traceback.format_exc()) - - try: - if DEBUG >= 4: print(f"Trying AutoTokenizer for {repo_id_or_local_path}") - return AutoTokenizer.from_pretrained(repo_id_or_local_path, trust_remote_code=True) - except Exception as e: - if DEBUG >= 4: print(f"Failed to load tokenizer for {repo_id_or_local_path}. Falling back to tinygrad tokenizer. Error: {e}") - if DEBUG >= 4: print(traceback.format_exc()) - - raise ValueError(f"[TODO] Unsupported model: {repo_id_or_local_path}") diff --git a/exo/main.py b/exo/main.py deleted file mode 100644 index 1ac5672d..00000000 --- a/exo/main.py +++ /dev/null @@ -1,402 +0,0 @@ -import argparse -import asyncio -import atexit -import signal -import json -import platform -import os -import time -import traceback -import uuid -import numpy as np -from tqdm import tqdm -from exo.train.dataset import load_dataset, iterate_batches -from exo.networking.manual.manual_discovery import ManualDiscovery -from exo.orchestration.node import Node -from exo.networking.grpc.grpc_server import GRPCServer -from exo.networking.udp.udp_discovery import UDPDiscovery -from exo.networking.tailscale.tailscale_discovery import TailscaleDiscovery -from exo.networking.grpc.grpc_peer_handle import GRPCPeerHandle -from exo.topology.ring_memory_weighted_partitioning_strategy import RingMemoryWeightedPartitioningStrategy -from exo.api import ChatGPTAPI -from exo.download.shard_download import ShardDownloader, NoopShardDownloader -from exo.download.download_progress import RepoProgressEvent -from exo.download.new_shard_download import new_shard_downloader, has_exo_home_read_access, has_exo_home_write_access, ensure_exo_home, seed_models -from exo.helpers import print_yellow_exo, find_available_port, DEBUG, get_system_info, get_or_create_node_id, get_all_ip_addresses_and_interfaces, terminal_link, shutdown -from exo.inference.shard import Shard -from exo.inference.inference_engine import get_inference_engine -from exo.inference.tokenizers import resolve_tokenizer -from exo.models import build_base_shard, get_repo -from exo.viz.topology_viz import TopologyViz -import uvloop -import concurrent.futures -import resource -import psutil - -# TODO: figure out why this is happening -os.environ["GRPC_VERBOSITY"] = "error" -os.environ["TRANSFORMERS_VERBOSITY"] = "error" -os.environ["TOKENIZERS_PARALLELISM"] = "true" - -# Configure uvloop for maximum performance -def configure_uvloop(): - uvloop.install() - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - - # Increase file descriptor limits on Unix systems - if not psutil.WINDOWS: - soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE) - try: resource.setrlimit(resource.RLIMIT_NOFILE, (hard, hard)) - except ValueError: - try: resource.setrlimit(resource.RLIMIT_NOFILE, (8192, hard)) - except ValueError: pass - - loop.set_default_executor(concurrent.futures.ThreadPoolExecutor(max_workers=min(32, (os.cpu_count() or 1) * 4))) - return loop - -# parse args -parser = argparse.ArgumentParser(description="Initialize GRPC Discovery") -parser.add_argument("command", nargs="?", choices=["run", "eval", "train"], help="Command to run") -parser.add_argument("model_name", nargs="?", help="Model name to run") -parser.add_argument("--default-model", type=str, default=None, help="Default model") -parser.add_argument("--iters", type=int, default=100, help="Training iterations") -parser.add_argument("--save-every", type=int, default=5, help="Save the model every N iterations.") -parser.add_argument("--data", type=str, default="exo/train/data/lora", help="Directory where training data lives") -parser.add_argument("--batch-size", type=int, default=1, help="Minibatch size.") -parser.add_argument("--resume-checkpoint", type=str, default=None, help="Path to a custom checkpoint to load") -parser.add_argument("--save-checkpoint-dir", type=str, default="checkpoints", help="Path to a folder where checkpoints are stored") -parser.add_argument("--node-id", type=str, default=None, help="Node ID") -parser.add_argument("--node-host", type=str, default="0.0.0.0", help="Node host") -parser.add_argument("--node-port", type=int, default=None, help="Node port") -parser.add_argument("--models-seed-dir", type=str, default=None, help="Model seed directory") -parser.add_argument("--listen-port", type=int, default=5678, help="Listening port for discovery") -parser.add_argument("--download-quick-check", action="store_true", help="Quick check local path for model shards download") -parser.add_argument("--max-parallel-downloads", type=int, default=8, help="Max parallel downloads for model shards download") -parser.add_argument("--broadcast-port", type=int, default=5678, help="Broadcast port for discovery") -parser.add_argument("--discovery-module", type=str, choices=["udp", "tailscale", "manual"], default="udp", help="Discovery module to use") -parser.add_argument("--discovery-timeout", type=int, default=30, help="Discovery timeout in seconds") -parser.add_argument("--discovery-config-path", type=str, default=None, help="Path to discovery config json file") -parser.add_argument("--wait-for-peers", type=int, default=0, help="Number of peers to wait to connect to before starting") -parser.add_argument("--chatgpt-api-port", type=int, default=52415, help="ChatGPT API port") -parser.add_argument("--chatgpt-api-response-timeout", type=int, default=900, help="ChatGPT API response timeout in seconds") -parser.add_argument("--max-generate-tokens", type=int, default=10000, help="Max tokens to generate in each request") -parser.add_argument("--inference-engine", type=str, default=None, help="Inference engine to use (mlx, tinygrad, or dummy)") -parser.add_argument("--disable-tui", action=argparse.BooleanOptionalAction, help="Disable TUI") -parser.add_argument("--run-model", type=str, help="Specify a model to run directly") -parser.add_argument("--prompt", type=str, help="Prompt for the model when using --run-model", default="Who are you?") -parser.add_argument("--default-temp", type=float, help="Default token sampling temperature", default=0.0) -parser.add_argument("--tailscale-api-key", type=str, default=None, help="Tailscale API key") -parser.add_argument("--tailnet-name", type=str, default=None, help="Tailnet name") -parser.add_argument("--node-id-filter", type=str, default=None, help="Comma separated list of allowed node IDs (only for UDP and Tailscale discovery)") -parser.add_argument("--interface-type-filter", type=str, default=None, help="Comma separated list of allowed interface types (only for UDP discovery)") -parser.add_argument("--system-prompt", type=str, default=None, help="System prompt for the ChatGPT API") -args = parser.parse_args() -print(f"Selected inference engine: {args.inference_engine}") - -print_yellow_exo() - -print("\n" + "="*80) -print("EXO") -print("="*80) -print("\nEXO started out of a desire to run research experiments on large language") -print("models using the hardware we already owned.") -print("\nWhat began here is becoming part of something much larger.") -print("\nsoon™") -print("\n- The EXO Team") -print("="*80 + "\n") - -system_info = get_system_info() -print(f"Detected system: {system_info}") - -shard_downloader: ShardDownloader = new_shard_downloader(args.max_parallel_downloads) if args.inference_engine != "dummy" else NoopShardDownloader() -inference_engine_name = args.inference_engine or ("mlx" if system_info == "Apple Silicon Mac" else "tinygrad") -print(f"Inference engine name after selection: {inference_engine_name}") - -inference_engine = get_inference_engine(inference_engine_name, shard_downloader) -print(f"Using inference engine: {inference_engine.__class__.__name__} with shard downloader: {shard_downloader.__class__.__name__}") - -if args.node_port is None: - args.node_port = find_available_port(args.node_host) - if DEBUG >= 1: print(f"Using available port: {args.node_port}") - -args.node_id = args.node_id or get_or_create_node_id() -chatgpt_api_endpoints = [f"http://{ip}:{args.chatgpt_api_port}/v1/chat/completions" for ip, _ in get_all_ip_addresses_and_interfaces()] -web_chat_urls = [f"http://{ip}:{args.chatgpt_api_port}" for ip, _ in get_all_ip_addresses_and_interfaces()] -if DEBUG >= 0: - print("Chat interface started:") - for web_chat_url in web_chat_urls: - print(f" - {terminal_link(web_chat_url)}") - print("ChatGPT API endpoint served at:") - for chatgpt_api_endpoint in chatgpt_api_endpoints: - print(f" - {terminal_link(chatgpt_api_endpoint)}") - -# Convert node-id-filter and interface-type-filter to lists if provided -allowed_node_ids = args.node_id_filter.split(',') if args.node_id_filter else None -allowed_interface_types = args.interface_type_filter.split(',') if args.interface_type_filter else None - -if args.discovery_module == "udp": - discovery = UDPDiscovery( - args.node_id, - args.node_port, - args.listen_port, - args.broadcast_port, - lambda peer_id, address, description, device_capabilities: GRPCPeerHandle(peer_id, address, description, device_capabilities), - discovery_timeout=args.discovery_timeout, - allowed_node_ids=allowed_node_ids, - allowed_interface_types=allowed_interface_types - ) -elif args.discovery_module == "tailscale": - discovery = TailscaleDiscovery( - args.node_id, - args.node_port, - lambda peer_id, address, description, device_capabilities: GRPCPeerHandle(peer_id, address, description, device_capabilities), - discovery_timeout=args.discovery_timeout, - tailscale_api_key=args.tailscale_api_key, - tailnet=args.tailnet_name, - allowed_node_ids=allowed_node_ids - ) -elif args.discovery_module == "manual": - if not args.discovery_config_path: - raise ValueError(f"--discovery-config-path is required when using manual discovery. Please provide a path to a config json file.") - discovery = ManualDiscovery(args.discovery_config_path, args.node_id, create_peer_handle=lambda peer_id, address, description, device_capabilities: GRPCPeerHandle(peer_id, address, description, device_capabilities)) -topology_viz = TopologyViz(chatgpt_api_endpoints=chatgpt_api_endpoints, web_chat_urls=web_chat_urls) if not args.disable_tui else None -node = Node( - args.node_id, - None, - inference_engine, - discovery, - shard_downloader, - partitioning_strategy=RingMemoryWeightedPartitioningStrategy(), - max_generate_tokens=args.max_generate_tokens, - topology_viz=topology_viz, - default_sample_temperature=args.default_temp -) -server = GRPCServer(node, args.node_host, args.node_port) -node.server = server -api = ChatGPTAPI( - node, - node.inference_engine.__class__.__name__, - response_timeout=args.chatgpt_api_response_timeout, - on_chat_completion_request=lambda req_id, __, prompt: topology_viz.update_prompt(req_id, prompt) if topology_viz else None, - default_model=args.default_model, - system_prompt=args.system_prompt -) -buffered_token_output = {} -def update_topology_viz(req_id, tokens, __): - if not topology_viz: return - if not node.inference_engine.shard: return - if node.inference_engine.shard.model_id == 'stable-diffusion-2-1-base': return - if req_id in buffered_token_output: buffered_token_output[req_id].extend(tokens) - else: buffered_token_output[req_id] = tokens - topology_viz.update_prompt_output(req_id, node.inference_engine.tokenizer.decode(buffered_token_output[req_id])) -node.on_token.register("update_topology_viz").on_next(update_topology_viz) -def update_prompt_viz(request_id, opaque_status: str): - if not topology_viz: return - try: - status = json.loads(opaque_status) - if status.get("type") != "node_status" or status.get("status") != "start_process_prompt": return - topology_viz.update_prompt(request_id, status.get("prompt", "corrupted prompt (this should never happen)")) - except Exception as e: - if DEBUG >= 2: - print(f"Failed to update prompt viz: {e}") - traceback.print_exc() -node.on_opaque_status.register("update_prompt_viz").on_next(update_prompt_viz) - -def preemptively_load_shard(request_id: str, opaque_status: str): - try: - status = json.loads(opaque_status) - if status.get("type") != "node_status" or status.get("status") != "start_process_prompt": return - current_shard = node.get_current_shard(Shard.from_dict(status.get("shard"))) - if DEBUG >= 2: print(f"Preemptively starting download for {current_shard}") - asyncio.create_task(node.inference_engine.ensure_shard(current_shard)) - except Exception as e: - if DEBUG >= 2: - print(f"Failed to preemptively start download: {e}") - traceback.print_exc() -node.on_opaque_status.register("preemptively_load_shard").on_next(preemptively_load_shard) - -last_events: dict[str, tuple[float, RepoProgressEvent]] = {} -def throttled_broadcast(shard: Shard, event: RepoProgressEvent): - global last_events - current_time = time.time() - if event.status == "not_started": return - last_event = last_events.get(shard.model_id) - if last_event and last_event[1].status == "complete" and event.status == "complete": return - if last_event and last_event[0] == event.status and current_time - last_event[0] < 0.2: return - last_events[shard.model_id] = (current_time, event) - asyncio.create_task(node.broadcast_opaque_status("", json.dumps({"type": "download_progress", "node_id": node.id, "progress": event.to_dict()}))) -shard_downloader.on_progress.register("broadcast").on_next(throttled_broadcast) - -async def run_model_cli(node: Node, model_name: str, prompt: str): - inference_class = node.inference_engine.__class__.__name__ - shard = build_base_shard(model_name, inference_class) - if not shard: - print(f"Error: Unsupported model '{model_name}' for inference engine {inference_class}") - return - tokenizer = await resolve_tokenizer(get_repo(shard.model_id, inference_class)) - request_id = str(uuid.uuid4()) - callback_id = f"cli-wait-response-{request_id}" - callback = node.on_token.register(callback_id) - if topology_viz: - topology_viz.update_prompt(request_id, prompt) - prompt = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True) - - try: - print(f"Processing prompt: {prompt}") - await node.process_prompt(shard, prompt, request_id=request_id) - - tokens = [] - def on_token(_request_id, _tokens, _is_finished): - tokens.extend(_tokens) - return _request_id == request_id and _is_finished - await callback.wait(on_token, timeout=300) - - print("\nGenerated response:") - print(tokenizer.decode(tokens)) - except Exception as e: - print(f"Error processing prompt: {str(e)}") - traceback.print_exc() - finally: - node.on_token.deregister(callback_id) - -def clean_path(path): - """Clean and resolve path""" - if path.startswith("Optional("): - path = path.strip('Optional("').rstrip('")') - return os.path.expanduser(path) - -async def hold_outstanding(node: Node): - while node.outstanding_requests: - await asyncio.sleep(.5) - return - -async def run_iter(node: Node, shard: Shard, train: bool, data, batch_size=1): - losses = [] - tokens = [] - for batch in tqdm(iterate_batches(data, batch_size), total=len(data) // batch_size): - _, _, lengths = batch - losses.append(np.sum(lengths * await node.enqueue_example(shard, *batch, train=train))) - tokens.append(np.sum(lengths)) - total_tokens = np.sum(tokens) - total_loss = np.sum(losses) / total_tokens - - return total_loss, total_tokens - -async def eval_model_cli(node: Node, model_name, dataloader, batch_size, num_batches=-1): - inference_class = node.inference_engine.__class__.__name__ - shard = build_base_shard(model_name, inference_class) - if not shard: - print(f"Error: Unsupported model '{model_name}' for inference engine {inference_class}") - return - tokenizer = await resolve_tokenizer(get_repo(shard.model_id, inference_class)) - train, val, test = dataloader(tokenizer.encode) - print(f"Evaluating {len(test)} examples with batch_size {batch_size}") - loss, tokens = await run_iter(node, shard, False, test, batch_size) - print(f"total | {loss=}, {tokens=}") - print("Waiting for outstanding tasks") - await hold_outstanding(node) - -async def train_model_cli(node: Node, model_name, dataloader, batch_size, iters, save_interval=0, checkpoint_dir=None): - inference_class = node.inference_engine.__class__.__name__ - shard = build_base_shard(model_name, inference_class) - if not shard: - print(f"Error: Unsupported model '{model_name}' for inference engine {inference_class}") - return - tokenizer = await resolve_tokenizer(get_repo(shard.model_id, inference_class)) - train, val, test = dataloader(tokenizer.encode) - print(f"Training on {len(train)} examples with batch_size {batch_size} for {iters} epochs") - for i in tqdm(range(3)): - await asyncio.sleep(1) - for epoch in range(iters): - loss, tokens = await run_iter(node, shard, True, train, batch_size) - print(f"epoch {epoch + 1}/{iters}\t| loss: {loss}, tokens: {tokens}") - if save_interval > 0 and epoch > 0 and (epoch % save_interval) == 0 and checkpoint_dir is not None: - await node.coordinate_save(shard, epoch, checkpoint_dir) - await hold_outstanding(node) - await hold_outstanding(node) - -async def check_exo_home(): - home, has_read, has_write = await ensure_exo_home(), await has_exo_home_read_access(), await has_exo_home_write_access() - if DEBUG >= 1: print(f"exo home directory: {home}") - print(f"{has_read=}, {has_write=}") - if not has_read or not has_write: - print(f""" - WARNING: Limited permissions for exo home directory: {home}. - This may prevent model downloads from working correctly. - {"❌ No read access" if not has_read else ""} - {"❌ No write access" if not has_write else ""} - """) - -async def main(): - loop = asyncio.get_running_loop() - - try: await check_exo_home() - except Exception as e: print(f"Error checking exo home directory: {e}") - - if not args.models_seed_dir is None: - try: - models_seed_dir = clean_path(args.models_seed_dir) - await seed_models(models_seed_dir) - except Exception as e: - print(f"Error seeding models: {e}") - - def restore_cursor(): - if platform.system() != "Windows": - os.system("tput cnorm") # Show cursor - - # Restore the cursor when the program exits - atexit.register(restore_cursor) - - # Use a more direct approach to handle signals - def handle_exit(): - asyncio.ensure_future(shutdown(signal.SIGTERM, loop, node.server)) - - if platform.system() != "Windows": - for s in [signal.SIGINT, signal.SIGTERM]: - loop.add_signal_handler(s, handle_exit) - - await node.start(wait_for_peers=args.wait_for_peers) - - if args.command == "run" or args.run_model: - model_name = args.model_name or args.run_model - if not model_name: - print("Error: Model name is required when using 'run' command or --run-model") - return - await run_model_cli(node, model_name, args.prompt) - elif args.command == "eval" or args.command == 'train': - model_name = args.model_name - dataloader = lambda tok: load_dataset(args.data, preprocess=lambda item: tok(item) - , loadline=lambda line: json.loads(line).get("text","")) - if args.command == 'eval': - if not model_name: - print("Error: Much like a human, I can't evaluate anything without a model") - return - await eval_model_cli(node, model_name, dataloader, args.batch_size) - else: - if not model_name: - print("Error: This train ain't leaving the station without a model") - return - await train_model_cli(node, model_name, dataloader, args.batch_size, args.iters, save_interval=args.save_every, checkpoint_dir=args.save_checkpoint_dir) - - else: - asyncio.create_task(api.run(port=args.chatgpt_api_port)) # Start the API server as a non-blocking task - await asyncio.Event().wait() - - if args.wait_for_peers > 0: - print("Cooldown to allow peers to exit gracefully") - for i in tqdm(range(50)): - await asyncio.sleep(.1) - -def run(): - loop = None - try: - loop = configure_uvloop() - loop.run_until_complete(main()) - except KeyboardInterrupt: - print("\nShutdown requested... exiting") - finally: - if loop: loop.close() - -if __name__ == "__main__": - run() diff --git a/exo/models.py b/exo/models.py deleted file mode 100644 index b64bcca1..00000000 --- a/exo/models.py +++ /dev/null @@ -1,273 +0,0 @@ -from exo.inference.shard import Shard -from typing import Optional, List - -model_cards = { - ### llama - "llama-3.3-70b": { - "layers": 80, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Llama-3.3-70B-Instruct-4bit", - "TinygradDynamicShardInferenceEngine": "unsloth/Llama-3.3-70B-Instruct", - }, - }, - "llama-3.2-1b": { - "layers": 16, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Llama-3.2-1B-Instruct-4bit", - "TinygradDynamicShardInferenceEngine": "unsloth/Llama-3.2-1B-Instruct", - }, - }, - "llama-3.2-1b-8bit": { - "layers": 16, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Llama-3.2-1B-Instruct-8bit", - "TinygradDynamicShardInferenceEngine": "unsloth/Llama-3.2-1B-Instruct", - }, - }, - "llama-3.2-3b": { - "layers": 28, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Llama-3.2-3B-Instruct-4bit", - "TinygradDynamicShardInferenceEngine": "unsloth/Llama-3.2-3B-Instruct", - }, - }, - "llama-3.2-3b-8bit": { - "layers": 28, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Llama-3.2-3B-Instruct-8bit", - "TinygradDynamicShardInferenceEngine": "unsloth/Llama-3.2-3B-Instruct", - }, - }, - "llama-3.2-3b-bf16": { - "layers": 28, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Llama-3.2-3B-Instruct", - "TinygradDynamicShardInferenceEngine": "unsloth/Llama-3.2-3B-Instruct", - }, - }, - "llama-3.1-8b": { - "layers": 32, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Meta-Llama-3.1-8B-Instruct-4bit", - "TinygradDynamicShardInferenceEngine": "mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated", - }, - }, - "llama-3.1-70b": { - "layers": 80, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Meta-Llama-3.1-70B-Instruct-4bit", - "TinygradDynamicShardInferenceEngine": "NousResearch/Meta-Llama-3.1-70B-Instruct", - }, - }, - "llama-3.1-70b-bf16": { - "layers": 80, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Meta-Llama-3.1-70B-Instruct-bf16-CORRECTED", - "TinygradDynamicShardInferenceEngine": "NousResearch/Meta-Llama-3.1-70B-Instruct", - }, - }, - "llama-3-8b": { - "layers": 32, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Meta-Llama-3-8B-Instruct-4bit", - "TinygradDynamicShardInferenceEngine": "TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-8B-R", - }, - }, - "llama-3-70b": { - "layers": 80, - "repo": { - "MLXDynamicShardInferenceEngine": "mlx-community/Meta-Llama-3-70B-Instruct-4bit", - "TinygradDynamicShardInferenceEngine": "TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-70B-R", - }, - }, - "llama-3.1-405b": { "layers": 126, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Meta-Llama-3.1-405B-4bit", }, }, - "llama-3.1-405b-8bit": { "layers": 126, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Meta-Llama-3.1-405B-Instruct-8bit", }, }, - ### mistral - "mistral-nemo": { "layers": 40, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Mistral-Nemo-Instruct-2407-4bit", }, }, - "mistral-large": { "layers": 88, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Mistral-Large-Instruct-2407-4bit", }, }, - ### deepseek - "deepseek-coder-v2-lite": { "layers": 27, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx", }, }, - "deepseek-coder-v2.5": { "layers": 60, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-V2.5-MLX-AQ4_1_64", }, }, - "deepseek-v3": { "layers": 61, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-V3-4bit", }, }, - "deepseek-v3-3bit": { "layers": 61, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-V3-3bit", }, }, - "deepseek-r1": { "layers": 61, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-4bit", }, }, - "deepseek-r1-3bit": { "layers": 61, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-3bit", }, }, - ### deepseek distills - "deepseek-r1-distill-qwen-1.5b": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/deepseek-r1-distill-qwen-1.5b", }, }, - "deepseek-r1-distill-qwen-1.5b-3bit": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-3bit", }, }, - "deepseek-r1-distill-qwen-1.5b-6bit": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-6bit", }, }, - "deepseek-r1-distill-qwen-1.5b-8bit": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-8bit", }, }, - "deepseek-r1-distill-qwen-1.5b-bf16": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-1.5B-bf16", }, }, - "deepseek-r1-distill-qwen-7b": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-7B-4bit", }, }, - "deepseek-r1-distill-qwen-7b-3bit": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-7B-3bit", }, }, - "deepseek-r1-distill-qwen-7b-6bit": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-7B-6bit", }, }, - "deepseek-r1-distill-qwen-7b-8bit": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-7B-8bit", }, }, - "deepseek-r1-distill-qwen-7b-bf16": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-7B-bf16", }, }, - "deepseek-r1-distill-qwen-14b": { "layers": 48, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-14B-4bit", }, }, - "deepseek-r1-distill-qwen-14b-3bit": { "layers": 48, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-14B-3bit", }, }, - "deepseek-r1-distill-qwen-14b-6bit": { "layers": 48, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-14B-6bit", }, }, - "deepseek-r1-distill-qwen-14b-8bit": { "layers": 48, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-14B-8bit", }, }, - "deepseek-r1-distill-qwen-14b-bf16": { "layers": 48, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-14B-bf16", }, }, - "deepseek-r1-distill-qwen-32b": { "layers": 64, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-32B-4bit", }, }, - "deepseek-r1-distill-qwen-32b-3bit": { "layers": 64, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-32B-3bit", }, }, - "deepseek-r1-distill-qwen-32b-6bit": { "layers": 64, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-32B-6bit", }, }, - "deepseek-r1-distill-qwen-32b-8bit": { "layers": 64, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-32B-MLX-8Bit", }, }, - "deepseek-r1-distill-qwen-32b-bf16": { "layers": 64, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Qwen-32B-bf16", }, }, - "deepseek-r1-distill-llama-8b": { "layers": 32, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-8B-4bit", }, }, - "deepseek-r1-distill-llama-8b-3bit": { "layers": 32, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-8B-3bit", }, }, - "deepseek-r1-distill-llama-8b-6bit": { "layers": 32, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-8B-6bit", }, }, - "deepseek-r1-distill-llama-8b-8bit": { "layers": 32, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-8B-8bit", }, }, - "deepseek-r1-distill-llama-8b-bf16": { "layers": 32, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-8B-bf16", }, }, - "deepseek-r1-distill-llama-70b": { "layers": 80, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-70B-4bit", }, }, - "deepseek-r1-distill-llama-70b-3bit": { "layers": 80, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-70B-3bit", }, }, - "deepseek-r1-distill-llama-70b-6bit": { "layers": 80, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-70B-6bit", }, }, - "deepseek-r1-distill-llama-70b-8bit": { "layers": 80, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/DeepSeek-R1-Distill-Llama-70B-8bit", }, }, - ### llava - "llava-1.5-7b-hf": { "layers": 32, "repo": { "MLXDynamicShardInferenceEngine": "llava-hf/llava-1.5-7b-hf", }, }, - ### qwen - "qwen-2.5-0.5b": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-0.5B-Instruct-4bit", }, }, - "qwen-2.5-1.5b": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-1.5B-Instruct-4bit", }, }, - "qwen-2.5-coder-1.5b": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-Coder-1.5B-Instruct-4bit", }, }, - "qwen-2.5-3b": { "layers": 36, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-3B-Instruct-4bit", }, }, - "qwen-2.5-coder-3b": { "layers": 36, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-Coder-3B-Instruct-4bit", }, }, - "qwen-2.5-7b": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-7B-Instruct-4bit", }, }, - "qwen-2.5-coder-7b": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-Coder-7B-Instruct-4bit", }, }, - "qwen-2.5-math-7b": { "layers": 28, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-Math-7B-Instruct-4bit", }, }, - "qwen-2.5-14b": { "layers": 48, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-14B-Instruct-4bit", }, }, - "qwen-2.5-coder-14b": { "layers": 48, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-Coder-14B-Instruct-4bit", }, }, - "qwen-2.5-32b": { "layers": 64, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-32B-Instruct-4bit", }, }, - "qwen-2.5-coder-32b": { "layers": 64, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-Coder-32B-Instruct-4bit", }, }, - "qwen-2.5-72b": { "layers": 80, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-72B-Instruct-4bit", }, }, - "qwen-2.5-math-72b": { "layers": 80, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Qwen2.5-Math-72B-Instruct-4bit", }, }, - ### nemotron - "nemotron-70b": { "layers": 80, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/nvidia_Llama-3.1-Nemotron-70B-Instruct-HF_4bit", }, }, - "nemotron-70b-bf16": { "layers": 80, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Llama-3.1-Nemotron-70B-Instruct-HF-bf16", }, }, - # gemma - "gemma2-9b": { "layers": 42, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/gemma-2-9b-it-4bit", }, }, - "gemma2-27b": { "layers": 46, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/gemma-2-27b-it-4bit", }, }, - # stable diffusion - "stable-diffusion-2-1-base": { "layers": 31, "repo": { "MLXDynamicShardInferenceEngine": "stabilityai/stable-diffusion-2-1-base" } }, - # phi - "phi-3.5-mini": { "layers": 32, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/Phi-3.5-mini-instruct-4bit", }, }, - "phi-4": { "layers": 40, "repo": { "MLXDynamicShardInferenceEngine": "mlx-community/phi-4-4bit", }, }, - # dummy - "dummy": { "layers": 8, "repo": { "DummyInferenceEngine": "dummy", }, }, -} - -pretty_name = { - "llama-3.3-70b": "Llama 3.3 70B", - "llama-3.2-1b": "Llama 3.2 1B", - "llama-3.2-1b-8bit": "Llama 3.2 1B (8-bit)", - "llama-3.2-3b": "Llama 3.2 3B", - "llama-3.2-3b-8bit": "Llama 3.2 3B (8-bit)", - "llama-3.2-3b-bf16": "Llama 3.2 3B (BF16)", - "llama-3.1-8b": "Llama 3.1 8B", - "llama-3.1-70b": "Llama 3.1 70B", - "llama-3.1-70b-bf16": "Llama 3.1 70B (BF16)", - "llama-3.1-405b": "Llama 3.1 405B", - "llama-3.1-405b-8bit": "Llama 3.1 405B (8-bit)", - "gemma2-9b": "Gemma2 9B", - "gemma2-27b": "Gemma2 27B", - "nemotron-70b": "Nemotron 70B", - "nemotron-70b-bf16": "Nemotron 70B (BF16)", - "mistral-nemo": "Mistral Nemo", - "mistral-large": "Mistral Large", - "deepseek-coder-v2-lite": "Deepseek Coder V2 Lite", - "deepseek-coder-v2.5": "Deepseek Coder V2.5", - "deepseek-v3": "Deepseek V3 (4-bit)", - "deepseek-v3-3bit": "Deepseek V3 (3-bit)", - "deepseek-r1": "Deepseek R1 (4-bit)", - "deepseek-r1-3bit": "Deepseek R1 (3-bit)", - "llava-1.5-7b-hf": "LLaVa 1.5 7B (Vision Model)", - "qwen-2.5-0.5b": "Qwen 2.5 0.5B", - "qwen-2.5-1.5b": "Qwen 2.5 1.5B", - "qwen-2.5-coder-1.5b": "Qwen 2.5 Coder 1.5B", - "qwen-2.5-3b": "Qwen 2.5 3B", - "qwen-2.5-coder-3b": "Qwen 2.5 Coder 3B", - "qwen-2.5-7b": "Qwen 2.5 7B", - "qwen-2.5-coder-7b": "Qwen 2.5 Coder 7B", - "qwen-2.5-math-7b": "Qwen 2.5 7B (Math)", - "qwen-2.5-14b": "Qwen 2.5 14B", - "qwen-2.5-coder-14b": "Qwen 2.5 Coder 14B", - "qwen-2.5-32b": "Qwen 2.5 32B", - "qwen-2.5-coder-32b": "Qwen 2.5 Coder 32B", - "qwen-2.5-72b": "Qwen 2.5 72B", - "qwen-2.5-math-72b": "Qwen 2.5 72B (Math)", - "phi-3.5-mini": "Phi-3.5 Mini", - "phi-4": "Phi-4", - "llama-3-8b": "Llama 3 8B", - "llama-3-70b": "Llama 3 70B", - "stable-diffusion-2-1-base": "Stable Diffusion 2.1", - "deepseek-r1-distill-qwen-1.5b": "DeepSeek R1 Distill Qwen 1.5B", - "deepseek-r1-distill-qwen-1.5b-3bit": "DeepSeek R1 Distill Qwen 1.5B (3-bit)", - "deepseek-r1-distill-qwen-1.5b-6bit": "DeepSeek R1 Distill Qwen 1.5B (6-bit)", - "deepseek-r1-distill-qwen-1.5b-8bit": "DeepSeek R1 Distill Qwen 1.5B (8-bit)", - "deepseek-r1-distill-qwen-1.5b-bf16": "DeepSeek R1 Distill Qwen 1.5B (BF16)", - "deepseek-r1-distill-qwen-7b": "DeepSeek R1 Distill Qwen 7B", - "deepseek-r1-distill-qwen-7b-3bit": "DeepSeek R1 Distill Qwen 7B (3-bit)", - "deepseek-r1-distill-qwen-7b-6bit": "DeepSeek R1 Distill Qwen 7B (6-bit)", - "deepseek-r1-distill-qwen-7b-8bit": "DeepSeek R1 Distill Qwen 7B (8-bit)", - "deepseek-r1-distill-qwen-7b-bf16": "DeepSeek R1 Distill Qwen 7B (BF16)", - "deepseek-r1-distill-qwen-14b": "DeepSeek R1 Distill Qwen 14B", - "deepseek-r1-distill-qwen-14b-3bit": "DeepSeek R1 Distill Qwen 14B (3-bit)", - "deepseek-r1-distill-qwen-14b-6bit": "DeepSeek R1 Distill Qwen 14B (6-bit)", - "deepseek-r1-distill-qwen-14b-8bit": "DeepSeek R1 Distill Qwen 14B (8-bit)", - "deepseek-r1-distill-qwen-14b-bf16": "DeepSeek R1 Distill Qwen 14B (BF16)", - "deepseek-r1-distill-qwen-32b": "DeepSeek R1 Distill Qwen 32B", - "deepseek-r1-distill-qwen-32b-3bit": "DeepSeek R1 Distill Qwen 32B (3-bit)", - "deepseek-r1-distill-qwen-32b-8bit": "DeepSeek R1 Distill Qwen 32B (8-bit)", - "deepseek-r1-distill-qwen-32b-bf16": "DeepSeek R1 Distill Qwen 32B (BF16)", - "deepseek-r1-distill-llama-8b-8bit": "DeepSeek R1 Distill Llama 8B (8-bit)", - "deepseek-r1-distill-llama-70b-6bit": "DeepSeek R1 Distill Llama 70B (6-bit)", - "deepseek-r1-distill-llama-70b-8bit": "DeepSeek R1 Distill Llama 70B (8-bit)", - "deepseek-r1-distill-llama-8b": "DeepSeek R1 Distill Llama 8B", - "deepseek-r1-distill-llama-8b-3bit": "DeepSeek R1 Distill Llama 8B (3-bit)", - "deepseek-r1-distill-llama-8b-6bit": "DeepSeek R1 Distill Llama 8B (6-bit)", - "deepseek-r1-distill-llama-8b-8bit": "DeepSeek R1 Distill Llama 8B (8-bit)", - "deepseek-r1-distill-llama-8b-bf16": "DeepSeek R1 Distill Llama 8B (BF16)", - "deepseek-r1-distill-llama-70b": "DeepSeek R1 Distill Llama 70B", - "deepseek-r1-distill-llama-70b-3bit": "DeepSeek R1 Distill Llama 70B (3-bit)", - "deepseek-r1-distill-llama-70b-6bit": "DeepSeek R1 Distill Llama 70B (6-bit)", - "deepseek-r1-distill-llama-70b-8bit": "DeepSeek R1 Distill Llama 70B (8-bit)", - "deepseek-r1-distill-qwen-32b-6bit": "DeepSeek R1 Distill Qwen 32B (6-bit)", -} - -def get_repo(model_id: str, inference_engine_classname: str) -> Optional[str]: - return model_cards.get(model_id, {}).get("repo", {}).get(inference_engine_classname, None) - -def get_pretty_name(model_id: str) -> Optional[str]: - return pretty_name.get(model_id, None) - -def build_base_shard(model_id: str, inference_engine_classname: str) -> Optional[Shard]: - repo = get_repo(model_id, inference_engine_classname) - n_layers = model_cards.get(model_id, {}).get("layers", 0) - if repo is None or n_layers < 1: - return None - return Shard(model_id, 0, 0, n_layers) - -def build_full_shard(model_id: str, inference_engine_classname: str) -> Optional[Shard]: - base_shard = build_base_shard(model_id, inference_engine_classname) - if base_shard is None: return None - return Shard(base_shard.model_id, 0, base_shard.n_layers - 1, base_shard.n_layers) - -def get_supported_models(supported_inference_engine_lists: Optional[List[List[str]]] = None) -> List[str]: - if not supported_inference_engine_lists: - return list(model_cards.keys()) - - from exo.inference.inference_engine import inference_engine_classes - supported_inference_engine_lists = [ - [inference_engine_classes[engine] if engine in inference_engine_classes else engine for engine in engine_list] - for engine_list in supported_inference_engine_lists - ] - - def has_any_engine(model_info: dict, engine_list: List[str]) -> bool: - return any(engine in model_info.get("repo", {}) for engine in engine_list) - - def supports_all_engine_lists(model_info: dict) -> bool: - return all(has_any_engine(model_info, engine_list) - for engine_list in supported_inference_engine_lists) - - return [ - model_id for model_id, model_info in model_cards.items() - if supports_all_engine_lists(model_info) - ] diff --git a/exo/networking/__init__.py b/exo/networking/__init__.py deleted file mode 100644 index 44a10a30..00000000 --- a/exo/networking/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .discovery import Discovery -from .peer_handle import PeerHandle -from .server import Server - -__all__ = ["Discovery", "PeerHandle", "Server"] diff --git a/exo/networking/discovery.py b/exo/networking/discovery.py deleted file mode 100644 index cdcbfabc..00000000 --- a/exo/networking/discovery.py +++ /dev/null @@ -1,17 +0,0 @@ -from abc import ABC, abstractmethod -from typing import List -from .peer_handle import PeerHandle - - -class Discovery(ABC): - @abstractmethod - async def start(self) -> None: - pass - - @abstractmethod - async def stop(self) -> None: - pass - - @abstractmethod - async def discover_peers(self, wait_for_peers: int = 0) -> List[PeerHandle]: - pass diff --git a/exo/networking/grpc/__init__.py b/exo/networking/grpc/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/networking/grpc/grpc_peer_handle.py b/exo/networking/grpc/grpc_peer_handle.py deleted file mode 100644 index 997d43ef..00000000 --- a/exo/networking/grpc/grpc_peer_handle.py +++ /dev/null @@ -1,226 +0,0 @@ -import grpc -import numpy as np -import asyncio -from typing import Optional, Tuple, List - -from . import node_service_pb2 -from . import node_service_pb2_grpc - -from ..peer_handle import PeerHandle -from exo.inference.shard import Shard -from exo.topology.topology import Topology -from exo.topology.device_capabilities import DeviceCapabilities, DeviceFlops -from exo.helpers import DEBUG -import json -import platform - -if platform.system().lower() == "darwin" and platform.machine().lower() == "arm64": - import mlx.core as mx -else: - import numpy as mx - - -class GRPCPeerHandle(PeerHandle): - def __init__(self, _id: str, address: str, desc: str, device_capabilities: DeviceCapabilities): - self._id = _id - self.address = address - self.desc = desc - self._device_capabilities = device_capabilities - self.channel = None - self.stub = None - self.channel_options = [ - ("grpc.max_metadata_size", 32 * 1024 * 1024), - ("grpc.max_receive_message_length", 256 * 1024 * 1024), - ("grpc.max_send_message_length", 256 * 1024 * 1024), - ("grpc.max_concurrent_streams", 100), - ("grpc.http2.min_time_between_pings_ms", 10000), - ("grpc.keepalive_time_ms", 10000), - ("grpc.keepalive_timeout_ms", 5000), - ("grpc.keepalive_permit_without_calls", 1), - ("grpc.http2.max_pings_without_data", 0), - ("grpc.http2.min_ping_interval_without_data_ms", 5000), - ("grpc.tcp_nodelay", 1), - ("grpc.optimization_target", "throughput"), - ] - - def id(self) -> str: - return self._id - - def addr(self) -> str: - return self.address - - def description(self) -> str: - return self.desc - - def device_capabilities(self) -> DeviceCapabilities: - return self._device_capabilities - - async def connect(self): - self.channel = grpc.aio.insecure_channel( - self.address, - options=self.channel_options, - compression=grpc.Compression.Gzip - ) - self.stub = node_service_pb2_grpc.NodeServiceStub(self.channel) - await asyncio.wait_for(self.channel.channel_ready(), timeout=10.0) - - async def is_connected(self) -> bool: - return self.channel is not None and self.channel.get_state() == grpc.ChannelConnectivity.READY - - async def disconnect(self): - if self.channel: - await self.channel.close() - self.channel = None - self.stub = None - - async def _ensure_connected(self): - if not (await self.is_connected()): - try: - await asyncio.wait_for(self.connect(), timeout=10.0) - except asyncio.TimeoutError: - if DEBUG >= 2: print(f"Connection timeout for {self._id}@{self.address}") - await self.disconnect() - raise - - async def health_check(self) -> bool: - try: - await self._ensure_connected() - request = node_service_pb2.HealthCheckRequest() - response = await asyncio.wait_for(self.stub.HealthCheck(request), timeout=5) - return response.is_healthy - except asyncio.TimeoutError: - return False - except Exception: - if DEBUG >= 4: - print(f"Health check failed for {self._id}@{self.address}.") - import traceback - traceback.print_exc() - return False - - async def send_prompt(self, shard: Shard, prompt: str, inference_state: Optional[dict] = None, request_id: Optional[str] = None) -> Optional[np.array]: - await self._ensure_connected() - request = node_service_pb2.PromptRequest( - prompt=prompt, - shard=node_service_pb2.Shard( - model_id=shard.model_id, - start_layer=shard.start_layer, - end_layer=shard.end_layer, - n_layers=shard.n_layers, - ), - request_id=request_id, - inference_state=None if inference_state is None else self.serialize_inference_state(inference_state) - ) - await self.stub.SendPrompt(request) - - async def send_tensor(self, shard: Shard, tensor: np.ndarray, inference_state: Optional[dict] = None, request_id: Optional[str] = None) -> Optional[np.array]: - await self._ensure_connected() - request = node_service_pb2.TensorRequest( - shard=node_service_pb2.Shard( - model_id=shard.model_id, - start_layer=shard.start_layer, - end_layer=shard.end_layer, - n_layers=shard.n_layers, - ), - tensor=node_service_pb2.Tensor(tensor_data=tensor.tobytes(), shape=tensor.shape, dtype=str(tensor.dtype)), - request_id=request_id, - inference_state=None if inference_state is None else self.serialize_inference_state(inference_state) - ) - response = await self.stub.SendTensor(request) - - if not response.tensor_data or not response.shape or not response.dtype: - return None - - return np.frombuffer(response.tensor_data, dtype=np.dtype(response.dtype)).reshape(response.shape) - - async def send_example(self, shard: Shard, example: np.ndarray, target: np.ndarray, length: np.ndarray, train: bool, request_id: Optional[str] = None) -> Optional[np.array]: - await self._ensure_connected() - request = node_service_pb2.ExampleRequest( - shard=node_service_pb2.Shard( - model_id=shard.model_id, - start_layer=shard.start_layer, - end_layer=shard.end_layer, - n_layers=shard.n_layers, - ), - example=node_service_pb2.Tensor(tensor_data=example.tobytes(), shape=example.shape, dtype=str(example.dtype)), - target=node_service_pb2.Tensor(tensor_data=target.tobytes(), shape=target.shape, dtype=str(target.dtype)), - length=node_service_pb2.Tensor(tensor_data=length.tobytes(), shape=length.shape, dtype=str(length.dtype)), - train=train, - request_id=request_id, - ) - response = await self.stub.SendExample(request) - loss = response.loss - if train and not shard.is_first_layer(): - grads = np.frombuffer(response.grads.tensor_data, dtype=np.dtype(response.grads.dtype)).reshape(response.grads.shape) - return loss, grads - else: - return loss - - async def send_loss(self, shard: Shard, tensor: np.ndarray, request_id: Optional[str] = None) -> Optional[np.array]: - await self._ensure_connected() - request = node_service_pb2.TensorRequest( - shard=node_service_pb2.Shard( - model_id=shard.model_id, - start_layer=shard.start_layer, - end_layer=shard.end_layer, - n_layers=shard.n_layers, - ), - tensor=node_service_pb2.Tensor(tensor_data=tensor.tobytes(), shape=tensor.shape, dtype=str(tensor.dtype)), - request_id=request_id, - ) - response = await self.stub.SendLoss(request) - - if not response.tensor_data or not response.shape or not response.dtype: - return None - - return np.frombuffer(response.tensor_data, dtype=np.dtype(response.dtype)).reshape(response.shape) - - async def collect_topology(self, visited: set[str], max_depth: int) -> Topology: - await self._ensure_connected() - request = node_service_pb2.CollectTopologyRequest(visited=visited, max_depth=max_depth) - response = await self.stub.CollectTopology(request) - topology = Topology() - for node_id, capabilities in response.nodes.items(): - device_capabilities = DeviceCapabilities( - model=capabilities.model, chip=capabilities.chip, memory=capabilities.memory, flops=DeviceFlops(fp16=capabilities.flops.fp16, fp32=capabilities.flops.fp32, int8=capabilities.flops.int8) - ) - topology.update_node(node_id, device_capabilities) - for node_id, peer_connections in response.peer_graph.items(): - for conn in peer_connections.connections: - topology.add_edge(node_id, conn.to_id, conn.description) - return topology - - async def send_result(self, request_id: str, result: List[int], is_finished: bool) -> None: - await self._ensure_connected() - tensor = None - if isinstance(result, np.ndarray): - tensor = node_service_pb2.Tensor(tensor_data=result.tobytes(), shape=result.shape, dtype=str(result.dtype)) - result = [] - request = node_service_pb2.SendResultRequest(request_id=request_id, result=result, tensor=tensor, is_finished=is_finished) - await self.stub.SendResult(request) - - async def send_opaque_status(self, request_id: str, status: str) -> None: - await self._ensure_connected() - request = node_service_pb2.SendOpaqueStatusRequest(request_id=request_id, status=status) - await asyncio.wait_for(self.stub.SendOpaqueStatus(request), timeout=10.0) - - def serialize_inference_state(self, inference_state: dict) -> node_service_pb2.InferenceState: - proto_inference_state = node_service_pb2.InferenceState() - other_data = {} - for k, v in inference_state.items(): - if isinstance(v, mx.array): - np_array = np.array(v) - tensor_data = node_service_pb2.Tensor(tensor_data=np_array.tobytes(), shape=list(np_array.shape), dtype=str(np_array.dtype)) - proto_inference_state.tensor_data[k].CopyFrom(tensor_data) - elif isinstance(v, list) and all(isinstance(item, mx.array) for item in v): - tensor_list = node_service_pb2.TensorList() - for tensor in v: - np_array = np.array(tensor) - tensor_data = node_service_pb2.Tensor(tensor_data=np_array.tobytes(), shape=list(np_array.shape), dtype=str(np_array.dtype)) - tensor_list.tensors.append(tensor_data) - proto_inference_state.tensor_list_data[k].CopyFrom(tensor_list) - else: - # For non-tensor data, we'll still use JSON - other_data[k] = v - if other_data: - proto_inference_state.other_data_json = json.dumps(other_data) - return proto_inference_state diff --git a/exo/networking/grpc/grpc_server.py b/exo/networking/grpc/grpc_server.py deleted file mode 100644 index cbc33f94..00000000 --- a/exo/networking/grpc/grpc_server.py +++ /dev/null @@ -1,173 +0,0 @@ -import grpc -from concurrent import futures -import numpy as np -from asyncio import CancelledError - -import platform - -from . import node_service_pb2 -from . import node_service_pb2_grpc -from exo import DEBUG -from exo.inference.shard import Shard -from exo.orchestration import Node -import json - -if platform.system().lower() == "darwin" and platform.machine().lower() == "arm64": - import mlx.core as mx -else: - import numpy as mx - - -class GRPCServer(node_service_pb2_grpc.NodeServiceServicer): - def __init__(self, node: Node, host: str, port: int): - self.node = node - self.host = host - self.port = port - self.server = None - - async def start(self) -> None: - self.server = grpc.aio.server( - futures.ThreadPoolExecutor(max_workers=32), - options=[ - ("grpc.max_metadata_size", 32*1024*1024), - ("grpc.max_send_message_length", 256*1024*1024), - ("grpc.max_receive_message_length", 256*1024*1024), - ("grpc.keepalive_time_ms", 10000), - ("grpc.keepalive_timeout_ms", 5000), - ("grpc.http2.max_pings_without_data", 0), - ("grpc.http2.min_time_between_pings_ms", 10000), - ("grpc.http2.min_ping_interval_without_data_ms", 5000), - ("grpc.max_concurrent_streams", 100), - ("grpc.tcp_nodelay", 1), - ("grpc.optimization_target", "throughput"), - ("grpc.keepalive_permit_without_calls", 1), - ("grpc.http2.max_concurrent_streams", 0), # Unlimited concurrent streams - ], - ) - node_service_pb2_grpc.add_NodeServiceServicer_to_server(self, self.server) - listen_addr = f"{self.host}:{self.port}" - self.server.add_insecure_port(listen_addr) - await self.server.start() - if DEBUG >= 1: print(f"Server started, listening on {listen_addr}") - - async def stop(self) -> None: - if self.server: - try: - await self.server.stop(grace=5) - await self.server.wait_for_termination() - except CancelledError: - pass - if DEBUG >= 1: print("Server stopped and all connections are closed") - - async def SendPrompt(self, request, context): - shard = Shard( - model_id=request.shard.model_id, - start_layer=request.shard.start_layer, - end_layer=request.shard.end_layer, - n_layers=request.shard.n_layers, - ) - prompt = request.prompt - request_id = request.request_id - inference_state = None if request.inference_state is None else self.deserialize_inference_state(request.inference_state) - result = await self.node.process_prompt(shard, prompt, request_id, inference_state) - if DEBUG >= 5: print(f"SendPrompt {shard=} {prompt=} {request_id=} result: {result}") - tensor_data = result.tobytes() if result is not None else None - return node_service_pb2.Tensor(tensor_data=tensor_data, shape=result.shape, dtype=str(result.dtype)) if result is not None else node_service_pb2.Tensor() - - async def SendTensor(self, request, context): - shard = Shard( - model_id=request.shard.model_id, - start_layer=request.shard.start_layer, - end_layer=request.shard.end_layer, - n_layers=request.shard.n_layers, - ) - tensor = np.frombuffer(request.tensor.tensor_data, dtype=np.dtype(request.tensor.dtype)).reshape(request.tensor.shape) - request_id = request.request_id - - inference_state = None if request.inference_state is None else self.deserialize_inference_state(request.inference_state) - - result = await self.node.process_tensor(shard, tensor, request_id, inference_state) - if DEBUG >= 5: print(f"SendTensor tensor {shard=} {tensor=} {request_id=} result: {result}") - tensor_data = result.tobytes() if result is not None else None - return node_service_pb2.Tensor(tensor_data=tensor_data, shape=result.shape, dtype=str(result.dtype)) if result is not None else node_service_pb2.Tensor() - - async def SendExample(self, request, context): - shard = Shard( - model_id=request.shard.model_id, - start_layer=request.shard.start_layer, - end_layer=request.shard.end_layer, - n_layers=request.shard.n_layers, - ) - example = np.frombuffer(request.example.tensor_data, dtype=np.dtype(request.example.dtype)).reshape(request.example.shape) - target = np.frombuffer(request.target.tensor_data, dtype=np.dtype(request.target.dtype)).reshape(request.target.shape) - length = np.frombuffer(request.length.tensor_data, dtype=np.dtype(request.length.dtype)).reshape(request.length.shape) - train = request.train - request_id = request.request_id - - if train and not shard.is_first_layer(): - loss, grad = await self.node.process_example(shard, example, target, length, train, request_id) - tensor_data = grad.tobytes() - grad_tensor = node_service_pb2.Tensor(tensor_data=tensor_data, shape=grad.shape, dtype=str(grad.dtype)) - return node_service_pb2.Loss(loss=loss, grads=grad_tensor) - else: - loss = await self.node.process_example(shard, example, target, length, train, request_id) - return node_service_pb2.Loss(loss=loss, grads=None) - - async def CollectTopology(self, request, context): - max_depth = request.max_depth - visited = set(request.visited) - topology = self.node.current_topology - nodes = { - node_id: - node_service_pb2.DeviceCapabilities( - model=cap.model, - chip=cap.chip, - memory=cap.memory, - flops=node_service_pb2.DeviceFlops(fp32=cap.flops.fp32, fp16=cap.flops.fp16, int8=cap.flops.int8), - ) - for node_id, cap in topology.nodes.items() - } - peer_graph = { - node_id: node_service_pb2.PeerConnections(connections=[node_service_pb2.PeerConnection(to_id=conn.to_id, description=conn.description) for conn in connections]) - for node_id, connections in topology.peer_graph.items() - } - if DEBUG >= 5: print(f"CollectTopology {max_depth=} {visited=} {nodes=} {peer_graph=}") - return node_service_pb2.Topology(nodes=nodes, peer_graph=peer_graph) - - async def SendResult(self, request, context): - request_id = request.request_id - result = request.result - is_finished = request.is_finished - img = request.tensor - if DEBUG >= 5: print(f"Received SendResult request: {request_id=} {result=} {is_finished=}") - result = list(result) - if len(img.tensor_data) > 0: - result = np.frombuffer(img.tensor_data, dtype=np.dtype(img.dtype)).reshape(img.shape) - self.node.on_token.trigger_all(request_id, result, is_finished) - return node_service_pb2.Empty() - - async def SendOpaqueStatus(self, request, context): - request_id = request.request_id - status = request.status - if DEBUG >= 8: print(f"Received SendOpaqueStatus request: {request_id=} {status=}") - self.node.on_opaque_status.trigger_all(request_id, status) - return node_service_pb2.Empty() - - async def HealthCheck(self, request, context): - return node_service_pb2.HealthCheckResponse(is_healthy=True) - - def deserialize_inference_state(self, inference_state_proto: node_service_pb2.InferenceState) -> dict: - inference_state = {} - - for k, tensor_data in inference_state_proto.tensor_data.items(): - np_array = np.frombuffer(tensor_data.tensor_data, dtype=tensor_data.dtype).reshape(tensor_data.shape) - inference_state[k] = mx.array(np_array) - - for k, tensor_list in inference_state_proto.tensor_list_data.items(): - inference_state[k] = [mx.array(np.frombuffer(tensor.tensor_data, dtype=tensor.dtype).reshape(tensor.shape)) for tensor in tensor_list.tensors] - - if inference_state_proto.other_data_json: - other_data = json.loads(inference_state_proto.other_data_json) - inference_state.update(other_data) - - return inference_state diff --git a/exo/networking/grpc/node_service.proto b/exo/networking/grpc/node_service.proto deleted file mode 100644 index 882a5247..00000000 --- a/exo/networking/grpc/node_service.proto +++ /dev/null @@ -1,116 +0,0 @@ -syntax = "proto3"; - -package node_service; - -service NodeService { - rpc SendPrompt (PromptRequest) returns (Tensor) {} - rpc SendTensor (TensorRequest) returns (Tensor) {} - rpc SendExample (ExampleRequest) returns (Loss) {} - rpc CollectTopology (CollectTopologyRequest) returns (Topology) {} - rpc SendResult (SendResultRequest) returns (Empty) {} - rpc SendOpaqueStatus (SendOpaqueStatusRequest) returns (Empty) {} - rpc HealthCheck (HealthCheckRequest) returns (HealthCheckResponse) {} -} - -message Shard { - string model_id = 1; - int32 start_layer = 2; - int32 end_layer = 3; - int32 n_layers = 4; -} - -message PromptRequest { - Shard shard = 1; - string prompt = 2; - optional string request_id = 3; - optional InferenceState inference_state = 4; -} - -message TensorRequest { - Shard shard = 1; - Tensor tensor = 2; - optional string request_id = 3; - optional InferenceState inference_state = 4; -} - -message ExampleRequest { - Shard shard = 1; - Tensor example = 2; - Tensor target = 3; - Tensor length = 4; - bool train = 5; - optional string request_id = 6; -} - -message Loss { - float loss = 1; - optional Tensor grads = 2; -} - -message Tensor { - bytes tensor_data = 1; - repeated int32 shape = 2; - string dtype = 3; -} - -message TensorList { - repeated Tensor tensors = 1; -} - -message InferenceState { - map tensor_data = 1; - map tensor_list_data = 2; - string other_data_json = 3; -} - -message CollectTopologyRequest { - repeated string visited = 1; - int32 max_depth = 2; -} - -message Topology { - map nodes = 1; - map peer_graph = 2; -} - -message PeerConnection { - string to_id = 1; - optional string description = 2; -} - -message PeerConnections { - repeated PeerConnection connections = 1; -} - -message DeviceFlops { - double fp32 = 1; - double fp16 = 2; - double int8 = 3; -} - -message DeviceCapabilities { - string model = 1; - string chip = 2; - int32 memory = 3; - DeviceFlops flops = 4; -} - -message SendResultRequest { - string request_id = 1; - repeated int32 result = 2; - optional Tensor tensor = 3; - bool is_finished = 4; -} - -message SendOpaqueStatusRequest { - string request_id = 1; - string status = 2; -} - -message HealthCheckRequest {} - -message HealthCheckResponse { - bool is_healthy = 1; -} - -message Empty {} diff --git a/exo/networking/grpc/node_service_pb2.py b/exo/networking/grpc/node_service_pb2.py deleted file mode 100644 index 9a83380d..00000000 --- a/exo/networking/grpc/node_service_pb2.py +++ /dev/null @@ -1,90 +0,0 @@ -# -*- coding: utf-8 -*- -# Generated by the protocol buffer compiler. DO NOT EDIT! -# NO CHECKED-IN PROTOBUF GENCODE -# source: node_service.proto -# Protobuf Python Version: 5.27.2 -"""Generated protocol buffer code.""" -from google.protobuf import descriptor as _descriptor -from google.protobuf import descriptor_pool as _descriptor_pool -from google.protobuf import runtime_version as _runtime_version -from google.protobuf import symbol_database as _symbol_database -from google.protobuf.internal import builder as _builder -_runtime_version.ValidateProtobufRuntimeVersion( - _runtime_version.Domain.PUBLIC, - 5, - 27, - 2, - '', - 'node_service.proto' -) -# @@protoc_insertion_point(imports) - -_sym_db = _symbol_database.Default() - - - - -DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x12node_service.proto\x12\x0cnode_service\"S\n\x05Shard\x12\x10\n\x08model_id\x18\x01 \x01(\t\x12\x13\n\x0bstart_layer\x18\x02 \x01(\x05\x12\x11\n\tend_layer\x18\x03 \x01(\x05\x12\x10\n\x08n_layers\x18\x04 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\x01(\x0b\x32\x14.node_service.Tensor\x12\r\n\x05train\x18\x05 \x01(\x08\x12\x17\n\nrequest_id\x18\x06 \x01(\tH\x00\x88\x01\x01\x42\r\n\x0b_request_id\"H\n\x04Loss\x12\x0c\n\x04loss\x18\x01 \x01(\x02\x12(\n\x05grads\x18\x02 \x01(\x0b\x32\x14.node_service.TensorH\x00\x88\x01\x01\x42\x08\n\x06_grads\";\n\x06Tensor\x12\x13\n\x0btensor_data\x18\x01 \x01(\x0c\x12\r\n\x05shape\x18\x02 \x03(\x05\x12\r\n\x05\x64type\x18\x03 \x01(\t\"3\n\nTensorList\x12%\n\x07tensors\x18\x01 \x03(\x0b\x32\x14.node_service.Tensor\"\xd2\x02\n\x0eInferenceState\x12\x41\n\x0btensor_data\x18\x01 \x03(\x0b\x32,.node_service.InferenceState.TensorDataEntry\x12J\n\x10tensor_list_data\x18\x02 \x03(\x0b\x32\x30.node_service.InferenceState.TensorListDataEntry\x12\x17\n\x0fother_data_json\x18\x03 \x01(\t\x1aG\n\x0fTensorDataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12#\n\x05value\x18\x02 \x01(\x0b\x32\x14.node_service.Tensor:\x02\x38\x01\x1aO\n\x13TensorListDataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\'\n\x05value\x18\x02 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\x01(\x01\x12\x0c\n\x04int8\x18\x03 \x01(\x01\"k\n\x12\x44\x65viceCapabilities\x12\r\n\x05model\x18\x01 \x01(\t\x12\x0c\n\x04\x63hip\x18\x02 \x01(\t\x12\x0e\n\x06memory\x18\x03 \x01(\x05\x12(\n\x05\x66lops\x18\x04 \x01(\x0b\x32\x19.node_service.DeviceFlops\"\x82\x01\n\x11SendResultRequest\x12\x12\n\nrequest_id\x18\x01 \x01(\t\x12\x0e\n\x06result\x18\x02 \x03(\x05\x12)\n\x06tensor\x18\x03 \x01(\x0b\x32\x14.node_service.TensorH\x00\x88\x01\x01\x12\x13\n\x0bis_finished\x18\x04 \x01(\x08\x42\t\n\x07_tensor\"=\n\x17SendOpaqueStatusRequest\x12\x12\n\nrequest_id\x18\x01 \x01(\t\x12\x0e\n\x06status\x18\x02 \x01(\t\"\x14\n\x12HealthCheckRequest\")\n\x13HealthCheckResponse\x12\x12\n\nis_healthy\x18\x01 \x01(\x08\"\x07\n\x05\x45mpty2\x97\x04\n\x0bNodeService\x12\x41\n\nSendPrompt\x12\x1b.node_service.PromptRequest\x1a\x14.node_service.Tensor\"\x00\x12\x41\n\nSendTensor\x12\x1b.node_service.TensorRequest\x1a\x14.node_service.Tensor\"\x00\x12\x41\n\x0bSendExample\x12\x1c.node_service.ExampleRequest\x1a\x12.node_service.Loss\"\x00\x12Q\n\x0f\x43ollectTopology\x12$.node_service.CollectTopologyRequest\x1a\x16.node_service.Topology\"\x00\x12\x44\n\nSendResult\x12\x1f.node_service.SendResultRequest\x1a\x13.node_service.Empty\"\x00\x12P\n\x10SendOpaqueStatus\x12%.node_service.SendOpaqueStatusRequest\x1a\x13.node_service.Empty\"\x00\x12T\n\x0bHealthCheck\x12 .node_service.HealthCheckRequest\x1a!.node_service.HealthCheckResponse\"\x00\x62\x06proto3') - -_globals = globals() -_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) -_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'node_service_pb2', _globals) -if not _descriptor._USE_C_DESCRIPTORS: - DESCRIPTOR._loaded_options = None - _globals['_INFERENCESTATE_TENSORDATAENTRY']._loaded_options = None - _globals['_INFERENCESTATE_TENSORDATAENTRY']._serialized_options = b'8\001' - _globals['_INFERENCESTATE_TENSORLISTDATAENTRY']._loaded_options = None - _globals['_INFERENCESTATE_TENSORLISTDATAENTRY']._serialized_options = b'8\001' - _globals['_TOPOLOGY_NODESENTRY']._loaded_options = None - _globals['_TOPOLOGY_NODESENTRY']._serialized_options = b'8\001' - _globals['_TOPOLOGY_PEERGRAPHENTRY']._loaded_options = None - _globals['_TOPOLOGY_PEERGRAPHENTRY']._serialized_options = b'8\001' - _globals['_SHARD']._serialized_start=36 - _globals['_SHARD']._serialized_end=119 - _globals['_PROMPTREQUEST']._serialized_start=122 - _globals['_PROMPTREQUEST']._serialized_end=309 - _globals['_TENSORREQUEST']._serialized_start=312 - _globals['_TENSORREQUEST']._serialized_end=521 - _globals['_EXAMPLEREQUEST']._serialized_start=524 - _globals['_EXAMPLEREQUEST']._serialized_end=746 - _globals['_LOSS']._serialized_start=748 - _globals['_LOSS']._serialized_end=820 - _globals['_TENSOR']._serialized_start=822 - _globals['_TENSOR']._serialized_end=881 - _globals['_TENSORLIST']._serialized_start=883 - _globals['_TENSORLIST']._serialized_end=934 - _globals['_INFERENCESTATE']._serialized_start=937 - _globals['_INFERENCESTATE']._serialized_end=1275 - _globals['_INFERENCESTATE_TENSORDATAENTRY']._serialized_start=1123 - _globals['_INFERENCESTATE_TENSORDATAENTRY']._serialized_end=1194 - _globals['_INFERENCESTATE_TENSORLISTDATAENTRY']._serialized_start=1196 - _globals['_INFERENCESTATE_TENSORLISTDATAENTRY']._serialized_end=1275 - _globals['_COLLECTTOPOLOGYREQUEST']._serialized_start=1277 - _globals['_COLLECTTOPOLOGYREQUEST']._serialized_end=1337 - _globals['_TOPOLOGY']._serialized_start=1340 - _globals['_TOPOLOGY']._serialized_end=1620 - _globals['_TOPOLOGY_NODESENTRY']._serialized_start=1461 - _globals['_TOPOLOGY_NODESENTRY']._serialized_end=1539 - _globals['_TOPOLOGY_PEERGRAPHENTRY']._serialized_start=1541 - _globals['_TOPOLOGY_PEERGRAPHENTRY']._serialized_end=1620 - _globals['_PEERCONNECTION']._serialized_start=1622 - _globals['_PEERCONNECTION']._serialized_end=1695 - _globals['_PEERCONNECTIONS']._serialized_start=1697 - _globals['_PEERCONNECTIONS']._serialized_end=1765 - _globals['_DEVICEFLOPS']._serialized_start=1767 - _globals['_DEVICEFLOPS']._serialized_end=1822 - _globals['_DEVICECAPABILITIES']._serialized_start=1824 - _globals['_DEVICECAPABILITIES']._serialized_end=1931 - _globals['_SENDRESULTREQUEST']._serialized_start=1934 - _globals['_SENDRESULTREQUEST']._serialized_end=2064 - _globals['_SENDOPAQUESTATUSREQUEST']._serialized_start=2066 - _globals['_SENDOPAQUESTATUSREQUEST']._serialized_end=2127 - _globals['_HEALTHCHECKREQUEST']._serialized_start=2129 - _globals['_HEALTHCHECKREQUEST']._serialized_end=2149 - _globals['_HEALTHCHECKRESPONSE']._serialized_start=2151 - _globals['_HEALTHCHECKRESPONSE']._serialized_end=2192 - _globals['_EMPTY']._serialized_start=2194 - _globals['_EMPTY']._serialized_end=2201 - _globals['_NODESERVICE']._serialized_start=2204 - _globals['_NODESERVICE']._serialized_end=2739 -# @@protoc_insertion_point(module_scope) diff --git a/exo/networking/grpc/node_service_pb2_grpc.py b/exo/networking/grpc/node_service_pb2_grpc.py deleted file mode 100644 index ac12e2f7..00000000 --- a/exo/networking/grpc/node_service_pb2_grpc.py +++ /dev/null @@ -1,355 +0,0 @@ -# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! -"""Client and server classes corresponding to protobuf-defined services.""" -import grpc -import warnings - -from . import node_service_pb2 as node__service__pb2 - -GRPC_GENERATED_VERSION = '1.67.0' -GRPC_VERSION = grpc.__version__ -_version_not_supported = False - -try: - from grpc._utilities import first_version_is_lower - _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) -except ImportError: - _version_not_supported = True - -if _version_not_supported: - raise RuntimeError( - f'The grpc package installed is at version {GRPC_VERSION},' - + f' but the generated code in node_service_pb2_grpc.py depends on' - + f' grpcio>={GRPC_GENERATED_VERSION}.' - + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' - + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' - ) - - -class NodeServiceStub(object): - """Missing associated documentation comment in .proto file.""" - - def __init__(self, channel): - """Constructor. - - Args: - channel: A grpc.Channel. - """ - self.SendPrompt = channel.unary_unary( - '/node_service.NodeService/SendPrompt', - request_serializer=node__service__pb2.PromptRequest.SerializeToString, - response_deserializer=node__service__pb2.Tensor.FromString, - _registered_method=True) - self.SendTensor = channel.unary_unary( - '/node_service.NodeService/SendTensor', - request_serializer=node__service__pb2.TensorRequest.SerializeToString, - response_deserializer=node__service__pb2.Tensor.FromString, - _registered_method=True) - self.SendExample = channel.unary_unary( - '/node_service.NodeService/SendExample', - request_serializer=node__service__pb2.ExampleRequest.SerializeToString, - response_deserializer=node__service__pb2.Loss.FromString, - _registered_method=True) - self.CollectTopology = channel.unary_unary( - '/node_service.NodeService/CollectTopology', - request_serializer=node__service__pb2.CollectTopologyRequest.SerializeToString, - response_deserializer=node__service__pb2.Topology.FromString, - _registered_method=True) - self.SendResult = channel.unary_unary( - '/node_service.NodeService/SendResult', - request_serializer=node__service__pb2.SendResultRequest.SerializeToString, - response_deserializer=node__service__pb2.Empty.FromString, - _registered_method=True) - self.SendOpaqueStatus = channel.unary_unary( - '/node_service.NodeService/SendOpaqueStatus', - request_serializer=node__service__pb2.SendOpaqueStatusRequest.SerializeToString, - response_deserializer=node__service__pb2.Empty.FromString, - _registered_method=True) - self.HealthCheck = channel.unary_unary( - '/node_service.NodeService/HealthCheck', - request_serializer=node__service__pb2.HealthCheckRequest.SerializeToString, - response_deserializer=node__service__pb2.HealthCheckResponse.FromString, - _registered_method=True) - - -class NodeServiceServicer(object): - """Missing associated documentation comment in .proto file.""" - - def SendPrompt(self, request, context): - """Missing associated documentation comment in .proto file.""" - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - def SendTensor(self, request, context): - """Missing associated documentation comment in .proto file.""" - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - def SendExample(self, request, context): - """Missing associated documentation comment in .proto file.""" - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - def CollectTopology(self, request, context): - """Missing associated documentation comment in .proto file.""" - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - def SendResult(self, request, context): - """Missing associated documentation comment in .proto file.""" - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - def SendOpaqueStatus(self, request, context): - """Missing associated documentation comment in .proto file.""" - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - def HealthCheck(self, request, context): - """Missing associated documentation comment in .proto file.""" - context.set_code(grpc.StatusCode.UNIMPLEMENTED) - context.set_details('Method not implemented!') - raise NotImplementedError('Method not implemented!') - - -def add_NodeServiceServicer_to_server(servicer, server): - rpc_method_handlers = { - 'SendPrompt': grpc.unary_unary_rpc_method_handler( - servicer.SendPrompt, - request_deserializer=node__service__pb2.PromptRequest.FromString, - response_serializer=node__service__pb2.Tensor.SerializeToString, - ), - 'SendTensor': grpc.unary_unary_rpc_method_handler( - servicer.SendTensor, - request_deserializer=node__service__pb2.TensorRequest.FromString, - response_serializer=node__service__pb2.Tensor.SerializeToString, - ), - 'SendExample': grpc.unary_unary_rpc_method_handler( - servicer.SendExample, - request_deserializer=node__service__pb2.ExampleRequest.FromString, - response_serializer=node__service__pb2.Loss.SerializeToString, - ), - 'CollectTopology': grpc.unary_unary_rpc_method_handler( - servicer.CollectTopology, - request_deserializer=node__service__pb2.CollectTopologyRequest.FromString, - response_serializer=node__service__pb2.Topology.SerializeToString, - ), - 'SendResult': grpc.unary_unary_rpc_method_handler( - servicer.SendResult, - request_deserializer=node__service__pb2.SendResultRequest.FromString, - response_serializer=node__service__pb2.Empty.SerializeToString, - ), - 'SendOpaqueStatus': grpc.unary_unary_rpc_method_handler( - servicer.SendOpaqueStatus, - request_deserializer=node__service__pb2.SendOpaqueStatusRequest.FromString, - response_serializer=node__service__pb2.Empty.SerializeToString, - ), - 'HealthCheck': grpc.unary_unary_rpc_method_handler( - servicer.HealthCheck, - request_deserializer=node__service__pb2.HealthCheckRequest.FromString, - response_serializer=node__service__pb2.HealthCheckResponse.SerializeToString, - ), - } - generic_handler = grpc.method_handlers_generic_handler( - 'node_service.NodeService', rpc_method_handlers) - server.add_generic_rpc_handlers((generic_handler,)) - server.add_registered_method_handlers('node_service.NodeService', rpc_method_handlers) - - - # This class is part of an EXPERIMENTAL API. -class NodeService(object): - """Missing associated documentation comment in .proto file.""" - - @staticmethod - def SendPrompt(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/node_service.NodeService/SendPrompt', - node__service__pb2.PromptRequest.SerializeToString, - node__service__pb2.Tensor.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) - - @staticmethod - def SendTensor(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/node_service.NodeService/SendTensor', - node__service__pb2.TensorRequest.SerializeToString, - node__service__pb2.Tensor.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) - - @staticmethod - def SendExample(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/node_service.NodeService/SendExample', - node__service__pb2.ExampleRequest.SerializeToString, - node__service__pb2.Loss.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) - - @staticmethod - def CollectTopology(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/node_service.NodeService/CollectTopology', - node__service__pb2.CollectTopologyRequest.SerializeToString, - node__service__pb2.Topology.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) - - @staticmethod - def SendResult(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/node_service.NodeService/SendResult', - node__service__pb2.SendResultRequest.SerializeToString, - node__service__pb2.Empty.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) - - @staticmethod - def SendOpaqueStatus(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/node_service.NodeService/SendOpaqueStatus', - node__service__pb2.SendOpaqueStatusRequest.SerializeToString, - node__service__pb2.Empty.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) - - @staticmethod - def HealthCheck(request, - target, - options=(), - channel_credentials=None, - call_credentials=None, - insecure=False, - compression=None, - wait_for_ready=None, - timeout=None, - metadata=None): - return grpc.experimental.unary_unary( - request, - target, - '/node_service.NodeService/HealthCheck', - node__service__pb2.HealthCheckRequest.SerializeToString, - node__service__pb2.HealthCheckResponse.FromString, - options, - channel_credentials, - insecure, - call_credentials, - compression, - wait_for_ready, - timeout, - metadata, - _registered_method=True) diff --git a/exo/networking/manual/__init__.py b/exo/networking/manual/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/networking/manual/manual_discovery.py b/exo/networking/manual/manual_discovery.py deleted file mode 100644 index 041eafa6..00000000 --- a/exo/networking/manual/manual_discovery.py +++ /dev/null @@ -1,101 +0,0 @@ -import os -import asyncio -from typing import Dict, List, Callable, Optional -from concurrent.futures import ThreadPoolExecutor - -from exo.networking.discovery import Discovery -from exo.topology.device_capabilities import DeviceCapabilities -from exo.networking.manual.network_topology_config import NetworkTopology, PeerConfig -from exo.helpers import DEBUG_DISCOVERY -from exo.networking.peer_handle import PeerHandle - - -class ManualDiscovery(Discovery): - def __init__( - self, - network_config_path: str, - node_id: str, - create_peer_handle: Callable[[str, str, str, DeviceCapabilities], PeerHandle], - ): - self.network_config_path = network_config_path - self.node_id = node_id - self.create_peer_handle = create_peer_handle - - self.listen_task = None - self.known_peers: Dict[str, PeerHandle] = {} - - self._cached_peers: Dict[str, PeerConfig] = {} - self._last_modified_time: Optional[float] = None - self._file_executor = ThreadPoolExecutor(max_workers=1) - - async def start(self) -> None: - self.listen_task = asyncio.create_task(self.task_find_peers_from_config()) - - async def stop(self) -> None: - if self.listen_task: self.listen_task.cancel() - self._file_executor.shutdown(wait=True) - - async def discover_peers(self, wait_for_peers: int = 0) -> List[PeerHandle]: - if wait_for_peers > 0: - while len(self.known_peers) < wait_for_peers: - if DEBUG_DISCOVERY >= 2: print(f"Current peers: {len(self.known_peers)}/{wait_for_peers}. Waiting for more peers...") - await asyncio.sleep(0.1) - if DEBUG_DISCOVERY >= 2: print(f"Discovered peers: {[peer.id() for peer in self.known_peers.values()]}") - return list(self.known_peers.values()) - - async def task_find_peers_from_config(self): - if DEBUG_DISCOVERY >= 2: print("Starting task to find peers from config...") - while True: - peers_from_config = await self._get_peers() - new_known_peers = {} - for peer_id, peer_config in peers_from_config.items(): - try: - if DEBUG_DISCOVERY >= 2: print(f"Checking peer {peer_id=} at {peer_config.address}:{peer_config.port}") - peer = self.known_peers.get(peer_id) - if not peer: - if DEBUG_DISCOVERY >= 2: print(f"{peer_id=} not found in known peers. Adding.") - peer = self.create_peer_handle(peer_id, f"{peer_config.address}:{peer_config.port}", "MAN", peer_config.device_capabilities) - is_healthy = await peer.health_check() - if is_healthy: - if DEBUG_DISCOVERY >= 2: print(f"{peer_id=} at {peer_config.address}:{peer_config.port} is healthy.") - new_known_peers[peer_id] = peer - elif DEBUG_DISCOVERY >= 2: - print(f"{peer_id=} at {peer_config.address}:{peer_config.port} is not healthy. Removing.") - except Exception as e: - if DEBUG_DISCOVERY >= 2: print(f"Exception occurred when attempting to add {peer_id=}: {e}") - self.known_peers = new_known_peers - await asyncio.sleep(5.0) - - if DEBUG_DISCOVERY >= 2: print(f"Current known peers: {[peer.id() for peer in self.known_peers.values()]}") - - async def _get_peers(self): - try: - loop = asyncio.get_running_loop() - current_mtime = await loop.run_in_executor(self._file_executor, os.path.getmtime, self.network_config_path) - - if (self._cached_peers is not None and self._last_modified_time is not None and current_mtime <= self._last_modified_time): - return self._cached_peers - - topology = await loop.run_in_executor(self._file_executor, NetworkTopology.from_path, self.network_config_path) - - if self.node_id not in topology.peers: - raise ValueError( - f"Node ID {self.node_id} not found in network config file " - f"{self.network_config_path}. Please run with `node_id` set to " - f"one of the keys in the config file: {[k for k, _ in topology.peers]}" - ) - - peers_in_network = topology.peers - peers_in_network.pop(self.node_id) - - self._cached_peers = peers_in_network - self._last_modified_time = current_mtime - - return peers_in_network - - except Exception as e: - if DEBUG_DISCOVERY >= 2: - print(f"Error when loading network config file from {self.network_config_path}. " - f"Please update the config file in order to successfully discover peers. " - f"Exception: {e}") - return self._cached_peers diff --git a/exo/networking/manual/network_topology_config.py b/exo/networking/manual/network_topology_config.py deleted file mode 100644 index c0246ec3..00000000 --- a/exo/networking/manual/network_topology_config.py +++ /dev/null @@ -1,31 +0,0 @@ -from typing import Dict -from pydantic import BaseModel, ValidationError - -from exo.topology.device_capabilities import DeviceCapabilities - - -class PeerConfig(BaseModel): - address: str - port: int - device_capabilities: DeviceCapabilities - - -class NetworkTopology(BaseModel): - """Configuration of the network. A collection outlining all nodes in the network, including the node this is running from.""" - - peers: Dict[str, PeerConfig] - """ - node_id to PeerConfig. The node_id is used to identify the peer in the discovery process. The node that this is running from should be included in this dict. - """ - @classmethod - def from_path(cls, path: str) -> "NetworkTopology": - try: - with open(path, "r") as f: - config_data = f.read() - except FileNotFoundError as e: - raise FileNotFoundError(f"Config file not found at {path}") from e - - try: - return cls.model_validate_json(config_data) - except ValidationError as e: - raise ValueError(f"Error validating network topology config from {path}: {e}") from e diff --git a/exo/networking/manual/test_data/invalid_config.json b/exo/networking/manual/test_data/invalid_config.json deleted file mode 100644 index 283feadf..00000000 --- a/exo/networking/manual/test_data/invalid_config.json +++ /dev/null @@ -1,17 +0,0 @@ -{ - "peers": { - "node1": { - "address": "localhost", - "device_capabilities": { - "model": "Unknown Model", - "chip": "Unknown Chip", - "memory": 0, - "flops": { - "fp32": 0, - "fp16": 0, - "int8": 0 - } - } - } - } -} diff --git a/exo/networking/manual/test_data/invalid_json.json b/exo/networking/manual/test_data/invalid_json.json deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/networking/manual/test_data/test_config.json b/exo/networking/manual/test_data/test_config.json deleted file mode 100644 index 54eced72..00000000 --- a/exo/networking/manual/test_data/test_config.json +++ /dev/null @@ -1,32 +0,0 @@ -{ - "peers": { - "node1": { - "address": "localhost", - "port": 50051, - "device_capabilities": { - "model": "Unknown Model", - "chip": "Unknown Chip", - "memory": 0, - "flops": { - "fp32": 0, - "fp16": 0, - "int8": 0 - } - } - }, - "node2": { - "address": "localhost", - "port": 50052, - "device_capabilities": { - "model": "Unknown Model", - "chip": "Unknown Chip", - "memory": 0, - "flops": { - "fp32": 0, - "fp16": 0, - "int8": 0 - } - } - } - } -} \ No newline at end of file diff --git a/exo/networking/manual/test_data/test_config_single_node.json b/exo/networking/manual/test_data/test_config_single_node.json deleted file mode 100644 index 81a0670f..00000000 --- a/exo/networking/manual/test_data/test_config_single_node.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "peers": { - "node1": { - "address": "localhost", - "port": 50051, - "device_capabilities": { - "model": "Unknown Model", - "chip": "Unknown Chip", - "memory": 0, - "flops": { - "fp32": 0, - "fp16": 0, - "int8": 0 - } - } - } - } -} diff --git a/exo/networking/manual/test_manual_discovery.py b/exo/networking/manual/test_manual_discovery.py deleted file mode 100644 index 317fba9d..00000000 --- a/exo/networking/manual/test_manual_discovery.py +++ /dev/null @@ -1,181 +0,0 @@ -import json -import asyncio -import unittest -from unittest import mock -from exo.networking.manual.manual_discovery import ManualDiscovery -from exo.networking.manual.network_topology_config import NetworkTopology -from exo.networking.grpc.grpc_peer_handle import GRPCPeerHandle -from exo.networking.grpc.grpc_server import GRPCServer -from exo.orchestration.node import Node - -root_path = "./exo/networking/manual/test_data/test_config.json" - - -class TestSingleNodeManualDiscovery(unittest.IsolatedAsyncioTestCase): - async def asyncSetUp(self): - self.peer1 = mock.AsyncMock() - self.peer1.connect = mock.AsyncMock() - self.discovery1 = ManualDiscovery( - root_path, - "node1", - create_peer_handle=lambda peer_id, address, description, device_capabilities: self.peer1, - ) - await self.discovery1.start() - - async def asyncTearDown(self): - await self.discovery1.stop() - - async def test_discovery(self): - peers1 = await self.discovery1.discover_peers(wait_for_peers=0) - assert len(peers1) == 0 - - self.peer1.connect.assert_not_called() - - -class TestManualDiscovery(unittest.IsolatedAsyncioTestCase): - async def asyncSetUp(self): - self.peer1 = mock.AsyncMock() - self.peer2 = mock.AsyncMock() - self.peer1.connect = mock.AsyncMock() - self.peer2.connect = mock.AsyncMock() - self.discovery1 = ManualDiscovery( - root_path, - "node1", - create_peer_handle=lambda peer_id, address, description, device_capabilities: self.peer1, - ) - self.discovery2 = ManualDiscovery( - root_path, - "node2", - create_peer_handle=lambda peer_id, address, description, device_capabilities: self.peer2, - ) - await self.discovery1.start() - await self.discovery2.start() - - async def asyncTearDown(self): - await self.discovery1.stop() - await self.discovery2.stop() - - async def test_discovery(self): - peers1 = await self.discovery1.discover_peers(wait_for_peers=1) - assert len(peers1) == 1 - peers2 = await self.discovery2.discover_peers(wait_for_peers=1) - assert len(peers2) == 1 - - # connect has to be explicitly called after discovery - self.peer1.connect.assert_not_called() - self.peer2.connect.assert_not_called() - - -class TestManualDiscoveryWithGRPCPeerHandle(unittest.IsolatedAsyncioTestCase): - async def asyncSetUp(self): - config = NetworkTopology.from_path(root_path) - - self.node1 = mock.AsyncMock(spec=Node) - self.node2 = mock.AsyncMock(spec=Node) - self.server1 = GRPCServer(self.node1, config.peers["node1"].address, config.peers["node1"].port) - self.server2 = GRPCServer(self.node2, config.peers["node2"].address, config.peers["node2"].port) - await self.server1.start() - await self.server2.start() - self.discovery1 = ManualDiscovery( - root_path, - "node1", - create_peer_handle=lambda peer_id, address, description, device_capabilities: GRPCPeerHandle(peer_id, address, description, device_capabilities), - ) - self.discovery2 = ManualDiscovery( - root_path, - "node2", - create_peer_handle=lambda peer_id, address, description, device_capabilities: GRPCPeerHandle(peer_id, address, description, device_capabilities), - ) - await self.discovery1.start() - await self.discovery2.start() - - async def asyncTearDown(self): - await self.discovery1.stop() - await self.discovery2.stop() - await self.server1.stop() - await self.server2.stop() - - async def test_grpc_discovery(self): - peers1 = await self.discovery1.discover_peers(wait_for_peers=1) - assert len(peers1) == 1 - peers2 = await self.discovery2.discover_peers(wait_for_peers=1) - assert len(peers2) == 1 - - # Connect - await peers1[0].connect() - await peers2[0].connect() - self.assertTrue(await peers1[0].is_connected()) - self.assertTrue(await peers2[0].is_connected()) - - # Kill server1 - await self.server1.stop() - - self.assertTrue(await peers1[0].is_connected()) - self.assertFalse(await peers2[0].is_connected()) - - # Kill server2 - await self.server2.stop() - - self.assertFalse(await peers1[0].is_connected()) - self.assertFalse(await peers2[0].is_connected()) - - async def test_dynamic_config_update(self): - initial_peers = await self.discovery1.discover_peers(wait_for_peers=1) - self.assertEqual(len(initial_peers), 1) - - # Save original config for cleanup - with open(root_path, "r") as f: - original_config = json.load(f) - - try: - updated_config = { - "peers": { - **original_config["peers"], - "node3": { - "address": "localhost", - "port": 50053, - "device_capabilities": { - "model": "Unknown Model", - "chip": "Unknown Chip", - "memory": 0, - "flops": {"fp32": 0, "fp16": 0, "int8": 0}, - }, - }, - } - } - - with open(root_path, "w") as f: - json.dump(updated_config, f, indent=2) - - node3 = mock.AsyncMock(spec=Node) - server3 = GRPCServer(node3, "localhost", 50053) - await server3.start() - - try: - # Wait for the config to be reloaded - await asyncio.sleep(1.5) - - updated_peers = await self.discovery1.discover_peers(wait_for_peers=2) - self.assertEqual(len(updated_peers), 2) - - for peer in updated_peers: - await peer.connect() - self.assertTrue(await peer.is_connected()) - - finally: - await server3.stop() - - finally: - # Restore the original config file - with open(root_path, "w") as f: - json.dump(original_config, f, indent=2) - - # Wait for the config to be reloaded again - await asyncio.sleep(1.5) - - updated_peers = await self.discovery1.discover_peers(wait_for_peers=1) - self.assertEqual(len(updated_peers), 1) - - -if __name__ == "__main__": - asyncio.run(unittest.main()) diff --git a/exo/networking/manual/test_network_topology_config.py b/exo/networking/manual/test_network_topology_config.py deleted file mode 100644 index b5b2fbd5..00000000 --- a/exo/networking/manual/test_network_topology_config.py +++ /dev/null @@ -1,49 +0,0 @@ -import unittest - -from exo.networking.manual.network_topology_config import NetworkTopology - -root_path = "./exo/networking/manual/test_data/" - - -class TestNetworkTopologyConfig(unittest.TestCase): - def test_from_path_invalid_path(self): - with self.assertRaises(FileNotFoundError) as e: - NetworkTopology.from_path("invalid_path") - self.assertEqual(str(e.exception), "Config file not found at invalid_path") - - def test_from_path_invalid_json(self): - with self.assertRaises(ValueError) as e: - NetworkTopology.from_path(root_path + "invalid_json.json") - self.assertIn("Error validating network topology config from", str(e.exception)) - self.assertIn("1 validation error for NetworkTopology\n Invalid JSON: EOF while parsing a value at line 1 column 0", str(e.exception)) - - def test_from_path_invalid_config(self): - with self.assertRaises(ValueError) as e: - NetworkTopology.from_path(root_path + "invalid_config.json") - self.assertIn("Error validating network topology config from", str(e.exception)) - self.assertIn("port\n Field required", str(e.exception)) - - def test_from_path_valid(self): - config = NetworkTopology.from_path(root_path + "test_config.json") - - self.assertEqual(config.peers["node1"].port, 50051) - self.assertEqual(config.peers["node1"].device_capabilities.model, "Unknown Model") - self.assertEqual(config.peers["node1"].address, "localhost") - self.assertEqual(config.peers["node1"].device_capabilities.chip, "Unknown Chip") - self.assertEqual(config.peers["node1"].device_capabilities.memory, 0) - self.assertEqual(config.peers["node1"].device_capabilities.flops.fp32, 0) - self.assertEqual(config.peers["node1"].device_capabilities.flops.fp16, 0) - self.assertEqual(config.peers["node1"].device_capabilities.flops.int8, 0) - - self.assertEqual(config.peers["node2"].port, 50052) - self.assertEqual(config.peers["node2"].device_capabilities.model, "Unknown Model") - self.assertEqual(config.peers["node2"].address, "localhost") - self.assertEqual(config.peers["node2"].device_capabilities.chip, "Unknown Chip") - self.assertEqual(config.peers["node2"].device_capabilities.memory, 0) - self.assertEqual(config.peers["node2"].device_capabilities.flops.fp32, 0) - self.assertEqual(config.peers["node2"].device_capabilities.flops.fp16, 0) - self.assertEqual(config.peers["node2"].device_capabilities.flops.int8, 0) - - -if __name__ == "__main__": - unittest.main() diff --git a/exo/networking/peer_handle.py b/exo/networking/peer_handle.py deleted file mode 100644 index d75318ef..00000000 --- a/exo/networking/peer_handle.py +++ /dev/null @@ -1,56 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Optional, Tuple, List -import numpy as np -from exo.inference.shard import Shard -from exo.topology.device_capabilities import DeviceCapabilities -from exo.topology.topology import Topology - - -class PeerHandle(ABC): - @abstractmethod - def id(self) -> str: - pass - - @abstractmethod - def addr(self) -> str: - pass - - @abstractmethod - def description(self) -> str: - pass - - @abstractmethod - def device_capabilities(self) -> DeviceCapabilities: - pass - - @abstractmethod - async def connect(self) -> None: - pass - - @abstractmethod - async def is_connected(self) -> bool: - pass - - @abstractmethod - async def disconnect(self) -> None: - pass - - @abstractmethod - async def health_check(self) -> bool: - pass - - @abstractmethod - async def send_prompt(self, shard: Shard, prompt: str, request_id: Optional[str] = None) -> Optional[np.array]: - pass - - @abstractmethod - async def send_tensor(self, shard: Shard, tensor: np.array, request_id: Optional[str] = None) -> Optional[np.array]: - pass - - @abstractmethod - async def send_result(self, request_id: str, result: List[int], is_finished: bool) -> None: - pass - - @abstractmethod - async def collect_topology(self, visited: set[str], max_depth: int) -> Topology: - pass diff --git a/exo/networking/server.py b/exo/networking/server.py deleted file mode 100644 index 8e7f9812..00000000 --- a/exo/networking/server.py +++ /dev/null @@ -1,11 +0,0 @@ -from abc import ABC, abstractmethod - - -class Server(ABC): - @abstractmethod - async def start(self) -> None: - pass - - @abstractmethod - async def stop(self) -> None: - pass diff --git a/exo/networking/tailscale/__init__.py b/exo/networking/tailscale/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/networking/tailscale/tailscale_discovery.py b/exo/networking/tailscale/tailscale_discovery.py deleted file mode 100644 index 61a13507..00000000 --- a/exo/networking/tailscale/tailscale_discovery.py +++ /dev/null @@ -1,178 +0,0 @@ -import asyncio -import time -import traceback -from typing import List, Dict, Callable, Tuple -from exo.networking.discovery import Discovery -from exo.networking.peer_handle import PeerHandle -from exo.topology.device_capabilities import DeviceCapabilities, device_capabilities, UNKNOWN_DEVICE_CAPABILITIES -from exo.helpers import DEBUG, DEBUG_DISCOVERY -from .tailscale_helpers import get_device_id, update_device_attributes, get_device_attributes, get_tailscale_devices, Device - - -class TailscaleDiscovery(Discovery): - def __init__( - self, - node_id: str, - node_port: int, - create_peer_handle: Callable[[str, str, str, DeviceCapabilities], PeerHandle], - discovery_interval: int = 5, - discovery_timeout: int = 30, - update_interval: int = 15, - device_capabilities: DeviceCapabilities = UNKNOWN_DEVICE_CAPABILITIES, - tailscale_api_key: str = None, - tailnet: str = None, - allowed_node_ids: List[str] = None, - ): - self.node_id = node_id - self.node_port = node_port - self.create_peer_handle = create_peer_handle - self.discovery_interval = discovery_interval - self.discovery_timeout = discovery_timeout - self.update_interval = update_interval - self.device_capabilities = device_capabilities - self.known_peers: Dict[str, Tuple[PeerHandle, float, float]] = {} - self.discovery_task = None - self.cleanup_task = None - self.tailscale_api_key = tailscale_api_key - self.tailnet = tailnet - self.allowed_node_ids = allowed_node_ids - self._device_id = None - self.update_task = None - - async def start(self): - self.device_capabilities = await device_capabilities() - self.discovery_task = asyncio.create_task(self.task_discover_peers()) - self.cleanup_task = asyncio.create_task(self.task_cleanup_peers()) - self.update_task = asyncio.create_task(self.task_update_device_posture_attributes()) - - async def task_update_device_posture_attributes(self): - while True: - try: - await self.update_device_posture_attributes() - if DEBUG_DISCOVERY >= 2: - print(f"Updated device posture attributes") - except Exception as e: - print(f"Error updating device posture attributes: {e}") - print(traceback.format_exc()) - finally: - await asyncio.sleep(self.update_interval) - - async def get_device_id(self): - if self._device_id: - return self._device_id - self._device_id = await get_device_id() - return self._device_id - - async def update_device_posture_attributes(self): - await update_device_attributes(await self.get_device_id(), self.tailscale_api_key, self.node_id, self.node_port, self.device_capabilities) - - async def task_discover_peers(self): - while True: - try: - devices: dict[str, Device] = await get_tailscale_devices(self.tailscale_api_key, self.tailnet) - current_time = time.time() - - active_devices = {name: device for name, device in devices.items() if device.last_seen is not None and (current_time - device.last_seen.timestamp()) < 30} - - if DEBUG_DISCOVERY >= 4: print(f"Found tailscale devices: {devices}") - if DEBUG_DISCOVERY >= 2: print(f"Active tailscale devices: {len(active_devices)}/{len(devices)}") - if DEBUG_DISCOVERY >= 2: print("Time since last seen tailscale devices", [(current_time - device.last_seen.timestamp()) for device in devices.values()]) - - for device in active_devices.values(): - if device.name == self.node_id: continue - peer_host = device.addresses[0] - peer_id, peer_port, device_capabilities = await get_device_attributes(device.device_id, self.tailscale_api_key) - if not peer_id: - if DEBUG_DISCOVERY >= 4: print(f"{device.device_id} does not have exo node attributes. skipping.") - continue - - if self.allowed_node_ids and peer_id not in self.allowed_node_ids: - if DEBUG_DISCOVERY >= 2: print(f"Ignoring peer {peer_id} as it's not in the allowed node IDs list") - continue - - if peer_id not in self.known_peers or self.known_peers[peer_id][0].addr() != f"{peer_host}:{peer_port}": - new_peer_handle = self.create_peer_handle(peer_id, f"{peer_host}:{peer_port}", "TS", device_capabilities) - if not await new_peer_handle.health_check(): - if DEBUG >= 1: print(f"Peer {peer_id} at {peer_host}:{peer_port} is not healthy. Skipping.") - continue - - if DEBUG >= 1: print(f"Adding {peer_id=} at {peer_host}:{peer_port}. Replace existing peer_id: {peer_id in self.known_peers}") - self.known_peers[peer_id] = ( - new_peer_handle, - current_time, - current_time, - ) - else: - if not await self.known_peers[peer_id][0].health_check(): - if DEBUG >= 1: print(f"Peer {peer_id} at {peer_host}:{peer_port} is not healthy. Removing.") - if peer_id in self.known_peers: del self.known_peers[peer_id] - continue - self.known_peers[peer_id] = (self.known_peers[peer_id][0], self.known_peers[peer_id][1], current_time) - - except Exception as e: - print(f"Error in discover peers: {e}") - print(traceback.format_exc()) - finally: - await asyncio.sleep(self.discovery_interval) - - async def stop(self): - if self.discovery_task: - self.discovery_task.cancel() - if self.cleanup_task: - self.cleanup_task.cancel() - if self.update_task: - self.update_task.cancel() - if self.discovery_task or self.cleanup_task or self.update_task: - await asyncio.gather(self.discovery_task, self.cleanup_task, self.update_task, return_exceptions=True) - - async def discover_peers(self, wait_for_peers: int = 0) -> List[PeerHandle]: - if wait_for_peers > 0: - while len(self.known_peers) < wait_for_peers: - if DEBUG_DISCOVERY >= 2: - print(f"Current peers: {len(self.known_peers)}/{wait_for_peers}. Waiting for more peers...") - await asyncio.sleep(0.1) - return [peer_handle for peer_handle, _, _ in self.known_peers.values()] - - async def task_cleanup_peers(self): - while True: - try: - current_time = time.time() - peers_to_remove = [] - - peer_ids = list(self.known_peers.keys()) - results = await asyncio.gather(*[self.check_peer(peer_id, current_time) for peer_id in peer_ids], return_exceptions=True) - - for peer_id, should_remove in zip(peer_ids, results): - if should_remove: peers_to_remove.append(peer_id) - - if DEBUG_DISCOVERY >= 2: - print( - "Peer statuses:", { - peer_handle.id(): f"is_connected={await peer_handle.is_connected()}, health_check={await peer_handle.health_check()}, connected_at={connected_at}, last_seen={last_seen}" - for peer_handle, connected_at, last_seen in self.known_peers.values() - } - ) - - for peer_id in peers_to_remove: - if peer_id in self.known_peers: - del self.known_peers[peer_id] - if DEBUG_DISCOVERY >= 2: print(f"Removed peer {peer_id} due to inactivity or failed health check.") - except Exception as e: - print(f"Error in cleanup peers: {e}") - print(traceback.format_exc()) - finally: - await asyncio.sleep(self.discovery_interval) - - async def check_peer(self, peer_id: str, current_time: float) -> bool: - peer_handle, connected_at, last_seen = self.known_peers.get(peer_id, (None, None, None)) - if peer_handle is None: return False - - try: - is_connected = await peer_handle.is_connected() - health_ok = await peer_handle.health_check() - except Exception as e: - if DEBUG_DISCOVERY >= 2: print(f"Error checking peer {peer_id}: {e}") - return True - - should_remove = ((not is_connected and current_time - connected_at > self.discovery_timeout) or (current_time - last_seen > self.discovery_timeout) or (not health_ok)) - return should_remove diff --git a/exo/networking/tailscale/tailscale_helpers.py b/exo/networking/tailscale/tailscale_helpers.py deleted file mode 100644 index 969a72da..00000000 --- a/exo/networking/tailscale/tailscale_helpers.py +++ /dev/null @@ -1,125 +0,0 @@ -import json -import asyncio -import aiohttp -import re -from typing import Dict, Any, Tuple, List, Optional -from exo.helpers import DEBUG_DISCOVERY -from exo.topology.device_capabilities import DeviceCapabilities, DeviceFlops -from datetime import datetime, timezone - - -class Device: - def __init__(self, device_id: str, name: str, addresses: List[str], last_seen: Optional[datetime] = None): - self.device_id = device_id - self.name = name - self.addresses = addresses - self.last_seen = last_seen - - @classmethod - def from_dict(cls, data: Dict[str, Any]) -> 'Device': - return cls(device_id=data.get('id', ''), name=data.get('name', ''), addresses=data.get('addresses', []), last_seen=cls.parse_datetime(data.get('lastSeen'))) - - @staticmethod - def parse_datetime(date_string: Optional[str]) -> Optional[datetime]: - if not date_string: - return None - return datetime.strptime(date_string, "%Y-%m-%dT%H:%M:%SZ").replace(tzinfo=timezone.utc) - - -async def get_device_id() -> str: - try: - process = await asyncio.create_subprocess_exec('tailscale', 'status', '--json', stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE) - stdout, stderr = await process.communicate() - if process.returncode != 0: - raise Exception(f"Command failed with exit code {process.returncode}: {stderr.decode().strip()}.") - if DEBUG_DISCOVERY >= 4: print(f"tailscale status: {stdout.decode()}") - data = json.loads(stdout.decode()) - return data['Self']['ID'] - except Exception as e: - raise Exception(f"{str(e)} Do you have the tailscale cli installed? See: https://tailscale.com/kb/1080/cli") - - -async def update_device_attributes(device_id: str, api_key: str, node_id: str, node_port: int, device_capabilities: DeviceCapabilities): - async with aiohttp.ClientSession() as session: - base_url = f"https://api.tailscale.com/api/v2/device/{device_id}/attributes" - headers = {'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json'} - - attributes = { - "custom:exo_node_id": node_id.replace('-', '_'), "custom:exo_node_port": node_port, "custom:exo_device_capability_chip": sanitize_attribute(device_capabilities.chip), - "custom:exo_device_capability_model": sanitize_attribute(device_capabilities.model), "custom:exo_device_capability_memory": str(device_capabilities.memory), - "custom:exo_device_capability_flops_fp16": str(device_capabilities.flops.fp16), "custom:exo_device_capability_flops_fp32": str(device_capabilities.flops.fp32), - "custom:exo_device_capability_flops_int8": str(device_capabilities.flops.int8) - } - - for attr_name, attr_value in attributes.items(): - url = f"{base_url}/{attr_name}" - data = {"value": str(attr_value).replace(' ', '_')} # Ensure all values are strings for JSON - async with session.post(url, headers=headers, json=data) as response: - if response.status == 200: - if DEBUG_DISCOVERY >= 1: print(f"Updated device posture attribute {attr_name} for device {device_id}") - else: - print(f"Failed to update device posture attribute {attr_name}: {response.status} {await response.text()}") - - -async def get_device_attributes(device_id: str, api_key: str) -> Tuple[str, int, DeviceCapabilities]: - async with aiohttp.ClientSession() as session: - url = f"https://api.tailscale.com/api/v2/device/{device_id}/attributes" - headers = {'Authorization': f'Bearer {api_key}'} - async with session.get(url, headers=headers) as response: - if response.status == 200: - data = await response.json() - attributes = data.get("attributes", {}) - node_id = attributes.get("custom:exo_node_id", "").replace('_', '-') - node_port = int(attributes.get("custom:exo_node_port", 0)) - device_capabilities = DeviceCapabilities( - model=attributes.get("custom:exo_device_capability_model", "").replace('_', ' '), - chip=attributes.get("custom:exo_device_capability_chip", "").replace('_', ' '), - memory=int(attributes.get("custom:exo_device_capability_memory", 0)), - flops=DeviceFlops( - fp16=float(attributes.get("custom:exo_device_capability_flops_fp16", 0)), - fp32=float(attributes.get("custom:exo_device_capability_flops_fp32", 0)), - int8=float(attributes.get("custom:exo_device_capability_flops_int8", 0)) - ) - ) - return node_id, node_port, device_capabilities - else: - print(f"Failed to fetch posture attributes for {device_id}: {response.status}") - return "", 0, DeviceCapabilities(model="", chip="", memory=0, flops=DeviceFlops(fp16=0, fp32=0, int8=0)) - - -def parse_device_attributes(data: Dict[str, str]) -> Dict[str, Any]: - result = {} - prefix = "custom:exo_" - for key, value in data.items(): - if key.startswith(prefix): - attr_name = key.replace(prefix, "") - if attr_name in ["node_id", "node_port", "device_capability_chip", "device_capability_model"]: - result[attr_name] = value.replace('_', ' ') - elif attr_name in ["device_capability_memory", "device_capability_flops_fp16", "device_capability_flops_fp32", "device_capability_flops_int8"]: - result[attr_name] = float(value) - return result - - -def sanitize_attribute(value: str) -> str: - # Replace invalid characters with underscores - sanitized_value = re.sub(r'[^a-zA-Z0-9_.]', '_', value) - # Truncate to 50 characters - return sanitized_value[:50] - - -async def get_tailscale_devices(api_key: str, tailnet: str) -> Dict[str, Device]: - async with aiohttp.ClientSession() as session: - url = f"https://api.tailscale.com/api/v2/tailnet/{tailnet}/devices" - headers = {"Authorization": f"Bearer {api_key}"} - - async with session.get(url, headers=headers) as response: - response.raise_for_status() - data = await response.json() - - devices = {} - for device_data in data.get("devices", []): - print("Device data: ", device_data) - device = Device.from_dict(device_data) - devices[device.name] = device - - return devices diff --git a/exo/networking/tailscale/test_tailscale_discovery.py b/exo/networking/tailscale/test_tailscale_discovery.py deleted file mode 100644 index 63cc9d04..00000000 --- a/exo/networking/tailscale/test_tailscale_discovery.py +++ /dev/null @@ -1,43 +0,0 @@ -import os -import asyncio -import unittest -from unittest import mock -from exo.networking.tailscale.tailscale_discovery import TailscaleDiscovery -from exo.networking.peer_handle import PeerHandle - - -class TestTailscaleDiscovery(unittest.IsolatedAsyncioTestCase): - async def asyncSetUp(self): - self.tailscale_api_key = os.environ.get("TAILSCALE_API_KEY", "") - self.tailnet = os.environ.get("TAILSCALE_TAILNET", "") - self.discovery = TailscaleDiscovery( - node_id="test_node", - node_port=50051, - create_peer_handle=lambda peer_id, address, description, device_capabilities: unittest.mock.Mock(spec=PeerHandle, id=lambda: peer_id), - tailscale_api_key=self.tailscale_api_key, - tailnet=self.tailnet - ) - await self.discovery.start() - - async def asyncTearDown(self): - await self.discovery.stop() - - async def test_discovery(self): - # Wait for a short period to allow discovery to happen - await asyncio.sleep(15) - - # Get discovered peers - peers = await self.discovery.discover_peers() - - # Check if any peers were discovered - self.assertGreater(len(peers), 0, "No peers were discovered") - - # Print discovered peers for debugging - print(f"Discovered peers: {[peer.id() for peer in peers]}") - - # Check if discovered peers are instances of GRPCPeerHandle - print(peers) - - -if __name__ == '__main__': - unittest.main() diff --git a/exo/networking/udp/__init__.py b/exo/networking/udp/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/exo/networking/udp/test_udp_discovery.py b/exo/networking/udp/test_udp_discovery.py deleted file mode 100644 index ff1dc5c2..00000000 --- a/exo/networking/udp/test_udp_discovery.py +++ /dev/null @@ -1,77 +0,0 @@ -import asyncio -import unittest -from unittest import mock -from exo.networking.udp.udp_discovery import UDPDiscovery -from exo.networking.grpc.grpc_peer_handle import GRPCPeerHandle -from exo.networking.grpc.grpc_server import GRPCServer -from exo.orchestration.node import Node - - -class TestUDPDiscovery(unittest.IsolatedAsyncioTestCase): - async def asyncSetUp(self): - self.peer1 = mock.AsyncMock() - self.peer2 = mock.AsyncMock() - self.peer1.connect = mock.AsyncMock() - self.peer2.connect = mock.AsyncMock() - self.discovery1 = UDPDiscovery("discovery1", 50051, 5678, 5679, create_peer_handle=lambda peer_id, address, description, device_capabilities: self.peer1) - self.discovery2 = UDPDiscovery("discovery2", 50052, 5679, 5678, create_peer_handle=lambda peer_id, address, description, device_capabilities: self.peer2) - await self.discovery1.start() - await self.discovery2.start() - - async def asyncTearDown(self): - await self.discovery1.stop() - await self.discovery2.stop() - - async def test_discovery(self): - peers1 = await self.discovery1.discover_peers(wait_for_peers=1) - assert len(peers1) == 1 - peers2 = await self.discovery2.discover_peers(wait_for_peers=1) - assert len(peers2) == 1 - - # connect has to be explicitly called after discovery - self.peer1.connect.assert_not_called() - self.peer2.connect.assert_not_called() - - -class TestUDPDiscoveryWithGRPCPeerHandle(unittest.IsolatedAsyncioTestCase): - async def asyncSetUp(self): - self.node1 = mock.AsyncMock(spec=Node) - self.node2 = mock.AsyncMock(spec=Node) - self.server1 = GRPCServer(self.node1, "localhost", 50053) - self.server2 = GRPCServer(self.node2, "localhost", 50054) - await self.server1.start() - await self.server2.start() - self.discovery1 = UDPDiscovery("discovery1", 50053, 5678, 5679, lambda peer_id, address, description, device_capabilities: GRPCPeerHandle(peer_id, address, description, device_capabilities)) - self.discovery2 = UDPDiscovery("discovery2", 50054, 5679, 5678, lambda peer_id, address, description, device_capabilities: GRPCPeerHandle(peer_id, address, description, device_capabilities)) - await self.discovery1.start() - await self.discovery2.start() - - async def asyncTearDown(self): - await self.discovery1.stop() - await self.discovery2.stop() - await self.server1.stop() - await self.server2.stop() - - async def test_grpc_discovery(self): - peers1 = await self.discovery1.discover_peers(wait_for_peers=1) - assert len(peers1) == 1 - peers2 = await self.discovery2.discover_peers(wait_for_peers=1) - assert len(peers2) == 1 - assert not await peers1[0].is_connected() - assert not await peers2[0].is_connected() - - # Connect - await peers1[0].connect() - await peers2[0].connect() - assert await peers1[0].is_connected() - assert await peers2[0].is_connected() - - # Kill server1 - await self.server1.stop() - - assert await peers1[0].is_connected() - assert not await peers2[0].is_connected() - - -if __name__ == "__main__": - asyncio.run(unittest.main()) diff --git a/exo/networking/udp/udp_discovery.py b/exo/networking/udp/udp_discovery.py deleted file mode 100644 index 7117c7cd..00000000 --- a/exo/networking/udp/udp_discovery.py +++ /dev/null @@ -1,246 +0,0 @@ -import asyncio -import json -import socket -import time -import traceback -from typing import List, Dict, Callable, Tuple, Coroutine, Optional -from exo.networking.discovery import Discovery -from exo.networking.peer_handle import PeerHandle -from exo.topology.device_capabilities import DeviceCapabilities, device_capabilities, UNKNOWN_DEVICE_CAPABILITIES -from exo.helpers import DEBUG, DEBUG_DISCOVERY, get_all_ip_addresses_and_interfaces, get_interface_priority_and_type - - -class ListenProtocol(asyncio.DatagramProtocol): - def __init__(self, on_message: Callable[[bytes, Tuple[str, int]], Coroutine]): - super().__init__() - self.on_message = on_message - self.loop = asyncio.get_event_loop() - - def connection_made(self, transport): - self.transport = transport - - def datagram_received(self, data, addr): - asyncio.create_task(self.on_message(data, addr)) - - -def get_broadcast_address(ip_addr: str) -> str: - try: - # Split IP into octets and create broadcast address for the subnet - ip_parts = ip_addr.split('.') - return f"{ip_parts[0]}.{ip_parts[1]}.{ip_parts[2]}.255" - except: - return "255.255.255.255" - - -class BroadcastProtocol(asyncio.DatagramProtocol): - def __init__(self, message: str, broadcast_port: int, source_ip: str): - self.message = message - self.broadcast_port = broadcast_port - self.source_ip = source_ip - - def connection_made(self, transport): - sock = transport.get_extra_info("socket") - sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1) - # Try both subnet-specific and global broadcast - broadcast_addr = get_broadcast_address(self.source_ip) - transport.sendto(self.message.encode("utf-8"), (broadcast_addr, self.broadcast_port)) - if broadcast_addr != "255.255.255.255": - transport.sendto(self.message.encode("utf-8"), ("255.255.255.255", self.broadcast_port)) - - -class UDPDiscovery(Discovery): - def __init__( - self, - node_id: str, - node_port: int, - listen_port: int, - broadcast_port: int, - create_peer_handle: Callable[[str, str, str, DeviceCapabilities], PeerHandle], - broadcast_interval: int = 2.5, - discovery_timeout: int = 30, - device_capabilities: DeviceCapabilities = UNKNOWN_DEVICE_CAPABILITIES, - allowed_node_ids: Optional[List[str]] = None, - allowed_interface_types: Optional[List[str]] = None, - ): - self.node_id = node_id - self.node_port = node_port - self.listen_port = listen_port - self.broadcast_port = broadcast_port - self.create_peer_handle = create_peer_handle - self.broadcast_interval = broadcast_interval - self.discovery_timeout = discovery_timeout - self.device_capabilities = device_capabilities - self.allowed_node_ids = allowed_node_ids - self.allowed_interface_types = allowed_interface_types - self.known_peers: Dict[str, Tuple[PeerHandle, float, float, int]] = {} - self.broadcast_task = None - self.listen_task = None - self.cleanup_task = None - - async def start(self): - self.device_capabilities = await device_capabilities() - self.broadcast_task = asyncio.create_task(self.task_broadcast_presence()) - self.listen_task = asyncio.create_task(self.task_listen_for_peers()) - self.cleanup_task = asyncio.create_task(self.task_cleanup_peers()) - - async def stop(self): - if self.broadcast_task: self.broadcast_task.cancel() - if self.listen_task: self.listen_task.cancel() - if self.cleanup_task: self.cleanup_task.cancel() - if self.broadcast_task or self.listen_task or self.cleanup_task: - await asyncio.gather(self.broadcast_task, self.listen_task, self.cleanup_task, return_exceptions=True) - - async def discover_peers(self, wait_for_peers: int = 0) -> List[PeerHandle]: - if wait_for_peers > 0: - while len(self.known_peers) < wait_for_peers: - if DEBUG_DISCOVERY >= 2: print(f"Current peers: {len(self.known_peers)}/{wait_for_peers}. Waiting for more peers...") - await asyncio.sleep(0.1) - return [peer_handle for peer_handle, _, _, _ in self.known_peers.values()] - - async def task_broadcast_presence(self): - while True: - for addr, interface_name in get_all_ip_addresses_and_interfaces(): - interface_priority, interface_type = await get_interface_priority_and_type(interface_name) - message = json.dumps({ - "type": "discovery", - "node_id": self.node_id, - "grpc_port": self.node_port, - "device_capabilities": self.device_capabilities.to_dict(), - "priority": interface_priority, - "interface_name": interface_name, - "interface_type": interface_type, - }) - - transport = None - try: - sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) - sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1) - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - try: - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) - except AttributeError: - pass - sock.bind((addr, 0)) - - transport, _ = await asyncio.get_event_loop().create_datagram_endpoint( - lambda: BroadcastProtocol(message, self.broadcast_port, addr), - sock=sock - ) - except Exception as e: - print(f"Error in broadcast presence ({addr} - {interface_name} - {interface_priority}): {e}") - finally: - if transport: - try: transport.close() - except Exception as e: - if DEBUG_DISCOVERY >= 2: print(f"Error closing transport: {e}") - - await asyncio.sleep(self.broadcast_interval) - - async def on_listen_message(self, data, addr): - if not data: - return - - decoded_data = data.decode("utf-8", errors="ignore") - - # Check if the decoded data starts with a valid JSON character - if not (decoded_data.strip() and decoded_data.strip()[0] in "{["): - if DEBUG_DISCOVERY >= 2: print(f"Received invalid JSON data from {addr}: {decoded_data[:100]}") - return - - try: - decoder = json.JSONDecoder(strict=False) - message = decoder.decode(decoded_data) - except json.JSONDecodeError as e: - if DEBUG_DISCOVERY >= 2: print(f"Error decoding JSON data from {addr}: {e}") - return - - if DEBUG_DISCOVERY >= 2: print(f"received from peer {addr}: {message}") - - if message["type"] == "discovery" and message["node_id"] != self.node_id: - peer_id = message["node_id"] - - # Skip if peer_id is not in allowed list - if self.allowed_node_ids and peer_id not in self.allowed_node_ids: - if DEBUG_DISCOVERY >= 2: print(f"Ignoring peer {peer_id} as it's not in the allowed node IDs list") - return - - peer_host = addr[0] - peer_port = message["grpc_port"] - peer_prio = message["priority"] - peer_interface_name = message["interface_name"] - peer_interface_type = message["interface_type"] - - # Skip if interface type is not in allowed list - if self.allowed_interface_types and peer_interface_type not in self.allowed_interface_types: - if DEBUG_DISCOVERY >= 2: print(f"Ignoring peer {peer_id} as its interface type {peer_interface_type} is not in the allowed interface types list") - return - - device_capabilities = DeviceCapabilities(**message["device_capabilities"]) - - if peer_id not in self.known_peers or self.known_peers[peer_id][0].addr() != f"{peer_host}:{peer_port}": - if peer_id in self.known_peers: - existing_peer_prio = self.known_peers[peer_id][3] - if existing_peer_prio >= peer_prio: - if DEBUG >= 1: - print(f"Ignoring peer {peer_id} at {peer_host}:{peer_port} with priority {peer_prio} because we already know about a peer with higher or equal priority: {existing_peer_prio}") - return - new_peer_handle = self.create_peer_handle(peer_id, f"{peer_host}:{peer_port}", f"{peer_interface_type} ({peer_interface_name})", device_capabilities) - if not await new_peer_handle.health_check(): - if DEBUG >= 1: print(f"Peer {peer_id} at {peer_host}:{peer_port} is not healthy. Skipping.") - return - if DEBUG >= 1: print(f"Adding {peer_id=} at {peer_host}:{peer_port}. Replace existing peer_id: {peer_id in self.known_peers}") - self.known_peers[peer_id] = (new_peer_handle, time.time(), time.time(), peer_prio) - else: - if not await self.known_peers[peer_id][0].health_check(): - if DEBUG >= 1: print(f"Peer {peer_id} at {peer_host}:{peer_port} is not healthy. Removing.") - if peer_id in self.known_peers: del self.known_peers[peer_id] - return - if peer_id in self.known_peers: self.known_peers[peer_id] = (self.known_peers[peer_id][0], self.known_peers[peer_id][1], time.time(), peer_prio) - - async def task_listen_for_peers(self): - await asyncio.get_event_loop().create_datagram_endpoint(lambda: ListenProtocol(self.on_listen_message), local_addr=("0.0.0.0", self.listen_port)) - if DEBUG_DISCOVERY >= 2: print("Started listen task") - - async def task_cleanup_peers(self): - while True: - try: - current_time = time.time() - peers_to_remove = [] - - peer_ids = list(self.known_peers.keys()) - results = await asyncio.gather(*[self.check_peer(peer_id, current_time) for peer_id in peer_ids], return_exceptions=True) - - for peer_id, should_remove in zip(peer_ids, results): - if should_remove: peers_to_remove.append(peer_id) - - if DEBUG_DISCOVERY >= 2: - print( - "Peer statuses:", { - peer_handle.id(): f"is_connected={await peer_handle.is_connected()}, health_check={await peer_handle.health_check()}, connected_at={connected_at}, last_seen={last_seen}, prio={prio}" - for peer_handle, connected_at, last_seen, prio in self.known_peers.values() - } - ) - - for peer_id in peers_to_remove: - if peer_id in self.known_peers: - del self.known_peers[peer_id] - if DEBUG_DISCOVERY >= 2: print(f"Removed peer {peer_id} due to inactivity or failed health check.") - except Exception as e: - print(f"Error in cleanup peers: {e}") - print(traceback.format_exc()) - finally: - await asyncio.sleep(self.broadcast_interval) - - async def check_peer(self, peer_id: str, current_time: float) -> bool: - peer_handle, connected_at, last_seen, prio = self.known_peers.get(peer_id, (None, None, None, None)) - if peer_handle is None: return False - - try: - is_connected = await peer_handle.is_connected() - health_ok = await peer_handle.health_check() - except Exception as e: - if DEBUG_DISCOVERY >= 2: print(f"Error checking peer {peer_id}: {e}") - return True - - should_remove = ((not is_connected and current_time - connected_at > self.discovery_timeout) or (current_time - last_seen > self.discovery_timeout) or (not health_ok)) - return should_remove diff --git a/exo/orchestration/__init__.py b/exo/orchestration/__init__.py deleted file mode 100644 index 022a9160..00000000 --- a/exo/orchestration/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .node import Node - -__all__ = ["Node"] diff --git a/exo/orchestration/node.py b/exo/orchestration/node.py deleted file mode 100644 index 1281aa8a..00000000 --- a/exo/orchestration/node.py +++ /dev/null @@ -1,628 +0,0 @@ -import numpy as np -import json -import asyncio -import uuid -import time -import traceback -from typing import List, Dict, Optional, Tuple, Union, Set -from exo.networking import Discovery, PeerHandle, Server -from exo.inference.inference_engine import InferenceEngine, Shard -from exo.topology.topology import Topology -from exo.topology.device_capabilities import device_capabilities, UNKNOWN_DEVICE_CAPABILITIES -from exo.topology.partitioning_strategy import Partition, PartitioningStrategy, map_partitions_to_shards -from exo import DEBUG -from exo.helpers import AsyncCallbackSystem -from exo.viz.topology_viz import TopologyViz -from exo.download.download_progress import RepoProgressEvent -from exo.inference.inference_engine import get_inference_engine, InferenceEngine -from exo.download.shard_download import ShardDownloader - -class Node: - def __init__( - self, - _id: str, - server: Server, - inference_engine: InferenceEngine, - discovery: Discovery, - shard_downloader: ShardDownloader, - partitioning_strategy: PartitioningStrategy = None, - max_generate_tokens: int = 1024, - default_sample_temperature: float = 0.0, - topology_viz: Optional[TopologyViz] = None, - ): - self.id = _id - self.inference_engine = inference_engine - self.server = server - self.discovery = discovery - self.shard_downloader = shard_downloader - self.partitioning_strategy = partitioning_strategy - self.peers: List[PeerHandle] = {} - self.topology: Topology = Topology() - self.device_capabilities = UNKNOWN_DEVICE_CAPABILITIES - self.buffered_token_output: Dict[str, Tuple[List[int], bool]] = {} - self.buffered_logits: Dict[str, List[np.ndarray]] = {} - self.buffered_inputs: Dict[str, List[np.ndarray]] = {} - self.buffered_partials: Dict[str, List[np.ndarray]] = {} - self.checkpoints: Dict[str, Dict[str, int]] = {} - - self.max_generate_tokens = max_generate_tokens - self.topology_viz = topology_viz - self.default_sample_temperature = default_sample_temperature - self._on_token = AsyncCallbackSystem[str, Tuple[str, List[int], bool]]() - self._on_opaque_status = AsyncCallbackSystem[str, Tuple[str, str]]() - self._on_opaque_status.register("node_status").on_next(self.on_node_status) - self.node_download_progress: Dict[str, RepoProgressEvent] = {} - self.topology_inference_engines_pool: List[List[str]] = [] - self.outstanding_requests = {} - - async def start(self, wait_for_peers: int = 0) -> None: - self.device_capabilities = await device_capabilities() - await self.server.start() - await self.discovery.start() - await self.update_peers(wait_for_peers) - await self.collect_topology(set()) - if DEBUG >= 2: print(f"Collected topology: {self.topology}") - asyncio.create_task(self.periodic_topology_collection(2.0)) - - async def stop(self) -> None: - await self.discovery.stop() - await self.server.stop() - - def on_node_status(self, request_id, opaque_status): - try: - status_data = json.loads(opaque_status) - status_type = status_data.get("type", "") - if status_type == "supported_inference_engines": - node_id = status_data.get("node_id") - engines = status_data.get("engines", []) - self.topology_inference_engines_pool.append(engines) - elif status_type == "node_status": - if status_data.get("status", "").startswith("start_"): - self.current_topology.active_node_id = status_data.get("node_id") - elif status_data.get("status", "").startswith("end_"): - if status_data.get("node_id") == self.current_topology.active_node_id: - self.current_topology.active_node_id = None - - download_progress = None - if status_type == "download_progress": - if DEBUG >= 8: print(f"Download progress from {status_data.get('node_id')}: {status_data.get('progress')}") - download_progress = RepoProgressEvent.from_dict(status_data.get('progress')) - self.node_download_progress[status_data.get('node_id')] = download_progress - - if self.topology_viz: - self.topology_viz.update_visualization(self.topology, self.partitioning_strategy.partition(self.topology), self.id, self.node_download_progress) - except Exception as e: - if DEBUG >= 1: print(f"Error on_node_status: {e}") - if DEBUG >= 1: traceback.print_exc() - - def get_supported_inference_engines(self): - supported_engine_names = [] - if self.inference_engine.__class__.__name__ == 'MLXDynamicShardInferenceEngine': - supported_engine_names.append('mlx') - supported_engine_names.append('tinygrad') - else: - supported_engine_names.append('tinygrad') - return supported_engine_names - - async def broadcast_supported_engines(self, supported_engines_names: List[str]): - status_message = json.dumps({"type": "supported_inference_engines", "node_id": self.id, "engines": supported_engines_names}) - await self.broadcast_opaque_status("", status_message) - - def get_topology_inference_engines(self) -> List[List[str]]: - return self.topology_inference_engines_pool - - token_count = 0 - first_token_time = 0 - async def process_inference_result( - self, - shard, - result: np.ndarray, - request_id: Optional[str] = None, - inference_state: Optional[dict] = None, - ): - if shard.model_id != 'stable-diffusion-2-1-base': - if request_id not in self.buffered_token_output: - self.buffered_token_output[request_id] = ([], False) - is_finished = len(self.buffered_token_output[request_id][0]) >= self.max_generate_tokens - if shard.is_last_layer() and not is_finished: - token = await self.inference_engine.sample(result, temp=self.default_sample_temperature) - await self.inference_engine.ensure_shard(shard) - self.buffered_token_output[request_id][0].append(token.item()) - is_finished = token.item() == self.inference_engine.tokenizer.eos_token_id or is_finished or len(self.buffered_token_output[request_id][0]) >= self.max_generate_tokens - if DEBUG >= 2: print(f"[{request_id}] result size: {result.size}, is finished: {is_finished}, buffered tokens: {len(self.buffered_token_output[request_id][0])}") - forward = token.reshape(1, -1) - intermediate_result = [self.buffered_token_output[request_id][0][-1]] - else: - forward = result - else: - await self.inference_engine.ensure_shard(shard) - is_finished = inference_state.get("is_finished", False) - intermediate_result, inference_state = self.handle_stable_diffusion(inference_state, result) - forward = result - if shard.is_last_layer(): - self.trigger_on_token_callbacks(request_id, intermediate_result, is_finished) - asyncio.create_task(self.broadcast_result(request_id, intermediate_result, is_finished)) - - if is_finished: - if shard.model_id != 'stable-diffusion-2-1-base': - self.buffered_token_output[request_id] = (self.buffered_token_output[request_id][0], True) - self.outstanding_requests.pop(request_id) - else: - self.outstanding_requests[request_id] = "waiting" - asyncio.create_task(self.forward_tensor(shard, forward, request_id, self.get_partition_index(offset = 1), inference_state)) - - return np.array(self.buffered_token_output[request_id][0]) if shard.model_id != 'stable-diffusion-2-1-base' else intermediate_result - - - async def process_prompt( - self, - base_shard: Shard, - prompt: str, - request_id: Optional[str] = None, - inference_state: Optional[dict] = {}, - ) -> Optional[np.ndarray]: - shard = self.get_current_shard(base_shard) - start_time = time.perf_counter_ns() - asyncio.create_task( - self.broadcast_opaque_status( - request_id, - json.dumps({ - "type": "node_status", - "node_id": self.id, - "status": "start_process_prompt", - "base_shard": base_shard.to_dict(), - "shard": shard.to_dict(), - "prompt": prompt, - "request_id": request_id, - }), - ) - ) - start_time = time.perf_counter_ns() - resp = await self._process_prompt(base_shard, prompt, request_id, inference_state) - end_time = time.perf_counter_ns() - elapsed_time_ns = end_time - start_time - asyncio.create_task( - self.broadcast_opaque_status( - request_id, - json.dumps({ - "type": "node_status", - "node_id": self.id, - "status": "end_process_prompt", - "base_shard": base_shard.to_dict(), - "shard": shard.to_dict(), - "prompt": prompt, - "request_id": request_id, - "elapsed_time_ns": elapsed_time_ns, - }), - ) - ) - if DEBUG >= 2: print(f"[{request_id}] process prompt: {base_shard=} {shard=} {prompt=} {elapsed_time_ns=}") - - async def _process_prompt(self, base_shard: Shard, prompt: str, request_id: Optional[str] = None, inference_state: Optional[dict] = None) -> Optional[np.ndarray]: - if request_id is None: - request_id = str(uuid.uuid4()) - shard = self.get_current_shard(base_shard) - if DEBUG >= 2: print(f"[{request_id}] process prompt: {base_shard=} {shard=} {prompt=}") - - if not shard.is_first_layer(): - if DEBUG >= 2: print(f"[{request_id}] forwarding to next shard: {base_shard=} {shard=} {prompt=}") - self.outstanding_requests[request_id] = "waiting" - resp = await self.forward_prompt(shard, prompt, request_id, 0, inference_state) - return None - else: - self.outstanding_requests[request_id] = "processing" - result, inference_state = await self.inference_engine.infer_prompt(request_id, shard, prompt, inference_state) - ret = await self.process_inference_result(shard, result, request_id, inference_state) - return result - - async def enqueue_example( - self, - base_shard: Shard, - example: np.ndarray, - target: np.ndarray, - length: np.ndarray, - request_id: Optional[str] = None, - train: bool = False, - ): - shard = self.get_current_shard(base_shard) - if shard.is_first_layer(): - loss = await self.process_example(shard, example, target, length, train, request_id) - return loss - else: - if request_id is None: - request_id = str(uuid.uuid4()) - self.outstanding_requests[request_id] = "waiting" - loss = await self.forward_example(shard, example, target, length, train, request_id, 0) - return loss - - async def coordinate_save( - self, - base_shard: Shard, - iteration: int, - destination: str, - ): - shard = self.get_current_shard(base_shard) - model = shard.model_id - sid = shard.__hash__() - path = f"{destination}/{model}/{sid}-{iteration}.safetensors" - self.outstanding_requests[f"{sid}::{iteration}"] = "Checking" - if model not in self.checkpoints: - self.checkpoints[model] = {} - if sid not in self.checkpoints[model]: - self.checkpoints[model][sid] = [] - if len(self.checkpoints[model][sid]) < 1 or self.checkpoints[model][sid][-1] < iteration: - print(f"Saving checkpoint to {path}") - self.outstanding_requests[f"{sid}::{iteration}"] = "Saving" - import os - os.makedirs("/".join(path.split("/")[:-1]), exist_ok=True) - await self.inference_engine.save_checkpoint(shard, path) - self.checkpoints[model][sid] = sorted(self.checkpoints[model][sid] + [iteration]) - self.outstanding_requests.pop(f"{sid}::{iteration}") - - async def process_example( - self, - base_shard: Shard, - example: np.ndarray, - target: np.ndarray, - length: np.ndarray, - train: bool = False, - request_id: Optional[str] = None, - ): - shard = self.get_current_shard(base_shard) - asyncio.create_task( - self.broadcast_opaque_status( - request_id, - json.dumps({ - "type": "node_status", - "node_id": self.id, - "status": f"start_{'train' if train else 'eval'}_example", - "base_shard": base_shard.to_dict(), - "shard": shard.to_dict(), - "example_size": example.size, - "example_shape": example.shape, - "request_id": request_id, - }), - ) - ) - start_time = time.perf_counter_ns() - resp = await self._process_example(shard, example, target, length, train, request_id) - end_time = time.perf_counter_ns() - elapsed_time_ns = end_time - start_time - asyncio.create_task( - self.broadcast_opaque_status( - request_id, - json.dumps({ - "type": "node_status", - "node_id": self.id, - "status": f"end_{'train' if train else 'eval'}_example", - "base_shard": base_shard.to_dict(), - "shard": shard.to_dict(), - "request_id": request_id, - "elapsed_time_ns": elapsed_time_ns, - }), - ) - ) - return resp - - async def _process_example( - self, - base_shard: Shard, - example: np.ndarray, - target: np.ndarray, - length: np.ndarray, - train: bool = False, - request_id: Optional[str] = None, - ) -> Optional[np.ndarray]: - if request_id is None: - request_id = str(uuid.uuid4()) - shard = self.get_current_shard(base_shard) - if DEBUG >= 1: print(f"[{request_id}] process_example: {example.shape=}") - try: - target = target.astype(int) - if train: - if shard.is_last_layer(): - self.outstanding_requests[request_id] = "training" - loss, grad = await self.inference_engine.train(request_id, shard, example, target, length) - else: - self.outstanding_requests[request_id] = "preprocessing" - step, _ = await self.inference_engine.infer_tensor(request_id, shard, example) - self.outstanding_requests[request_id] = "waiting" - loss, backgrad = await self.forward_example(shard, step, target, length, train, request_id, self.get_partition_index(offset = 1)) - self.outstanding_requests[request_id] = "training" - partial_loss, grad = await self.inference_engine.train(request_id, shard, example, backgrad, length, loss="back_gradient") - self.outstanding_requests.pop(request_id) - if shard.is_first_layer(): - return loss - else: - return loss, grad - else: - if shard.is_last_layer(): - self.outstanding_requests[request_id] = "evaluating" - loss = await self.inference_engine.evaluate(request_id, shard, example, target, length) - else: - self.outstanding_requests[request_id] = "preprocessing" - step, _ = await self.inference_engine.infer_tensor(request_id, shard, example) - self.outstanding_requests[request_id] = "waiting" - loss = await self.forward_example(shard, step, target, length, train, request_id, self.get_partition_index(offset = 1)) - self.outstanding_requests.pop(request_id) - return loss - except Exception as e: - self.outstanding_requests.pop(request_id) - print(f"Error processing example for shard {shard}: {e}") - traceback.print_exc() - return None - - async def process_tensor( - self, - base_shard: Shard, - tensor: np.ndarray, - request_id: Optional[str] = None, - inference_state: Optional[dict] = None, - ) -> Optional[np.ndarray]: - shard = self.get_current_shard(base_shard) - start_time = time.perf_counter_ns() - resp = await self._process_tensor(shard, tensor, request_id, inference_state) - end_time = time.perf_counter_ns() - elapsed_time_ns = end_time - start_time - if DEBUG >= 2: print(f"[{request_id}] process_tensor: {base_shard=} {shard=} {tensor.size=} {tensor.shape=} {elapsed_time_ns=}") - - async def _process_tensor( - self, - base_shard: Shard, - tensor: np.ndarray, - request_id: Optional[str] = None, - inference_state: Optional[dict] = None, - ) -> Optional[np.ndarray]: - if request_id is None: - request_id = str(uuid.uuid4()) - shard = self.get_current_shard(base_shard) - - try: - self.outstanding_requests[request_id] = "processing" - result, inference_state = await self.inference_engine.infer_tensor(request_id, shard, tensor, inference_state) - ret = await self.process_inference_result(shard, result, request_id, inference_state) - return ret - except Exception as e: - self.outstanding_requests.pop(request_id) - print(f"Error processing tensor for shard {shard}: {e}") - traceback.print_exc() - - async def forward_example( - self, - base_shard: Shard, - step: np.ndarray, - target: np.ndarray, - length: np.ndarray, - train: bool, - request_id: str, - target_index: int, - ) -> None: - if DEBUG >= 1: print(f"target partition index: {target_index}") - target_id = self.partitioning_strategy.partition(self.topology)[target_index].node_id - target_shard = self.get_current_shard(base_shard, target_index) - if DEBUG >= 2: print(f"computed target from: {base_shard} {target_index}, {self.topology}. target shard: {target_shard}") - target_peer = next((p for p in self.peers if p.id() == target_id), None) - if not target_peer: - raise ValueError(f"peer for {target_index} not found") - if DEBUG >= 1: print(f"sending example to {target_peer.id()}: {step} => {target} ({length})") - resp = await target_peer.send_example(target_shard, step, target, length, request_id=request_id, train=train) - return resp - - async def forward_prompt( - self, - base_shard: Shard, - prompt: str, - request_id: str, - target_index: int, - inference_state: Optional[dict] = None, - ) -> None: - if DEBUG >= 1: print(f"target partition index: {target_index}") - target_id = self.partitioning_strategy.partition(self.topology)[target_index].node_id - next_shard = self.get_current_shard(base_shard, target_index) - if DEBUG >= 2: print(f"Computed target from: {base_shard} {target_index}, {self.topology}. next shard: {next_shard}") - if target_id == self.id: - await self.process_prompt(next_shard, prompt, request_id, inference_state) - else: - target_peer = next((p for p in self.peers if p.id() == target_id), None) - if not target_peer: - raise ValueError(f"Peer for {target_index} not found") - if DEBUG >= 1: print(f"Sending prompt to {target_peer.id()}: {prompt}") - await target_peer.send_prompt(next_shard, prompt, request_id=request_id, inference_state=inference_state) - - async def forward_tensor( - self, - base_shard: Shard, - tensor: np.ndarray, - request_id: str, - target_index: int, - inference_state: Optional[dict] = None, - ) -> None: - if DEBUG >= 1: print(f"target partition index: {target_index}") - target_id = self.partitioning_strategy.partition(self.topology)[target_index].node_id - next_shard = self.get_current_shard(base_shard, target_index) - if DEBUG >= 2: print(f"Computed target from: {base_shard} {target_index}, {self.topology}. target shard: {next_shard}") - if target_id == self.id: - await self.process_tensor(next_shard, tensor, request_id, inference_state) - else: - target_peer = next((p for p in self.peers if p.id() == target_id), None) - if not target_peer: - raise ValueError(f"Peer for {target_index} not found") - if DEBUG >= 1: print(f"Sending tensor to {target_peer.id()}: {tensor}") - await target_peer.send_tensor(next_shard, tensor, request_id=request_id, inference_state=inference_state) - - def get_partition_index(self, offset: int = 0): - if not self.partitioning_strategy: - if DEBUG >= 1: print("No partitioning strategy found. Skipping forward.") - return None - partitions = self.partitioning_strategy.partition(self.topology) - current_partition_index = next((i for i, p in enumerate(partitions) if p.node_id == self.id), None) - if current_partition_index is None: - raise ValueError(f"No current partition found for node: {self.id}") - return (current_partition_index + offset) % len(partitions) - - def get_current_shard(self, base_shard: Shard, index: Optional[int] = None) -> Shard: - if index is None: - index = self.get_partition_index() - partitions = self.partitioning_strategy.partition(self.topology) - shards = map_partitions_to_shards(partitions, base_shard.n_layers, base_shard.model_id) - return shards[index] - - async def update_peers(self, wait_for_peers: int = 0) -> bool: - next_peers = await self.discovery.discover_peers(wait_for_peers) - current_peer_ids = {peer.id() for peer in self.peers} - next_peer_ids = {peer.id() for peer in next_peers} - peers_added = [peer for peer in next_peers if peer.id() not in current_peer_ids] - peers_removed = [peer for peer in self.peers if peer.id() not in next_peer_ids] - peers_updated = [peer for peer in next_peers if peer.id() in current_peer_ids and any(p.addr() != peer.addr() for p in self.peers if p.id() == peer.id())] - peers_unchanged = [peer for peer in next_peers if peer.id() in current_peer_ids and all(p.addr() == peer.addr() for p in self.peers if p.id() == peer.id())] - peers_to_disconnect = [peer for peer in peers_removed if await peer.is_connected()] - peers_to_connect = [peer for peer in peers_added + peers_updated + peers_unchanged if not await peer.is_connected()] - - def _pretty(peers: List[PeerHandle]) -> List[str]: - return [f"{peer.id()}@{peer.addr()}" for peer in peers] - - if DEBUG >= 2: - print(f"update_peers: added={peers_added} removed={peers_removed} updated={peers_updated} unchanged={peers_unchanged} to_disconnect={peers_to_disconnect} to_connect={peers_to_connect}") - - async def disconnect_with_timeout(peer, timeout=5): - try: - await asyncio.wait_for(peer.disconnect(), timeout) - return True - except Exception as e: - print(f"Error disconnecting peer {peer.id()}@{peer.addr()}: {e}") - traceback.print_exc() - return False - - async def connect_with_timeout(peer, timeout=5): - try: - await asyncio.wait_for(peer.connect(), timeout) - return True - except Exception as e: - print(f"Error connecting peer {peer.id()}@{peer.addr()}: {e}") - traceback.print_exc() - return False - - disconnect_results = await asyncio.gather(*(disconnect_with_timeout(peer) for peer in peers_to_disconnect), return_exceptions=True) - connect_results = await asyncio.gather(*(connect_with_timeout(peer) for peer in peers_to_connect), return_exceptions=True) - - successful_disconnects = [peer for peer, result in zip(peers_to_disconnect, disconnect_results) if result is True] - failed_disconnects = [peer for peer, result in zip(peers_to_disconnect, disconnect_results) if result is False] - successful_connects = [peer for peer, result in zip(peers_to_connect, connect_results) if result is True] - failed_connects = [peer for peer, result in zip(peers_to_connect, connect_results) if result is False] - if DEBUG >= 1: - if successful_disconnects: print(f"Successfully disconnected peers: {_pretty(successful_disconnects)}") - if failed_disconnects: print(f"Failed to disconnect peers: {_pretty(failed_disconnects)}") - if successful_connects: print(f"Successfully connected peers: {_pretty(successful_connects)}") - if failed_connects: print(f"Failed to connect peers: {_pretty(failed_connects)}") - - self.peers = next_peers - return len(peers_added) > 0 or len(peers_removed) > 0 or len(peers_updated) > 0 - - async def select_best_inference_engine(self): - if self.inference_engine.__class__.__name__ == 'DummyInferenceEngine': return - supported_engines = self.get_supported_inference_engines() - await self.broadcast_supported_engines(supported_engines) - if len(self.get_topology_inference_engines()): - self.inference_engine = get_inference_engine(supported_engines[0], self.shard_downloader) - - async def periodic_topology_collection(self, interval: int): - while True: - await asyncio.sleep(interval) - try: - did_peers_change = await self.update_peers() - if DEBUG >= 2: print(f"{did_peers_change=}") - await self.collect_topology(set()) - if did_peers_change: - await self.select_best_inference_engine() - except Exception as e: - print(f"Error collecting topology: {e}") - traceback.print_exc() - - async def collect_topology(self, visited: set[str], max_depth: int = 4) -> Topology: - next_topology = Topology() - next_topology.update_node(self.id, self.device_capabilities) - - if DEBUG >= 2: print(f"Collecting topology {max_depth=} {visited=}") - - prev_visited = visited.copy() - visited.add(self.id) - visited.update(p.id() for p in self.peers) - - for peer in self.peers: - next_topology.update_node(peer.id(), peer.device_capabilities()) - next_topology.add_edge(self.id, peer.id(), peer.description()) - - if peer.id() in prev_visited: - continue - - if max_depth <= 0: - if DEBUG >= 2: print("Max depth reached. Skipping...") - continue - - try: - other_topology = await asyncio.wait_for(peer.collect_topology(visited, max_depth=max_depth - 1), timeout=5.0) - if DEBUG >= 2: print(f"Collected topology from: {peer.id()}: {other_topology}") - next_topology.merge(peer.id(), other_topology) - except Exception as e: - print(f"Error collecting topology from {peer.id()}: {e}") - traceback.print_exc() - - next_topology.active_node_id = self.topology.active_node_id - self.topology = next_topology - if self.topology_viz: - self.topology_viz.update_visualization(self.topology, self.partitioning_strategy.partition(self.topology), self.id) - return self.topology - - @property - def on_token(self) -> AsyncCallbackSystem[str, Tuple[str, List[int], bool]]: - return self._on_token - - @property - def on_opaque_status(self) -> AsyncCallbackSystem[str, Tuple[str, str]]: - return self._on_opaque_status - - def trigger_on_token_callbacks(self, request_id: str, tokens: List[int], is_finished: bool) -> None: - if DEBUG >= 2: print(f"Triggering all on_token callbacks with {request_id=} {tokens=} {is_finished=}") - self.on_token.trigger_all(request_id, tokens, is_finished) - - async def broadcast_result(self, request_id: str, result: List[int], is_finished: bool) -> None: - if DEBUG >= 2: print(f"Broadcasting result: {request_id=} {result=} {is_finished=}") - async def send_result_to_peer(peer): - try: - await asyncio.wait_for(peer.send_result(request_id, result, is_finished), timeout=15.0) - except asyncio.TimeoutError: - print(f"Timeout broadcasting result to {peer.id()}") - except Exception as e: - print(f"Error broadcasting result to {peer.id()}: {e}") - traceback.print_exc() - - await asyncio.gather(*[send_result_to_peer(peer) for peer in self.peers], return_exceptions=True) - - async def broadcast_opaque_status(self, request_id: str, status: str) -> None: - if DEBUG >= 8: print(f"Broadcasting opaque status: {request_id=} {status=}") - - async def send_status_to_peer(peer): - try: - await asyncio.wait_for(peer.send_opaque_status(request_id, status), timeout=15.0) - except asyncio.TimeoutError: - print(f"Timeout sending opaque status to {peer.id()}") - except Exception as e: - print(f"Error sending opaque status to {peer.id()}: {e}") - traceback.print_exc() - - await asyncio.gather(*[send_status_to_peer(peer) for peer in self.peers], return_exceptions=True) - # in the case of opaque status, we also want to receive our own opaque statuses - self.on_opaque_status.trigger_all(request_id, status) - - @property - def current_topology(self) -> Topology: - return self.topology - - def handle_stable_diffusion(self, inference_state, result): - if inference_state['is_step_finished']: - inference_state['step']+=1 - progress = [inference_state['step'],inference_state['total_steps']] - intermediate_result = result - if progress[0] == progress[1]: - intermediate_result = result - return intermediate_result, inference_state diff --git a/exo/orchestration/test_node.py b/exo/orchestration/test_node.py deleted file mode 100644 index 6f992bbe..00000000 --- a/exo/orchestration/test_node.py +++ /dev/null @@ -1,66 +0,0 @@ -import unittest -from unittest.mock import Mock, AsyncMock -import numpy as np -import pytest - -from .node import Node -from exo.networking.peer_handle import PeerHandle -from exo.download.shard_download import NoopShardDownloader - -class TestNode(unittest.IsolatedAsyncioTestCase): - def setUp(self): - self.mock_inference_engine = AsyncMock() - self.mock_server = AsyncMock() - self.mock_server.start = AsyncMock() - self.mock_server.stop = AsyncMock() - self.mock_discovery = AsyncMock() - self.mock_discovery.start = AsyncMock() - self.mock_discovery.stop = AsyncMock() - mock_peer1 = Mock(spec=PeerHandle) - mock_peer1.id.return_value = "peer1" - mock_peer2 = Mock(spec=PeerHandle) - mock_peer2.id.return_value = "peer2" - self.mock_discovery.discover_peers = AsyncMock(return_value=[mock_peer1, mock_peer2]) - - self.node = Node("test_node", self.mock_server, self.mock_inference_engine, "localhost", 50051, self.mock_discovery, NoopShardDownloader()) - - async def asyncSetUp(self): - await self.node.start() - - async def asyncTearDown(self): - await self.node.stop() - - async def test_node_initialization(self): - self.assertEqual(self.node.node_id, "test_node") - self.assertEqual(self.node.host, "localhost") - self.assertEqual(self.node.port, 50051) - - async def test_node_start(self): - self.mock_server.start.assert_called_once_with("localhost", 50051) - - async def test_node_stop(self): - await self.node.stop() - self.mock_server.stop.assert_called_once() - - async def test_discover_and_connect_to_peers(self): - await self.node.discover_and_connect_to_peers() - self.assertEqual(len(self.node.peers), 2) - self.assertIn("peer1", map(lambda p: p.id(), self.node.peers)) - self.assertIn("peer2", map(lambda p: p.id(), self.node.peers)) - - async def test_process_tensor_calls_inference_engine(self): - mock_peer = Mock() - self.node.peers = [mock_peer] - - input_tensor = np.array([69, 1, 2]) - await self.node.process_tensor(input_tensor, None) - - self.node.inference_engine.process_shard.assert_called_once_with(input_tensor) - - @pytest.mark.asyncio - async def test_node_capabilities(): - node = Node() - await node.initialize() - caps = await node.get_device_capabilities() - assert caps is not None - assert caps.model != "" diff --git a/exo/orchestration/tracing.py b/exo/orchestration/tracing.py deleted file mode 100644 index 4466fc7d..00000000 --- a/exo/orchestration/tracing.py +++ /dev/null @@ -1,166 +0,0 @@ -from dataclasses import dataclass -from typing import Dict, Optional, Any -from opentelemetry import trace, context -from opentelemetry.trace import Status, StatusCode, SpanContext -from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator -from contextlib import contextmanager -import time -from threading import Lock - -@dataclass -class TraceContext: - request_id: str - sequence_number: int - current_span: Optional[trace.Span] = None - trace_parent: Optional[str] = None - token_group_span: Optional[trace.Span] = None - token_count: int = 0 - token_group_size: int = 10 # Default group size - request_span: Optional[trace.Span] = None # Track the main request span - -class Tracer: - def __init__(self): - self.tracer = trace.get_tracer("exo") - self.contexts: Dict[str, TraceContext] = {} - self._lock = Lock() - self.propagator = TraceContextTextMapPropagator() - - def get_context(self, request_id: str) -> Optional[TraceContext]: - with self._lock: - return self.contexts.get(request_id) - - def set_context(self, request_id: str, context: TraceContext): - with self._lock: - self.contexts[request_id] = context - - def inject_context(self, span: trace.Span) -> str: - """Inject current span context into carrier for propagation""" - carrier = {} - ctx = trace.set_span_in_context(span) - self.propagator.inject(carrier, context=ctx) - return carrier.get("traceparent", "") - - def extract_context(self, trace_parent: str) -> Optional[context.Context]: - """Extract span context from carrier""" - if not trace_parent: - return None - carrier = {"traceparent": trace_parent} - return self.propagator.extract(carrier) - - def create_context_from_parent(self, request_id: str, trace_parent: str, sequence_number: int = 0) -> TraceContext: - """Create a new context with the given trace parent""" - parent_ctx = self.extract_context(trace_parent) - if parent_ctx: - # Create a new request span that links to the parent context - request_span = self.tracer.start_span( - "request", - context=parent_ctx, - attributes={ - "request_id": request_id, - "sequence_number": sequence_number - } - ) - return TraceContext( - request_id=request_id, - sequence_number=sequence_number, - request_span=request_span, - current_span=request_span, - trace_parent=trace_parent - ) - return TraceContext(request_id=request_id, sequence_number=sequence_number) - - def handle_token(self, context: TraceContext, token: int, is_finished: bool = False): - """Handle token generation and manage token group spans""" - context.token_count += 1 - - # Start a new token group span if needed - if not context.token_group_span and context.request_span: - group_number = (context.token_count - 1) // context.token_group_size + 1 - - # Create token group span as child of request span - parent_ctx = trace.set_span_in_context(context.request_span) - context.token_group_span = self.tracer.start_span( - f"token_group_{group_number}", - context=parent_ctx, - attributes={ - "request_id": context.request_id, - "group.number": group_number, - "group.start_token": context.token_count, - "group.max_tokens": context.token_group_size - } - ) - - # Add token to current group span - if context.token_group_span: - relative_pos = ((context.token_count - 1) % context.token_group_size) + 1 - context.token_group_span.set_attribute(f"token.{relative_pos}", token) - context.token_group_span.set_attribute("token.count", relative_pos) - - # End current group span if we've reached the group size or if generation is finished - if context.token_count % context.token_group_size == 0 or is_finished: - context.token_group_span.set_attribute("token.final_count", relative_pos) - context.token_group_span.end() - context.token_group_span = None - - @contextmanager - def start_span(self, name: str, context: TraceContext, extra_attributes: Optional[Dict[str, Any]] = None): - """Start a new span with proper parent context""" - attributes = { - "request_id": context.request_id, - "sequence_number": context.sequence_number - } - if extra_attributes: - attributes.update(extra_attributes) - - # Use request span as parent if available - parent_ctx = None - if context.request_span: - parent_ctx = trace.set_span_in_context(context.request_span) - elif context.trace_parent: - parent_ctx = self.extract_context(context.trace_parent) - if parent_ctx and not context.request_span: - # Create a new request span that links to the parent context - context.request_span = self.tracer.start_span( - "request", - context=parent_ctx, - attributes={ - "request_id": context.request_id, - "sequence_number": context.sequence_number - } - ) - parent_ctx = trace.set_span_in_context(context.request_span) - elif context.current_span: - parent_ctx = trace.set_span_in_context(context.current_span) - - # Create span with parent context if it exists - if parent_ctx: - span = self.tracer.start_span( - name, - context=parent_ctx, - attributes=attributes - ) - else: - span = self.tracer.start_span( - name, - attributes=attributes - ) - - # Update context with current span - prev_span = context.current_span - context.current_span = span - - try: - start_time = time.perf_counter() - yield span - duration = time.perf_counter() - start_time - span.set_attribute("duration_s", duration) - span.set_status(Status(StatusCode.OK)) - except Exception as e: - span.set_status(Status(StatusCode.ERROR, str(e))) - raise - finally: - span.end() - context.current_span = prev_span - -# Global tracer instance -tracer = Tracer() \ No newline at end of file diff --git a/exo/test_callbacks.py b/exo/test_callbacks.py deleted file mode 100644 index c10083d6..00000000 --- a/exo/test_callbacks.py +++ /dev/null @@ -1,50 +0,0 @@ -import asyncio -from typing import Any, Callable -from exo.helpers import AsyncCallbackSystem, AsyncCallback - - -# Usage example -async def main() -> None: - callback_system = AsyncCallbackSystem[str, Any]() - - # Register callbacks - callback1 = callback_system.register("callback1") - callback2 = callback_system.register("callback2") - - def on_next_callback(name: str) -> Callable[..., None]: - def callback(*args: Any) -> None: - print(f"{name} received values: {args}") - - return callback - - callback1.on_next(on_next_callback("Callback1")) - callback2.on_next(on_next_callback("Callback2")) - - async def wait_for_callback(name: str, callback: AsyncCallback[Any], condition: Callable[..., bool]) -> None: - try: - result = await callback.wait(condition, timeout=2) - print(f"{name} wait completed with result: {result}") - except asyncio.TimeoutError: - print(f"{name} wait timed out") - - # Trigger all callbacks at once - callback_system.trigger_all("Hello", 42, True) - - # Wait for all callbacks with different conditions - await asyncio.gather( - wait_for_callback("Callback1", callback1, lambda msg, num, flag: isinstance(msg, str) and num > 0), - wait_for_callback("Callback2", callback2, lambda msg, num, flag: flag is True), - ) - - # Trigger individual callback - callback_system.trigger("callback2", "World", -10, False) - - # Demonstrate timeout - new_callback = callback_system.register("new_callback") - new_callback.on_next(on_next_callback("NewCallback")) - await wait_for_callback("NewCallback", new_callback, lambda msg, num, flag: num > 100) - - callback_system.trigger("callback2", "World", 200, False) - - -asyncio.run(main()) diff --git a/exo/tinychat/common.css b/exo/tinychat/common.css deleted file mode 100644 index e654d6a1..00000000 --- a/exo/tinychat/common.css +++ /dev/null @@ -1,130 +0,0 @@ -/* make it responsive */ -@media(min-width: 852px) { - body { - font-size: 14px; - } -} -@media(max-width: 852px) { - body { - font-size: 12px; - } -} - -/* resets */ -html, body { - width: 100%; - height: 100%; -} - -*::-webkit-scrollbar { - display: none; -} - -* { - -ms-overflow-style: none; - scrollbar-width: none; -} - -* { - -moz-box-sizing: border-box; - -webkit-box-sizing: border-box; - box-sizing: border-box; -} - -/* default */ -body { - margin: 0; - background-color: var(--primary-bg-color); - color: var(--foreground-color); -} - -h1, h2, h3, h4, h5, h6 { - margin: 0em; -} - -hr { - width: 92%; -} - -button { - cursor: pointer; - border: none; - background-color: transparent; -} -button:hover { -} -button:active { -} - -/* components */ -.container { - margin: 0 auto; - padding: 1rem; -} - -.centered { - display: flex; - flex-direction: column; - justify-content: center; - align-items: center; -} - -.centered-w-only { - position: absolute; - left: 50%; - transform: translateX(-50%); -} - -.centered-h-only { - position: absolute; - top: 50%; - transform: translateY(-50%); -} - -.card { - padding: 0; -} - -.card-header { - padding: 0.5rem 1rem; -} - -.card-container { - width: 96vw; - height: 100%; - gap: 1rem; - display: flex; - flex-direction: row; - flex-wrap: wrap; - justify-content: center; - align-items: center; -} - -.clean-a { - text-decoration: underline; - text-decoration-color: #006fc1; - text-decoration-thickness: 2px; - color: inherit; -} - -.hover-underline { - text-decoration: underline; - text-decoration-color: #228039; - text-decoration-thickness: 2px; - color: inherit; -} - -.flex-horizontal { - display: flex; - flex-direction: row; - justify-content: space-between; - align-items: center; -} - -.vertical-separator { - padding: 0 0.5rem; -} - -[x-cloak] { - display: none !important; -} diff --git a/exo/tinychat/favicon.svg b/exo/tinychat/favicon.svg deleted file mode 100644 index 420a33e0..00000000 --- a/exo/tinychat/favicon.svg +++ /dev/null @@ -1,25 +0,0 @@ - - - - diff --git a/exo/tinychat/index.css b/exo/tinychat/index.css deleted file mode 100644 index 1971795a..00000000 --- a/exo/tinychat/index.css +++ /dev/null @@ -1,846 +0,0 @@ -/* define colors */ -:root { - --primary-color: #fff; - --secondary-color: #2a2a2a; - --secondary-color-transparent: #ffffff66; - --primary-bg-color: #1a1a1a; - --foreground-color: #f0f0f0; - --red-color: #a52e4d; -} - -main { - width: 100%; - height: 100%; - - display: flex; - flex-direction: column; - - place-items: center; -} - -.home { - width: 100%; - height: 90%; - margin-bottom: 10rem; - padding-top: 2rem; -} - -.title { - font-size: 3rem; - margin: 1rem 0; - margin-top: 3rem; -} - -.histories-container-container { - width: 100%; - max-height: 75%; - - position: relative; -} - -.histories-container { - overflow-y: auto; - overflow-x: hidden; - width: 100%; - height: 100%; - - display: flex; - flex-direction: column; - gap: 1rem; - align-items: center; - - margin: 0; - padding: 3rem 1rem; -} - -.histories-start { - height: 3rem; - width: 100%; - - z-index: 999; - top: 0; - position: absolute; - - background: linear-gradient( - 180deg, - var(--primary-bg-color) 0%, - transparent 100% - ); -} -.histories-end { - height: 3rem; - width: 100%; - - z-index: 999; - bottom: 0; - position: absolute; - - background: linear-gradient( - 0deg, - var(--primary-bg-color) 0%, - transparent 100% - ); -} - -.history { - padding: 1rem; - width: 100%; - max-width: 40rem; - - background-color: var(--secondary-color); - border-radius: 10px; - border-left: 2px solid var(--primary-color); - - cursor: pointer; - - transform: translateX(calc(1px * var(--tx, 0))); - opacity: var(--opacity, 1); -} -.history:hover { - background-color: var(--secondary-color); -} - -.history-delete-button { - position: absolute; - top: 0; - right: 0; - padding: 0.5rem; - margin: 0; - outline: none; - border: none; - background-color: var(--secondary-color); - color: var(--foreground-color); - border-radius: 0 0 0 10px; - cursor: pointer; - transition: 0.2s; -} -.history-delete-button:hover { - background-color: var(--secondary-color); - padding: 0.75rem; -} - -.messages { - overflow-y: auto; - height: 100%; - width: 100%; - max-width: 1200px; - - display: flex; - flex-direction: column; - gap: 1rem; - align-items: center; - padding-top: 4rem; - padding-bottom: 11rem; - margin: 0 auto; -} - -.message { - max-width: 75%; - padding: 0.5rem 1rem; - border-radius: 20px; -} - -@media(max-width: 1482px) { - .messages { - padding-left: 16px; - padding-right: 16px; - } -} - -.message-role-assistant { - background-color: var(--secondary-color); - margin-right: auto; - color: #fff; -} -.message-role-user { - margin-left: auto; - background-color: var(--primary-color); - color: #000; -} - -.message-role-user p { - white-space: pre-wrap; - word-wrap: break-word; -} - -.download-progress { - position: fixed; - bottom: 11rem; - left: 50%; - transform: translateX(-50%); - margin-left: 125px; - width: 100%; - max-width: 1200px; - overflow-y: auto; - min-height: 350px; - padding: 2rem; - z-index: 998; -} -.message > pre { - white-space: pre-wrap; -} - -.progress-bar-container { - width: 100%; - background-color: #e0e0e0; - border-radius: 4px; - margin: 10px 0; -} -.progress-bar { - height: 20px; - border-radius: 4px; - transition: width 0.5s ease-in-out; -} -.progress-bar.complete { - background-color: #4CAF50; -} -.progress-bar.in-progress { - background-color: #2196F3; -} - -.toast { - width: 100%; - background-color: #fc2a2a; - color: #fff; - text-align: left; - border-radius: 2px; - padding: 16px; - position: fixed; - z-index: 9999; - top: 0; - left: 0; - right: 0; - display: flex; - flex-direction: column; - white-space: pre-wrap; - font-family: monospace; -} - -.toast-header { - display: flex; - justify-content: space-between; - align-items: center; - width: 100%; -} - -.toast-error-message { - flex-grow: 1; -} - -.toast-header-buttons { - display: flex; - align-items: center; - gap: 16px; - margin-left: 24px; -} - -.toast-expand-button { - background: none; - border: none; - color: white; - padding: 4px; - cursor: pointer; - font-size: 1em; -} - -.toast-close-button { - background: none; - border: none; - color: white; - padding: 4px; - cursor: pointer; - font-size: 1.2em; - line-height: 1; -} - -.toast-expand-button:hover, -.toast-close-button:hover { - opacity: 0.8; -} - -.toast-content { - margin-top: 10px; - padding: 10px; - background-color: rgba(0, 0, 0, 0.2); - border-radius: 4px; -} - -.hljs { - width: 100%; - position: relative; - border-radius: 10px; - /* wrap code blocks */ - white-space: pre-wrap; -} -/* put clipboard button in the top right corner of the code block */ -.clipboard-button { - position: absolute; - top: 0; - right: 0; - padding: 0.5rem; - margin: 0; - outline: none; - border: none; - background-color: var(--secondary-color); - color: var(--foreground-color); - border-radius: 0 0 0 10px; - cursor: pointer; - transition: 0.2s; -} -.clipboard-button:hover { - background-color: var(--secondary-color); - padding: 0.75rem; -} - -.input-container { - position: fixed; - bottom: 0; - left: 250px; - width: calc(100% - 250px); - max-width: 1200px; - display: flex; - flex-direction: column; - justify-content: center; - align-items: center; - z-index: 999; - background: linear-gradient( - 0deg, - var(--primary-bg-color) 55%, - transparent 100% - ); - left: 50%; - transform: translateX(-50%); - margin-left: 125px; -} - -.input-performance { - margin-top: 4rem; - - display: flex; - flex-direction: row; - gap: 1rem; -} - -.input-performance-point { - display: flex; - flex-direction: row; - place-items: center; - gap: 0.5rem; -} -.input-performance-point > p { - height: 1rem; - line-height: normal; -} - -.input { - width: 90%; - min-height: 3rem; - flex-shrink: 0; - - display: flex; - flex-direction: row; - justify-content: center; - gap: 0.5rem; - - align-items: flex-end; - margin-bottom: 2rem; -} - -.input-form { - width: 100%; - padding: 1rem; - min-height: 3rem; - max-height: 8rem; - - background-color: var(--secondary-color); - color: var(--foreground-color); - border-radius: 10px; - border: none; - resize: none; - outline: none; -} - -.input-button { - height: 3rem; - width: 4rem; - - background-color: var(--primary-color); - color: var(--secondary-color); - border-radius: 10px; - padding: 0.5rem; - cursor: pointer; -} -.input-button:hover { - background-color: var(--secondary-color-transparent); -} -.input-button:disabled { - background-color: var(--secondary-color); - cursor: not-allowed; -} - -/* wrap text */ -p { - white-space: pre-wrap; -} - -/* fonts */ -.megrim-regular { - font-family: "Megrim", system-ui; - font-weight: 400; - font-style: normal; -} - -.monospace { - font-family: monospace; -} - -.model-selector { - display: none; -} - -/* Image upload button styles */ -.image-input-button { - background-color: var(--secondary-color); - color: var(--foreground-color); - border: none; - border-radius: 50%; - width: 40px; - height: 40px; - font-size: 18px; - cursor: pointer; - transition: all 0.3s ease; - display: flex; - align-items: center; - justify-content: center; - margin-right: 10px; -} - -.image-input-button:hover { - background-color: var(--secondary-color-transparent); - transform: scale(1.1); -} - -.image-input-button:focus { - outline: none; - box-shadow: 0 0 0 3px rgba(var(--secondary-color-rgb), 0.5); -} - -.image-input-button i { - transition: all 0.3s ease; -} - -.image-input-button:hover i { - transform: scale(1.2); -} - -/* Hidden file input styles */ -#image-upload { - display: none; -} - -.image-preview-container { - position: relative; - display: inline-block; - margin-right: 10px; -} - -.image-preview { - max-width: 100px; - max-height: 100px; - object-fit: cover; - border-radius: 5px; -} - -.remove-image-button { - position: absolute; - top: -5px; - right: -5px; - background-color: rgba(255, 255, 255, 0.8); - border: none; - border-radius: 50%; - padding: 2px 5px; - cursor: pointer; -} - -.message > p > img { - max-width: 100%; - max-height: 100%; - object-fit: contain; -} - -.clear-history-button { - background-color: var(--red-color); - color: white; - padding: 10px 20px; - border-radius: 5px; - display: flex; - align-items: center; - gap: 8px; - transition: all 0.3s ease; - margin: 1rem auto; - border: none; - cursor: pointer; -} - -.clear-history-button:hover { - opacity: 0.8; - transform: scale(1.05); -} - -.clear-history-button i { - font-size: 14px; -} - -/* Add new sidebar styles */ -.sidebar { - position: fixed; - left: 0; - top: 0; - bottom: 0; - width: 250px; - background-color: var(--secondary-color); - padding: 20px; - overflow-y: auto; - z-index: 1000; -} - -.model-option { - padding: 12px; - margin: 8px 0; - border-radius: 8px; - background-color: var(--primary-bg-color); - cursor: pointer; - transition: all 0.2s ease; -} - -.model-option:hover { - transform: translateX(5px); -} - -.model-option.selected { - border-left: 3px solid var(--primary-color); - background-color: var(--secondary-color-transparent); -} - -.model-name { - font-weight: bold; - margin-bottom: 4px; -} - -.model-progress { - font-size: 0.9em; - color: var(--secondary-color-transparent); - display: flex; - flex-direction: column; - gap: 0.5rem; -} - -.model-progress-info { - display: flex; - flex-direction: column; - gap: 0.5rem; -} - -.model-progress i { - font-size: 0.9em; - color: var(--primary-color); -} - -/* Adjust main content to accommodate sidebar */ -main { - margin-left: 250px; - width: calc(100% - 250px); -} - -/* Add styles for the back button */ -.back-button { - position: fixed; - top: 1rem; - left: calc(250px + 1rem); /* Sidebar width + padding */ - background-color: var(--secondary-color); - color: var(--foreground-color); - padding: 0.5rem 1rem; - border-radius: 8px; - border: none; - cursor: pointer; - display: flex; - align-items: center; - gap: 0.5rem; - z-index: 1000; - transition: all 0.2s ease; -} - -.back-button:hover { - transform: translateX(-5px); - background-color: var(--secondary-color-transparent); -} - -.model-info { - display: flex; - flex-direction: column; - gap: 4px; -} - -.model-size { - font-size: 0.8em; - color: var(--secondary-color-transparent); - opacity: 0.8; -} - -.model-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 4px; -} - -.model-delete-button { - background: none; - border: none; - color: var(--red-color); - padding: 4px 8px; - cursor: pointer; - transition: all 0.2s ease; - opacity: 0.7; -} - -.model-delete-button:hover { - opacity: 1; - transform: scale(1.1); -} - -.model-option:hover .model-delete-button { - opacity: 1; -} - -.loading-container { - display: flex; - flex-direction: column; - align-items: center; - gap: 10px; - padding: 20px; - color: var(--secondary-color-transparent); -} - -.loading-container i { - font-size: 24px; -} - -.loading-container span { - font-size: 14px; -} - -/* Add this to your CSS */ -.fa-spin { - animation: fa-spin 2s infinite linear; -} - -@keyframes fa-spin { - 0% { - transform: rotate(0deg); - } - 100% { - transform: rotate(360deg); - } -} - -.model-download-button { - background: none; - border: none; - color: var(--primary-color); - padding: 4px 8px; - border-radius: 4px; - cursor: pointer; - transition: all 0.2s ease; - display: inline-flex; - align-items: center; - gap: 6px; - background-color: var(--primary-bg-color); - font-size: 0.9em; - width: fit-content; - align-self: flex-start; -} - -.model-download-button:hover { - transform: scale(1.05); - background-color: var(--secondary-color-transparent); -} - -.model-download-button i { - font-size: 0.9em; -} - -.topology-section { - margin-bottom: 30px; - padding: 15px; - background: rgba(255, 255, 255, 0.05); - border-radius: 8px; -} - -.topology-visualization { - min-height: 150px; - position: relative; - margin-top: 10px; -} - -.topology-loading { - display: flex; - align-items: center; - gap: 10px; - color: #666; - font-size: 0.9em; -} - -.topology-node { - padding: 8px; - background: rgba(255, 255, 255, 0.05); - border-radius: 4px; - margin: 4px 0; - display: flex; - flex-direction: column; - gap: 4px; -} - -.node-info { - display: flex; - align-items: center; - gap: 6px; - font-size: 0.9em; -} - -.topology-node .status { - width: 6px; - height: 6px; - border-radius: 50%; - flex-shrink: 0; -} - -.topology-node .status.active { - background: #4CAF50; -} - -.topology-node .status.inactive { - background: #666; -} - -.node-details { - padding-left: 12px; - display: flex; - flex-direction: column; - gap: 2px; - font-size: 0.8em; - opacity: 0.6; -} - -.node-details span { - display: flex; - align-items: center; -} - -.peer-connections { - margin-top: 8px; - padding-left: 12px; - display: flex; - flex-direction: column; - gap: 4px; -} - -.peer-connection { - display: flex; - align-items: center; - gap: 8px; - font-size: 0.85em; - color: #a0a0a0; -} - -.peer-connection i { - font-size: 0.8em; - color: #666; -} - -.thinking-block { - background-color: rgba(255, 255, 255, 0.05); - border-radius: 8px; - margin: 8px 0; - overflow: hidden; -} - -.thinking-header { - background-color: rgba(255, 255, 255, 0.1); - padding: 8px 12px; - font-size: 0.9em; - color: #a0a0a0; - display: flex; - align-items: center; - gap: 8px; -} - -.thinking-content { - padding: 12px; - white-space: pre-wrap; -} - -@keyframes thinking-spin { - to { transform: rotate(360deg); } -} - -.thinking-header.thinking::before { - content: ''; - width: 12px; - height: 12px; - border: 2px solid #a0a0a0; - border-top-color: transparent; - border-radius: 50%; - animation: thinking-spin 1s linear infinite; -} - -.model-group { - margin-bottom: 12px; -} - -.model-group-header, -.model-subgroup-header { - display: flex; - justify-content: space-between; - align-items: center; - padding: 8px 12px; - background-color: var(--primary-bg-color); - border-radius: 6px; - cursor: pointer; - transition: all 0.2s ease; - margin-bottom: 8px; -} - -.model-group-header:hover, -.model-subgroup-header:hover { - background-color: var(--secondary-color-transparent); -} - -.model-group-content { - padding-left: 12px; -} - -.model-subgroup { - margin-bottom: 8px; -} - -.model-subgroup-header { - font-size: 0.9em; - background-color: rgba(255, 255, 255, 0.05); -} - -.model-subgroup-content { - padding-left: 12px; -} - -.group-header-content { - display: flex; - align-items: center; - gap: 8px; -} - -.model-count { - font-size: 0.8em; - color: var(--secondary-color-transparent); - font-family: monospace; -} diff --git a/exo/tinychat/index.html b/exo/tinychat/index.html deleted file mode 100644 index 63e37c8a..00000000 --- a/exo/tinychat/index.html +++ /dev/null @@ -1,454 +0,0 @@ - - - -tinychat - - - - - - - - - - - - - - - - - - - - - - - -

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- - - diff --git a/exo/tinychat/index.js b/exo/tinychat/index.js deleted file mode 100644 index 5b39e64a..00000000 --- a/exo/tinychat/index.js +++ /dev/null @@ -1,889 +0,0 @@ -document.addEventListener("alpine:init", () => { - Alpine.data("state", () => ({ - // current state - cstate: { - time: null, - messages: [], - selectedModel: 'llama-3.2-1b', - }, - - // historical state - histories: JSON.parse(localStorage.getItem("histories")) || [], - - home: 0, - generating: false, - endpoint: `${window.location.origin}/v1`, - - // Initialize error message structure - errorMessage: null, - errorExpanded: false, - errorTimeout: null, - - // performance tracking - time_till_first: 0, - tokens_per_second: 0, - total_tokens: 0, - - // image handling - imagePreview: null, - - // download progress - downloadProgress: null, - downloadProgressInterval: null, // To keep track of the polling interval - - // Pending message storage - pendingMessage: null, - - modelPoolInterval: null, - - // Add models state alongside existing state - models: {}, - - // Show only models available locally - showDownloadedOnly: false, - - topology: null, - topologyInterval: null, - - // Add these new properties - expandedGroups: {}, - - init() { - // Clean up any pending messages - localStorage.removeItem("pendingMessage"); - - // Get initial model list - this.fetchInitialModels(); - - // Start polling for download progress - this.startDownloadProgressPolling(); - - // Start model polling with the new pattern - this.startModelPolling(); - }, - - async fetchInitialModels() { - try { - const response = await fetch(`${window.location.origin}/initial_models`); - if (response.ok) { - const initialModels = await response.json(); - this.models = initialModels; - } - } catch (error) { - console.error('Error fetching initial models:', error); - } - }, - - async startModelPolling() { - while (true) { - try { - await this.populateSelector(); - // Wait 15 seconds before next poll - await new Promise(resolve => setTimeout(resolve, 15000)); - } catch (error) { - console.error('Model polling error:', error); - // If there's an error, wait before retrying - await new Promise(resolve => setTimeout(resolve, 15000)); - } - } - }, - - async populateSelector() { - return new Promise((resolve, reject) => { - const evtSource = new EventSource(`${window.location.origin}/modelpool`); - - evtSource.onmessage = (event) => { - if (event.data === "[DONE]") { - evtSource.close(); - resolve(); - return; - } - - const modelData = JSON.parse(event.data); - // Update existing model data while preserving other properties - Object.entries(modelData).forEach(([modelName, data]) => { - if (this.models[modelName]) { - this.models[modelName] = { - ...this.models[modelName], - ...data, - loading: false - }; - } - }); - }; - - evtSource.onerror = (error) => { - console.error('EventSource failed:', error); - evtSource.close(); - reject(error); - }; - }); - }, - - removeHistory(cstate) { - const index = this.histories.findIndex((state) => { - return state.time === cstate.time; - }); - if (index !== -1) { - this.histories.splice(index, 1); - localStorage.setItem("histories", JSON.stringify(this.histories)); - } - }, - - clearAllHistory() { - this.histories = []; - localStorage.setItem("histories", JSON.stringify([])); - }, - - // Utility functions - formatBytes(bytes) { - if (bytes === 0) return '0 B'; - const k = 1024; - const sizes = ['B', 'KB', 'MB', 'GB', 'TB']; - const i = Math.floor(Math.log(bytes) / Math.log(k)); - return parseFloat((bytes / Math.pow(k, i)).toFixed(2)) + ' ' + sizes[i]; - }, - - formatDuration(seconds) { - if (seconds === null || seconds === undefined || isNaN(seconds)) return ''; - const h = Math.floor(seconds / 3600); - const m = Math.floor((seconds % 3600) / 60); - const s = Math.floor(seconds % 60); - if (h > 0) return `${h}h ${m}m ${s}s`; - if (m > 0) return `${m}m ${s}s`; - return `${s}s`; - }, - - async handleImageUpload(event) { - const file = event.target.files[0]; - if (file) { - const reader = new FileReader(); - reader.onload = (e) => { - this.imagePreview = e.target.result; - this.imageUrl = e.target.result; // Store the image URL - // Add image preview to the chat - this.cstate.messages.push({ - role: "user", - content: `![Uploaded Image](${this.imagePreview})`, - }); - }; - reader.readAsDataURL(file); - } - }, - - - async handleSend() { - try { - const el = document.getElementById("input-form"); - const value = el.value.trim(); - if (!value && !this.imagePreview) return; - - if (this.generating) return; - this.generating = true; - if (this.home === 0) this.home = 1; - - // ensure that going back in history will go back to home - window.history.pushState({}, "", "/"); - - // add message to list - if (value) { - this.cstate.messages.push({ role: "user", content: value }); - } - - // clear textarea - el.value = ""; - el.style.height = "auto"; - el.style.height = el.scrollHeight + "px"; - - localStorage.setItem("pendingMessage", value); - this.processMessage(value); - } catch (error) { - console.error('error', error); - this.setError(error); - this.generating = false; - } - }, - - async processMessage(value) { - try { - // reset performance tracking - const prefill_start = Date.now(); - let start_time = 0; - let tokens = 0; - this.tokens_per_second = 0; - - // prepare messages for API request - let apiMessages = this.cstate.messages.map(msg => { - if (msg.content.startsWith('![Uploaded Image]')) { - return { - role: "user", - content: [ - { - type: "image_url", - image_url: { - url: this.imageUrl - } - }, - { - type: "text", - text: value // Use the actual text the user typed - } - ] - }; - } else { - return { - role: msg.role, - content: msg.content - }; - } - }); - - if (this.cstate.selectedModel === "stable-diffusion-2-1-base") { - // Send a request to the image generation endpoint - console.log(apiMessages[apiMessages.length - 1].content) - console.log(this.cstate.selectedModel) - console.log(this.endpoint) - const response = await fetch(`${this.endpoint}/image/generations`, { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - "model": 'stable-diffusion-2-1-base', - "prompt": apiMessages[apiMessages.length - 1].content, - "image_url": this.imageUrl - }), - }); - - if (!response.ok) { - throw new Error("Failed to fetch"); - } - const reader = response.body.getReader(); - let done = false; - let gottenFirstChunk = false; - - while (!done) { - const { value, done: readerDone } = await reader.read(); - done = readerDone; - const decoder = new TextDecoder(); - - if (value) { - // Assume non-binary data (text) comes first - const chunk = decoder.decode(value, { stream: true }); - const parsed = JSON.parse(chunk); - console.log(parsed) - - if (parsed.progress) { - if (!gottenFirstChunk) { - this.cstate.messages.push({ role: "assistant", content: "" }); - gottenFirstChunk = true; - } - this.cstate.messages[this.cstate.messages.length - 1].content = parsed.progress; - } - else if (parsed.images) { - if (!gottenFirstChunk) { - this.cstate.messages.push({ role: "assistant", content: "" }); - gottenFirstChunk = true; - } - const imageUrl = parsed.images[0].url; - console.log(imageUrl) - this.cstate.messages[this.cstate.messages.length - 1].content = `![Generated Image](${imageUrl}?t=${Date.now()})`; - } - } - } - } - - else{ - const containsImage = apiMessages.some(msg => Array.isArray(msg.content) && msg.content.some(item => item.type === 'image_url')); - if (containsImage) { - // Map all messages with string content to object with type text - apiMessages = apiMessages.map(msg => { - if (typeof msg.content === 'string') { - return { - ...msg, - content: [ - { - type: "text", - text: msg.content - } - ] - }; - } - return msg; - }); - } - - console.log(apiMessages) - //start receiving server sent events - let gottenFirstChunk = false; - for await ( - const chunk of this.openaiChatCompletion(this.cstate.selectedModel, apiMessages) - ) { - if (!gottenFirstChunk) { - this.cstate.messages.push({ role: "assistant", content: "" }); - gottenFirstChunk = true; - } - - // add chunk to the last message - this.cstate.messages[this.cstate.messages.length - 1].content += chunk; - - // calculate performance tracking - tokens += 1; - this.total_tokens += 1; - if (start_time === 0) { - start_time = Date.now(); - this.time_till_first = start_time - prefill_start; - } else { - const diff = Date.now() - start_time; - if (diff > 0) { - this.tokens_per_second = tokens / (diff / 1000); - } - } - } - } - // Clean the cstate before adding it to histories - const cleanedCstate = JSON.parse(JSON.stringify(this.cstate)); - cleanedCstate.messages = cleanedCstate.messages.map(msg => { - if (Array.isArray(msg.content)) { - return { - ...msg, - content: msg.content.map(item => - item.type === 'image_url' ? { type: 'image_url', image_url: { url: '[IMAGE_PLACEHOLDER]' } } : item - ) - }; - } - return msg; - }); - - // Update the state in histories or add it if it doesn't exist - const index = this.histories.findIndex((cstate) => cstate.time === cleanedCstate.time); - cleanedCstate.time = Date.now(); - if (index !== -1) { - // Update the existing entry - this.histories[index] = cleanedCstate; - } else { - // Add a new entry - this.histories.push(cleanedCstate); - } - console.log(this.histories) - // update in local storage - try { - localStorage.setItem("histories", JSON.stringify(this.histories)); - } catch (error) { - console.error("Failed to save histories to localStorage:", error); - } - } catch (error) { - console.error('error', error); - this.setError(error); - } finally { - this.generating = false; - } - }, - - async handleEnter(event) { - // if shift is not pressed - if (!event.shiftKey) { - event.preventDefault(); - await this.handleSend(); - } - }, - - updateTotalTokens(messages) { - fetch(`${this.endpoint}/chat/token/encode`, { - method: "POST", - headers: { "Content-Type": "application/json" }, - body: JSON.stringify({ messages }), - }).then((response) => response.json()).then((data) => { - this.total_tokens = data.length; - }).catch(console.error); - }, - - async *openaiChatCompletion(model, messages) { - const response = await fetch(`${this.endpoint}/chat/completions`, { - method: "POST", - headers: { - "Content-Type": "application/json", - }, - body: JSON.stringify({ - "model": model, - "messages": messages, - "stream": true, - }), - }); - if (!response.ok) { - const errorResBody = await response.json() - if (errorResBody?.detail) { - throw new Error(`Failed to fetch completions: ${errorResBody.detail}`); - } else { - throw new Error("Failed to fetch completions: Unknown error"); - } - } - - const reader = response.body.pipeThrough(new TextDecoderStream()) - .pipeThrough(new EventSourceParserStream()).getReader(); - - while (true) { - const { done, value } = await reader.read(); - if (done) break; - - if (value.type === "event") { - const json = JSON.parse(value.data); - if (json.choices) { - const choice = json.choices[0]; - if (choice.finish_reason === "stop") break; - if (choice.delta.content) yield choice.delta.content; - } - } - } - }, - - async fetchDownloadProgress() { - try { - const response = await fetch(`${this.endpoint}/download/progress`); - if (response.ok) { - const data = await response.json(); - const progressArray = Object.values(data); - if (progressArray.length > 0) { - this.downloadProgress = progressArray.map(progress => { - // Check if download is complete - if (progress.status === "complete") { - return { - ...progress, - isComplete: true, - percentage: 100 - }; - } else if (progress.status === "failed") { - return { - ...progress, - isComplete: false, - errorMessage: "Download failed" - }; - } else { - return { - ...progress, - isComplete: false, - downloaded_bytes_display: this.formatBytes(progress.downloaded_bytes), - total_bytes_display: this.formatBytes(progress.total_bytes), - overall_speed_display: progress.overall_speed ? this.formatBytes(progress.overall_speed) + '/s' : '', - overall_eta_display: progress.overall_eta ? this.formatDuration(progress.overall_eta) : '', - percentage: ((progress.downloaded_bytes / progress.total_bytes) * 100).toFixed(2) - }; - } - }); - const allComplete = this.downloadProgress.every(progress => progress.isComplete); - if (allComplete) { - // Check for pendingMessage - const savedMessage = localStorage.getItem("pendingMessage"); - if (savedMessage) { - // Clear pendingMessage - localStorage.removeItem("pendingMessage"); - // Call processMessage() with savedMessage - if (this.lastErrorMessage) { - await this.processMessage(savedMessage); - } - } - this.lastErrorMessage = null; - this.downloadProgress = null; - } - } else { - // No ongoing download - this.downloadProgress = null; - } - } - } catch (error) { - console.error("Error fetching download progress:", error); - this.downloadProgress = null; - } - }, - - startDownloadProgressPolling() { - if (this.downloadProgressInterval) { - // Already polling - return; - } - this.fetchDownloadProgress(); // Fetch immediately - this.downloadProgressInterval = setInterval(() => { - this.fetchDownloadProgress(); - }, 1000); // Poll every second - }, - - // Add a helper method to set errors consistently - setError(error) { - this.errorMessage = { - basic: error.message || "An unknown error occurred", - stack: error.stack || "" - }; - this.errorExpanded = false; - - if (this.errorTimeout) { - clearTimeout(this.errorTimeout); - } - - if (!this.errorExpanded) { - this.errorTimeout = setTimeout(() => { - this.errorMessage = null; - this.errorExpanded = false; - }, 30 * 1000); - } - }, - - async deleteModel(modelName, model) { - const downloadedSize = model.total_downloaded || 0; - const sizeMessage = downloadedSize > 0 ? - `This will free up ${this.formatBytes(downloadedSize)} of space.` : - 'This will remove any partially downloaded files.'; - - if (!confirm(`Are you sure you want to delete ${model.name}? ${sizeMessage}`)) { - return; - } - - try { - const response = await fetch(`${window.location.origin}/models/${modelName}`, { - method: 'DELETE', - headers: { - 'Content-Type': 'application/json' - } - }); - - const data = await response.json(); - - if (!response.ok) { - throw new Error(data.detail || 'Failed to delete model'); - } - - // Update the model status in the UI - if (this.models[modelName]) { - this.models[modelName].downloaded = false; 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Calling Alpine.start() more than once can cause problems."),mr=!0,document.body||E("Unable to initialize. Trying to load Alpine before `` is available. Did you forget to add `defer` in Alpine's ` - - - - - - - """ - - # Write the dashboard - dashboard_path = output_dir / "dashboard.html" - with open(dashboard_path, "w") as f: - f.write(dashboard_html) - - # Generate summary with available metrics - latest_data = {} - - if not df_size.empty: - latest = df_size.iloc[-1] - previous = df_size.iloc[-2] if len(df_size) > 1 else latest - size_change = float(latest['total_size_mb'] - previous['total_size_mb']) - latest_data.update({ - 'timestamp': latest['timestamp'].isoformat(), - 'commit_hash': latest['commit_hash'], - 'commit_url': latest['commit_url'], - 'total_size_mb': float(latest['total_size_mb']), - 'size_change_mb': size_change, - 'packages': latest.get('packages', []) - }) - - if not df_lines.empty: - latest = df_lines.iloc[-1] - previous = df_lines.iloc[-2] if len(df_lines) > 1 else latest - linecount_change = int(latest['total_lines'] - previous['total_lines']) - if not latest_data: # Only add timestamp and commit info if not already added - latest_data.update({ - 'timestamp': latest['timestamp'].isoformat(), - 'commit_hash': latest['commit_hash'], - 'commit_url': latest['commit_url'], - }) - latest_data.update({ - 'total_lines': int(latest['total_lines']), - 'linecount_change': linecount_change - }) - - if not df_benchmark.empty: - latest = df_benchmark.iloc[-1] - previous = df_benchmark.iloc[-2] if len(df_benchmark) > 1 else latest - tokens_change = float(latest['tokens_per_second'] - previous['tokens_per_second']) - if not latest_data: # Only add timestamp and commit info if not already added - latest_data.update({ - 'timestamp': latest['timestamp'].isoformat(), - 'commit_hash': latest['commit_hash'], - 'commit_url': latest['commit_url'], - }) - latest_data.update({ - 'tokens_per_second': float(latest['tokens_per_second']), - 'tokens_change': tokens_change - }) - - if latest_data: - with open(output_dir / 'latest_data.json', 'w') as f: - json.dump(latest_data, f, indent=2) - - self._print_summary(latest_data) - self.logger.info(f"Report generated in {output_dir}") - return str(output_dir) - - return None - - def _print_summary(self, latest_data: Dict): - print("\n=== Package Size Summary ===") - print(f"Timestamp: {latest_data['timestamp']}") - print(f"Commit: {latest_data['commit_hash'][:7]}") - - if 'total_size_mb' in latest_data: - print(f"Total Size: {latest_data['total_size_mb']:.2f}MB") - change = latest_data['size_change_mb'] - change_symbol = "↓" if change <= 0 else "↑" - print(f"Change: {change_symbol} {abs(change):.2f}MB") - - if latest_data.get('packages'): - print("\nTop 5 Largest Packages:") - sorted_packages = sorted(latest_data['packages'], key=lambda x: x['size_mb'], reverse=True) - for pkg in sorted_packages[:5]: - print(f"- {pkg['name']}: {pkg['size_mb']:.2f}MB") - - if 'total_lines' in latest_data: - print("\nLine Count Stats:") - print(f"Total Lines: {latest_data['total_lines']:,}") - change = latest_data['linecount_change'] - change_symbol = "↓" if change <= 0 else "↑" - print(f"Change: {change_symbol} {abs(change):,}") - - if 'tokens_per_second' in latest_data: - print("\nBenchmark Stats:") - print(f"Tokens per Second: {latest_data['tokens_per_second']:.2f}") - if 'time_to_first_token' in latest_data: - print(f"Time to First Token: {latest_data['time_to_first_token']:.3f}s") - - print("\n") - - def _calculate_data_hash(self, data: List[Dict]) -> str: - """Calculate a hash of the data to detect changes""" - return hash(str(sorted([ - (d.get('commit_hash'), d.get('timestamp')) - for d in data - ]))) - - def _play_sound(self, sound_key: str): - """Play a specific notification sound using pygame""" - try: - sound_path = self.sounds.get(sound_key) - if sound_path and sound_path.exists(): - sound = pygame.mixer.Sound(str(sound_path)) - sound.play() - # Wait for the sound to finish playing - pygame.time.wait(int(sound.get_length() * 1000)) - else: - self.logger.warning(f"Sound file not found: {sound_key} at {sound_path}") - except Exception as e: - self.logger.error(f"Failed to play sound {sound_key}: {e}") - - def _check_metrics_changes(self, current_data: List[Dict], previous_data: List[Dict]): - # Sort data by timestamp in descending order (most recent first) - def sort_by_timestamp(data): - return sorted( - data, - key=lambda x: x.get('timestamp', ''), - reverse=True # Most recent first - ) - - current_data = sort_by_timestamp(current_data) - previous_data = sort_by_timestamp(previous_data) - - # Helper to find latest entry with a specific metric - def find_latest_with_metric(data: List[Dict], metric: str) -> Optional[Dict]: - return next((d for d in data if metric in d), None) - - # Check line count changes - current_lines = find_latest_with_metric(current_data, 'total_lines') - previous_lines = find_latest_with_metric(previous_data, 'total_lines') - - if current_lines and previous_lines: - diff = current_lines['total_lines'] - previous_lines['total_lines'] - self.logger.debug(f"Lines of code diff: {diff}") - if diff > 0: - self.logger.info(f"Lines of code increased by {diff:,}") - self._play_sound('lines_up') - elif diff < 0: - self.logger.info(f"Lines of code decreased by {abs(diff):,}") - self._play_sound('lines_down') - else: - self.logger.debug("No lines of code data found") - - # Check tokens per second changes - current_tokens = find_latest_with_metric(current_data, 'tokens_per_second') - previous_tokens = find_latest_with_metric(previous_data, 'tokens_per_second') - - if current_tokens and previous_tokens: - diff = current_tokens['tokens_per_second'] - previous_tokens['tokens_per_second'] - self.logger.debug(f"Tokens per second diff: {diff}") - if diff > 0: - self.logger.info(f"Tokens per second increased by {diff:.2f}") - self._play_sound('tokens_up') - elif diff < 0: - self.logger.info(f"Tokens per second decreased by {abs(diff):.2f}") - self._play_sound('tokens_down') - else: - self.logger.debug("No tokens per second data found") - - # Check package size changes - current_size = find_latest_with_metric(current_data, 'total_size_mb') - previous_size = find_latest_with_metric(previous_data, 'total_size_mb') - - if current_size and previous_size: - diff = current_size['total_size_mb'] - previous_size['total_size_mb'] - self.logger.debug(f"Package size diff: {diff:.2f}MB") - if diff > 0: - self.logger.info(f"Package size increased by {diff:.2f}MB") - self._play_sound('size_up') - elif diff < 0: - self.logger.info(f"Package size decreased by {abs(diff):.2f}MB") - self._play_sound('size_down') - else: - self.logger.debug("No package size data found") - - async def run_dashboard(self, update_interval: int = 10): - """Run the dashboard with periodic updates""" - try: - update_interval = float(update_interval) - self.logger.debug(f"Update interval type: {type(update_interval)}, value: {update_interval}") - except ValueError as e: - self.logger.error(f"Failed to convert update_interval to float: {update_interval}") - raise - - self.logger.info(f"Starting real-time dashboard with {update_interval}s updates") - previous_data = None - - while True: - try: - start_time = time.time() - - # Collect new data - current_data = await self.collect_data() - if not current_data: - self.logger.warning("No data collected") - await asyncio.sleep(update_interval) - continue - - # Generate report - report_path = self.generate_report(current_data) - if report_path: - self.logger.info( - f"Dashboard updated at {datetime.now().strftime('%H:%M:%S')}" - ) - - print("Curr:", len(current_data)) - print("Prev:", len(previous_data) if previous_data else "None") - if previous_data: - # Check for metric changes and play appropriate sounds - self.logger.debug(f"Checking metrics changes between {len(current_data)} current and {len(previous_data)} previous data points") - self._check_metrics_changes(current_data, previous_data) - - # Update previous data - previous_data = current_data.copy() # Make a copy to prevent reference issues - - # Calculate sleep time - elapsed = float(time.time() - start_time) - sleep_time = max(0.0, update_interval - elapsed) - await asyncio.sleep(sleep_time) - - except Exception as e: - self.logger.error(f"Error in dashboard update loop: {e}", exc_info=True) - if self.debug: - raise - await asyncio.sleep(update_interval) - -async def main(): - token = os.getenv("CIRCLECI_TOKEN") - project_slug = os.getenv("CIRCLECI_PROJECT_SLUG") - debug = os.getenv("DEBUG", "").lower() in ("true", "1", "yes") - - try: - # Get update interval from environment or use default - update_interval = float(os.getenv("UPDATE_INTERVAL", "10")) - print(f"Update interval type: {type(update_interval)}, value: {update_interval}") # Debug print - except ValueError as e: - print(f"Error converting UPDATE_INTERVAL to float: {os.getenv('UPDATE_INTERVAL')}") - update_interval = 10.0 - - if not token or not project_slug: - print("Error: Please set CIRCLECI_TOKEN and CIRCLECI_PROJECT_SLUG environment variables") - return - - tracker = PackageSizeTracker(token, project_slug, debug) - - try: - await tracker.run_dashboard(update_interval) - except KeyboardInterrupt: - print("\nDashboard stopped by user") - except Exception as e: - logging.error(f"Error: {str(e)}", exc_info=True) - if debug: - raise - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/extra/dashboard/requirements.txt b/extra/dashboard/requirements.txt deleted file mode 100644 index 7b978bc0..00000000 --- a/extra/dashboard/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -plotly -pandas -requests -aiohttp -pygame \ No newline at end of file diff --git a/extra/dashboard/sounds/gta5_wasted.mp3 b/extra/dashboard/sounds/gta5_wasted.mp3 deleted file mode 100644 index 2723ff8a..00000000 --- a/extra/dashboard/sounds/gta5_wasted.mp3 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fb3fb66dd02827fbff86ef1ce3bc6438371c823aed7d4c3803ed522f008e4947 -size 206399 diff --git a/extra/dashboard/sounds/pokemon_evolve.mp3 b/extra/dashboard/sounds/pokemon_evolve.mp3 deleted file mode 100644 index 35b01432..00000000 --- a/extra/dashboard/sounds/pokemon_evolve.mp3 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d99cc9bdab4a4639d50f439b424547000e7c79f195b5b121734ad4ead435911c -size 633345 diff --git a/extra/line_counter.py b/extra/line_counter.py deleted file mode 100644 index 01e7ee20..00000000 --- a/extra/line_counter.py +++ /dev/null @@ -1,210 +0,0 @@ -#!/usr/bin/env python3 -import os -import sys -import json -import token -import tokenize -from datetime import datetime, timezone - -TOKEN_WHITELIST = [token.OP, token.NAME, token.NUMBER, token.STRING] - -def is_docstring(t): - return t.type == token.STRING and t.string.startswith('"""') and t.line.strip().startswith('"""') - -def gen_stats(base_path="."): - table = [] - exo_path = os.path.join(base_path, "exo") - if not os.path.exists(exo_path): - print(f"Warning: {exo_path} directory not found") - return table - - for path, _, files in os.walk(exo_path): - for name in files: - if not name.endswith(".py"): - continue - - filepath = os.path.join(path, name) - relfilepath = os.path.relpath(filepath, base_path).replace('\\', '/') - - try: - with tokenize.open(filepath) as file_: - tokens = [t for t in tokenize.generate_tokens(file_.readline) - if t.type in TOKEN_WHITELIST and not is_docstring(t)] - token_count = len(tokens) - line_count = len(set([x for t in tokens - for x in range(t.start[0], t.end[0]+1)])) - if line_count > 0: - table.append([relfilepath, line_count, token_count/line_count]) - except Exception as e: - print(f"Error processing {filepath}: {e}") - continue - - return table - -def gen_diff(table_old, table_new): - table = [] - files_new = set([x[0] for x in table_new]) - files_old = set([x[0] for x in table_old]) - - added = files_new - files_old - deleted = files_old - files_new - unchanged = files_new & files_old - - for file in added: - file_stat = [stats for stats in table_new if file in stats][0] - table.append([file_stat[0], file_stat[1], file_stat[1], file_stat[2], file_stat[2]]) - - for file in deleted: - file_stat = [stats for stats in table_old if file in stats][0] - table.append([file_stat[0], 0, -file_stat[1], 0, -file_stat[2]]) - - for file in unchanged: - file_stat_old = [stats for stats in table_old if file in stats][0] - file_stat_new = [stats for stats in table_new if file in stats][0] - if file_stat_new[1] != file_stat_old[1] or file_stat_new[2] != file_stat_old[2]: - table.append([ - file_stat_new[0], - file_stat_new[1], - file_stat_new[1] - file_stat_old[1], - file_stat_new[2], - file_stat_new[2] - file_stat_old[2] - ]) - - return table - -def create_json_report(table, is_diff=False): - timestamp = datetime.now(timezone.utc).isoformat() - commit_sha = os.environ.get('GITHUB_SHA', 'unknown') - branch = os.environ.get('GITHUB_REF_NAME', 'unknown') - pr_number = os.environ.get('GITHUB_EVENT_NUMBER', '') - - if is_diff: - files = [{ - 'name': row[0], - 'current_lines': row[1], - 'line_diff': row[2], - 'current_tokens_per_line': row[3], - 'tokens_per_line_diff': row[4] - } for row in table] - - report = { - 'type': 'diff', - 'timestamp': timestamp, - 'commit_sha': commit_sha, - 'branch': branch, - 'pr_number': pr_number, - 'files': files, - 'total_line_changes': sum(row[2] for row in table), - 'total_files_changed': len(files) - } - else: - files = [{ - 'name': row[0], - 'lines': row[1], - 'tokens_per_line': row[2] - } for row in table] - - report = { - 'type': 'snapshot', - 'timestamp': timestamp, - 'commit_sha': commit_sha, - 'branch': branch, - 'files': files, - 'total_lines': sum(row[1] for row in table), - 'total_files': len(files) - } - - return report - -def display_diff(diff): - return "+" + str(diff) if diff > 0 else str(diff) - -def format_table(rows, headers, floatfmt): - if not rows: - return "" - - # Add headers as first row - all_rows = [headers] + rows - - # Calculate column widths - col_widths = [] - for col in range(len(headers)): - col_width = max(len(str(row[col])) for row in all_rows) - col_widths.append(col_width) - - # Format rows - output = [] - for row_idx, row in enumerate(all_rows): - formatted_cols = [] - for col_idx, (value, width) in enumerate(zip(row, col_widths)): - if isinstance(value, float): - # Handle float formatting based on floatfmt - fmt = floatfmt[col_idx] - if fmt.startswith('+'): - value = f"{value:+.1f}" - else: - value = f"{value:.1f}" - elif isinstance(value, int) and col_idx > 0: # Skip filename column - # Handle integer formatting based on floatfmt - fmt = floatfmt[col_idx] - if fmt.startswith('+'): - value = f"{value:+d}" - else: - value = f"{value:d}" - formatted_cols.append(str(value).ljust(width)) - output.append(" ".join(formatted_cols)) - - # Add separator line after headers - if row_idx == 0: - separator = [] - for width in col_widths: - separator.append("-" * width) - output.append(" ".join(separator)) - - return "\n".join(output) - -if __name__ == "__main__": - if len(sys.argv) == 3: - # Comparing two directories - headers = ["File", "Lines", "Diff", "Tokens/Line", "Diff"] - table = gen_diff(gen_stats(sys.argv[1]), gen_stats(sys.argv[2])) - - if table: - # Print table output - print("### Code Changes in 'exo' Directory") - print("```") - print(format_table( - sorted(table, key=lambda x: abs(x[2]) if len(x) > 2 else 0, reverse=True), - headers, - (".1f", "d", "+d", ".1f", "+.1f") - )) - total_changes = sum(row[2] for row in table) - print(f"\nTotal line changes: {display_diff(total_changes)}") - print("```") - - # Generate JSON report - report = create_json_report(table, is_diff=True) - with open('line-count-diff.json', 'w') as f: - json.dump(report, f, indent=2) - else: - # Single directory analysis - headers = ["File", "Lines", "Tokens/Line"] - table = gen_stats(sys.argv[1] if len(sys.argv) > 1 else ".") - - if table: - # Print table output - print("### Code Statistics for 'exo' Directory") - print("```") - print(format_table( - sorted(table, key=lambda x: x[1], reverse=True), - headers, - (".1f", "d", ".1f") - )) - total_lines = sum(row[1] for row in table) - print(f"\nTotal lines: {total_lines}") - print("```") - - # Generate JSON report - report = create_json_report(table, is_diff=False) - with open('line-count-snapshot.json', 'w') as f: - json.dump(report, f, indent=2) diff --git a/extra/pipsize.py b/extra/pipsize.py deleted file mode 100644 index 406d177c..00000000 --- a/extra/pipsize.py +++ /dev/null @@ -1,113 +0,0 @@ -import os -import importlib.metadata -import importlib.util -import json -import sys - - -def calc_container(path): - """Calculate total size of a directory or file.""" - if os.path.isfile(path): - try: - return os.path.getsize(path) - except (OSError, FileNotFoundError): - return 0 - - total_size = 0 - for dirpath, dirnames, filenames in os.walk(path): - for f in filenames: - fp = os.path.join(dirpath, f) - try: - total_size += os.path.getsize(fp) - except (OSError, FileNotFoundError): - continue - return total_size - - -def get_package_location(package_name): - """Get the actual location of a package's files.""" - try: - spec = importlib.util.find_spec(package_name) - if spec is None: - return None - - if spec.submodule_search_locations: - # Return the first location for namespace packages - return spec.submodule_search_locations[0] - elif spec.origin: - # For single-file modules, return the file path itself - return spec.origin - except ImportError: - return None - - -def get_package_sizes(min_size_mb=0.1): - """Get sizes of installed packages above minimum size threshold.""" - package_sizes = [] - - # Get all installed distributions - for dist in importlib.metadata.distributions(): - try: - package_name = dist.metadata["Name"] - location = get_package_location(package_name.replace("-", "_")) - - if location and os.path.exists(location): - size = calc_container(location) - size_mb = size / (1024 * 1024) - - if size_mb > min_size_mb: - package_sizes.append((package_name, size)) - except Exception as e: - print( - f"Error processing {dist.metadata.get('Name', 'Unknown package')}: {e}" - ) - - return package_sizes - - -def main(): - # Get and sort package sizes - package_sizes = get_package_sizes() - package_sizes.sort(key=lambda x: x[1], reverse=True) - - # Convert sizes to MB and prepare data - table_data = [(name, size/(1024*1024)) for name, size in package_sizes] - total_size = sum(size for _, size in package_sizes)/(1024*1024) - - # Check if --json flag is present - if "--json" in sys.argv: - try: - output_file = sys.argv[sys.argv.index("--json") + 1] - json_data = { - "packages": [{ - "name": name, - "size_mb": round(size, 2) - } for name, size in table_data], - "total_size_mb": round(total_size, 2) - } - - with open(output_file, 'w') as f: - json.dump(json_data, f, indent=2) - print(f"JSON data written to {output_file}") - return - except IndexError: - print("Error: Please provide a filename after --json") - sys.exit(1) - except Exception as e: - print(f"Error writing JSON file: {e}") - sys.exit(1) - - # Original table output code - max_name_width = max(len(name) for name, _ in table_data) - max_name_width = max(max_name_width, len("Package")) - - print(f"\n{'Package':<{max_name_width}} | Size (MB)") - print("-" * max_name_width + "-+-" + "-" * 10) - - for name, size in table_data: - print(f"{name:<{max_name_width}} | {size:>8.2f}") - - print(f"\nTotal size: {total_size:.2f} MB\n") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/extra/start_openwebui.sh b/extra/start_openwebui.sh deleted file mode 100755 index 2cf3d66a..00000000 --- a/extra/start_openwebui.sh +++ /dev/null @@ -1,3 +0,0 @@ -API_ENDPOINT="http://${API_ENDPOINT:-$(ifconfig | grep 'inet ' | grep -v '127.0.0.1' | awk '{print $2}' | head -n 1):52415}" -echo "Using API_ENDPOINT=${API_ENDPOINT}" -docker run -d -p 3000:8080 -e OPENAI_API_BASE_URL="${API_ENDPOINT}" -e OPENAI_API_KEY=your_secret_key -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main diff --git a/format.py b/format.py deleted file mode 100644 index 68842493..00000000 --- a/format.py +++ /dev/null @@ -1,33 +0,0 @@ -#!/usr/bin/env python -import subprocess -import sys -import os - - -def run_yapf(target): - if os.path.isfile(target): - files = [target] - else: - files = [os.path.join(root, file) for root, _, files in os.walk(target) for file in files if file.endswith('.py')] - - for file in files: - try: - command = ["yapf", "-i", file] - subprocess.run(command, check=True, capture_output=True, text=True) - print(f"Formatted: {file}") - except subprocess.CalledProcessError as e: - print(f"Error formatting {file}: {e.stderr}") - - -def main(): - if len(sys.argv) < 2: - print("Usage: python3 format.py e.g. python3 format.py ./exo") - sys.exit(1) - - target = sys.argv[1] - run_yapf(target) - print("Formatting completed.") - - -if __name__ == "__main__": - main() diff --git a/install.sh b/install.sh deleted file mode 100755 index a5fffec6..00000000 --- a/install.sh +++ /dev/null @@ -1,11 +0,0 @@ -#!/usr/bin/env bash - -if command -v python3.12 &>/dev/null; then - echo "Python 3.12 is installed, proceeding with python3.12..." - python3.12 -m venv .venv -else - echo "The recommended version of Python to run exo with is Python 3.12, but $(python3 --version) is installed. Proceeding with $(python3 --version)" - python3 -m venv .venv -fi -source .venv/bin/activate -pip install -e . diff --git a/scripts/build_exo.py b/scripts/build_exo.py deleted file mode 100644 index be3704e1..00000000 --- a/scripts/build_exo.py +++ /dev/null @@ -1,64 +0,0 @@ -import site -import subprocess -import sys -import os -import pkgutil - -def run(): - site_packages = site.getsitepackages()[0] - base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - baseimages_dir = os.path.join(base_dir, "exo", "apputil", "baseimages") - - command = [ - f"{sys.executable}", "-m", "nuitka", "exo/main.py", - "--company-name=exolabs", - "--product-name=exo", - "--output-dir=dist", - "--follow-imports", - "--standalone", - "--output-filename=exo", - "--python-flag=no_site", - "--onefile", - f"--include-data-dir={baseimages_dir}=exo/apputil/baseimages" - ] - - if sys.platform == "darwin": - command.extend([ - "--macos-app-name=exo", - "--macos-app-mode=gui", - "--macos-app-version=0.0.1", - "--macos-signed-app-name=net.exolabs.exo", - "--include-distribution-meta=mlx", - "--include-module=mlx._reprlib_fix", - "--include-module=mlx._os_warning", - "--include-distribution-meta=huggingface_hub", - "--include-module=huggingface_hub.repocard", - f"--include-data-files={site_packages}/mlx/lib/mlx.metallib=mlx/lib/mlx.metallib", - f"--include-data-files={site_packages}/mlx/lib/mlx.metallib=./mlx.metallib", - "--include-distribution-meta=pygments", - "--nofollow-import-to=tinygrad" - ]) - inference_modules = [ - name for _, name, _ in pkgutil.iter_modules(['exo/inference/mlx/models']) - ] - for module in inference_modules: - command.append(f"--include-module=exo.inference.mlx.models.{module}") - elif sys.platform == "win32": - command.extend([ - "--windows-icon-from-ico=docs/exo-logo-win.ico", - "--file-version=0.0.1", - "--product-version=0.0.1" - ]) - elif sys.platform.startswith("linux"): - command.extend([ - "--include-distribution-metadata=pygments", - "--linux-icon=docs/exo-rounded.png" - ]) - try: - subprocess.run(command, check=True) - print("Build completed!") - except subprocess.CalledProcessError as e: - print(f"An error occurred: {e}") - -if __name__ == "__main__": - run() diff --git a/scripts/compile_grpc.sh b/scripts/compile_grpc.sh deleted file mode 100755 index b0333bb5..00000000 --- a/scripts/compile_grpc.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/usr/bin/env bash -source ./install.sh -pushd exo/networking/grpc -python3 -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. node_service.proto -sed -i '' "s/import\ node_service_pb2/from . &/" node_service_pb2_grpc.py -popd - diff --git a/setup.py b/setup.py deleted file mode 100644 index 6f922c43..00000000 --- a/setup.py +++ /dev/null @@ -1,89 +0,0 @@ -import sys -import platform -import subprocess - -from setuptools import find_packages, setup - -# Base requirements for all platforms -install_requires = [ - "aiohttp==3.10.11", - "aiohttp_cors==0.7.0", - "aiofiles==24.1.0", - "grpcio==1.70.0", - "grpcio-tools==1.70.0", - "Jinja2==3.1.4", - "numpy==2.0.0", - "nuitka==2.5.1", - "nvidia-ml-py==12.560.30", - "opencv-python==4.10.0.84", - "pillow==10.4.0", - "prometheus-client==0.20.0", - "protobuf==5.28.1", - "psutil==6.0.0", - "pyamdgpuinfo==2.1.6;platform_system=='Linux'", - "pydantic==2.9.2", - "requests==2.32.3", - "rich==13.7.1", - "scapy==2.6.1", - "tqdm==4.66.4", - "transformers==4.46.3", - "uuid==1.30", - "uvloop==0.21.0", - "tinygrad @ git+https://github.com/tinygrad/tinygrad.git@ec120ce6b9ce8e4ff4b5692566a683ef240e8bc8", -] - -extras_require = { - "formatting": ["yapf==0.40.2",], - "apple_silicon": [ - "mlx==0.22.0", - "mlx-lm==0.21.1", - ], - "windows": ["pywin32==308",], - "nvidia-gpu": ["nvidia-ml-py==12.560.30",], - "amd-gpu": ["pyrsmi==0.2.0"], -} - -# Check if running on macOS with Apple Silicon -if sys.platform.startswith("darwin") and platform.machine() == "arm64": - install_requires.extend(extras_require["apple_silicon"]) - -# Check if running Windows -if sys.platform.startswith("win32"): - install_requires.extend(extras_require["windows"]) - - -def _add_gpu_requires(): - global install_requires - # Add Nvidia-GPU - try: - out = subprocess.run(['nvidia-smi', '--query-gpu=name', '--format=csv,noheader'], shell=True, text=True, capture_output=True, check=False) - if out.returncode == 0: - install_requires.extend(extras_require["nvidia-gpu"]) - except subprocess.CalledProcessError: - pass - - # Add AMD-GPU - # This will mostly work only on Linux, amd/rocm-smi is not yet supported on Windows - try: - out = subprocess.run(['amd-smi', 'list', '--csv'], shell=True, text=True, capture_output=True, check=False) - if out.returncode == 0: - install_requires.extend(extras_require["amd-gpu"]) - except: - out = subprocess.run(['rocm-smi', 'list', '--csv'], shell=True, text=True, capture_output=True, check=False) - if out.returncode == 0: - install_requires.extend(extras_require["amd-gpu"]) - finally: - pass - - -_add_gpu_requires() - -setup( - name="exo", - version="0.0.1", - packages=find_packages(), - install_requires=install_requires, - extras_require=extras_require, - package_data={"exo": ["tinychat/**/*"]}, - entry_points={"console_scripts": ["exo = exo.main:run"]}, -) diff --git a/test/reconnect.sh b/test/reconnect.sh deleted file mode 100755 index 1e9a2add..00000000 --- a/test/reconnect.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env bash - -echo "Starting node 1" -DEBUG_DISCOVERY=7 DEBUG=7 python3 main.py --node-id "node1" --listen-port 5678 --broadcast-port 5679 --chatgpt-api-port 52415 --chatgpt-api-response-timeout 900 > output1.log 2>&1 & -PID1=$! -echo "Started node 1 PID: $PID1" -echo "Starting node 2" -DEBUG_DISCOVERY=7 DEBUG=7 python3 main.py --node-id "node2" --listen-port 5679 --broadcast-port 5678 --chatgpt-api-port 8001 --chatgpt-api-response-timeout 900 > output2.log 2>&1 & -PID2=$! -echo "Started node 2 PID: $PID2" -sleep 5 -kill $PID2 -sleep 5 -echo "Starting node 2 again..." -DEBUG_DISCOVERY=7 DEBUG=7 python3 main.py --node-id "node2" --listen-port 5679 --broadcast-port 5678 --chatgpt-api-port 8001 --chatgpt-api-response-timeout 900 > output3.log 2>&1 & -PID2=$! -sleep 5 -echo "Killing nodes and ending test..." -kill $PID1 -kill $PID2 -echo "Test complete." \ No newline at end of file diff --git a/test/test_model_helpers.py b/test/test_model_helpers.py deleted file mode 100644 index a8215017..00000000 --- a/test/test_model_helpers.py +++ /dev/null @@ -1,121 +0,0 @@ -import unittest -from exo.models import get_supported_models, model_cards -from exo.inference.inference_engine import inference_engine_classes -from typing import NamedTuple - -class TestCase(NamedTuple): - name: str - engine_lists: list # Will contain short names, will be mapped to class names - expected_models_contains: list - min_count: int | None - exact_count: int | None - max_count: int | None - -# Helper function to map short names to class names -def expand_engine_lists(engine_lists): - def map_engine(engine): - return inference_engine_classes.get(engine, engine) # Return original name if not found - - return [[map_engine(engine) for engine in sublist] - for sublist in engine_lists] - -test_cases = [ - TestCase( - name="single_mlx_engine", - engine_lists=[["mlx"]], - expected_models_contains=["llama-3.2-1b", "llama-3.1-70b", "mistral-nemo"], - min_count=10, - exact_count=None, - max_count=None - ), - TestCase( - name="single_tinygrad_engine", - engine_lists=[["tinygrad"]], - expected_models_contains=["llama-3.2-1b", "llama-3.2-3b"], - min_count=5, - exact_count=None, - max_count=15 - ), - TestCase( - name="multiple_engines_or", - engine_lists=[["mlx", "tinygrad"], ["mlx"]], - expected_models_contains=["llama-3.2-1b", "llama-3.2-3b", "mistral-nemo"], - min_count=10, - exact_count=None, - max_count=None - ), - TestCase( - name="multiple_engines_all", - engine_lists=[["mlx", "tinygrad"], ["mlx", "tinygrad"]], - expected_models_contains=["llama-3.2-1b", "llama-3.2-3b", "mistral-nemo"], - min_count=10, - exact_count=None, - max_count=None - ), - TestCase( - name="distinct_engine_lists", - engine_lists=[["mlx"], ["tinygrad"]], - expected_models_contains=["llama-3.2-1b"], - min_count=5, - exact_count=None, - max_count=15 - ), - TestCase( - name="no_engines", - engine_lists=[], - expected_models_contains=None, - min_count=None, - exact_count=len(model_cards), - max_count=None - ), - TestCase( - name="nonexistent_engine", - engine_lists=[["NonexistentEngine"]], - expected_models_contains=[], - min_count=None, - exact_count=0, - max_count=None - ), - TestCase( - name="dummy_engine", - engine_lists=[["dummy"]], - expected_models_contains=["dummy"], - min_count=None, - exact_count=1, - max_count=None - ), -] - -class TestModelHelpers(unittest.TestCase): - def test_get_supported_models(self): - for case in test_cases: - with self.subTest(f"{case.name}_short_names"): - result = get_supported_models(case.engine_lists) - self._verify_results(case, result) - - with self.subTest(f"{case.name}_class_names"): - class_name_lists = expand_engine_lists(case.engine_lists) - result = get_supported_models(class_name_lists) - self._verify_results(case, result) - - def _verify_results(self, case, result): - if case.expected_models_contains: - for model in case.expected_models_contains: - self.assertIn(model, result) - - if case.min_count: - self.assertGreater(len(result), case.min_count) - - if case.exact_count is not None: - self.assertEqual(len(result), case.exact_count) - - # Special case for distinct lists test - if case.name == "distinct_engine_lists": - self.assertLess(len(result), 15) - self.assertNotIn("mistral-nemo", result) - - if case.max_count: - self.assertLess(len(result), case.max_count) - -if __name__ == '__main__': - unittest.main() diff --git a/test/test_tokenizers.py b/test/test_tokenizers.py deleted file mode 100644 index 6e1b383e..00000000 --- a/test/test_tokenizers.py +++ /dev/null @@ -1,42 +0,0 @@ -import os -import re -from transformers import AutoTokenizer, AutoProcessor -from exo.models import model_cards - - -def test_tokenizer(name, tokenizer, verbose=False): - print(f"--- {name} ({tokenizer.__class__.__name__}) ---") - text = "Hello! How can I assist you today? Let me know if you need help with something or just want to chat." - encoded = tokenizer.encode(text) - decoded = tokenizer.decode(encoded) - - print(f"{encoded=}") - print(f"{decoded=}") - - reconstructed = "" - for token in encoded: - if verbose: - print(f"{token=}") - print(f"{tokenizer.decode([token])=}") - reconstructed += tokenizer.decode([token]) - print(f"{reconstructed=}") - - strip_tokens = lambda s: s.lstrip(tokenizer.decode([tokenizer.bos_token_id])).rstrip(tokenizer.decode([tokenizer.eos_token_id])) - assert text == strip_tokens(decoded) == strip_tokens(reconstructed) - -ignore = ["TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-70B-R", "mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx", "mlx-community/DeepSeek-V2.5-MLX-AQ4_1_64", "llava-hf/llava-1.5-7b-hf", "mlx-community/Qwen*", "dummy", "mlx-community/Meta-Llama-3.1-405B-Instruct-8bit", "mlx-community/Phi-3.5-mini-instruct-4bit", "mlx-community/phi-4-4bit", "stabilityai/stable-diffusion-2-1-base"] -ignore_pattern = re.compile(r"^(" + "|".join(model.replace("*", ".*") for model in ignore) + r")") -models = [] -for model_id in model_cards: - for engine_type, repo_id in model_cards[model_id].get("repo", {}).items(): - if not ignore_pattern.match(repo_id): - models.append(repo_id) -models = list(set(models)) - -verbose = os.environ.get("VERBOSE", "0").lower() == "1" -for m in models: - # TODO: figure out why use_fast=False is giving inconsistent behaviour (no spaces decoding invididual tokens) for Mistral-Large-Instruct-2407-4bit - # test_tokenizer(m, AutoProcessor.from_pretrained(m, use_fast=False), verbose) - if m not in ["mlx-community/DeepSeek-R1-4bit", "mlx-community/DeepSeek-R1-3bit", "mlx-community/DeepSeek-V3-4bit", "mlx-community/DeepSeek-V3-3bit"]: - test_tokenizer(m, AutoProcessor.from_pretrained(m, use_fast=True, trust_remote_code=True), verbose) - test_tokenizer(m, AutoTokenizer.from_pretrained(m, trust_remote_code=True), verbose)