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35 lines
1.6 KiB
Python
35 lines
1.6 KiB
Python
from transformers import AutoTokenizer, AutoProcessor
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from exo.models import model_base_shards
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def test_tokenizer(name, tokenizer, verbose=False):
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print(f"--- {name} ({tokenizer.__class__.__name__}) ---")
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text = "Hello! How can I assist you today? Let me know if you need help with something or just want to chat."
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encoded = tokenizer.encode(text)
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decoded = tokenizer.decode(encoded)
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print(f"{encoded=}")
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print(f"{decoded=}")
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reconstructed = ""
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for token in encoded:
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if verbose:
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print(f"{token=}")
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print(f"{tokenizer.decode([token])=}")
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reconstructed += tokenizer.decode([token])
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print(f"{reconstructed=}")
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strip_tokens = lambda s: s.lstrip(tokenizer.decode([tokenizer.bos_token_id])).rstrip(tokenizer.decode([tokenizer.eos_token_id]))
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assert text == strip_tokens(decoded) == strip_tokens(reconstructed)
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ignore = ["TriAiExperiments/SFR-Iterative-DPO-LLaMA-3-70B-R", "mlx-community/DeepSeek-Coder-V2-Lite-Instruct-4bit-mlx", "llava-hf/llava-1.5-7b-hf"]
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models = [shard.model_id for shards in model_base_shards.values() for shard in shards.values() if shard.model_id not in ignore]
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import os
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verbose = os.environ.get("VERBOSE", "0").lower() == "1"
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for m in models:
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# TODO: figure out why use_fast=False is giving inconsistent behaviour (no spaces decoding invididual tokens) for Mistral-Large-Instruct-2407-4bit
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# test_tokenizer(m, AutoProcessor.from_pretrained(m, use_fast=False), verbose)
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test_tokenizer(m, AutoProcessor.from_pretrained(m, use_fast=True), verbose)
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test_tokenizer(m, AutoTokenizer.from_pretrained(m), verbose)
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