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60 lines
4.4 KiB
Python
60 lines
4.4 KiB
Python
# mypy: disable-error-code="name-defined,no-redef"
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from __future__ import annotations
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import logging, typing as t
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from openllm_core.utils import LazyLoader, is_autogptq_available, is_bitsandbytes_available, is_transformers_supports_kbit, pkg
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from openllm_core._typing_compat import overload
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if t.TYPE_CHECKING:
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from ._llm import LLM
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from openllm_core._typing_compat import DictStrAny
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autogptq, torch, transformers = LazyLoader("autogptq", globals(), "auto_gptq"), LazyLoader("torch", globals(), "torch"), LazyLoader("transformers", globals(), "transformers")
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logger = logging.getLogger(__name__)
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QuantiseMode = t.Literal["int8", "int4", "gptq"]
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@overload
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def infer_quantisation_config(cls: type[LLM[t.Any, t.Any]], quantise: t.Literal["int8", "int4"], **attrs: t.Any) -> tuple[transformers.BitsAndBytesConfig, DictStrAny]:
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...
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@overload
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def infer_quantisation_config(cls: type[LLM[t.Any, t.Any]], quantise: t.Literal["gptq"], **attrs: t.Any) -> tuple[autogptq.BaseQuantizeConfig, DictStrAny]:
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...
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def infer_quantisation_config(cls: type[LLM[t.Any, t.Any]], quantise: QuantiseMode, **attrs: t.Any) -> tuple[transformers.BitsAndBytesConfig | autogptq.BaseQuantizeConfig, DictStrAny]:
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# 8 bit configuration
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int8_threshold = attrs.pop("llm_int8_threshhold", 6.0)
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int8_enable_fp32_cpu_offload = attrs.pop("llm_int8_enable_fp32_cpu_offload", False)
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int8_skip_modules: list[str] | None = attrs.pop("llm_int8_skip_modules", None)
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int8_has_fp16_weight = attrs.pop("llm_int8_has_fp16_weight", False)
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autogptq_attrs: DictStrAny = {"bits": attrs.pop("gptq_bits", 4), "group_size": attrs.pop("gptq_group_size", -1), "damp_percent": attrs.pop("gptq_damp_percent", 0.01), "desc_act": attrs.pop("gptq_desc_act", True), "sym": attrs.pop("gptq_sym", True), "true_sequential": attrs.pop("gptq_true_sequential", True),}
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def create_int8_config(int8_skip_modules: list[str] | None) -> transformers.BitsAndBytesConfig:
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if int8_skip_modules is None: int8_skip_modules = []
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if "lm_head" not in int8_skip_modules and cls.config_class.__openllm_model_type__ == "causal_lm":
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logger.debug("Skipping 'lm_head' for quantization for %s", cls.__name__)
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int8_skip_modules.append("lm_head")
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return transformers.BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=int8_enable_fp32_cpu_offload, llm_int8_threshhold=int8_threshold, llm_int8_skip_modules=int8_skip_modules, llm_int8_has_fp16_weight=int8_has_fp16_weight,)
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# 4 bit configuration
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int4_compute_dtype = attrs.pop("bnb_4bit_compute_dtype", torch.bfloat16)
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int4_quant_type = attrs.pop("bnb_4bit_quant_type", "nf4")
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int4_use_double_quant = attrs.pop("bnb_4bit_use_double_quant", True)
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# NOTE: Quantization setup
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# quantize is a openllm.LLM feature, where we can quantize the model
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# with bitsandbytes or quantization aware training.
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if not is_bitsandbytes_available(): raise RuntimeError("Quantization requires bitsandbytes to be installed. Make sure to install OpenLLM with 'pip install \"openllm[fine-tune]\"'")
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if quantise == "int8": quantisation_config = create_int8_config(int8_skip_modules)
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elif quantise == "int4":
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if is_transformers_supports_kbit(): quantisation_config = transformers.BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=int4_compute_dtype, bnb_4bit_quant_type=int4_quant_type, bnb_4bit_use_double_quant=int4_use_double_quant)
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else:
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logger.warning("'quantize' is set to int4, while the current transformers version %s does not support k-bit quantization. k-bit quantization is supported since transformers 4.30, therefore make sure to install the latest version of transformers either via PyPI or from git source: 'pip install git+https://github.com/huggingface/transformers'. Fallback to int8 quantisation.", pkg.pkg_version_info("transformers"))
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quantisation_config = create_int8_config(int8_skip_modules)
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elif quantise == "gptq":
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if not is_autogptq_available():
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logger.warning("'quantize=\"gptq\"' requires 'auto-gptq' to be installed (not available with local environment). Make sure to have 'auto-gptq' available locally: 'pip install \"openllm[gptq]\"'. OpenLLM will fallback to int8 with bitsandbytes.")
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quantisation_config = create_int8_config(int8_skip_modules)
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else:
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quantisation_config = autogptq.BaseQuantizeConfig(**autogptq_attrs)
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else:
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raise ValueError(f"'quantize' must be one of ['int8', 'int4', 'gptq'], got {quantise} instead.")
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return quantisation_config, attrs
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