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https://github.com/huggingface/text-generation-inference.git
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8511669cb2
Quantized weights were loaded in the `Weights` class, but this was getting quite unwieldy, where every higher level method to load weights was a long conditional to cover all the different quantizers. This change moves loading of quantized weights out of the `Weights` class. This is done by defining a simple `WeightsLoader` interface that is implemented by `Exl2WeightsLoader`, `GPTQWeightsLoader`, and `MarlinWeightsLoader`. These implementations are in the quantizers' respective modules. The `Weights` class provides the low-level load operations (such as loading tensors or sharded tensors), but delegates loads that need quantizer-specific weight processing to a loader. The loaders still use the low-level functionality provided by `Weights`. I initially tried making a hierarchy where a class like `GPTQWeights` would inherit from `Weights`. But it is not very flexible (e.g. does not work well with the new weight storage mock used in tests) and the implicit indirections made the code harder to follow.
120 lines
4.0 KiB
Python
120 lines
4.0 KiB
Python
from typing import Optional
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import os
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import json
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from dataclasses import dataclass
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from huggingface_hub import hf_hub_download
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from text_generation_server.utils.weights import DefaultWeightsLoader, WeightsLoader
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@dataclass
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class _QuantizerConfig:
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bits: int
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checkpoint_format: Optional[str]
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desc_act: bool
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groupsize: int
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quant_method: str
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sym: bool
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# We should probably do this with Pytantic JSON deserialization,
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# but for now we'll stay close to the old _set_gptq_params.
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def _get_quantizer_config(model_id, revision):
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bits = 4
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groupsize = -1
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quant_method = "gptq"
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checkpoint_format = None
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sym = True
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desc_act = False
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filename = "config.json"
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try:
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if os.path.exists(os.path.join(model_id, filename)):
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filename = os.path.join(model_id, filename)
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else:
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filename = hf_hub_download(model_id, filename=filename, revision=revision)
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with open(filename, "r") as f:
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data = json.load(f)
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bits = data["quantization_config"]["bits"]
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groupsize = data["quantization_config"]["group_size"]
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# Order is important here, desc_act is missing on some real models
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quant_method = data["quantization_config"]["quant_method"]
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checkpoint_format = data["quantization_config"].get("checkpoint_format")
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sym = data["quantization_config"]["sym"]
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desc_act = data["quantization_config"]["desc_act"]
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except Exception:
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filename = "quantize_config.json"
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try:
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if os.path.exists(os.path.join(model_id, filename)):
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filename = os.path.join(model_id, filename)
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else:
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filename = hf_hub_download(
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model_id, filename=filename, revision=revision
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)
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with open(filename, "r") as f:
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data = json.load(f)
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bits = data["bits"]
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groupsize = data["group_size"]
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sym = data["sym"]
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desc_act = data["desc_act"]
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if "version" in data and data["version"] == "GEMM":
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quant_method = "awq"
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except Exception:
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filename = "quant_config.json"
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try:
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if os.path.exists(os.path.join(model_id, filename)):
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filename = os.path.join(model_id, filename)
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else:
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filename = hf_hub_download(
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model_id, filename=filename, revision=revision
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)
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with open(filename, "r") as f:
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data = json.load(f)
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bits = data["w_bit"]
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groupsize = data["q_group_size"]
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desc_act = data["desc_act"]
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if "version" in data and data["version"] == "GEMM":
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quant_method = "awq"
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except Exception:
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pass
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return _QuantizerConfig(
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bits=bits,
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groupsize=groupsize,
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quant_method=quant_method,
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checkpoint_format=checkpoint_format,
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sym=sym,
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desc_act=desc_act,
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)
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def get_loader(
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quantize: Optional[str], model_id: str, revision: Optional[str]
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) -> WeightsLoader:
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quantizer_config = _get_quantizer_config(model_id, revision)
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if quantize in {"awq", "gptq"}:
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from text_generation_server.layers.gptq import GPTQWeightsLoader
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return GPTQWeightsLoader(
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bits=quantizer_config.bits,
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desc_act=quantizer_config.desc_act,
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groupsize=quantizer_config.groupsize,
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quant_method=quantizer_config.quant_method,
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quantize=quantize,
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sym=quantizer_config.sym,
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)
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elif quantize == "exl2":
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from text_generation_server.layers.exl2 import Exl2WeightsLoader
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return Exl2WeightsLoader()
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elif quantize == "marlin":
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from text_generation_server.layers.marlin import MarlinWeightsLoader
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return MarlinWeightsLoader(
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bits=quantizer_config.bits,
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is_marlin_24=quantizer_config.checkpoint_format == "marlin_24",
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)
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else:
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return DefaultWeightsLoader()
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