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263 lines
11 KiB
263 lines
11 KiB
4 days ago
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING
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from ..integrations import prepare_for_hqq_linear
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from ..utils import is_hqq_available, is_torch_available, logging
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from .base import HfQuantizer
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from .quantizers_utils import get_module_from_name
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if TYPE_CHECKING:
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from ..modeling_utils import PreTrainedModel
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if is_torch_available():
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import torch
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if is_hqq_available():
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from hqq.core.quantize import HQQLinear
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# This is a compatibility hack. HQQ-quantized linear layers do not have a `weight` attribute,
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# but some models attempt to access `weight.dtype` during the forward pass. To prevent runtime errors,
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# we patch HQQLinear with a dummy `weight` property that returns an empty tensor with the correct dtype and device.
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@property
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def weight(self):
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return torch.empty(0, dtype=self.compute_dtype, device=self.device)
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HQQLinear.weight = weight
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logger = logging.get_logger(__name__)
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class HqqHfQuantizer(HfQuantizer):
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"""
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HQQ quantizer base HF class.
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nn.Linear modules are first tagged with quant_config in _process_model_before_weight_loading().
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"""
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requires_calibration = False
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def __init__(self, quantization_config, **kwargs):
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if not is_hqq_available():
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raise ImportError(
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"A valid HQQ version (>=0.2.1) is not available. Please follow the instructions to install it: `https://github.com/mobiusml/hqq/`."
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)
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super().__init__(quantization_config, **kwargs)
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self.dtype = None
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self.using_multi_gpu = False
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# Keys that are serialized specifically by hqq
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self.hqq_keys = HQQLinear(None, None).state_dict_keys() - {"bias"}
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def validate_environment(self, *args, **kwargs):
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if self.dtype is None:
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if "dtype" in kwargs:
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self.dtype = kwargs["dtype"]
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else:
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self.dtype = torch.float32
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logger.info("Setting dtype to torch.float32 as the default value since it was not specified.")
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device_map = kwargs.get("device_map")
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if isinstance(device_map, dict):
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if "cpu" in device_map.values() or "disk" in device_map.values():
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raise ValueError(
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"You are attempting to use an HQQ model with a device_map that contains a CPU or disk device."
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" This is not supported. Please remove the CPU or disk device from the device_map."
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)
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else:
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self.using_multi_gpu = len(set(device_map.values())) > 1
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# TODO: to remove
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# Kept here in case we see some interest in adding support for it
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# # Adds missing keys for HQQLinear modules that are loaded but the model with initialized with torch.nn.Linear
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# def update_expected_keys(
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# self, model: "PreTrainedModel", expected_keys: list[str], loaded_keys: list[str]
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# ) -> list[str]:
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# if not self.pre_quantized:
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# return expected_keys
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# # Collects all quantizable (linear) layers
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# def _find_hqq_quantizable_layers(model, layers):
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# for name, module in model.named_children():
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# if isinstance(module, (torch.nn.Linear)):
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# layers.add(module.name)
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# _find_hqq_quantizable_layers(module, layers)
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# new_keys = set(expected_keys)
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# # Name modules
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# for name, module in model.named_modules():
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# module.name = name
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# # valid modules are Linear layers that have HQQLinear state_dict. We ignore skip_modules and any layers with Linear state_dict() params
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# _valid_modules = set()
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# _find_hqq_quantizable_layers(model, _valid_modules)
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# # Remove skipped modules
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# _skipped_modules = set()
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# for _module in _valid_modules:
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# for _skip_module in model.config.quantization_config["skip_modules"]:
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# if _skip_module in _module:
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# _skipped_modules.add(_module)
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# _valid_modules -= _skipped_modules
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# # Append new expected layers based on _ref_keys
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# _ref_keys = HQQLinear(
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# linear_layer=None,
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# quant_config=None,
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# compute_dtype=torch.float16,
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# device="cpu",
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# del_orig=False,
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# ).state_dict_keys() - {"bias"}
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# # Clean-up
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# _rm_keys = set()
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# for key in new_keys:
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# if any(_module in key for _module in _valid_modules):
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# _rm_keys.add(key)
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# new_keys -= _rm_keys
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# # At this point, new_keys contains all the keys of the layers that are NOT HQQLinear or torch.nn.Linear
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# # Re-populate Linear/HQQLinear
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# for _module in _valid_modules:
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# if _module + ".weight" in loaded_keys:
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# new_keys.add(_module + ".weight")
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# else:
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# new_keys.update({_module + "." + _ref_key for _ref_key in _ref_keys})
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# if _module + ".bias" in loaded_keys:
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# new_keys.add(_module + ".bias")
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# return list(new_keys)
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def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
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module, _ = get_module_from_name(model, param_name)
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# Since we do not prepare the modules in advance, we need every param of the Linear layer to go through
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# `create_quantized_param`, even when `self.is_quantized == True`
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return isinstance(module, torch.nn.Linear)
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# TODO: to remove
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# def create_quantized_param(
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# self,
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# model: "PreTrainedModel",
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# param_value: "torch.Tensor",
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# param_name: str,
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# target_device: "torch.device",
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# **kwargs,
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# ):
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# module, tensor_name = get_module_from_name(model, param_name)
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# module_name = param_name.rsplit(".", 1)[0]
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# parent_module, node = get_module_from_name(model, module_name)
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# quant_config = model.config.quantization_config["quant_config"]
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# skip_modules = model.config.quantization_config["skip_modules"]
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# # In this case we do not quantize this layer (it's explicitly skipped) -> simply load param
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# if any(skip_module in module.name for skip_module in skip_modules):
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# module.load_state_dict(
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# {tensor_name: param_value.to(device=target_device, dtype=self.dtype)}, strict=False, assign=True
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# )
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# return
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# # We need this hack as the model is not pre-prepared as an empty skeleton on meta device
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# if self.pre_quantized:
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# # Save them for later
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# if not hasattr(self, "hqq_params"):
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# self.hqq_params = defaultdict(dict)
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# self.hqq_params[module_name].update({tensor_name: param_value})
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# hqq_params = self.hqq_params[module_name]
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# # If they are all present and saved, make it a HQQLinear layer! (we cannot do it param after param because
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# # hqq does not support it...)
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# if all(k in hqq_params for k in self.hqq_keys) and ("bias" in hqq_params or module.bias is None):
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# hqq_layer = HQQLinear(
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# linear_layer=None,
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# quant_config=None,
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# compute_dtype=self.dtype,
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# device=target_device,
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# del_orig=False,
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# )
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# hqq_layer.load_state_dict(hqq_params)
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# if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
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# hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)
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# if self.using_multi_gpu:
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# hqq_layer = self._patch_layer_for_multigpu(hqq_layer)
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# setattr(parent_module, node, hqq_layer)
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# del self.hqq_params[module_name], module
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# return
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# # Load param in the module (without caring about device or dtype, it will be changed later)
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# module.load_state_dict({tensor_name: param_value}, strict=False, assign=True)
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# # If both the weight and bias have already been loaded, time to quantize!
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# module_is_ready = module.weight.device.type != "meta" and (
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# module.bias is None or module.bias.device.type != "meta"
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# )
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# if module_is_ready:
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# module_tag = ".".join(module.name.split(".")[-2:])
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# if "weight_quant_params" in quant_config:
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# module_quant_config = quant_config
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# elif module_tag in quant_config:
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# module_quant_config = quant_config[module_tag]
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# hqq_layer = HQQLinear(
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# module,
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# quant_config=module_quant_config,
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# compute_dtype=self.dtype,
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# device=target_device,
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# del_orig=True,
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# )
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# if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
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# hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)
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# if self.using_multi_gpu:
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# hqq_layer = self._patch_layer_for_multigpu(hqq_layer)
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# setattr(parent_module, node, hqq_layer)
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def _patch_layer_for_multigpu(self, hqq_layer):
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def forward_with_device(self, x):
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out = torch.matmul(x.to(self.device), self.dequantize().t())
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if self.bias is not None:
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out += self.bias
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return out
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hqq_layer.forward = lambda x: forward_with_device(hqq_layer, x)
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return hqq_layer
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def _process_model_before_weight_loading(
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self,
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model: "PreTrainedModel",
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**kwargs,
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):
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# Add the corresponding quant_config to each valid module. This allows us to do the actual nn.Linear -> HQQLinear conversion in create_quantized_param().
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# prepare_for_hqq_linear() also sets the right quantization config inside the model (model.config.quantization_config) and the layers (hqq_layer.quant_config)
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model = prepare_for_hqq_linear(model, quantization_config=self.quantization_config)
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def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
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model.is_hqq_quantized = True
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model.is_hqq_serializable = self.is_serializable()
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return model
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def is_serializable(self):
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return True
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@property
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def is_trainable(self) -> bool:
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return True
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