262 lines
12 KiB
Python
262 lines
12 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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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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#
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import torch
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import os
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import json
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from .transformers import ggml_convert_low_bit
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from torch.nn.modules import Module
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from torch.nn.modules.module import _IncompatibleKeys
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from accelerate import init_empty_weights
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from accelerate.utils import set_module_tensor_to_device
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from ipex_llm.ggml.quantize import ggml_tensor_qtype
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.utils import extract_local_archive_file, get_local_shard_files
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import transformers
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import warnings
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from transformers import PreTrainedModel
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from .utils.common import MuteHFLogger
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from .utils.lazy_load_torch import LazyLoadTensors
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from contextlib import ExitStack, contextmanager
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# Simulate the Hugging Face format
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PYTORCH_MODEL_NAME = "pytorch_model.bin"
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CONFIG_NAME = "bigdl_config.json"
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def _save_low_bit(self, save_dir, *args, **kwargs):
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invalidInputError(self._bigdl_config.get("bigdl_transformers_low_bit", False),
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f"Detected this model is not a low-bit model, please use from_pretrained's"
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f" load_in_4bit or load_in_low_bit parameter to load a 4-bit model first.")
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os.makedirs(save_dir, exist_ok=True)
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model_path = os.path.join(save_dir, PYTORCH_MODEL_NAME)
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if isinstance(self, PreTrainedModel):
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# We borrowed this method to adapt to Transformer model cases
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# as much as possible, and later we may merge these two situations
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self.save_pretrained(save_dir)
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else:
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# TODO: For the lowbit model still larger than 8GB,
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# save it into shards.
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torch.save(self.state_dict(), model_path, *args, **kwargs)
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with open(os.path.join(save_dir, CONFIG_NAME), "w") as json_file:
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json.dump(self._bigdl_config, json_file)
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# Under `init_empty_weights()`, we need to disable all actions
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# that may lead to any parameter allocation", otherwise may need to error:
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# NotImplementedError: Cannot copy out of meta tensor; no data!
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class DisableTorchAllocTensor():
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def __init__(self) -> None:
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self._old_torch_load_state_dict = Module.load_state_dict
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self._old_torch_to_device = Module.to
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self._old_torch_load_from_state_dict = Module._load_from_state_dict
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# Chatglm2 init weights manually,
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# and `skip_init` init on `cpu` by default
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self._old_skip_init = torch.nn.utils.skip_init
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def __enter__(self):
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Module.load_state_dict = lambda *args, **kwargs: _IncompatibleKeys([], [])
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Module._load_from_state_dict = lambda *args, **kwargs: None
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Module.to = lambda self, *args, **kwargs: self
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def skip_init_on_meta(module_cls, *args, **kwargs):
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kwargs['device'] = 'meta'
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return self._old_skip_init(module_cls, *args, **kwargs)
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torch.nn.utils.skip_init = skip_init_on_meta
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def __exit__(self, exc_type, exc_value, traceback):
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Module.load_state_dict = self._old_torch_load_state_dict
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Module._load_from_state_dict = self._old_torch_load_from_state_dict
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Module.to = self._old_torch_to_device
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torch.nn.utils.skip_init = self._old_skip_init
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class ContextManagers:
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"""
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Wrapper for `contextlib.ExitStack` which enters a collection of context managers.
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Adaptation of `ContextManagers` in the `fastcore` library.
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"""
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def __init__(self, context_managers):
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self.context_managers = context_managers
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self.stack = ExitStack()
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def __enter__(self):
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for context_manager in self.context_managers:
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self.stack.enter_context(context_manager)
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def __exit__(self, *args, **kwargs):
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self.stack.__exit__(*args, **kwargs)
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def low_bit_sanity_check(model_path):
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invalidInputError(os.path.isdir(model_path),
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"model_path should be a valid directory path.")
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invalidInputError(os.path.isfile(os.path.join(model_path, CONFIG_NAME)),
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"bigdl_config.json should be under your model directory,"
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"please check your input path.")
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with open(os.path.join(model_path, CONFIG_NAME), 'r') as f:
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_config = json.load(f)
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low_bit = _config.get("bigdl_transformers_low_bit", None)
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invalidInputError(low_bit,
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"Detect this model is not a low-bit model, Please use `optimize_model`"
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" with low_bit to get a low-bit model , and "
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" serialize the model using save_low_bit first.")
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return low_bit
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@contextmanager
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def low_memory_init():
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init_contexts = []
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init_contexts.extend([init_empty_weights(), DisableTorchAllocTensor()])
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# Load everything except Tensors' parameters
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init_contexts.append(LazyLoadTensors())
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# As we have muted the `torch.load`, this will trigger a key missing warning in hf
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# but this matters not for we will load again later.
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init_contexts.append(MuteHFLogger(logger=transformers.modeling_utils.logger))
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with ContextManagers(init_contexts):
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yield
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def load_low_bit(model, model_path):
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"""
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Load the optimized pytorch model.
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:param model: The PyTorch model instance
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:param model_path: The path of saved optimized model
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:return: The optimized model.
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>>> # Example 1:
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>>> # Take ChatGLM2-6B model as an example
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>>> # Make sure you have saved the optimized model by calling 'save_low_bit'
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>>> from ipex_llm.optimize import low_memory_init, load_low_bit
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>>> with low_memory_init(): # Fast and low cost by loading model on meta device
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>>> model = AutoModel.from_pretrained(saved_dir,
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>>> torch_dtype="auto",
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>>> trust_remote_code=True)
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>>> model = load_low_bit(model, saved_dir) # Load the optimized model
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>>> # Example 2:
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>>> # If the model doesn't fit 'low_memory_init' method,
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>>> # alternatively, you can obtain the model instance through traditional loading method.
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>>> # Take OpenAI Whisper model as an example
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>>> # Make sure you have saved the optimized model by calling 'save_low_bit'
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>>> from ipex_llm.optimize import load_low_bit
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>>> model = whisper.load_model('tiny') # A model instance through traditional loading method
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>>> model = load_low_bit(model, saved_dir) # Load the optimized model
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"""
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low_bit = low_bit_sanity_check(model_path)
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invalidInputError(isinstance(model, torch.nn.Module),
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"model should be a instance of "
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f"`torch.nn.Module`, but got {type(model)} at last.")
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if low_bit:
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invalidInputError(isinstance(model, torch.nn.Module),
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"model should be an instance of `torch.nn.Module`, "
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f"but got {type(model)} at last.")
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invalidInputError(model.device.type in ('cpu', 'meta'),
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"Expect model on device `cpu` or `meta`, "
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f"but got device type {model.device.type}")
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qtype = ggml_tensor_qtype[low_bit]
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model = ggml_convert_low_bit(model, qtype=qtype, convert_shape_only=True)
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resolved_archive_file, is_sharded = extract_local_archive_file(model_path, subfolder="")
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if is_sharded:
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# For now only shards transformers models
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# can run in this branch.
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resolved_archive_file, _ = \
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get_local_shard_files(model_path,
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resolved_archive_file,
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subfolder="")
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else:
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resolved_archive_file = [os.path.join(model_path, PYTORCH_MODEL_NAME)]
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for model_file in resolved_archive_file:
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state_dict = torch.load(model_file)
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for param_name, param in state_dict.items():
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set_module_tensor_to_device(model, param_name, "cpu", param)
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return model
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def optimize_model(model, low_bit='sym_int4', optimize_llm=True, modules_to_not_convert=None,
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cpu_embedding=False, lightweight_bmm=False, **kwargs):
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"""
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A method to optimize any pytorch model.
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:param model: The original PyTorch model (nn.module)
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:param low_bit: str value, options are ``'sym_int4'``, ``'asym_int4'``, ``'sym_int5'``,
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``'asym_int5'``, ``'sym_int8'``, ``'nf3'``, ``'nf4'``, ``'fp4'``,
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``'fp8'``, ``'fp8_e4m3'``, ``'fp8_e5m2'``, ``'fp16'`` or ``'bf16'``,
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``'sym_int4'`` means symmetric int 4, ``'asym_int4'`` means
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asymmetric int 4, ``'nf4'`` means 4-bit NormalFloat, etc.
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Relevant low bit optimizations will be applied to the model.
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:param optimize_llm: Whether to further optimize llm model. Default to be ``True``.
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:param modules_to_not_convert: list of str value, modules (nn.Module) that are skipped
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when conducting model optimizations. Default to be ``None``.
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:param cpu_embedding: Whether to replace the Embedding layer, may need to set it
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to ``True`` when running BigDL-LLM on GPU on Windows. Default to be ``False``.
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:param lightweight_bmm: Whether to replace the torch.bmm ops, may need to set it
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to ``True`` when running BigDL-LLM on GPU on Windows. Default to be ``False``.
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:return: The optimized model.
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>>> # Take OpenAI Whisper model as an example
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>>> from ipex_llm import optimize_model
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>>> model = whisper.load_model('tiny') # Load whisper model under pytorch framework
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>>> model = optimize_model(model) # With only one line code change
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>>> # Use the optimized model without other API change
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>>> result = model.transcribe(audio, verbose=True, language="English")
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>>> # (Optional) you can also save the optimized model by calling 'save_low_bit'
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>>> model.save_low_bit(saved_dir)
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"""
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invalidInputError(low_bit in ggml_tensor_qtype,
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f"Unknown load_in_low_bit value: {low_bit}, expected:"
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f" sym_int4, asym_int4, sym_int5, asym_int5 or sym_int8.")
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invalidInputError(isinstance(model, torch.nn.Module),
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"model should be an instance of "
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f"`torch.nn.Module`, but got {type(model)} at last.")
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invalidInputError(model.device.type in ('cpu', 'meta'),
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"Expect model on device `cpu` or `meta`, "
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f"but got device type {model.device.type}")
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if kwargs.pop("replace_embedding", False):
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warnings.warn("replace_embedding is deprecated and will be removed in a future version,"
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" please use cpu_embedding instead.", FutureWarning)
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cpu_embedding = True
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if low_bit == "fp16":
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torch_dtype = kwargs.get("torch_dtype", None)
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if torch_dtype is not None and torch_dtype != torch.float16:
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invalidInputError(False,
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"Please use torch_dtype=torch.float16 when setting low_bit='fp16'.")
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else:
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torch_dtype = torch.float16
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else:
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torch_dtype = kwargs.get("torch_dtype", "auto")
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qtype = ggml_tensor_qtype[low_bit]
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model = ggml_convert_low_bit(model,
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qtype=qtype,
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torch_dtype=torch_dtype,
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optimize_model=optimize_llm,
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modules_to_not_convert=modules_to_not_convert,
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cpu_embedding=cpu_embedding,
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lightweight_bmm=lightweight_bmm)
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# add save_low_bit to pretrained model dynamically
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import types
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model._bigdl_config = dict()
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model._bigdl_config["bigdl_transformers_low_bit"] = low_bit
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model.save_low_bit = types.MethodType(_save_low_bit, model)
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return model
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