mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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9dbc5b38ba
* Update TensorRT-LLM --------- Co-authored-by: Marks101 <markus.schnoes@gmx.de> Co-authored-by: lkm2835 <lkm2835@gmail.com>
241 lines
8.0 KiB
Python
241 lines
8.0 KiB
Python
import re
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from pathlib import Path
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from typing import Dict, Optional, Union
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import torch
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from datasets import load_dataset
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from ..quantization import QuantAlgo
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def split(v, tp_size, idx, dim=0):
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if tp_size == 1:
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return v
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if len(v.shape) == 1:
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return torch.chunk(v, tp_size)[idx].contiguous()
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else:
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return torch.chunk(v, tp_size, dim=dim)[idx].clone()
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def split_qkv_tp(v, n_head, n_hidden, tensor_parallel, rank):
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"""
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Splits the QKV matrix according to tensor parallelism
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"""
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v = v.reshape(3, n_hidden, n_hidden)
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split_v = split(v, tensor_parallel, rank, dim=1)
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split_v = split_v.reshape(3 * (n_hidden // tensor_parallel), n_hidden)
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return split_v.clone()
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def split_qkv_bias_tp(v, n_head, n_hidden, tensor_parallel, rank):
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"""
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Splits the QKV bias according to tensor parallelism
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"""
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v = v.reshape(3, n_hidden)
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split_v = split(v, tensor_parallel, rank, dim=1)
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split_v = split_v.reshape(3 * (n_hidden // tensor_parallel))
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return split_v.clone()
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def split_matrix_tp(v, tensor_parallel, rank, dim):
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return split(v, tensor_parallel, rank, dim=dim)
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def weight_only_quantize(weight: torch.Tensor,
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quant_algo: str,
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plugin: bool = True):
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assert quant_algo in [QuantAlgo.W4A16, QuantAlgo.W8A16
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], f'unsupported quant algo: {quant_algo}'
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if quant_algo == QuantAlgo.W4A16:
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assert plugin, 'W4A16 is only supported with plugin'
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if weight.dim() > 2:
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v = weight.transpose(-1, -2)
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else:
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v = weight.t()
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t = torch.quint4x2 if quant_algo == QuantAlgo.W4A16 else torch.int8
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processed_torch_weights, torch_weight_scales = \
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torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
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v.contiguous(), t)
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if plugin:
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return processed_torch_weights, torch_weight_scales
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else:
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return v, torch_weight_scales
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def weight_only_quantize_dict(weights: Dict[str, torch.Tensor],
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quant_algo: str,
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quant_weights=[
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'qkv.weight', 'dense.weight', 'fc.weight',
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'proj.weight', 'gate.weight'
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],
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exclude_weights=['shared_expert_gate.weight'],
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plugin: bool = True):
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if quant_algo not in [QuantAlgo.W4A16, QuantAlgo.W8A16]:
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return weights
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for name in list(weights):
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if any([_name in name for _name in exclude_weights]):
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continue
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if any([_name in name for _name in quant_weights
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]) and weights[name].dtype != torch.int8:
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quant_weight, quant_scale = weight_only_quantize(
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weight=weights[name], quant_algo=quant_algo, plugin=plugin)
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weights[name] = quant_weight
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weights[name.replace('.weight', '.per_channel_scale')] = quant_scale
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return weights
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def load_state_dict(
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file_path: Union[str, Path],
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dtype: Optional[torch.dtype] = None,
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device: Optional[Union[str, torch.device]] = None,
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) -> Dict[str, torch.Tensor]:
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""" Load weights from model file.
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`safetensors` or `pytorch binary` is supported.
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Args:
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file_path: model file path, ends with .bin or .safetensors.
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dtype: torch.dtype, data type.
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device: torch device like, optional. If None, load to cpu.
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Returns:
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Weights as state dict.
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"""
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file_path = Path(file_path)
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if dtype is not None:
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assert isinstance(dtype, torch.dtype)
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if device is None:
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device = 'cpu'
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model_params = {}
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if file_path.suffix == '.safetensors':
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# load from safetensors file
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from safetensors import safe_open
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with safe_open(file_path, framework='pt', device=device) as f:
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for name in f.keys():
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tensor = f.get_tensor(name)
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if dtype is not None:
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tensor = tensor.to(dtype)
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model_params[name] = tensor
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elif file_path.suffix == '.bin':
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# load from pytorch bin file
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state_dict = torch.load(file_path, map_location=device)
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for name in state_dict:
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tensor = state_dict[name]
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if dtype is not None:
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tensor = tensor.to(dtype)
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model_params[name] = tensor
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else:
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raise NotImplementedError(
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f'Support .safetensors or .bin files, but got {str(file_path)}')
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return model_params
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def get_model_path(
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model_dir: Union[str, Path],
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name: Optional[str] = None,
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) -> Optional[str]:
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""" Get model path from model directory.
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`safetensors` or `pytorch binary` is supported.
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Args:
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model_dir: model directory.
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name: model file name without suffix.
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Returns:
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Full model path.
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"""
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model_dir = Path(model_dir)
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if name is not None:
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if (model_dir / f"{name}.safetensors").exists():
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return str(model_dir / f"{name}.safetensors")
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elif (model_dir / f"{name}.bin").exists():
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return str(model_dir / f"{name}.bin")
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else:
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return None
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else:
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model_files = list(model_dir.glob('*.safetensors'))
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if len(model_files) > 0:
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assert len(
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model_files
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) == 1, f"find multiple safetensors files in {model_dir}, please specify one"
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return str(model_files[0])
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model_files = list(model_dir.glob('*.bin'))
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if len(model_files) > 0:
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assert len(
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model_files
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) == 1, f"find multiple bin files in {model_dir}, please specify one"
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return str(model_files[0])
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return None
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def retrieved_layer_index_from_name(name: str) -> Optional[int]:
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# This method is a hacky function to retrieve the layer index from
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# HF model. Most of HF models have similar naming convention but
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# please check carefully before applying if this method works well
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# on your target model.
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res = re.search(r'\d+', name)
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return int(res.group()) if res is not None else res
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def iterate_shard_files(model_dir: Union[Path, str],
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rank: int,
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progress_bar: bool = True):
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model_dir = Path(model_dir)
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# '.bin' or '.safetensors'. In case that both exist, '.safetensor'
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# files will be loaded first.
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shard_files = list(model_dir.glob('*.safetensors'))
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if not shard_files:
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# The model checkpoint is stored in .bin file.
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shard_files = list(model_dir.glob('*.bin'))
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if not shard_files:
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raise RuntimeError(
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f"Could not find any .safetensors or .bin files in {model_dir}")
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try:
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import tqdm
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if progress_bar:
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# Show a progress bar per rank.
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desc = f'Rank [{rank}] Loading weights'
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shard_files = tqdm.tqdm(shard_files, desc=desc, position=rank)
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except ImportError:
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pass
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for shard_file in shard_files:
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yield shard_file
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def has_safetensors(model_dir: str):
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return len(list(Path(model_dir).glob('*.safetensors'))) > 0
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DEFAULT_HF_DATASET_META = {
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'ccdv/cnn_dailymail': ('3.0.0', 'train', 'article'),
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'cnn_dailymail': ('3.0.0', 'train', 'article'),
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'lambada': (None, 'validation', 'text'),
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}
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def load_calib_dataset(dataset_name_or_dir: str,
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config_name: Optional[str] = None,
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split: Optional[str] = None,
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key: Optional[str] = None,
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trust_remote_code=True,
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**kwargs):
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if config_name is None:
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for name, meta in DEFAULT_HF_DATASET_META.items():
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if name in dataset_name_or_dir:
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if config_name is None:
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config_name = meta[0]
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if split is None:
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split = meta[1]
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if key is None:
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key = meta[2]
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break
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dataset = load_dataset(dataset_name_or_dir,
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name=config_name,
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split=split,
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**kwargs)
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return dataset[key]
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