mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
276 lines
9.3 KiB
Python
276 lines
9.3 KiB
Python
import fnmatch
|
|
import re
|
|
from pathlib import Path
|
|
from typing import Dict, Optional, Tuple, Union
|
|
|
|
import torch
|
|
from datasets import load_dataset
|
|
|
|
from ..quantization import QuantAlgo
|
|
|
|
|
|
def split(v, tp_size, idx, dim=0):
|
|
if tp_size == 1:
|
|
return v
|
|
if len(v.shape) == 1:
|
|
return torch.chunk(v, tp_size)[idx].contiguous()
|
|
else:
|
|
return torch.chunk(v, tp_size, dim=dim)[idx].contiguous()
|
|
|
|
|
|
def split_qkv_tp(v, n_head, n_hidden, tensor_parallel, rank):
|
|
"""
|
|
Splits the QKV matrix according to tensor parallelism
|
|
"""
|
|
v = v.reshape(3, n_hidden, n_hidden)
|
|
split_v = split(v, tensor_parallel, rank, dim=1)
|
|
split_v = split_v.reshape(3 * (n_hidden // tensor_parallel), n_hidden)
|
|
return split_v.contiguous()
|
|
|
|
|
|
def split_qkv_bias_tp(v, n_head, n_hidden, tensor_parallel, rank):
|
|
"""
|
|
Splits the QKV bias according to tensor parallelism
|
|
"""
|
|
v = v.reshape(3, n_hidden)
|
|
split_v = split(v, tensor_parallel, rank, dim=1)
|
|
split_v = split_v.reshape(3 * (n_hidden // tensor_parallel))
|
|
return split_v.contiguous()
|
|
|
|
|
|
def split_matrix_tp(v, tensor_parallel, rank, dim):
|
|
return split(v, tensor_parallel, rank, dim=dim)
|
|
|
|
|
|
def weight_only_quantize(weight: torch.Tensor,
|
|
quant_algo: str,
|
|
plugin: bool = True):
|
|
assert quant_algo in [QuantAlgo.W4A16, QuantAlgo.W8A16
|
|
], f'unsupported quant algo: {quant_algo}'
|
|
if quant_algo == QuantAlgo.W4A16:
|
|
assert plugin, 'W4A16 is only supported with plugin'
|
|
if weight.dim() > 2:
|
|
v = weight.transpose(-1, -2)
|
|
else:
|
|
v = weight.t()
|
|
t = torch.quint4x2 if quant_algo == QuantAlgo.W4A16 else torch.int8
|
|
processed_torch_weights, torch_weight_scales = \
|
|
torch.ops.trtllm.symmetric_quantize_last_axis_of_batched_matrix(
|
|
v.contiguous(), t)
|
|
if plugin:
|
|
return processed_torch_weights, torch_weight_scales
|
|
else:
|
|
return v, torch_weight_scales
|
|
|
|
|
|
def get_weight(params: Dict[str, torch.Tensor], prefix: str,
|
|
dtype: torch.dtype) -> torch.Tensor:
|
|
if f'{prefix}.weight' not in params:
|
|
return None
|
|
return params[f'{prefix}.weight'].to(dtype).detach().cpu()
|
|
|
|
|
|
def get_bias(params: Dict[str, torch.Tensor], prefix: str,
|
|
dtype: torch.dtype) -> torch.Tensor:
|
|
if f'{prefix}.bias' not in params:
|
|
return None
|
|
return params[f'{prefix}.bias'].to(dtype).detach().cpu()
|
|
|
|
|
|
def get_weight_and_bias(params: Dict[str, torch.Tensor], prefix: str,
|
|
dtype: torch.dtype) -> Tuple[torch.Tensor]:
|
|
return get_weight(params, prefix, dtype), get_bias(params, prefix, dtype)
|
|
|
|
|
|
def dup_kv_weight(v, num_head, tp_size):
|
|
assert tp_size % num_head == 0
|
|
reps = tp_size // num_head
|
|
head_size = v.shape[0] // num_head
|
|
v = v.reshape(num_head, head_size,
|
|
-1)[:, None, :, :].expand(num_head, reps, head_size,
|
|
v.shape[1])
|
|
return v.reshape(num_head * reps * head_size, -1).clone().detach()
|
|
|
|
|
|
def weight_only_quantize_dict(weights: Dict[str, torch.Tensor],
|
|
quant_algo: str,
|
|
quant_weights=[
|
|
'qkv.weight', 'dense.weight', 'fc.weight',
|
|
'proj.weight', 'gate.weight'
|
|
],
|
|
exclude_modules=None,
|
|
plugin: bool = True):
|
|
if quant_algo not in [QuantAlgo.W4A16, QuantAlgo.W8A16]:
|
|
return weights
|
|
if exclude_modules is None:
|
|
exclude_modules = ['*shared_expert_gate.weight']
|
|
for name in list(weights):
|
|
is_excluded = False
|
|
for exclude_module in exclude_modules:
|
|
if fnmatch.fnmatchcase(name, exclude_module):
|
|
is_excluded = True
|
|
break
|
|
if not is_excluded and any([_name in name for _name in quant_weights
|
|
]) and weights[name].dtype != torch.int8:
|
|
quant_weight, quant_scale = weight_only_quantize(
|
|
weight=weights[name], quant_algo=quant_algo, plugin=plugin)
|
|
weights[name] = quant_weight
|
|
weights[name.replace('.weight', '.per_channel_scale')] = quant_scale
|
|
return weights
|
|
|
|
|
|
def load_state_dict(
|
|
file_path: Union[str, Path],
|
|
dtype: Optional[torch.dtype] = None,
|
|
device: Optional[Union[str, torch.device]] = None,
|
|
) -> Dict[str, torch.Tensor]:
|
|
""" Load weights from model file.
|
|
|
|
`safetensors` or `pytorch binary` is supported.
|
|
Args:
|
|
file_path: model file path, ends with .bin or .safetensors.
|
|
dtype: torch.dtype, data type.
|
|
device: torch device like, optional. If None, load to cpu.
|
|
Returns:
|
|
Weights as state dict.
|
|
"""
|
|
file_path = Path(file_path)
|
|
if dtype is not None:
|
|
assert isinstance(dtype, torch.dtype)
|
|
|
|
if device is None:
|
|
device = 'cpu'
|
|
|
|
model_params = {}
|
|
if file_path.suffix == '.safetensors':
|
|
# load from safetensors file
|
|
from safetensors import safe_open
|
|
with safe_open(file_path, framework='pt', device=device) as f:
|
|
for name in f.keys():
|
|
tensor = f.get_tensor(name)
|
|
if dtype is not None:
|
|
tensor = tensor.to(dtype)
|
|
model_params[name] = tensor
|
|
elif file_path.suffix == '.bin':
|
|
# load from pytorch bin file
|
|
state_dict = torch.load(file_path, map_location=device)
|
|
for name in state_dict:
|
|
tensor = state_dict[name]
|
|
if dtype is not None:
|
|
tensor = tensor.to(dtype)
|
|
model_params[name] = tensor
|
|
else:
|
|
raise NotImplementedError(
|
|
f'Support .safetensors or .bin files, but got {str(file_path)}')
|
|
return model_params
|
|
|
|
|
|
def get_model_path(
|
|
model_dir: Union[str, Path],
|
|
name: Optional[str] = None,
|
|
) -> Optional[str]:
|
|
""" Get model path from model directory.
|
|
|
|
`safetensors` or `pytorch binary` is supported.
|
|
Args:
|
|
model_dir: model directory.
|
|
name: model file name without suffix.
|
|
Returns:
|
|
Full model path.
|
|
"""
|
|
model_dir = Path(model_dir)
|
|
if name is not None:
|
|
if (model_dir / f"{name}.safetensors").exists():
|
|
return str(model_dir / f"{name}.safetensors")
|
|
elif (model_dir / f"{name}.bin").exists():
|
|
return str(model_dir / f"{name}.bin")
|
|
else:
|
|
return None
|
|
else:
|
|
model_files = list(model_dir.glob('*.safetensors'))
|
|
if len(model_files) > 0:
|
|
assert len(
|
|
model_files
|
|
) == 1, f"find multiple safetensors files in {model_dir}, please specify one"
|
|
return str(model_files[0])
|
|
model_files = list(model_dir.glob('*.bin'))
|
|
if len(model_files) > 0:
|
|
assert len(
|
|
model_files
|
|
) == 1, f"find multiple bin files in {model_dir}, please specify one"
|
|
return str(model_files[0])
|
|
return None
|
|
|
|
|
|
def retrieved_layer_index_from_name(name: str) -> Optional[int]:
|
|
# This method is a hacky function to retrieve the layer index from
|
|
# HF model. Most of HF models have similar naming convention but
|
|
# please check carefully before applying if this method works well
|
|
# on your target model.
|
|
res = re.search(r'\d+', name)
|
|
return int(res.group()) if res is not None else res
|
|
|
|
|
|
def iterate_shard_files(model_dir: Union[Path, str],
|
|
rank: int,
|
|
progress_bar: bool = True):
|
|
model_dir = Path(model_dir)
|
|
|
|
# '.bin' or '.safetensors'. In case that both exist, '.safetensor'
|
|
# files will be loaded first.
|
|
shard_files = list(model_dir.glob('*.safetensors'))
|
|
if not shard_files:
|
|
# The model checkpoint is stored in .bin file.
|
|
shard_files = list(model_dir.glob('*.bin'))
|
|
if not shard_files:
|
|
raise RuntimeError(
|
|
f"Could not find any .safetensors or .bin files in {model_dir}")
|
|
|
|
try:
|
|
import tqdm
|
|
if progress_bar:
|
|
# Show a progress bar per rank.
|
|
desc = f'Rank [{rank}] Loading weights'
|
|
shard_files = tqdm.tqdm(shard_files, desc=desc, position=rank)
|
|
|
|
except ImportError:
|
|
pass
|
|
|
|
for shard_file in shard_files:
|
|
yield shard_file
|
|
|
|
|
|
def has_safetensors(model_dir: str):
|
|
return len(list(Path(model_dir).glob('*.safetensors'))) > 0
|
|
|
|
|
|
DEFAULT_HF_DATASET_META = {
|
|
'ccdv/cnn_dailymail': ('3.0.0', 'train', 'article'),
|
|
'cnn_dailymail': ('3.0.0', 'train', 'article'),
|
|
'lambada': (None, 'validation', 'text'),
|
|
}
|
|
|
|
|
|
def load_calib_dataset(dataset_name_or_dir: str,
|
|
config_name: Optional[str] = None,
|
|
split: Optional[str] = None,
|
|
key: Optional[str] = None,
|
|
trust_remote_code=True,
|
|
**kwargs):
|
|
if config_name is None:
|
|
for name, meta in DEFAULT_HF_DATASET_META.items():
|
|
if name in dataset_name_or_dir:
|
|
if config_name is None:
|
|
config_name = meta[0]
|
|
if split is None:
|
|
split = meta[1]
|
|
if key is None:
|
|
key = meta[2]
|
|
break
|
|
|
|
dataset = load_dataset(dataset_name_or_dir,
|
|
name=config_name,
|
|
split=split,
|
|
**kwargs)
|
|
return dataset[key]
|