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Unverified Commit ee962784 authored by lvhan028's avatar lvhan028 Committed by GitHub
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remove duplicate model converter script (#13) 

* remove constraints on model name

* remove duplicate model converter
parent 4b121180
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Showing with 1 addition and 220 deletions
examples/cpp/llama/llama_ckpt_convert.py deleted 100644 → 0
View file @ 4b121180
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import fire
import os.path as osp
from os import makedirs
from pathlib import Path
import safetensors
from typing import List
from tqdm import tqdm
def import_fb(ckpt_dir: str):
checkpoints = []
for pattern in ['*.pth', '*.pt']:
checkpoints += sorted(Path(ckpt_dir).glob(pattern))
print(checkpoints)
n_ckpt = len(checkpoints)
model_params = {}
def get_param(name, size):
print(name, size)
if name not in model_params:
model_params[name] = torch.zeros(
size, dtype=torch.float16, device='cpu')
return model_params[name]
for i, ckpt_path in enumerate(checkpoints):
ckpt = torch.load(ckpt_path, map_location='cpu')
for param_name, param_data in ckpt.items():
key = param_name.split('.')[-2]
if key in ['w1', 'w3', 'wq', 'wk', 'wv', 'output']: # column-parallel
size = param_data.size(0)
param = get_param(
param_name, [size * n_ckpt, param_data.size(1)])
param.data[size * i: size * (i + 1), :] = param_data
elif key in ['w2', 'wo', 'tok_embeddings']: # row-parallel
size = param_data.size(-1)
param = get_param(
param_name, [param_data.size(0), size * n_ckpt])
param.data[:, size * i: size * (i + 1)] = param_data
elif i == 0:
param = get_param(param_name, param_data.size())
param.data = param_data
del ckpt
for name, param in model_params.items():
# transpose all weights as FasterTransformer is expecting column-major weights
# (output_dims, input_dims) -> (input_dims, output_dims)
key = name.split('.')[-2]
if key in ['w1', 'w3', 'wq', 'wk', 'wv', 'w2', 'wo']:
param.data = param.data.t()
# concat qkv projection
for i in range(1000):
_qkv = [f'layers.{i}.attention.{k}.weight' for k in ['wq', 'wk', 'wv']]
try:
qkv = tuple(map(model_params.pop, _qkv))
except KeyError:
break
qkv = torch.stack(qkv, dim=1)
model_params[f'layers.{i}.attention.w_qkv.weight'] = qkv
print(qkv.shape, qkv.dtype)
return model_params
def permute(x: torch.Tensor):
SIZE_PER_HEAD = 128
if x.shape[-1] > 1: # qweights
dim = x.shape[-1]
n_heads = dim // SIZE_PER_HEAD
return x.view(-1, n_heads, 2, dim // n_heads // 2).transpose(2, 3).reshape(-1, dim)
else: # scales, zeros
dim = x.shape[0]
n_heads = dim // SIZE_PER_HEAD
return x.view(n_heads, 2, dim // n_heads // 2, 1).transpose(1, 2).reshape(dim, 1)
def check_zero(x: torch.Tensor):
sum = x.flatten().sum().item()
assert sum == 0, str(sum)
def import_gptq(path: str):
model_params = {}
_qweight = 'weight'
_suffixes = [_qweight]
n_split = 3
if True:
_params = {}
for i in tqdm(range(0, n_split)):
filename = "pytorch_model-{:05d}-of-{:05d}.bin".format(i + 1, n_split)
_tmp = torch.load(osp.join(path, filename), map_location='cpu')
_params.update(_tmp)
# print('\n'.join(_params.keys()))
def get_tensor(name):
return _params[name]
def get_tensor_transposed(name):
return _params[name].t()
# _qweight = 'qweight'
# _suffixes = [_qweight, 'bias', 'scales', 'zeros']
# with safetensors.safe_open(path, framework='pt') as f:
# get_tensor = f.get_tensor
# # quantized weights are already in column major, no need to transpose
# get_tensor_transposed = get_tensor
for i in range(1000):
try:
# attention weights
_qkvo = [f'model.layers.{i}.self_attn.{t}_proj' for t in 'qkvo']
for suffix in _suffixes:
q, k, v, o = map(get_tensor_transposed, map(('{}.' + suffix).format, _qkvo))
if suffix == 'bias':
check_zero(q), check_zero(k), check_zero(v), check_zero(o)
else:
# q, k has different layout for fb & hf, convert to fb's layout
q = permute(q)
k = permute(k)
if suffix == _qweight: # weight, qweight
# insert a dimension for splitting heads later
# qkv = torch.cat([q[:, None, :], k[:, None, :], v[:, None, :]], dim=1)
qkv = torch.stack((q, k, v), dim=1)
else: # scales, zeros
# qkv = torch.cat([q[None, :], k[None, :], v[None, :]], dim=0).squeeze(dim=-1)
qkv = torch.stack((q, k, v), dim=0).squeeze(dim=-1)
for k, v in [('w_qkv', qkv), ('wo', o)]:
model_params[f'layers.{i}.attention.{k}.{suffix}'] = v
# ffn weights
_w123 = [f'model.layers.{i}.mlp.{t}_proj' for t in ['gate', 'down', 'up']]
for suffix in _suffixes:
w1, w2, w3 = map(get_tensor_transposed, map(('{}.' + suffix).format, _w123))
if suffix == 'bias':
check_zero(w1), check_zero(w2), check_zero(w3)
else:
if suffix in ['scales', 'zeros']:
w1, w2, w3 = map(lambda x: x.squeeze(dim=-1), [w1, w2, w3])
for k, v in [('w1', w1), ('w2', w2), ('w3', w3)]:
model_params[f'layers.{i}.feed_forward.{k}.{suffix}'] = v
other = [('attention_norm.weight', 'input_layernorm.weight'),
('ffn_norm.weight', 'post_attention_layernorm.weight')]
for ours, theirs in other:
model_params[f'layers.{i}.' + ours] = get_tensor(f'model.layers.{i}.' + theirs)
except safetensors.SafetensorError:
break
except KeyError:
break
print(i)
other = [('tok_embeddings.weight', 'model.embed_tokens.weight'),
('norm.weight', 'model.norm.weight'),
('output.weight', 'lm_head.weight')]
for ours, theirs in other:
model_params[ours] = get_tensor(theirs)
return model_params
def export(model_params: dict, out_dir: str, n_inference: int):
makedirs(out_dir, exist_ok=True)
def save_bin(param: torch.Tensor, name):
print(name, param.shape)
if param.dtype in [torch.float, torch.bfloat16]:
param = param.half()
param.contiguous().numpy().tofile(osp.join(out_dir, name))
# reverse the spliting axes since the weights are transposed above
for param_name, param_data in model_params.items():
split_dim = None
key, ext = param_name.split('.')[-2:]
copy = False
if key in ['w1', 'w3', 'w_qkv']:
split_dim = -1
elif key in ['w2', 'wo']:
if ext in ['scales', 'zeros']:
copy = True
else:
split_dim = 0
if split_dim is not None:
print(f'*** spliting {param_name}, shape={param_data.shape}, split_dim={split_dim}')
assert param_data.shape[split_dim] % n_inference == 0
split_size = param_data.shape[split_dim] // n_inference
splits = torch.split(param_data, split_size, dim=split_dim)
for i, split in enumerate(splits):
prefix, ext = osp.splitext(param_name)
save_bin(split, f'{prefix}.{i}{ext}')
elif copy:
print(f'### copying {param_name}, shape={param_data.shape}')
copies = [param_data] * n_inference
for i, copy in enumerate(copies):
prefix, ext = osp.splitext(param_name)
save_bin(copy, f'{prefix}.{i}{ext}')
else:
save_bin(param_data, param_name)
def main(kind: str, input_path: str, out_dir: str, n_inference: int = 1):
if kind == 'fb':
model_params = import_fb(input_path)
elif kind == 'gptq':
model_params = import_gptq(input_path)
else:
raise RuntimeError(f'Unsupported kind: {kind}')
export(model_params, out_dir, n_inference)
if __name__ == '__main__':
fire.Fire(main)
\ No newline at end of file
llmdeploy/serve/fastertransformer/deploy.py
View file @ ee962784
......@@ -12,10 +12,6 @@ import safetensors
import torch
from sentencepiece import SentencePieceProcessor
supported_models = [
'vicuna-7b', 'vicuna-13b', 'llama-7b', 'llama-13b', 'llama-30b',
'llama-65b'
]
supported_formats = ['llama', 'hf']
......@@ -379,7 +375,7 @@ def main(model_name: str,
Args:
model_name (str): the name of the to-be-deployed model, such as
llama-7b, llama-13b and etc
llama-7b, llama-13b, vicuna-7b and etc
model_path (str): the directory path of the model
model_format (str): the format of the model, fb or hf. 'fb' stands for
META's llama format, and 'hf' means huggingface format
......@@ -387,10 +383,6 @@ def main(model_name: str,
dst_path (str): the destination path that saves outputs
tp (int): the number of GPUs used for tensor parallelism
"""
if model_name.lower() not in supported_models:
print(f'"{model_name}" is not supported. The supported models are: '
f'{supported_models}')
exit(-1)
if model_format not in supported_formats:
print(f'the model format "{model_format}" is not supported. '
......@@ -409,7 +401,6 @@ def main(model_name: str,
if triton_models_path is None:
exit(-1)
model_name = model_name.lower()
if model_format == 'llama':
res = deploy_llama(model_name, model_path, tokenizer_path,
triton_models_path, tp)
......
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