forked from NVIDIA/TensorRT-LLM
-
Notifications
You must be signed in to change notification settings - Fork 0
/
convert.py
186 lines (155 loc) · 8.24 KB
/
convert.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utilities for exporting a model to our custom format.
"""
import numpy as np
def save_val(val, dir, key, tp_num=None):
suffix = "bin" if tp_num is None else f"{tp_num}.bin"
val.tofile(dir / f"model.{key}.{suffix}")
def save_split(split_vals, dir, key, i, factor):
for j, val in enumerate(split_vals):
save_val(val, dir, key, i * factor + j)
def generate_int8(weights, act_range, is_qkv=False):
"""
This function has two purposes:
- compute quantized weights, scaled either per-tensor or per-column
- compute scaling factors
Depending on the GEMM API (CUTLASS/CUBLAS) the required scaling factors differ.
CUTLASS uses two sets of scaling factors. One for the activation X, one for the weight W.
CUBLAS only has one (we can't do per-row scaling). So we must provide pre-multiplied scaling factor.
Here is the list of what we need (T means per-tensor, C per-column):
- scale_x_orig_quant puts fp activation into the quantized range (i.e. [-128, 127], for int8). Used before the GEMM. (T)
- scale_y_quant_orig puts quantized activation into the fp range. Used if the GEMM outputs int8. (T)
- scale_w_quant_orig puts weights from quant range to fp range (used with CUTLASS) (T, C)
- scale_y_accum_quant puts the GEMM result (XW) from accumulation range (int32)
to quant range (int8) (used for CUBLAS) (T, C)
Note that we don't do anything special about row-parallel GEMM. Theorically, we could have per-GPU scaling factors too,
but then the model would change depending on the number of GPUs used.
For QKV projection, the behavior is special. Even if we have a single matrix to perform QKV projection, we consider it
as three different matrices: Q, K, and V. So per-tensor actually means one scaling factor for each Q, K and V.
"""
# compute weight scaling factors for fp->int8 and int8->fp
if is_qkv:
scale_w_orig_quant_t = 127. / act_range["w"].reshape(3, -1).max(
dim=-1, keepdims=True)[0].cpu().numpy()
scale_w_orig_quant_c = 127. / act_range["w"].reshape(3,
-1).cpu().numpy()
else:
scale_w_orig_quant_t = 127. / act_range["w"].max().cpu().numpy()
scale_w_orig_quant_c = 127. / act_range["w"].cpu().numpy()
scale_w_quant_orig_t = 1.0 / scale_w_orig_quant_t
scale_w_quant_orig_c = 1.0 / scale_w_orig_quant_c
# compute the rest of needed scaling factors
scale_x_orig_quant_t = np.array(127. / act_range["x"].max().item())
scale_y_orig_quant_t = np.array(127. / act_range["y"].max().item())
scale_y_quant_orig_t = np.array(act_range["y"].max().item() / 127.)
scale_y_accum_quant_t = scale_y_orig_quant_t / (scale_x_orig_quant_t *
scale_w_orig_quant_t)
scale_y_accum_quant_c = scale_y_orig_quant_t / (scale_x_orig_quant_t *
scale_w_orig_quant_c)
if is_qkv:
scale_y_accum_quant_t = np.broadcast_to(scale_y_accum_quant_t,
scale_w_orig_quant_c.shape)
scale_w_quant_orig_t = np.broadcast_to(scale_w_quant_orig_t,
scale_w_orig_quant_c.shape)
to_i8 = lambda x: x.round().clip(-127, 127).astype(np.int8)
return {
"weight.int8": to_i8(weights * scale_w_orig_quant_t),
"weight.int8.col": to_i8(weights * scale_w_orig_quant_c),
"scale_x_orig_quant": scale_x_orig_quant_t.astype(np.float32),
"scale_w_quant_orig": scale_w_quant_orig_t.astype(np.float32),
"scale_w_quant_orig.col": scale_w_quant_orig_c.astype(np.float32),
"scale_y_accum_quant": scale_y_accum_quant_t.astype(np.float32),
"scale_y_accum_quant.col": scale_y_accum_quant_c.astype(np.float32),
"scale_y_quant_orig": scale_y_quant_orig_t.astype(np.float32),
}
def write_int8(vals, dir, base_key, split_dim, i, factor):
save_split(np.split(vals["weight.int8"], factor, axis=split_dim), dir,
f"{base_key}.weight.int8", i, factor)
save_split(np.split(vals["weight.int8.col"], factor, axis=split_dim), dir,
f"{base_key}.weight.int8.col", i, factor)
saved_keys_once = [
"scale_x_orig_quant", "scale_w_quant_orig", "scale_y_accum_quant",
"scale_y_quant_orig"
]
# per-column scaling factors are loaded per-gpu for ColumnParallel GEMMs (QKV, FC1)
if split_dim == -1:
save_split(
np.split(vals["scale_w_quant_orig.col"], factor, axis=split_dim),
dir, f"{base_key}.scale_w_quant_orig.col", i, factor)
save_split(
np.split(vals["scale_y_accum_quant.col"], factor, axis=split_dim),
dir, f"{base_key}.scale_y_accum_quant.col", i, factor)
else:
saved_keys_once += ["scale_w_quant_orig.col", "scale_y_accum_quant.col"]
if i == 0:
for save_key in saved_keys_once:
save_val(vals[save_key], dir, f"{base_key}.{save_key}")
def str_to_np_dtype(type_str):
convert_dict = {
"fp32": np.float32,
"fp16": np.float16,
}
dtype = convert_dict.get(type_str)
if dtype is None:
raise ValueError(f"{type_str} is an invalid storage type")
return dtype
def split_and_save_weight(i, saved_dir, factor, key, args, val, act_range):
save_int8 = act_range is not None
if "input_layernorm.weight" in key or "input_layernorm.bias" in key or \
"attention.dense.bias" in key or "post_attention_layernorm.weight" in key or \
"post_attention_layernorm.bias" in key or "mlp.dense_4h_to_h.bias" in key or \
"final_layernorm.weight" in key or "final_layernorm.bias" in key:
# shared weights, only need to convert the weights of rank 0
if i == 0:
save_val(val, saved_dir, key)
elif "attention.dense.weight" in key or "mlp.dense_4h_to_h.weight" in key:
split_dim = 0
split_vals = np.split(val, factor, axis=split_dim)
save_split(split_vals, saved_dir, key, i, factor)
if save_int8:
base_key = key.replace(".weight", "")
vals_i8 = generate_int8(val, act_range)
write_int8(vals_i8, saved_dir, base_key, split_dim, i, factor)
elif "mlp.dense_h_to_4h.weight" in key:
split_dim = -1
split_vals = np.split(val, factor, axis=split_dim)
save_split(split_vals, saved_dir, key, i, factor)
if save_int8:
base_key = key.replace(".weight", "")
vals_i8 = generate_int8(val, act_range)
write_int8(vals_i8, saved_dir, base_key, split_dim, i, factor)
elif "mlp.dense_h_to_4h.bias" in key:
split_vals = np.split(val, factor, axis=-1)
save_split(split_vals, saved_dir, key, i, factor)
elif "attention.query_key_value.bias" in key:
local_dim = val.shape[-1] // 3
val = val.reshape(3, local_dim)
split_vals = np.split(val, factor, axis=-1)
save_split(split_vals, saved_dir, key, i, factor)
elif "attention.query_key_value.weight" in key:
hidden_dim = val.shape[0] // 3
local_dim = val.shape[-1]
val = val.reshape(3, hidden_dim, local_dim)
split_dim = -1
split_vals = np.split(val, factor, axis=split_dim)
save_split(split_vals, saved_dir, key, i, factor)
if save_int8:
base_key = key.replace(".weight", "")
vals_i8 = generate_int8(val, act_range, is_qkv=True)
write_int8(vals_i8, saved_dir, base_key, split_dim, i, factor)
else:
print(f"[WARNING] {key} not handled by converter")