Upload 21 files
Browse filesMirror of Qwen-VL-Chat
- .gitattributes +1 -0
- SimSun.ttf +3 -0
- config.json +49 -0
- configuration_qwen.py +65 -0
- generation_config.json +11 -0
- modeling_qwen.py +1154 -0
- pytorch_model-00001-of-00010.bin +3 -0
- pytorch_model-00002-of-00010.bin +3 -0
- pytorch_model-00003-of-00010.bin +3 -0
- pytorch_model-00004-of-00010.bin +3 -0
- pytorch_model-00005-of-00010.bin +3 -0
- pytorch_model-00006-of-00010.bin +3 -0
- pytorch_model-00007-of-00010.bin +3 -0
- pytorch_model-00008-of-00010.bin +3 -0
- pytorch_model-00009-of-00010.bin +3 -0
- pytorch_model-00010-of-00010.bin +3 -0
- pytorch_model.bin.index.json +860 -0
- qwen.tiktoken +0 -0
- qwen_generation_utils.py +420 -0
- tokenization_qwen.py +590 -0
- tokenizer_config.json +10 -0
- visual.py +426 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
36 |
+
SimSun.ttf filter=lfs diff=lfs merge=lfs -text
|
SimSun.ttf
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ca4da082cd970f0c8abaa79f213ddcbc475f7b5afabcb81b385998f9ebfbb53f
|
3 |
+
size 10499104
|
config.json
ADDED
@@ -0,0 +1,49 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_name_or_path": "./",
|
3 |
+
"architectures": [
|
4 |
+
"QWenLMHeadModel"
|
5 |
+
],
|
6 |
+
"attn_dropout_prob": 0.0,
|
7 |
+
"auto_map": {
|
8 |
+
"AutoConfig": "configuration_qwen.QWenConfig",
|
9 |
+
"AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel"
|
10 |
+
},
|
11 |
+
"bf16": false,
|
12 |
+
"emb_dropout_prob": 0.0,
|
13 |
+
"fp16": false,
|
14 |
+
"fp32": false,
|
15 |
+
"hidden_size": 4096,
|
16 |
+
"initializer_range": 0.02,
|
17 |
+
"intermediate_size": 22016,
|
18 |
+
"kv_channels": 128,
|
19 |
+
"layer_norm_epsilon": 1e-06,
|
20 |
+
"max_position_embeddings": 8192,
|
21 |
+
"model_type": "qwen",
|
22 |
+
"no_bias": true,
|
23 |
+
"num_attention_heads": 32,
|
24 |
+
"num_hidden_layers": 32,
|
25 |
+
"onnx_safe": null,
|
26 |
+
"rotary_emb_base": 10000,
|
27 |
+
"rotary_pct": 1.0,
|
28 |
+
"scale_attn_weights": true,
|
29 |
+
"seq_length": 2048,
|
30 |
+
"tie_word_embeddings": false,
|
31 |
+
"tokenizer_type": "QWenTokenizer",
|
32 |
+
"torch_dtype": "bfloat16",
|
33 |
+
"transformers_version": "4.31.0",
|
34 |
+
"use_cache": true,
|
35 |
+
"use_dynamic_ntk": true,
|
36 |
+
"use_flash_attn": false,
|
37 |
+
"use_logn_attn": true,
|
38 |
+
"visual": {
|
39 |
+
"heads": 16,
|
40 |
+
"image_size": 448,
|
41 |
+
"image_start_id": 151857,
|
42 |
+
"layers": 48,
|
43 |
+
"mlp_ratio": 4.9231,
|
44 |
+
"output_dim": 4096,
|
45 |
+
"patch_size": 14,
|
46 |
+
"width": 1664
|
47 |
+
},
|
48 |
+
"vocab_size": 151936
|
49 |
+
}
|
configuration_qwen.py
ADDED
@@ -0,0 +1,65 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Alibaba Cloud.
|
2 |
+
#
|
3 |
+
# This source code is licensed under the license found in the
|
4 |
+
# LICENSE file in the root directory of this source tree.
|
5 |
+
|
6 |
+
from transformers import PretrainedConfig
|
7 |
+
|
8 |
+
|
9 |
+
class QWenConfig(PretrainedConfig):
|
10 |
+
model_type = "qwen"
|
11 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
12 |
+
|
13 |
+
def __init__(
|
14 |
+
self,
|
15 |
+
vocab_size=151936,
|
16 |
+
hidden_size=4096,
|
17 |
+
num_hidden_layers=32,
|
18 |
+
num_attention_heads=32,
|
19 |
+
emb_dropout_prob=0.0,
|
20 |
+
attn_dropout_prob=0.0,
|
21 |
+
layer_norm_epsilon=1e-6,
|
22 |
+
initializer_range=0.02,
|
23 |
+
max_position_embeddings=8192,
|
24 |
+
scale_attn_weights=True,
|
25 |
+
use_cache=True,
|
26 |
+
bf16=False,
|
27 |
+
fp16=False,
|
28 |
+
fp32=False,
|
29 |
+
kv_channels=128,
|
30 |
+
rotary_pct=1.0,
|
31 |
+
rotary_emb_base=10000,
|
32 |
+
use_dynamic_ntk=True,
|
33 |
+
use_logn_attn=True,
|
34 |
+
use_flash_attn="auto",
|
35 |
+
intermediate_size=22016,
|
36 |
+
no_bias=True,
|
37 |
+
tie_word_embeddings=False,
|
38 |
+
**kwargs,
|
39 |
+
):
|
40 |
+
self.vocab_size = vocab_size
|
41 |
+
self.hidden_size = hidden_size
|
42 |
+
self.intermediate_size = intermediate_size
|
43 |
+
self.num_hidden_layers = num_hidden_layers
|
44 |
+
self.num_attention_heads = num_attention_heads
|
45 |
+
self.emb_dropout_prob = emb_dropout_prob
|
46 |
+
self.attn_dropout_prob = attn_dropout_prob
|
47 |
+
self.layer_norm_epsilon = layer_norm_epsilon
|
48 |
+
self.initializer_range = initializer_range
|
49 |
+
self.scale_attn_weights = scale_attn_weights
|
50 |
+
self.use_cache = use_cache
|
51 |
+
self.max_position_embeddings = max_position_embeddings
|
52 |
+
self.bf16 = bf16
|
53 |
+
self.fp16 = fp16
|
54 |
+
self.fp32 = fp32
|
55 |
+
self.kv_channels = kv_channels
|
56 |
+
self.rotary_pct = rotary_pct
|
57 |
+
self.rotary_emb_base = rotary_emb_base
|
58 |
+
self.use_dynamic_ntk = use_dynamic_ntk
|
59 |
+
self.use_logn_attn = use_logn_attn
|
60 |
+
self.use_flash_attn = use_flash_attn
|
61 |
+
self.no_bias = no_bias
|
62 |
+
super().__init__(
|
63 |
+
tie_word_embeddings=tie_word_embeddings,
|
64 |
+
**kwargs
|
65 |
+
)
|
generation_config.json
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"chat_format": "chatml",
|
3 |
+
"do_sample": true,
|
4 |
+
"eos_token_id": 151643,
|
5 |
+
"max_new_tokens": 512,
|
6 |
+
"max_window_size": 6144,
|
7 |
+
"pad_token_id": 151643,
|
8 |
+
"top_k": 0,
|
9 |
+
"top_p": 0.3,
|
10 |
+
"transformers_version": "4.31.0"
|
11 |
+
}
|
modeling_qwen.py
ADDED
@@ -0,0 +1,1154 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Alibaba Cloud.
|
2 |
+
#
|
3 |
+
# This source code is licensed under the license found in the
|
4 |
+
# LICENSE file in the root directory of this source tree.
|
5 |
+
|
6 |
+
import importlib
|
7 |
+
import math
|
8 |
+
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator
|
9 |
+
|
10 |
+
import torch
|
11 |
+
import torch.nn.functional as F
|
12 |
+
import torch.utils.checkpoint
|
13 |
+
from torch.cuda.amp import autocast
|
14 |
+
|
15 |
+
from torch.nn import CrossEntropyLoss
|
16 |
+
from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
|
17 |
+
from transformers.generation.logits_process import LogitsProcessorList
|
18 |
+
|
19 |
+
if TYPE_CHECKING:
|
20 |
+
from transformers.generation.streamers import BaseStreamer
|
21 |
+
from transformers.generation.utils import GenerateOutput
|
22 |
+
from transformers.modeling_outputs import (
|
23 |
+
BaseModelOutputWithPast,
|
24 |
+
CausalLMOutputWithPast,
|
25 |
+
)
|
26 |
+
from transformers.modeling_utils import PreTrainedModel
|
27 |
+
from transformers.utils import logging
|
28 |
+
|
29 |
+
try:
|
30 |
+
from einops import rearrange
|
31 |
+
except ImportError:
|
32 |
+
rearrange = None
|
33 |
+
from torch import nn
|
34 |
+
|
35 |
+
SUPPORT_CUDA = torch.cuda.is_available()
|
36 |
+
SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
|
37 |
+
SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7
|
38 |
+
|
39 |
+
from .configuration_qwen import QWenConfig
|
40 |
+
from .qwen_generation_utils import (
|
41 |
+
HistoryType,
|
42 |
+
make_context,
|
43 |
+
decode_tokens,
|
44 |
+
get_stop_words_ids,
|
45 |
+
StopWordsLogitsProcessor,
|
46 |
+
)
|
47 |
+
from .visual import VisionTransformer
|
48 |
+
|
49 |
+
|
50 |
+
logger = logging.get_logger(__name__)
|
51 |
+
|
52 |
+
_CHECKPOINT_FOR_DOC = "qwen"
|
53 |
+
_CONFIG_FOR_DOC = "QWenConfig"
|
54 |
+
|
55 |
+
QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"]
|
56 |
+
|
57 |
+
_ERROR_BAD_CHAT_FORMAT = """\
|
58 |
+
We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml".
|
59 |
+
If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat().
|
60 |
+
我们检测到您可能在使用预训练模型(而非chat模型)进行多轮chat,因为您当前在generation_config指定的chat_format,并未设置为我们在对话中所支持的"chatml"格式。
|
61 |
+
如果您在直接使用我们从Huggingface提供的模型,请确保您在调用model.chat()时,使用的是"Qwen/Qwen-7B-Chat"模型(而非"Qwen/Qwen-7B"预训练模型)。
|
62 |
+
"""
|
63 |
+
|
64 |
+
_SENTINEL = object()
|
65 |
+
_ERROR_STREAM_IN_CHAT = """\
|
66 |
+
Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True).
|
67 |
+
向model.chat()传入参数stream的用法可能存在Bug,该用法已被废弃,将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。
|
68 |
+
"""
|
69 |
+
|
70 |
+
apply_rotary_emb_func = None
|
71 |
+
rms_norm = None
|
72 |
+
|
73 |
+
|
74 |
+
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
75 |
+
def _make_causal_mask(
|
76 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
77 |
+
):
|
78 |
+
"""
|
79 |
+
Make causal mask used for bi-directional self-attention.
|
80 |
+
"""
|
81 |
+
bsz, tgt_len = input_ids_shape
|
82 |
+
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
|
83 |
+
mask_cond = torch.arange(mask.size(-1), device=device)
|
84 |
+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
85 |
+
mask = mask.to(dtype)
|
86 |
+
|
87 |
+
if past_key_values_length > 0:
|
88 |
+
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
89 |
+
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
90 |
+
|
91 |
+
|
92 |
+
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
93 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
94 |
+
"""
|
95 |
+
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
96 |
+
"""
|
97 |
+
bsz, src_len = mask.size()
|
98 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
99 |
+
|
100 |
+
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
101 |
+
|
102 |
+
inverted_mask = 1.0 - expanded_mask
|
103 |
+
|
104 |
+
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
105 |
+
|
106 |
+
|
107 |
+
class QWenAttention(nn.Module):
|
108 |
+
def __init__(self, config):
|
109 |
+
super().__init__()
|
110 |
+
|
111 |
+
self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
|
112 |
+
self.seq_length = config.seq_length
|
113 |
+
|
114 |
+
self.hidden_size = config.hidden_size
|
115 |
+
self.split_size = config.hidden_size
|
116 |
+
self.num_heads = config.num_attention_heads
|
117 |
+
self.head_dim = self.hidden_size // self.num_heads
|
118 |
+
|
119 |
+
self.scale_attn_weights = True
|
120 |
+
|
121 |
+
self.projection_size = config.kv_channels * config.num_attention_heads
|
122 |
+
|
123 |
+
assert self.projection_size % config.num_attention_heads == 0
|
124 |
+
self.hidden_size_per_attention_head = (
|
125 |
+
self.projection_size // config.num_attention_heads
|
126 |
+
)
|
127 |
+
|
128 |
+
self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)
|
129 |
+
|
130 |
+
self.c_proj = nn.Linear(
|
131 |
+
config.hidden_size, self.projection_size, bias=not config.no_bias
|
132 |
+
)
|
133 |
+
|
134 |
+
self.is_fp32 = not (config.bf16 or config.fp16)
|
135 |
+
self.bf16 = config.bf16
|
136 |
+
|
137 |
+
self.use_dynamic_ntk = config.use_dynamic_ntk
|
138 |
+
self.use_logn_attn = config.use_logn_attn
|
139 |
+
|
140 |
+
logn_list = [
|
141 |
+
math.log(i, self.seq_length) if i > self.seq_length else 1
|
142 |
+
for i in range(1, 32768)
|
143 |
+
]
|
144 |
+
self.logn_tensor = torch.tensor(logn_list)[None, :, None, None]
|
145 |
+
|
146 |
+
self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
|
147 |
+
|
148 |
+
def _attn(self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None):
|
149 |
+
attn_weights = torch.matmul(query, key.transpose(-1, -2))
|
150 |
+
|
151 |
+
if self.scale_attn_weights:
|
152 |
+
attn_weights = attn_weights / torch.full(
|
153 |
+
[],
|
154 |
+
value.size(-1) ** 0.5,
|
155 |
+
dtype=attn_weights.dtype,
|
156 |
+
device=attn_weights.device,
|
157 |
+
)
|
158 |
+
|
159 |
+
query_length, key_length = query.size(-2), key.size(-2)
|
160 |
+
# causal_mask = self.bias[
|
161 |
+
# :, :, key_length - query_length : key_length, :key_length
|
162 |
+
# ]
|
163 |
+
# mask_value = torch.finfo(attn_weights.dtype).min
|
164 |
+
# mask_value = torch.full([], mask_value, dtype=attn_weights.dtype).to(
|
165 |
+
# attn_weights.device
|
166 |
+
# )
|
167 |
+
# attn_weights = torch.where(
|
168 |
+
# causal_mask, attn_weights.to(attn_weights.dtype), mask_value
|
169 |
+
# )
|
170 |
+
attn_weights = attn_weights + attention_mask
|
171 |
+
|
172 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
173 |
+
|
174 |
+
attn_weights = attn_weights.type(value.dtype)
|
175 |
+
attn_weights = self.attn_dropout(attn_weights)
|
176 |
+
|
177 |
+
if head_mask is not None:
|
178 |
+
attn_weights = attn_weights * head_mask
|
179 |
+
|
180 |
+
attn_output = torch.matmul(attn_weights, value)
|
181 |
+
attn_output = attn_output.transpose(1, 2)
|
182 |
+
|
183 |
+
return attn_output, attn_weights
|
184 |
+
|
185 |
+
def _upcast_and_reordered_attn(
|
186 |
+
self, query, key, value, registered_causal_mask, attention_mask=None, head_mask=None
|
187 |
+
):
|
188 |
+
bsz, num_heads, q_seq_len, dk = query.size()
|
189 |
+
_, _, k_seq_len, _ = key.size()
|
190 |
+
|
191 |
+
attn_weights = torch.empty(
|
192 |
+
bsz * num_heads,
|
193 |
+
q_seq_len,
|
194 |
+
k_seq_len,
|
195 |
+
dtype=torch.float32,
|
196 |
+
device=query.device,
|
197 |
+
)
|
198 |
+
|
199 |
+
scale_factor = 1.0
|
200 |
+
if self.scale_attn_weights:
|
201 |
+
scale_factor /= float(value.size(-1)) ** 0.5
|
202 |
+
|
203 |
+
with autocast(enabled=False):
|
204 |
+
q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(
|
205 |
+
-1, dk, k_seq_len
|
206 |
+
)
|
207 |
+
attn_weights = torch.baddbmm(
|
208 |
+
attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor
|
209 |
+
)
|
210 |
+
attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len)
|
211 |
+
|
212 |
+
query_length, key_length = query.size(-2), key.size(-2)
|
213 |
+
causal_mask = registered_causal_mask[
|
214 |
+
:, :, key_length - query_length : key_length, :key_length
|
215 |
+
]
|
216 |
+
mask_value = torch.finfo(attn_weights.dtype).min
|
217 |
+
mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(
|
218 |
+
attn_weights.device
|
219 |
+
)
|
220 |
+
attn_weights = torch.where(causal_mask, attn_weights, mask_value)
|
221 |
+
|
222 |
+
if attention_mask is not None:
|
223 |
+
attn_weights = attn_weights + attention_mask
|
224 |
+
|
225 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
226 |
+
|
227 |
+
if attn_weights.dtype != torch.float32:
|
228 |
+
raise RuntimeError(
|
229 |
+
"Error with upcasting, attn_weights does not have dtype torch.float32"
|
230 |
+
)
|
231 |
+
attn_weights = attn_weights.type(value.dtype)
|
232 |
+
attn_weights = self.attn_dropout(attn_weights)
|
233 |
+
|
234 |
+
if head_mask is not None:
|
235 |
+
attn_weights = attn_weights * head_mask
|
236 |
+
|
237 |
+
attn_output = torch.matmul(attn_weights, value)
|
238 |
+
|
239 |
+
return attn_output, attn_weights
|
240 |
+
|
241 |
+
def _split_heads(self, tensor, num_heads, attn_head_size):
|
242 |
+
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
|
243 |
+
tensor = tensor.view(new_shape)
|
244 |
+
return tensor
|
245 |
+
|
246 |
+
def _merge_heads(self, tensor, num_heads, attn_head_size):
|
247 |
+
tensor = tensor.contiguous()
|
248 |
+
new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
|
249 |
+
return tensor.view(new_shape)
|
250 |
+
|
251 |
+
def forward(
|
252 |
+
self,
|
253 |
+
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
254 |
+
rotary_pos_emb: Optional[List[torch.Tensor]] = None,
|
255 |
+
registered_causal_mask: Optional[torch.Tensor] = None,
|
256 |
+
layer_past: Optional[Tuple[torch.Tensor]] = None,
|
257 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
258 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
259 |
+
encoder_hidden_states: Optional[torch.Tensor] = None,
|
260 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
261 |
+
output_attentions: Optional[bool] = False,
|
262 |
+
use_cache: Optional[bool] = False,
|
263 |
+
):
|
264 |
+
|
265 |
+
mixed_x_layer = self.c_attn(hidden_states)
|
266 |
+
|
267 |
+
query, key, value = mixed_x_layer.split(self.split_size, dim=2)
|
268 |
+
|
269 |
+
query = self._split_heads(query, self.num_heads, self.head_dim)
|
270 |
+
key = self._split_heads(key, self.num_heads, self.head_dim)
|
271 |
+
value = self._split_heads(value, self.num_heads, self.head_dim)
|
272 |
+
|
273 |
+
if rotary_pos_emb is not None:
|
274 |
+
cur_len = query.shape[1]
|
275 |
+
rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
|
276 |
+
rotary_pos_emb = (rotary_pos_emb,) * 2
|
277 |
+
q_pos_emb, k_pos_emb = rotary_pos_emb
|
278 |
+
# Slice the pos emb for current inference
|
279 |
+
query = apply_rotary_pos_emb(query, q_pos_emb)
|
280 |
+
key = apply_rotary_pos_emb(key, k_pos_emb)
|
281 |
+
|
282 |
+
if layer_past is not None:
|
283 |
+
past_key, past_value = layer_past[0], layer_past[1]
|
284 |
+
key = torch.cat((past_key, key), dim=1)
|
285 |
+
value = torch.cat((past_value, value), dim=1)
|
286 |
+
|
287 |
+
if use_cache:
|
288 |
+
present = (key, value)
|
289 |
+
else:
|
290 |
+
present = None
|
291 |
+
|
292 |
+
if self.use_logn_attn and not self.training:
|
293 |
+
if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype:
|
294 |
+
self.logn_tensor = self.logn_tensor.to(query.device).type_as(query)
|
295 |
+
seq_start = key.size(1) - query.size(1)
|
296 |
+
seq_end = key.size(1)
|
297 |
+
logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :]
|
298 |
+
query = query * logn_tensor.expand_as(query)
|
299 |
+
|
300 |
+
query = query.permute(0, 2, 1, 3)
|
301 |
+
key = key.permute(0, 2, 1, 3)
|
302 |
+
value = value.permute(0, 2, 1, 3)
|
303 |
+
attn_output, attn_weight = self._attn(
|
304 |
+
query, key, value, registered_causal_mask, attention_mask, head_mask
|
305 |
+
)
|
306 |
+
context_layer = self._merge_heads(
|
307 |
+
attn_output, self.num_heads, self.head_dim
|
308 |
+
)
|
309 |
+
|
310 |
+
attn_output = self.c_proj(context_layer)
|
311 |
+
|
312 |
+
outputs = (attn_output, present)
|
313 |
+
if output_attentions:
|
314 |
+
outputs += (attn_weight,)
|
315 |
+
|
316 |
+
return outputs
|
317 |
+
|
318 |
+
|
319 |
+
class QWenMLP(nn.Module):
|
320 |
+
def __init__(self, config):
|
321 |
+
super().__init__()
|
322 |
+
self.w1 = nn.Linear(
|
323 |
+
config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
|
324 |
+
)
|
325 |
+
self.w2 = nn.Linear(
|
326 |
+
config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
|
327 |
+
)
|
328 |
+
ff_dim_in = config.intermediate_size // 2
|
329 |
+
self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)
|
330 |
+
|
331 |
+
def forward(self, hidden_states):
|
332 |
+
a1 = self.w1(hidden_states)
|
333 |
+
a2 = self.w2(hidden_states)
|
334 |
+
intermediate_parallel = a1 * F.silu(a2)
|
335 |
+
output = self.c_proj(intermediate_parallel)
|
336 |
+
return output
|
337 |
+
|
338 |
+
class QWenBlock(nn.Module):
|
339 |
+
def __init__(self, config):
|
340 |
+
super().__init__()
|
341 |
+
hidden_size = config.hidden_size
|
342 |
+
self.bf16 = config.bf16
|
343 |
+
|
344 |
+
self.ln_1 = RMSNorm(
|
345 |
+
hidden_size,
|
346 |
+
eps=config.layer_norm_epsilon,
|
347 |
+
)
|
348 |
+
self.attn = QWenAttention(config)
|
349 |
+
self.ln_2 = RMSNorm(
|
350 |
+
hidden_size,
|
351 |
+
eps=config.layer_norm_epsilon,
|
352 |
+
)
|
353 |
+
|
354 |
+
self.mlp = QWenMLP(config)
|
355 |
+
|
356 |
+
def forward(
|
357 |
+
self,
|
358 |
+
hidden_states: Optional[Tuple[torch.FloatTensor]],
|
359 |
+
rotary_pos_emb: Optional[List[torch.Tensor]] = None,
|
360 |
+
registered_causal_mask: Optional[torch.Tensor] = None,
|
361 |
+
layer_past: Optional[Tuple[torch.Tensor]] = None,
|
362 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
363 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
364 |
+
encoder_hidden_states: Optional[torch.Tensor] = None,
|
365 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
366 |
+
use_cache: Optional[bool] = False,
|
367 |
+
output_attentions: Optional[bool] = False,
|
368 |
+
):
|
369 |
+
layernorm_output = self.ln_1(hidden_states)
|
370 |
+
|
371 |
+
attn_outputs = self.attn(
|
372 |
+
layernorm_output,
|
373 |
+
rotary_pos_emb,
|
374 |
+
registered_causal_mask=registered_causal_mask,
|
375 |
+
layer_past=layer_past,
|
376 |
+
attention_mask=attention_mask,
|
377 |
+
head_mask=head_mask,
|
378 |
+
use_cache=use_cache,
|
379 |
+
output_attentions=output_attentions,
|
380 |
+
)
|
381 |
+
attn_output = attn_outputs[0]
|
382 |
+
|
383 |
+
outputs = attn_outputs[1:]
|
384 |
+
|
385 |
+
residual = hidden_states
|
386 |
+
layernorm_input = attn_output + residual
|
387 |
+
|
388 |
+
layernorm_output = self.ln_2(layernorm_input)
|
389 |
+
|
390 |
+
residual = layernorm_input
|
391 |
+
mlp_output = self.mlp(layernorm_output)
|
392 |
+
hidden_states = residual + mlp_output
|
393 |
+
|
394 |
+
if use_cache:
|
395 |
+
outputs = (hidden_states,) + outputs
|
396 |
+
else:
|
397 |
+
outputs = (hidden_states,) + outputs[1:]
|
398 |
+
|
399 |
+
return outputs
|
400 |
+
|
401 |
+
|
402 |
+
class QWenPreTrainedModel(PreTrainedModel):
|
403 |
+
config_class = QWenConfig
|
404 |
+
base_model_prefix = "transformer"
|
405 |
+
is_parallelizable = False
|
406 |
+
supports_gradient_checkpointing = True
|
407 |
+
_no_split_modules = ["QWenBlock"]
|
408 |
+
|
409 |
+
def __init__(self, *inputs, **kwargs):
|
410 |
+
super().__init__(*inputs, **kwargs)
|
411 |
+
|
412 |
+
def _init_weights(self, module):
|
413 |
+
"""Initialize the weights."""
|
414 |
+
if isinstance(module, nn.Linear):
|
415 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
416 |
+
if module.bias is not None:
|
417 |
+
module.bias.data.zero_()
|
418 |
+
elif isinstance(module, nn.Embedding):
|
419 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
420 |
+
if module.padding_idx is not None:
|
421 |
+
module.weight.data[module.padding_idx].zero_()
|
422 |
+
elif isinstance(module, RMSNorm):
|
423 |
+
module.weight.data.fill_(1.0)
|
424 |
+
|
425 |
+
for name, p in module.named_parameters():
|
426 |
+
if name == "c_proj.weight":
|
427 |
+
p.data.normal_(
|
428 |
+
mean=0.0,
|
429 |
+
std=(
|
430 |
+
self.config.initializer_range
|
431 |
+
/ math.sqrt(2 * self.config.num_hidden_layers)
|
432 |
+
),
|
433 |
+
)
|
434 |
+
|
435 |
+
def _set_gradient_checkpointing(self, module, value=False):
|
436 |
+
if isinstance(module, QWenModel):
|
437 |
+
module.gradient_checkpointing = value
|
438 |
+
|
439 |
+
|
440 |
+
class QWenModel(QWenPreTrainedModel):
|
441 |
+
_keys_to_ignore_on_load_missing = ["attn.masked_bias"]
|
442 |
+
|
443 |
+
def __init__(self, config):
|
444 |
+
super().__init__(config)
|
445 |
+
self.vocab_size = config.vocab_size
|
446 |
+
self.num_hidden_layers = config.num_hidden_layers
|
447 |
+
self.embed_dim = config.hidden_size
|
448 |
+
|
449 |
+
self.gradient_checkpointing = False
|
450 |
+
self.use_dynamic_ntk = config.use_dynamic_ntk
|
451 |
+
self.seq_length = config.seq_length
|
452 |
+
|
453 |
+
self.wte = nn.Embedding(self.vocab_size, self.embed_dim)
|
454 |
+
|
455 |
+
self.drop = nn.Dropout(config.emb_dropout_prob)
|
456 |
+
|
457 |
+
if config.rotary_pct == 1.0:
|
458 |
+
self.rotary_ndims = None
|
459 |
+
else:
|
460 |
+
assert config.rotary_pct < 1
|
461 |
+
self.rotary_ndims = int(
|
462 |
+
config.kv_channels * config.rotary_pct
|
463 |
+
)
|
464 |
+
dim = (
|
465 |
+
self.rotary_ndims
|
466 |
+
if self.rotary_ndims is not None
|
467 |
+
else config.kv_channels
|
468 |
+
)
|
469 |
+
self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)
|
470 |
+
|
471 |
+
self.use_flash_attn = config.use_flash_attn
|
472 |
+
self.is_fp32 = not (config.bf16 or config.fp16)
|
473 |
+
self.registered_causal_mask = None
|
474 |
+
# if (
|
475 |
+
# self.use_flash_attn
|
476 |
+
# and flash_attn_unpadded_func is not None
|
477 |
+
# and not self.is_fp32
|
478 |
+
# ):
|
479 |
+
# self.registered_causal_mask = None
|
480 |
+
# else:
|
481 |
+
# max_positions = config.max_position_embeddings
|
482 |
+
# self.register_buffer(
|
483 |
+
# "registered_causal_mask",
|
484 |
+
# torch.tril(
|
485 |
+
# torch.ones((max_positions, max_positions), dtype=torch.bool)
|
486 |
+
# ).view(1, 1, max_positions, max_positions),
|
487 |
+
# persistent=False,
|
488 |
+
# )
|
489 |
+
|
490 |
+
self.h = nn.ModuleList(
|
491 |
+
[
|
492 |
+
QWenBlock(
|
493 |
+
config
|
494 |
+
)
|
495 |
+
for i in range(config.num_hidden_layers)
|
496 |
+
]
|
497 |
+
)
|
498 |
+
self.ln_f = RMSNorm(
|
499 |
+
self.embed_dim,
|
500 |
+
eps=config.layer_norm_epsilon,
|
501 |
+
)
|
502 |
+
|
503 |
+
self.visual = VisionTransformer(**config.visual)
|
504 |
+
|
505 |
+
self.post_init()
|
506 |
+
|
507 |
+
def get_input_embeddings(self):
|
508 |
+
return self.wte
|
509 |
+
|
510 |
+
def set_input_embeddings(self, new_embeddings):
|
511 |
+
self.wte = new_embeddings
|
512 |
+
|
513 |
+
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
514 |
+
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
515 |
+
# create causal mask
|
516 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
517 |
+
combined_attention_mask = None
|
518 |
+
if input_shape[-1] > 1:
|
519 |
+
combined_attention_mask = _make_causal_mask(
|
520 |
+
input_shape,
|
521 |
+
inputs_embeds.dtype,
|
522 |
+
device=inputs_embeds.device,
|
523 |
+
past_key_values_length=past_key_values_length,
|
524 |
+
)
|
525 |
+
|
526 |
+
if attention_mask is not None:
|
527 |
+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
528 |
+
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
529 |
+
inputs_embeds.device
|
530 |
+
)
|
531 |
+
combined_attention_mask = (
|
532 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
533 |
+
)
|
534 |
+
|
535 |
+
return combined_attention_mask
|
536 |
+
|
537 |
+
|
538 |
+
def forward(
|
539 |
+
self,
|
540 |
+
input_ids: Optional[torch.LongTensor] = None,
|
541 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
542 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
543 |
+
token_type_ids: Optional[torch.LongTensor] = None,
|
544 |
+
position_ids: Optional[torch.LongTensor] = None,
|
545 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
546 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
547 |
+
encoder_hidden_states: Optional[torch.Tensor] = None,
|
548 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
549 |
+
use_cache: Optional[bool] = None,
|
550 |
+
output_attentions: Optional[bool] = None,
|
551 |
+
output_hidden_states: Optional[bool] = None,
|
552 |
+
return_dict: Optional[bool] = None,
|
553 |
+
):
|
554 |
+
if past_key_values is None and torch.any(input_ids == self.config.visual['image_start_id']):
|
555 |
+
bos_pos = torch.where(input_ids == self.config.visual['image_start_id'])
|
556 |
+
eos_pos = torch.where(input_ids == self.config.visual['image_start_id'] + 1)
|
557 |
+
assert (bos_pos[0] == eos_pos[0]).all()
|
558 |
+
img_pos = torch.stack((bos_pos[0], bos_pos[1], eos_pos[1]), dim=1)
|
559 |
+
images = []
|
560 |
+
for i, a, b in img_pos:
|
561 |
+
image = input_ids[i][a + 1 : b - 1].tolist()
|
562 |
+
image = image[ : image.index(self.config.visual['image_start_id'] + 2)]
|
563 |
+
images.append(bytes(image).decode('utf-8'))
|
564 |
+
|
565 |
+
images = self.visual.encode(images)
|
566 |
+
assert images.shape[0] == len(images)
|
567 |
+
else:
|
568 |
+
images = None
|
569 |
+
|
570 |
+
output_attentions = (
|
571 |
+
output_attentions
|
572 |
+
if output_attentions is not None
|
573 |
+
else self.config.output_attentions
|
574 |
+
)
|
575 |
+
output_hidden_states = (
|
576 |
+
output_hidden_states
|
577 |
+
if output_hidden_states is not None
|
578 |
+
else self.config.output_hidden_states
|
579 |
+
)
|
580 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
581 |
+
return_dict = (
|
582 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
583 |
+
)
|
584 |
+
|
585 |
+
if input_ids is not None and inputs_embeds is not None:
|
586 |
+
raise ValueError(
|
587 |
+
"You cannot specify both input_ids and inputs_embeds at the same time"
|
588 |
+
)
|
589 |
+
elif input_ids is not None:
|
590 |
+
input_shape = input_ids.size()
|
591 |
+
input_ids = input_ids.view(-1, input_shape[-1])
|
592 |
+
batch_size = input_ids.shape[0]
|
593 |
+
elif inputs_embeds is not None:
|
594 |
+
input_shape = inputs_embeds.size()[:-1]
|
595 |
+
batch_size = inputs_embeds.shape[0]
|
596 |
+
else:
|
597 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
598 |
+
|
599 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
600 |
+
|
601 |
+
if token_type_ids is not None:
|
602 |
+
token_type_ids = token_type_ids.view(-1, input_shape[-1])
|
603 |
+
if position_ids is not None:
|
604 |
+
position_ids = position_ids.view(-1, input_shape[-1])
|
605 |
+
|
606 |
+
if past_key_values is None:
|
607 |
+
past_length = 0
|
608 |
+
past_key_values = tuple([None] * len(self.h))
|
609 |
+
else:
|
610 |
+
past_length = past_key_values[0][0].size(-2)
|
611 |
+
|
612 |
+
if position_ids is None:
|
613 |
+
position_ids = torch.arange(
|
614 |
+
past_length,
|
615 |
+
input_shape[-1] + past_length,
|
616 |
+
dtype=torch.long,
|
617 |
+
device=device,
|
618 |
+
)
|
619 |
+
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
|
620 |
+
|
621 |
+
encoder_attention_mask = None
|
622 |
+
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
623 |
+
|
624 |
+
if inputs_embeds is None:
|
625 |
+
inputs_embeds = self.wte(input_ids)
|
626 |
+
|
627 |
+
if batch_size <= 0:
|
628 |
+
raise ValueError("batch_size has to be defined and > 0")
|
629 |
+
attention_mask = self._prepare_decoder_attention_mask(
|
630 |
+
attention_mask, input_shape, inputs_embeds, past_length
|
631 |
+
)
|
632 |
+
|
633 |
+
hidden_states = inputs_embeds
|
634 |
+
|
635 |
+
kv_seq_len = hidden_states.size()[1]
|
636 |
+
if past_key_values[0] is not None:
|
637 |
+
# past key values[0][0] shape: bs * seq_len * head_num * dim
|
638 |
+
kv_seq_len += past_key_values[0][0].shape[1]
|
639 |
+
if (
|
640 |
+
self.use_dynamic_ntk
|
641 |
+
and kv_seq_len == hidden_states.size()[1]
|
642 |
+
and not self.training
|
643 |
+
):
|
644 |
+
context_value = math.log(kv_seq_len / self.seq_length, 2) + 1
|
645 |
+
ntk_alpha = 2 ** math.ceil(context_value) - 1
|
646 |
+
ntk_alpha = max(ntk_alpha, 1)
|
647 |
+
else:
|
648 |
+
ntk_alpha = self.rotary_emb._ntk_alpha_cached
|
649 |
+
|
650 |
+
rotary_pos_emb = self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha)
|
651 |
+
for idx in range(len(rotary_pos_emb)):
|
652 |
+
rotary_pos_emb[idx] = rotary_pos_emb[idx].to(hidden_states.device)
|
653 |
+
|
654 |
+
hidden_states = self.drop(hidden_states)
|
655 |
+
if images is not None:
|
656 |
+
for idx, (i, a, b) in enumerate(img_pos):
|
657 |
+
hidden_states[i][a + 1 : b] = images[idx]
|
658 |
+
output_shape = input_shape + (hidden_states.size(-1),)
|
659 |
+
|
660 |
+
if self.gradient_checkpointing and self.training:
|
661 |
+
if use_cache:
|
662 |
+
logger.warning_once(
|
663 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
664 |
+
)
|
665 |
+
use_cache = False
|
666 |
+
|
667 |
+
presents = () if use_cache else None
|
668 |
+
all_self_attentions = () if output_attentions else None
|
669 |
+
all_hidden_states = () if output_hidden_states else None
|
670 |
+
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
671 |
+
|
672 |
+
if output_hidden_states:
|
673 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
674 |
+
|
675 |
+
if self.gradient_checkpointing and self.training:
|
676 |
+
|
677 |
+
def create_custom_forward(module):
|
678 |
+
def custom_forward(*inputs):
|
679 |
+
# None for past_key_value
|
680 |
+
return module(*inputs, use_cache, output_attentions)
|
681 |
+
|
682 |
+
return custom_forward
|
683 |
+
|
684 |
+
outputs = torch.utils.checkpoint.checkpoint(
|
685 |
+
create_custom_forward(block),
|
686 |
+
hidden_states,
|
687 |
+
rotary_pos_emb,
|
688 |
+
self.registered_causal_mask,
|
689 |
+
None,
|
690 |
+
attention_mask,
|
691 |
+
head_mask[i],
|
692 |
+
encoder_hidden_states,
|
693 |
+
encoder_attention_mask,
|
694 |
+
)
|
695 |
+
else:
|
696 |
+
outputs = block(
|
697 |
+
hidden_states,
|
698 |
+
layer_past=layer_past,
|
699 |
+
rotary_pos_emb=rotary_pos_emb,
|
700 |
+
registered_causal_mask=self.registered_causal_mask,
|
701 |
+
attention_mask=attention_mask,
|
702 |
+
head_mask=head_mask[i],
|
703 |
+
encoder_hidden_states=encoder_hidden_states,
|
704 |
+
encoder_attention_mask=encoder_attention_mask,
|
705 |
+
use_cache=use_cache,
|
706 |
+
output_attentions=output_attentions,
|
707 |
+
)
|
708 |
+
|
709 |
+
hidden_states = outputs[0]
|
710 |
+
if use_cache is True:
|
711 |
+
presents = presents + (outputs[1],)
|
712 |
+
|
713 |
+
if output_attentions:
|
714 |
+
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
715 |
+
|
716 |
+
hidden_states = self.ln_f(hidden_states)
|
717 |
+
hidden_states = hidden_states.view(output_shape)
|
718 |
+
# Add last hidden state
|
719 |
+
if output_hidden_states:
|
720 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
721 |
+
|
722 |
+
if not return_dict:
|
723 |
+
return tuple(
|
724 |
+
v for v in [hidden_states, presents, all_hidden_states] if v is not None
|
725 |
+
)
|
726 |
+
|
727 |
+
return BaseModelOutputWithPast(
|
728 |
+
last_hidden_state=hidden_states,
|
729 |
+
past_key_values=presents,
|
730 |
+
hidden_states=all_hidden_states,
|
731 |
+
attentions=all_self_attentions,
|
732 |
+
)
|
733 |
+
|
734 |
+
|
735 |
+
class QWenLMHeadModel(QWenPreTrainedModel):
|
736 |
+
_keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"]
|
737 |
+
_keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]
|
738 |
+
|
739 |
+
def __init__(self, config):
|
740 |
+
super().__init__(config)
|
741 |
+
assert (
|
742 |
+
config.bf16 + config.fp16 + config.fp32 <= 1
|
743 |
+
), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true"
|
744 |
+
|
745 |
+
autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0
|
746 |
+
|
747 |
+
if autoset_precision:
|
748 |
+
if SUPPORT_BF16:
|
749 |
+
logger.warn(
|
750 |
+
"The model is automatically converting to bf16 for faster inference. "
|
751 |
+
"If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
|
752 |
+
)
|
753 |
+
config.bf16 = True
|
754 |
+
elif SUPPORT_FP16:
|
755 |
+
logger.warn(
|
756 |
+
"The model is automatically converting to fp16 for faster inference. "
|
757 |
+
"If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
|
758 |
+
)
|
759 |
+
config.fp16 = True
|
760 |
+
else:
|
761 |
+
config.fp32 = True
|
762 |
+
|
763 |
+
if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
|
764 |
+
logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
|
765 |
+
if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
|
766 |
+
logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
|
767 |
+
if config.fp32:
|
768 |
+
if SUPPORT_BF16:
|
769 |
+
logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
|
770 |
+
elif SUPPORT_FP16:
|
771 |
+
logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")
|
772 |
+
|
773 |
+
self.transformer = QWenModel(config)
|
774 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
775 |
+
|
776 |
+
if config.bf16:
|
777 |
+
self.transformer.bfloat16()
|
778 |
+
self.lm_head.bfloat16()
|
779 |
+
if config.fp16:
|
780 |
+
self.transformer.half()
|
781 |
+
self.lm_head.half()
|
782 |
+
self.post_init()
|
783 |
+
|
784 |
+
def get_output_embeddings(self):
|
785 |
+
return self.lm_head
|
786 |
+
|
787 |
+
def set_output_embeddings(self, new_embeddings):
|
788 |
+
self.lm_head = new_embeddings
|
789 |
+
|
790 |
+
def prepare_inputs_for_generation(
|
791 |
+
self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
|
792 |
+
):
|
793 |
+
token_type_ids = kwargs.get("token_type_ids", None)
|
794 |
+
if past_key_values:
|
795 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
796 |
+
if token_type_ids is not None:
|
797 |
+
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
|
798 |
+
|
799 |
+
attention_mask = kwargs.get("attention_mask", None)
|
800 |
+
position_ids = kwargs.get("position_ids", None)
|
801 |
+
|
802 |
+
if attention_mask is not None and position_ids is None:
|
803 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
804 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
805 |
+
if past_key_values:
|
806 |
+
position_ids = position_ids[:, -1].unsqueeze(-1)
|
807 |
+
else:
|
808 |
+
position_ids = None
|
809 |
+
|
810 |
+
if inputs_embeds is not None and past_key_values is None:
|
811 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
812 |
+
else:
|
813 |
+
model_inputs = {"input_ids": input_ids}
|
814 |
+
|
815 |
+
model_inputs.update(
|
816 |
+
{
|
817 |
+
"past_key_values": past_key_values,
|
818 |
+
"use_cache": kwargs.get("use_cache"),
|
819 |
+
"position_ids": position_ids,
|
820 |
+
"attention_mask": attention_mask,
|
821 |
+
"token_type_ids": token_type_ids,
|
822 |
+
}
|
823 |
+
)
|
824 |
+
return model_inputs
|
825 |
+
|
826 |
+
def forward(
|
827 |
+
self,
|
828 |
+
input_ids: Optional[torch.LongTensor] = None,
|
829 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
830 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
831 |
+
token_type_ids: Optional[torch.LongTensor] = None,
|
832 |
+
position_ids: Optional[torch.LongTensor] = None,
|
833 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
834 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
835 |
+
encoder_hidden_states: Optional[torch.Tensor] = None,
|
836 |
+
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
837 |
+
labels: Optional[torch.LongTensor] = None,
|
838 |
+
use_cache: Optional[bool] = None,
|
839 |
+
output_attentions: Optional[bool] = None,
|
840 |
+
output_hidden_states: Optional[bool] = None,
|
841 |
+
return_dict: Optional[bool] = None,
|
842 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
843 |
+
|
844 |
+
return_dict = (
|
845 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
846 |
+
)
|
847 |
+
|
848 |
+
transformer_outputs = self.transformer(
|
849 |
+
input_ids,
|
850 |
+
past_key_values=past_key_values,
|
851 |
+
attention_mask=attention_mask,
|
852 |
+
token_type_ids=token_type_ids,
|
853 |
+
position_ids=position_ids,
|
854 |
+
head_mask=head_mask,
|
855 |
+
inputs_embeds=inputs_embeds,
|
856 |
+
encoder_hidden_states=encoder_hidden_states,
|
857 |
+
encoder_attention_mask=encoder_attention_mask,
|
858 |
+
use_cache=use_cache,
|
859 |
+
output_attentions=output_attentions,
|
860 |
+
output_hidden_states=output_hidden_states,
|
861 |
+
return_dict=return_dict,
|
862 |
+
)
|
863 |
+
hidden_states = transformer_outputs[0]
|
864 |
+
|
865 |
+
lm_logits = self.lm_head(hidden_states)
|
866 |
+
|
867 |
+
loss = None
|
868 |
+
if labels is not None:
|
869 |
+
labels = labels.to(lm_logits.device)
|
870 |
+
shift_logits = lm_logits[..., :-1, :].contiguous()
|
871 |
+
shift_labels = labels[..., 1:].contiguous()
|
872 |
+
loss_fct = CrossEntropyLoss()
|
873 |
+
loss = loss_fct(
|
874 |
+
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
|
875 |
+
)
|
876 |
+
|
877 |
+
if not return_dict:
|
878 |
+
output = (lm_logits,) + transformer_outputs[1:]
|
879 |
+
return ((loss,) + output) if loss is not None else output
|
880 |
+
|
881 |
+
return CausalLMOutputWithPast(
|
882 |
+
loss=loss,
|
883 |
+
logits=lm_logits,
|
884 |
+
past_key_values=transformer_outputs.past_key_values,
|
885 |
+
hidden_states=transformer_outputs.hidden_states,
|
886 |
+
attentions=transformer_outputs.attentions,
|
887 |
+
)
|
888 |
+
|
889 |
+
@staticmethod
|
890 |
+
def _reorder_cache(
|
891 |
+
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
|
892 |
+
) -> Tuple[Tuple[torch.Tensor]]:
|
893 |
+
|
894 |
+
return tuple(
|
895 |
+
tuple(
|
896 |
+
past_state.index_select(0, beam_idx.to(past_state.device))
|
897 |
+
for past_state in layer_past
|
898 |
+
)
|
899 |
+
for layer_past in past_key_values
|
900 |
+
)
|
901 |
+
|
902 |
+
def chat(
|
903 |
+
self,
|
904 |
+
tokenizer: PreTrainedTokenizer,
|
905 |
+
query: str,
|
906 |
+
history: Optional[HistoryType],
|
907 |
+
system: str = "You are a helpful assistant.",
|
908 |
+
append_history: bool = True,
|
909 |
+
stream: Optional[bool] = _SENTINEL,
|
910 |
+
stop_words_ids: Optional[List[List[int]]] = None,
|
911 |
+
generation_config: Optional[GenerationConfig] = None,
|
912 |
+
**kwargs,
|
913 |
+
) -> Tuple[str, HistoryType]:
|
914 |
+
generation_config = generation_config if generation_config is not None else self.generation_config
|
915 |
+
|
916 |
+
assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT
|
917 |
+
assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
|
918 |
+
if history is None:
|
919 |
+
history = []
|
920 |
+
if stop_words_ids is None:
|
921 |
+
stop_words_ids = []
|
922 |
+
|
923 |
+
max_window_size = kwargs.get('max_window_size', None)
|
924 |
+
if max_window_size is None:
|
925 |
+
max_window_size = generation_config.max_window_size
|
926 |
+
raw_text, context_tokens = make_context(
|
927 |
+
tokenizer,
|
928 |
+
query,
|
929 |
+
history=history,
|
930 |
+
system=system,
|
931 |
+
max_window_size=max_window_size,
|
932 |
+
chat_format=generation_config.chat_format,
|
933 |
+
)
|
934 |
+
|
935 |
+
stop_words_ids.extend(get_stop_words_ids(
|
936 |
+
generation_config.chat_format, tokenizer
|
937 |
+
))
|
938 |
+
input_ids = torch.tensor([context_tokens]).to(self.device)
|
939 |
+
outputs = self.generate(
|
940 |
+
input_ids,
|
941 |
+
stop_words_ids=stop_words_ids,
|
942 |
+
return_dict_in_generate=False,
|
943 |
+
generation_config=generation_config,
|
944 |
+
**kwargs,
|
945 |
+
)
|
946 |
+
|
947 |
+
response = decode_tokens(
|
948 |
+
outputs[0],
|
949 |
+
tokenizer,
|
950 |
+
raw_text_len=len(raw_text),
|
951 |
+
context_length=len(context_tokens),
|
952 |
+
chat_format=generation_config.chat_format,
|
953 |
+
verbose=False,
|
954 |
+
errors='replace'
|
955 |
+
)
|
956 |
+
|
957 |
+
if append_history:
|
958 |
+
history.append((query, response))
|
959 |
+
|
960 |
+
return response, history
|
961 |
+
|
962 |
+
def chat_stream(
|
963 |
+
self,
|
964 |
+
tokenizer: PreTrainedTokenizer,
|
965 |
+
query: str,
|
966 |
+
history: Optional[HistoryType],
|
967 |
+
system: str = "You are a helpful assistant.",
|
968 |
+
stop_words_ids: Optional[List[List[int]]] = None,
|
969 |
+
logits_processor: Optional[LogitsProcessorList] = None,
|
970 |
+
generation_config: Optional[GenerationConfig] = None,
|
971 |
+
**kwargs,
|
972 |
+
) -> Generator[str, Any, None]:
|
973 |
+
generation_config = generation_config if generation_config is not None else self.generation_config
|
974 |
+
assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
|
975 |
+
if history is None:
|
976 |
+
history = []
|
977 |
+
if stop_words_ids is None:
|
978 |
+
stop_words_ids = []
|
979 |
+
|
980 |
+
max_window_size = kwargs.get('max_window_size', None)
|
981 |
+
if max_window_size is None:
|
982 |
+
max_window_size = generation_config.max_window_size
|
983 |
+
raw_text, context_tokens = make_context(
|
984 |
+
tokenizer,
|
985 |
+
query,
|
986 |
+
history=history,
|
987 |
+
system=system,
|
988 |
+
max_window_size=max_window_size,
|
989 |
+
chat_format=generation_config.chat_format,
|
990 |
+
)
|
991 |
+
|
992 |
+
stop_words_ids.extend(get_stop_words_ids(
|
993 |
+
generation_config.chat_format, tokenizer
|
994 |
+
))
|
995 |
+
if stop_words_ids is not None:
|
996 |
+
stop_words_logits_processor = StopWordsLogitsProcessor(
|
997 |
+
stop_words_ids=stop_words_ids,
|
998 |
+
eos_token_id=generation_config.eos_token_id,
|
999 |
+
)
|
1000 |
+
if logits_processor is None:
|
1001 |
+
logits_processor = LogitsProcessorList([stop_words_logits_processor])
|
1002 |
+
else:
|
1003 |
+
logits_processor.append(stop_words_logits_processor)
|
1004 |
+
input_ids = torch.tensor([context_tokens]).to(self.device)
|
1005 |
+
|
1006 |
+
from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
|
1007 |
+
self.__class__.generate_stream = NewGenerationMixin.generate
|
1008 |
+
self.__class__.sample_stream = NewGenerationMixin.sample_stream
|
1009 |
+
stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True)
|
1010 |
+
|
1011 |
+
def stream_generator():
|
1012 |
+
outputs = []
|
1013 |
+
for token in self.generate_stream(
|
1014 |
+
input_ids,
|
1015 |
+
return_dict_in_generate=False,
|
1016 |
+
generation_config=stream_config,
|
1017 |
+
logits_processor=logits_processor,
|
1018 |
+
seed=-1,
|
1019 |
+
**kwargs):
|
1020 |
+
outputs.append(token.item())
|
1021 |
+
yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore')
|
1022 |
+
|
1023 |
+
return stream_generator()
|
1024 |
+
|
1025 |
+
def generate(
|
1026 |
+
self,
|
1027 |
+
inputs: Optional[torch.Tensor] = None,
|
1028 |
+
generation_config: Optional[GenerationConfig] = None,
|
1029 |
+
logits_processor: Optional[LogitsProcessorList] = None,
|
1030 |
+
stopping_criteria: Optional[StoppingCriteriaList] = None,
|
1031 |
+
prefix_allowed_tokens_fn: Optional[
|
1032 |
+
Callable[[int, torch.Tensor], List[int]]
|
1033 |
+
] = None,
|
1034 |
+
synced_gpus: Optional[bool] = None,
|
1035 |
+
assistant_model: Optional["PreTrainedModel"] = None,
|
1036 |
+
streamer: Optional["BaseStreamer"] = None,
|
1037 |
+
**kwargs,
|
1038 |
+
) -> Union[GenerateOutput, torch.LongTensor]:
|
1039 |
+
generation_config = generation_config if generation_config is not None else self.generation_config
|
1040 |
+
|
1041 |
+
# Process stop_words_ids.
|
1042 |
+
stop_words_ids = kwargs.pop("stop_words_ids", None)
|
1043 |
+
if stop_words_ids is None and generation_config is not None:
|
1044 |
+
stop_words_ids = getattr(generation_config, "stop_words_ids", None)
|
1045 |
+
if stop_words_ids is None:
|
1046 |
+
stop_words_ids = getattr(generation_config, "stop_words_ids", None)
|
1047 |
+
|
1048 |
+
if stop_words_ids is not None:
|
1049 |
+
stop_words_logits_processor = StopWordsLogitsProcessor(
|
1050 |
+
stop_words_ids=stop_words_ids,
|
1051 |
+
eos_token_id=generation_config.eos_token_id,
|
1052 |
+
)
|
1053 |
+
if logits_processor is None:
|
1054 |
+
logits_processor = LogitsProcessorList([stop_words_logits_processor])
|
1055 |
+
else:
|
1056 |
+
logits_processor.append(stop_words_logits_processor)
|
1057 |
+
|
1058 |
+
return super().generate(
|
1059 |
+
inputs,
|
1060 |
+
generation_config=generation_config,
|
1061 |
+
logits_processor=logits_processor,
|
1062 |
+
stopping_criteria=stopping_criteria,
|
1063 |
+
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
|
1064 |
+
synced_gpus=synced_gpus,
|
1065 |
+
assistant_model=assistant_model,
|
1066 |
+
streamer=streamer,
|
1067 |
+
**kwargs,
|
1068 |
+
)
|
1069 |
+
|
1070 |
+
|
1071 |
+
class RotaryEmbedding(torch.nn.Module):
|
1072 |
+
def __init__(self, dim, base=10000):
|
1073 |
+
super().__init__()
|
1074 |
+
self.dim = dim
|
1075 |
+
self.base = base
|
1076 |
+
self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
|
1077 |
+
if importlib.util.find_spec("einops") is None:
|
1078 |
+
raise RuntimeError("einops is required for Rotary Embedding")
|
1079 |
+
|
1080 |
+
self._rotary_pos_emb_cache = None
|
1081 |
+
self._seq_len_cached = 0
|
1082 |
+
self._ntk_alpha_cached = 1.0
|
1083 |
+
|
1084 |
+
def update_rotary_pos_emb_cache(self, max_seq_len, offset=0, ntk_alpha=1.0):
|
1085 |
+
seqlen = max_seq_len + offset
|
1086 |
+
if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
|
1087 |
+
base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
|
1088 |
+
self.inv_freq = 1.0 / (
|
1089 |
+
base
|
1090 |
+
** (
|
1091 |
+
torch.arange(0, self.dim, 2, device=self.inv_freq.device).float()
|
1092 |
+
/ self.dim
|
1093 |
+
)
|
1094 |
+
)
|
1095 |
+
self._seq_len_cached = max(2 * seqlen, 16)
|
1096 |
+
self._ntk_alpha_cached = ntk_alpha
|
1097 |
+
seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
|
1098 |
+
freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
|
1099 |
+
|
1100 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
1101 |
+
from einops import rearrange
|
1102 |
+
|
1103 |
+
emb = rearrange(emb, "n d -> 1 n 1 d")
|
1104 |
+
|
1105 |
+
cos, sin = emb.cos(), emb.sin()
|
1106 |
+
self._rotary_pos_emb_cache = [cos, sin]
|
1107 |
+
|
1108 |
+
def forward(self, max_seq_len, offset=0, ntk_alpha=1.0):
|
1109 |
+
self.update_rotary_pos_emb_cache(max_seq_len, offset, ntk_alpha)
|
1110 |
+
cos, sin = self._rotary_pos_emb_cache
|
1111 |
+
return [cos[:, offset : offset + max_seq_len], sin[:, offset : offset + max_seq_len]]
|
1112 |
+
|
1113 |
+
|
1114 |
+
def _rotate_half(x):
|
1115 |
+
from einops import rearrange
|
1116 |
+
|
1117 |
+
x = rearrange(x, "... (j d) -> ... j d", j=2)
|
1118 |
+
x1, x2 = x.unbind(dim=-2)
|
1119 |
+
return torch.cat((-x2, x1), dim=-1)
|
1120 |
+
|
1121 |
+
|
1122 |
+
def apply_rotary_pos_emb(t, freqs):
|
1123 |
+
cos, sin = freqs
|
1124 |
+
if apply_rotary_emb_func is not None and t.is_cuda:
|
1125 |
+
t_ = t.float()
|
1126 |
+
cos = cos.squeeze(0).squeeze(1)[:, : cos.shape[-1] // 2]
|
1127 |
+
sin = sin.squeeze(0).squeeze(1)[:, : sin.shape[-1] // 2]
|
1128 |
+
output = apply_rotary_emb_func(t_, cos, sin).type_as(t)
|
1129 |
+
return output
|
1130 |
+
else:
|
1131 |
+
rot_dim = freqs[0].shape[-1]
|
1132 |
+
cos, sin = freqs
|
1133 |
+
t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:]
|
1134 |
+
t_ = t_.float()
|
1135 |
+
t_pass_ = t_pass_.float()
|
1136 |
+
t_ = (t_ * cos) + (_rotate_half(t_) * sin)
|
1137 |
+
return torch.cat((t_, t_pass_), dim=-1).type_as(t)
|
1138 |
+
|
1139 |
+
|
1140 |
+
class RMSNorm(torch.nn.Module):
|
1141 |
+
def __init__(self, dim: int, eps: float = 1e-6):
|
1142 |
+
super().__init__()
|
1143 |
+
self.eps = eps
|
1144 |
+
self.weight = nn.Parameter(torch.ones(dim))
|
1145 |
+
|
1146 |
+
def _norm(self, x):
|
1147 |
+
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
|
1148 |
+
|
1149 |
+
def forward(self, x):
|
1150 |
+
if rms_norm is not None and x.is_cuda:
|
1151 |
+
return rms_norm(x, self.weight, self.eps)
|
1152 |
+
else:
|
1153 |
+
output = self._norm(x.float()).type_as(x)
|
1154 |
+
return output * self.weight
|
pytorch_model-00001-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8096d6aefba5b263f06ecd9f19b76e1780fefe2aa7010eab4f1235b18253e8f7
|
3 |
+
size 1964070447
|
pytorch_model-00002-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:244b3c40adad8ec6f8e94d59a443607e2d6d11fee4838ca787ea1873ef3c3c20
|
3 |
+
size 1933792141
|
pytorch_model-00003-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:7fce23c694efdf7b28c152f9ac77ba40a5e114d066708fab8079db0ee967db93
|
3 |
+
size 1933792141
|
pytorch_model-00004-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0fcc2950c12804b413838b291bbd302b375a2caf80aa5ec5dd5588e9083675bd
|
3 |
+
size 1990406779
|
pytorch_model-00005-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8a83706861cd18b0b7a58a67022b92a13d94d6c740fa003a0c4a44bc18938988
|
3 |
+
size 1923281531
|
pytorch_model-00006-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4af6fbf1423f86de3f88942ec2460641a22570d1c41347625043d9933e51c3ca
|
3 |
+
size 1933783675
|
pytorch_model-00007-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5171efa571c59248f314a4ebfb56d5f4dede27f89a126d17ce5400eb6220d82f
|
3 |
+
size 1933792205
|
pytorch_model-00008-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5f79876d7cc162272687daac6dd09778b4bce04895bc44f67c53cc4a9b803fc5
|
3 |
+
size 1975364669
|
pytorch_model-00009-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:02b652b95c6dffc2754d2cc3d8166b35c542726a571b979fc2b59417c218f196
|
3 |
+
size 1994925011
|
pytorch_model-00010-of-00010.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:23baadda455bbc1f5238bd7b1eb2386e1d17312c8cfaa3f9a3e5bdde44883aa4
|
3 |
+
size 1730968463
|
pytorch_model.bin.index.json
ADDED
@@ -0,0 +1,860 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"metadata": {
|
3 |
+
"total_size": 19313870336
|
4 |
+
},
|
5 |
+
"weight_map": {
|
6 |
+
"lm_head.weight": "pytorch_model-00010-of-00010.bin",
|
7 |
+
"transformer.h.0.attn.c_attn.bias": "pytorch_model-00001-of-00010.bin",
|
8 |
+
"transformer.h.0.attn.c_attn.weight": "pytorch_model-00001-of-00010.bin",
|
9 |
+
"transformer.h.0.attn.c_proj.weight": "pytorch_model-00001-of-00010.bin",
|
10 |
+
"transformer.h.0.ln_1.weight": "pytorch_model-00001-of-00010.bin",
|
11 |
+
"transformer.h.0.ln_2.weight": "pytorch_model-00001-of-00010.bin",
|
12 |
+
"transformer.h.0.mlp.c_proj.weight": "pytorch_model-00001-of-00010.bin",
|
13 |
+
"transformer.h.0.mlp.w1.weight": "pytorch_model-00001-of-00010.bin",
|
14 |
+
"transformer.h.0.mlp.w2.weight": "pytorch_model-00001-of-00010.bin",
|
15 |
+
"transformer.h.1.attn.c_attn.bias": "pytorch_model-00001-of-00010.bin",
|
16 |
+
"transformer.h.1.attn.c_attn.weight": "pytorch_model-00001-of-00010.bin",
|
17 |
+
"transformer.h.1.attn.c_proj.weight": "pytorch_model-00001-of-00010.bin",
|
18 |
+
"transformer.h.1.ln_1.weight": "pytorch_model-00001-of-00010.bin",
|
19 |
+
"transformer.h.1.ln_2.weight": "pytorch_model-00001-of-00010.bin",
|
20 |
+
"transformer.h.1.mlp.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
21 |
+
"transformer.h.1.mlp.w1.weight": "pytorch_model-00001-of-00010.bin",
|
22 |
+
"transformer.h.1.mlp.w2.weight": "pytorch_model-00001-of-00010.bin",
|
23 |
+
"transformer.h.10.attn.c_attn.bias": "pytorch_model-00003-of-00010.bin",
|
24 |
+
"transformer.h.10.attn.c_attn.weight": "pytorch_model-00003-of-00010.bin",
|
25 |
+
"transformer.h.10.attn.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
26 |
+
"transformer.h.10.ln_1.weight": "pytorch_model-00003-of-00010.bin",
|
27 |
+
"transformer.h.10.ln_2.weight": "pytorch_model-00003-of-00010.bin",
|
28 |
+
"transformer.h.10.mlp.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
29 |
+
"transformer.h.10.mlp.w1.weight": "pytorch_model-00003-of-00010.bin",
|
30 |
+
"transformer.h.10.mlp.w2.weight": "pytorch_model-00003-of-00010.bin",
|
31 |
+
"transformer.h.11.attn.c_attn.bias": "pytorch_model-00003-of-00010.bin",
|
32 |
+
"transformer.h.11.attn.c_attn.weight": "pytorch_model-00003-of-00010.bin",
|
33 |
+
"transformer.h.11.attn.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
34 |
+
"transformer.h.11.ln_1.weight": "pytorch_model-00003-of-00010.bin",
|
35 |
+
"transformer.h.11.ln_2.weight": "pytorch_model-00003-of-00010.bin",
|
36 |
+
"transformer.h.11.mlp.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
37 |
+
"transformer.h.11.mlp.w1.weight": "pytorch_model-00004-of-00010.bin",
|
38 |
+
"transformer.h.11.mlp.w2.weight": "pytorch_model-00004-of-00010.bin",
|
39 |
+
"transformer.h.12.attn.c_attn.bias": "pytorch_model-00004-of-00010.bin",
|
40 |
+
"transformer.h.12.attn.c_attn.weight": "pytorch_model-00004-of-00010.bin",
|
41 |
+
"transformer.h.12.attn.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
42 |
+
"transformer.h.12.ln_1.weight": "pytorch_model-00004-of-00010.bin",
|
43 |
+
"transformer.h.12.ln_2.weight": "pytorch_model-00004-of-00010.bin",
|
44 |
+
"transformer.h.12.mlp.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
45 |
+
"transformer.h.12.mlp.w1.weight": "pytorch_model-00004-of-00010.bin",
|
46 |
+
"transformer.h.12.mlp.w2.weight": "pytorch_model-00004-of-00010.bin",
|
47 |
+
"transformer.h.13.attn.c_attn.bias": "pytorch_model-00004-of-00010.bin",
|
48 |
+
"transformer.h.13.attn.c_attn.weight": "pytorch_model-00004-of-00010.bin",
|
49 |
+
"transformer.h.13.attn.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
50 |
+
"transformer.h.13.ln_1.weight": "pytorch_model-00004-of-00010.bin",
|
51 |
+
"transformer.h.13.ln_2.weight": "pytorch_model-00004-of-00010.bin",
|
52 |
+
"transformer.h.13.mlp.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
53 |
+
"transformer.h.13.mlp.w1.weight": "pytorch_model-00004-of-00010.bin",
|
54 |
+
"transformer.h.13.mlp.w2.weight": "pytorch_model-00004-of-00010.bin",
|
55 |
+
"transformer.h.14.attn.c_attn.bias": "pytorch_model-00004-of-00010.bin",
|
56 |
+
"transformer.h.14.attn.c_attn.weight": "pytorch_model-00004-of-00010.bin",
|
57 |
+
"transformer.h.14.attn.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
58 |
+
"transformer.h.14.ln_1.weight": "pytorch_model-00004-of-00010.bin",
|
59 |
+
"transformer.h.14.ln_2.weight": "pytorch_model-00004-of-00010.bin",
|
60 |
+
"transformer.h.14.mlp.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
61 |
+
"transformer.h.14.mlp.w1.weight": "pytorch_model-00004-of-00010.bin",
|
62 |
+
"transformer.h.14.mlp.w2.weight": "pytorch_model-00004-of-00010.bin",
|
63 |
+
"transformer.h.15.attn.c_attn.bias": "pytorch_model-00004-of-00010.bin",
|
64 |
+
"transformer.h.15.attn.c_attn.weight": "pytorch_model-00004-of-00010.bin",
|
65 |
+
"transformer.h.15.attn.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
66 |
+
"transformer.h.15.ln_1.weight": "pytorch_model-00004-of-00010.bin",
|
67 |
+
"transformer.h.15.ln_2.weight": "pytorch_model-00004-of-00010.bin",
|
68 |
+
"transformer.h.15.mlp.c_proj.weight": "pytorch_model-00004-of-00010.bin",
|
69 |
+
"transformer.h.15.mlp.w1.weight": "pytorch_model-00004-of-00010.bin",
|
70 |
+
"transformer.h.15.mlp.w2.weight": "pytorch_model-00004-of-00010.bin",
|
71 |
+
"transformer.h.16.attn.c_attn.bias": "pytorch_model-00004-of-00010.bin",
|
72 |
+
"transformer.h.16.attn.c_attn.weight": "pytorch_model-00004-of-00010.bin",
|
73 |
+
"transformer.h.16.attn.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
74 |
+
"transformer.h.16.ln_1.weight": "pytorch_model-00004-of-00010.bin",
|
75 |
+
"transformer.h.16.ln_2.weight": "pytorch_model-00005-of-00010.bin",
|
76 |
+
"transformer.h.16.mlp.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
77 |
+
"transformer.h.16.mlp.w1.weight": "pytorch_model-00005-of-00010.bin",
|
78 |
+
"transformer.h.16.mlp.w2.weight": "pytorch_model-00005-of-00010.bin",
|
79 |
+
"transformer.h.17.attn.c_attn.bias": "pytorch_model-00005-of-00010.bin",
|
80 |
+
"transformer.h.17.attn.c_attn.weight": "pytorch_model-00005-of-00010.bin",
|
81 |
+
"transformer.h.17.attn.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
82 |
+
"transformer.h.17.ln_1.weight": "pytorch_model-00005-of-00010.bin",
|
83 |
+
"transformer.h.17.ln_2.weight": "pytorch_model-00005-of-00010.bin",
|
84 |
+
"transformer.h.17.mlp.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
85 |
+
"transformer.h.17.mlp.w1.weight": "pytorch_model-00005-of-00010.bin",
|
86 |
+
"transformer.h.17.mlp.w2.weight": "pytorch_model-00005-of-00010.bin",
|
87 |
+
"transformer.h.18.attn.c_attn.bias": "pytorch_model-00005-of-00010.bin",
|
88 |
+
"transformer.h.18.attn.c_attn.weight": "pytorch_model-00005-of-00010.bin",
|
89 |
+
"transformer.h.18.attn.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
90 |
+
"transformer.h.18.ln_1.weight": "pytorch_model-00005-of-00010.bin",
|
91 |
+
"transformer.h.18.ln_2.weight": "pytorch_model-00005-of-00010.bin",
|
92 |
+
"transformer.h.18.mlp.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
93 |
+
"transformer.h.18.mlp.w1.weight": "pytorch_model-00005-of-00010.bin",
|
94 |
+
"transformer.h.18.mlp.w2.weight": "pytorch_model-00005-of-00010.bin",
|
95 |
+
"transformer.h.19.attn.c_attn.bias": "pytorch_model-00005-of-00010.bin",
|
96 |
+
"transformer.h.19.attn.c_attn.weight": "pytorch_model-00005-of-00010.bin",
|
97 |
+
"transformer.h.19.attn.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
98 |
+
"transformer.h.19.ln_1.weight": "pytorch_model-00005-of-00010.bin",
|
99 |
+
"transformer.h.19.ln_2.weight": "pytorch_model-00005-of-00010.bin",
|
100 |
+
"transformer.h.19.mlp.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
101 |
+
"transformer.h.19.mlp.w1.weight": "pytorch_model-00005-of-00010.bin",
|
102 |
+
"transformer.h.19.mlp.w2.weight": "pytorch_model-00005-of-00010.bin",
|
103 |
+
"transformer.h.2.attn.c_attn.bias": "pytorch_model-00002-of-00010.bin",
|
104 |
+
"transformer.h.2.attn.c_attn.weight": "pytorch_model-00002-of-00010.bin",
|
105 |
+
"transformer.h.2.attn.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
106 |
+
"transformer.h.2.ln_1.weight": "pytorch_model-00002-of-00010.bin",
|
107 |
+
"transformer.h.2.ln_2.weight": "pytorch_model-00002-of-00010.bin",
|
108 |
+
"transformer.h.2.mlp.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
109 |
+
"transformer.h.2.mlp.w1.weight": "pytorch_model-00002-of-00010.bin",
|
110 |
+
"transformer.h.2.mlp.w2.weight": "pytorch_model-00002-of-00010.bin",
|
111 |
+
"transformer.h.20.attn.c_attn.bias": "pytorch_model-00005-of-00010.bin",
|
112 |
+
"transformer.h.20.attn.c_attn.weight": "pytorch_model-00005-of-00010.bin",
|
113 |
+
"transformer.h.20.attn.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
114 |
+
"transformer.h.20.ln_1.weight": "pytorch_model-00005-of-00010.bin",
|
115 |
+
"transformer.h.20.ln_2.weight": "pytorch_model-00005-of-00010.bin",
|
116 |
+
"transformer.h.20.mlp.c_proj.weight": "pytorch_model-00005-of-00010.bin",
|
117 |
+
"transformer.h.20.mlp.w1.weight": "pytorch_model-00005-of-00010.bin",
|
118 |
+
"transformer.h.20.mlp.w2.weight": "pytorch_model-00005-of-00010.bin",
|
119 |
+
"transformer.h.21.attn.c_attn.bias": "pytorch_model-00006-of-00010.bin",
|
120 |
+
"transformer.h.21.attn.c_attn.weight": "pytorch_model-00006-of-00010.bin",
|
121 |
+
"transformer.h.21.attn.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
122 |
+
"transformer.h.21.ln_1.weight": "pytorch_model-00005-of-00010.bin",
|
123 |
+
"transformer.h.21.ln_2.weight": "pytorch_model-00006-of-00010.bin",
|
124 |
+
"transformer.h.21.mlp.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
125 |
+
"transformer.h.21.mlp.w1.weight": "pytorch_model-00006-of-00010.bin",
|
126 |
+
"transformer.h.21.mlp.w2.weight": "pytorch_model-00006-of-00010.bin",
|
127 |
+
"transformer.h.22.attn.c_attn.bias": "pytorch_model-00006-of-00010.bin",
|
128 |
+
"transformer.h.22.attn.c_attn.weight": "pytorch_model-00006-of-00010.bin",
|
129 |
+
"transformer.h.22.attn.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
130 |
+
"transformer.h.22.ln_1.weight": "pytorch_model-00006-of-00010.bin",
|
131 |
+
"transformer.h.22.ln_2.weight": "pytorch_model-00006-of-00010.bin",
|
132 |
+
"transformer.h.22.mlp.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
133 |
+
"transformer.h.22.mlp.w1.weight": "pytorch_model-00006-of-00010.bin",
|
134 |
+
"transformer.h.22.mlp.w2.weight": "pytorch_model-00006-of-00010.bin",
|
135 |
+
"transformer.h.23.attn.c_attn.bias": "pytorch_model-00006-of-00010.bin",
|
136 |
+
"transformer.h.23.attn.c_attn.weight": "pytorch_model-00006-of-00010.bin",
|
137 |
+
"transformer.h.23.attn.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
138 |
+
"transformer.h.23.ln_1.weight": "pytorch_model-00006-of-00010.bin",
|
139 |
+
"transformer.h.23.ln_2.weight": "pytorch_model-00006-of-00010.bin",
|
140 |
+
"transformer.h.23.mlp.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
141 |
+
"transformer.h.23.mlp.w1.weight": "pytorch_model-00006-of-00010.bin",
|
142 |
+
"transformer.h.23.mlp.w2.weight": "pytorch_model-00006-of-00010.bin",
|
143 |
+
"transformer.h.24.attn.c_attn.bias": "pytorch_model-00006-of-00010.bin",
|
144 |
+
"transformer.h.24.attn.c_attn.weight": "pytorch_model-00006-of-00010.bin",
|
145 |
+
"transformer.h.24.attn.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
146 |
+
"transformer.h.24.ln_1.weight": "pytorch_model-00006-of-00010.bin",
|
147 |
+
"transformer.h.24.ln_2.weight": "pytorch_model-00006-of-00010.bin",
|
148 |
+
"transformer.h.24.mlp.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
149 |
+
"transformer.h.24.mlp.w1.weight": "pytorch_model-00006-of-00010.bin",
|
150 |
+
"transformer.h.24.mlp.w2.weight": "pytorch_model-00006-of-00010.bin",
|
151 |
+
"transformer.h.25.attn.c_attn.bias": "pytorch_model-00006-of-00010.bin",
|
152 |
+
"transformer.h.25.attn.c_attn.weight": "pytorch_model-00006-of-00010.bin",
|
153 |
+
"transformer.h.25.attn.c_proj.weight": "pytorch_model-00006-of-00010.bin",
|
154 |
+
"transformer.h.25.ln_1.weight": "pytorch_model-00006-of-00010.bin",
|
155 |
+
"transformer.h.25.ln_2.weight": "pytorch_model-00006-of-00010.bin",
|
156 |
+
"transformer.h.25.mlp.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
157 |
+
"transformer.h.25.mlp.w1.weight": "pytorch_model-00006-of-00010.bin",
|
158 |
+
"transformer.h.25.mlp.w2.weight": "pytorch_model-00006-of-00010.bin",
|
159 |
+
"transformer.h.26.attn.c_attn.bias": "pytorch_model-00007-of-00010.bin",
|
160 |
+
"transformer.h.26.attn.c_attn.weight": "pytorch_model-00007-of-00010.bin",
|
161 |
+
"transformer.h.26.attn.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
162 |
+
"transformer.h.26.ln_1.weight": "pytorch_model-00007-of-00010.bin",
|
163 |
+
"transformer.h.26.ln_2.weight": "pytorch_model-00007-of-00010.bin",
|
164 |
+
"transformer.h.26.mlp.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
165 |
+
"transformer.h.26.mlp.w1.weight": "pytorch_model-00007-of-00010.bin",
|
166 |
+
"transformer.h.26.mlp.w2.weight": "pytorch_model-00007-of-00010.bin",
|
167 |
+
"transformer.h.27.attn.c_attn.bias": "pytorch_model-00007-of-00010.bin",
|
168 |
+
"transformer.h.27.attn.c_attn.weight": "pytorch_model-00007-of-00010.bin",
|
169 |
+
"transformer.h.27.attn.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
170 |
+
"transformer.h.27.ln_1.weight": "pytorch_model-00007-of-00010.bin",
|
171 |
+
"transformer.h.27.ln_2.weight": "pytorch_model-00007-of-00010.bin",
|
172 |
+
"transformer.h.27.mlp.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
173 |
+
"transformer.h.27.mlp.w1.weight": "pytorch_model-00007-of-00010.bin",
|
174 |
+
"transformer.h.27.mlp.w2.weight": "pytorch_model-00007-of-00010.bin",
|
175 |
+
"transformer.h.28.attn.c_attn.bias": "pytorch_model-00007-of-00010.bin",
|
176 |
+
"transformer.h.28.attn.c_attn.weight": "pytorch_model-00007-of-00010.bin",
|
177 |
+
"transformer.h.28.attn.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
178 |
+
"transformer.h.28.ln_1.weight": "pytorch_model-00007-of-00010.bin",
|
179 |
+
"transformer.h.28.ln_2.weight": "pytorch_model-00007-of-00010.bin",
|
180 |
+
"transformer.h.28.mlp.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
181 |
+
"transformer.h.28.mlp.w1.weight": "pytorch_model-00007-of-00010.bin",
|
182 |
+
"transformer.h.28.mlp.w2.weight": "pytorch_model-00007-of-00010.bin",
|
183 |
+
"transformer.h.29.attn.c_attn.bias": "pytorch_model-00007-of-00010.bin",
|
184 |
+
"transformer.h.29.attn.c_attn.weight": "pytorch_model-00007-of-00010.bin",
|
185 |
+
"transformer.h.29.attn.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
186 |
+
"transformer.h.29.ln_1.weight": "pytorch_model-00007-of-00010.bin",
|
187 |
+
"transformer.h.29.ln_2.weight": "pytorch_model-00007-of-00010.bin",
|
188 |
+
"transformer.h.29.mlp.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
189 |
+
"transformer.h.29.mlp.w1.weight": "pytorch_model-00007-of-00010.bin",
|
190 |
+
"transformer.h.29.mlp.w2.weight": "pytorch_model-00007-of-00010.bin",
|
191 |
+
"transformer.h.3.attn.c_attn.bias": "pytorch_model-00002-of-00010.bin",
|
192 |
+
"transformer.h.3.attn.c_attn.weight": "pytorch_model-00002-of-00010.bin",
|
193 |
+
"transformer.h.3.attn.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
194 |
+
"transformer.h.3.ln_1.weight": "pytorch_model-00002-of-00010.bin",
|
195 |
+
"transformer.h.3.ln_2.weight": "pytorch_model-00002-of-00010.bin",
|
196 |
+
"transformer.h.3.mlp.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
197 |
+
"transformer.h.3.mlp.w1.weight": "pytorch_model-00002-of-00010.bin",
|
198 |
+
"transformer.h.3.mlp.w2.weight": "pytorch_model-00002-of-00010.bin",
|
199 |
+
"transformer.h.30.attn.c_attn.bias": "pytorch_model-00007-of-00010.bin",
|
200 |
+
"transformer.h.30.attn.c_attn.weight": "pytorch_model-00007-of-00010.bin",
|
201 |
+
"transformer.h.30.attn.c_proj.weight": "pytorch_model-00007-of-00010.bin",
|
202 |
+
"transformer.h.30.ln_1.weight": "pytorch_model-00007-of-00010.bin",
|
203 |
+
"transformer.h.30.ln_2.weight": "pytorch_model-00007-of-00010.bin",
|
204 |
+
"transformer.h.30.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
205 |
+
"transformer.h.30.mlp.w1.weight": "pytorch_model-00007-of-00010.bin",
|
206 |
+
"transformer.h.30.mlp.w2.weight": "pytorch_model-00008-of-00010.bin",
|
207 |
+
"transformer.h.31.attn.c_attn.bias": "pytorch_model-00008-of-00010.bin",
|
208 |
+
"transformer.h.31.attn.c_attn.weight": "pytorch_model-00008-of-00010.bin",
|
209 |
+
"transformer.h.31.attn.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
210 |
+
"transformer.h.31.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
211 |
+
"transformer.h.31.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
212 |
+
"transformer.h.31.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
213 |
+
"transformer.h.31.mlp.w1.weight": "pytorch_model-00008-of-00010.bin",
|
214 |
+
"transformer.h.31.mlp.w2.weight": "pytorch_model-00008-of-00010.bin",
|
215 |
+
"transformer.h.4.attn.c_attn.bias": "pytorch_model-00002-of-00010.bin",
|
216 |
+
"transformer.h.4.attn.c_attn.weight": "pytorch_model-00002-of-00010.bin",
|
217 |
+
"transformer.h.4.attn.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
218 |
+
"transformer.h.4.ln_1.weight": "pytorch_model-00002-of-00010.bin",
|
219 |
+
"transformer.h.4.ln_2.weight": "pytorch_model-00002-of-00010.bin",
|
220 |
+
"transformer.h.4.mlp.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
221 |
+
"transformer.h.4.mlp.w1.weight": "pytorch_model-00002-of-00010.bin",
|
222 |
+
"transformer.h.4.mlp.w2.weight": "pytorch_model-00002-of-00010.bin",
|
223 |
+
"transformer.h.5.attn.c_attn.bias": "pytorch_model-00002-of-00010.bin",
|
224 |
+
"transformer.h.5.attn.c_attn.weight": "pytorch_model-00002-of-00010.bin",
|
225 |
+
"transformer.h.5.attn.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
226 |
+
"transformer.h.5.ln_1.weight": "pytorch_model-00002-of-00010.bin",
|
227 |
+
"transformer.h.5.ln_2.weight": "pytorch_model-00002-of-00010.bin",
|
228 |
+
"transformer.h.5.mlp.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
229 |
+
"transformer.h.5.mlp.w1.weight": "pytorch_model-00002-of-00010.bin",
|
230 |
+
"transformer.h.5.mlp.w2.weight": "pytorch_model-00002-of-00010.bin",
|
231 |
+
"transformer.h.6.attn.c_attn.bias": "pytorch_model-00002-of-00010.bin",
|
232 |
+
"transformer.h.6.attn.c_attn.weight": "pytorch_model-00002-of-00010.bin",
|
233 |
+
"transformer.h.6.attn.c_proj.weight": "pytorch_model-00002-of-00010.bin",
|
234 |
+
"transformer.h.6.ln_1.weight": "pytorch_model-00002-of-00010.bin",
|
235 |
+
"transformer.h.6.ln_2.weight": "pytorch_model-00002-of-00010.bin",
|
236 |
+
"transformer.h.6.mlp.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
237 |
+
"transformer.h.6.mlp.w1.weight": "pytorch_model-00002-of-00010.bin",
|
238 |
+
"transformer.h.6.mlp.w2.weight": "pytorch_model-00003-of-00010.bin",
|
239 |
+
"transformer.h.7.attn.c_attn.bias": "pytorch_model-00003-of-00010.bin",
|
240 |
+
"transformer.h.7.attn.c_attn.weight": "pytorch_model-00003-of-00010.bin",
|
241 |
+
"transformer.h.7.attn.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
242 |
+
"transformer.h.7.ln_1.weight": "pytorch_model-00003-of-00010.bin",
|
243 |
+
"transformer.h.7.ln_2.weight": "pytorch_model-00003-of-00010.bin",
|
244 |
+
"transformer.h.7.mlp.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
245 |
+
"transformer.h.7.mlp.w1.weight": "pytorch_model-00003-of-00010.bin",
|
246 |
+
"transformer.h.7.mlp.w2.weight": "pytorch_model-00003-of-00010.bin",
|
247 |
+
"transformer.h.8.attn.c_attn.bias": "pytorch_model-00003-of-00010.bin",
|
248 |
+
"transformer.h.8.attn.c_attn.weight": "pytorch_model-00003-of-00010.bin",
|
249 |
+
"transformer.h.8.attn.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
250 |
+
"transformer.h.8.ln_1.weight": "pytorch_model-00003-of-00010.bin",
|
251 |
+
"transformer.h.8.ln_2.weight": "pytorch_model-00003-of-00010.bin",
|
252 |
+
"transformer.h.8.mlp.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
253 |
+
"transformer.h.8.mlp.w1.weight": "pytorch_model-00003-of-00010.bin",
|
254 |
+
"transformer.h.8.mlp.w2.weight": "pytorch_model-00003-of-00010.bin",
|
255 |
+
"transformer.h.9.attn.c_attn.bias": "pytorch_model-00003-of-00010.bin",
|
256 |
+
"transformer.h.9.attn.c_attn.weight": "pytorch_model-00003-of-00010.bin",
|
257 |
+
"transformer.h.9.attn.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
258 |
+
"transformer.h.9.ln_1.weight": "pytorch_model-00003-of-00010.bin",
|
259 |
+
"transformer.h.9.ln_2.weight": "pytorch_model-00003-of-00010.bin",
|
260 |
+
"transformer.h.9.mlp.c_proj.weight": "pytorch_model-00003-of-00010.bin",
|
261 |
+
"transformer.h.9.mlp.w1.weight": "pytorch_model-00003-of-00010.bin",
|
262 |
+
"transformer.h.9.mlp.w2.weight": "pytorch_model-00003-of-00010.bin",
|
263 |
+
"transformer.ln_f.weight": "pytorch_model-00008-of-00010.bin",
|
264 |
+
"transformer.visual.attn_pool.attn.in_proj_bias": "pytorch_model-00010-of-00010.bin",
|
265 |
+
"transformer.visual.attn_pool.attn.in_proj_weight": "pytorch_model-00010-of-00010.bin",
|
266 |
+
"transformer.visual.attn_pool.attn.out_proj.bias": "pytorch_model-00010-of-00010.bin",
|
267 |
+
"transformer.visual.attn_pool.attn.out_proj.weight": "pytorch_model-00010-of-00010.bin",
|
268 |
+
"transformer.visual.attn_pool.kv_proj.weight": "pytorch_model-00010-of-00010.bin",
|
269 |
+
"transformer.visual.attn_pool.ln_kv.bias": "pytorch_model-00010-of-00010.bin",
|
270 |
+
"transformer.visual.attn_pool.ln_kv.weight": "pytorch_model-00010-of-00010.bin",
|
271 |
+
"transformer.visual.attn_pool.ln_q.bias": "pytorch_model-00010-of-00010.bin",
|
272 |
+
"transformer.visual.attn_pool.ln_q.weight": "pytorch_model-00010-of-00010.bin",
|
273 |
+
"transformer.visual.attn_pool.pos_embed": "pytorch_model-00010-of-00010.bin",
|
274 |
+
"transformer.visual.attn_pool.query": "pytorch_model-00010-of-00010.bin",
|
275 |
+
"transformer.visual.conv1.weight": "pytorch_model-00008-of-00010.bin",
|
276 |
+
"transformer.visual.ln_post.bias": "pytorch_model-00010-of-00010.bin",
|
277 |
+
"transformer.visual.ln_post.weight": "pytorch_model-00010-of-00010.bin",
|
278 |
+
"transformer.visual.ln_pre.bias": "pytorch_model-00008-of-00010.bin",
|
279 |
+
"transformer.visual.ln_pre.weight": "pytorch_model-00008-of-00010.bin",
|
280 |
+
"transformer.visual.positional_embedding": "pytorch_model-00008-of-00010.bin",
|
281 |
+
"transformer.visual.proj": "pytorch_model-00008-of-00010.bin",
|
282 |
+
"transformer.visual.transformer.resblocks.0.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
283 |
+
"transformer.visual.transformer.resblocks.0.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
284 |
+
"transformer.visual.transformer.resblocks.0.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
285 |
+
"transformer.visual.transformer.resblocks.0.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
286 |
+
"transformer.visual.transformer.resblocks.0.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
287 |
+
"transformer.visual.transformer.resblocks.0.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
288 |
+
"transformer.visual.transformer.resblocks.0.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
289 |
+
"transformer.visual.transformer.resblocks.0.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
290 |
+
"transformer.visual.transformer.resblocks.0.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
291 |
+
"transformer.visual.transformer.resblocks.0.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
292 |
+
"transformer.visual.transformer.resblocks.0.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
293 |
+
"transformer.visual.transformer.resblocks.0.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
294 |
+
"transformer.visual.transformer.resblocks.1.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
295 |
+
"transformer.visual.transformer.resblocks.1.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
296 |
+
"transformer.visual.transformer.resblocks.1.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
297 |
+
"transformer.visual.transformer.resblocks.1.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
298 |
+
"transformer.visual.transformer.resblocks.1.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
299 |
+
"transformer.visual.transformer.resblocks.1.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
300 |
+
"transformer.visual.transformer.resblocks.1.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
301 |
+
"transformer.visual.transformer.resblocks.1.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
302 |
+
"transformer.visual.transformer.resblocks.1.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
303 |
+
"transformer.visual.transformer.resblocks.1.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
304 |
+
"transformer.visual.transformer.resblocks.1.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
305 |
+
"transformer.visual.transformer.resblocks.1.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
306 |
+
"transformer.visual.transformer.resblocks.10.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
307 |
+
"transformer.visual.transformer.resblocks.10.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
308 |
+
"transformer.visual.transformer.resblocks.10.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
309 |
+
"transformer.visual.transformer.resblocks.10.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
310 |
+
"transformer.visual.transformer.resblocks.10.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
311 |
+
"transformer.visual.transformer.resblocks.10.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
312 |
+
"transformer.visual.transformer.resblocks.10.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
313 |
+
"transformer.visual.transformer.resblocks.10.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
314 |
+
"transformer.visual.transformer.resblocks.10.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
315 |
+
"transformer.visual.transformer.resblocks.10.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
316 |
+
"transformer.visual.transformer.resblocks.10.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
317 |
+
"transformer.visual.transformer.resblocks.10.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
318 |
+
"transformer.visual.transformer.resblocks.11.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
319 |
+
"transformer.visual.transformer.resblocks.11.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
320 |
+
"transformer.visual.transformer.resblocks.11.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
321 |
+
"transformer.visual.transformer.resblocks.11.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
322 |
+
"transformer.visual.transformer.resblocks.11.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
323 |
+
"transformer.visual.transformer.resblocks.11.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
324 |
+
"transformer.visual.transformer.resblocks.11.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
325 |
+
"transformer.visual.transformer.resblocks.11.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
326 |
+
"transformer.visual.transformer.resblocks.11.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
327 |
+
"transformer.visual.transformer.resblocks.11.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
328 |
+
"transformer.visual.transformer.resblocks.11.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
329 |
+
"transformer.visual.transformer.resblocks.11.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
330 |
+
"transformer.visual.transformer.resblocks.12.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
331 |
+
"transformer.visual.transformer.resblocks.12.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
332 |
+
"transformer.visual.transformer.resblocks.12.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
333 |
+
"transformer.visual.transformer.resblocks.12.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
334 |
+
"transformer.visual.transformer.resblocks.12.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
335 |
+
"transformer.visual.transformer.resblocks.12.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
336 |
+
"transformer.visual.transformer.resblocks.12.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
337 |
+
"transformer.visual.transformer.resblocks.12.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
338 |
+
"transformer.visual.transformer.resblocks.12.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
339 |
+
"transformer.visual.transformer.resblocks.12.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
340 |
+
"transformer.visual.transformer.resblocks.12.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
341 |
+
"transformer.visual.transformer.resblocks.12.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
342 |
+
"transformer.visual.transformer.resblocks.13.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
343 |
+
"transformer.visual.transformer.resblocks.13.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
344 |
+
"transformer.visual.transformer.resblocks.13.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
345 |
+
"transformer.visual.transformer.resblocks.13.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
346 |
+
"transformer.visual.transformer.resblocks.13.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
347 |
+
"transformer.visual.transformer.resblocks.13.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
348 |
+
"transformer.visual.transformer.resblocks.13.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
349 |
+
"transformer.visual.transformer.resblocks.13.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
350 |
+
"transformer.visual.transformer.resblocks.13.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
351 |
+
"transformer.visual.transformer.resblocks.13.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
352 |
+
"transformer.visual.transformer.resblocks.13.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
353 |
+
"transformer.visual.transformer.resblocks.13.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
354 |
+
"transformer.visual.transformer.resblocks.14.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
355 |
+
"transformer.visual.transformer.resblocks.14.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
356 |
+
"transformer.visual.transformer.resblocks.14.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
357 |
+
"transformer.visual.transformer.resblocks.14.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
358 |
+
"transformer.visual.transformer.resblocks.14.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
359 |
+
"transformer.visual.transformer.resblocks.14.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
360 |
+
"transformer.visual.transformer.resblocks.14.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
361 |
+
"transformer.visual.transformer.resblocks.14.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
362 |
+
"transformer.visual.transformer.resblocks.14.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
363 |
+
"transformer.visual.transformer.resblocks.14.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
364 |
+
"transformer.visual.transformer.resblocks.14.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
365 |
+
"transformer.visual.transformer.resblocks.14.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
366 |
+
"transformer.visual.transformer.resblocks.15.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
367 |
+
"transformer.visual.transformer.resblocks.15.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
368 |
+
"transformer.visual.transformer.resblocks.15.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
369 |
+
"transformer.visual.transformer.resblocks.15.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
370 |
+
"transformer.visual.transformer.resblocks.15.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
371 |
+
"transformer.visual.transformer.resblocks.15.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
372 |
+
"transformer.visual.transformer.resblocks.15.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
373 |
+
"transformer.visual.transformer.resblocks.15.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
374 |
+
"transformer.visual.transformer.resblocks.15.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
375 |
+
"transformer.visual.transformer.resblocks.15.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
376 |
+
"transformer.visual.transformer.resblocks.15.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
377 |
+
"transformer.visual.transformer.resblocks.15.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
378 |
+
"transformer.visual.transformer.resblocks.16.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
379 |
+
"transformer.visual.transformer.resblocks.16.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
380 |
+
"transformer.visual.transformer.resblocks.16.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
381 |
+
"transformer.visual.transformer.resblocks.16.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
382 |
+
"transformer.visual.transformer.resblocks.16.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
383 |
+
"transformer.visual.transformer.resblocks.16.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
384 |
+
"transformer.visual.transformer.resblocks.16.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
385 |
+
"transformer.visual.transformer.resblocks.16.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
386 |
+
"transformer.visual.transformer.resblocks.16.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
387 |
+
"transformer.visual.transformer.resblocks.16.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
388 |
+
"transformer.visual.transformer.resblocks.16.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
389 |
+
"transformer.visual.transformer.resblocks.16.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
390 |
+
"transformer.visual.transformer.resblocks.17.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
391 |
+
"transformer.visual.transformer.resblocks.17.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
392 |
+
"transformer.visual.transformer.resblocks.17.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
393 |
+
"transformer.visual.transformer.resblocks.17.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
394 |
+
"transformer.visual.transformer.resblocks.17.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
395 |
+
"transformer.visual.transformer.resblocks.17.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
396 |
+
"transformer.visual.transformer.resblocks.17.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
397 |
+
"transformer.visual.transformer.resblocks.17.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
398 |
+
"transformer.visual.transformer.resblocks.17.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
399 |
+
"transformer.visual.transformer.resblocks.17.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
400 |
+
"transformer.visual.transformer.resblocks.17.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
401 |
+
"transformer.visual.transformer.resblocks.17.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
402 |
+
"transformer.visual.transformer.resblocks.18.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
403 |
+
"transformer.visual.transformer.resblocks.18.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
404 |
+
"transformer.visual.transformer.resblocks.18.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
405 |
+
"transformer.visual.transformer.resblocks.18.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
406 |
+
"transformer.visual.transformer.resblocks.18.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
407 |
+
"transformer.visual.transformer.resblocks.18.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
408 |
+
"transformer.visual.transformer.resblocks.18.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
409 |
+
"transformer.visual.transformer.resblocks.18.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
410 |
+
"transformer.visual.transformer.resblocks.18.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
411 |
+
"transformer.visual.transformer.resblocks.18.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
412 |
+
"transformer.visual.transformer.resblocks.18.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
413 |
+
"transformer.visual.transformer.resblocks.18.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
414 |
+
"transformer.visual.transformer.resblocks.19.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
415 |
+
"transformer.visual.transformer.resblocks.19.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
416 |
+
"transformer.visual.transformer.resblocks.19.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
417 |
+
"transformer.visual.transformer.resblocks.19.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
418 |
+
"transformer.visual.transformer.resblocks.19.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
419 |
+
"transformer.visual.transformer.resblocks.19.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
420 |
+
"transformer.visual.transformer.resblocks.19.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
421 |
+
"transformer.visual.transformer.resblocks.19.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
422 |
+
"transformer.visual.transformer.resblocks.19.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
423 |
+
"transformer.visual.transformer.resblocks.19.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
424 |
+
"transformer.visual.transformer.resblocks.19.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
425 |
+
"transformer.visual.transformer.resblocks.19.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
426 |
+
"transformer.visual.transformer.resblocks.2.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
427 |
+
"transformer.visual.transformer.resblocks.2.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
428 |
+
"transformer.visual.transformer.resblocks.2.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
429 |
+
"transformer.visual.transformer.resblocks.2.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
430 |
+
"transformer.visual.transformer.resblocks.2.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
431 |
+
"transformer.visual.transformer.resblocks.2.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
432 |
+
"transformer.visual.transformer.resblocks.2.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
433 |
+
"transformer.visual.transformer.resblocks.2.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
434 |
+
"transformer.visual.transformer.resblocks.2.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
435 |
+
"transformer.visual.transformer.resblocks.2.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
436 |
+
"transformer.visual.transformer.resblocks.2.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
437 |
+
"transformer.visual.transformer.resblocks.2.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
438 |
+
"transformer.visual.transformer.resblocks.20.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
439 |
+
"transformer.visual.transformer.resblocks.20.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
440 |
+
"transformer.visual.transformer.resblocks.20.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
441 |
+
"transformer.visual.transformer.resblocks.20.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
442 |
+
"transformer.visual.transformer.resblocks.20.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
443 |
+
"transformer.visual.transformer.resblocks.20.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
444 |
+
"transformer.visual.transformer.resblocks.20.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
445 |
+
"transformer.visual.transformer.resblocks.20.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
446 |
+
"transformer.visual.transformer.resblocks.20.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
447 |
+
"transformer.visual.transformer.resblocks.20.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
448 |
+
"transformer.visual.transformer.resblocks.20.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
449 |
+
"transformer.visual.transformer.resblocks.20.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
450 |
+
"transformer.visual.transformer.resblocks.21.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
451 |
+
"transformer.visual.transformer.resblocks.21.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
452 |
+
"transformer.visual.transformer.resblocks.21.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
453 |
+
"transformer.visual.transformer.resblocks.21.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
454 |
+
"transformer.visual.transformer.resblocks.21.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
455 |
+
"transformer.visual.transformer.resblocks.21.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
456 |
+
"transformer.visual.transformer.resblocks.21.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
457 |
+
"transformer.visual.transformer.resblocks.21.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
458 |
+
"transformer.visual.transformer.resblocks.21.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
459 |
+
"transformer.visual.transformer.resblocks.21.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
460 |
+
"transformer.visual.transformer.resblocks.21.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
461 |
+
"transformer.visual.transformer.resblocks.21.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
462 |
+
"transformer.visual.transformer.resblocks.22.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
463 |
+
"transformer.visual.transformer.resblocks.22.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
464 |
+
"transformer.visual.transformer.resblocks.22.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
465 |
+
"transformer.visual.transformer.resblocks.22.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
466 |
+
"transformer.visual.transformer.resblocks.22.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
467 |
+
"transformer.visual.transformer.resblocks.22.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
468 |
+
"transformer.visual.transformer.resblocks.22.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
469 |
+
"transformer.visual.transformer.resblocks.22.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
470 |
+
"transformer.visual.transformer.resblocks.22.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
471 |
+
"transformer.visual.transformer.resblocks.22.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
472 |
+
"transformer.visual.transformer.resblocks.22.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
473 |
+
"transformer.visual.transformer.resblocks.22.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
474 |
+
"transformer.visual.transformer.resblocks.23.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
475 |
+
"transformer.visual.transformer.resblocks.23.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
476 |
+
"transformer.visual.transformer.resblocks.23.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
477 |
+
"transformer.visual.transformer.resblocks.23.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
478 |
+
"transformer.visual.transformer.resblocks.23.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
479 |
+
"transformer.visual.transformer.resblocks.23.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
480 |
+
"transformer.visual.transformer.resblocks.23.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
481 |
+
"transformer.visual.transformer.resblocks.23.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
482 |
+
"transformer.visual.transformer.resblocks.23.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
483 |
+
"transformer.visual.transformer.resblocks.23.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
484 |
+
"transformer.visual.transformer.resblocks.23.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
485 |
+
"transformer.visual.transformer.resblocks.23.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
486 |
+
"transformer.visual.transformer.resblocks.24.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
487 |
+
"transformer.visual.transformer.resblocks.24.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
488 |
+
"transformer.visual.transformer.resblocks.24.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
489 |
+
"transformer.visual.transformer.resblocks.24.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
490 |
+
"transformer.visual.transformer.resblocks.24.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
491 |
+
"transformer.visual.transformer.resblocks.24.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
492 |
+
"transformer.visual.transformer.resblocks.24.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
493 |
+
"transformer.visual.transformer.resblocks.24.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
494 |
+
"transformer.visual.transformer.resblocks.24.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
495 |
+
"transformer.visual.transformer.resblocks.24.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
496 |
+
"transformer.visual.transformer.resblocks.24.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
497 |
+
"transformer.visual.transformer.resblocks.24.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
498 |
+
"transformer.visual.transformer.resblocks.25.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
499 |
+
"transformer.visual.transformer.resblocks.25.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
500 |
+
"transformer.visual.transformer.resblocks.25.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
501 |
+
"transformer.visual.transformer.resblocks.25.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
502 |
+
"transformer.visual.transformer.resblocks.25.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
503 |
+
"transformer.visual.transformer.resblocks.25.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
504 |
+
"transformer.visual.transformer.resblocks.25.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
505 |
+
"transformer.visual.transformer.resblocks.25.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
506 |
+
"transformer.visual.transformer.resblocks.25.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
507 |
+
"transformer.visual.transformer.resblocks.25.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
508 |
+
"transformer.visual.transformer.resblocks.25.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
509 |
+
"transformer.visual.transformer.resblocks.25.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
510 |
+
"transformer.visual.transformer.resblocks.26.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
511 |
+
"transformer.visual.transformer.resblocks.26.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
512 |
+
"transformer.visual.transformer.resblocks.26.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
513 |
+
"transformer.visual.transformer.resblocks.26.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
514 |
+
"transformer.visual.transformer.resblocks.26.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
515 |
+
"transformer.visual.transformer.resblocks.26.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
516 |
+
"transformer.visual.transformer.resblocks.26.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
517 |
+
"transformer.visual.transformer.resblocks.26.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
518 |
+
"transformer.visual.transformer.resblocks.26.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
519 |
+
"transformer.visual.transformer.resblocks.26.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
520 |
+
"transformer.visual.transformer.resblocks.26.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
521 |
+
"transformer.visual.transformer.resblocks.26.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
522 |
+
"transformer.visual.transformer.resblocks.27.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
523 |
+
"transformer.visual.transformer.resblocks.27.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
524 |
+
"transformer.visual.transformer.resblocks.27.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
525 |
+
"transformer.visual.transformer.resblocks.27.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
526 |
+
"transformer.visual.transformer.resblocks.27.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
527 |
+
"transformer.visual.transformer.resblocks.27.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
528 |
+
"transformer.visual.transformer.resblocks.27.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
529 |
+
"transformer.visual.transformer.resblocks.27.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
530 |
+
"transformer.visual.transformer.resblocks.27.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
531 |
+
"transformer.visual.transformer.resblocks.27.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
532 |
+
"transformer.visual.transformer.resblocks.27.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
533 |
+
"transformer.visual.transformer.resblocks.27.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
534 |
+
"transformer.visual.transformer.resblocks.28.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
535 |
+
"transformer.visual.transformer.resblocks.28.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
536 |
+
"transformer.visual.transformer.resblocks.28.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
537 |
+
"transformer.visual.transformer.resblocks.28.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
538 |
+
"transformer.visual.transformer.resblocks.28.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
539 |
+
"transformer.visual.transformer.resblocks.28.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
540 |
+
"transformer.visual.transformer.resblocks.28.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
541 |
+
"transformer.visual.transformer.resblocks.28.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
542 |
+
"transformer.visual.transformer.resblocks.28.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
543 |
+
"transformer.visual.transformer.resblocks.28.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
544 |
+
"transformer.visual.transformer.resblocks.28.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
545 |
+
"transformer.visual.transformer.resblocks.28.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
546 |
+
"transformer.visual.transformer.resblocks.29.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
547 |
+
"transformer.visual.transformer.resblocks.29.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
548 |
+
"transformer.visual.transformer.resblocks.29.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
549 |
+
"transformer.visual.transformer.resblocks.29.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
550 |
+
"transformer.visual.transformer.resblocks.29.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
551 |
+
"transformer.visual.transformer.resblocks.29.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
552 |
+
"transformer.visual.transformer.resblocks.29.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
553 |
+
"transformer.visual.transformer.resblocks.29.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
554 |
+
"transformer.visual.transformer.resblocks.29.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
555 |
+
"transformer.visual.transformer.resblocks.29.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
556 |
+
"transformer.visual.transformer.resblocks.29.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
557 |
+
"transformer.visual.transformer.resblocks.29.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
558 |
+
"transformer.visual.transformer.resblocks.3.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
559 |
+
"transformer.visual.transformer.resblocks.3.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
560 |
+
"transformer.visual.transformer.resblocks.3.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
561 |
+
"transformer.visual.transformer.resblocks.3.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
562 |
+
"transformer.visual.transformer.resblocks.3.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
563 |
+
"transformer.visual.transformer.resblocks.3.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
564 |
+
"transformer.visual.transformer.resblocks.3.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
565 |
+
"transformer.visual.transformer.resblocks.3.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
566 |
+
"transformer.visual.transformer.resblocks.3.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
567 |
+
"transformer.visual.transformer.resblocks.3.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
568 |
+
"transformer.visual.transformer.resblocks.3.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
569 |
+
"transformer.visual.transformer.resblocks.3.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
570 |
+
"transformer.visual.transformer.resblocks.30.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
571 |
+
"transformer.visual.transformer.resblocks.30.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
572 |
+
"transformer.visual.transformer.resblocks.30.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
573 |
+
"transformer.visual.transformer.resblocks.30.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
574 |
+
"transformer.visual.transformer.resblocks.30.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
575 |
+
"transformer.visual.transformer.resblocks.30.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
576 |
+
"transformer.visual.transformer.resblocks.30.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
577 |
+
"transformer.visual.transformer.resblocks.30.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
578 |
+
"transformer.visual.transformer.resblocks.30.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
579 |
+
"transformer.visual.transformer.resblocks.30.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
580 |
+
"transformer.visual.transformer.resblocks.30.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
581 |
+
"transformer.visual.transformer.resblocks.30.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
582 |
+
"transformer.visual.transformer.resblocks.31.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
583 |
+
"transformer.visual.transformer.resblocks.31.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
584 |
+
"transformer.visual.transformer.resblocks.31.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
585 |
+
"transformer.visual.transformer.resblocks.31.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
586 |
+
"transformer.visual.transformer.resblocks.31.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
587 |
+
"transformer.visual.transformer.resblocks.31.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
588 |
+
"transformer.visual.transformer.resblocks.31.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
589 |
+
"transformer.visual.transformer.resblocks.31.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
590 |
+
"transformer.visual.transformer.resblocks.31.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
591 |
+
"transformer.visual.transformer.resblocks.31.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
592 |
+
"transformer.visual.transformer.resblocks.31.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
593 |
+
"transformer.visual.transformer.resblocks.31.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
594 |
+
"transformer.visual.transformer.resblocks.32.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
595 |
+
"transformer.visual.transformer.resblocks.32.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
596 |
+
"transformer.visual.transformer.resblocks.32.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
597 |
+
"transformer.visual.transformer.resblocks.32.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
598 |
+
"transformer.visual.transformer.resblocks.32.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
599 |
+
"transformer.visual.transformer.resblocks.32.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
600 |
+
"transformer.visual.transformer.resblocks.32.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
601 |
+
"transformer.visual.transformer.resblocks.32.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
602 |
+
"transformer.visual.transformer.resblocks.32.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
603 |
+
"transformer.visual.transformer.resblocks.32.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
604 |
+
"transformer.visual.transformer.resblocks.32.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
605 |
+
"transformer.visual.transformer.resblocks.32.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
606 |
+
"transformer.visual.transformer.resblocks.33.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
607 |
+
"transformer.visual.transformer.resblocks.33.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
608 |
+
"transformer.visual.transformer.resblocks.33.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
609 |
+
"transformer.visual.transformer.resblocks.33.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
610 |
+
"transformer.visual.transformer.resblocks.33.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
611 |
+
"transformer.visual.transformer.resblocks.33.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
612 |
+
"transformer.visual.transformer.resblocks.33.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
613 |
+
"transformer.visual.transformer.resblocks.33.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
614 |
+
"transformer.visual.transformer.resblocks.33.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
615 |
+
"transformer.visual.transformer.resblocks.33.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
616 |
+
"transformer.visual.transformer.resblocks.33.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
617 |
+
"transformer.visual.transformer.resblocks.33.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
618 |
+
"transformer.visual.transformer.resblocks.34.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
619 |
+
"transformer.visual.transformer.resblocks.34.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
620 |
+
"transformer.visual.transformer.resblocks.34.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
621 |
+
"transformer.visual.transformer.resblocks.34.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
622 |
+
"transformer.visual.transformer.resblocks.34.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
623 |
+
"transformer.visual.transformer.resblocks.34.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
624 |
+
"transformer.visual.transformer.resblocks.34.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
625 |
+
"transformer.visual.transformer.resblocks.34.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
626 |
+
"transformer.visual.transformer.resblocks.34.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
627 |
+
"transformer.visual.transformer.resblocks.34.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
628 |
+
"transformer.visual.transformer.resblocks.34.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
629 |
+
"transformer.visual.transformer.resblocks.34.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
630 |
+
"transformer.visual.transformer.resblocks.35.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
631 |
+
"transformer.visual.transformer.resblocks.35.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
632 |
+
"transformer.visual.transformer.resblocks.35.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
633 |
+
"transformer.visual.transformer.resblocks.35.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
634 |
+
"transformer.visual.transformer.resblocks.35.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
635 |
+
"transformer.visual.transformer.resblocks.35.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
636 |
+
"transformer.visual.transformer.resblocks.35.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
637 |
+
"transformer.visual.transformer.resblocks.35.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
638 |
+
"transformer.visual.transformer.resblocks.35.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
639 |
+
"transformer.visual.transformer.resblocks.35.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
640 |
+
"transformer.visual.transformer.resblocks.35.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
641 |
+
"transformer.visual.transformer.resblocks.35.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
642 |
+
"transformer.visual.transformer.resblocks.36.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
643 |
+
"transformer.visual.transformer.resblocks.36.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
644 |
+
"transformer.visual.transformer.resblocks.36.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
645 |
+
"transformer.visual.transformer.resblocks.36.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
646 |
+
"transformer.visual.transformer.resblocks.36.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
647 |
+
"transformer.visual.transformer.resblocks.36.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
648 |
+
"transformer.visual.transformer.resblocks.36.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
649 |
+
"transformer.visual.transformer.resblocks.36.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
650 |
+
"transformer.visual.transformer.resblocks.36.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
651 |
+
"transformer.visual.transformer.resblocks.36.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
652 |
+
"transformer.visual.transformer.resblocks.36.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
653 |
+
"transformer.visual.transformer.resblocks.36.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
654 |
+
"transformer.visual.transformer.resblocks.37.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
655 |
+
"transformer.visual.transformer.resblocks.37.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
656 |
+
"transformer.visual.transformer.resblocks.37.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
657 |
+
"transformer.visual.transformer.resblocks.37.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
658 |
+
"transformer.visual.transformer.resblocks.37.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
659 |
+
"transformer.visual.transformer.resblocks.37.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
660 |
+
"transformer.visual.transformer.resblocks.37.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
661 |
+
"transformer.visual.transformer.resblocks.37.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
662 |
+
"transformer.visual.transformer.resblocks.37.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
663 |
+
"transformer.visual.transformer.resblocks.37.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
664 |
+
"transformer.visual.transformer.resblocks.37.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
665 |
+
"transformer.visual.transformer.resblocks.37.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
666 |
+
"transformer.visual.transformer.resblocks.38.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
667 |
+
"transformer.visual.transformer.resblocks.38.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
668 |
+
"transformer.visual.transformer.resblocks.38.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
669 |
+
"transformer.visual.transformer.resblocks.38.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
670 |
+
"transformer.visual.transformer.resblocks.38.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
671 |
+
"transformer.visual.transformer.resblocks.38.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
672 |
+
"transformer.visual.transformer.resblocks.38.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
673 |
+
"transformer.visual.transformer.resblocks.38.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
674 |
+
"transformer.visual.transformer.resblocks.38.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
675 |
+
"transformer.visual.transformer.resblocks.38.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
676 |
+
"transformer.visual.transformer.resblocks.38.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
677 |
+
"transformer.visual.transformer.resblocks.38.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
678 |
+
"transformer.visual.transformer.resblocks.39.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
679 |
+
"transformer.visual.transformer.resblocks.39.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
680 |
+
"transformer.visual.transformer.resblocks.39.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
681 |
+
"transformer.visual.transformer.resblocks.39.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
682 |
+
"transformer.visual.transformer.resblocks.39.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
683 |
+
"transformer.visual.transformer.resblocks.39.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
684 |
+
"transformer.visual.transformer.resblocks.39.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
685 |
+
"transformer.visual.transformer.resblocks.39.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
686 |
+
"transformer.visual.transformer.resblocks.39.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
687 |
+
"transformer.visual.transformer.resblocks.39.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
688 |
+
"transformer.visual.transformer.resblocks.39.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
689 |
+
"transformer.visual.transformer.resblocks.39.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
690 |
+
"transformer.visual.transformer.resblocks.4.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
691 |
+
"transformer.visual.transformer.resblocks.4.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
692 |
+
"transformer.visual.transformer.resblocks.4.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
693 |
+
"transformer.visual.transformer.resblocks.4.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
694 |
+
"transformer.visual.transformer.resblocks.4.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
695 |
+
"transformer.visual.transformer.resblocks.4.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
696 |
+
"transformer.visual.transformer.resblocks.4.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
697 |
+
"transformer.visual.transformer.resblocks.4.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
698 |
+
"transformer.visual.transformer.resblocks.4.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
699 |
+
"transformer.visual.transformer.resblocks.4.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
700 |
+
"transformer.visual.transformer.resblocks.4.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
701 |
+
"transformer.visual.transformer.resblocks.4.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
702 |
+
"transformer.visual.transformer.resblocks.40.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
703 |
+
"transformer.visual.transformer.resblocks.40.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
704 |
+
"transformer.visual.transformer.resblocks.40.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
705 |
+
"transformer.visual.transformer.resblocks.40.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
706 |
+
"transformer.visual.transformer.resblocks.40.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
707 |
+
"transformer.visual.transformer.resblocks.40.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
708 |
+
"transformer.visual.transformer.resblocks.40.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
709 |
+
"transformer.visual.transformer.resblocks.40.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
710 |
+
"transformer.visual.transformer.resblocks.40.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
711 |
+
"transformer.visual.transformer.resblocks.40.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
712 |
+
"transformer.visual.transformer.resblocks.40.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
713 |
+
"transformer.visual.transformer.resblocks.40.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
714 |
+
"transformer.visual.transformer.resblocks.41.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
715 |
+
"transformer.visual.transformer.resblocks.41.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
716 |
+
"transformer.visual.transformer.resblocks.41.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
717 |
+
"transformer.visual.transformer.resblocks.41.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
718 |
+
"transformer.visual.transformer.resblocks.41.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
719 |
+
"transformer.visual.transformer.resblocks.41.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
720 |
+
"transformer.visual.transformer.resblocks.41.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
721 |
+
"transformer.visual.transformer.resblocks.41.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
722 |
+
"transformer.visual.transformer.resblocks.41.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
723 |
+
"transformer.visual.transformer.resblocks.41.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
724 |
+
"transformer.visual.transformer.resblocks.41.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
725 |
+
"transformer.visual.transformer.resblocks.41.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
726 |
+
"transformer.visual.transformer.resblocks.42.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
727 |
+
"transformer.visual.transformer.resblocks.42.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
728 |
+
"transformer.visual.transformer.resblocks.42.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
729 |
+
"transformer.visual.transformer.resblocks.42.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
730 |
+
"transformer.visual.transformer.resblocks.42.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
731 |
+
"transformer.visual.transformer.resblocks.42.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
732 |
+
"transformer.visual.transformer.resblocks.42.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
733 |
+
"transformer.visual.transformer.resblocks.42.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
734 |
+
"transformer.visual.transformer.resblocks.42.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
735 |
+
"transformer.visual.transformer.resblocks.42.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
736 |
+
"transformer.visual.transformer.resblocks.42.mlp.c_proj.bias": "pytorch_model-00009-of-00010.bin",
|
737 |
+
"transformer.visual.transformer.resblocks.42.mlp.c_proj.weight": "pytorch_model-00009-of-00010.bin",
|
738 |
+
"transformer.visual.transformer.resblocks.43.attn.in_proj.bias": "pytorch_model-00009-of-00010.bin",
|
739 |
+
"transformer.visual.transformer.resblocks.43.attn.in_proj.weight": "pytorch_model-00009-of-00010.bin",
|
740 |
+
"transformer.visual.transformer.resblocks.43.attn.out_proj.bias": "pytorch_model-00009-of-00010.bin",
|
741 |
+
"transformer.visual.transformer.resblocks.43.attn.out_proj.weight": "pytorch_model-00009-of-00010.bin",
|
742 |
+
"transformer.visual.transformer.resblocks.43.ln_1.bias": "pytorch_model-00009-of-00010.bin",
|
743 |
+
"transformer.visual.transformer.resblocks.43.ln_1.weight": "pytorch_model-00009-of-00010.bin",
|
744 |
+
"transformer.visual.transformer.resblocks.43.ln_2.bias": "pytorch_model-00009-of-00010.bin",
|
745 |
+
"transformer.visual.transformer.resblocks.43.ln_2.weight": "pytorch_model-00009-of-00010.bin",
|
746 |
+
"transformer.visual.transformer.resblocks.43.mlp.c_fc.bias": "pytorch_model-00009-of-00010.bin",
|
747 |
+
"transformer.visual.transformer.resblocks.43.mlp.c_fc.weight": "pytorch_model-00009-of-00010.bin",
|
748 |
+
"transformer.visual.transformer.resblocks.43.mlp.c_proj.bias": "pytorch_model-00010-of-00010.bin",
|
749 |
+
"transformer.visual.transformer.resblocks.43.mlp.c_proj.weight": "pytorch_model-00010-of-00010.bin",
|
750 |
+
"transformer.visual.transformer.resblocks.44.attn.in_proj.bias": "pytorch_model-00010-of-00010.bin",
|
751 |
+
"transformer.visual.transformer.resblocks.44.attn.in_proj.weight": "pytorch_model-00010-of-00010.bin",
|
752 |
+
"transformer.visual.transformer.resblocks.44.attn.out_proj.bias": "pytorch_model-00010-of-00010.bin",
|
753 |
+
"transformer.visual.transformer.resblocks.44.attn.out_proj.weight": "pytorch_model-00010-of-00010.bin",
|
754 |
+
"transformer.visual.transformer.resblocks.44.ln_1.bias": "pytorch_model-00010-of-00010.bin",
|
755 |
+
"transformer.visual.transformer.resblocks.44.ln_1.weight": "pytorch_model-00010-of-00010.bin",
|
756 |
+
"transformer.visual.transformer.resblocks.44.ln_2.bias": "pytorch_model-00010-of-00010.bin",
|
757 |
+
"transformer.visual.transformer.resblocks.44.ln_2.weight": "pytorch_model-00010-of-00010.bin",
|
758 |
+
"transformer.visual.transformer.resblocks.44.mlp.c_fc.bias": "pytorch_model-00010-of-00010.bin",
|
759 |
+
"transformer.visual.transformer.resblocks.44.mlp.c_fc.weight": "pytorch_model-00010-of-00010.bin",
|
760 |
+
"transformer.visual.transformer.resblocks.44.mlp.c_proj.bias": "pytorch_model-00010-of-00010.bin",
|
761 |
+
"transformer.visual.transformer.resblocks.44.mlp.c_proj.weight": "pytorch_model-00010-of-00010.bin",
|
762 |
+
"transformer.visual.transformer.resblocks.45.attn.in_proj.bias": "pytorch_model-00010-of-00010.bin",
|
763 |
+
"transformer.visual.transformer.resblocks.45.attn.in_proj.weight": "pytorch_model-00010-of-00010.bin",
|
764 |
+
"transformer.visual.transformer.resblocks.45.attn.out_proj.bias": "pytorch_model-00010-of-00010.bin",
|
765 |
+
"transformer.visual.transformer.resblocks.45.attn.out_proj.weight": "pytorch_model-00010-of-00010.bin",
|
766 |
+
"transformer.visual.transformer.resblocks.45.ln_1.bias": "pytorch_model-00010-of-00010.bin",
|
767 |
+
"transformer.visual.transformer.resblocks.45.ln_1.weight": "pytorch_model-00010-of-00010.bin",
|
768 |
+
"transformer.visual.transformer.resblocks.45.ln_2.bias": "pytorch_model-00010-of-00010.bin",
|
769 |
+
"transformer.visual.transformer.resblocks.45.ln_2.weight": "pytorch_model-00010-of-00010.bin",
|
770 |
+
"transformer.visual.transformer.resblocks.45.mlp.c_fc.bias": "pytorch_model-00010-of-00010.bin",
|
771 |
+
"transformer.visual.transformer.resblocks.45.mlp.c_fc.weight": "pytorch_model-00010-of-00010.bin",
|
772 |
+
"transformer.visual.transformer.resblocks.45.mlp.c_proj.bias": "pytorch_model-00010-of-00010.bin",
|
773 |
+
"transformer.visual.transformer.resblocks.45.mlp.c_proj.weight": "pytorch_model-00010-of-00010.bin",
|
774 |
+
"transformer.visual.transformer.resblocks.46.attn.in_proj.bias": "pytorch_model-00010-of-00010.bin",
|
775 |
+
"transformer.visual.transformer.resblocks.46.attn.in_proj.weight": "pytorch_model-00010-of-00010.bin",
|
776 |
+
"transformer.visual.transformer.resblocks.46.attn.out_proj.bias": "pytorch_model-00010-of-00010.bin",
|
777 |
+
"transformer.visual.transformer.resblocks.46.attn.out_proj.weight": "pytorch_model-00010-of-00010.bin",
|
778 |
+
"transformer.visual.transformer.resblocks.46.ln_1.bias": "pytorch_model-00010-of-00010.bin",
|
779 |
+
"transformer.visual.transformer.resblocks.46.ln_1.weight": "pytorch_model-00010-of-00010.bin",
|
780 |
+
"transformer.visual.transformer.resblocks.46.ln_2.bias": "pytorch_model-00010-of-00010.bin",
|
781 |
+
"transformer.visual.transformer.resblocks.46.ln_2.weight": "pytorch_model-00010-of-00010.bin",
|
782 |
+
"transformer.visual.transformer.resblocks.46.mlp.c_fc.bias": "pytorch_model-00010-of-00010.bin",
|
783 |
+
"transformer.visual.transformer.resblocks.46.mlp.c_fc.weight": "pytorch_model-00010-of-00010.bin",
|
784 |
+
"transformer.visual.transformer.resblocks.46.mlp.c_proj.bias": "pytorch_model-00010-of-00010.bin",
|
785 |
+
"transformer.visual.transformer.resblocks.46.mlp.c_proj.weight": "pytorch_model-00010-of-00010.bin",
|
786 |
+
"transformer.visual.transformer.resblocks.47.attn.in_proj.bias": "pytorch_model-00010-of-00010.bin",
|
787 |
+
"transformer.visual.transformer.resblocks.47.attn.in_proj.weight": "pytorch_model-00010-of-00010.bin",
|
788 |
+
"transformer.visual.transformer.resblocks.47.attn.out_proj.bias": "pytorch_model-00010-of-00010.bin",
|
789 |
+
"transformer.visual.transformer.resblocks.47.attn.out_proj.weight": "pytorch_model-00010-of-00010.bin",
|
790 |
+
"transformer.visual.transformer.resblocks.47.ln_1.bias": "pytorch_model-00010-of-00010.bin",
|
791 |
+
"transformer.visual.transformer.resblocks.47.ln_1.weight": "pytorch_model-00010-of-00010.bin",
|
792 |
+
"transformer.visual.transformer.resblocks.47.ln_2.bias": "pytorch_model-00010-of-00010.bin",
|
793 |
+
"transformer.visual.transformer.resblocks.47.ln_2.weight": "pytorch_model-00010-of-00010.bin",
|
794 |
+
"transformer.visual.transformer.resblocks.47.mlp.c_fc.bias": "pytorch_model-00010-of-00010.bin",
|
795 |
+
"transformer.visual.transformer.resblocks.47.mlp.c_fc.weight": "pytorch_model-00010-of-00010.bin",
|
796 |
+
"transformer.visual.transformer.resblocks.47.mlp.c_proj.bias": "pytorch_model-00010-of-00010.bin",
|
797 |
+
"transformer.visual.transformer.resblocks.47.mlp.c_proj.weight": "pytorch_model-00010-of-00010.bin",
|
798 |
+
"transformer.visual.transformer.resblocks.5.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
799 |
+
"transformer.visual.transformer.resblocks.5.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
800 |
+
"transformer.visual.transformer.resblocks.5.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
801 |
+
"transformer.visual.transformer.resblocks.5.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
802 |
+
"transformer.visual.transformer.resblocks.5.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
803 |
+
"transformer.visual.transformer.resblocks.5.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
804 |
+
"transformer.visual.transformer.resblocks.5.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
805 |
+
"transformer.visual.transformer.resblocks.5.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
806 |
+
"transformer.visual.transformer.resblocks.5.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
807 |
+
"transformer.visual.transformer.resblocks.5.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
808 |
+
"transformer.visual.transformer.resblocks.5.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
809 |
+
"transformer.visual.transformer.resblocks.5.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
810 |
+
"transformer.visual.transformer.resblocks.6.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
811 |
+
"transformer.visual.transformer.resblocks.6.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
812 |
+
"transformer.visual.transformer.resblocks.6.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
813 |
+
"transformer.visual.transformer.resblocks.6.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
814 |
+
"transformer.visual.transformer.resblocks.6.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
815 |
+
"transformer.visual.transformer.resblocks.6.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
816 |
+
"transformer.visual.transformer.resblocks.6.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
817 |
+
"transformer.visual.transformer.resblocks.6.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
818 |
+
"transformer.visual.transformer.resblocks.6.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
819 |
+
"transformer.visual.transformer.resblocks.6.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
820 |
+
"transformer.visual.transformer.resblocks.6.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
821 |
+
"transformer.visual.transformer.resblocks.6.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
822 |
+
"transformer.visual.transformer.resblocks.7.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
823 |
+
"transformer.visual.transformer.resblocks.7.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
824 |
+
"transformer.visual.transformer.resblocks.7.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
825 |
+
"transformer.visual.transformer.resblocks.7.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
826 |
+
"transformer.visual.transformer.resblocks.7.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
827 |
+
"transformer.visual.transformer.resblocks.7.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
828 |
+
"transformer.visual.transformer.resblocks.7.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
829 |
+
"transformer.visual.transformer.resblocks.7.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
830 |
+
"transformer.visual.transformer.resblocks.7.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
831 |
+
"transformer.visual.transformer.resblocks.7.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
832 |
+
"transformer.visual.transformer.resblocks.7.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
833 |
+
"transformer.visual.transformer.resblocks.7.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
834 |
+
"transformer.visual.transformer.resblocks.8.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
835 |
+
"transformer.visual.transformer.resblocks.8.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
836 |
+
"transformer.visual.transformer.resblocks.8.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
837 |
+
"transformer.visual.transformer.resblocks.8.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
838 |
+
"transformer.visual.transformer.resblocks.8.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
839 |
+
"transformer.visual.transformer.resblocks.8.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
840 |
+
"transformer.visual.transformer.resblocks.8.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
841 |
+
"transformer.visual.transformer.resblocks.8.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
842 |
+
"transformer.visual.transformer.resblocks.8.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
843 |
+
"transformer.visual.transformer.resblocks.8.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
844 |
+
"transformer.visual.transformer.resblocks.8.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
845 |
+
"transformer.visual.transformer.resblocks.8.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
846 |
+
"transformer.visual.transformer.resblocks.9.attn.in_proj.bias": "pytorch_model-00008-of-00010.bin",
|
847 |
+
"transformer.visual.transformer.resblocks.9.attn.in_proj.weight": "pytorch_model-00008-of-00010.bin",
|
848 |
+
"transformer.visual.transformer.resblocks.9.attn.out_proj.bias": "pytorch_model-00008-of-00010.bin",
|
849 |
+
"transformer.visual.transformer.resblocks.9.attn.out_proj.weight": "pytorch_model-00008-of-00010.bin",
|
850 |
+
"transformer.visual.transformer.resblocks.9.ln_1.bias": "pytorch_model-00008-of-00010.bin",
|
851 |
+
"transformer.visual.transformer.resblocks.9.ln_1.weight": "pytorch_model-00008-of-00010.bin",
|
852 |
+
"transformer.visual.transformer.resblocks.9.ln_2.bias": "pytorch_model-00008-of-00010.bin",
|
853 |
+
"transformer.visual.transformer.resblocks.9.ln_2.weight": "pytorch_model-00008-of-00010.bin",
|
854 |
+
"transformer.visual.transformer.resblocks.9.mlp.c_fc.bias": "pytorch_model-00008-of-00010.bin",
|
855 |
+
"transformer.visual.transformer.resblocks.9.mlp.c_fc.weight": "pytorch_model-00008-of-00010.bin",
|
856 |
+
"transformer.visual.transformer.resblocks.9.mlp.c_proj.bias": "pytorch_model-00008-of-00010.bin",
|
857 |
+
"transformer.visual.transformer.resblocks.9.mlp.c_proj.weight": "pytorch_model-00008-of-00010.bin",
|
858 |
+
"transformer.wte.weight": "pytorch_model-00001-of-00010.bin"
|
859 |
+
}
|
860 |
+
}
|
qwen.tiktoken
ADDED
The diff for this file is too large to render.
See raw diff
|
|
qwen_generation_utils.py
ADDED
@@ -0,0 +1,420 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Alibaba Cloud.
|
2 |
+
#
|
3 |
+
# This source code is licensed under the license found in the
|
4 |
+
# LICENSE file in the root directory of this source tree.
|
5 |
+
|
6 |
+
"""Generation support."""
|
7 |
+
|
8 |
+
from typing import Tuple, List, Union, Iterable
|
9 |
+
|
10 |
+
import numpy as np
|
11 |
+
import torch
|
12 |
+
import torch.nn.functional as F
|
13 |
+
from transformers import PreTrainedTokenizer
|
14 |
+
from transformers import logging
|
15 |
+
from transformers.generation import LogitsProcessor
|
16 |
+
|
17 |
+
logger = logging.get_logger(__name__)
|
18 |
+
|
19 |
+
# Types.
|
20 |
+
HistoryType = List[Tuple[str, str]]
|
21 |
+
TokensType = List[int]
|
22 |
+
BatchTokensType = List[List[int]]
|
23 |
+
|
24 |
+
|
25 |
+
def pad_batch(batch: BatchTokensType, pad_id: int, seq_length: int) -> BatchTokensType:
|
26 |
+
for tokens in batch:
|
27 |
+
context_length = len(tokens)
|
28 |
+
if context_length < seq_length:
|
29 |
+
tokens.extend([pad_id] * (seq_length - context_length))
|
30 |
+
return batch
|
31 |
+
|
32 |
+
|
33 |
+
def get_ltor_masks_and_position_ids(
|
34 |
+
data,
|
35 |
+
eod_token,
|
36 |
+
reset_position_ids,
|
37 |
+
reset_attention_mask,
|
38 |
+
eod_mask_loss,
|
39 |
+
):
|
40 |
+
"""Build masks and position id for left to right model."""
|
41 |
+
|
42 |
+
# Extract batch size and sequence length.
|
43 |
+
micro_batch_size, seq_length = data.size()
|
44 |
+
|
45 |
+
# Attention mask (lower triangular).
|
46 |
+
if reset_attention_mask:
|
47 |
+
att_mask_batch = micro_batch_size
|
48 |
+
else:
|
49 |
+
att_mask_batch = 1
|
50 |
+
attention_mask = torch.tril(
|
51 |
+
torch.ones((att_mask_batch, seq_length, seq_length), device=data.device)
|
52 |
+
).view(att_mask_batch, 1, seq_length, seq_length)
|
53 |
+
|
54 |
+
# Loss mask.
|
55 |
+
loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
|
56 |
+
if eod_mask_loss:
|
57 |
+
loss_mask[data == eod_token] = 0.0
|
58 |
+
|
59 |
+
# Position ids.
|
60 |
+
position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
|
61 |
+
position_ids = position_ids.unsqueeze(0).expand_as(data)
|
62 |
+
# We need to clone as the ids will be modifed based on batch index.
|
63 |
+
if reset_position_ids:
|
64 |
+
position_ids = position_ids.clone()
|
65 |
+
|
66 |
+
if reset_position_ids or reset_attention_mask:
|
67 |
+
# Loop through the batches:
|
68 |
+
for b in range(micro_batch_size):
|
69 |
+
|
70 |
+
# Find indecies where EOD token is.
|
71 |
+
eod_index = position_ids[b, data[b] == eod_token]
|
72 |
+
# Detach indecies from positions if going to modify positions.
|
73 |
+
if reset_position_ids:
|
74 |
+
eod_index = eod_index.clone()
|
75 |
+
|
76 |
+
# Loop through EOD indecies:
|
77 |
+
prev_index = 0
|
78 |
+
for j in range(eod_index.size()[0]):
|
79 |
+
i = eod_index[j]
|
80 |
+
# Mask attention loss.
|
81 |
+
if reset_attention_mask:
|
82 |
+
attention_mask[b, 0, (i + 1) :, : (i + 1)] = 0
|
83 |
+
# Reset positions.
|
84 |
+
if reset_position_ids:
|
85 |
+
position_ids[b, (i + 1) :] -= i + 1 - prev_index
|
86 |
+
prev_index = i + 1
|
87 |
+
|
88 |
+
# Convert attention mask to binary:
|
89 |
+
attention_mask = attention_mask < 0.5
|
90 |
+
|
91 |
+
return attention_mask, loss_mask, position_ids
|
92 |
+
|
93 |
+
|
94 |
+
def get_batch(context_tokens: torch.LongTensor, eod_id: int):
|
95 |
+
"""Generate batch from context tokens."""
|
96 |
+
# Move to GPU.
|
97 |
+
tokens = context_tokens.contiguous().to(context_tokens.device)
|
98 |
+
# Get the attention mask and postition ids.
|
99 |
+
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
|
100 |
+
tokens,
|
101 |
+
eod_id,
|
102 |
+
reset_position_ids=False,
|
103 |
+
reset_attention_mask=False,
|
104 |
+
eod_mask_loss=False,
|
105 |
+
)
|
106 |
+
return tokens, attention_mask, position_ids
|
107 |
+
|
108 |
+
|
109 |
+
def get_stop_words_ids(chat_format, tokenizer):
|
110 |
+
if chat_format == "raw":
|
111 |
+
stop_words_ids = [tokenizer.encode("Human:"), [tokenizer.eod_id]]
|
112 |
+
elif chat_format == "chatml":
|
113 |
+
stop_words_ids = [[tokenizer.im_end_id], [tokenizer.im_start_id]]
|
114 |
+
else:
|
115 |
+
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
|
116 |
+
return stop_words_ids
|
117 |
+
|
118 |
+
|
119 |
+
def make_context(
|
120 |
+
tokenizer: PreTrainedTokenizer,
|
121 |
+
query: str,
|
122 |
+
history: List[Tuple[str, str]] = None,
|
123 |
+
system: str = "",
|
124 |
+
max_window_size: int = 6144,
|
125 |
+
chat_format: str = "chatml",
|
126 |
+
):
|
127 |
+
if history is None:
|
128 |
+
history = []
|
129 |
+
|
130 |
+
if chat_format == "chatml":
|
131 |
+
im_start, im_end = "<|im_start|>", "<|im_end|>"
|
132 |
+
im_start_tokens = [tokenizer.im_start_id]
|
133 |
+
im_end_tokens = [tokenizer.im_end_id]
|
134 |
+
nl_tokens = tokenizer.encode("\n")
|
135 |
+
|
136 |
+
def _tokenize_str(role, content):
|
137 |
+
return f"{role}\n{content}", tokenizer.encode(
|
138 |
+
role, allowed_special=set(tokenizer.IMAGE_ST)
|
139 |
+
) + nl_tokens + tokenizer.encode(content, allowed_special=set(tokenizer.IMAGE_ST))
|
140 |
+
|
141 |
+
system_text, system_tokens_part = _tokenize_str("system", system)
|
142 |
+
system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
|
143 |
+
|
144 |
+
raw_text = ""
|
145 |
+
context_tokens = []
|
146 |
+
|
147 |
+
for turn_query, turn_response in reversed(history):
|
148 |
+
query_text, query_tokens_part = _tokenize_str("user", turn_query)
|
149 |
+
query_tokens = im_start_tokens + query_tokens_part + im_end_tokens
|
150 |
+
if turn_response is not None:
|
151 |
+
response_text, response_tokens_part = _tokenize_str(
|
152 |
+
"assistant", turn_response
|
153 |
+
)
|
154 |
+
response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
|
155 |
+
|
156 |
+
next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
|
157 |
+
prev_chat = (
|
158 |
+
f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
|
159 |
+
)
|
160 |
+
else:
|
161 |
+
next_context_tokens = nl_tokens + query_tokens + nl_tokens
|
162 |
+
prev_chat = f"\n{im_start}{query_text}{im_end}\n"
|
163 |
+
|
164 |
+
current_context_size = (
|
165 |
+
len(system_tokens) + len(next_context_tokens) + len(context_tokens)
|
166 |
+
)
|
167 |
+
if current_context_size < max_window_size:
|
168 |
+
context_tokens = next_context_tokens + context_tokens
|
169 |
+
raw_text = prev_chat + raw_text
|
170 |
+
else:
|
171 |
+
break
|
172 |
+
|
173 |
+
context_tokens = system_tokens + context_tokens
|
174 |
+
raw_text = f"{im_start}{system_text}{im_end}" + raw_text
|
175 |
+
context_tokens += (
|
176 |
+
nl_tokens
|
177 |
+
+ im_start_tokens
|
178 |
+
+ _tokenize_str("user", query)[1]
|
179 |
+
+ im_end_tokens
|
180 |
+
+ nl_tokens
|
181 |
+
+ im_start_tokens
|
182 |
+
+ tokenizer.encode("assistant")
|
183 |
+
+ nl_tokens
|
184 |
+
)
|
185 |
+
raw_text += f"\n{im_start}user\n{query}{im_end}\n{im_start}assistant\n"
|
186 |
+
|
187 |
+
elif chat_format == "raw":
|
188 |
+
raw_text = query
|
189 |
+
context_tokens = tokenizer.encode(raw_text)
|
190 |
+
else:
|
191 |
+
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
|
192 |
+
|
193 |
+
return raw_text, context_tokens
|
194 |
+
|
195 |
+
|
196 |
+
def _decode_default(
|
197 |
+
tokens: List[int],
|
198 |
+
*,
|
199 |
+
stop_words: List[str],
|
200 |
+
eod_words: List[str],
|
201 |
+
tokenizer: PreTrainedTokenizer,
|
202 |
+
raw_text_len: int,
|
203 |
+
verbose: bool = False,
|
204 |
+
return_end_reason: bool = False,
|
205 |
+
errors: str='replace',
|
206 |
+
):
|
207 |
+
trim_decode_tokens = tokenizer.decode(tokens, errors=errors)[raw_text_len:]
|
208 |
+
if verbose:
|
209 |
+
print("\nRaw Generate: ", trim_decode_tokens)
|
210 |
+
|
211 |
+
end_reason = f"Gen length {len(tokens)}"
|
212 |
+
for stop_word in stop_words:
|
213 |
+
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
|
214 |
+
for eod_word in eod_words:
|
215 |
+
if eod_word in trim_decode_tokens:
|
216 |
+
end_reason = f"Gen {eod_word!r}"
|
217 |
+
trim_decode_tokens = trim_decode_tokens.split(eod_word)[0]
|
218 |
+
trim_decode_tokens = trim_decode_tokens.strip()
|
219 |
+
if verbose:
|
220 |
+
print("\nEnd Reason:", end_reason)
|
221 |
+
print("\nGenerate: ", trim_decode_tokens)
|
222 |
+
|
223 |
+
if return_end_reason:
|
224 |
+
return trim_decode_tokens, end_reason
|
225 |
+
else:
|
226 |
+
return trim_decode_tokens
|
227 |
+
|
228 |
+
|
229 |
+
def _decode_chatml(
|
230 |
+
tokens: List[int],
|
231 |
+
*,
|
232 |
+
stop_words: List[str],
|
233 |
+
eod_token_ids: List[int],
|
234 |
+
tokenizer: PreTrainedTokenizer,
|
235 |
+
raw_text_len: int,
|
236 |
+
context_length: int,
|
237 |
+
verbose: bool = False,
|
238 |
+
return_end_reason: bool = False,
|
239 |
+
errors: str='replace'
|
240 |
+
):
|
241 |
+
end_reason = f"Gen length {len(tokens)}"
|
242 |
+
eod_token_idx = context_length
|
243 |
+
for eod_token_idx in range(context_length, len(tokens)):
|
244 |
+
if tokens[eod_token_idx] in eod_token_ids:
|
245 |
+
end_reason = f"Gen {tokenizer.decode([tokens[eod_token_idx]])!r}"
|
246 |
+
break
|
247 |
+
|
248 |
+
trim_decode_tokens = tokenizer.decode(tokens[:eod_token_idx], errors=errors)[raw_text_len:]
|
249 |
+
if verbose:
|
250 |
+
print("\nRaw Generate w/o EOD:", tokenizer.decode(tokens, errors=errors)[raw_text_len:])
|
251 |
+
print("\nRaw Generate:", trim_decode_tokens)
|
252 |
+
print("\nEnd Reason:", end_reason)
|
253 |
+
for stop_word in stop_words:
|
254 |
+
trim_decode_tokens = trim_decode_tokens.replace(stop_word, "").strip()
|
255 |
+
trim_decode_tokens = trim_decode_tokens.strip()
|
256 |
+
if verbose:
|
257 |
+
print("\nGenerate:", trim_decode_tokens)
|
258 |
+
|
259 |
+
if return_end_reason:
|
260 |
+
return trim_decode_tokens, end_reason
|
261 |
+
else:
|
262 |
+
return trim_decode_tokens
|
263 |
+
|
264 |
+
|
265 |
+
def decode_tokens(
|
266 |
+
tokens: Union[torch.LongTensor, TokensType],
|
267 |
+
tokenizer: PreTrainedTokenizer,
|
268 |
+
raw_text_len: int,
|
269 |
+
context_length: int,
|
270 |
+
chat_format: str,
|
271 |
+
verbose: bool = False,
|
272 |
+
return_end_reason: bool = False,
|
273 |
+
errors: str="replace",
|
274 |
+
) -> str:
|
275 |
+
if torch.is_tensor(tokens):
|
276 |
+
tokens = tokens.cpu().numpy().tolist()
|
277 |
+
|
278 |
+
if chat_format == "chatml":
|
279 |
+
return _decode_chatml(
|
280 |
+
tokens,
|
281 |
+
stop_words=[],
|
282 |
+
eod_token_ids=[tokenizer.im_start_id, tokenizer.im_end_id],
|
283 |
+
tokenizer=tokenizer,
|
284 |
+
raw_text_len=raw_text_len,
|
285 |
+
context_length=context_length,
|
286 |
+
verbose=verbose,
|
287 |
+
return_end_reason=return_end_reason,
|
288 |
+
errors=errors,
|
289 |
+
)
|
290 |
+
elif chat_format == "raw":
|
291 |
+
return _decode_default(
|
292 |
+
tokens,
|
293 |
+
stop_words=["<|endoftext|>"],
|
294 |
+
eod_words=["<|endoftext|>"],
|
295 |
+
tokenizer=tokenizer,
|
296 |
+
raw_text_len=raw_text_len,
|
297 |
+
verbose=verbose,
|
298 |
+
return_end_reason=return_end_reason,
|
299 |
+
errors=errors,
|
300 |
+
)
|
301 |
+
else:
|
302 |
+
raise NotImplementedError(f"Unknown chat format {chat_format!r}")
|
303 |
+
|
304 |
+
|
305 |
+
class StopWordsLogitsProcessor(LogitsProcessor):
|
306 |
+
"""
|
307 |
+
:class:`transformers.LogitsProcessor` that enforces that when specified sequences appear, stop geration.
|
308 |
+
|
309 |
+
Args:
|
310 |
+
stop_words_ids (:obj:`List[List[int]]`):
|
311 |
+
List of list of token ids of stop ids. In order to get the tokens of the words
|
312 |
+
that should not appear in the generated text, use :obj:`tokenizer(bad_word,
|
313 |
+
add_prefix_space=True).input_ids`.
|
314 |
+
eos_token_id (:obj:`int`):
|
315 |
+
The id of the `end-of-sequence` token.
|
316 |
+
"""
|
317 |
+
|
318 |
+
def __init__(self, stop_words_ids: Iterable[Iterable[int]], eos_token_id: int):
|
319 |
+
|
320 |
+
if not isinstance(stop_words_ids, List) or len(stop_words_ids) == 0:
|
321 |
+
raise ValueError(
|
322 |
+
f"`stop_words_ids` has to be a non-emtpy list, but is {stop_words_ids}."
|
323 |
+
)
|
324 |
+
if any(not isinstance(bad_word_ids, list) for bad_word_ids in stop_words_ids):
|
325 |
+
raise ValueError(
|
326 |
+
f"`stop_words_ids` has to be a list of lists, but is {stop_words_ids}."
|
327 |
+
)
|
328 |
+
if any(
|
329 |
+
any(
|
330 |
+
(not isinstance(token_id, (int, np.integer)) or token_id < 0)
|
331 |
+
for token_id in stop_word_ids
|
332 |
+
)
|
333 |
+
for stop_word_ids in stop_words_ids
|
334 |
+
):
|
335 |
+
raise ValueError(
|
336 |
+
f"Each list in `stop_words_ids` has to be a list of positive integers, but is {stop_words_ids}."
|
337 |
+
)
|
338 |
+
|
339 |
+
self.stop_words_ids = list(
|
340 |
+
filter(
|
341 |
+
lambda bad_token_seq: bad_token_seq != [eos_token_id], stop_words_ids
|
342 |
+
)
|
343 |
+
)
|
344 |
+
self.eos_token_id = eos_token_id
|
345 |
+
for stop_token_seq in self.stop_words_ids:
|
346 |
+
assert (
|
347 |
+
len(stop_token_seq) > 0
|
348 |
+
), "Stop words token sequences {} cannot have an empty list".format(
|
349 |
+
stop_words_ids
|
350 |
+
)
|
351 |
+
|
352 |
+
def __call__(
|
353 |
+
self, input_ids: torch.LongTensor, scores: torch.FloatTensor
|
354 |
+
) -> torch.FloatTensor:
|
355 |
+
stopped_samples = self._calc_stopped_samples(input_ids)
|
356 |
+
for i, should_stop in enumerate(stopped_samples):
|
357 |
+
if should_stop:
|
358 |
+
scores[i, self.eos_token_id] = float(2**15)
|
359 |
+
return scores
|
360 |
+
|
361 |
+
def _tokens_match(self, prev_tokens: torch.LongTensor, tokens: List[int]) -> bool:
|
362 |
+
if len(tokens) == 0:
|
363 |
+
# if bad word tokens is just one token always ban it
|
364 |
+
return True
|
365 |
+
elif len(tokens) > len(prev_tokens):
|
366 |
+
# if bad word tokens are longer then prev input_ids they can't be equal
|
367 |
+
return False
|
368 |
+
elif prev_tokens[-len(tokens) :].tolist() == tokens:
|
369 |
+
# if tokens match
|
370 |
+
return True
|
371 |
+
else:
|
372 |
+
return False
|
373 |
+
|
374 |
+
def _calc_stopped_samples(self, prev_input_ids: Iterable[int]) -> Iterable[int]:
|
375 |
+
stopped_samples = []
|
376 |
+
for prev_input_ids_slice in prev_input_ids:
|
377 |
+
match = False
|
378 |
+
for stop_token_seq in self.stop_words_ids:
|
379 |
+
if self._tokens_match(prev_input_ids_slice, stop_token_seq):
|
380 |
+
# if tokens do not match continue
|
381 |
+
match = True
|
382 |
+
break
|
383 |
+
stopped_samples.append(match)
|
384 |
+
|
385 |
+
return stopped_samples
|
386 |
+
|
387 |
+
|
388 |
+
def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float("Inf")):
|
389 |
+
"""This function has been mostly taken from huggingface conversational
|
390 |
+
ai code at
|
391 |
+
https://medium.com/huggingface/how-to-build-a-state-of-the-art-
|
392 |
+
conversational-ai-with-transfer-learning-2d818ac26313"""
|
393 |
+
|
394 |
+
if top_k > 0:
|
395 |
+
# Remove all tokens with a probability less than the
|
396 |
+
# last token of the top-k
|
397 |
+
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
|
398 |
+
logits[indices_to_remove] = filter_value
|
399 |
+
|
400 |
+
if top_p > 0.0:
|
401 |
+
# Cconvert to 1D
|
402 |
+
sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
|
403 |
+
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
|
404 |
+
|
405 |
+
# Remove tokens with cumulative probability above the threshold
|
406 |
+
sorted_indices_to_remove = cumulative_probs > top_p
|
407 |
+
# Shift the indices to the right to keep also the first token
|
408 |
+
# above the threshold
|
409 |
+
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
|
410 |
+
sorted_indices_to_remove[..., 0] = 0
|
411 |
+
for i in range(sorted_indices.size(0)):
|
412 |
+
indices_to_remove = sorted_indices[i][sorted_indices_to_remove[i]]
|
413 |
+
logits[i][indices_to_remove] = filter_value
|
414 |
+
|
415 |
+
return logits
|
416 |
+
|
417 |
+
|
418 |
+
def switch(val1, val2, boolean):
|
419 |
+
boolean = boolean.type_as(val1)
|
420 |
+
return (1 - boolean) * val1 + boolean * val2
|
tokenization_qwen.py
ADDED
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Alibaba Cloud.
|
2 |
+
#
|
3 |
+
# This source code is licensed under the license found in the
|
4 |
+
# LICENSE file in the root directory of this source tree.
|
5 |
+
|
6 |
+
"""Tokenization classes for QWen."""
|
7 |
+
|
8 |
+
import base64
|
9 |
+
import logging
|
10 |
+
import os
|
11 |
+
import requests
|
12 |
+
import unicodedata
|
13 |
+
from typing import Collection, Dict, List, Set, Tuple, Union, Any, Callable, Optional
|
14 |
+
|
15 |
+
import tiktoken
|
16 |
+
import numpy as np
|
17 |
+
from PIL import Image
|
18 |
+
from PIL import ImageFont
|
19 |
+
from PIL import ImageDraw
|
20 |
+
from transformers import PreTrainedTokenizer, AddedToken
|
21 |
+
from transformers.utils import try_to_load_from_cache
|
22 |
+
|
23 |
+
import matplotlib.colors as mcolors
|
24 |
+
from matplotlib.font_manager import FontProperties
|
25 |
+
|
26 |
+
logger = logging.getLogger(__name__)
|
27 |
+
|
28 |
+
|
29 |
+
VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken", "ttf": "SimSun.ttf"}
|
30 |
+
FONT_PATH = try_to_load_from_cache("Qwen/Qwen-VL-Chat", "SimSun.ttf")
|
31 |
+
if FONT_PATH is None:
|
32 |
+
if not os.path.exists("SimSun.ttf"):
|
33 |
+
ttf = requests.get("https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/SimSun.ttf")
|
34 |
+
open("SimSun.ttf", "wb").write(ttf.content)
|
35 |
+
FONT_PATH = "SimSun.ttf"
|
36 |
+
|
37 |
+
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
|
38 |
+
ENDOFTEXT = "<|endoftext|>"
|
39 |
+
IMSTART = "<|im_start|>"
|
40 |
+
IMEND = "<|im_end|>"
|
41 |
+
# as the default behavior is changed to allow special tokens in
|
42 |
+
# regular texts, the surface forms of special tokens need to be
|
43 |
+
# as different as possible to minimize the impact
|
44 |
+
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
|
45 |
+
SPECIAL_TOKENS = (
|
46 |
+
ENDOFTEXT,
|
47 |
+
IMSTART,
|
48 |
+
IMEND,
|
49 |
+
) + EXTRAS
|
50 |
+
IMG_TOKEN_SPAN = 256
|
51 |
+
|
52 |
+
|
53 |
+
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
|
54 |
+
with open(tiktoken_bpe_file, "rb") as f:
|
55 |
+
contents = f.read()
|
56 |
+
return {
|
57 |
+
base64.b64decode(token): int(rank)
|
58 |
+
for token, rank in (line.split() for line in contents.splitlines() if line)
|
59 |
+
}
|
60 |
+
|
61 |
+
def _list_find(
|
62 |
+
input_list: List[Any],
|
63 |
+
candidates: Tuple[Any],
|
64 |
+
start: int = 0,
|
65 |
+
):
|
66 |
+
for i in range(start, len(input_list)):
|
67 |
+
if input_list[i] in candidates:
|
68 |
+
return i
|
69 |
+
return -1
|
70 |
+
|
71 |
+
def _replace_closed_tag(
|
72 |
+
input_tokens: List[Any],
|
73 |
+
start_tags: Union[Any, Tuple[Any]],
|
74 |
+
end_tags: Union[Any, Tuple[Any]],
|
75 |
+
inclusive_replace_func: Callable,
|
76 |
+
exclusive_replace_func: Callable = lambda x: x,
|
77 |
+
):
|
78 |
+
if isinstance(start_tags, (str, int)):
|
79 |
+
start_tags = (start_tags,)
|
80 |
+
if isinstance(end_tags, (str, int)):
|
81 |
+
end_tags = (end_tags,)
|
82 |
+
assert len(start_tags) == len(end_tags)
|
83 |
+
|
84 |
+
output_tokens = []
|
85 |
+
end = 0
|
86 |
+
while True:
|
87 |
+
start = _list_find(input_tokens, start_tags, end)
|
88 |
+
if start == -1:
|
89 |
+
break
|
90 |
+
output_tokens.extend(exclusive_replace_func(input_tokens[end : start]))
|
91 |
+
tag_idx = start_tags.index(input_tokens[start])
|
92 |
+
end = _list_find(input_tokens, (end_tags[tag_idx],), start)
|
93 |
+
if end == -1:
|
94 |
+
raise ValueError("Unclosed image token")
|
95 |
+
output_tokens.extend(inclusive_replace_func(input_tokens[start : end + 1]))
|
96 |
+
end += 1
|
97 |
+
output_tokens.extend(exclusive_replace_func(input_tokens[end : ]))
|
98 |
+
return output_tokens
|
99 |
+
|
100 |
+
class QWenTokenizer(PreTrainedTokenizer):
|
101 |
+
"""QWen tokenizer."""
|
102 |
+
|
103 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
104 |
+
|
105 |
+
def __init__(
|
106 |
+
self,
|
107 |
+
vocab_file,
|
108 |
+
errors="replace",
|
109 |
+
image_start_tag='<img>',
|
110 |
+
image_end_tag='</img>',
|
111 |
+
image_pad_tag='<imgpad>',
|
112 |
+
ref_start_tag='<ref>',
|
113 |
+
ref_end_tag='</ref>',
|
114 |
+
box_start_tag='<box>',
|
115 |
+
box_end_tag='</box>',
|
116 |
+
quad_start_tag='<quad>',
|
117 |
+
quad_end_tag='</quad>',
|
118 |
+
**kwargs,
|
119 |
+
):
|
120 |
+
super().__init__(**kwargs)
|
121 |
+
self.image_start_tag = image_start_tag
|
122 |
+
self.image_end_tag = image_end_tag
|
123 |
+
self.image_pad_tag = image_pad_tag
|
124 |
+
self.ref_start_tag = ref_start_tag
|
125 |
+
self.ref_end_tag = ref_end_tag
|
126 |
+
self.box_start_tag = box_start_tag
|
127 |
+
self.box_end_tag = box_end_tag
|
128 |
+
self.quad_start_tag = quad_start_tag
|
129 |
+
self.quad_end_tag = quad_end_tag
|
130 |
+
self.IMAGE_ST = (
|
131 |
+
ref_start_tag, ref_end_tag,
|
132 |
+
box_start_tag, box_end_tag,
|
133 |
+
quad_start_tag, quad_end_tag,
|
134 |
+
image_start_tag, image_end_tag,
|
135 |
+
image_pad_tag
|
136 |
+
)
|
137 |
+
|
138 |
+
self.errors = errors # how to handle errors in decoding
|
139 |
+
|
140 |
+
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: dict[bytes, int]
|
141 |
+
self.special_tokens = {
|
142 |
+
token: index
|
143 |
+
for index, token in enumerate(
|
144 |
+
SPECIAL_TOKENS + self.IMAGE_ST, start=len(self.mergeable_ranks)
|
145 |
+
)
|
146 |
+
}
|
147 |
+
self.img_start_id = self.special_tokens[self.image_start_tag]
|
148 |
+
self.img_end_id = self.special_tokens[self.image_end_tag]
|
149 |
+
self.img_pad_id = self.special_tokens[self.image_pad_tag]
|
150 |
+
self.ref_start_id = self.special_tokens[self.ref_start_tag]
|
151 |
+
self.ref_end_id = self.special_tokens[self.ref_end_tag]
|
152 |
+
self.box_start_id = self.special_tokens[self.box_start_tag]
|
153 |
+
self.box_end_id = self.special_tokens[self.box_end_tag]
|
154 |
+
self.quad_start_id = self.special_tokens[self.quad_start_tag]
|
155 |
+
self.quad_end_id = self.special_tokens[self.quad_end_tag]
|
156 |
+
|
157 |
+
enc = tiktoken.Encoding(
|
158 |
+
"Qwen",
|
159 |
+
pat_str=PAT_STR,
|
160 |
+
mergeable_ranks=self.mergeable_ranks,
|
161 |
+
special_tokens=self.special_tokens,
|
162 |
+
)
|
163 |
+
assert (
|
164 |
+
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
|
165 |
+
), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
|
166 |
+
|
167 |
+
self.decoder = {
|
168 |
+
v: k for k, v in self.mergeable_ranks.items()
|
169 |
+
} # type: dict[int, bytes|str]
|
170 |
+
self.decoder.update({v: k for k, v in self.special_tokens.items()})
|
171 |
+
|
172 |
+
self.tokenizer = enc # type: tiktoken.Encoding
|
173 |
+
|
174 |
+
self.eod_id = self.tokenizer.eot_token
|
175 |
+
self.im_start_id = self.special_tokens[IMSTART]
|
176 |
+
self.im_end_id = self.special_tokens[IMEND]
|
177 |
+
|
178 |
+
def __getstate__(self):
|
179 |
+
# for pickle lovers
|
180 |
+
state = self.__dict__.copy()
|
181 |
+
del state['tokenizer']
|
182 |
+
return state
|
183 |
+
|
184 |
+
def __setstate__(self, state):
|
185 |
+
# tokenizer is not python native; don't pass it; rebuild it
|
186 |
+
self.__dict__.update(state)
|
187 |
+
enc = tiktoken.Encoding(
|
188 |
+
"Qwen",
|
189 |
+
pat_str=PAT_STR,
|
190 |
+
mergeable_ranks=self.mergeable_ranks,
|
191 |
+
special_tokens=self.special_tokens,
|
192 |
+
)
|
193 |
+
self.tokenizer = enc
|
194 |
+
|
195 |
+
|
196 |
+
def __len__(self) -> int:
|
197 |
+
return self.tokenizer.n_vocab
|
198 |
+
|
199 |
+
def get_vocab(self) -> Dict[bytes, int]:
|
200 |
+
return self.mergeable_ranks
|
201 |
+
|
202 |
+
def convert_tokens_to_ids(
|
203 |
+
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
|
204 |
+
) -> List[int]:
|
205 |
+
ids = []
|
206 |
+
if isinstance(tokens, (str, bytes)):
|
207 |
+
if tokens in self.special_tokens:
|
208 |
+
return self.special_tokens[tokens]
|
209 |
+
else:
|
210 |
+
return self.mergeable_ranks.get(tokens)
|
211 |
+
for token in tokens:
|
212 |
+
if token in self.special_tokens:
|
213 |
+
ids.append(self.special_tokens[token])
|
214 |
+
else:
|
215 |
+
ids.append(self.mergeable_ranks.get(token))
|
216 |
+
return ids
|
217 |
+
|
218 |
+
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
|
219 |
+
if not special_tokens and new_tokens:
|
220 |
+
raise ValueError('Adding regular tokens is not supported')
|
221 |
+
for token in new_tokens:
|
222 |
+
surface_form = token.content if isinstance(token, AddedToken) else token
|
223 |
+
if surface_form not in SPECIAL_TOKENS + self.IMAGE_ST:
|
224 |
+
raise ValueError('Adding unknown special tokens is not supported')
|
225 |
+
return 0
|
226 |
+
|
227 |
+
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
|
228 |
+
"""
|
229 |
+
Save only the vocabulary of the tokenizer (vocabulary).
|
230 |
+
|
231 |
+
Returns:
|
232 |
+
`Tuple(str)`: Paths to the files saved.
|
233 |
+
"""
|
234 |
+
file_path = os.path.join(save_directory, "qwen.tiktoken")
|
235 |
+
with open(file_path, "w", encoding="utf8") as w:
|
236 |
+
for k, v in self.mergeable_ranks.items():
|
237 |
+
line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
|
238 |
+
w.write(line)
|
239 |
+
return (file_path,)
|
240 |
+
|
241 |
+
def tokenize(
|
242 |
+
self,
|
243 |
+
text: str,
|
244 |
+
allowed_special: Union[Set, str] = "all",
|
245 |
+
disallowed_special: Union[Collection, str] = (),
|
246 |
+
**kwargs,
|
247 |
+
) -> List[Union[bytes, str]]:
|
248 |
+
"""
|
249 |
+
Converts a string in a sequence of tokens.
|
250 |
+
|
251 |
+
Args:
|
252 |
+
text (`str`):
|
253 |
+
The sequence to be encoded.
|
254 |
+
allowed_special (`Literal["all"]` or `set`):
|
255 |
+
The surface forms of the tokens to be encoded as special tokens in regular texts.
|
256 |
+
Default to "all".
|
257 |
+
disallowed_special (`Literal["all"]` or `Collection`):
|
258 |
+
The surface forms of the tokens that should not be in regular texts and trigger errors.
|
259 |
+
Default to an empty tuple.
|
260 |
+
|
261 |
+
kwargs (additional keyword arguments, *optional*):
|
262 |
+
Will be passed to the underlying model specific encode method.
|
263 |
+
|
264 |
+
Returns:
|
265 |
+
`List[bytes|str]`: The list of tokens.
|
266 |
+
"""
|
267 |
+
tokens = []
|
268 |
+
text = unicodedata.normalize("NFC", text)
|
269 |
+
|
270 |
+
# this implementation takes a detour: text -> token id -> token surface forms
|
271 |
+
for t in self.tokenizer.encode(
|
272 |
+
text, allowed_special=allowed_special, disallowed_special=disallowed_special
|
273 |
+
):
|
274 |
+
tokens.append(self.decoder[t])
|
275 |
+
|
276 |
+
def _encode_imgurl(img_tokens):
|
277 |
+
assert img_tokens[0] == self.image_start_tag and img_tokens[-1] == self.image_end_tag
|
278 |
+
img_tokens = img_tokens[1:-1]
|
279 |
+
img_url = b''.join(img_tokens)
|
280 |
+
out_img_tokens = list(map(self.decoder.get, img_url))
|
281 |
+
if len(out_img_tokens) > IMG_TOKEN_SPAN:
|
282 |
+
raise ValueError("The content in {}..{} is too long".format(
|
283 |
+
self.image_start_tag, self.image_end_tag))
|
284 |
+
out_img_tokens.extend([self.image_pad_tag] * (IMG_TOKEN_SPAN - len(out_img_tokens)))
|
285 |
+
out_img_tokens = [self.image_start_tag] + out_img_tokens + [self.image_end_tag]
|
286 |
+
return out_img_tokens
|
287 |
+
|
288 |
+
return _replace_closed_tag(tokens, self.image_start_tag, self.image_end_tag, _encode_imgurl)
|
289 |
+
|
290 |
+
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
291 |
+
"""
|
292 |
+
Converts a sequence of tokens in a single string.
|
293 |
+
"""
|
294 |
+
text = ""
|
295 |
+
temp = b""
|
296 |
+
for t in tokens:
|
297 |
+
if isinstance(t, str):
|
298 |
+
if temp:
|
299 |
+
text += temp.decode("utf-8", errors=self.errors)
|
300 |
+
temp = b""
|
301 |
+
text += t
|
302 |
+
elif isinstance(t, bytes):
|
303 |
+
temp += t
|
304 |
+
else:
|
305 |
+
raise TypeError("token should only be of type types or str")
|
306 |
+
if temp:
|
307 |
+
text += temp.decode("utf-8", errors=self.errors)
|
308 |
+
return text
|
309 |
+
|
310 |
+
@property
|
311 |
+
def vocab_size(self):
|
312 |
+
return self.tokenizer.n_vocab
|
313 |
+
|
314 |
+
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
|
315 |
+
"""Converts an id to a token, special tokens included"""
|
316 |
+
if index in self.decoder:
|
317 |
+
return self.decoder[index]
|
318 |
+
raise ValueError("unknown ids")
|
319 |
+
|
320 |
+
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
|
321 |
+
"""Converts a token to an id using the vocab, special tokens included"""
|
322 |
+
if token in self.special_tokens:
|
323 |
+
return self.special_tokens[token]
|
324 |
+
if token in self.mergeable_ranks:
|
325 |
+
return self.mergeable_ranks[token]
|
326 |
+
raise ValueError("unknown token")
|
327 |
+
|
328 |
+
def _tokenize(self, text: str, **kwargs):
|
329 |
+
"""
|
330 |
+
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
|
331 |
+
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
|
332 |
+
|
333 |
+
Do NOT take care of added tokens.
|
334 |
+
"""
|
335 |
+
raise NotImplementedError
|
336 |
+
|
337 |
+
def _decode(
|
338 |
+
self,
|
339 |
+
token_ids: Union[int, List[int]],
|
340 |
+
skip_special_tokens: bool = False,
|
341 |
+
errors: str = None,
|
342 |
+
**kwargs,
|
343 |
+
) -> str:
|
344 |
+
if isinstance(token_ids, int):
|
345 |
+
token_ids = [token_ids]
|
346 |
+
|
347 |
+
def _decode_imgurl(img_token_ids):
|
348 |
+
assert img_token_ids[0] == self.img_start_id and img_token_ids[-1] == self.img_end_id
|
349 |
+
img_token_ids = img_token_ids[1:-1]
|
350 |
+
img_token_ids = img_token_ids[ : img_token_ids.index(self.img_pad_id)]
|
351 |
+
img_url = bytes(img_token_ids).decode('utf-8')
|
352 |
+
return [self.img_start_id] + self.tokenizer.encode(img_url) + [self.img_end_id]
|
353 |
+
|
354 |
+
token_ids = _replace_closed_tag(token_ids, self.img_start_id, self.img_end_id, _decode_imgurl)
|
355 |
+
|
356 |
+
if skip_special_tokens:
|
357 |
+
token_ids = [i for i in token_ids if i < self.eod_id]
|
358 |
+
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
|
359 |
+
|
360 |
+
def to_list_format(self, text: str):
|
361 |
+
text = unicodedata.normalize("NFC", text)
|
362 |
+
token_ids = self.tokenizer.encode(
|
363 |
+
text, allowed_special=set(self.IMAGE_ST + (ENDOFTEXT,)))
|
364 |
+
|
365 |
+
def _encode_vl_info(tokens):
|
366 |
+
if len(tokens) == 0:
|
367 |
+
return []
|
368 |
+
if tokens[0] == self.img_start_id and tokens[-1] == self.img_end_id:
|
369 |
+
key = 'image'
|
370 |
+
elif tokens[0] == self.ref_start_id and tokens[-1] == self.ref_end_id:
|
371 |
+
key = 'ref'
|
372 |
+
elif tokens[0] == self.box_start_id and tokens[-1] == self.box_end_id:
|
373 |
+
key = 'box'
|
374 |
+
elif tokens[0] == self.quad_start_id and tokens[-1] == self.quad_end_id:
|
375 |
+
key = 'quad'
|
376 |
+
else:
|
377 |
+
_tobytes = lambda x: x.encode('utf-8') if isinstance(x, str) else x
|
378 |
+
return [{'text': b''.join(map(_tobytes, map(self.decoder.get, tokens))).decode('utf-8')}]
|
379 |
+
_tobytes = lambda x: x.encode('utf-8') if isinstance(x, str) else x
|
380 |
+
val = b''.join(map(_tobytes, map(self.decoder.get, tokens[1:-1]))).decode('utf-8')
|
381 |
+
return [{key: val}]
|
382 |
+
|
383 |
+
return _replace_closed_tag(
|
384 |
+
token_ids,
|
385 |
+
(self.img_start_id, self.ref_start_id, self.box_start_id, self.quad_start_id),
|
386 |
+
(self.img_end_id, self.ref_end_id, self.box_end_id, self.quad_end_id),
|
387 |
+
_encode_vl_info,
|
388 |
+
_encode_vl_info,
|
389 |
+
)
|
390 |
+
|
391 |
+
def from_list_format(self, list_format: List[Dict]):
|
392 |
+
text = ''
|
393 |
+
num_images = 0
|
394 |
+
for ele in list_format:
|
395 |
+
if 'image' in ele:
|
396 |
+
num_images += 1
|
397 |
+
text += f'Picture {num_images}:'
|
398 |
+
text += self.image_start_tag + ele['image'] + self.image_end_tag
|
399 |
+
text += '\n'
|
400 |
+
elif 'text' in ele:
|
401 |
+
text += ele['text']
|
402 |
+
elif 'box' in ele:
|
403 |
+
if 'ref' in ele:
|
404 |
+
text += self.ref_start_tag + ele['ref'] + self.ref_end_tag
|
405 |
+
for box in ele['box']:
|
406 |
+
text += self.box_start_tag + '(%d,%d),(%d,%d)' % (box[0], box[1], box[2], box[3]) + self.box_end_tag
|
407 |
+
else:
|
408 |
+
raise ValueError("Unsupport element: " + str(ele))
|
409 |
+
return text
|
410 |
+
|
411 |
+
def _fetch_latest_picture(self, response, history):
|
412 |
+
if history is None:
|
413 |
+
history = []
|
414 |
+
_history = history + [(response, None)]
|
415 |
+
for q, r in _history[::-1]:
|
416 |
+
for ele in self.to_list_format(q)[::-1]:
|
417 |
+
if 'image' in ele:
|
418 |
+
return ele['image']
|
419 |
+
return None
|
420 |
+
|
421 |
+
def _fetch_all_box_with_ref(self, text):
|
422 |
+
list_format = self.to_list_format(text)
|
423 |
+
output = []
|
424 |
+
for i, ele in enumerate(list_format):
|
425 |
+
if 'box' in ele:
|
426 |
+
bbox = tuple(map(int, ele['box'].replace('(', '').replace(')', '').split(',')))
|
427 |
+
assert len(bbox) == 4
|
428 |
+
output.append({'box': bbox})
|
429 |
+
if i > 0 and 'ref' in list_format[i-1]:
|
430 |
+
output[-1]['ref'] = list_format[i-1]['ref'].strip()
|
431 |
+
return output
|
432 |
+
|
433 |
+
def draw_bbox_on_latest_picture(
|
434 |
+
self,
|
435 |
+
response,
|
436 |
+
history=None,
|
437 |
+
) -> Optional[Image.Image]:
|
438 |
+
image = self._fetch_latest_picture(response, history)
|
439 |
+
if image is None:
|
440 |
+
return None
|
441 |
+
if image.startswith("http://") or image.startswith("https://"):
|
442 |
+
image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
|
443 |
+
h, w = image.height, image.width
|
444 |
+
else:
|
445 |
+
image = np.asarray(Image.open(image).convert("RGB"))
|
446 |
+
h, w = image.shape[0], image.shape[1]
|
447 |
+
visualizer = Visualizer(image)
|
448 |
+
|
449 |
+
boxes = self._fetch_all_box_with_ref(response)
|
450 |
+
if not boxes:
|
451 |
+
return None
|
452 |
+
color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()]) # init color
|
453 |
+
for box in boxes:
|
454 |
+
if 'ref' in box: # random new color for new refexps
|
455 |
+
color = random.choice([_ for _ in mcolors.TABLEAU_COLORS.keys()])
|
456 |
+
x1, y1, x2, y2 = box['box']
|
457 |
+
x1, y1, x2, y2 = (int(x1 / 1000 * w), int(y1 / 1000 * h), int(x2 / 1000 * w), int(y2 / 1000 * h))
|
458 |
+
visualizer.draw_box((x1, y1, x2, y2), alpha=1, edge_color=color)
|
459 |
+
if 'ref' in box:
|
460 |
+
visualizer.draw_text(box['ref'], (x1, y1), color=color, horizontal_alignment="left")
|
461 |
+
return visualizer.output
|
462 |
+
|
463 |
+
|
464 |
+
import colorsys
|
465 |
+
import logging
|
466 |
+
import math
|
467 |
+
import numpy as np
|
468 |
+
import matplotlib as mpl
|
469 |
+
import matplotlib.colors as mplc
|
470 |
+
import matplotlib.figure as mplfigure
|
471 |
+
import torch
|
472 |
+
from matplotlib.backends.backend_agg import FigureCanvasAgg
|
473 |
+
from PIL import Image
|
474 |
+
import random
|
475 |
+
|
476 |
+
logger = logging.getLogger(__name__)
|
477 |
+
|
478 |
+
|
479 |
+
class VisImage:
|
480 |
+
def __init__(self, img, scale=1.0):
|
481 |
+
self.img = img
|
482 |
+
self.scale = scale
|
483 |
+
self.width, self.height = img.shape[1], img.shape[0]
|
484 |
+
self._setup_figure(img)
|
485 |
+
|
486 |
+
def _setup_figure(self, img):
|
487 |
+
fig = mplfigure.Figure(frameon=False)
|
488 |
+
self.dpi = fig.get_dpi()
|
489 |
+
# add a small 1e-2 to avoid precision lost due to matplotlib's truncation
|
490 |
+
# (https://github.com/matplotlib/matplotlib/issues/15363)
|
491 |
+
fig.set_size_inches(
|
492 |
+
(self.width * self.scale + 1e-2) / self.dpi,
|
493 |
+
(self.height * self.scale + 1e-2) / self.dpi,
|
494 |
+
)
|
495 |
+
self.canvas = FigureCanvasAgg(fig)
|
496 |
+
# self.canvas = mpl.backends.backend_cairo.FigureCanvasCairo(fig)
|
497 |
+
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0])
|
498 |
+
ax.axis("off")
|
499 |
+
self.fig = fig
|
500 |
+
self.ax = ax
|
501 |
+
self.reset_image(img)
|
502 |
+
|
503 |
+
def reset_image(self, img):
|
504 |
+
img = img.astype("uint8")
|
505 |
+
self.ax.imshow(img, extent=(0, self.width, self.height, 0), interpolation="nearest")
|
506 |
+
|
507 |
+
def save(self, filepath):
|
508 |
+
self.fig.savefig(filepath)
|
509 |
+
|
510 |
+
def get_image(self):
|
511 |
+
canvas = self.canvas
|
512 |
+
s, (width, height) = canvas.print_to_buffer()
|
513 |
+
|
514 |
+
buffer = np.frombuffer(s, dtype="uint8")
|
515 |
+
|
516 |
+
img_rgba = buffer.reshape(height, width, 4)
|
517 |
+
rgb, alpha = np.split(img_rgba, [3], axis=2)
|
518 |
+
return rgb.astype("uint8")
|
519 |
+
|
520 |
+
|
521 |
+
class Visualizer:
|
522 |
+
def __init__(self, img_rgb, metadata=None, scale=1.0):
|
523 |
+
self.img = np.asarray(img_rgb).clip(0, 255).astype(np.uint8)
|
524 |
+
self.font_path = FONT_PATH
|
525 |
+
self.output = VisImage(self.img, scale=scale)
|
526 |
+
self.cpu_device = torch.device("cpu")
|
527 |
+
|
528 |
+
# too small texts are useless, therefore clamp to 14
|
529 |
+
self._default_font_size = max(
|
530 |
+
np.sqrt(self.output.height * self.output.width) // 30, 15 // scale
|
531 |
+
)
|
532 |
+
|
533 |
+
def draw_text(
|
534 |
+
self,
|
535 |
+
text,
|
536 |
+
position,
|
537 |
+
*,
|
538 |
+
font_size=None,
|
539 |
+
color="g",
|
540 |
+
horizontal_alignment="center",
|
541 |
+
rotation=0,
|
542 |
+
):
|
543 |
+
if not font_size:
|
544 |
+
font_size = self._default_font_size
|
545 |
+
|
546 |
+
# since the text background is dark, we don't want the text to be dark
|
547 |
+
color = np.maximum(list(mplc.to_rgb(color)), 0.2)
|
548 |
+
color[np.argmax(color)] = max(0.8, np.max(color))
|
549 |
+
|
550 |
+
x, y = position
|
551 |
+
self.output.ax.text(
|
552 |
+
x,
|
553 |
+
y,
|
554 |
+
text,
|
555 |
+
size=font_size * self.output.scale,
|
556 |
+
fontproperties=FontProperties(fname=self.font_path),
|
557 |
+
bbox={"facecolor": "black", "alpha": 0.8, "pad": 0.7, "edgecolor": "none"},
|
558 |
+
verticalalignment="top",
|
559 |
+
horizontalalignment=horizontal_alignment,
|
560 |
+
color=color,
|
561 |
+
zorder=10,
|
562 |
+
rotation=rotation,
|
563 |
+
)
|
564 |
+
return self.output
|
565 |
+
|
566 |
+
def draw_box(self, box_coord, alpha=0.5, edge_color="g", line_style="-"):
|
567 |
+
|
568 |
+
x0, y0, x1, y1 = box_coord
|
569 |
+
width = x1 - x0
|
570 |
+
height = y1 - y0
|
571 |
+
|
572 |
+
linewidth = max(self._default_font_size / 4, 1)
|
573 |
+
|
574 |
+
self.output.ax.add_patch(
|
575 |
+
mpl.patches.Rectangle(
|
576 |
+
(x0, y0),
|
577 |
+
width,
|
578 |
+
height,
|
579 |
+
fill=False,
|
580 |
+
edgecolor=edge_color,
|
581 |
+
linewidth=linewidth * self.output.scale,
|
582 |
+
alpha=alpha,
|
583 |
+
linestyle=line_style,
|
584 |
+
)
|
585 |
+
)
|
586 |
+
return self.output
|
587 |
+
|
588 |
+
def get_output(self):
|
589 |
+
|
590 |
+
return self.output
|
tokenizer_config.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"model_max_length": 8192,
|
3 |
+
"tokenizer_class": "QWenTokenizer",
|
4 |
+
"auto_map": {
|
5 |
+
"AutoTokenizer": [
|
6 |
+
"tokenization_qwen.QWenTokenizer",
|
7 |
+
null
|
8 |
+
]
|
9 |
+
}
|
10 |
+
}
|
visual.py
ADDED
@@ -0,0 +1,426 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright (c) Alibaba Cloud.
|
2 |
+
#
|
3 |
+
# This source code is licensed under the license found in the
|
4 |
+
# LICENSE file in the root directory of this source tree.
|
5 |
+
|
6 |
+
from collections import OrderedDict
|
7 |
+
import math
|
8 |
+
import requests
|
9 |
+
from io import BytesIO
|
10 |
+
from functools import partial
|
11 |
+
from PIL import Image
|
12 |
+
from typing import Callable, Optional, Sequence, Tuple, List
|
13 |
+
import numpy as np
|
14 |
+
|
15 |
+
import torch
|
16 |
+
from torch import nn
|
17 |
+
from torch.nn import functional as F
|
18 |
+
from torch.nn.init import trunc_normal_
|
19 |
+
from torchvision import transforms
|
20 |
+
from torchvision.transforms import InterpolationMode
|
21 |
+
|
22 |
+
|
23 |
+
def get_abs_pos(abs_pos, tgt_size):
|
24 |
+
# abs_pos: L, C
|
25 |
+
# tgt_size: M
|
26 |
+
# return: M, C
|
27 |
+
src_size = int(math.sqrt(abs_pos.size(0)))
|
28 |
+
tgt_size = int(math.sqrt(tgt_size))
|
29 |
+
dtype = abs_pos.dtype
|
30 |
+
|
31 |
+
if src_size != tgt_size:
|
32 |
+
return F.interpolate(
|
33 |
+
abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
|
34 |
+
size=(tgt_size, tgt_size),
|
35 |
+
mode="bicubic",
|
36 |
+
align_corners=False,
|
37 |
+
).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
|
38 |
+
else:
|
39 |
+
return abs_pos
|
40 |
+
|
41 |
+
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
|
42 |
+
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
|
43 |
+
"""
|
44 |
+
grid_size: int of the grid height and width
|
45 |
+
return:
|
46 |
+
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
47 |
+
"""
|
48 |
+
grid_h = np.arange(grid_size, dtype=np.float32)
|
49 |
+
grid_w = np.arange(grid_size, dtype=np.float32)
|
50 |
+
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
51 |
+
grid = np.stack(grid, axis=0)
|
52 |
+
|
53 |
+
grid = grid.reshape([2, 1, grid_size, grid_size])
|
54 |
+
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
55 |
+
if cls_token:
|
56 |
+
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
|
57 |
+
return pos_embed
|
58 |
+
|
59 |
+
|
60 |
+
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
61 |
+
assert embed_dim % 2 == 0
|
62 |
+
|
63 |
+
# use half of dimensions to encode grid_h
|
64 |
+
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
|
65 |
+
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
|
66 |
+
|
67 |
+
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
|
68 |
+
return emb
|
69 |
+
|
70 |
+
|
71 |
+
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
72 |
+
"""
|
73 |
+
embed_dim: output dimension for each position
|
74 |
+
pos: a list of positions to be encoded: size (M,)
|
75 |
+
out: (M, D)
|
76 |
+
"""
|
77 |
+
assert embed_dim % 2 == 0
|
78 |
+
omega = np.arange(embed_dim // 2, dtype=np.float32)
|
79 |
+
omega /= embed_dim / 2.
|
80 |
+
omega = 1. / 10000**omega # (D/2,)
|
81 |
+
|
82 |
+
pos = pos.reshape(-1) # (M,)
|
83 |
+
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
|
84 |
+
|
85 |
+
emb_sin = np.sin(out) # (M, D/2)
|
86 |
+
emb_cos = np.cos(out) # (M, D/2)
|
87 |
+
|
88 |
+
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
|
89 |
+
return emb
|
90 |
+
|
91 |
+
|
92 |
+
class Resampler(nn.Module):
|
93 |
+
"""
|
94 |
+
A 2D perceiver-resampler network with one cross attention layers by
|
95 |
+
(grid_size**2) learnable queries and 2d sincos pos_emb
|
96 |
+
Outputs:
|
97 |
+
A tensor with the shape of (grid_size**2, embed_dim)
|
98 |
+
"""
|
99 |
+
def __init__(
|
100 |
+
self,
|
101 |
+
grid_size,
|
102 |
+
embed_dim,
|
103 |
+
num_heads,
|
104 |
+
kv_dim=None,
|
105 |
+
norm_layer=nn.LayerNorm
|
106 |
+
):
|
107 |
+
super().__init__()
|
108 |
+
self.num_queries = grid_size ** 2
|
109 |
+
self.embed_dim = embed_dim
|
110 |
+
self.num_heads = num_heads
|
111 |
+
|
112 |
+
self.pos_embed = nn.Parameter(
|
113 |
+
torch.from_numpy(get_2d_sincos_pos_embed(embed_dim, grid_size)).float()
|
114 |
+
).requires_grad_(False)
|
115 |
+
|
116 |
+
self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
|
117 |
+
trunc_normal_(self.query, std=.02)
|
118 |
+
|
119 |
+
if kv_dim is not None and kv_dim != embed_dim:
|
120 |
+
self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
|
121 |
+
else:
|
122 |
+
self.kv_proj = nn.Identity()
|
123 |
+
|
124 |
+
self.attn = nn.MultiheadAttention(embed_dim, num_heads)
|
125 |
+
self.ln_q = norm_layer(embed_dim)
|
126 |
+
self.ln_kv = norm_layer(embed_dim)
|
127 |
+
|
128 |
+
self.apply(self._init_weights)
|
129 |
+
|
130 |
+
def _init_weights(self, m):
|
131 |
+
if isinstance(m, nn.Linear):
|
132 |
+
trunc_normal_(m.weight, std=.02)
|
133 |
+
if isinstance(m, nn.Linear) and m.bias is not None:
|
134 |
+
nn.init.constant_(m.bias, 0)
|
135 |
+
elif isinstance(m, nn.LayerNorm):
|
136 |
+
nn.init.constant_(m.bias, 0)
|
137 |
+
nn.init.constant_(m.weight, 1.0)
|
138 |
+
|
139 |
+
def forward(self, x, attn_mask=None):
|
140 |
+
|
141 |
+
pos_embed = get_abs_pos(self.pos_embed, x.size(1))
|
142 |
+
|
143 |
+
x = self.kv_proj(x)
|
144 |
+
x = self.ln_kv(x).permute(1, 0, 2)
|
145 |
+
|
146 |
+
N = x.shape[1]
|
147 |
+
q = self.ln_q(self.query)
|
148 |
+
out = self.attn(
|
149 |
+
self._repeat(q, N) + self.pos_embed.unsqueeze(1),
|
150 |
+
x + pos_embed.unsqueeze(1),
|
151 |
+
x,
|
152 |
+
attn_mask=attn_mask)[0]
|
153 |
+
return out.permute(1, 0, 2)
|
154 |
+
|
155 |
+
def _repeat(self, query, N: int):
|
156 |
+
return query.unsqueeze(1).repeat(1, N, 1)
|
157 |
+
|
158 |
+
|
159 |
+
class VisualAttention(nn.Module):
|
160 |
+
"""self-attention layer class.
|
161 |
+
|
162 |
+
Self-attention layer takes input with size [s, b, h]
|
163 |
+
and returns output of the same size.
|
164 |
+
"""
|
165 |
+
|
166 |
+
def __init__(self, embed_dim, num_heads,
|
167 |
+
bias=True, kdim=None, vdim=None):
|
168 |
+
super(VisualAttention, self).__init__()
|
169 |
+
self.embed_dim = embed_dim
|
170 |
+
self.kdim = kdim if kdim is not None else embed_dim
|
171 |
+
self.vdim = vdim if vdim is not None else embed_dim
|
172 |
+
self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
|
173 |
+
|
174 |
+
self.num_heads = num_heads
|
175 |
+
|
176 |
+
# Per attention head and per partition values.
|
177 |
+
assert embed_dim % num_heads == 0
|
178 |
+
self.hidden_size_per_attention_head = embed_dim // num_heads
|
179 |
+
self.num_attention_heads_per_partition = num_heads
|
180 |
+
self.hidden_size_per_partition = embed_dim
|
181 |
+
|
182 |
+
# Strided linear layer.
|
183 |
+
assert self._qkv_same_embed_dim, 'Only Support SelfAttention Currently'
|
184 |
+
self.in_proj = nn.Linear(embed_dim, 3 * embed_dim)
|
185 |
+
self.out_proj = nn.Linear(embed_dim, embed_dim)
|
186 |
+
self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
|
187 |
+
|
188 |
+
def forward(self, query, key, value, attn_mask = None):
|
189 |
+
# query/key/value: [sq, b, h]
|
190 |
+
sq, b, _ = query.size()
|
191 |
+
|
192 |
+
assert query is key, 'Only Support Self-Attention Currently'
|
193 |
+
sk = sq
|
194 |
+
mixed_x_layer = self.in_proj(query)
|
195 |
+
|
196 |
+
# [sq, b, (np * 3 * hn)] --> [sq, b, np, 3 * hn]
|
197 |
+
new_tensor_shape = mixed_x_layer.size()[:-1] + \
|
198 |
+
(self.num_attention_heads_per_partition,
|
199 |
+
3 * self.hidden_size_per_attention_head)
|
200 |
+
mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
|
201 |
+
|
202 |
+
# [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
|
203 |
+
query_layer, key_layer, value_layer = mixed_x_layer.split(
|
204 |
+
self.hidden_size_per_attention_head, dim=-1)
|
205 |
+
|
206 |
+
# [sq, b, np, hn] -> [sq, b * np, hn]
|
207 |
+
query_layer = query_layer.view(sq,
|
208 |
+
b * self.num_attention_heads_per_partition,
|
209 |
+
self.hidden_size_per_attention_head).transpose(0, 1)
|
210 |
+
# [sk, b, np, hn] -> [sk, b * np, hn]
|
211 |
+
key_layer = key_layer.view(sk,
|
212 |
+
b * self.num_attention_heads_per_partition,
|
213 |
+
self.hidden_size_per_attention_head).transpose(0, 1)
|
214 |
+
|
215 |
+
q_scaled = query_layer / self.norm_factor
|
216 |
+
if attn_mask is not None:
|
217 |
+
attention_probs = torch.baddbmm(attn_mask, q_scaled, key_layer.transpose(-2, -1))
|
218 |
+
else:
|
219 |
+
attention_probs = torch.bmm(q_scaled, key_layer.transpose(-2, -1))
|
220 |
+
attention_probs = attention_probs.softmax(dim=-1)
|
221 |
+
|
222 |
+
value_layer = value_layer.view(sk,
|
223 |
+
b * self.num_attention_heads_per_partition,
|
224 |
+
self.hidden_size_per_attention_head).transpose(0, 1)
|
225 |
+
|
226 |
+
# matmul: [b * np, sq, hn]
|
227 |
+
context_layer = torch.bmm(attention_probs, value_layer)
|
228 |
+
|
229 |
+
# change view [b, np, sq, hn]
|
230 |
+
context_layer = context_layer.view(b,
|
231 |
+
self.num_attention_heads_per_partition,
|
232 |
+
sq, self.hidden_size_per_attention_head)
|
233 |
+
|
234 |
+
# [b, np, sq, hn] --> [sq, b, np, hn]
|
235 |
+
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
|
236 |
+
|
237 |
+
# [sq, b, np, hn] --> [sq, b, hp]
|
238 |
+
new_context_layer_shape = context_layer.size()[:-2] + \
|
239 |
+
(self.hidden_size_per_partition,)
|
240 |
+
context_layer = context_layer.view(*new_context_layer_shape)
|
241 |
+
|
242 |
+
output = self.out_proj(context_layer)
|
243 |
+
|
244 |
+
return output
|
245 |
+
|
246 |
+
|
247 |
+
class VisualAttentionBlock(nn.Module):
|
248 |
+
def __init__(
|
249 |
+
self,
|
250 |
+
d_model: int,
|
251 |
+
n_head: int,
|
252 |
+
mlp_ratio: float = 4.0,
|
253 |
+
act_layer: Callable = nn.GELU,
|
254 |
+
norm_layer: Callable = nn.LayerNorm,
|
255 |
+
is_cross_attention: bool = False,
|
256 |
+
):
|
257 |
+
super().__init__()
|
258 |
+
|
259 |
+
self.ln_1 = norm_layer(d_model)
|
260 |
+
if is_cross_attention:
|
261 |
+
self.ln_1_kv = norm_layer(d_model)
|
262 |
+
|
263 |
+
self.ln_2 = norm_layer(d_model)
|
264 |
+
mlp_width = int(d_model * mlp_ratio)
|
265 |
+
self.attn = VisualAttention(d_model, n_head)
|
266 |
+
self.mlp = nn.Sequential(OrderedDict([
|
267 |
+
("c_fc", nn.Linear(d_model, mlp_width)),
|
268 |
+
("gelu", act_layer()),
|
269 |
+
("c_proj", nn.Linear(mlp_width, d_model))
|
270 |
+
]))
|
271 |
+
|
272 |
+
def attention(
|
273 |
+
self,
|
274 |
+
q_x: torch.Tensor,
|
275 |
+
k_x: Optional[torch.Tensor] = None,
|
276 |
+
v_x: Optional[torch.Tensor] = None,
|
277 |
+
attn_mask: Optional[torch.Tensor] = None,
|
278 |
+
):
|
279 |
+
k_x = k_x if k_x is not None else q_x
|
280 |
+
v_x = v_x if v_x is not None else q_x
|
281 |
+
|
282 |
+
attn_mask = attn_mask.to(q_x.dtype) if attn_mask is not None else None
|
283 |
+
return self.attn(q_x, k_x, v_x, attn_mask=attn_mask)
|
284 |
+
|
285 |
+
def forward(
|
286 |
+
self,
|
287 |
+
q_x: torch.Tensor,
|
288 |
+
k_x: Optional[torch.Tensor] = None,
|
289 |
+
v_x: Optional[torch.Tensor] = None,
|
290 |
+
attn_mask: Optional[torch.Tensor] = None,
|
291 |
+
):
|
292 |
+
k_x = self.ln_1_kv(k_x) if hasattr(self, "ln_1_kv") and k_x is not None else None
|
293 |
+
v_x = self.ln_1_kv(v_x) if hasattr(self, "ln_1_kv") and v_x is not None else None
|
294 |
+
|
295 |
+
x = q_x + self.attention(q_x=self.ln_1(q_x), k_x=k_x, v_x=v_x, attn_mask=attn_mask)
|
296 |
+
x = x + self.mlp(self.ln_2(x))
|
297 |
+
return x
|
298 |
+
|
299 |
+
|
300 |
+
class TransformerBlock(nn.Module):
|
301 |
+
def __init__(
|
302 |
+
self,
|
303 |
+
width: int,
|
304 |
+
layers: int,
|
305 |
+
heads: int,
|
306 |
+
mlp_ratio: float = 4.0,
|
307 |
+
act_layer: Callable = nn.GELU,
|
308 |
+
norm_layer: Callable = nn.LayerNorm,
|
309 |
+
):
|
310 |
+
super().__init__()
|
311 |
+
self.width = width
|
312 |
+
self.layers = layers
|
313 |
+
|
314 |
+
self.resblocks = nn.ModuleList([
|
315 |
+
VisualAttentionBlock(
|
316 |
+
width, heads, mlp_ratio, act_layer=act_layer, norm_layer=norm_layer)
|
317 |
+
for _ in range(layers)
|
318 |
+
])
|
319 |
+
|
320 |
+
def get_cast_dtype(self) -> torch.dtype:
|
321 |
+
return self.resblocks[0].mlp.c_fc.weight.dtype
|
322 |
+
|
323 |
+
def get_cast_device(self) -> torch.device:
|
324 |
+
return self.resblocks[0].mlp.c_fc.weight.device
|
325 |
+
|
326 |
+
def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
|
327 |
+
for r in self.resblocks:
|
328 |
+
x = r(x, attn_mask=attn_mask)
|
329 |
+
return x
|
330 |
+
|
331 |
+
|
332 |
+
class VisionTransformer(nn.Module):
|
333 |
+
|
334 |
+
def __init__(
|
335 |
+
self,
|
336 |
+
image_size: int,
|
337 |
+
patch_size: int,
|
338 |
+
width: int,
|
339 |
+
layers: int,
|
340 |
+
heads: int,
|
341 |
+
mlp_ratio: float,
|
342 |
+
n_queries: int = 256,
|
343 |
+
output_dim: int = 512,
|
344 |
+
**kwargs
|
345 |
+
):
|
346 |
+
super().__init__()
|
347 |
+
image_height, image_width = self.image_size = (image_size, image_size)
|
348 |
+
patch_height, patch_width = self.patch_size = (patch_size, patch_size)
|
349 |
+
self.grid_size = (image_height // patch_height, image_width // patch_width)
|
350 |
+
self.output_dim = output_dim
|
351 |
+
|
352 |
+
mean = (0.48145466, 0.4578275, 0.40821073)
|
353 |
+
std = (0.26862954, 0.26130258, 0.27577711)
|
354 |
+
self.image_transform = transforms.Compose([
|
355 |
+
transforms.Resize(
|
356 |
+
(image_size, image_size),
|
357 |
+
interpolation=InterpolationMode.BICUBIC
|
358 |
+
),
|
359 |
+
transforms.ToTensor(),
|
360 |
+
transforms.Normalize(mean=mean, std=std),
|
361 |
+
])
|
362 |
+
|
363 |
+
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
|
364 |
+
|
365 |
+
# class embeddings and positional embeddings
|
366 |
+
scale = width ** -0.5
|
367 |
+
self.positional_embedding = nn.Parameter(scale * torch.randn(256, width))
|
368 |
+
|
369 |
+
norm_layer = partial(nn.LayerNorm, eps=1e-6)
|
370 |
+
act_layer = nn.GELU
|
371 |
+
|
372 |
+
self.ln_pre = norm_layer(width)
|
373 |
+
self.transformer = TransformerBlock(
|
374 |
+
width,
|
375 |
+
layers,
|
376 |
+
heads,
|
377 |
+
mlp_ratio,
|
378 |
+
act_layer=act_layer,
|
379 |
+
norm_layer=norm_layer,
|
380 |
+
)
|
381 |
+
|
382 |
+
self.attn_pool = Resampler(
|
383 |
+
grid_size=int(math.sqrt(n_queries)),
|
384 |
+
embed_dim=output_dim,
|
385 |
+
num_heads=output_dim // 128,
|
386 |
+
kv_dim=width,
|
387 |
+
norm_layer=norm_layer,
|
388 |
+
)
|
389 |
+
self.ln_post = norm_layer(output_dim)
|
390 |
+
self.proj = nn.Parameter((output_dim** -0.5) * torch.randn(output_dim, output_dim))
|
391 |
+
|
392 |
+
def forward(self, x: torch.Tensor):
|
393 |
+
x = x.to(
|
394 |
+
dtype=self.transformer.get_cast_dtype(),
|
395 |
+
device=self.transformer.get_cast_device(),
|
396 |
+
)
|
397 |
+
# to patches
|
398 |
+
x = self.conv1(x) # shape = [*, width, grid, grid]
|
399 |
+
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
|
400 |
+
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
|
401 |
+
|
402 |
+
x = x + get_abs_pos(self.positional_embedding, x.size(1))
|
403 |
+
|
404 |
+
x = self.ln_pre(x)
|
405 |
+
|
406 |
+
x = x.permute(1, 0, 2) # NLD -> LND
|
407 |
+
x = self.transformer(x)
|
408 |
+
x = x.permute(1, 0, 2) # LND -> NLD
|
409 |
+
|
410 |
+
x = self.attn_pool(x)
|
411 |
+
x = self.ln_post(x)
|
412 |
+
x = x @ self.proj
|
413 |
+
|
414 |
+
return x
|
415 |
+
|
416 |
+
def encode(self, image_paths: List[str]):
|
417 |
+
images = []
|
418 |
+
for image_path in image_paths:
|
419 |
+
if image_path.startswith("http://") or image_path.startswith("https://"):
|
420 |
+
image = Image.open(requests.get(image_path, stream=True).raw)
|
421 |
+
else:
|
422 |
+
image = Image.open(image_path)
|
423 |
+
image = image.convert("RGB")
|
424 |
+
images.append(self.image_transform(image))
|
425 |
+
images = torch.stack(images, dim=0)
|
426 |
+
return self(images)
|