markoarnauto
commited on
Commit
•
22183ef
1
Parent(s):
347294c
model
Browse files- __init__.py +0 -0
- config.json +1047 -0
- configuration_decilm.py +99 -0
- generation_config.json +10 -0
- model-00001-of-00011.safetensors +3 -0
- model-00002-of-00011.safetensors +3 -0
- model-00003-of-00011.safetensors +3 -0
- model-00004-of-00011.safetensors +3 -0
- model-00005-of-00011.safetensors +3 -0
- model-00006-of-00011.safetensors +3 -0
- model-00007-of-00011.safetensors +3 -0
- model-00008-of-00011.safetensors +3 -0
- model-00009-of-00011.safetensors +3 -0
- model-00010-of-00011.safetensors +3 -0
- model-00011-of-00011.safetensors +3 -0
- model.safetensors.index.json +1110 -0
- modeling_decilm.py +1709 -0
- recipe.yaml +6 -0
- transformers_4_44_2__activations.py +239 -0
- transformers_4_44_2__cache_utils.py +325 -0
- transformers_4_44_2__configuration_llama.py +203 -0
- transformers_4_44_2__modeling_attn_mask_utils.py +482 -0
- transformers_4_44_2__modeling_flash_attention_utils_backward_compat.py +348 -0
- transformers_4_44_2__modeling_outputs.py +0 -0
- transformers_4_44_2__modeling_rope_utils.py +559 -0
- transformers_4_44_2__pytorch_utils.py +17 -0
- variable_cache.py +113 -0
__init__.py
ADDED
File without changes
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config.json
ADDED
@@ -0,0 +1,1047 @@
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1 |
+
{
|
2 |
+
"_name_or_path": "nvidia/Llama-3_1-Nemotron-51B-Instruct",
|
3 |
+
"architectures": [
|
4 |
+
"DeciLMForCausalLM"
|
5 |
+
],
|
6 |
+
"attention_bias": false,
|
7 |
+
"attention_dropout": 0.0,
|
8 |
+
"auto_map": {
|
9 |
+
"AutoConfig": "nvidia/Llama-3_1-Nemotron-51B-Instruct--configuration_decilm.DeciLMConfig",
|
10 |
+
"AutoModelForCausalLM": "nvidia/Llama-3_1-Nemotron-51B-Instruct--modeling_decilm.DeciLMForCausalLM"
|
11 |
+
},
|
12 |
+
"block_configs": [
|
13 |
+
{
|
14 |
+
"attention": {
|
15 |
+
"n_heads_in_group": 8,
|
16 |
+
"no_op": false,
|
17 |
+
"replace_with_linear": false
|
18 |
+
},
|
19 |
+
"ffn": {
|
20 |
+
"ffn_mult": 1.3125,
|
21 |
+
"no_op": false,
|
22 |
+
"replace_with_linear": false
|
23 |
+
}
|
24 |
+
},
|
25 |
+
{
|
26 |
+
"attention": {
|
27 |
+
"n_heads_in_group": 16,
|
28 |
+
"no_op": false,
|
29 |
+
"replace_with_linear": false
|
30 |
+
},
|
31 |
+
"ffn": {
|
32 |
+
"ffn_mult": 2.625,
|
33 |
+
"no_op": false,
|
34 |
+
"replace_with_linear": false
|
35 |
+
}
|
36 |
+
},
|
37 |
+
{
|
38 |
+
"attention": {
|
39 |
+
"n_heads_in_group": 8,
|
40 |
+
"no_op": false,
|
41 |
+
"replace_with_linear": false
|
42 |
+
},
|
43 |
+
"ffn": {
|
44 |
+
"ffn_mult": 5.25,
|
45 |
+
"no_op": false,
|
46 |
+
"replace_with_linear": false
|
47 |
+
}
|
48 |
+
},
|
49 |
+
{
|
50 |
+
"attention": {
|
51 |
+
"n_heads_in_group": 8,
|
52 |
+
"no_op": false,
|
53 |
+
"replace_with_linear": false
|
54 |
+
},
|
55 |
+
"ffn": {
|
56 |
+
"ffn_mult": 5.25,
|
57 |
+
"no_op": false,
|
58 |
+
"replace_with_linear": false
|
59 |
+
}
|
60 |
+
},
|
61 |
+
{
|
62 |
+
"attention": {
|
63 |
+
"n_heads_in_group": 8,
|
64 |
+
"no_op": false,
|
65 |
+
"replace_with_linear": false
|
66 |
+
},
|
67 |
+
"ffn": {
|
68 |
+
"ffn_mult": 5.25,
|
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|
1003 |
+
"symmetric": true,
|
1004 |
+
"type": "float"
|
1005 |
+
},
|
1006 |
+
"output_activations": null,
|
1007 |
+
"targets": [
|
1008 |
+
"Linear"
|
1009 |
+
],
|
1010 |
+
"weights": {
|
1011 |
+
"actorder": null,
|
1012 |
+
"block_structure": null,
|
1013 |
+
"dynamic": false,
|
1014 |
+
"group_size": null,
|
1015 |
+
"num_bits": 8,
|
1016 |
+
"observer": "minmax",
|
1017 |
+
"observer_kwargs": {},
|
1018 |
+
"strategy": "channel",
|
1019 |
+
"symmetric": true,
|
1020 |
+
"type": "float"
|
1021 |
+
}
|
1022 |
+
}
|
1023 |
+
},
|
1024 |
+
"format": "float-quantized",
|
1025 |
+
"global_compression_ratio": 1.4496834038723068,
|
1026 |
+
"ignore": [
|
1027 |
+
"lm_head"
|
1028 |
+
],
|
1029 |
+
"kv_cache_scheme": null,
|
1030 |
+
"quant_method": "compressed-tensors",
|
1031 |
+
"quantization_status": "compressed"
|
1032 |
+
},
|
1033 |
+
"rms_norm_eps": 1e-05,
|
1034 |
+
"rope_scaling": {
|
1035 |
+
"factor": 8.0,
|
1036 |
+
"high_freq_factor": 4.0,
|
1037 |
+
"low_freq_factor": 1.0,
|
1038 |
+
"original_max_position_embeddings": 8192,
|
1039 |
+
"rope_type": "llama3"
|
1040 |
+
},
|
1041 |
+
"rope_theta": 500000.0,
|
1042 |
+
"tie_word_embeddings": false,
|
1043 |
+
"torch_dtype": "bfloat16",
|
1044 |
+
"transformers_version": "4.47.0",
|
1045 |
+
"use_cache": true,
|
1046 |
+
"vocab_size": 128256
|
1047 |
+
}
|
configuration_decilm.py
ADDED
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2024 Nvidia Corporation. All rights reserved.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
|
16 |
+
import dataclasses
|
17 |
+
import warnings
|
18 |
+
from dataclasses import dataclass, MISSING
|
19 |
+
from functools import partial
|
20 |
+
from typing import Optional, Dict, Any
|
21 |
+
|
22 |
+
from .transformers_4_44_2__configuration_llama import LlamaConfig
|
23 |
+
from .transformers_4_44_2__modeling_rope_utils import \
|
24 |
+
rope_config_validation # fake import to make AutoConfig infer the dependency
|
25 |
+
|
26 |
+
|
27 |
+
class DeciLMConfig(LlamaConfig):
|
28 |
+
model_type = "nemotron-nas"
|
29 |
+
|
30 |
+
def __init__(
|
31 |
+
self,
|
32 |
+
block_configs: list[dict] | list["BlockConfig"] = None,
|
33 |
+
**kwargs,
|
34 |
+
):
|
35 |
+
super().__init__(**kwargs)
|
36 |
+
self.intermediate_size = None
|
37 |
+
self.num_key_value_heads = None
|
38 |
+
|
39 |
+
if block_configs is not None:
|
40 |
+
assert len(block_configs) == self.num_hidden_layers
|
41 |
+
if isinstance(block_configs[0], dict):
|
42 |
+
block_configs = [BlockConfig(**conf) for conf in block_configs]
|
43 |
+
self.block_configs: list[BlockConfig] = block_configs
|
44 |
+
|
45 |
+
def to_dict(self) -> Dict[str, Any]:
|
46 |
+
self_dict = super().to_dict()
|
47 |
+
if self.block_configs is not None:
|
48 |
+
self_dict["block_configs"] = [dataclasses.asdict(conf) for conf in self.block_configs]
|
49 |
+
return self_dict
|
50 |
+
|
51 |
+
|
52 |
+
@partial(dataclass, frozen=True, eq=True, unsafe_hash=True, order=True)
|
53 |
+
class AttentionConfig:
|
54 |
+
no_op: bool = False
|
55 |
+
replace_with_linear: bool = False
|
56 |
+
n_heads_in_group: Optional[int] = None
|
57 |
+
|
58 |
+
def __post_init__(self):
|
59 |
+
assert not (self.no_op and self.replace_with_linear)
|
60 |
+
if self.no_op or self.replace_with_linear:
|
61 |
+
object.__setattr__(self, 'n_heads_in_group', None) # __setattr__ to overcome frozen=True
|
62 |
+
else:
|
63 |
+
assert self.n_heads_in_group is not None
|
64 |
+
|
65 |
+
|
66 |
+
@partial(dataclass, frozen=True, eq=True, unsafe_hash=True, order=True)
|
67 |
+
class FFNConfig:
|
68 |
+
no_op: bool = False
|
69 |
+
replace_with_linear: bool = False
|
70 |
+
ffn_mult: Optional[float] = None
|
71 |
+
|
72 |
+
def __post_init__(self):
|
73 |
+
assert not (self.no_op and self.replace_with_linear)
|
74 |
+
if self.no_op or self.replace_with_linear:
|
75 |
+
object.__setattr__(self, 'ffn_mult', None) # __setattr__ to overcome frozen=True
|
76 |
+
else:
|
77 |
+
assert self.ffn_mult is not None
|
78 |
+
|
79 |
+
|
80 |
+
@partial(dataclass, frozen=True, eq=True, unsafe_hash=True, order=True)
|
81 |
+
class BlockConfig:
|
82 |
+
attention: AttentionConfig = MISSING
|
83 |
+
ffn: FFNConfig = MISSING
|
84 |
+
|
85 |
+
def __post_init__(self):
|
86 |
+
"""
|
87 |
+
Init subblock dataclasses from dicts
|
88 |
+
"""
|
89 |
+
for subblock_name in dataclasses.fields(self):
|
90 |
+
subblock_config = getattr(self, subblock_name.name)
|
91 |
+
if isinstance(subblock_config, dict):
|
92 |
+
subblock_fields = [field.name for field in dataclasses.fields(subblock_name.type)]
|
93 |
+
unsupported_fields = [field_name for field_name in subblock_config.keys()
|
94 |
+
if field_name not in subblock_fields]
|
95 |
+
if len(unsupported_fields) > 0:
|
96 |
+
warnings.warn(f"Removed unsupported fields {unsupported_fields} from {subblock_name.type.__name__}")
|
97 |
+
subblock_config = {k: v for k, v in subblock_config.items() if k not in unsupported_fields}
|
98 |
+
object.__setattr__(self, subblock_name.name,
|
99 |
+
subblock_name.type(**subblock_config)) # __setattr__ to overcome frozen=True
|
generation_config.json
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"bos_token_id": 128000,
|
4 |
+
"eos_token_id": [
|
5 |
+
128001,
|
6 |
+
128008,
|
7 |
+
128009
|
8 |
+
],
|
9 |
+
"transformers_version": "4.47.0"
|
10 |
+
}
|
model-00001-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:86b4e1cf8165a3d2dabc3a30521d5938c3db690a1921a1fc1a33fb716cc33eae
|
3 |
+
size 4786436800
|
model-00002-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:47b5ebe6a53af41aca799b97e10bb9a90d01a0d1869a2a23fd1a5f87bed258fa
|
3 |
+
size 4927584624
|
model-00003-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:54bab02ef162a6198cff12b72cd6a9ccaba5ac46c2659febad6a284cdc70742d
|
3 |
+
size 4896002976
|
model-00004-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:8ae55f7375d8741e668a3000dabb3ca624f6e259659e5ed070d6b9c40017432f
|
3 |
+
size 4900104792
|
model-00005-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
|
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|
|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:93b80d7bc2c89e9c1f267f094bab4f2dff66794387ddf5bd87faa6c5490a8e6e
|
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size 4900104792
|
model-00006-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:4ee48e16f0c3ef9f46ddaea6cbee6acc845dec640d1cd65ef946dad09281a829
|
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+
size 4983994144
|
model-00007-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:986104d1572b49c79582f89578f43f37999302700015b8b89ad0a42f4aa5f939
|
3 |
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size 4849806496
|
model-00008-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:8df013bdbd2bb2bc6f123aad8cdc908bcf0714270bde4f9894fae97fccc8a922
|
3 |
+
size 4967428552
|
model-00009-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
|
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|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:ea2183caf19cc604cc03ae483dc78b566d614bd6c9b1070b4201cd3504f4837f
|
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+
size 4984457240
|
model-00010-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:68f68ce3ed8722add26123b0fd6a39c2fee84f96fa0813b3db02d8682d2d7366
|
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size 4900104792
|
model-00011-of-00011.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
|
|
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|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:38a032b98e29936f83f3cec13e3dd9359d54ac84e797f55636df798d8aebdb28
|
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size 4517819024
|
model.safetensors.index.json
ADDED
@@ -0,0 +1,1110 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
|
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|
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|
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|
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|
|
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|
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|
|
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|
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|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
|
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|
|
|
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|
|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
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|
|
|
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|
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|
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modeling_decilm.py
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1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2024 EleutherAI, HuggingFace Inc, Nvidia Corporation. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on the Llama modeling code by HuggingFace, which is in turn based on
|
5 |
+
# EleutherAI's GPT-NeoX library and the GPT-NeoX and OPT implementations in this library.
|
6 |
+
#
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+
# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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+
#
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+
# http://www.apache.org/licenses/LICENSE-2.0
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+
#
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+
# Unless required by applicable law or agreed to in writing, software
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+
# distributed under the License is distributed on an "AS IS" BASIS,
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+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+
# See the License for the specific language governing permissions and
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+
# limitations under the License.
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+
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+
import math
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+
from typing import List, Optional, Tuple, Union
|
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+
|
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+
import torch
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+
import torch.nn.functional as F
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+
import torch.utils.checkpoint
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+
from torch import nn
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+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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+
from transformers import GenerationConfig
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+
from transformers.generation.utils import NEED_SETUP_CACHE_CLASSES_MAPPING, GenerationMixin, GenerateOutput
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+
from transformers.modeling_utils import PreTrainedModel
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+
from transformers.utils import (
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+
add_start_docstrings,
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+
add_start_docstrings_to_model_forward,
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+
is_flash_attn_greater_or_equal_2_10,
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+
logging,
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+
replace_return_docstrings,
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+
)
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+
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
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+
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+
from .configuration_decilm import DeciLMConfig, AttentionConfig, FFNConfig
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+
from .transformers_4_44_2__activations import ACT2FN
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+
from .transformers_4_44_2__cache_utils import Cache, StaticCache
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+
from .transformers_4_44_2__modeling_attn_mask_utils import AttentionMaskConverter
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+
from .transformers_4_44_2__modeling_flash_attention_utils_backward_compat import _flash_attention_forward
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+
from .transformers_4_44_2__modeling_outputs import (
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+
BaseModelOutputWithPast,
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+
CausalLMOutputWithPast,
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+
QuestionAnsweringModelOutput,
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+
SequenceClassifierOutputWithPast,
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+
TokenClassifierOutput,
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+
)
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+
from .transformers_4_44_2__modeling_rope_utils import ROPE_INIT_FUNCTIONS
|
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+
from .transformers_4_44_2__pytorch_utils import ALL_LAYERNORM_LAYERS
|
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+
from .variable_cache import VariableCache
|
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+
|
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+
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[DeciLMConfig.model_type] = "DeciLMForCausalLM"
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+
logger = logging.get_logger(__name__)
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+
|
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+
_CONFIG_FOR_DOC = "DeciLMConfig"
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+
|
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+
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+
def _prepare_4d_causal_attention_mask_with_cache_position(
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+
attention_mask: torch.Tensor,
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+
sequence_length: int,
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+
target_length: int,
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+
dtype: torch.dtype,
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+
device: torch.device,
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+
min_dtype: float,
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+
cache_position: torch.Tensor,
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+
batch_size: int,
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+
):
|
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+
"""
|
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+
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
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+
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
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+
|
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+
Args:
|
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+
attention_mask (`torch.Tensor`):
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+
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
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+
sequence_length (`int`):
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+
The sequence length being processed.
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+
target_length (`int`):
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+
The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
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+
dtype (`torch.dtype`):
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+
The dtype to use for the 4D attention mask.
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+
device (`torch.device`):
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+
The device to plcae the 4D attention mask on.
|
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+
min_dtype (`float`):
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+
The minimum value representable with the dtype `dtype`.
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+
cache_position (`torch.Tensor`):
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+
Indices depicting the position of the input sequence tokens in the sequence.
|
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+
batch_size (`torch.Tensor`):
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+
Batch size.
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+
"""
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+
if attention_mask is not None and attention_mask.dim() == 4:
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+
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
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+
causal_mask = attention_mask
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+
else:
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+
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
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+
if sequence_length != 1:
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+
causal_mask = torch.triu(causal_mask, diagonal=1)
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+
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
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+
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
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+
if attention_mask is not None:
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+
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
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+
mask_length = attention_mask.shape[-1]
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+
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
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+
padding_mask = padding_mask == 0
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+
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
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+
padding_mask, min_dtype
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+
)
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+
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+
return causal_mask
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+
|
113 |
+
|
114 |
+
class DeciLMRMSNorm(nn.Module):
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+
def __init__(self, hidden_size, eps=1e-6):
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+
"""
|
117 |
+
DeciLMRMSNorm is equivalent to T5LayerNorm
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+
"""
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+
super().__init__()
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+
self.weight = nn.Parameter(torch.ones(hidden_size))
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+
self.variance_epsilon = eps
|
122 |
+
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+
def forward(self, hidden_states):
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+
input_dtype = hidden_states.dtype
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+
hidden_states = hidden_states.to(torch.float32)
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+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
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+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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+
return self.weight * hidden_states.to(input_dtype)
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129 |
+
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+
def extra_repr(self):
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+
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
132 |
+
|
133 |
+
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+
ALL_LAYERNORM_LAYERS.append(DeciLMRMSNorm)
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+
|
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+
|
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+
class DeciLMRotaryEmbedding(nn.Module):
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+
def __init__(
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+
self,
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+
dim=None,
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+
max_position_embeddings=2048,
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142 |
+
base=10000,
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143 |
+
device=None,
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144 |
+
scaling_factor=1.0,
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145 |
+
rope_type="default",
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146 |
+
config: Optional[DeciLMConfig] = None,
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147 |
+
):
|
148 |
+
super().__init__()
|
149 |
+
# TODO (joao): remove the `if` below, only used for BC
|
150 |
+
self.rope_kwargs = {}
|
151 |
+
if config is None:
|
152 |
+
logger.warning_once(
|
153 |
+
"`DeciLMRotaryEmbedding` can now be fully parameterized by passing the model config through the "
|
154 |
+
"`config` argument. All other arguments will be removed in v4.45"
|
155 |
+
)
|
156 |
+
self.rope_kwargs = {
|
157 |
+
"rope_type": rope_type,
|
158 |
+
"factor": scaling_factor,
|
159 |
+
"dim": dim,
|
160 |
+
"base": base,
|
161 |
+
"max_position_embeddings": max_position_embeddings,
|
162 |
+
}
|
163 |
+
self.rope_type = rope_type
|
164 |
+
self.max_seq_len_cached = max_position_embeddings
|
165 |
+
self.original_max_seq_len = max_position_embeddings
|
166 |
+
else:
|
167 |
+
# BC: "rope_type" was originally "type"
|
168 |
+
if config.rope_scaling is not None:
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169 |
+
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
170 |
+
else:
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171 |
+
self.rope_type = "default"
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172 |
+
self.max_seq_len_cached = config.max_position_embeddings
|
173 |
+
self.original_max_seq_len = config.max_position_embeddings
|
174 |
+
|
175 |
+
self.config = config
|
176 |
+
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
177 |
+
|
178 |
+
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
|
179 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
180 |
+
self.original_inv_freq = self.inv_freq
|
181 |
+
|
182 |
+
def _dynamic_frequency_update(self, position_ids, device):
|
183 |
+
"""
|
184 |
+
dynamic RoPE layers should recompute `inv_freq` in the following situations:
|
185 |
+
1 - growing beyond the cached sequence length (allow scaling)
|
186 |
+
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
|
187 |
+
"""
|
188 |
+
seq_len = torch.max(position_ids) + 1
|
189 |
+
if seq_len > self.max_seq_len_cached: # growth
|
190 |
+
inv_freq, self.attention_scaling = self.rope_init_fn(
|
191 |
+
self.config, device, seq_len=seq_len, **self.rope_kwargs
|
192 |
+
)
|
193 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
|
194 |
+
self.max_seq_len_cached = seq_len
|
195 |
+
|
196 |
+
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
|
197 |
+
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
|
198 |
+
self.max_seq_len_cached = self.original_max_seq_len
|
199 |
+
|
200 |
+
@torch.no_grad()
|
201 |
+
def forward(self, x, position_ids):
|
202 |
+
if "dynamic" in self.rope_type:
|
203 |
+
self._dynamic_frequency_update(position_ids, device=x.device)
|
204 |
+
|
205 |
+
# Core RoPE block
|
206 |
+
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
207 |
+
position_ids_expanded = position_ids[:, None, :].float()
|
208 |
+
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
|
209 |
+
device_type = x.device.type
|
210 |
+
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
|
211 |
+
with torch.autocast(device_type=device_type, enabled=False):
|
212 |
+
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
213 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
214 |
+
cos = emb.cos()
|
215 |
+
sin = emb.sin()
|
216 |
+
|
217 |
+
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
|
218 |
+
cos = cos * self.attention_scaling
|
219 |
+
sin = sin * self.attention_scaling
|
220 |
+
|
221 |
+
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
222 |
+
|
223 |
+
|
224 |
+
class DeciLMLinearScalingRotaryEmbedding(DeciLMRotaryEmbedding):
|
225 |
+
"""DeciLMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
226 |
+
|
227 |
+
def __init__(self, *args, **kwargs):
|
228 |
+
logger.warning_once(
|
229 |
+
"`DeciLMLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.45. Please use "
|
230 |
+
"`DeciLMRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)."
|
231 |
+
)
|
232 |
+
kwargs["rope_type"] = "linear"
|
233 |
+
super().__init__(*args, **kwargs)
|
234 |
+
|
235 |
+
|
236 |
+
class DeciLMDynamicNTKScalingRotaryEmbedding(DeciLMRotaryEmbedding):
|
237 |
+
"""DeciLMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
|
238 |
+
|
239 |
+
def __init__(self, *args, **kwargs):
|
240 |
+
logger.warning_once(
|
241 |
+
"`DeciLMDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.45. Please use "
|
242 |
+
"`DeciLMRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to "
|
243 |
+
"__init__)."
|
244 |
+
)
|
245 |
+
kwargs["rope_type"] = "dynamic"
|
246 |
+
super().__init__(*args, **kwargs)
|
247 |
+
|
248 |
+
|
249 |
+
def rotate_half(x):
|
250 |
+
"""Rotates half the hidden dims of the input."""
|
251 |
+
x1 = x[..., : x.shape[-1] // 2]
|
252 |
+
x2 = x[..., x.shape[-1] // 2:]
|
253 |
+
return torch.cat((-x2, x1), dim=-1)
|
254 |
+
|
255 |
+
|
256 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
257 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
258 |
+
|
259 |
+
Args:
|
260 |
+
q (`torch.Tensor`): The query tensor.
|
261 |
+
k (`torch.Tensor`): The key tensor.
|
262 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
263 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
264 |
+
position_ids (`torch.Tensor`, *optional*):
|
265 |
+
Deprecated and unused.
|
266 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
267 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
268 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
269 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
270 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
271 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
272 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
273 |
+
Returns:
|
274 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
275 |
+
"""
|
276 |
+
cos = cos.unsqueeze(unsqueeze_dim)
|
277 |
+
sin = sin.unsqueeze(unsqueeze_dim)
|
278 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
279 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
280 |
+
return q_embed, k_embed
|
281 |
+
|
282 |
+
|
283 |
+
class DeciLMMLP(nn.Module):
|
284 |
+
def __init__(self,
|
285 |
+
config: DeciLMConfig,
|
286 |
+
ffn_config: FFNConfig,
|
287 |
+
):
|
288 |
+
super().__init__()
|
289 |
+
self.config = config
|
290 |
+
self.hidden_size = config.hidden_size
|
291 |
+
self.intermediate_size = _ffn_mult_to_intermediate_size(
|
292 |
+
ffn_config.ffn_mult, config.hidden_size) # DeciLM-specific code
|
293 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
294 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
|
295 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
|
296 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
297 |
+
|
298 |
+
def forward(self, x):
|
299 |
+
if self.config.pretraining_tp > 1:
|
300 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
301 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
302 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
303 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
304 |
+
|
305 |
+
gate_proj = torch.cat(
|
306 |
+
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
|
307 |
+
)
|
308 |
+
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
309 |
+
|
310 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
311 |
+
down_proj = [
|
312 |
+
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
313 |
+
]
|
314 |
+
down_proj = sum(down_proj)
|
315 |
+
else:
|
316 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
317 |
+
|
318 |
+
return down_proj
|
319 |
+
|
320 |
+
|
321 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
322 |
+
"""
|
323 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
324 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
325 |
+
"""
|
326 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
327 |
+
if n_rep == 1:
|
328 |
+
return hidden_states
|
329 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
330 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
331 |
+
|
332 |
+
|
333 |
+
class DeciLMAttention(nn.Module):
|
334 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
335 |
+
|
336 |
+
def __init__(self,
|
337 |
+
config: DeciLMConfig,
|
338 |
+
attention_config: AttentionConfig,
|
339 |
+
layer_idx: Optional[int] = None,
|
340 |
+
):
|
341 |
+
super().__init__()
|
342 |
+
self.config = config
|
343 |
+
self.layer_idx = layer_idx
|
344 |
+
if layer_idx is None:
|
345 |
+
logger.warning_once(
|
346 |
+
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
|
347 |
+
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
|
348 |
+
"when creating this class."
|
349 |
+
)
|
350 |
+
|
351 |
+
self.attention_dropout = config.attention_dropout
|
352 |
+
self.hidden_size = config.hidden_size
|
353 |
+
self.num_heads = config.num_attention_heads
|
354 |
+
self.head_dim = self.hidden_size // self.num_heads
|
355 |
+
self.num_key_value_groups = attention_config.n_heads_in_group # DeciLM-specific code
|
356 |
+
self.num_key_value_heads = self.num_heads // self.num_key_value_groups # DeciLM-specific code
|
357 |
+
self.max_position_embeddings = config.max_position_embeddings
|
358 |
+
self.rope_theta = config.rope_theta
|
359 |
+
self.is_causal = True
|
360 |
+
|
361 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
362 |
+
raise ValueError(
|
363 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
364 |
+
f" and `num_heads`: {self.num_heads})."
|
365 |
+
)
|
366 |
+
|
367 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
368 |
+
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
369 |
+
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
|
370 |
+
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
|
371 |
+
|
372 |
+
# TODO (joao): remove in v4.45 (RoPE is computed in the model, not in the decoder layers)
|
373 |
+
self.rotary_emb = DeciLMRotaryEmbedding(config=self.config)
|
374 |
+
|
375 |
+
def forward(
|
376 |
+
self,
|
377 |
+
hidden_states: torch.Tensor,
|
378 |
+
attention_mask: Optional[torch.Tensor] = None,
|
379 |
+
position_ids: Optional[torch.LongTensor] = None,
|
380 |
+
past_key_value: Optional[Cache] = None,
|
381 |
+
output_attentions: bool = False,
|
382 |
+
use_cache: bool = False,
|
383 |
+
cache_position: Optional[torch.LongTensor] = None,
|
384 |
+
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
|
385 |
+
**kwargs,
|
386 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
387 |
+
bsz, q_len, _ = hidden_states.size()
|
388 |
+
if self.config.pretraining_tp > 1:
|
389 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
390 |
+
query_slices = self.q_proj.weight.split(
|
391 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
392 |
+
)
|
393 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
394 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
395 |
+
|
396 |
+
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
397 |
+
query_states = torch.cat(query_states, dim=-1)
|
398 |
+
|
399 |
+
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
400 |
+
key_states = torch.cat(key_states, dim=-1)
|
401 |
+
|
402 |
+
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
403 |
+
value_states = torch.cat(value_states, dim=-1)
|
404 |
+
|
405 |
+
else:
|
406 |
+
query_states = self.q_proj(hidden_states)
|
407 |
+
key_states = self.k_proj(hidden_states)
|
408 |
+
value_states = self.v_proj(hidden_states)
|
409 |
+
|
410 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
411 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
412 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
413 |
+
|
414 |
+
if position_embeddings is None:
|
415 |
+
logger.warning_once(
|
416 |
+
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
|
417 |
+
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
|
418 |
+
"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.45 `position_ids` will be "
|
419 |
+
"removed and `position_embeddings` will be mandatory."
|
420 |
+
)
|
421 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
422 |
+
else:
|
423 |
+
cos, sin = position_embeddings
|
424 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
425 |
+
|
426 |
+
if past_key_value is not None:
|
427 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
428 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
429 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
430 |
+
|
431 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
432 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
433 |
+
|
434 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
435 |
+
|
436 |
+
if attention_mask is not None: # no matter the length, we just slice it
|
437 |
+
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
438 |
+
attn_weights = attn_weights + causal_mask
|
439 |
+
|
440 |
+
# upcast attention to fp32
|
441 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
442 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
443 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
444 |
+
|
445 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
446 |
+
raise ValueError(
|
447 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
448 |
+
f" {attn_output.size()}"
|
449 |
+
)
|
450 |
+
|
451 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
452 |
+
|
453 |
+
attn_output = attn_output.reshape(bsz, q_len, -1)
|
454 |
+
|
455 |
+
if self.config.pretraining_tp > 1:
|
456 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
457 |
+
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
458 |
+
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
459 |
+
else:
|
460 |
+
attn_output = self.o_proj(attn_output)
|
461 |
+
|
462 |
+
if not output_attentions:
|
463 |
+
attn_weights = None
|
464 |
+
|
465 |
+
return attn_output, attn_weights, past_key_value
|
466 |
+
|
467 |
+
|
468 |
+
class DeciLMFlashAttention2(DeciLMAttention):
|
469 |
+
"""
|
470 |
+
DeciLM flash attention module. This module inherits from `DeciLMAttention` as the weights of the module stays
|
471 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
472 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
473 |
+
"""
|
474 |
+
|
475 |
+
def __init__(self, *args, **kwargs):
|
476 |
+
super().__init__(*args, **kwargs)
|
477 |
+
|
478 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
479 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
480 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
481 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
482 |
+
|
483 |
+
def forward(
|
484 |
+
self,
|
485 |
+
hidden_states: torch.Tensor,
|
486 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
487 |
+
position_ids: Optional[torch.LongTensor] = None,
|
488 |
+
past_key_value: Optional[Cache] = None,
|
489 |
+
output_attentions: bool = False,
|
490 |
+
use_cache: bool = False,
|
491 |
+
cache_position: Optional[torch.LongTensor] = None,
|
492 |
+
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
|
493 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
494 |
+
if isinstance(past_key_value, StaticCache):
|
495 |
+
raise ValueError(
|
496 |
+
"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
|
497 |
+
"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
|
498 |
+
)
|
499 |
+
output_attentions = False
|
500 |
+
|
501 |
+
bsz, q_len, _ = hidden_states.size()
|
502 |
+
|
503 |
+
query_states = self.q_proj(hidden_states)
|
504 |
+
key_states = self.k_proj(hidden_states)
|
505 |
+
value_states = self.v_proj(hidden_states)
|
506 |
+
|
507 |
+
# Flash attention requires the input to have the shape
|
508 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
509 |
+
# therefore we just need to keep the original shape
|
510 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
511 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
512 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
513 |
+
|
514 |
+
if position_embeddings is None:
|
515 |
+
logger.warning_once(
|
516 |
+
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
|
517 |
+
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
|
518 |
+
"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.45 `position_ids` will be "
|
519 |
+
"removed and `position_embeddings` will be mandatory."
|
520 |
+
)
|
521 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
522 |
+
else:
|
523 |
+
cos, sin = position_embeddings
|
524 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
525 |
+
|
526 |
+
if past_key_value is not None:
|
527 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
528 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
529 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
530 |
+
|
531 |
+
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
|
532 |
+
# to be able to avoid many of these transpose/reshape/view.
|
533 |
+
query_states = query_states.transpose(1, 2)
|
534 |
+
key_states = key_states.transpose(1, 2)
|
535 |
+
value_states = value_states.transpose(1, 2)
|
536 |
+
|
537 |
+
dropout_rate = self.attention_dropout if self.training else 0.0
|
538 |
+
|
539 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
540 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
541 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
542 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
543 |
+
# in fp32. (DeciLMRMSNorm handles it correctly)
|
544 |
+
|
545 |
+
input_dtype = query_states.dtype
|
546 |
+
if input_dtype == torch.float32:
|
547 |
+
if torch.is_autocast_enabled():
|
548 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
549 |
+
# Handle the case where the model is quantized
|
550 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
551 |
+
target_dtype = self.config._pre_quantization_dtype
|
552 |
+
else:
|
553 |
+
target_dtype = self.q_proj.weight.dtype
|
554 |
+
|
555 |
+
logger.warning_once(
|
556 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
557 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
558 |
+
f" {target_dtype}."
|
559 |
+
)
|
560 |
+
|
561 |
+
query_states = query_states.to(target_dtype)
|
562 |
+
key_states = key_states.to(target_dtype)
|
563 |
+
value_states = value_states.to(target_dtype)
|
564 |
+
|
565 |
+
attn_output = _flash_attention_forward(
|
566 |
+
query_states,
|
567 |
+
key_states,
|
568 |
+
value_states,
|
569 |
+
attention_mask,
|
570 |
+
q_len,
|
571 |
+
position_ids=position_ids,
|
572 |
+
dropout=dropout_rate,
|
573 |
+
sliding_window=getattr(self, "sliding_window", None),
|
574 |
+
use_top_left_mask=self._flash_attn_uses_top_left_mask,
|
575 |
+
is_causal=self.is_causal,
|
576 |
+
)
|
577 |
+
|
578 |
+
attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
|
579 |
+
attn_output = self.o_proj(attn_output)
|
580 |
+
|
581 |
+
if not output_attentions:
|
582 |
+
attn_weights = None
|
583 |
+
|
584 |
+
return attn_output, attn_weights, past_key_value
|
585 |
+
|
586 |
+
|
587 |
+
class DeciLMSdpaAttention(DeciLMAttention):
|
588 |
+
"""
|
589 |
+
DeciLM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
590 |
+
`DeciLMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
591 |
+
SDPA API.
|
592 |
+
"""
|
593 |
+
|
594 |
+
# Adapted from DeciLMAttention.forward
|
595 |
+
def forward(
|
596 |
+
self,
|
597 |
+
hidden_states: torch.Tensor,
|
598 |
+
attention_mask: Optional[torch.Tensor] = None,
|
599 |
+
position_ids: Optional[torch.LongTensor] = None,
|
600 |
+
past_key_value: Optional[Cache] = None,
|
601 |
+
output_attentions: bool = False,
|
602 |
+
use_cache: bool = False,
|
603 |
+
cache_position: Optional[torch.LongTensor] = None,
|
604 |
+
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
|
605 |
+
**kwargs,
|
606 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
607 |
+
if output_attentions:
|
608 |
+
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
609 |
+
logger.warning_once(
|
610 |
+
"DeciLMModel is using DeciLMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
611 |
+
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
612 |
+
)
|
613 |
+
return super().forward(
|
614 |
+
hidden_states=hidden_states,
|
615 |
+
attention_mask=attention_mask,
|
616 |
+
position_ids=position_ids,
|
617 |
+
past_key_value=past_key_value,
|
618 |
+
output_attentions=output_attentions,
|
619 |
+
use_cache=use_cache,
|
620 |
+
cache_position=cache_position,
|
621 |
+
position_embeddings=position_embeddings,
|
622 |
+
)
|
623 |
+
|
624 |
+
bsz, q_len, _ = hidden_states.size()
|
625 |
+
|
626 |
+
query_states = self.q_proj(hidden_states)
|
627 |
+
key_states = self.k_proj(hidden_states)
|
628 |
+
value_states = self.v_proj(hidden_states)
|
629 |
+
|
630 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
631 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
632 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
633 |
+
|
634 |
+
if position_embeddings is None:
|
635 |
+
logger.warning_once(
|
636 |
+
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
|
637 |
+
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
|
638 |
+
"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.45 `position_ids` will be "
|
639 |
+
"removed and `position_embeddings` will be mandatory."
|
640 |
+
)
|
641 |
+
cos, sin = self.rotary_emb(value_states, position_ids)
|
642 |
+
else:
|
643 |
+
cos, sin = position_embeddings
|
644 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
645 |
+
|
646 |
+
if past_key_value is not None:
|
647 |
+
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
648 |
+
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
649 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
650 |
+
|
651 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
652 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
653 |
+
|
654 |
+
causal_mask = attention_mask
|
655 |
+
if attention_mask is not None:
|
656 |
+
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
657 |
+
|
658 |
+
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
659 |
+
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
660 |
+
if query_states.device.type == "cuda" and causal_mask is not None:
|
661 |
+
query_states = query_states.contiguous()
|
662 |
+
key_states = key_states.contiguous()
|
663 |
+
value_states = value_states.contiguous()
|
664 |
+
|
665 |
+
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
666 |
+
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
667 |
+
is_causal = True if causal_mask is None and q_len > 1 else False
|
668 |
+
|
669 |
+
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
670 |
+
query_states,
|
671 |
+
key_states,
|
672 |
+
value_states,
|
673 |
+
attn_mask=causal_mask,
|
674 |
+
dropout_p=self.attention_dropout if self.training else 0.0,
|
675 |
+
is_causal=is_causal,
|
676 |
+
)
|
677 |
+
|
678 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
679 |
+
attn_output = attn_output.view(bsz, q_len, -1)
|
680 |
+
|
681 |
+
attn_output = self.o_proj(attn_output)
|
682 |
+
|
683 |
+
return attn_output, None, past_key_value
|
684 |
+
|
685 |
+
|
686 |
+
DECILM_ATTENTION_CLASSES = {
|
687 |
+
"eager": DeciLMAttention,
|
688 |
+
"flash_attention_2": DeciLMFlashAttention2,
|
689 |
+
"sdpa": DeciLMSdpaAttention,
|
690 |
+
}
|
691 |
+
|
692 |
+
|
693 |
+
class DeciLMDecoderLayer(nn.Module):
|
694 |
+
# DeciLM-specific code
|
695 |
+
def __init__(self, config: DeciLMConfig, layer_idx: int):
|
696 |
+
super().__init__()
|
697 |
+
self.hidden_size = config.hidden_size
|
698 |
+
self.block_config = config.block_configs[layer_idx]
|
699 |
+
|
700 |
+
if not self.block_config.attention.no_op:
|
701 |
+
self.input_layernorm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
702 |
+
if not self.block_config.attention.replace_with_linear:
|
703 |
+
self.self_attn = DECILM_ATTENTION_CLASSES[config._attn_implementation](
|
704 |
+
config=config, attention_config=self.block_config.attention, layer_idx=layer_idx)
|
705 |
+
else:
|
706 |
+
self.self_attn = DeciLMLinearAttention(config)
|
707 |
+
|
708 |
+
if not self.block_config.ffn.no_op:
|
709 |
+
self.post_attention_layernorm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
710 |
+
if not self.block_config.ffn.replace_with_linear:
|
711 |
+
self.mlp = DeciLMMLP(config, self.block_config.ffn)
|
712 |
+
else:
|
713 |
+
self.mlp = DeciLMLinearMLP(config)
|
714 |
+
|
715 |
+
def forward(
|
716 |
+
self,
|
717 |
+
hidden_states: torch.Tensor,
|
718 |
+
attention_mask: Optional[torch.Tensor] = None,
|
719 |
+
position_ids: Optional[torch.LongTensor] = None,
|
720 |
+
past_key_value: Optional[Cache] = None,
|
721 |
+
output_attentions: Optional[bool] = False,
|
722 |
+
use_cache: Optional[bool] = False,
|
723 |
+
cache_position: Optional[torch.LongTensor] = None,
|
724 |
+
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
|
725 |
+
**kwargs,
|
726 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
727 |
+
"""
|
728 |
+
Args:
|
729 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
730 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
731 |
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
732 |
+
query_sequence_length, key_sequence_length)` if default attention is used.
|
733 |
+
output_attentions (`bool`, *optional*):
|
734 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
735 |
+
returned tensors for more detail.
|
736 |
+
use_cache (`bool`, *optional*):
|
737 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
738 |
+
(see `past_key_values`).
|
739 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
740 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
741 |
+
Indices depicting the position of the input sequence tokens in the sequence
|
742 |
+
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
|
743 |
+
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
744 |
+
with `head_dim` being the embedding dimension of each attention head.
|
745 |
+
kwargs (`dict`, *optional*):
|
746 |
+
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
747 |
+
into the model
|
748 |
+
"""
|
749 |
+
self_attn_weights = None
|
750 |
+
present_key_value = past_key_value
|
751 |
+
if self.block_config.attention.no_op:
|
752 |
+
pass
|
753 |
+
elif self.block_config.attention.replace_with_linear:
|
754 |
+
residual = hidden_states
|
755 |
+
hidden_states = self.input_layernorm(hidden_states)
|
756 |
+
hidden_states = self.self_attn(hidden_states)
|
757 |
+
hidden_states = residual + hidden_states
|
758 |
+
else:
|
759 |
+
residual = hidden_states
|
760 |
+
hidden_states = self.input_layernorm(hidden_states)
|
761 |
+
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
762 |
+
hidden_states=hidden_states,
|
763 |
+
attention_mask=attention_mask,
|
764 |
+
position_ids=position_ids,
|
765 |
+
past_key_value=past_key_value,
|
766 |
+
output_attentions=output_attentions,
|
767 |
+
use_cache=use_cache,
|
768 |
+
cache_position=cache_position,
|
769 |
+
position_embeddings=position_embeddings,
|
770 |
+
**kwargs,
|
771 |
+
)
|
772 |
+
hidden_states = residual + hidden_states
|
773 |
+
|
774 |
+
if not self.block_config.ffn.no_op:
|
775 |
+
residual = hidden_states
|
776 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
777 |
+
hidden_states = self.mlp(hidden_states)
|
778 |
+
hidden_states = residual + hidden_states
|
779 |
+
|
780 |
+
outputs = (hidden_states,)
|
781 |
+
|
782 |
+
if output_attentions:
|
783 |
+
outputs += (self_attn_weights,)
|
784 |
+
|
785 |
+
if use_cache:
|
786 |
+
outputs += (present_key_value,)
|
787 |
+
|
788 |
+
return outputs
|
789 |
+
|
790 |
+
|
791 |
+
DECILM_START_DOCSTRING = r"""
|
792 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
793 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
794 |
+
etc.)
|
795 |
+
|
796 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
797 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
798 |
+
and behavior.
|
799 |
+
|
800 |
+
Parameters:
|
801 |
+
config ([`DeciLMConfig`]):
|
802 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
803 |
+
load the weights associated with the model, only the configuration. Check out the
|
804 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
805 |
+
"""
|
806 |
+
|
807 |
+
|
808 |
+
@add_start_docstrings(
|
809 |
+
"The bare DeciLM Model outputting raw hidden-states without any specific head on top.",
|
810 |
+
DECILM_START_DOCSTRING,
|
811 |
+
)
|
812 |
+
class DeciLMPreTrainedModel(PreTrainedModel):
|
813 |
+
config_class = DeciLMConfig
|
814 |
+
base_model_prefix = "model"
|
815 |
+
supports_gradient_checkpointing = True
|
816 |
+
_no_split_modules = ["DeciLMDecoderLayer"]
|
817 |
+
_skip_keys_device_placement = ["past_key_values"]
|
818 |
+
_supports_flash_attn_2 = True
|
819 |
+
_supports_sdpa = True
|
820 |
+
_supports_cache_class = True
|
821 |
+
_supports_quantized_cache = True
|
822 |
+
_supports_static_cache = True
|
823 |
+
|
824 |
+
def _init_weights(self, module):
|
825 |
+
std = self.config.initializer_range
|
826 |
+
if isinstance(module, nn.Linear):
|
827 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
828 |
+
if module.bias is not None:
|
829 |
+
module.bias.data.zero_()
|
830 |
+
elif isinstance(module, nn.Embedding):
|
831 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
832 |
+
if module.padding_idx is not None:
|
833 |
+
module.weight.data[module.padding_idx].zero_()
|
834 |
+
|
835 |
+
def _prepare_generation_config(
|
836 |
+
self, generation_config: Optional[GenerationConfig], **kwargs: dict
|
837 |
+
) -> tuple[GenerationConfig, dict]:
|
838 |
+
# DeciLM-specific code
|
839 |
+
generation_config, model_kwargs = super()._prepare_generation_config(generation_config, **kwargs)
|
840 |
+
generation_config.cache_implementation = "variable"
|
841 |
+
NEED_SETUP_CACHE_CLASSES_MAPPING["variable"] = VariableCache
|
842 |
+
return generation_config, model_kwargs
|
843 |
+
|
844 |
+
|
845 |
+
DECILM_INPUTS_DOCSTRING = r"""
|
846 |
+
Args:
|
847 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
848 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
849 |
+
it.
|
850 |
+
|
851 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
852 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
853 |
+
|
854 |
+
[What are input IDs?](../glossary#input-ids)
|
855 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
856 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
857 |
+
|
858 |
+
- 1 for tokens that are **not masked**,
|
859 |
+
- 0 for tokens that are **masked**.
|
860 |
+
|
861 |
+
[What are attention masks?](../glossary#attention-mask)
|
862 |
+
|
863 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
864 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
865 |
+
|
866 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
867 |
+
`past_key_values`).
|
868 |
+
|
869 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
870 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
871 |
+
information on the default strategy.
|
872 |
+
|
873 |
+
- 1 indicates the head is **not masked**,
|
874 |
+
- 0 indicates the head is **masked**.
|
875 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
876 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
877 |
+
config.n_positions - 1]`.
|
878 |
+
|
879 |
+
[What are position IDs?](../glossary#position-ids)
|
880 |
+
past_key_values (`VariableCache`, *optional*):
|
881 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
882 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
883 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
884 |
+
|
885 |
+
If passed to the forward function, past_key_values must be a VariableCache object (see imports).
|
886 |
+
For generation purposes, this is already handled inside model.generate().
|
887 |
+
|
888 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
889 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
890 |
+
of shape `(batch_size, sequence_length)`.
|
891 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
892 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
893 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
894 |
+
model's internal embedding lookup matrix.
|
895 |
+
use_cache (`bool`, *optional*):
|
896 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
897 |
+
`past_key_values`).
|
898 |
+
output_attentions (`bool`, *optional*):
|
899 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
900 |
+
tensors for more detail.
|
901 |
+
output_hidden_states (`bool`, *optional*):
|
902 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
903 |
+
more detail.
|
904 |
+
return_dict (`bool`, *optional*):
|
905 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
906 |
+
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
907 |
+
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
908 |
+
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
909 |
+
the complete sequence length.
|
910 |
+
"""
|
911 |
+
|
912 |
+
|
913 |
+
@add_start_docstrings(
|
914 |
+
"The bare DeciLM Model outputting raw hidden-states without any specific head on top.",
|
915 |
+
DECILM_START_DOCSTRING,
|
916 |
+
)
|
917 |
+
class DeciLMModel(DeciLMPreTrainedModel):
|
918 |
+
"""
|
919 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeciLMDecoderLayer`]
|
920 |
+
|
921 |
+
Args:
|
922 |
+
config: DeciLMConfig
|
923 |
+
"""
|
924 |
+
|
925 |
+
def __init__(self, config: DeciLMConfig):
|
926 |
+
super().__init__(config)
|
927 |
+
self.padding_idx = config.pad_token_id
|
928 |
+
self.vocab_size = config.vocab_size
|
929 |
+
|
930 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
931 |
+
self.layers = nn.ModuleList(
|
932 |
+
[DeciLMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
933 |
+
)
|
934 |
+
self.norm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
935 |
+
self.rotary_emb = DeciLMRotaryEmbedding(config=config)
|
936 |
+
self.gradient_checkpointing = False
|
937 |
+
|
938 |
+
# Initialize weights and apply final processing
|
939 |
+
self.post_init()
|
940 |
+
|
941 |
+
def get_input_embeddings(self):
|
942 |
+
return self.embed_tokens
|
943 |
+
|
944 |
+
def set_input_embeddings(self, value):
|
945 |
+
self.embed_tokens = value
|
946 |
+
|
947 |
+
@add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
|
948 |
+
def forward(
|
949 |
+
self,
|
950 |
+
input_ids: torch.LongTensor = None,
|
951 |
+
attention_mask: Optional[torch.Tensor] = None,
|
952 |
+
position_ids: Optional[torch.LongTensor] = None,
|
953 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
954 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
955 |
+
use_cache: Optional[bool] = None,
|
956 |
+
output_attentions: Optional[bool] = None,
|
957 |
+
output_hidden_states: Optional[bool] = None,
|
958 |
+
return_dict: Optional[bool] = None,
|
959 |
+
cache_position: Optional[torch.LongTensor] = None,
|
960 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
961 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
962 |
+
output_hidden_states = (
|
963 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
964 |
+
)
|
965 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
966 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
967 |
+
|
968 |
+
if (input_ids is None) ^ (inputs_embeds is not None):
|
969 |
+
raise ValueError(
|
970 |
+
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
|
971 |
+
)
|
972 |
+
|
973 |
+
if self.gradient_checkpointing and self.training and use_cache:
|
974 |
+
logger.warning_once(
|
975 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
976 |
+
)
|
977 |
+
use_cache = False
|
978 |
+
|
979 |
+
if inputs_embeds is None:
|
980 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
981 |
+
|
982 |
+
is_legacy_cache_format = (past_key_values is not None) and not isinstance(past_key_values, Cache)
|
983 |
+
if is_legacy_cache_format:
|
984 |
+
raise NotImplementedError("DeciLMModel does not support legacy cache format, please use a newer "
|
985 |
+
"transformers version or use VariableCache explicitly (see import in this file).")
|
986 |
+
|
987 |
+
if cache_position is None:
|
988 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
989 |
+
cache_position = torch.arange(
|
990 |
+
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
991 |
+
)
|
992 |
+
if position_ids is None:
|
993 |
+
position_ids = cache_position.unsqueeze(0)
|
994 |
+
|
995 |
+
causal_mask = self._update_causal_mask(
|
996 |
+
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
997 |
+
)
|
998 |
+
hidden_states = inputs_embeds
|
999 |
+
|
1000 |
+
# create position embeddings to be shared across the decoder layers
|
1001 |
+
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
1002 |
+
|
1003 |
+
# decoder layers
|
1004 |
+
all_hidden_states = () if output_hidden_states else None
|
1005 |
+
all_self_attns = () if output_attentions else None
|
1006 |
+
next_decoder_cache = None
|
1007 |
+
|
1008 |
+
for decoder_layer in self.layers:
|
1009 |
+
if output_hidden_states:
|
1010 |
+
all_hidden_states += (hidden_states,)
|
1011 |
+
|
1012 |
+
if self.gradient_checkpointing and self.training:
|
1013 |
+
layer_outputs = self._gradient_checkpointing_func(
|
1014 |
+
decoder_layer.__call__,
|
1015 |
+
hidden_states,
|
1016 |
+
causal_mask,
|
1017 |
+
position_ids,
|
1018 |
+
past_key_values,
|
1019 |
+
output_attentions,
|
1020 |
+
use_cache,
|
1021 |
+
cache_position,
|
1022 |
+
position_embeddings,
|
1023 |
+
)
|
1024 |
+
else:
|
1025 |
+
layer_outputs = decoder_layer(
|
1026 |
+
hidden_states,
|
1027 |
+
attention_mask=causal_mask,
|
1028 |
+
position_ids=position_ids,
|
1029 |
+
past_key_value=past_key_values,
|
1030 |
+
output_attentions=output_attentions,
|
1031 |
+
use_cache=use_cache,
|
1032 |
+
cache_position=cache_position,
|
1033 |
+
position_embeddings=position_embeddings,
|
1034 |
+
)
|
1035 |
+
|
1036 |
+
hidden_states = layer_outputs[0]
|
1037 |
+
|
1038 |
+
if use_cache:
|
1039 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
1040 |
+
|
1041 |
+
if output_attentions:
|
1042 |
+
all_self_attns += (layer_outputs[1],)
|
1043 |
+
|
1044 |
+
hidden_states = self.norm(hidden_states)
|
1045 |
+
|
1046 |
+
# add hidden states from the last decoder layer
|
1047 |
+
if output_hidden_states:
|
1048 |
+
all_hidden_states += (hidden_states,)
|
1049 |
+
|
1050 |
+
next_cache = next_decoder_cache if use_cache else None
|
1051 |
+
|
1052 |
+
if not return_dict:
|
1053 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
1054 |
+
return BaseModelOutputWithPast(
|
1055 |
+
last_hidden_state=hidden_states,
|
1056 |
+
past_key_values=next_cache,
|
1057 |
+
hidden_states=all_hidden_states,
|
1058 |
+
attentions=all_self_attns,
|
1059 |
+
)
|
1060 |
+
|
1061 |
+
def _update_causal_mask(
|
1062 |
+
self,
|
1063 |
+
attention_mask: torch.Tensor,
|
1064 |
+
input_tensor: torch.Tensor,
|
1065 |
+
cache_position: torch.Tensor,
|
1066 |
+
past_key_values: Cache,
|
1067 |
+
output_attentions: bool,
|
1068 |
+
):
|
1069 |
+
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
|
1070 |
+
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
|
1071 |
+
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
|
1072 |
+
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
|
1073 |
+
|
1074 |
+
if self.config._attn_implementation == "flash_attention_2":
|
1075 |
+
if attention_mask is not None and 0.0 in attention_mask:
|
1076 |
+
return attention_mask
|
1077 |
+
return None
|
1078 |
+
|
1079 |
+
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
|
1080 |
+
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
|
1081 |
+
# to infer the attention mask.
|
1082 |
+
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
1083 |
+
assert not isinstance(past_key_values, StaticCache), "DeciLM does not support StaticCache"
|
1084 |
+
using_static_cache = isinstance(past_key_values, StaticCache)
|
1085 |
+
|
1086 |
+
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
|
1087 |
+
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
|
1088 |
+
if AttentionMaskConverter._ignore_causal_mask_sdpa(
|
1089 |
+
attention_mask,
|
1090 |
+
inputs_embeds=input_tensor,
|
1091 |
+
past_key_values_length=past_seen_tokens,
|
1092 |
+
is_training=self.training,
|
1093 |
+
):
|
1094 |
+
return None
|
1095 |
+
|
1096 |
+
dtype, device = input_tensor.dtype, input_tensor.device
|
1097 |
+
min_dtype = torch.finfo(dtype).min
|
1098 |
+
sequence_length = input_tensor.shape[1]
|
1099 |
+
if using_static_cache:
|
1100 |
+
target_length = past_key_values.get_max_length()
|
1101 |
+
else:
|
1102 |
+
target_length = (
|
1103 |
+
attention_mask.shape[-1]
|
1104 |
+
if isinstance(attention_mask, torch.Tensor)
|
1105 |
+
else past_seen_tokens + sequence_length + 1
|
1106 |
+
)
|
1107 |
+
|
1108 |
+
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
|
1109 |
+
causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
1110 |
+
attention_mask,
|
1111 |
+
sequence_length=sequence_length,
|
1112 |
+
target_length=target_length,
|
1113 |
+
dtype=dtype,
|
1114 |
+
device=device,
|
1115 |
+
min_dtype=min_dtype,
|
1116 |
+
cache_position=cache_position,
|
1117 |
+
batch_size=input_tensor.shape[0],
|
1118 |
+
)
|
1119 |
+
|
1120 |
+
if (
|
1121 |
+
self.config._attn_implementation == "sdpa"
|
1122 |
+
and attention_mask is not None
|
1123 |
+
and attention_mask.device.type == "cuda"
|
1124 |
+
and not output_attentions
|
1125 |
+
):
|
1126 |
+
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
|
1127 |
+
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
1128 |
+
# Details: https://github.com/pytorch/pytorch/issues/110213
|
1129 |
+
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
|
1130 |
+
|
1131 |
+
return causal_mask
|
1132 |
+
|
1133 |
+
|
1134 |
+
class DeciLMForCausalLM(DeciLMPreTrainedModel, GenerationMixin):
|
1135 |
+
_tied_weights_keys = ["lm_head.weight"]
|
1136 |
+
|
1137 |
+
def __init__(self, config):
|
1138 |
+
super().__init__(config)
|
1139 |
+
self.model = DeciLMModel(config)
|
1140 |
+
self.vocab_size = config.vocab_size
|
1141 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
1142 |
+
|
1143 |
+
# Initialize weights and apply final processing
|
1144 |
+
self.post_init()
|
1145 |
+
|
1146 |
+
def get_input_embeddings(self):
|
1147 |
+
return self.model.embed_tokens
|
1148 |
+
|
1149 |
+
def set_input_embeddings(self, value):
|
1150 |
+
self.model.embed_tokens = value
|
1151 |
+
|
1152 |
+
def get_output_embeddings(self):
|
1153 |
+
return self.lm_head
|
1154 |
+
|
1155 |
+
def set_output_embeddings(self, new_embeddings):
|
1156 |
+
self.lm_head = new_embeddings
|
1157 |
+
|
1158 |
+
def set_decoder(self, decoder):
|
1159 |
+
self.model = decoder
|
1160 |
+
|
1161 |
+
def get_decoder(self):
|
1162 |
+
return self.model
|
1163 |
+
|
1164 |
+
@add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
|
1165 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
1166 |
+
def forward(
|
1167 |
+
self,
|
1168 |
+
input_ids: torch.LongTensor = None,
|
1169 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1170 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1171 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
1172 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1173 |
+
labels: Optional[torch.LongTensor] = None,
|
1174 |
+
use_cache: Optional[bool] = None,
|
1175 |
+
output_attentions: Optional[bool] = None,
|
1176 |
+
output_hidden_states: Optional[bool] = None,
|
1177 |
+
return_dict: Optional[bool] = None,
|
1178 |
+
cache_position: Optional[torch.LongTensor] = None,
|
1179 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1180 |
+
r"""
|
1181 |
+
Args:
|
1182 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1183 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
1184 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1185 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
1186 |
+
|
1187 |
+
Return:
|
1188 |
+
"""
|
1189 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1190 |
+
output_hidden_states = (
|
1191 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1192 |
+
)
|
1193 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1194 |
+
|
1195 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1196 |
+
outputs = self.model(
|
1197 |
+
input_ids=input_ids,
|
1198 |
+
attention_mask=attention_mask,
|
1199 |
+
position_ids=position_ids,
|
1200 |
+
past_key_values=past_key_values,
|
1201 |
+
inputs_embeds=inputs_embeds,
|
1202 |
+
use_cache=use_cache,
|
1203 |
+
output_attentions=output_attentions,
|
1204 |
+
output_hidden_states=output_hidden_states,
|
1205 |
+
return_dict=return_dict,
|
1206 |
+
cache_position=cache_position,
|
1207 |
+
)
|
1208 |
+
|
1209 |
+
hidden_states = outputs[0]
|
1210 |
+
if self.config.pretraining_tp > 1:
|
1211 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
1212 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
1213 |
+
logits = torch.cat(logits, dim=-1)
|
1214 |
+
else:
|
1215 |
+
logits = self.lm_head(hidden_states)
|
1216 |
+
logits = logits.float()
|
1217 |
+
|
1218 |
+
loss = None
|
1219 |
+
if labels is not None:
|
1220 |
+
# Shift so that tokens < n predict n
|
1221 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
1222 |
+
shift_labels = labels[..., 1:].contiguous()
|
1223 |
+
# Flatten the tokens
|
1224 |
+
loss_fct = CrossEntropyLoss()
|
1225 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1226 |
+
shift_labels = shift_labels.view(-1)
|
1227 |
+
# Enable model parallelism
|
1228 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
1229 |
+
loss = loss_fct(shift_logits, shift_labels)
|
1230 |
+
|
1231 |
+
if not return_dict:
|
1232 |
+
output = (logits,) + outputs[1:]
|
1233 |
+
return (loss,) + output if loss is not None else output
|
1234 |
+
|
1235 |
+
return CausalLMOutputWithPast(
|
1236 |
+
loss=loss,
|
1237 |
+
logits=logits,
|
1238 |
+
past_key_values=outputs.past_key_values,
|
1239 |
+
hidden_states=outputs.hidden_states,
|
1240 |
+
attentions=outputs.attentions,
|
1241 |
+
)
|
1242 |
+
|
1243 |
+
def prepare_inputs_for_generation(
|
1244 |
+
self,
|
1245 |
+
input_ids,
|
1246 |
+
past_key_values=None,
|
1247 |
+
attention_mask=None,
|
1248 |
+
inputs_embeds=None,
|
1249 |
+
cache_position=None,
|
1250 |
+
position_ids=None,
|
1251 |
+
use_cache=True,
|
1252 |
+
**kwargs,
|
1253 |
+
):
|
1254 |
+
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
|
1255 |
+
# Exception 1: when passing input_embeds, input_ids may be missing entries
|
1256 |
+
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
|
1257 |
+
if past_key_values is not None:
|
1258 |
+
if inputs_embeds is not None: # Exception 1
|
1259 |
+
input_ids = input_ids[:, -cache_position.shape[0]:]
|
1260 |
+
elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
|
1261 |
+
input_ids = input_ids[:, cache_position]
|
1262 |
+
|
1263 |
+
if attention_mask is not None and position_ids is None:
|
1264 |
+
# create position_ids on the fly for batch generation
|
1265 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
1266 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
1267 |
+
if past_key_values:
|
1268 |
+
position_ids = position_ids[:, -input_ids.shape[1]:]
|
1269 |
+
|
1270 |
+
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
|
1271 |
+
position_ids = position_ids.clone(memory_format=torch.contiguous_format)
|
1272 |
+
|
1273 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1274 |
+
if inputs_embeds is not None and cache_position[0] == 0:
|
1275 |
+
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
|
1276 |
+
else:
|
1277 |
+
# The clone here is for the same reason as for `position_ids`.
|
1278 |
+
model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
|
1279 |
+
|
1280 |
+
assert not isinstance(past_key_values, StaticCache), "DeciLM does not support StaticCache"
|
1281 |
+
if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
|
1282 |
+
if model_inputs["inputs_embeds"] is not None:
|
1283 |
+
batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
|
1284 |
+
device = model_inputs["inputs_embeds"].device
|
1285 |
+
else:
|
1286 |
+
batch_size, sequence_length = model_inputs["input_ids"].shape
|
1287 |
+
device = model_inputs["input_ids"].device
|
1288 |
+
|
1289 |
+
dtype = self.lm_head.weight.dtype
|
1290 |
+
min_dtype = torch.finfo(dtype).min
|
1291 |
+
|
1292 |
+
attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
|
1293 |
+
attention_mask,
|
1294 |
+
sequence_length=sequence_length,
|
1295 |
+
target_length=past_key_values.get_max_length(),
|
1296 |
+
dtype=dtype,
|
1297 |
+
device=device,
|
1298 |
+
min_dtype=min_dtype,
|
1299 |
+
cache_position=cache_position,
|
1300 |
+
batch_size=batch_size,
|
1301 |
+
)
|
1302 |
+
|
1303 |
+
model_inputs.update(
|
1304 |
+
{
|
1305 |
+
"position_ids": position_ids,
|
1306 |
+
"cache_position": cache_position,
|
1307 |
+
"past_key_values": past_key_values,
|
1308 |
+
"use_cache": use_cache,
|
1309 |
+
"attention_mask": attention_mask,
|
1310 |
+
}
|
1311 |
+
)
|
1312 |
+
return model_inputs
|
1313 |
+
|
1314 |
+
def _maybe_initialize_input_ids_for_generation(
|
1315 |
+
self,
|
1316 |
+
inputs: Optional[torch.Tensor] = None,
|
1317 |
+
bos_token_id: Optional[torch.Tensor] = None,
|
1318 |
+
model_kwargs: Optional[dict[str, torch.Tensor]] = None,
|
1319 |
+
) -> torch.LongTensor:
|
1320 |
+
"""
|
1321 |
+
Patching hf bug that creates wrong cache length if only inputs_embeds are passed to the model
|
1322 |
+
"""
|
1323 |
+
input_ids = super()._maybe_initialize_input_ids_for_generation(
|
1324 |
+
inputs=inputs, bos_token_id=bos_token_id, model_kwargs=model_kwargs)
|
1325 |
+
if (
|
1326 |
+
"inputs_embeds" in model_kwargs
|
1327 |
+
and input_ids is not None
|
1328 |
+
and input_ids.shape[1] == 0
|
1329 |
+
):
|
1330 |
+
batch_size, input_sequence_length = model_kwargs["inputs_embeds"].shape[:2]
|
1331 |
+
input_ids = torch.zeros((batch_size, input_sequence_length), dtype=torch.long, device=self.device)
|
1332 |
+
return input_ids
|
1333 |
+
|
1334 |
+
def generate(
|
1335 |
+
self,
|
1336 |
+
inputs: Optional[torch.Tensor] = None,
|
1337 |
+
*args,
|
1338 |
+
**kwargs,
|
1339 |
+
) -> Union[GenerateOutput, torch.LongTensor]:
|
1340 |
+
"""
|
1341 |
+
Patching hf bug that creates wrong cache length if only inputs_embeds are passed to the model
|
1342 |
+
"""
|
1343 |
+
only_passed_inputs_embeds = (
|
1344 |
+
"inputs_embeds" in kwargs and
|
1345 |
+
"input_ids" not in kwargs and
|
1346 |
+
inputs is None
|
1347 |
+
)
|
1348 |
+
if only_passed_inputs_embeds:
|
1349 |
+
input_sequence_length = kwargs["inputs_embeds"].shape[1]
|
1350 |
+
|
1351 |
+
generation_output = super().generate(inputs=inputs, *args, **kwargs)
|
1352 |
+
|
1353 |
+
if only_passed_inputs_embeds and isinstance(generation_output, torch.Tensor):
|
1354 |
+
generation_output = generation_output[:, input_sequence_length:]
|
1355 |
+
|
1356 |
+
return generation_output
|
1357 |
+
|
1358 |
+
|
1359 |
+
@add_start_docstrings(
|
1360 |
+
"""
|
1361 |
+
The DeciLM Model transformer with a sequence classification head on top (linear layer).
|
1362 |
+
|
1363 |
+
[`DeciLMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
1364 |
+
(e.g. GPT-2) do.
|
1365 |
+
|
1366 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1367 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
1368 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
1369 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
1370 |
+
each row of the batch).
|
1371 |
+
""",
|
1372 |
+
DECILM_START_DOCSTRING,
|
1373 |
+
)
|
1374 |
+
class DeciLMForSequenceClassification(DeciLMPreTrainedModel):
|
1375 |
+
def __init__(self, config):
|
1376 |
+
super().__init__(config)
|
1377 |
+
self.num_labels = config.num_labels
|
1378 |
+
self.model = DeciLMModel(config)
|
1379 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
1380 |
+
|
1381 |
+
# Initialize weights and apply final processing
|
1382 |
+
self.post_init()
|
1383 |
+
|
1384 |
+
def get_input_embeddings(self):
|
1385 |
+
return self.model.embed_tokens
|
1386 |
+
|
1387 |
+
def set_input_embeddings(self, value):
|
1388 |
+
self.model.embed_tokens = value
|
1389 |
+
|
1390 |
+
@add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
|
1391 |
+
def forward(
|
1392 |
+
self,
|
1393 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1394 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1395 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1396 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
1397 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1398 |
+
labels: Optional[torch.LongTensor] = None,
|
1399 |
+
use_cache: Optional[bool] = None,
|
1400 |
+
output_attentions: Optional[bool] = None,
|
1401 |
+
output_hidden_states: Optional[bool] = None,
|
1402 |
+
return_dict: Optional[bool] = None,
|
1403 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
1404 |
+
r"""
|
1405 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1406 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1407 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1408 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1409 |
+
"""
|
1410 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1411 |
+
|
1412 |
+
transformer_outputs = self.model(
|
1413 |
+
input_ids,
|
1414 |
+
attention_mask=attention_mask,
|
1415 |
+
position_ids=position_ids,
|
1416 |
+
past_key_values=past_key_values,
|
1417 |
+
inputs_embeds=inputs_embeds,
|
1418 |
+
use_cache=use_cache,
|
1419 |
+
output_attentions=output_attentions,
|
1420 |
+
output_hidden_states=output_hidden_states,
|
1421 |
+
return_dict=return_dict,
|
1422 |
+
)
|
1423 |
+
hidden_states = transformer_outputs[0]
|
1424 |
+
logits = self.score(hidden_states)
|
1425 |
+
|
1426 |
+
if input_ids is not None:
|
1427 |
+
batch_size = input_ids.shape[0]
|
1428 |
+
else:
|
1429 |
+
batch_size = inputs_embeds.shape[0]
|
1430 |
+
|
1431 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
1432 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
1433 |
+
if self.config.pad_token_id is None:
|
1434 |
+
sequence_lengths = -1
|
1435 |
+
else:
|
1436 |
+
if input_ids is not None:
|
1437 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
1438 |
+
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
1439 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
1440 |
+
sequence_lengths = sequence_lengths.to(logits.device)
|
1441 |
+
else:
|
1442 |
+
sequence_lengths = -1
|
1443 |
+
|
1444 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
1445 |
+
|
1446 |
+
loss = None
|
1447 |
+
if labels is not None:
|
1448 |
+
labels = labels.to(logits.device)
|
1449 |
+
if self.config.problem_type is None:
|
1450 |
+
if self.num_labels == 1:
|
1451 |
+
self.config.problem_type = "regression"
|
1452 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
1453 |
+
self.config.problem_type = "single_label_classification"
|
1454 |
+
else:
|
1455 |
+
self.config.problem_type = "multi_label_classification"
|
1456 |
+
|
1457 |
+
if self.config.problem_type == "regression":
|
1458 |
+
loss_fct = MSELoss()
|
1459 |
+
if self.num_labels == 1:
|
1460 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
1461 |
+
else:
|
1462 |
+
loss = loss_fct(pooled_logits, labels)
|
1463 |
+
elif self.config.problem_type == "single_label_classification":
|
1464 |
+
loss_fct = CrossEntropyLoss()
|
1465 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
1466 |
+
elif self.config.problem_type == "multi_label_classification":
|
1467 |
+
loss_fct = BCEWithLogitsLoss()
|
1468 |
+
loss = loss_fct(pooled_logits, labels)
|
1469 |
+
if not return_dict:
|
1470 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
1471 |
+
return ((loss,) + output) if loss is not None else output
|
1472 |
+
|
1473 |
+
return SequenceClassifierOutputWithPast(
|
1474 |
+
loss=loss,
|
1475 |
+
logits=pooled_logits,
|
1476 |
+
past_key_values=transformer_outputs.past_key_values,
|
1477 |
+
hidden_states=transformer_outputs.hidden_states,
|
1478 |
+
attentions=transformer_outputs.attentions,
|
1479 |
+
)
|
1480 |
+
|
1481 |
+
|
1482 |
+
@add_start_docstrings(
|
1483 |
+
"""
|
1484 |
+
The DeciLM Model transformer with a span classification head on top for extractive question-answering tasks like
|
1485 |
+
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
1486 |
+
""",
|
1487 |
+
DECILM_START_DOCSTRING,
|
1488 |
+
)
|
1489 |
+
class DeciLMForQuestionAnswering(DeciLMPreTrainedModel):
|
1490 |
+
base_model_prefix = "transformer"
|
1491 |
+
|
1492 |
+
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->DeciLM
|
1493 |
+
def __init__(self, config):
|
1494 |
+
super().__init__(config)
|
1495 |
+
self.transformer = DeciLMModel(config)
|
1496 |
+
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
1497 |
+
|
1498 |
+
# Initialize weights and apply final processing
|
1499 |
+
self.post_init()
|
1500 |
+
|
1501 |
+
def get_input_embeddings(self):
|
1502 |
+
return self.transformer.embed_tokens
|
1503 |
+
|
1504 |
+
def set_input_embeddings(self, value):
|
1505 |
+
self.transformer.embed_tokens = value
|
1506 |
+
|
1507 |
+
@add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
|
1508 |
+
def forward(
|
1509 |
+
self,
|
1510 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1511 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
1512 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1513 |
+
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
|
1514 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1515 |
+
start_positions: Optional[torch.LongTensor] = None,
|
1516 |
+
end_positions: Optional[torch.LongTensor] = None,
|
1517 |
+
output_attentions: Optional[bool] = None,
|
1518 |
+
output_hidden_states: Optional[bool] = None,
|
1519 |
+
return_dict: Optional[bool] = None,
|
1520 |
+
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
1521 |
+
r"""
|
1522 |
+
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1523 |
+
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
1524 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1525 |
+
are not taken into account for computing the loss.
|
1526 |
+
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1527 |
+
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
1528 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1529 |
+
are not taken into account for computing the loss.
|
1530 |
+
"""
|
1531 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1532 |
+
|
1533 |
+
outputs = self.transformer(
|
1534 |
+
input_ids,
|
1535 |
+
attention_mask=attention_mask,
|
1536 |
+
position_ids=position_ids,
|
1537 |
+
past_key_values=past_key_values,
|
1538 |
+
inputs_embeds=inputs_embeds,
|
1539 |
+
output_attentions=output_attentions,
|
1540 |
+
output_hidden_states=output_hidden_states,
|
1541 |
+
return_dict=return_dict,
|
1542 |
+
)
|
1543 |
+
|
1544 |
+
sequence_output = outputs[0]
|
1545 |
+
|
1546 |
+
logits = self.qa_outputs(sequence_output)
|
1547 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
1548 |
+
start_logits = start_logits.squeeze(-1).contiguous()
|
1549 |
+
end_logits = end_logits.squeeze(-1).contiguous()
|
1550 |
+
|
1551 |
+
total_loss = None
|
1552 |
+
if start_positions is not None and end_positions is not None:
|
1553 |
+
# If we are on multi-GPU, split add a dimension
|
1554 |
+
if len(start_positions.size()) > 1:
|
1555 |
+
start_positions = start_positions.squeeze(-1).to(start_logits.device)
|
1556 |
+
if len(end_positions.size()) > 1:
|
1557 |
+
end_positions = end_positions.squeeze(-1).to(end_logits.device)
|
1558 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
1559 |
+
ignored_index = start_logits.size(1)
|
1560 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
1561 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
1562 |
+
|
1563 |
+
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
1564 |
+
start_loss = loss_fct(start_logits, start_positions)
|
1565 |
+
end_loss = loss_fct(end_logits, end_positions)
|
1566 |
+
total_loss = (start_loss + end_loss) / 2
|
1567 |
+
|
1568 |
+
if not return_dict:
|
1569 |
+
output = (start_logits, end_logits) + outputs[2:]
|
1570 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
1571 |
+
|
1572 |
+
return QuestionAnsweringModelOutput(
|
1573 |
+
loss=total_loss,
|
1574 |
+
start_logits=start_logits,
|
1575 |
+
end_logits=end_logits,
|
1576 |
+
hidden_states=outputs.hidden_states,
|
1577 |
+
attentions=outputs.attentions,
|
1578 |
+
)
|
1579 |
+
|
1580 |
+
|
1581 |
+
@add_start_docstrings(
|
1582 |
+
"""
|
1583 |
+
The DeciLM Model transformer with a token classification head on top (a linear layer on top of the hidden-states
|
1584 |
+
output) e.g. for Named-Entity-Recognition (NER) tasks.
|
1585 |
+
""",
|
1586 |
+
DECILM_START_DOCSTRING,
|
1587 |
+
)
|
1588 |
+
class DeciLMForTokenClassification(DeciLMPreTrainedModel):
|
1589 |
+
def __init__(self, config):
|
1590 |
+
super().__init__(config)
|
1591 |
+
self.num_labels = config.num_labels
|
1592 |
+
self.model = DeciLMModel(config)
|
1593 |
+
if getattr(config, "classifier_dropout", None) is not None:
|
1594 |
+
classifier_dropout = config.classifier_dropout
|
1595 |
+
elif getattr(config, "hidden_dropout", None) is not None:
|
1596 |
+
classifier_dropout = config.hidden_dropout
|
1597 |
+
else:
|
1598 |
+
classifier_dropout = 0.1
|
1599 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
1600 |
+
self.score = nn.Linear(config.hidden_size, config.num_labels)
|
1601 |
+
|
1602 |
+
# Initialize weights and apply final processing
|
1603 |
+
self.post_init()
|
1604 |
+
|
1605 |
+
def get_input_embeddings(self):
|
1606 |
+
return self.model.embed_tokens
|
1607 |
+
|
1608 |
+
def set_input_embeddings(self, value):
|
1609 |
+
self.model.embed_tokens = value
|
1610 |
+
|
1611 |
+
@add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
|
1612 |
+
def forward(
|
1613 |
+
self,
|
1614 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1615 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1616 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1617 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1618 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1619 |
+
labels: Optional[torch.LongTensor] = None,
|
1620 |
+
use_cache: Optional[bool] = None,
|
1621 |
+
output_attentions: Optional[bool] = None,
|
1622 |
+
output_hidden_states: Optional[bool] = None,
|
1623 |
+
return_dict: Optional[bool] = None,
|
1624 |
+
) -> Union[Tuple, TokenClassifierOutput]:
|
1625 |
+
r"""
|
1626 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1627 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1628 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1629 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1630 |
+
"""
|
1631 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1632 |
+
|
1633 |
+
outputs = self.model(
|
1634 |
+
input_ids,
|
1635 |
+
attention_mask=attention_mask,
|
1636 |
+
position_ids=position_ids,
|
1637 |
+
past_key_values=past_key_values,
|
1638 |
+
inputs_embeds=inputs_embeds,
|
1639 |
+
use_cache=use_cache,
|
1640 |
+
output_attentions=output_attentions,
|
1641 |
+
output_hidden_states=output_hidden_states,
|
1642 |
+
return_dict=return_dict,
|
1643 |
+
)
|
1644 |
+
sequence_output = outputs[0]
|
1645 |
+
sequence_output = self.dropout(sequence_output)
|
1646 |
+
logits = self.score(sequence_output)
|
1647 |
+
|
1648 |
+
loss = None
|
1649 |
+
if labels is not None:
|
1650 |
+
loss_fct = CrossEntropyLoss()
|
1651 |
+
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
1652 |
+
|
1653 |
+
if not return_dict:
|
1654 |
+
output = (logits,) + outputs[2:]
|
1655 |
+
return ((loss,) + output) if loss is not None else output
|
1656 |
+
|
1657 |
+
return TokenClassifierOutput(
|
1658 |
+
loss=loss,
|
1659 |
+
logits=logits,
|
1660 |
+
hidden_states=outputs.hidden_states,
|
1661 |
+
attentions=outputs.attentions,
|
1662 |
+
)
|
1663 |
+
|
1664 |
+
|
1665 |
+
########################################################################
|
1666 |
+
# DeciLM-specific code
|
1667 |
+
########################################################################
|
1668 |
+
|
1669 |
+
|
1670 |
+
def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
|
1671 |
+
# DeciLM-specific code
|
1672 |
+
intermediate_size = int(2 * ffn_mult * n_embd / 3)
|
1673 |
+
return _find_multiple(intermediate_size, 256)
|
1674 |
+
|
1675 |
+
|
1676 |
+
def _find_multiple(n: int, k: int) -> int:
|
1677 |
+
# DeciLM-specific code
|
1678 |
+
if n % k == 0:
|
1679 |
+
return n
|
1680 |
+
return n + k - (n % k)
|
1681 |
+
|
1682 |
+
|
1683 |
+
class DeciLMLinearMLP(nn.Module):
|
1684 |
+
# DeciLM-specific code
|
1685 |
+
def __init__(self,
|
1686 |
+
config: DeciLMConfig,
|
1687 |
+
):
|
1688 |
+
super().__init__()
|
1689 |
+
self.linear_mlp = nn.Linear(in_features=config.hidden_size,
|
1690 |
+
out_features=config.hidden_size,
|
1691 |
+
bias=False)
|
1692 |
+
|
1693 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
1694 |
+
return self.linear_mlp.forward(x)
|
1695 |
+
|
1696 |
+
|
1697 |
+
class DeciLMLinearAttention(nn.Module):
|
1698 |
+
# DeciLM-specific code
|
1699 |
+
def __init__(self,
|
1700 |
+
config: DeciLMConfig,
|
1701 |
+
):
|
1702 |
+
super().__init__()
|
1703 |
+
self.linear_attn = nn.Linear(in_features=config.hidden_size,
|
1704 |
+
out_features=config.hidden_size,
|
1705 |
+
bias=False)
|
1706 |
+
|
1707 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
1708 |
+
return self.linear_attn.forward(x)
|
1709 |
+
|
recipe.yaml
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
DEFAULT_stage:
|
2 |
+
DEFAULT_modifiers:
|
3 |
+
QuantizationModifier:
|
4 |
+
ignore: [lm_head]
|
5 |
+
targets: [Linear]
|
6 |
+
scheme: FP8_DYNAMIC
|
transformers_4_44_2__activations.py
ADDED
@@ -0,0 +1,239 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
1 |
+
# Copyright 2020 The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
import math
|
16 |
+
from collections import OrderedDict
|
17 |
+
|
18 |
+
import torch
|
19 |
+
from packaging import version
|
20 |
+
from torch import Tensor, nn
|
21 |
+
|
22 |
+
from transformers.utils import logging
|
23 |
+
|
24 |
+
|
25 |
+
logger = logging.get_logger(__name__)
|
26 |
+
|
27 |
+
|
28 |
+
class PytorchGELUTanh(nn.Module):
|
29 |
+
"""
|
30 |
+
A fast C implementation of the tanh approximation of the GeLU activation function. See
|
31 |
+
https://arxiv.org/abs/1606.08415.
|
32 |
+
|
33 |
+
This implementation is equivalent to NewGELU and FastGELU but much faster. However, it is not an exact numerical
|
34 |
+
match due to rounding errors.
|
35 |
+
"""
|
36 |
+
|
37 |
+
def __init__(self):
|
38 |
+
super().__init__()
|
39 |
+
if version.parse(torch.__version__) < version.parse("1.12.0"):
|
40 |
+
raise ImportError(
|
41 |
+
f"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use "
|
42 |
+
"PytorchGELUTanh. Please upgrade torch."
|
43 |
+
)
|
44 |
+
|
45 |
+
def forward(self, input: Tensor) -> Tensor:
|
46 |
+
return nn.functional.gelu(input, approximate="tanh")
|
47 |
+
|
48 |
+
|
49 |
+
class NewGELUActivation(nn.Module):
|
50 |
+
"""
|
51 |
+
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
|
52 |
+
the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
|
53 |
+
"""
|
54 |
+
|
55 |
+
def forward(self, input: Tensor) -> Tensor:
|
56 |
+
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))
|
57 |
+
|
58 |
+
|
59 |
+
class GELUActivation(nn.Module):
|
60 |
+
"""
|
61 |
+
Original Implementation of the GELU activation function in Google BERT repo when initially created. For
|
62 |
+
information: OpenAI GPT's GELU is slightly different (and gives slightly different results): 0.5 * x * (1 +
|
63 |
+
torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) This is now written in C in nn.functional
|
64 |
+
Also see the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
|
65 |
+
"""
|
66 |
+
|
67 |
+
def __init__(self, use_gelu_python: bool = False):
|
68 |
+
super().__init__()
|
69 |
+
if use_gelu_python:
|
70 |
+
self.act = self._gelu_python
|
71 |
+
else:
|
72 |
+
self.act = nn.functional.gelu
|
73 |
+
|
74 |
+
def _gelu_python(self, input: Tensor) -> Tensor:
|
75 |
+
return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0)))
|
76 |
+
|
77 |
+
def forward(self, input: Tensor) -> Tensor:
|
78 |
+
return self.act(input)
|
79 |
+
|
80 |
+
|
81 |
+
class FastGELUActivation(nn.Module):
|
82 |
+
"""
|
83 |
+
Applies GELU approximation that is slower than QuickGELU but more accurate. See: https://github.com/hendrycks/GELUs
|
84 |
+
"""
|
85 |
+
|
86 |
+
def forward(self, input: Tensor) -> Tensor:
|
87 |
+
return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)))
|
88 |
+
|
89 |
+
|
90 |
+
class QuickGELUActivation(nn.Module):
|
91 |
+
"""
|
92 |
+
Applies GELU approximation that is fast but somewhat inaccurate. See: https://github.com/hendrycks/GELUs
|
93 |
+
"""
|
94 |
+
|
95 |
+
def forward(self, input: Tensor) -> Tensor:
|
96 |
+
return input * torch.sigmoid(1.702 * input)
|
97 |
+
|
98 |
+
|
99 |
+
class ClippedGELUActivation(nn.Module):
|
100 |
+
"""
|
101 |
+
Clip the range of possible GeLU outputs between [min, max]. This is especially useful for quantization purpose, as
|
102 |
+
it allows mapping negatives values in the GeLU spectrum. For more information on this trick, please refer to
|
103 |
+
https://arxiv.org/abs/2004.09602.
|
104 |
+
|
105 |
+
Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when
|
106 |
+
initially created.
|
107 |
+
|
108 |
+
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results): 0.5 * x * (1 +
|
109 |
+
torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))). See https://arxiv.org/abs/1606.08415
|
110 |
+
"""
|
111 |
+
|
112 |
+
def __init__(self, min: float, max: float):
|
113 |
+
if min > max:
|
114 |
+
raise ValueError(f"min should be < max (got min: {min}, max: {max})")
|
115 |
+
|
116 |
+
super().__init__()
|
117 |
+
self.min = min
|
118 |
+
self.max = max
|
119 |
+
|
120 |
+
def forward(self, x: Tensor) -> Tensor:
|
121 |
+
return torch.clip(gelu(x), self.min, self.max)
|
122 |
+
|
123 |
+
|
124 |
+
class AccurateGELUActivation(nn.Module):
|
125 |
+
"""
|
126 |
+
Applies GELU approximation that is faster than default and more accurate than QuickGELU. See:
|
127 |
+
https://github.com/hendrycks/GELUs
|
128 |
+
|
129 |
+
Implemented along with MEGA (Moving Average Equipped Gated Attention)
|
130 |
+
"""
|
131 |
+
|
132 |
+
def __init__(self):
|
133 |
+
super().__init__()
|
134 |
+
self.precomputed_constant = math.sqrt(2 / math.pi)
|
135 |
+
|
136 |
+
def forward(self, input: Tensor) -> Tensor:
|
137 |
+
return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(input, 3))))
|
138 |
+
|
139 |
+
|
140 |
+
class MishActivation(nn.Module):
|
141 |
+
"""
|
142 |
+
See Mish: A Self-Regularized Non-Monotonic Activation Function (Misra., https://arxiv.org/abs/1908.08681). Also
|
143 |
+
visit the official repository for the paper: https://github.com/digantamisra98/Mish
|
144 |
+
"""
|
145 |
+
|
146 |
+
def __init__(self):
|
147 |
+
super().__init__()
|
148 |
+
if version.parse(torch.__version__) < version.parse("1.9.0"):
|
149 |
+
self.act = self._mish_python
|
150 |
+
else:
|
151 |
+
self.act = nn.functional.mish
|
152 |
+
|
153 |
+
def _mish_python(self, input: Tensor) -> Tensor:
|
154 |
+
return input * torch.tanh(nn.functional.softplus(input))
|
155 |
+
|
156 |
+
def forward(self, input: Tensor) -> Tensor:
|
157 |
+
return self.act(input)
|
158 |
+
|
159 |
+
|
160 |
+
class LinearActivation(nn.Module):
|
161 |
+
"""
|
162 |
+
Applies the linear activation function, i.e. forwarding input directly to output.
|
163 |
+
"""
|
164 |
+
|
165 |
+
def forward(self, input: Tensor) -> Tensor:
|
166 |
+
return input
|
167 |
+
|
168 |
+
|
169 |
+
class LaplaceActivation(nn.Module):
|
170 |
+
"""
|
171 |
+
Applies elementwise activation based on Laplace function, introduced in MEGA as an attention activation. See
|
172 |
+
https://arxiv.org/abs/2209.10655
|
173 |
+
|
174 |
+
Inspired by squared relu, but with bounded range and gradient for better stability
|
175 |
+
"""
|
176 |
+
|
177 |
+
def forward(self, input, mu=0.707107, sigma=0.282095):
|
178 |
+
input = (input - mu).div(sigma * math.sqrt(2.0))
|
179 |
+
return 0.5 * (1.0 + torch.erf(input))
|
180 |
+
|
181 |
+
|
182 |
+
class ReLUSquaredActivation(nn.Module):
|
183 |
+
"""
|
184 |
+
Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2
|
185 |
+
"""
|
186 |
+
|
187 |
+
def forward(self, input):
|
188 |
+
relu_applied = nn.functional.relu(input)
|
189 |
+
squared = torch.square(relu_applied)
|
190 |
+
return squared
|
191 |
+
|
192 |
+
|
193 |
+
class ClassInstantier(OrderedDict):
|
194 |
+
def __getitem__(self, key):
|
195 |
+
content = super().__getitem__(key)
|
196 |
+
cls, kwargs = content if isinstance(content, tuple) else (content, {})
|
197 |
+
return cls(**kwargs)
|
198 |
+
|
199 |
+
|
200 |
+
ACT2CLS = {
|
201 |
+
"gelu": GELUActivation,
|
202 |
+
"gelu_10": (ClippedGELUActivation, {"min": -10, "max": 10}),
|
203 |
+
"gelu_fast": FastGELUActivation,
|
204 |
+
"gelu_new": NewGELUActivation,
|
205 |
+
"gelu_python": (GELUActivation, {"use_gelu_python": True}),
|
206 |
+
"gelu_pytorch_tanh": PytorchGELUTanh,
|
207 |
+
"gelu_accurate": AccurateGELUActivation,
|
208 |
+
"laplace": LaplaceActivation,
|
209 |
+
"leaky_relu": nn.LeakyReLU,
|
210 |
+
"linear": LinearActivation,
|
211 |
+
"mish": MishActivation,
|
212 |
+
"quick_gelu": QuickGELUActivation,
|
213 |
+
"relu": nn.ReLU,
|
214 |
+
"relu2": ReLUSquaredActivation,
|
215 |
+
"relu6": nn.ReLU6,
|
216 |
+
"sigmoid": nn.Sigmoid,
|
217 |
+
"silu": nn.SiLU,
|
218 |
+
"swish": nn.SiLU,
|
219 |
+
"tanh": nn.Tanh,
|
220 |
+
}
|
221 |
+
ACT2FN = ClassInstantier(ACT2CLS)
|
222 |
+
|
223 |
+
|
224 |
+
def get_activation(activation_string):
|
225 |
+
if activation_string in ACT2FN:
|
226 |
+
return ACT2FN[activation_string]
|
227 |
+
else:
|
228 |
+
raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACT2FN.keys())}")
|
229 |
+
|
230 |
+
|
231 |
+
# For backwards compatibility with: from activations import gelu_python
|
232 |
+
gelu_python = get_activation("gelu_python")
|
233 |
+
gelu_new = get_activation("gelu_new")
|
234 |
+
gelu = get_activation("gelu")
|
235 |
+
gelu_fast = get_activation("gelu_fast")
|
236 |
+
quick_gelu = get_activation("quick_gelu")
|
237 |
+
silu = get_activation("silu")
|
238 |
+
mish = get_activation("mish")
|
239 |
+
linear_act = get_activation("linear")
|
transformers_4_44_2__cache_utils.py
ADDED
@@ -0,0 +1,325 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
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|
|
|
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|
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|
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|
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|
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|
|
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|
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|
|
|
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|
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|
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|
|
|
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|
|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
|
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|
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|
|
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|
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|
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|
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|
|
|
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|
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|
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|
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|
|
|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import copy
|
2 |
+
import importlib.metadata
|
3 |
+
import json
|
4 |
+
import os
|
5 |
+
from dataclasses import dataclass
|
6 |
+
from typing import Any, Dict, List, Optional, Tuple, Union
|
7 |
+
|
8 |
+
import torch
|
9 |
+
from packaging import version
|
10 |
+
|
11 |
+
from transformers.configuration_utils import PretrainedConfig
|
12 |
+
from transformers.utils import is_torchdynamo_compiling, logging
|
13 |
+
|
14 |
+
|
15 |
+
logger = logging.get_logger(__name__)
|
16 |
+
|
17 |
+
|
18 |
+
class Cache(torch.nn.Module):
|
19 |
+
"""
|
20 |
+
Base, abstract class for all caches. The actual data structure is specific to each subclass.
|
21 |
+
"""
|
22 |
+
|
23 |
+
def __init__(self):
|
24 |
+
super().__init__()
|
25 |
+
|
26 |
+
def update(
|
27 |
+
self,
|
28 |
+
key_states: torch.Tensor,
|
29 |
+
value_states: torch.Tensor,
|
30 |
+
layer_idx: int,
|
31 |
+
cache_kwargs: Optional[Dict[str, Any]] = None,
|
32 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
33 |
+
"""
|
34 |
+
Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
|
35 |
+
|
36 |
+
Parameters:
|
37 |
+
key_states (`torch.Tensor`):
|
38 |
+
The new key states to cache.
|
39 |
+
value_states (`torch.Tensor`):
|
40 |
+
The new value states to cache.
|
41 |
+
layer_idx (`int`):
|
42 |
+
The index of the layer to cache the states for.
|
43 |
+
cache_kwargs (`Dict[str, Any]`, `optional`):
|
44 |
+
Additional arguments for the cache subclass. These are specific to each subclass and allow new types of
|
45 |
+
cache to be created.
|
46 |
+
|
47 |
+
Return:
|
48 |
+
A tuple containing the updated key and value states.
|
49 |
+
"""
|
50 |
+
raise NotImplementedError("Make sure to implement `update` in a subclass.")
|
51 |
+
|
52 |
+
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
|
53 |
+
"""Returns the sequence length of the cached states. A layer index can be optionally passed."""
|
54 |
+
# TODO: deprecate this function in favor of `cache_position`
|
55 |
+
raise NotImplementedError("Make sure to implement `get_seq_length` in a subclass.")
|
56 |
+
|
57 |
+
def get_max_length(self) -> Optional[int]:
|
58 |
+
"""Returns the maximum sequence length of the cached states, if there is any."""
|
59 |
+
raise NotImplementedError("Make sure to implement `get_max_length` in a subclass.")
|
60 |
+
|
61 |
+
def get_usable_length(self, new_seq_length: int, layer_idx: Optional[int] = 0) -> int:
|
62 |
+
"""Given the sequence length of the new inputs, returns the usable length of the cache."""
|
63 |
+
# Cache without size limit -> all cache is usable
|
64 |
+
# Cache with size limit -> if the length cache plus the length of the new inputs is larger the maximum cache
|
65 |
+
# length, we will need to evict part of the cache (and thus not all cache is usable)
|
66 |
+
max_length = self.get_max_length()
|
67 |
+
previous_seq_length = self.get_seq_length(layer_idx)
|
68 |
+
if max_length is not None and previous_seq_length + new_seq_length > max_length:
|
69 |
+
return max_length - new_seq_length
|
70 |
+
return previous_seq_length
|
71 |
+
|
72 |
+
def reorder_cache(self, beam_idx: torch.LongTensor):
|
73 |
+
"""Reorders the cache for beam search, given the selected beam indices."""
|
74 |
+
for layer_idx in range(len(self.key_cache)):
|
75 |
+
device = self.key_cache[layer_idx].device
|
76 |
+
self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
|
77 |
+
device = self.value_cache[layer_idx].device
|
78 |
+
self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
|
79 |
+
|
80 |
+
@property
|
81 |
+
def seen_tokens(self):
|
82 |
+
logger.warning_once(
|
83 |
+
"The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` "
|
84 |
+
"model input instead."
|
85 |
+
)
|
86 |
+
if hasattr(self, "_seen_tokens"):
|
87 |
+
return self._seen_tokens
|
88 |
+
else:
|
89 |
+
return None
|
90 |
+
|
91 |
+
|
92 |
+
@dataclass
|
93 |
+
class CacheConfig:
|
94 |
+
"""
|
95 |
+
Base class for cache configs
|
96 |
+
"""
|
97 |
+
|
98 |
+
cache_implementation: None
|
99 |
+
|
100 |
+
@classmethod
|
101 |
+
def from_dict(cls, config_dict, **kwargs):
|
102 |
+
"""
|
103 |
+
Constructs a CacheConfig instance from a dictionary of parameters.
|
104 |
+
Args:
|
105 |
+
config_dict (Dict[str, Any]): Dictionary containing configuration parameters.
|
106 |
+
**kwargs: Additional keyword arguments to override dictionary values.
|
107 |
+
|
108 |
+
Returns:
|
109 |
+
CacheConfig: Instance of CacheConfig constructed from the dictionary.
|
110 |
+
"""
|
111 |
+
config = cls(**config_dict)
|
112 |
+
to_remove = []
|
113 |
+
for key, value in kwargs.items():
|
114 |
+
if hasattr(config, key):
|
115 |
+
setattr(config, key, value)
|
116 |
+
to_remove.append(key)
|
117 |
+
for key in to_remove:
|
118 |
+
kwargs.pop(key, None)
|
119 |
+
return config
|
120 |
+
|
121 |
+
# Copied from transformers.utils.quantization_config.QuantizationConfigMixin.to_json_file
|
122 |
+
def to_json_file(self, json_file_path: Union[str, os.PathLike]):
|
123 |
+
"""
|
124 |
+
Save this instance to a JSON file.
|
125 |
+
|
126 |
+
Args:
|
127 |
+
json_file_path (`str` or `os.PathLike`):
|
128 |
+
Path to the JSON file in which this configuration instance's parameters will be saved.
|
129 |
+
use_diff (`bool`, *optional*, defaults to `True`):
|
130 |
+
If set to `True`, only the difference between the config instance and the default
|
131 |
+
`QuantizationConfig()` is serialized to JSON file.
|
132 |
+
"""
|
133 |
+
with open(json_file_path, "w", encoding="utf-8") as writer:
|
134 |
+
config_dict = self.to_dict()
|
135 |
+
json_string = json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
|
136 |
+
|
137 |
+
writer.write(json_string)
|
138 |
+
|
139 |
+
# Copied from transformers.utils.quantization_config.QuantizationConfigMixin.to_dict
|
140 |
+
def to_dict(self) -> Dict[str, Any]:
|
141 |
+
"""
|
142 |
+
Serializes this instance to a Python dictionary. Returns:
|
143 |
+
`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
|
144 |
+
"""
|
145 |
+
return copy.deepcopy(self.__dict__)
|
146 |
+
|
147 |
+
# Copied from transformers.utils.quantization_config.QuantizationConfigMixin.__iter__
|
148 |
+
def __iter__(self):
|
149 |
+
"""allows `dict(obj)` for situations where obj may be a dict or QuantizationConfigMixin"""
|
150 |
+
for attr, value in copy.deepcopy(self.__dict__).items():
|
151 |
+
yield attr, value
|
152 |
+
|
153 |
+
# Copied from transformers.utils.quantization_config.QuantizationConfigMixin.__repr__
|
154 |
+
def __repr__(self):
|
155 |
+
return f"{self.__class__.__name__} {self.to_json_string()}"
|
156 |
+
|
157 |
+
def to_json_string(self):
|
158 |
+
"""
|
159 |
+
Serializes this instance to a JSON formatted string.
|
160 |
+
Returns:
|
161 |
+
str: JSON formatted string representing the configuration instance.
|
162 |
+
"""
|
163 |
+
return json.dumps(self.__dict__, indent=2) + "\n"
|
164 |
+
|
165 |
+
# Copied from transformers.utils.quantization_config.QuantizationConfigMixin.update
|
166 |
+
def update(self, **kwargs):
|
167 |
+
"""
|
168 |
+
Updates attributes of this class instance with attributes from `kwargs` if they match existing attributes,
|
169 |
+
returning all the unused kwargs.
|
170 |
+
|
171 |
+
Args:
|
172 |
+
kwargs (`Dict[str, Any]`):
|
173 |
+
Dictionary of attributes to tentatively update this class.
|
174 |
+
|
175 |
+
Returns:
|
176 |
+
`Dict[str, Any]`: Dictionary containing all the key-value pairs that were not used to update the instance.
|
177 |
+
"""
|
178 |
+
to_remove = []
|
179 |
+
for key, value in kwargs.items():
|
180 |
+
if hasattr(self, key):
|
181 |
+
setattr(self, key, value)
|
182 |
+
to_remove.append(key)
|
183 |
+
|
184 |
+
# Remove all the attributes that were updated, without modifying the input dict
|
185 |
+
unused_kwargs = {key: value for key, value in kwargs.items() if key not in to_remove}
|
186 |
+
return unused_kwargs
|
187 |
+
|
188 |
+
|
189 |
+
class StaticCache(Cache):
|
190 |
+
"""
|
191 |
+
Static Cache class to be used with `torch.compile(model)` and `torch.export()`.
|
192 |
+
|
193 |
+
Parameters:
|
194 |
+
config (`PretrainedConfig`):
|
195 |
+
The configuration file defining the shape-related attributes required to initialize the static cache.
|
196 |
+
max_batch_size (`int`):
|
197 |
+
The maximum batch size with which the model will be used.
|
198 |
+
max_cache_len (`int`):
|
199 |
+
The maximum sequence length with which the model will be used.
|
200 |
+
device (`torch.device`):
|
201 |
+
The device on which the cache should be initialized. Should be the same as the layer.
|
202 |
+
dtype (*optional*, defaults to `torch.float32`):
|
203 |
+
The default `dtype` to use when initializing the layer.
|
204 |
+
|
205 |
+
Example:
|
206 |
+
|
207 |
+
```python
|
208 |
+
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, StaticCache
|
209 |
+
|
210 |
+
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
|
211 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
|
212 |
+
|
213 |
+
>>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
|
214 |
+
|
215 |
+
>>> # Prepare a cache class and pass it to model's forward
|
216 |
+
>>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
|
217 |
+
>>> max_generated_length = inputs.input_ids.shape[1] + 10
|
218 |
+
>>> past_key_values = StaticCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
|
219 |
+
>>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
|
220 |
+
>>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
|
221 |
+
```
|
222 |
+
"""
|
223 |
+
|
224 |
+
def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device, dtype=None) -> None:
|
225 |
+
super().__init__()
|
226 |
+
self.max_batch_size = max_batch_size
|
227 |
+
self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
|
228 |
+
# Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
|
229 |
+
self.head_dim = (
|
230 |
+
config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
|
231 |
+
)
|
232 |
+
|
233 |
+
self.dtype = dtype if dtype is not None else torch.float32
|
234 |
+
self.num_key_value_heads = (
|
235 |
+
config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
|
236 |
+
)
|
237 |
+
|
238 |
+
self.key_cache: List[torch.Tensor] = []
|
239 |
+
self.value_cache: List[torch.Tensor] = []
|
240 |
+
# Note: There will be significant perf decrease if switching to use 5D tensors instead.
|
241 |
+
cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim)
|
242 |
+
for idx in range(config.num_hidden_layers):
|
243 |
+
new_layer_key_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
|
244 |
+
new_layer_value_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
|
245 |
+
# Notes:
|
246 |
+
# 1. `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
|
247 |
+
# breaks when updating the cache. It can't be used if the cache code is being compiled (but in that case
|
248 |
+
# it is not needed anyway)
|
249 |
+
# 2. `torch.export()` requires mutations to be registered as buffers.
|
250 |
+
if not is_torchdynamo_compiling():
|
251 |
+
self.register_buffer(f"key_cache_{idx}", torch.zeros(cache_shape, dtype=dtype, device=device))
|
252 |
+
self.register_buffer(f"value_cache_{idx}", torch.zeros(cache_shape, dtype=dtype, device=device))
|
253 |
+
new_layer_key_cache = getattr(self, f"key_cache_{idx}")
|
254 |
+
new_layer_value_cache = getattr(self, f"value_cache_{idx}")
|
255 |
+
torch._dynamo.mark_static_address(new_layer_key_cache)
|
256 |
+
torch._dynamo.mark_static_address(new_layer_value_cache)
|
257 |
+
self.key_cache.append(new_layer_key_cache)
|
258 |
+
self.value_cache.append(new_layer_value_cache)
|
259 |
+
|
260 |
+
def update(
|
261 |
+
self,
|
262 |
+
key_states: torch.Tensor,
|
263 |
+
value_states: torch.Tensor,
|
264 |
+
layer_idx: int,
|
265 |
+
cache_kwargs: Optional[Dict[str, Any]] = None,
|
266 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
267 |
+
"""
|
268 |
+
Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
|
269 |
+
It is VERY important to index using a tensor, otherwise you introduce a copy to the device.
|
270 |
+
|
271 |
+
Parameters:
|
272 |
+
key_states (`torch.Tensor`):
|
273 |
+
The new key states to cache.
|
274 |
+
value_states (`torch.Tensor`):
|
275 |
+
The new value states to cache.
|
276 |
+
layer_idx (`int`):
|
277 |
+
The index of the layer to cache the states for.
|
278 |
+
cache_kwargs (`Dict[str, Any]`, `optional`):
|
279 |
+
Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
|
280 |
+
to know how where to write in the cache.
|
281 |
+
|
282 |
+
Return:
|
283 |
+
A tuple containing the updated key and value states.
|
284 |
+
"""
|
285 |
+
cache_position = cache_kwargs.get("cache_position")
|
286 |
+
self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device=key_states.device)
|
287 |
+
self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device=value_states.device)
|
288 |
+
k_out = self.key_cache[layer_idx]
|
289 |
+
v_out = self.value_cache[layer_idx]
|
290 |
+
|
291 |
+
if cache_position is None:
|
292 |
+
k_out.copy_(key_states)
|
293 |
+
v_out.copy_(value_states)
|
294 |
+
else:
|
295 |
+
# Note: here we use `tensor.index_copy_(dim, index, tensor)` that is equivalent to
|
296 |
+
# `tensor[:, :, index] = tensor`, but the first one is compile-friendly and it does explicitly an in-place
|
297 |
+
# operation, that avoids copies and uses less memory.
|
298 |
+
try:
|
299 |
+
k_out.index_copy_(2, cache_position, key_states)
|
300 |
+
v_out.index_copy_(2, cache_position, value_states)
|
301 |
+
except NotImplementedError:
|
302 |
+
# The operator 'aten::index_copy.out' is not currently implemented for the MPS device.
|
303 |
+
k_out[:, :, cache_position] = key_states
|
304 |
+
v_out[:, :, cache_position] = value_states
|
305 |
+
|
306 |
+
return k_out, v_out
|
307 |
+
|
308 |
+
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
|
309 |
+
"""Returns the sequence length of the cached states that were seen by the model."""
|
310 |
+
# Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
|
311 |
+
# limit the check to the first batch member and head dimension.
|
312 |
+
# TODO: deprecate this function in favor of `cache_position`
|
313 |
+
return (self.key_cache[layer_idx][0, 0].any(dim=-1)).sum()
|
314 |
+
|
315 |
+
def get_max_length(self) -> Optional[int]:
|
316 |
+
"""Returns the maximum sequence length of the cached states."""
|
317 |
+
return self.max_cache_len
|
318 |
+
|
319 |
+
def reset(self):
|
320 |
+
"""Resets the cache values while preserving the objects"""
|
321 |
+
for layer_idx in range(len(self.key_cache)):
|
322 |
+
# In-place ops prevent breaking the static address
|
323 |
+
self.key_cache[layer_idx].zero_()
|
324 |
+
self.value_cache[layer_idx].zero_()
|
325 |
+
|
transformers_4_44_2__configuration_llama.py
ADDED
@@ -0,0 +1,203 @@
|
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|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
5 |
+
# and OPT implementations in this library. It has been modified from its
|
6 |
+
# original forms to accommodate minor architectural differences compared
|
7 |
+
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
8 |
+
#
|
9 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
10 |
+
# you may not use this file except in compliance with the License.
|
11 |
+
# You may obtain a copy of the License at
|
12 |
+
#
|
13 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
14 |
+
#
|
15 |
+
# Unless required by applicable law or agreed to in writing, software
|
16 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
17 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
18 |
+
# See the License for the specific language governing permissions and
|
19 |
+
# limitations under the License.
|
20 |
+
"""LLaMA model configuration"""
|
21 |
+
|
22 |
+
from transformers.configuration_utils import PretrainedConfig
|
23 |
+
from .transformers_4_44_2__modeling_rope_utils import rope_config_validation
|
24 |
+
|
25 |
+
|
26 |
+
class LlamaConfig(PretrainedConfig):
|
27 |
+
r"""
|
28 |
+
This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
|
29 |
+
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
30 |
+
defaults will yield a similar configuration to that of the LLaMA-7B.
|
31 |
+
|
32 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
33 |
+
documentation from [`PretrainedConfig`] for more information.
|
34 |
+
|
35 |
+
|
36 |
+
Args:
|
37 |
+
vocab_size (`int`, *optional*, defaults to 32000):
|
38 |
+
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
|
39 |
+
`inputs_ids` passed when calling [`LlamaModel`]
|
40 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
41 |
+
Dimension of the hidden representations.
|
42 |
+
intermediate_size (`int`, *optional*, defaults to 11008):
|
43 |
+
Dimension of the MLP representations.
|
44 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
45 |
+
Number of hidden layers in the Transformer decoder.
|
46 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
47 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
48 |
+
num_key_value_heads (`int`, *optional*):
|
49 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
50 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
51 |
+
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
52 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
53 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
54 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
55 |
+
`num_attention_heads`.
|
56 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
57 |
+
The non-linear activation function (function or string) in the decoder.
|
58 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
59 |
+
The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
|
60 |
+
Llama 2 up to 4096, CodeLlama up to 16384.
|
61 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
62 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
63 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
64 |
+
The epsilon used by the rms normalization layers.
|
65 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
66 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
67 |
+
relevant if `config.is_decoder=True`.
|
68 |
+
pad_token_id (`int`, *optional*):
|
69 |
+
Padding token id.
|
70 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
71 |
+
Beginning of stream token id.
|
72 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
73 |
+
End of stream token id.
|
74 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
75 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
76 |
+
document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
|
77 |
+
understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
|
78 |
+
results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
|
79 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
80 |
+
Whether to tie weight embeddings
|
81 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
82 |
+
The base period of the RoPE embeddings.
|
83 |
+
rope_scaling (`Dict`, *optional*):
|
84 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
|
85 |
+
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
86 |
+
accordingly.
|
87 |
+
Expected contents:
|
88 |
+
`rope_type` (`str`):
|
89 |
+
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
90 |
+
'llama3'], with 'default' being the original RoPE implementation.
|
91 |
+
`factor` (`float`, *optional*):
|
92 |
+
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
93 |
+
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
94 |
+
original maximum pre-trained length.
|
95 |
+
`original_max_position_embeddings` (`int`, *optional*):
|
96 |
+
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
97 |
+
pretraining.
|
98 |
+
`attention_factor` (`float`, *optional*):
|
99 |
+
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
100 |
+
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
101 |
+
`factor` field to infer the suggested value.
|
102 |
+
`beta_fast` (`float`, *optional*):
|
103 |
+
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
104 |
+
ramp function. If unspecified, it defaults to 32.
|
105 |
+
`beta_slow` (`float`, *optional*):
|
106 |
+
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
107 |
+
ramp function. If unspecified, it defaults to 1.
|
108 |
+
`short_factor` (`List[float]`, *optional*):
|
109 |
+
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
110 |
+
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
111 |
+
size divided by the number of attention heads divided by 2
|
112 |
+
`long_factor` (`List[float]`, *optional*):
|
113 |
+
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
114 |
+
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
115 |
+
size divided by the number of attention heads divided by 2
|
116 |
+
`low_freq_factor` (`float`, *optional*):
|
117 |
+
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
118 |
+
`high_freq_factor` (`float`, *optional*):
|
119 |
+
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
120 |
+
attention_bias (`bool`, *optional*, defaults to `False`):
|
121 |
+
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
122 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
123 |
+
The dropout ratio for the attention probabilities.
|
124 |
+
mlp_bias (`bool`, *optional*, defaults to `False`):
|
125 |
+
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
|
126 |
+
|
127 |
+
```python
|
128 |
+
>>> from transformers import LlamaModel, LlamaConfig
|
129 |
+
|
130 |
+
>>> # Initializing a LLaMA llama-7b style configuration
|
131 |
+
>>> configuration = LlamaConfig()
|
132 |
+
|
133 |
+
>>> # Initializing a model from the llama-7b style configuration
|
134 |
+
>>> model = LlamaModel(configuration)
|
135 |
+
|
136 |
+
>>> # Accessing the model configuration
|
137 |
+
>>> configuration = model.config
|
138 |
+
```"""
|
139 |
+
|
140 |
+
model_type = "llama"
|
141 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
142 |
+
|
143 |
+
def __init__(
|
144 |
+
self,
|
145 |
+
vocab_size=32000,
|
146 |
+
hidden_size=4096,
|
147 |
+
intermediate_size=11008,
|
148 |
+
num_hidden_layers=32,
|
149 |
+
num_attention_heads=32,
|
150 |
+
num_key_value_heads=None,
|
151 |
+
hidden_act="silu",
|
152 |
+
max_position_embeddings=2048,
|
153 |
+
initializer_range=0.02,
|
154 |
+
rms_norm_eps=1e-6,
|
155 |
+
use_cache=True,
|
156 |
+
pad_token_id=None,
|
157 |
+
bos_token_id=1,
|
158 |
+
eos_token_id=2,
|
159 |
+
pretraining_tp=1,
|
160 |
+
tie_word_embeddings=False,
|
161 |
+
rope_theta=10000.0,
|
162 |
+
rope_scaling=None,
|
163 |
+
attention_bias=False,
|
164 |
+
attention_dropout=0.0,
|
165 |
+
mlp_bias=False,
|
166 |
+
**kwargs,
|
167 |
+
):
|
168 |
+
self.vocab_size = vocab_size
|
169 |
+
self.max_position_embeddings = max_position_embeddings
|
170 |
+
self.hidden_size = hidden_size
|
171 |
+
self.intermediate_size = intermediate_size
|
172 |
+
self.num_hidden_layers = num_hidden_layers
|
173 |
+
self.num_attention_heads = num_attention_heads
|
174 |
+
|
175 |
+
# for backward compatibility
|
176 |
+
if num_key_value_heads is None:
|
177 |
+
num_key_value_heads = num_attention_heads
|
178 |
+
|
179 |
+
self.num_key_value_heads = num_key_value_heads
|
180 |
+
self.hidden_act = hidden_act
|
181 |
+
self.initializer_range = initializer_range
|
182 |
+
self.rms_norm_eps = rms_norm_eps
|
183 |
+
self.pretraining_tp = pretraining_tp
|
184 |
+
self.use_cache = use_cache
|
185 |
+
self.rope_theta = rope_theta
|
186 |
+
self.rope_scaling = rope_scaling
|
187 |
+
self.attention_bias = attention_bias
|
188 |
+
self.attention_dropout = attention_dropout
|
189 |
+
self.mlp_bias = mlp_bias
|
190 |
+
|
191 |
+
# Validate the correctness of rotary position embeddings parameters
|
192 |
+
# BC: if there is a 'type' field, move it to 'rope_type'.
|
193 |
+
if self.rope_scaling is not None and "type" in self.rope_scaling:
|
194 |
+
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
|
195 |
+
rope_config_validation(self)
|
196 |
+
|
197 |
+
super().__init__(
|
198 |
+
pad_token_id=pad_token_id,
|
199 |
+
bos_token_id=bos_token_id,
|
200 |
+
eos_token_id=eos_token_id,
|
201 |
+
tie_word_embeddings=tie_word_embeddings,
|
202 |
+
**kwargs,
|
203 |
+
)
|
transformers_4_44_2__modeling_attn_mask_utils.py
ADDED
@@ -0,0 +1,482 @@
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|
1 |
+
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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+
from typing import List, Optional, Tuple, Union
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+
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+
import torch
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+
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+
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+
@dataclass
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class AttentionMaskConverter:
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"""
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A utility attention mask class that allows one to:
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- Create a causal 4d mask
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- Create a causal 4d mask with slided window
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- Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
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key_value_length) that can be multiplied with attention scores
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+
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Examples:
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+
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```python
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>>> import torch
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>>> from transformers.modeling_attn_mask_utils import AttentionMaskConverter
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+
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>>> converter = AttentionMaskConverter(True)
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>>> converter.to_4d(torch.tensor([[0, 0, 0, 1, 1]]), 5, key_value_length=5, dtype=torch.float32)
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+
tensor([[[[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
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[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
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[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
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[-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, -3.4028e+38],
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+
[-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, 0.0000e+00]]]])
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+
```
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+
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+
Parameters:
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+
is_causal (`bool`):
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Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
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+
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+
sliding_window (`int`, *optional*):
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+
Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
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"""
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+
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is_causal: bool
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sliding_window: int
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+
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def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
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self.is_causal = is_causal
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self.sliding_window = sliding_window
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+
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if self.sliding_window is not None and self.sliding_window <= 0:
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raise ValueError(
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f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`"
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)
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+
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+
def to_causal_4d(
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self,
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batch_size: int,
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+
query_length: int,
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key_value_length: int,
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dtype: torch.dtype,
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device: Union[torch.device, "str"] = "cpu",
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) -> Optional[torch.Tensor]:
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"""
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Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
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bias to upper right hand triangular matrix (causal mask).
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"""
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if not self.is_causal:
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raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
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+
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# If shape is not cached, create a new causal mask and cache it
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input_shape = (batch_size, query_length)
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+
past_key_values_length = key_value_length - query_length
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+
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# create causal mask
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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causal_4d_mask = None
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if input_shape[-1] > 1 or self.sliding_window is not None:
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causal_4d_mask = self._make_causal_mask(
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input_shape,
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dtype,
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device=device,
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past_key_values_length=past_key_values_length,
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sliding_window=self.sliding_window,
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)
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+
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return causal_4d_mask
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+
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def to_4d(
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self,
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attention_mask_2d: torch.Tensor,
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query_length: int,
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dtype: torch.dtype,
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key_value_length: Optional[int] = None,
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) -> torch.Tensor:
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"""
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+
Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
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+
key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
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+
causal, a causal mask will be added.
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+
"""
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+
input_shape = (attention_mask_2d.shape[0], query_length)
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+
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# create causal mask
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+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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+
causal_4d_mask = None
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+
if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
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+
if key_value_length is None:
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+
raise ValueError(
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"This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask."
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)
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+
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+
past_key_values_length = key_value_length - query_length
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causal_4d_mask = self._make_causal_mask(
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input_shape,
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dtype,
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+
device=attention_mask_2d.device,
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+
past_key_values_length=past_key_values_length,
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+
sliding_window=self.sliding_window,
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+
)
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+
elif self.sliding_window is not None:
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raise NotImplementedError("Sliding window is currently only implemented for causal masking")
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+
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+
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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+
expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
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+
attention_mask_2d.device
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+
)
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+
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+
if causal_4d_mask is not None:
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+
expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min)
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+
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+
# expanded_attn_mask + causal_4d_mask can cause some overflow
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+
expanded_4d_mask = expanded_attn_mask
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+
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+
return expanded_4d_mask
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+
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+
@staticmethod
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+
def _make_causal_mask(
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input_ids_shape: torch.Size,
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+
dtype: torch.dtype,
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+
device: torch.device,
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+
past_key_values_length: int = 0,
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+
sliding_window: Optional[int] = None,
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+
):
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152 |
+
"""
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153 |
+
Make causal mask used for bi-directional self-attention.
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154 |
+
"""
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+
bsz, tgt_len = input_ids_shape
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+
mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
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+
mask_cond = torch.arange(mask.size(-1), device=device)
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+
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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159 |
+
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+
mask = mask.to(dtype)
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161 |
+
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162 |
+
if past_key_values_length > 0:
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+
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
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164 |
+
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+
# add lower triangular sliding window mask if necessary
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+
if sliding_window is not None:
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+
diagonal = past_key_values_length - sliding_window - 1
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+
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+
context_mask = torch.tril(torch.ones_like(mask, dtype=torch.bool), diagonal=diagonal)
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+
mask.masked_fill_(context_mask, torch.finfo(dtype).min)
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+
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+
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
173 |
+
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+
@staticmethod
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+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
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176 |
+
"""
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+
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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178 |
+
"""
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179 |
+
bsz, src_len = mask.size()
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180 |
+
tgt_len = tgt_len if tgt_len is not None else src_len
|
181 |
+
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182 |
+
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
183 |
+
|
184 |
+
inverted_mask = 1.0 - expanded_mask
|
185 |
+
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186 |
+
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
187 |
+
|
188 |
+
@staticmethod
|
189 |
+
def _unmask_unattended(
|
190 |
+
expanded_mask: torch.FloatTensor,
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191 |
+
min_dtype: float,
|
192 |
+
):
|
193 |
+
# fmt: off
|
194 |
+
"""
|
195 |
+
Attend to all tokens in masked rows from the expanded attention mask, for example the relevant first rows when
|
196 |
+
using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
197 |
+
Details: https://github.com/pytorch/pytorch/issues/110213
|
198 |
+
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199 |
+
`expanded_mask` is [bsz, num_masks, tgt_seq_len, src_seq_len] or [bsz, tgt_seq_len, src_seq_len].
|
200 |
+
`attention_mask` is [bsz, src_seq_len].
|
201 |
+
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202 |
+
The dimension num_masks of `expanded_mask` is most often 1, but it can also be the number of heads in the case of alibi attention bias.
|
203 |
+
|
204 |
+
For example, if `expanded_mask` is (e.g. here left-padding case)
|
205 |
+
```
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206 |
+
[[[[0, 0, 0],
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207 |
+
[0, 0, 0],
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208 |
+
[0, 0, 1]]],
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209 |
+
[[[1, 0, 0],
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210 |
+
[1, 1, 0],
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+
[1, 1, 1]]],
|
212 |
+
[[[0, 0, 0],
|
213 |
+
[0, 1, 0],
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214 |
+
[0, 1, 1]]]]
|
215 |
+
```
|
216 |
+
then the modified `expanded_mask` will be
|
217 |
+
```
|
218 |
+
[[[[1, 1, 1], <-- modified
|
219 |
+
[1, 1, 1], <-- modified
|
220 |
+
[0, 0, 1]]],
|
221 |
+
[[[1, 0, 0],
|
222 |
+
[1, 1, 0],
|
223 |
+
[1, 1, 1]]],
|
224 |
+
[[[1, 1, 1], <-- modified
|
225 |
+
[0, 1, 0],
|
226 |
+
[0, 1, 1]]]]
|
227 |
+
```
|
228 |
+
"""
|
229 |
+
# fmt: on
|
230 |
+
if expanded_mask.dtype == torch.bool:
|
231 |
+
raise ValueError(
|
232 |
+
"AttentionMaskConverter._unmask_unattended expects a float `expanded_mask`, got a BoolTensor."
|
233 |
+
)
|
234 |
+
|
235 |
+
return expanded_mask.mul(~torch.all(expanded_mask == min_dtype, dim=-1, keepdim=True))
|
236 |
+
|
237 |
+
@staticmethod
|
238 |
+
def _ignore_causal_mask_sdpa(
|
239 |
+
attention_mask: Optional[torch.Tensor],
|
240 |
+
inputs_embeds: torch.Tensor,
|
241 |
+
past_key_values_length: int,
|
242 |
+
sliding_window: Optional[int] = None,
|
243 |
+
is_training: bool = False,
|
244 |
+
) -> bool:
|
245 |
+
"""
|
246 |
+
Detects whether the optional user-specified attention_mask & the automatically created causal mask can be ignored in case PyTorch's SDPA is used, rather relying on SDPA's `is_causal` argument.
|
247 |
+
|
248 |
+
In case no token is masked in the `attention_mask` argument, if `query_length == 1` or
|
249 |
+
`key_value_length == query_length`, we rather rely on SDPA `is_causal` argument to use causal/non-causal masks,
|
250 |
+
allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
|
251 |
+
"""
|
252 |
+
|
253 |
+
_, query_length = inputs_embeds.shape[0], inputs_embeds.shape[1]
|
254 |
+
key_value_length = query_length + past_key_values_length
|
255 |
+
|
256 |
+
is_tracing = (
|
257 |
+
torch.jit.is_tracing()
|
258 |
+
or isinstance(inputs_embeds, torch.fx.Proxy)
|
259 |
+
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
|
260 |
+
)
|
261 |
+
|
262 |
+
ignore_causal_mask = False
|
263 |
+
|
264 |
+
if attention_mask is None:
|
265 |
+
# TODO: When tracing with TorchDynamo with fullgraph=True, the model is recompiled depending on the input shape, thus SDPA's `is_causal` argument is rightfully updated (see https://gist.github.com/fxmarty/1313f39037fc1c112508989628c57363). However, when using `torch.export` or
|
266 |
+
# or `torch.onnx.dynamo_export`, we must pass an example input, and `is_causal` behavior is hard-coded. If a user exports a model with q_len > 1, the exported model will hard-code `is_causal=True` which is in general wrong (see https://github.com/pytorch/pytorch/issues/108108).
|
267 |
+
# Thus, we only set `ignore_causal_mask = True` if the model is set to training.
|
268 |
+
#
|
269 |
+
# Besides, jit.trace can not handle the `q_len > 1` condition for `is_causal` ("TypeError: scaled_dot_product_attention(): argument 'is_causal' must be bool, not Tensor").
|
270 |
+
if (
|
271 |
+
(is_training or not is_tracing)
|
272 |
+
and (query_length == 1 or key_value_length == query_length)
|
273 |
+
and (sliding_window is None or key_value_length < sliding_window)
|
274 |
+
):
|
275 |
+
ignore_causal_mask = True
|
276 |
+
elif sliding_window is None or key_value_length < sliding_window:
|
277 |
+
if len(attention_mask.shape) == 4:
|
278 |
+
return False
|
279 |
+
elif (is_training or not is_tracing) and torch.all(attention_mask == 1):
|
280 |
+
if query_length == 1 or key_value_length == query_length:
|
281 |
+
# For query_length == 1, causal attention and bi-directional attention are the same.
|
282 |
+
ignore_causal_mask = True
|
283 |
+
|
284 |
+
# Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
|
285 |
+
# may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
|
286 |
+
# Reference: https://github.com/pytorch/pytorch/issues/108108
|
287 |
+
# TODO: maybe revisit this with https://github.com/pytorch/pytorch/pull/114823 in PyTorch 2.3.
|
288 |
+
|
289 |
+
return ignore_causal_mask
|
290 |
+
|
291 |
+
|
292 |
+
def _prepare_4d_causal_attention_mask(
|
293 |
+
attention_mask: Optional[torch.Tensor],
|
294 |
+
input_shape: Union[torch.Size, Tuple, List],
|
295 |
+
inputs_embeds: torch.Tensor,
|
296 |
+
past_key_values_length: int,
|
297 |
+
sliding_window: Optional[int] = None,
|
298 |
+
):
|
299 |
+
"""
|
300 |
+
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
301 |
+
`(batch_size, key_value_length)`
|
302 |
+
|
303 |
+
Args:
|
304 |
+
attention_mask (`torch.Tensor` or `None`):
|
305 |
+
A 2D attention mask of shape `(batch_size, key_value_length)`
|
306 |
+
input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
|
307 |
+
The input shape should be a tuple that defines `(batch_size, query_length)`.
|
308 |
+
inputs_embeds (`torch.Tensor`):
|
309 |
+
The embedded inputs as a torch Tensor.
|
310 |
+
past_key_values_length (`int`):
|
311 |
+
The length of the key value cache.
|
312 |
+
sliding_window (`int`, *optional*):
|
313 |
+
If the model uses windowed attention, a sliding window should be passed.
|
314 |
+
"""
|
315 |
+
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
|
316 |
+
|
317 |
+
key_value_length = input_shape[-1] + past_key_values_length
|
318 |
+
|
319 |
+
# 4d mask is passed through the layers
|
320 |
+
if attention_mask is not None and len(attention_mask.shape) == 2:
|
321 |
+
attention_mask = attn_mask_converter.to_4d(
|
322 |
+
attention_mask, input_shape[-1], key_value_length=key_value_length, dtype=inputs_embeds.dtype
|
323 |
+
)
|
324 |
+
elif attention_mask is not None and len(attention_mask.shape) == 4:
|
325 |
+
expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
|
326 |
+
if tuple(attention_mask.shape) != expected_shape:
|
327 |
+
raise ValueError(
|
328 |
+
f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
|
329 |
+
)
|
330 |
+
else:
|
331 |
+
# if the 4D mask has correct shape - invert it and fill with negative infinity
|
332 |
+
inverted_mask = 1.0 - attention_mask
|
333 |
+
attention_mask = inverted_mask.masked_fill(
|
334 |
+
inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
|
335 |
+
)
|
336 |
+
else:
|
337 |
+
attention_mask = attn_mask_converter.to_causal_4d(
|
338 |
+
input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
|
339 |
+
)
|
340 |
+
|
341 |
+
return attention_mask
|
342 |
+
|
343 |
+
|
344 |
+
# Adapted from _prepare_4d_causal_attention_mask
|
345 |
+
def _prepare_4d_causal_attention_mask_for_sdpa(
|
346 |
+
attention_mask: Optional[torch.Tensor],
|
347 |
+
input_shape: Union[torch.Size, Tuple, List],
|
348 |
+
inputs_embeds: torch.Tensor,
|
349 |
+
past_key_values_length: int,
|
350 |
+
sliding_window: Optional[int] = None,
|
351 |
+
):
|
352 |
+
"""
|
353 |
+
Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`.
|
354 |
+
|
355 |
+
In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and
|
356 |
+
`key_value_length == query_length`, and rely instead on SDPA `is_causal` argument to use causal/non-causal masks,
|
357 |
+
allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
|
358 |
+
"""
|
359 |
+
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
|
360 |
+
|
361 |
+
key_value_length = input_shape[-1] + past_key_values_length
|
362 |
+
|
363 |
+
# torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
|
364 |
+
# used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
|
365 |
+
# TODO: For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
|
366 |
+
is_tracing = (
|
367 |
+
torch.jit.is_tracing()
|
368 |
+
or isinstance(inputs_embeds, torch.fx.Proxy)
|
369 |
+
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
|
370 |
+
)
|
371 |
+
|
372 |
+
ignore_causal_mask = AttentionMaskConverter._ignore_causal_mask_sdpa(
|
373 |
+
attention_mask=attention_mask,
|
374 |
+
inputs_embeds=inputs_embeds,
|
375 |
+
past_key_values_length=past_key_values_length,
|
376 |
+
sliding_window=sliding_window,
|
377 |
+
)
|
378 |
+
|
379 |
+
if ignore_causal_mask:
|
380 |
+
expanded_4d_mask = None
|
381 |
+
elif attention_mask is None:
|
382 |
+
expanded_4d_mask = attn_mask_converter.to_causal_4d(
|
383 |
+
input_shape[0], input_shape[-1], key_value_length, dtype=inputs_embeds.dtype, device=inputs_embeds.device
|
384 |
+
)
|
385 |
+
else:
|
386 |
+
if attention_mask.dim() == 4:
|
387 |
+
# in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
|
388 |
+
if attention_mask.max() != 0:
|
389 |
+
raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
|
390 |
+
expanded_4d_mask = attention_mask
|
391 |
+
else:
|
392 |
+
expanded_4d_mask = attn_mask_converter.to_4d(
|
393 |
+
attention_mask,
|
394 |
+
input_shape[-1],
|
395 |
+
dtype=inputs_embeds.dtype,
|
396 |
+
key_value_length=key_value_length,
|
397 |
+
)
|
398 |
+
|
399 |
+
# Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
|
400 |
+
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
|
401 |
+
# Details: https://github.com/pytorch/pytorch/issues/110213
|
402 |
+
if not is_tracing and expanded_4d_mask.device.type == "cuda":
|
403 |
+
expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
|
404 |
+
expanded_4d_mask, min_dtype=torch.finfo(inputs_embeds.dtype).min
|
405 |
+
)
|
406 |
+
|
407 |
+
return expanded_4d_mask
|
408 |
+
|
409 |
+
|
410 |
+
def _prepare_4d_attention_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
411 |
+
"""
|
412 |
+
Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
413 |
+
`(batch_size, key_value_length)`
|
414 |
+
|
415 |
+
Args:
|
416 |
+
mask (`torch.Tensor`):
|
417 |
+
A 2D attention mask of shape `(batch_size, key_value_length)`
|
418 |
+
dtype (`torch.dtype`):
|
419 |
+
The torch dtype the created mask shall have.
|
420 |
+
tgt_len (`int`):
|
421 |
+
The target length or query length the created mask shall have.
|
422 |
+
"""
|
423 |
+
return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
|
424 |
+
|
425 |
+
|
426 |
+
def _prepare_4d_attention_mask_for_sdpa(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
427 |
+
"""
|
428 |
+
Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
429 |
+
`(batch_size, key_value_length)`
|
430 |
+
|
431 |
+
Args:
|
432 |
+
mask (`torch.Tensor`):
|
433 |
+
A 2D attention mask of shape `(batch_size, key_value_length)`
|
434 |
+
dtype (`torch.dtype`):
|
435 |
+
The torch dtype the created mask shall have.
|
436 |
+
tgt_len (`int`):
|
437 |
+
The target length or query length the created mask shall have.
|
438 |
+
"""
|
439 |
+
_, key_value_length = mask.shape
|
440 |
+
tgt_len = tgt_len if tgt_len is not None else key_value_length
|
441 |
+
|
442 |
+
is_tracing = (
|
443 |
+
torch.jit.is_tracing()
|
444 |
+
or isinstance(mask, torch.fx.Proxy)
|
445 |
+
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
|
446 |
+
)
|
447 |
+
|
448 |
+
# torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture data-dependent controlflows.
|
449 |
+
if not is_tracing and torch.all(mask == 1):
|
450 |
+
return None
|
451 |
+
else:
|
452 |
+
return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
|
453 |
+
|
454 |
+
|
455 |
+
def _create_4d_causal_attention_mask(
|
456 |
+
input_shape: Union[torch.Size, Tuple, List],
|
457 |
+
dtype: torch.dtype,
|
458 |
+
device: torch.device,
|
459 |
+
past_key_values_length: int = 0,
|
460 |
+
sliding_window: Optional[int] = None,
|
461 |
+
) -> Optional[torch.Tensor]:
|
462 |
+
"""
|
463 |
+
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)`
|
464 |
+
|
465 |
+
Args:
|
466 |
+
input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
|
467 |
+
The input shape should be a tuple that defines `(batch_size, query_length)`.
|
468 |
+
dtype (`torch.dtype`):
|
469 |
+
The torch dtype the created mask shall have.
|
470 |
+
device (`int`):
|
471 |
+
The torch device the created mask shall have.
|
472 |
+
sliding_window (`int`, *optional*):
|
473 |
+
If the model uses windowed attention, a sliding window should be passed.
|
474 |
+
"""
|
475 |
+
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
|
476 |
+
|
477 |
+
key_value_length = past_key_values_length + input_shape[-1]
|
478 |
+
attention_mask = attn_mask_converter.to_causal_4d(
|
479 |
+
input_shape[0], input_shape[-1], key_value_length, dtype=dtype, device=device
|
480 |
+
)
|
481 |
+
|
482 |
+
return attention_mask
|
transformers_4_44_2__modeling_flash_attention_utils_backward_compat.py
ADDED
@@ -0,0 +1,348 @@
|
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|
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|
|
|
|
|
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|
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|
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|
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|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
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|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2024 The Fairseq Authors and the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
|
16 |
+
import inspect
|
17 |
+
import os
|
18 |
+
from typing import Optional, Tuple, Union
|
19 |
+
|
20 |
+
|
21 |
+
import torch
|
22 |
+
import torch.nn.functional as F
|
23 |
+
|
24 |
+
from functools import lru_cache
|
25 |
+
import importlib.metadata
|
26 |
+
import importlib.util
|
27 |
+
from packaging import version
|
28 |
+
|
29 |
+
from transformers.utils import is_flash_attn_2_available
|
30 |
+
|
31 |
+
|
32 |
+
if is_flash_attn_2_available():
|
33 |
+
try:
|
34 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
35 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
36 |
+
_flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
|
37 |
+
except ImportError:
|
38 |
+
raise "Unable to import flash_attn"
|
39 |
+
|
40 |
+
|
41 |
+
def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[Tuple[bool, str], bool]:
|
42 |
+
# Check if the package spec exists and grab its version to avoid importing a local directory
|
43 |
+
package_exists = importlib.util.find_spec(pkg_name) is not None
|
44 |
+
package_version = "N/A"
|
45 |
+
if package_exists:
|
46 |
+
try:
|
47 |
+
# Primary method to get the package version
|
48 |
+
package_version = importlib.metadata.version(pkg_name)
|
49 |
+
except importlib.metadata.PackageNotFoundError:
|
50 |
+
# Fallback method: Only for "torch" and versions containing "dev"
|
51 |
+
if pkg_name == "torch":
|
52 |
+
try:
|
53 |
+
package = importlib.import_module(pkg_name)
|
54 |
+
temp_version = getattr(package, "__version__", "N/A")
|
55 |
+
# Check if the version contains "dev"
|
56 |
+
if "dev" in temp_version:
|
57 |
+
package_version = temp_version
|
58 |
+
package_exists = True
|
59 |
+
else:
|
60 |
+
package_exists = False
|
61 |
+
except ImportError:
|
62 |
+
# If the package can't be imported, it's not available
|
63 |
+
package_exists = False
|
64 |
+
else:
|
65 |
+
# For packages other than "torch", don't attempt the fallback and set as not available
|
66 |
+
package_exists = False
|
67 |
+
if return_version:
|
68 |
+
return package_exists, package_version
|
69 |
+
else:
|
70 |
+
return package_exists
|
71 |
+
|
72 |
+
|
73 |
+
@lru_cache()
|
74 |
+
def is_flash_attn_greater_or_equal(library_version: str):
|
75 |
+
if not _is_package_available("flash_attn"):
|
76 |
+
return False
|
77 |
+
|
78 |
+
return version.parse(importlib.metadata.version("flash_attn")) >= version.parse(library_version)
|
79 |
+
|
80 |
+
|
81 |
+
def _get_unpad_data(attention_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, int]:
|
82 |
+
"""
|
83 |
+
Retrieves indexing data required to repad unpadded (ragged) tensors.
|
84 |
+
|
85 |
+
Arguments:
|
86 |
+
attention_mask (`torch.Tensor`):
|
87 |
+
Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
|
88 |
+
|
89 |
+
Return:
|
90 |
+
indices (`torch.Tensor`):
|
91 |
+
The indices of non-masked tokens from the flattened input sequence.
|
92 |
+
cu_seqlens (`torch.Tensor`):
|
93 |
+
The cumulative sequence lengths, used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
|
94 |
+
max_seqlen_in_batch (`int`):
|
95 |
+
Maximum sequence length in batch.
|
96 |
+
"""
|
97 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
98 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
99 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
100 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
|
101 |
+
return (
|
102 |
+
indices,
|
103 |
+
cu_seqlens,
|
104 |
+
max_seqlen_in_batch,
|
105 |
+
)
|
106 |
+
|
107 |
+
|
108 |
+
def _upad_input(
|
109 |
+
query_layer: torch.Tensor,
|
110 |
+
key_layer: torch.Tensor,
|
111 |
+
value_layer: torch.Tensor,
|
112 |
+
attention_mask: torch.Tensor,
|
113 |
+
query_length: int,
|
114 |
+
):
|
115 |
+
"""
|
116 |
+
Unpads query, key, and values tensors, using a single dimension for all tokens even though they belong to different batches.
|
117 |
+
|
118 |
+
This function is used instead of `flash_attn.bert_padding.unpad_input` in order to avoid the recomputation of the same intermediary
|
119 |
+
tensors for query, key, value tensors.
|
120 |
+
|
121 |
+
Arguments:
|
122 |
+
query_layer (`torch.Tensor`):
|
123 |
+
Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim).
|
124 |
+
key_layer (`torch.Tensor`):
|
125 |
+
Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
|
126 |
+
value_layer (`torch.Tensor`):
|
127 |
+
Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
|
128 |
+
attention_mask (`torch.Tensor`):
|
129 |
+
Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
|
130 |
+
query_length (`int`):
|
131 |
+
Target length.
|
132 |
+
|
133 |
+
Return:
|
134 |
+
query_layer (`torch.Tensor`):
|
135 |
+
Query state without padding. Shape: (total_target_length, num_heads, head_dim).
|
136 |
+
key_layer (`torch.Tensor`):
|
137 |
+
Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
|
138 |
+
value_layer (`torch.Tensor`):
|
139 |
+
Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
|
140 |
+
indices_q (`torch.Tensor`):
|
141 |
+
The indices of non-masked tokens from the flattened input target sequence.
|
142 |
+
(cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`):
|
143 |
+
The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
|
144 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`):
|
145 |
+
Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value).
|
146 |
+
"""
|
147 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
148 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
149 |
+
|
150 |
+
key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k)
|
151 |
+
value_layer = index_first_axis(
|
152 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
153 |
+
)
|
154 |
+
if query_length == kv_seq_len:
|
155 |
+
query_layer = index_first_axis(query_layer.reshape(batch_size * kv_seq_len, -1, head_dim), indices_k)
|
156 |
+
cu_seqlens_q = cu_seqlens_k
|
157 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
158 |
+
indices_q = indices_k
|
159 |
+
elif query_length == 1:
|
160 |
+
max_seqlen_in_batch_q = 1
|
161 |
+
cu_seqlens_q = torch.arange(
|
162 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
163 |
+
) # There is a memcpy here, that is very bad.
|
164 |
+
indices_q = cu_seqlens_q[:-1]
|
165 |
+
query_layer = query_layer.squeeze(1)
|
166 |
+
else:
|
167 |
+
# The -q_len: slice assumes left padding.
|
168 |
+
attention_mask = attention_mask[:, -query_length:]
|
169 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
170 |
+
|
171 |
+
return (
|
172 |
+
query_layer,
|
173 |
+
key_layer,
|
174 |
+
value_layer,
|
175 |
+
indices_q,
|
176 |
+
(cu_seqlens_q, cu_seqlens_k),
|
177 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
178 |
+
)
|
179 |
+
|
180 |
+
|
181 |
+
def prepare_fa2_from_position_ids(query, key, value, position_ids):
|
182 |
+
"""
|
183 |
+
This function returns necessary arguments to call `flash_attn_varlen_func`.
|
184 |
+
All three query, key, value states will be flattened.
|
185 |
+
Cummulative lengths of each examples in the batch will be extracted from position_ids.
|
186 |
+
|
187 |
+
NOTE: ideally cummulative lengths should be prepared at the data collator stage
|
188 |
+
|
189 |
+
Arguments:
|
190 |
+
query (`torch.Tensor`):
|
191 |
+
Query state with padding. Shape: (batch_size, query_length, num_heads, head_dim).
|
192 |
+
key (`torch.Tensor`):
|
193 |
+
Key state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
|
194 |
+
value (`torch.Tensor`):
|
195 |
+
Value state with padding. Shape: (batch_size, kv_seq_len, num_key_value_heads, head_dim).
|
196 |
+
position_ids (`torch.Tensor`):
|
197 |
+
Boolean or int tensor of shape (batch_size, sequence_length), 1 means valid and 0 means not valid.
|
198 |
+
|
199 |
+
Return:
|
200 |
+
query (`torch.Tensor`):
|
201 |
+
Query state without padding. Shape: (total_target_length, num_heads, head_dim).
|
202 |
+
key (`torch.Tensor`):
|
203 |
+
Key state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
|
204 |
+
value (`torch.Tensor`):
|
205 |
+
Value state with padding. Shape: (total_source_length, num_key_value_heads, head_dim).
|
206 |
+
indices_q (`torch.Tensor`):
|
207 |
+
The indices of non-masked tokens from the flattened input target sequence.
|
208 |
+
(cu_seqlens_q, cu_seqlens_k) (`Tuple[int]`):
|
209 |
+
The cumulative sequence lengths for the target (query) and source (key, value), used to index into ragged (unpadded) tensors. `cu_seqlens` shape is (batch_size + 1,).
|
210 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k) (`Tuple[int]`):
|
211 |
+
Maximum sequence length in batch (`max_seqlen_in_batch_q` for the target sequence i.e. query, `max_seqlen_in_batch_k` for the source sequence i.e. key/value).
|
212 |
+
"""
|
213 |
+
query = query.view(-1, query.size(-2), query.size(-1))
|
214 |
+
key = key.view(-1, key.size(-2), key.size(-1))
|
215 |
+
value = value.view(-1, value.size(-2), value.size(-1))
|
216 |
+
position_ids = position_ids.flatten()
|
217 |
+
indices_q = torch.arange(position_ids.size(0), device=position_ids.device, dtype=torch.int32)
|
218 |
+
|
219 |
+
cu_seq_lens = torch.cat(
|
220 |
+
(
|
221 |
+
indices_q[position_ids == 0],
|
222 |
+
torch.tensor(position_ids.size(), device=position_ids.device, dtype=torch.int32),
|
223 |
+
)
|
224 |
+
)
|
225 |
+
|
226 |
+
max_length = position_ids.max() + 1
|
227 |
+
|
228 |
+
return (query, key, value, indices_q, (cu_seq_lens, cu_seq_lens), (max_length, max_length))
|
229 |
+
|
230 |
+
|
231 |
+
def _flash_attention_forward(
|
232 |
+
query_states: torch.Tensor,
|
233 |
+
key_states: torch.Tensor,
|
234 |
+
value_states: torch.Tensor,
|
235 |
+
attention_mask: torch.Tensor,
|
236 |
+
query_length: int,
|
237 |
+
is_causal: bool,
|
238 |
+
dropout: float = 0.0,
|
239 |
+
position_ids: Optional[torch.Tensor] = None,
|
240 |
+
softmax_scale: Optional[float] = None,
|
241 |
+
sliding_window: Optional[int] = None,
|
242 |
+
use_top_left_mask: bool = False,
|
243 |
+
softcap: Optional[float] = None,
|
244 |
+
deterministic: bool = None,
|
245 |
+
):
|
246 |
+
"""
|
247 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
248 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
249 |
+
|
250 |
+
Args:
|
251 |
+
query_states (`torch.Tensor`):
|
252 |
+
Input query states to be passed to Flash Attention API
|
253 |
+
key_states (`torch.Tensor`):
|
254 |
+
Input key states to be passed to Flash Attention API
|
255 |
+
value_states (`torch.Tensor`):
|
256 |
+
Input value states to be passed to Flash Attention API
|
257 |
+
attention_mask (`torch.Tensor`):
|
258 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
259 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
260 |
+
dropout (`float`):
|
261 |
+
Attention dropout
|
262 |
+
softmax_scale (`float`, *optional*):
|
263 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
264 |
+
use_top_left_mask (`bool`, defaults to `False`):
|
265 |
+
flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference.
|
266 |
+
softcap (`float`, *optional*):
|
267 |
+
Softcap for the attention logits, used e.g. in gemma2.
|
268 |
+
deterministic (`bool`, *optional*):
|
269 |
+
Determines if the deterministic option introduced in flash_attn>=2.4.1 is enabled.
|
270 |
+
"""
|
271 |
+
if not use_top_left_mask:
|
272 |
+
causal = is_causal
|
273 |
+
else:
|
274 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__.
|
275 |
+
causal = is_causal and query_length != 1
|
276 |
+
|
277 |
+
# Assuming 4D tensors, key_states.shape[1] is the key/value sequence length (source length).
|
278 |
+
use_sliding_windows = (
|
279 |
+
_flash_supports_window_size and sliding_window is not None and key_states.shape[1] > sliding_window
|
280 |
+
)
|
281 |
+
flash_kwargs = {"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {}
|
282 |
+
|
283 |
+
if is_flash_attn_greater_or_equal("2.4.1"):
|
284 |
+
if deterministic is None:
|
285 |
+
deterministic = os.environ.get("FLASH_ATTENTION_DETERMINISTIC", "0") == "1"
|
286 |
+
flash_kwargs["deterministic"] = deterministic
|
287 |
+
|
288 |
+
if softcap is not None:
|
289 |
+
flash_kwargs["softcap"] = softcap
|
290 |
+
|
291 |
+
# Contains at least one padding token in the sequence
|
292 |
+
if attention_mask is not None:
|
293 |
+
batch_size = query_states.shape[0]
|
294 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = _upad_input(
|
295 |
+
query_states, key_states, value_states, attention_mask, query_length
|
296 |
+
)
|
297 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
298 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
299 |
+
|
300 |
+
attn_output_unpad = flash_attn_varlen_func(
|
301 |
+
query_states,
|
302 |
+
key_states,
|
303 |
+
value_states,
|
304 |
+
cu_seqlens_q=cu_seqlens_q,
|
305 |
+
cu_seqlens_k=cu_seqlens_k,
|
306 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
307 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
308 |
+
dropout_p=dropout,
|
309 |
+
softmax_scale=softmax_scale,
|
310 |
+
causal=causal,
|
311 |
+
**flash_kwargs,
|
312 |
+
)
|
313 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
314 |
+
|
315 |
+
# If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
|
316 |
+
# then we probably have one sequence, otherwise it is packed. Additionally check we are in pre-fill/training stage.
|
317 |
+
# Use `flash_attn_varlen_func` to prevent cross-example attention and also allow padding free approach
|
318 |
+
elif position_ids is not None and not (torch.diff(position_ids, dim=-1) >= 0).all() and query_length != 1:
|
319 |
+
batch_size = query_states.size(0)
|
320 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = prepare_fa2_from_position_ids(
|
321 |
+
query_states, key_states, value_states, position_ids
|
322 |
+
)
|
323 |
+
|
324 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
325 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
326 |
+
|
327 |
+
attn_output = flash_attn_varlen_func(
|
328 |
+
query_states,
|
329 |
+
key_states,
|
330 |
+
value_states,
|
331 |
+
cu_seqlens_q=cu_seqlens_q,
|
332 |
+
cu_seqlens_k=cu_seqlens_k,
|
333 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
334 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
335 |
+
dropout_p=dropout,
|
336 |
+
softmax_scale=softmax_scale,
|
337 |
+
causal=causal,
|
338 |
+
**flash_kwargs,
|
339 |
+
)
|
340 |
+
|
341 |
+
attn_output = attn_output.view(batch_size, -1, attn_output.size(-2), attn_output.size(-1))
|
342 |
+
|
343 |
+
else:
|
344 |
+
attn_output = flash_attn_func(
|
345 |
+
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal, **flash_kwargs
|
346 |
+
)
|
347 |
+
|
348 |
+
return attn_output
|
transformers_4_44_2__modeling_outputs.py
ADDED
The diff for this file is too large to render.
See raw diff
|
|
transformers_4_44_2__modeling_rope_utils.py
ADDED
@@ -0,0 +1,559 @@
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1 |
+
# Copyright 2024 The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
import math
|
16 |
+
from typing import Optional, Tuple
|
17 |
+
|
18 |
+
from transformers.configuration_utils import PretrainedConfig
|
19 |
+
from transformers.utils import is_torch_available, logging
|
20 |
+
|
21 |
+
|
22 |
+
logger = logging.get_logger(__name__)
|
23 |
+
|
24 |
+
|
25 |
+
if is_torch_available():
|
26 |
+
import torch
|
27 |
+
|
28 |
+
|
29 |
+
def _compute_default_rope_parameters(
|
30 |
+
config: Optional[PretrainedConfig] = None,
|
31 |
+
device: Optional["torch.device"] = None,
|
32 |
+
seq_len: Optional[int] = None,
|
33 |
+
**rope_kwargs,
|
34 |
+
) -> Tuple["torch.Tensor", float]:
|
35 |
+
"""
|
36 |
+
Computes the inverse frequencies according to the original RoPE implementation
|
37 |
+
Args:
|
38 |
+
config ([`~transformers.PretrainedConfig`]):
|
39 |
+
The model configuration.
|
40 |
+
device (`torch.device`):
|
41 |
+
The device to use for initialization of the inverse frequencies.
|
42 |
+
seq_len (`int`, *optional*):
|
43 |
+
The current sequence length. Unused for this type of RoPE.
|
44 |
+
rope_kwargs (`Dict`, *optional*):
|
45 |
+
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
46 |
+
Returns:
|
47 |
+
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
48 |
+
post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
|
49 |
+
"""
|
50 |
+
if config is not None and len(rope_kwargs) > 0:
|
51 |
+
raise ValueError(
|
52 |
+
"Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
|
53 |
+
f"`_compute_default_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
|
54 |
+
)
|
55 |
+
if len(rope_kwargs) > 0:
|
56 |
+
base = rope_kwargs["base"]
|
57 |
+
dim = rope_kwargs["dim"]
|
58 |
+
elif config is not None:
|
59 |
+
base = config.rope_theta
|
60 |
+
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
61 |
+
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
62 |
+
dim = int(head_dim * partial_rotary_factor)
|
63 |
+
|
64 |
+
attention_factor = 1.0 # Unused in this type of RoPE
|
65 |
+
|
66 |
+
# Compute the inverse frequencies
|
67 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
|
68 |
+
return inv_freq, attention_factor
|
69 |
+
|
70 |
+
|
71 |
+
def _compute_linear_scaling_rope_parameters(
|
72 |
+
config: Optional[PretrainedConfig] = None,
|
73 |
+
device: Optional["torch.device"] = None,
|
74 |
+
seq_len: Optional[int] = None,
|
75 |
+
**rope_kwargs,
|
76 |
+
) -> Tuple["torch.Tensor", float]:
|
77 |
+
"""
|
78 |
+
Computes the inverse frequencies with linear scaling. Credits to the Reddit user /u/kaiokendev
|
79 |
+
Args:
|
80 |
+
config ([`~transformers.PretrainedConfig`]):
|
81 |
+
The model configuration.
|
82 |
+
device (`torch.device`):
|
83 |
+
The device to use for initialization of the inverse frequencies.
|
84 |
+
seq_len (`int`, *optional*):
|
85 |
+
The current sequence length. Unused for this type of RoPE.
|
86 |
+
rope_kwargs (`Dict`, *optional*):
|
87 |
+
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
88 |
+
Returns:
|
89 |
+
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
90 |
+
post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
|
91 |
+
"""
|
92 |
+
if config is not None and len(rope_kwargs) > 0:
|
93 |
+
raise ValueError(
|
94 |
+
"Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
|
95 |
+
f"`_compute_linear_scaling_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
|
96 |
+
)
|
97 |
+
if len(rope_kwargs) > 0:
|
98 |
+
factor = rope_kwargs["factor"]
|
99 |
+
elif config is not None:
|
100 |
+
factor = config.rope_scaling["factor"]
|
101 |
+
|
102 |
+
# Gets the default RoPE parameters
|
103 |
+
inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
|
104 |
+
|
105 |
+
# Then applies linear scaling to the frequencies.
|
106 |
+
# NOTE: originally, scaling was applied to the position_ids. However, we get `embs = inv_freq @ position_ids`, so
|
107 |
+
# applying scaling to the inverse frequencies is equivalent.
|
108 |
+
inv_freq /= factor
|
109 |
+
return inv_freq, attention_factor
|
110 |
+
|
111 |
+
|
112 |
+
def _compute_dynamic_ntk_parameters(
|
113 |
+
config: Optional[PretrainedConfig] = None,
|
114 |
+
device: Optional["torch.device"] = None,
|
115 |
+
seq_len: Optional[int] = None,
|
116 |
+
**rope_kwargs,
|
117 |
+
) -> Tuple["torch.Tensor", float]:
|
118 |
+
"""
|
119 |
+
Computes the inverse frequencies with NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla
|
120 |
+
Args:
|
121 |
+
config ([`~transformers.PretrainedConfig`]):
|
122 |
+
The model configuration.
|
123 |
+
device (`torch.device`):
|
124 |
+
The device to use for initialization of the inverse frequencies.
|
125 |
+
seq_len (`int`, *optional*):
|
126 |
+
The current sequence length, used to update the dynamic RoPE at inference time.
|
127 |
+
rope_kwargs (`Dict`, *optional*):
|
128 |
+
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
129 |
+
Returns:
|
130 |
+
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
131 |
+
post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
|
132 |
+
"""
|
133 |
+
# TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
|
134 |
+
if config is not None and len(rope_kwargs) > 0:
|
135 |
+
raise ValueError(
|
136 |
+
"Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
|
137 |
+
f"`_compute_dynamic_ntk_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
|
138 |
+
)
|
139 |
+
if len(rope_kwargs) > 0:
|
140 |
+
base = rope_kwargs["base"]
|
141 |
+
dim = rope_kwargs["dim"]
|
142 |
+
max_position_embeddings = rope_kwargs["max_position_embeddings"]
|
143 |
+
factor = rope_kwargs["factor"]
|
144 |
+
elif config is not None:
|
145 |
+
base = config.rope_theta
|
146 |
+
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
147 |
+
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
148 |
+
dim = int(head_dim * partial_rotary_factor)
|
149 |
+
max_position_embeddings = config.max_position_embeddings
|
150 |
+
factor = config.rope_scaling["factor"]
|
151 |
+
|
152 |
+
attention_factor = 1.0 # Unused in this type of RoPE
|
153 |
+
|
154 |
+
# seq_len: default to max_position_embeddings, e.g. at init time
|
155 |
+
seq_len = seq_len if seq_len is not None and seq_len > max_position_embeddings else max_position_embeddings
|
156 |
+
|
157 |
+
# Compute the inverse frequencies
|
158 |
+
base = base * ((factor * seq_len / max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
|
159 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
|
160 |
+
return inv_freq, attention_factor
|
161 |
+
|
162 |
+
|
163 |
+
def _compute_yarn_parameters(
|
164 |
+
config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
|
165 |
+
) -> Tuple["torch.Tensor", float]:
|
166 |
+
"""
|
167 |
+
Computes the inverse frequencies with NTK scaling. Please refer to the
|
168 |
+
[original paper](https://arxiv.org/abs/2309.00071)
|
169 |
+
Args:
|
170 |
+
config ([`~transformers.PretrainedConfig`]):
|
171 |
+
The model configuration.
|
172 |
+
device (`torch.device`):
|
173 |
+
The device to use for initialization of the inverse frequencies.
|
174 |
+
seq_len (`int`, *optional*):
|
175 |
+
The current sequence length. Unused for this type of RoPE.
|
176 |
+
rope_kwargs (`Dict`, *optional*):
|
177 |
+
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
178 |
+
Returns:
|
179 |
+
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
180 |
+
post-processing scaling factor applied to the computed cos/sin.
|
181 |
+
"""
|
182 |
+
# No need to keep BC with yarn, unreleased when this new pattern was created.
|
183 |
+
if len(rope_kwargs) > 0:
|
184 |
+
raise ValueError(
|
185 |
+
f"Unexpected arguments: `**rope_kwargs` should be unset in `_compute_yarn_parameters`, got {rope_kwargs}"
|
186 |
+
)
|
187 |
+
|
188 |
+
base = config.rope_theta
|
189 |
+
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
190 |
+
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
191 |
+
dim = int(head_dim * partial_rotary_factor)
|
192 |
+
max_position_embeddings = config.max_position_embeddings
|
193 |
+
factor = config.rope_scaling["factor"]
|
194 |
+
|
195 |
+
# Sets the attention factor as suggested in the paper
|
196 |
+
attention_factor = config.rope_scaling.get("attention_factor")
|
197 |
+
if attention_factor is None:
|
198 |
+
attention_factor = 0.1 * math.log(factor) + 1.0
|
199 |
+
|
200 |
+
# Optional config options
|
201 |
+
# beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
|
202 |
+
beta_fast = config.rope_scaling.get("beta_fast") or 32
|
203 |
+
beta_slow = config.rope_scaling.get("beta_slow") or 1
|
204 |
+
|
205 |
+
# Compute the inverse frequencies
|
206 |
+
def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
|
207 |
+
"""Inverse dimension formula to find the dimension based on the number of rotations"""
|
208 |
+
return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
|
209 |
+
|
210 |
+
def find_correction_range(low_rot, high_rot, dim, base, max_position_embeddings):
|
211 |
+
"""Find dimension range bounds based on rotations"""
|
212 |
+
low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings))
|
213 |
+
high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
|
214 |
+
return max(low, 0), min(high, dim - 1)
|
215 |
+
|
216 |
+
def linear_ramp_factor(min, max, dim):
|
217 |
+
if min == max:
|
218 |
+
max += 0.001 # Prevent singularity
|
219 |
+
|
220 |
+
linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
|
221 |
+
ramp_func = torch.clamp(linear_func, 0, 1)
|
222 |
+
return ramp_func
|
223 |
+
|
224 |
+
# Note on variable naming: "interpolation" comes from the original technique, where we interpolate the position IDs
|
225 |
+
# to expand the possible context length. In other words, interpolation = apply scaling factor.
|
226 |
+
pos_freqs = base ** (torch.arange(0, dim, 2).float().to(device) / dim)
|
227 |
+
inv_freq_extrapolation = 1.0 / pos_freqs
|
228 |
+
inv_freq_interpolation = 1.0 / (factor * pos_freqs)
|
229 |
+
|
230 |
+
low, high = find_correction_range(beta_fast, beta_slow, dim, base, max_position_embeddings)
|
231 |
+
|
232 |
+
# Get n-dimensional rotational scaling corrected for extrapolation
|
233 |
+
inv_freq_extrapolation_factor = 1 - linear_ramp_factor(low, high, dim // 2).float().to(device)
|
234 |
+
inv_freq = (
|
235 |
+
inv_freq_interpolation * (1 - inv_freq_extrapolation_factor)
|
236 |
+
+ inv_freq_extrapolation * inv_freq_extrapolation_factor
|
237 |
+
)
|
238 |
+
|
239 |
+
return inv_freq, attention_factor
|
240 |
+
|
241 |
+
|
242 |
+
def _compute_longrope_parameters(
|
243 |
+
config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
|
244 |
+
) -> Tuple["torch.Tensor", float]:
|
245 |
+
"""
|
246 |
+
Computes the inverse frequencies with LongRoPE scaling. Please refer to the
|
247 |
+
[original implementation](https://github.com/microsoft/LongRoPE)
|
248 |
+
Args:
|
249 |
+
config ([`~transformers.PretrainedConfig`]):
|
250 |
+
The model configuration.
|
251 |
+
device (`torch.device`):
|
252 |
+
The device to use for initialization of the inverse frequencies.
|
253 |
+
seq_len (`int`, *optional*):
|
254 |
+
The current sequence length. Unused for this type of RoPE.
|
255 |
+
rope_kwargs (`Dict`, *optional*):
|
256 |
+
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
257 |
+
Returns:
|
258 |
+
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
259 |
+
post-processing scaling factor applied to the computed cos/sin.
|
260 |
+
"""
|
261 |
+
# TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
|
262 |
+
# No need to keep BC with longrope, unreleased when this new pattern was created.
|
263 |
+
if len(rope_kwargs) > 0:
|
264 |
+
raise ValueError(
|
265 |
+
"Unexpected arguments: `**rope_kwargs` should be unset in `_compute_longrope_parameters`, got "
|
266 |
+
f"{rope_kwargs}"
|
267 |
+
)
|
268 |
+
|
269 |
+
base = config.rope_theta
|
270 |
+
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
271 |
+
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
272 |
+
dim = int(head_dim * partial_rotary_factor)
|
273 |
+
long_factor = config.rope_scaling["long_factor"]
|
274 |
+
short_factor = config.rope_scaling["short_factor"]
|
275 |
+
factor = config.rope_scaling.get("factor")
|
276 |
+
attention_factor = config.rope_scaling.get("attention_factor")
|
277 |
+
|
278 |
+
# NOTE: Phi3 (and potentially other models) modify `max_position_embeddings` and have a
|
279 |
+
# `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
|
280 |
+
# values to compute the default attention scaling factor, instead of using `factor`.
|
281 |
+
if hasattr(config, "original_max_position_embeddings"):
|
282 |
+
max_position_embeddings = config.original_max_position_embeddings
|
283 |
+
expanded_max_position_embeddings = config.max_position_embeddings
|
284 |
+
factor = expanded_max_position_embeddings / max_position_embeddings
|
285 |
+
else:
|
286 |
+
max_position_embeddings = config.max_position_embeddings
|
287 |
+
expanded_max_position_embeddings = max_position_embeddings * factor
|
288 |
+
|
289 |
+
# Sets the attention factor as suggested in the paper
|
290 |
+
if attention_factor is None:
|
291 |
+
if factor <= 1.0:
|
292 |
+
attention_factor = 1.0
|
293 |
+
else:
|
294 |
+
attention_factor = math.sqrt(1 + math.log(factor) / math.log(max_position_embeddings))
|
295 |
+
|
296 |
+
# Compute the inverse frequencies -- scaled based on the target sequence length
|
297 |
+
if expanded_max_position_embeddings > max_position_embeddings:
|
298 |
+
ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
|
299 |
+
else:
|
300 |
+
ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)
|
301 |
+
inv_freq_shape = torch.arange(0, dim, 2, dtype=torch.int64, device=device).float() / dim
|
302 |
+
inv_freq = 1.0 / (ext_factors * base**inv_freq_shape)
|
303 |
+
|
304 |
+
return inv_freq, attention_factor
|
305 |
+
|
306 |
+
|
307 |
+
def _compute_llama3_parameters(
|
308 |
+
config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
|
309 |
+
) -> Tuple["torch.Tensor", float]:
|
310 |
+
"""
|
311 |
+
Computes the inverse frequencies for llama 3.1.
|
312 |
+
|
313 |
+
Args:
|
314 |
+
config ([`~transformers.PretrainedConfig`]):
|
315 |
+
The model configuration.
|
316 |
+
device (`torch.device`):
|
317 |
+
The device to use for initialization of the inverse frequencies.
|
318 |
+
seq_len (`int`, *optional*):
|
319 |
+
The current sequence length. Unused for this type of RoPE.
|
320 |
+
rope_kwargs (`Dict`, *optional*):
|
321 |
+
BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
|
322 |
+
Returns:
|
323 |
+
Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
|
324 |
+
post-processing scaling factor applied to the computed cos/sin.
|
325 |
+
"""
|
326 |
+
# Gets the default RoPE parameters
|
327 |
+
inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
|
328 |
+
|
329 |
+
factor = config.rope_scaling["factor"] # `8` in the original implementation
|
330 |
+
low_freq_factor = config.rope_scaling["low_freq_factor"] # `1` in the original implementation
|
331 |
+
high_freq_factor = config.rope_scaling["high_freq_factor"] # `4` in the original implementation
|
332 |
+
old_context_len = config.rope_scaling["original_max_position_embeddings"] # `8192` in the original implementation
|
333 |
+
|
334 |
+
low_freq_wavelen = old_context_len / low_freq_factor
|
335 |
+
high_freq_wavelen = old_context_len / high_freq_factor
|
336 |
+
|
337 |
+
wavelen = 2 * math.pi / inv_freq
|
338 |
+
# wavelen < high_freq_wavelen: do nothing
|
339 |
+
# wavelen > low_freq_wavelen: divide by factor
|
340 |
+
inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / factor, inv_freq)
|
341 |
+
# otherwise: interpolate between the two, using a smooth factor
|
342 |
+
smooth_factor = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
|
343 |
+
smoothed_inv_freq = (1 - smooth_factor) * inv_freq_llama / factor + smooth_factor * inv_freq_llama
|
344 |
+
is_medium_freq = ~(wavelen < high_freq_wavelen) * ~(wavelen > low_freq_wavelen)
|
345 |
+
inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
|
346 |
+
|
347 |
+
return inv_freq_llama, attention_factor
|
348 |
+
|
349 |
+
|
350 |
+
# This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
|
351 |
+
# from the model config. You can append new {'rope_type': callable} pairs to this dictionary to enable custom RoPE
|
352 |
+
# parameterizations, as long as the callable has the same signature.
|
353 |
+
ROPE_INIT_FUNCTIONS = {
|
354 |
+
"default": _compute_default_rope_parameters,
|
355 |
+
"linear": _compute_linear_scaling_rope_parameters,
|
356 |
+
"dynamic": _compute_dynamic_ntk_parameters,
|
357 |
+
"yarn": _compute_yarn_parameters,
|
358 |
+
"longrope": _compute_longrope_parameters,
|
359 |
+
"llama3": _compute_llama3_parameters,
|
360 |
+
}
|
361 |
+
|
362 |
+
|
363 |
+
def _check_received_keys(rope_type: str, received_keys: set, required_keys: set, optional_keys: Optional[set] = None):
|
364 |
+
"""Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
|
365 |
+
# BC: "rope_type" was originally "type" -- let's gracefully handle it
|
366 |
+
if "rope_type" not in received_keys and "type" in received_keys:
|
367 |
+
received_keys -= {"type"}
|
368 |
+
received_keys.add("rope_type")
|
369 |
+
|
370 |
+
missing_keys = required_keys - received_keys
|
371 |
+
if missing_keys:
|
372 |
+
raise KeyError(f"Missing required keys in `rope_scaling` for 'rope_type'='{rope_type}': {missing_keys}")
|
373 |
+
|
374 |
+
if optional_keys is not None:
|
375 |
+
unused_keys = received_keys - required_keys - optional_keys
|
376 |
+
else:
|
377 |
+
unused_keys = received_keys - required_keys
|
378 |
+
if unused_keys:
|
379 |
+
logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
|
380 |
+
|
381 |
+
|
382 |
+
def _validate_default_rope_parameters(config: PretrainedConfig):
|
383 |
+
rope_scaling = config.rope_scaling
|
384 |
+
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
385 |
+
required_keys = {"rope_type"}
|
386 |
+
received_keys = set(rope_scaling.keys())
|
387 |
+
_check_received_keys(rope_type, received_keys, required_keys)
|
388 |
+
|
389 |
+
|
390 |
+
def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
|
391 |
+
rope_scaling = config.rope_scaling
|
392 |
+
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
393 |
+
required_keys = {"rope_type", "factor"}
|
394 |
+
received_keys = set(rope_scaling.keys())
|
395 |
+
_check_received_keys(rope_type, received_keys, required_keys)
|
396 |
+
|
397 |
+
factor = rope_scaling["factor"]
|
398 |
+
if factor is None or not isinstance(factor, float) or factor < 1.0:
|
399 |
+
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
400 |
+
|
401 |
+
|
402 |
+
def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
|
403 |
+
rope_scaling = config.rope_scaling
|
404 |
+
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
405 |
+
required_keys = {"rope_type", "factor"}
|
406 |
+
# TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
|
407 |
+
optional_keys = {"original_max_position_embeddings"}
|
408 |
+
received_keys = set(rope_scaling.keys())
|
409 |
+
_check_received_keys(rope_type, received_keys, required_keys, optional_keys)
|
410 |
+
|
411 |
+
factor = rope_scaling["factor"]
|
412 |
+
if factor is None or not isinstance(factor, float) or factor < 1.0:
|
413 |
+
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
414 |
+
|
415 |
+
|
416 |
+
def _validate_yarn_parameters(config: PretrainedConfig):
|
417 |
+
rope_scaling = config.rope_scaling
|
418 |
+
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
419 |
+
required_keys = {"rope_type", "factor"}
|
420 |
+
optional_keys = {"attention_factor", "beta_fast", "beta_slow"}
|
421 |
+
received_keys = set(rope_scaling.keys())
|
422 |
+
_check_received_keys(rope_type, received_keys, required_keys, optional_keys)
|
423 |
+
|
424 |
+
factor = rope_scaling["factor"]
|
425 |
+
if factor is None or not isinstance(factor, float) or factor < 1.0:
|
426 |
+
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
427 |
+
|
428 |
+
attention_factor = rope_scaling.get("attention_factor")
|
429 |
+
if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
|
430 |
+
logger.warning(
|
431 |
+
f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
|
432 |
+
)
|
433 |
+
beta_fast = rope_scaling.get("beta_fast")
|
434 |
+
if beta_fast is not None and not isinstance(beta_fast, float):
|
435 |
+
logger.warning(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
|
436 |
+
beta_slow = rope_scaling.get("beta_slow")
|
437 |
+
if beta_slow is not None and not isinstance(beta_slow, float):
|
438 |
+
logger.warning(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
|
439 |
+
|
440 |
+
if (beta_fast or 32) < (beta_slow or 1):
|
441 |
+
logger.warning(
|
442 |
+
f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
|
443 |
+
f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
|
444 |
+
)
|
445 |
+
|
446 |
+
|
447 |
+
def _validate_longrope_parameters(config: PretrainedConfig):
|
448 |
+
rope_scaling = config.rope_scaling
|
449 |
+
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
450 |
+
required_keys = {"rope_type", "short_factor", "long_factor"}
|
451 |
+
# TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
|
452 |
+
optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
|
453 |
+
received_keys = set(rope_scaling.keys())
|
454 |
+
_check_received_keys(rope_type, received_keys, required_keys, optional_keys)
|
455 |
+
|
456 |
+
partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
|
457 |
+
head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
458 |
+
dim = int(head_dim * partial_rotary_factor)
|
459 |
+
|
460 |
+
short_factor = rope_scaling.get("short_factor")
|
461 |
+
if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
|
462 |
+
logger.warning(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
|
463 |
+
if not len(short_factor) == dim // 2:
|
464 |
+
logger.warning(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
|
465 |
+
|
466 |
+
long_factor = rope_scaling.get("long_factor")
|
467 |
+
if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
|
468 |
+
logger.warning(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
|
469 |
+
if not len(long_factor) == dim // 2:
|
470 |
+
logger.warning(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
|
471 |
+
|
472 |
+
# Handle Phi3 divergence: prefer the use of `attention_factor` and/or `factor` over
|
473 |
+
# `original_max_position_embeddings` to compute internal variables. The latter lives outside `rope_scaling` and is
|
474 |
+
# unique to longrope (= undesirable)
|
475 |
+
if hasattr(config, "original_max_position_embeddings"):
|
476 |
+
logger.warning_once(
|
477 |
+
"This model has set a `original_max_position_embeddings` field, to be used together with "
|
478 |
+
"`max_position_embeddings` to determine a scaling factor. Please set the `factor` field of `rope_scaling`"
|
479 |
+
"with this ratio instead -- we recommend the use of this field over `original_max_position_embeddings`, "
|
480 |
+
"as it is compatible with most model architectures."
|
481 |
+
)
|
482 |
+
else:
|
483 |
+
factor = rope_scaling.get("factor")
|
484 |
+
if factor is None:
|
485 |
+
logger.warning("Missing required keys in `rope_scaling`: 'factor'")
|
486 |
+
elif not isinstance(factor, float) or factor < 1.0:
|
487 |
+
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
488 |
+
|
489 |
+
attention_factor = rope_scaling.get("attention_factor")
|
490 |
+
if attention_factor is not None and not isinstance(attention_factor, float) or attention_factor < 0:
|
491 |
+
logger.warning(
|
492 |
+
f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
|
493 |
+
)
|
494 |
+
|
495 |
+
|
496 |
+
def _validate_llama3_parameters(config: PretrainedConfig):
|
497 |
+
rope_scaling = config.rope_scaling
|
498 |
+
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None)) # BC: "rope_type" was originally "type"
|
499 |
+
required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
|
500 |
+
received_keys = set(rope_scaling.keys())
|
501 |
+
_check_received_keys(rope_type, received_keys, required_keys)
|
502 |
+
|
503 |
+
factor = rope_scaling["factor"]
|
504 |
+
if factor is None or not isinstance(factor, float) or factor < 1.0:
|
505 |
+
logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
|
506 |
+
|
507 |
+
low_freq_factor = rope_scaling["low_freq_factor"]
|
508 |
+
high_freq_factor = rope_scaling["high_freq_factor"]
|
509 |
+
if low_freq_factor is None or not isinstance(low_freq_factor, float):
|
510 |
+
logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
|
511 |
+
if high_freq_factor is None or not isinstance(high_freq_factor, float):
|
512 |
+
logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
|
513 |
+
if high_freq_factor <= low_freq_factor:
|
514 |
+
logger.warning(
|
515 |
+
"`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
|
516 |
+
f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
|
517 |
+
)
|
518 |
+
|
519 |
+
original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
|
520 |
+
if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
|
521 |
+
logger.warning(
|
522 |
+
"`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
|
523 |
+
f"{original_max_position_embeddings}"
|
524 |
+
)
|
525 |
+
if original_max_position_embeddings >= config.max_position_embeddings:
|
526 |
+
logger.warning(
|
527 |
+
"`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
|
528 |
+
f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
|
529 |
+
)
|
530 |
+
|
531 |
+
|
532 |
+
# Like `ROPE_INIT_FUNCTIONS`, this validation function mapping can be dynamically updated for custom RoPE types.
|
533 |
+
ROPE_VALIDATION_FUNCTIONS = {
|
534 |
+
"default": _validate_default_rope_parameters,
|
535 |
+
"linear": _validate_linear_scaling_rope_parameters,
|
536 |
+
"dynamic": _validate_dynamic_scaling_rope_parameters,
|
537 |
+
"yarn": _validate_yarn_parameters,
|
538 |
+
"longrope": _validate_longrope_parameters,
|
539 |
+
"llama3": _validate_llama3_parameters,
|
540 |
+
}
|
541 |
+
|
542 |
+
|
543 |
+
def rope_config_validation(config: PretrainedConfig):
|
544 |
+
"""
|
545 |
+
Validate the RoPE config arguments, given a `PretrainedConfig` object
|
546 |
+
"""
|
547 |
+
rope_scaling = getattr(config, "rope_scaling", None) # not a default parameter in `PretrainedConfig`
|
548 |
+
if rope_scaling is None:
|
549 |
+
return
|
550 |
+
|
551 |
+
# BC: "rope_type" was originally "type"
|
552 |
+
rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
|
553 |
+
validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
|
554 |
+
if validation_fn is not None:
|
555 |
+
validation_fn(config)
|
556 |
+
else:
|
557 |
+
logger.warning(
|
558 |
+
f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"
|
559 |
+
)
|
transformers_4_44_2__pytorch_utils.py
ADDED
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Copyright 2022 The HuggingFace Team. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
|
15 |
+
from torch import nn
|
16 |
+
|
17 |
+
ALL_LAYERNORM_LAYERS = [nn.LayerNorm]
|
variable_cache.py
ADDED
@@ -0,0 +1,113 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2024 Nvidia Corporation. All rights reserved.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
|
16 |
+
from copy import deepcopy
|
17 |
+
from typing import Optional, Dict, Any, Tuple
|
18 |
+
|
19 |
+
import torch
|
20 |
+
from transformers.cache_utils import Cache # used to let GenerationMixin know that we use a Cache object
|
21 |
+
|
22 |
+
from .configuration_decilm import DeciLMConfig, AttentionConfig
|
23 |
+
from .transformers_4_44_2__cache_utils import Cache as Cache_4_44_2, StaticCache
|
24 |
+
|
25 |
+
|
26 |
+
class VariableCache(Cache_4_44_2, Cache):
|
27 |
+
"""
|
28 |
+
A Cache object that supports a different Cache implementation for every layer,
|
29 |
+
including layers without any kv-cache.
|
30 |
+
Implemented using a list of Cache objects, each represents a "model" with 1 layer.
|
31 |
+
The default implementation for the layer caches is StaticCache.
|
32 |
+
The cache of each layer is allocated to the same gpu as the layer itself.
|
33 |
+
"""
|
34 |
+
|
35 |
+
def __init__(
|
36 |
+
self,
|
37 |
+
*, # key-word only, no positional args allowed to avoid mix-ups with newer transformers versions
|
38 |
+
config: DeciLMConfig,
|
39 |
+
batch_size: int = None,
|
40 |
+
max_cache_len: int = None,
|
41 |
+
dtype: torch.dtype = torch.float32,
|
42 |
+
max_batch_size: Optional[int] = None,
|
43 |
+
**kwargs: Any,
|
44 |
+
) -> None:
|
45 |
+
Cache_4_44_2.__init__(self)
|
46 |
+
|
47 |
+
self.config = deepcopy(config)
|
48 |
+
self.max_batch_size = batch_size or max_batch_size
|
49 |
+
self.batch_size = self.max_batch_size
|
50 |
+
self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
|
51 |
+
self.dtype = dtype
|
52 |
+
|
53 |
+
self.layer_caches: list[Cache | None] = [None] * config.num_hidden_layers
|
54 |
+
|
55 |
+
def update(
|
56 |
+
self,
|
57 |
+
key_states: torch.Tensor,
|
58 |
+
value_states: torch.Tensor,
|
59 |
+
layer_idx: int,
|
60 |
+
cache_kwargs: Optional[Dict[str, Any]] = None,
|
61 |
+
) -> Tuple[torch.Tensor, torch.Tensor]:
|
62 |
+
layer_cache = self.layer_caches[layer_idx]
|
63 |
+
|
64 |
+
if layer_cache is None:
|
65 |
+
block_config = self.config.block_configs[layer_idx]
|
66 |
+
layer_cache = self._init_layer_cache(attention_config=block_config.attention, device=key_states.device)
|
67 |
+
assert layer_cache is not None, "Trying to update the cache of a cache-less layer"
|
68 |
+
self.layer_caches[layer_idx] = layer_cache
|
69 |
+
|
70 |
+
k_out, v_out = layer_cache.update(key_states=key_states,
|
71 |
+
value_states=value_states,
|
72 |
+
layer_idx=0,
|
73 |
+
cache_kwargs=cache_kwargs)
|
74 |
+
seq_len = self.get_seq_length(layer_idx)
|
75 |
+
k_out = k_out[:, :, :seq_len, :]
|
76 |
+
v_out = v_out[:, :, :seq_len, :]
|
77 |
+
return k_out, v_out
|
78 |
+
|
79 |
+
def _init_layer_cache(self,
|
80 |
+
attention_config: AttentionConfig,
|
81 |
+
device: torch.device,
|
82 |
+
) -> Cache | None:
|
83 |
+
if attention_config.no_op or attention_config.replace_with_linear:
|
84 |
+
return None
|
85 |
+
config = deepcopy(self.config)
|
86 |
+
config.num_hidden_layers = 1
|
87 |
+
config.num_key_value_heads = self.config.num_attention_heads // attention_config.n_heads_in_group
|
88 |
+
return StaticCache(config, self.max_batch_size, self.max_cache_len, device, self.dtype)
|
89 |
+
|
90 |
+
def _get_first_real_cache(self) -> Cache:
|
91 |
+
for layer_cache in self.layer_caches:
|
92 |
+
if layer_cache is not None:
|
93 |
+
return layer_cache
|
94 |
+
raise ValueError(f"No real cache found, all layer caches are None.")
|
95 |
+
|
96 |
+
def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
|
97 |
+
if layer_idx == 0 and self.layer_caches[0] is None:
|
98 |
+
try:
|
99 |
+
layer_cache = self._get_first_real_cache()
|
100 |
+
except ValueError:
|
101 |
+
return 0
|
102 |
+
else:
|
103 |
+
layer_cache = self.layer_caches[layer_idx]
|
104 |
+
return layer_cache.get_seq_length()
|
105 |
+
|
106 |
+
def get_max_length(self) -> Optional[int]:
|
107 |
+
"""Returns the maximum sequence length of the cached states."""
|
108 |
+
return self.max_cache_len
|
109 |
+
|
110 |
+
def reset(self):
|
111 |
+
for layer_cache in self.layer_caches:
|
112 |
+
if hasattr(layer_cache, "reset"):
|
113 |
+
layer_cache.reset()
|