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nvidia
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Llama-3_3-Nemotron-Super-49B-GenRM-Multilingual

Text Generation
Transformers
Safetensors
nemotron-nas
nvidia
llama3.3
conversational
custom_code
Model card Files
xet
Community
jiaqiz commited on
Commit
44697d6
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  1. .gitattributes +1 -0
  2. __init__.py +0 -0
  3. block_config.py +118 -0
  4. config.json +1484 -0
  5. configuration_decilm.py +65 -0
  6. model-00001-of-00011.safetensors +3 -0
  7. model-00002-of-00011.safetensors +3 -0
  8. model-00003-of-00011.safetensors +3 -0
  9. model-00004-of-00011.safetensors +3 -0
  10. model-00005-of-00011.safetensors +3 -0
  11. model-00006-of-00011.safetensors +3 -0
  12. model-00007-of-00011.safetensors +3 -0
  13. model-00008-of-00011.safetensors +3 -0
  14. model-00009-of-00011.safetensors +3 -0
  15. model-00010-of-00011.safetensors +3 -0
  16. model-00011-of-00011.safetensors +3 -0
  17. model.safetensors.index.json +575 -0
  18. modeling_decilm.py +1681 -0
  19. nemo_common.json +1 -0
  20. nemo_common.pt +3 -0
  21. nemo_model_config.yaml +215 -0
  22. special_tokens_map.json +16 -0
  23. tokenizer.json +3 -0
  24. tokenizer_config.json +2063 -0
  25. tokenizer_name.txt +1 -0
  26. transformers_4_44_2__activations.py +239 -0
  27. transformers_4_44_2__cache_utils.py +1347 -0
  28. transformers_4_44_2__configuration_llama.py +203 -0
  29. transformers_4_44_2__modeling_attn_mask_utils.py +482 -0
  30. transformers_4_44_2__modeling_flash_attention_utils_backward_compat.py +348 -0
  31. transformers_4_44_2__modeling_outputs.py +0 -0
  32. transformers_4_44_2__modeling_rope_utils.py +559 -0
  33. transformers_4_44_2__pytorch_utils.py +17 -0
  34. variable_cache.py +139 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
__init__.py ADDED
File without changes
block_config.py ADDED
@@ -0,0 +1,118 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import dataclasses
2
+ import json
3
+ import warnings
4
+ from dataclasses import dataclass, MISSING
5
+ from functools import partial
6
+ from typing import Optional, Any
7
+
8
+
9
+ @partial(dataclass, frozen=True, kw_only=True)
10
+ class JsonComparable:
11
+ def to_json(self) -> str:
12
+ return json.dumps(dataclasses.asdict(self))
13
+
14
+ def __eq__(self, other: "JsonComparable") -> bool:
15
+ return self.to_json() == other.to_json()
16
+
17
+ def __hash__(self) -> int:
18
+ return hash(self.to_json())
19
+
20
+ def __lt__(self, other: "JsonComparable") -> bool:
21
+ return self.to_json() < other.to_json()
22
+
23
+
24
+ @partial(dataclass, frozen=True, kw_only=True)
25
+ class SubblockConfig(JsonComparable):
26
+ no_op: bool = False
27
+ replace_with_linear: bool = False
28
+ sparsify: Optional[list[str]] = None
29
+
30
+ def __post_init__(self):
31
+ assert not (self.no_op and self.replace_with_linear)
32
+
33
+ def _force_setattr(self, name: str, value: Any) -> None:
34
+ """
35
+ Set an attribute even in frozen dataclasses.
36
+ Use only inside __post_init__!
37
+ """
38
+ object.__setattr__(self, name, value)
39
+
40
+
41
+ @partial(dataclass, frozen=True, kw_only=True)
42
+ class AttentionConfig(SubblockConfig):
43
+ n_heads_in_group: Optional[int] = None
44
+ window_length: Optional[int] = None
45
+ num_sink_tokens: Optional[int] = None
46
+ use_prefill_window_in_sink_attention: bool = False
47
+ unshifted_sink: bool = False
48
+
49
+ def __post_init__(self):
50
+ super().__post_init__()
51
+ assert not (self.no_op and self.replace_with_linear)
52
+
53
+ if self.no_op or self.replace_with_linear:
54
+ for irrelevant_att in ["n_heads_in_group", "window_length", "num_sink_tokens"]:
55
+ self._force_setattr(irrelevant_att, None)
56
+ else:
57
+ assert self.n_heads_in_group is not None
58
+
59
+ if self.is_sink:
60
+ assert not (self.unshifted_sink and self.use_prefill_window_in_sink_attention), \
61
+ ("Unshifted sink uses its own kind of explicit masking, not standard window. "
62
+ "Set use_prefill_window_in_sink_attention to False.")
63
+ assert not (self.num_sink_tokens == 0 and not self.unshifted_sink), \
64
+ "Fake sink attention with 0 sink tokens is only supported with unshifted_sink=True"
65
+
66
+ @property
67
+ def prefill_sliding_window(self) -> Optional[int]:
68
+ if self.window_length is not None:
69
+ if not self.is_sink or self.use_prefill_window_in_sink_attention:
70
+ return self.window_length
71
+ return None
72
+
73
+ @property
74
+ def is_sliding(self) -> bool:
75
+ return self.prefill_sliding_window is not None
76
+
77
+ @property
78
+ def is_sink(self) -> bool:
79
+ return (
80
+ (self.window_length is not None)
81
+ and
82
+ (self.num_sink_tokens is not None)
83
+ )
84
+
85
+
86
+ @partial(dataclass, frozen=True, kw_only=True)
87
+ class FFNConfig(SubblockConfig):
88
+ ffn_mult: Optional[float] = None
89
+
90
+ def __post_init__(self):
91
+ super().__post_init__()
92
+ if self.no_op or self.replace_with_linear:
93
+ self._force_setattr("ffn_mult", None)
94
+ else:
95
+ assert self.ffn_mult is not None
96
+ self._force_setattr("ffn_mult", round(self.ffn_mult, 6))
97
+
98
+
99
+ @partial(dataclass, frozen=True, kw_only=True)
100
+ class BlockConfig(JsonComparable):
101
+ attention: AttentionConfig = MISSING
102
+ ffn: FFNConfig = MISSING
103
+
104
+ def __post_init__(self):
105
+ """
106
+ Init subblock dataclasses from dicts
107
+ """
108
+ for subblock_name in dataclasses.fields(self):
109
+ subblock_config = getattr(self, subblock_name.name)
110
+ if isinstance(subblock_config, dict):
111
+ subblock_fields = [field.name for field in dataclasses.fields(subblock_name.type)]
112
+ unsupported_fields = [field_name for field_name in subblock_config.keys()
113
+ if field_name not in subblock_fields]
114
+ if len(unsupported_fields) > 0:
115
+ warnings.warn(f"Removed unsupported fields {unsupported_fields} from {subblock_name.type.__name__}")
116
+ subblock_config = {k: v for k, v in subblock_config.items() if k not in unsupported_fields}
117
+ object.__setattr__(self, subblock_name.name,
118
+ subblock_name.type(**subblock_config)) # __setattr__ to overcome frozen=True
config.json ADDED
@@ -0,0 +1,1484 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "DeciLMForCausalLM"
4
+ ],
5
+ "attention_bias": false,
6
+ "attention_dropout": 0.0,
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_decilm.DeciLMConfig",
9
+ "AutoModelForCausalLM": "modeling_decilm.DeciLMForCausalLM"
10
+ },
11
+ "block_configs": [
12
+ {
13
+ "attention": {
14
+ "n_heads_in_group": 8,
15
+ "no_op": false,
16
+ "num_sink_tokens": null,
17
+ "replace_with_linear": false,
18
+ "sparsify": null,
19
+ "unshifted_sink": false,
20
+ "use_prefill_window_in_sink_attention": false,
21
+ "window_length": null
22
+ },
23
+ "ffn": {
24
+ "ffn_mult": 2.625,
25
+ "no_op": false,
26
+ "replace_with_linear": false,
27
+ "sparsify": null
28
+ }
29
+ },
30
+ {
31
+ "attention": {
32
+ "n_heads_in_group": 8,
33
+ "no_op": false,
34
+ "num_sink_tokens": null,
35
+ "replace_with_linear": false,
36
+ "sparsify": null,
37
+ "unshifted_sink": false,
38
+ "use_prefill_window_in_sink_attention": false,
39
+ "window_length": null
40
+ },
41
+ "ffn": {
42
+ "ffn_mult": 5.25,
43
+ "no_op": false,
44
+ "replace_with_linear": false,
45
+ "sparsify": null
46
+ }
47
+ },
48
+ {
49
+ "attention": {
50
+ "n_heads_in_group": 8,
51
+ "no_op": false,
52
+ "num_sink_tokens": null,
53
+ "replace_with_linear": false,
54
+ "sparsify": null,
55
+ "unshifted_sink": false,
56
+ "use_prefill_window_in_sink_attention": false,
57
+ "window_length": null
58
+ },
59
+ "ffn": {
60
+ "ffn_mult": 5.25,
61
+ "no_op": false,
62
+ "replace_with_linear": false,
63
+ "sparsify": null
64
+ }
65
+ },
66
+ {
67
+ "attention": {
68
+ "n_heads_in_group": 8,
69
+ "no_op": false,
70
+ "num_sink_tokens": null,
71
+ "replace_with_linear": false,
72
+ "sparsify": null,
73
+ "unshifted_sink": false,
74
+ "use_prefill_window_in_sink_attention": false,
75
+ "window_length": null
76
+ },
77
+ "ffn": {
78
+ "ffn_mult": 5.25,
79
+ "no_op": false,
80
+ "replace_with_linear": false,
81
+ "sparsify": null
82
+ }
83
+ },
84
+ {
85
+ "attention": {
86
+ "n_heads_in_group": 8,
87
+ "no_op": false,
88
+ "num_sink_tokens": null,
89
+ "replace_with_linear": false,
90
+ "sparsify": null,
91
+ "unshifted_sink": false,
92
+ "use_prefill_window_in_sink_attention": false,
93
+ "window_length": null
94
+ },
95
+ "ffn": {
96
+ "ffn_mult": 5.25,
97
+ "no_op": false,
98
+ "replace_with_linear": false,
99
+ "sparsify": null
100
+ }
101
+ },
102
+ {
103
+ "attention": {
104
+ "n_heads_in_group": 8,
105
+ "no_op": false,
106
+ "num_sink_tokens": null,
107
+ "replace_with_linear": false,
108
+ "sparsify": null,
109
+ "unshifted_sink": false,
110
+ "use_prefill_window_in_sink_attention": false,
111
+ "window_length": null
112
+ },
113
+ "ffn": {
114
+ "ffn_mult": 5.25,
115
+ "no_op": false,
116
+ "replace_with_linear": false,
117
+ "sparsify": null
118
+ }
119
+ },
120
+ {
121
+ "attention": {
122
+ "n_heads_in_group": null,
123
+ "no_op": true,
124
+ "num_sink_tokens": null,
125
+ "replace_with_linear": false,
126
+ "sparsify": null,
127
+ "unshifted_sink": false,
128
+ "use_prefill_window_in_sink_attention": false,
129
+ "window_length": null
130
+ },
131
+ "ffn": {
132
+ "ffn_mult": 2.625,
133
+ "no_op": false,
134
+ "replace_with_linear": false,
135
+ "sparsify": null
136
+ }
137
+ },
138
+ {
139
+ "attention": {
140
+ "n_heads_in_group": null,
141
+ "no_op": true,
142
+ "num_sink_tokens": null,
143
+ "replace_with_linear": false,
144
+ "sparsify": null,
145
+ "unshifted_sink": false,
146
+ "use_prefill_window_in_sink_attention": false,
147
+ "window_length": null
148
+ },
149
+ "ffn": {
150
+ "ffn_mult": 2.625,
151
+ "no_op": false,
152
+ "replace_with_linear": false,
153
+ "sparsify": null
154
+ }
155
+ },
156
+ {
157
+ "attention": {
158
+ "n_heads_in_group": 8,
159
+ "no_op": false,
160
+ "num_sink_tokens": null,
161
+ "replace_with_linear": false,
162
+ "sparsify": null,
163
+ "unshifted_sink": false,
164
+ "use_prefill_window_in_sink_attention": false,
165
+ "window_length": null
166
+ },
167
+ "ffn": {
168
+ "ffn_mult": 5.25,
169
+ "no_op": false,
170
+ "replace_with_linear": false,
171
+ "sparsify": null
172
+ }
173
+ },
174
+ {
175
+ "attention": {
176
+ "n_heads_in_group": 8,
177
+ "no_op": false,
178
+ "num_sink_tokens": null,
179
+ "replace_with_linear": false,
180
+ "sparsify": null,
181
+ "unshifted_sink": false,
182
+ "use_prefill_window_in_sink_attention": false,
183
+ "window_length": null
184
+ },
185
+ "ffn": {
186
+ "ffn_mult": 5.25,
187
+ "no_op": false,
188
+ "replace_with_linear": false,
189
+ "sparsify": null
190
+ }
191
+ },
192
+ {
193
+ "attention": {
194
+ "n_heads_in_group": 8,
195
+ "no_op": false,
196
+ "num_sink_tokens": null,
197
+ "replace_with_linear": false,
198
+ "sparsify": null,
199
+ "unshifted_sink": false,
200
+ "use_prefill_window_in_sink_attention": false,
201
+ "window_length": null
202
+ },
203
+ "ffn": {
204
+ "ffn_mult": 5.25,
205
+ "no_op": false,
206
+ "replace_with_linear": false,
207
+ "sparsify": null
208
+ }
209
+ },
210
+ {
211
+ "attention": {
212
+ "n_heads_in_group": null,
213
+ "no_op": true,
214
+ "num_sink_tokens": null,
215
+ "replace_with_linear": false,
216
+ "sparsify": null,
217
+ "unshifted_sink": false,
218
+ "use_prefill_window_in_sink_attention": false,
219
+ "window_length": null
220
+ },
221
+ "ffn": {
222
+ "ffn_mult": 3.28125,
223
+ "no_op": false,
224
+ "replace_with_linear": false,
225
+ "sparsify": null
226
+ }
227
+ },
228
+ {
229
+ "attention": {
230
+ "n_heads_in_group": 8,
231
+ "no_op": false,
232
+ "num_sink_tokens": null,
233
+ "replace_with_linear": false,
234
+ "sparsify": null,
235
+ "unshifted_sink": false,
236
+ "use_prefill_window_in_sink_attention": false,
237
+ "window_length": null
238
+ },
239
+ "ffn": {
240
+ "ffn_mult": 5.25,
241
+ "no_op": false,
242
+ "replace_with_linear": false,
243
+ "sparsify": null
244
+ }
245
+ },
246
+ {
247
+ "attention": {
248
+ "n_heads_in_group": 8,
249
+ "no_op": false,
250
+ "num_sink_tokens": null,
251
+ "replace_with_linear": false,
252
+ "sparsify": null,
253
+ "unshifted_sink": false,
254
+ "use_prefill_window_in_sink_attention": false,
255
+ "window_length": null
256
+ },
257
+ "ffn": {
258
+ "ffn_mult": 5.25,
259
+ "no_op": false,
260
+ "replace_with_linear": false,
261
+ "sparsify": null
262
+ }
263
+ },
264
+ {
265
+ "attention": {
266
+ "n_heads_in_group": 8,
267
+ "no_op": false,
268
+ "num_sink_tokens": null,
269
+ "replace_with_linear": false,
270
+ "sparsify": null,
271
+ "unshifted_sink": false,
272
+ "use_prefill_window_in_sink_attention": false,
273
+ "window_length": null
274
+ },
275
+ "ffn": {
276
+ "ffn_mult": 5.25,
277
+ "no_op": false,
278
+ "replace_with_linear": false,
279
+ "sparsify": null
280
+ }
281
+ },
282
+ {
283
+ "attention": {
284
+ "n_heads_in_group": 8,
285
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+ }
configuration_decilm.py ADDED
@@ -0,0 +1,65 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 typing import Dict, Any
19
+
20
+ from transformers.utils import is_flash_attn_2_available
21
+
22
+ from .block_config import BlockConfig
23
+ from .transformers_4_44_2__configuration_llama import LlamaConfig
24
+ from .transformers_4_44_2__modeling_rope_utils import \
25
+ rope_config_validation # fake import to make AutoConfig infer the dependency
26
+
27
+ rope_config_validation # this line is here to make sure that auto-formatting doesn't remove the import
28
+
29
+
30
+ class DeciLMConfig(LlamaConfig):
31
+ model_type = "nemotron-nas"
32
+
33
+ def __init__(
34
+ self,
35
+ block_configs: list[dict] | list[BlockConfig] = None,
36
+ **kwargs,
37
+ ):
38
+ attn_implementation = kwargs.pop("attn_implementation", None)
39
+ if attn_implementation is None and is_flash_attn_2_available():
40
+ attn_implementation = "flash_attention_2"
41
+
42
+ if block_configs is not None:
43
+ if isinstance(block_configs[0], dict):
44
+ block_configs = [BlockConfig(**conf) for conf in block_configs]
45
+
46
+ using_unshifted_sink = any([block_config.attention.unshifted_sink for block_config in block_configs])
47
+ if using_unshifted_sink and attn_implementation != "eager":
48
+ warnings.warn("Forcing attn_implementation='eager' since some attention layers use unshifted sink")
49
+ attn_implementation = "eager"
50
+
51
+ super().__init__(attn_implementation=attn_implementation, **kwargs)
52
+
53
+ self.intermediate_size = None
54
+ self.num_key_value_heads = None
55
+
56
+ if block_configs is not None:
57
+ assert len(block_configs) == self.num_hidden_layers
58
+
59
+ self.block_configs: list[BlockConfig] = block_configs
60
+
61
+ def to_dict(self) -> Dict[str, Any]:
62
+ self_dict = super().to_dict()
63
+ if self.block_configs is not None:
64
+ self_dict["block_configs"] = [dataclasses.asdict(conf) for conf in self.block_configs]
65
+ return self_dict
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+ "model.layers.78.input_layernorm.weight": "model-00011-of-00011.safetensors",
571
+ "model.layers.79.post_attention_layernorm.weight": "model-00011-of-00011.safetensors",
572
+ "model.layers.79.input_layernorm.weight": "model-00011-of-00011.safetensors",
573
+ "model.layers.78.mlp.up_proj.weight": "model-00011-of-00011.safetensors"
574
+ }
575
+ }
modeling_decilm.py ADDED
@@ -0,0 +1,1681 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2024 Nvidia Corporation, Google Inc, HuggingFace Inc, EleutherAI. All rights reserved.
3
+ #
4
+ # This code for Nvidia's model is based on the Llama modeling code by HuggingFace,
5
+ # which is in turn based on EleutherAI's GPT-NeoX library and the GPT-NeoX and
6
+ # OPT implementations in this library.
7
+ # Sliding window code based on Gemma2 by Google.
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
+
21
+ import math
22
+ from typing import List, Optional, Tuple, Union
23
+
24
+ import torch
25
+ import torch.nn.functional as F
26
+ import torch.utils.checkpoint
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+ from transformers import GenerationConfig
30
+ from transformers.generation.utils import NEED_SETUP_CACHE_CLASSES_MAPPING, GenerationMixin, GenerateOutput
31
+ from transformers.modeling_utils import PreTrainedModel
32
+ from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
33
+ from transformers.utils import (
34
+ add_start_docstrings,
35
+ add_start_docstrings_to_model_forward,
36
+ is_flash_attn_greater_or_equal_2_10,
37
+ logging,
38
+ replace_return_docstrings,
39
+ )
40
+
41
+ from .block_config import AttentionConfig, FFNConfig
42
+ from .configuration_decilm import DeciLMConfig
43
+ from .transformers_4_44_2__activations import ACT2FN
44
+ from .transformers_4_44_2__cache_utils import Cache, StaticCache
45
+ from .transformers_4_44_2__modeling_attn_mask_utils import AttentionMaskConverter
46
+ from .transformers_4_44_2__modeling_flash_attention_utils_backward_compat import _flash_attention_forward
47
+ from .transformers_4_44_2__modeling_outputs import (
48
+ BaseModelOutputWithPast,
49
+ CausalLMOutputWithPast,
50
+ QuestionAnsweringModelOutput,
51
+ SequenceClassifierOutputWithPast,
52
+ TokenClassifierOutput,
53
+ )
54
+ from .transformers_4_44_2__modeling_rope_utils import ROPE_INIT_FUNCTIONS
55
+ from .transformers_4_44_2__pytorch_utils import ALL_LAYERNORM_LAYERS
56
+ from .variable_cache import VariableCache
57
+
58
+ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[DeciLMConfig.model_type] = "DeciLMForCausalLM"
59
+ logger = logging.get_logger(__name__)
60
+
61
+ _CONFIG_FOR_DOC = "DeciLMConfig"
62
+
63
+
64
+ def _prepare_4d_causal_attention_mask_with_cache_position(
65
+ attention_mask: torch.Tensor,
66
+ sequence_length: int,
67
+ target_length: int,
68
+ dtype: torch.dtype,
69
+ device: torch.device,
70
+ min_dtype: float,
71
+ cache_position: torch.Tensor,
72
+ batch_size: int,
73
+ ):
74
+ """
75
+ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
76
+ `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
77
+
78
+ Args:
79
+ attention_mask (`torch.Tensor`):
80
+ 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)`.
81
+ sequence_length (`int`):
82
+ The sequence length being processed.
83
+ target_length (`int`):
84
+ 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.
85
+ dtype (`torch.dtype`):
86
+ The dtype to use for the 4D attention mask.
87
+ device (`torch.device`):
88
+ The device to place the 4D attention mask on.
89
+ min_dtype (`float`):
90
+ The minimum value representable with the dtype `dtype`.
91
+ cache_position (`torch.Tensor`):
92
+ Indices depicting the position of the input sequence tokens in the sequence.
93
+ batch_size (`torch.Tensor`):
94
+ Batch size.
95
+ """
96
+ if attention_mask is not None and attention_mask.dim() == 4:
97
+ # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
98
+ causal_mask = attention_mask
99
+ else:
100
+ causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
101
+ if sequence_length != 1:
102
+ causal_mask = torch.triu(causal_mask, diagonal=1)
103
+ causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
104
+ causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
105
+ if attention_mask is not None:
106
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
107
+ mask_length = attention_mask.shape[-1]
108
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
109
+ padding_mask = padding_mask == 0
110
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
111
+ padding_mask, min_dtype
112
+ )
113
+
114
+ return causal_mask
115
+
116
+
117
+ class DeciLMRMSNorm(nn.Module):
118
+ def __init__(self, hidden_size, eps=1e-6):
119
+ """
120
+ DeciLMRMSNorm is equivalent to T5LayerNorm
121
+ """
122
+ super().__init__()
123
+ self.weight = nn.Parameter(torch.ones(hidden_size))
124
+ self.variance_epsilon = eps
125
+
126
+ def forward(self, hidden_states):
127
+ input_dtype = hidden_states.dtype
128
+ hidden_states = hidden_states.to(torch.float32)
129
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
130
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
131
+ return self.weight * hidden_states.to(input_dtype)
132
+
133
+ def extra_repr(self):
134
+ return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
135
+
136
+
137
+ ALL_LAYERNORM_LAYERS.append(DeciLMRMSNorm)
138
+
139
+
140
+ class DeciLMRotaryEmbedding(nn.Module):
141
+ def __init__(
142
+ self,
143
+ dim=None,
144
+ max_position_embeddings=2048,
145
+ base=10000,
146
+ device=None,
147
+ scaling_factor=1.0,
148
+ rope_type="default",
149
+ config: Optional[DeciLMConfig] = None,
150
+ ):
151
+ super().__init__()
152
+ # TODO (joao): remove the `if` below, only used for BC
153
+ self.rope_kwargs = {}
154
+ if config is None:
155
+ logger.warning_once(
156
+ "`DeciLMRotaryEmbedding` can now be fully parameterized by passing the model config through the "
157
+ "`config` argument. All other arguments will be removed in v4.45"
158
+ )
159
+ self.rope_kwargs = {
160
+ "rope_type": rope_type,
161
+ "factor": scaling_factor,
162
+ "dim": dim,
163
+ "base": base,
164
+ "max_position_embeddings": max_position_embeddings,
165
+ }
166
+ self.rope_type = rope_type
167
+ self.max_seq_len_cached = max_position_embeddings
168
+ self.original_max_seq_len = max_position_embeddings
169
+ else:
170
+ # BC: "rope_type" was originally "type"
171
+ if config.rope_scaling is not None:
172
+ self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
173
+ else:
174
+ self.rope_type = "default"
175
+ self.max_seq_len_cached = config.max_position_embeddings
176
+ self.original_max_seq_len = config.max_position_embeddings
177
+
178
+ self.config = config
179
+ self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
180
+
181
+ inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
182
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
183
+ self.original_inv_freq = self.inv_freq
184
+
185
+ def _dynamic_frequency_update(self, position_ids, device):
186
+ """
187
+ dynamic RoPE layers should recompute `inv_freq` in the following situations:
188
+ 1 - growing beyond the cached sequence length (allow scaling)
189
+ 2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
190
+ """
191
+ seq_len = torch.max(position_ids) + 1
192
+ if seq_len > self.max_seq_len_cached: # growth
193
+ inv_freq, self.attention_scaling = self.rope_init_fn(
194
+ self.config, device, seq_len=seq_len, **self.rope_kwargs
195
+ )
196
+ self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
197
+ self.max_seq_len_cached = seq_len
198
+
199
+ if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
200
+ self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
201
+ self.max_seq_len_cached = self.original_max_seq_len
202
+
203
+ @torch.no_grad()
204
+ def forward(self, x, position_ids):
205
+ if "dynamic" in self.rope_type:
206
+ self._dynamic_frequency_update(position_ids, device=x.device)
207
+
208
+ # Core RoPE block
209
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
210
+ position_ids_expanded = position_ids[:, None, :].float()
211
+ # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
212
+ device_type = x.device.type
213
+ device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
214
+ with torch.autocast(device_type=device_type, enabled=False):
215
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
216
+ emb = torch.cat((freqs, freqs), dim=-1)
217
+ cos = emb.cos()
218
+ sin = emb.sin()
219
+
220
+ # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
221
+ cos = cos * self.attention_scaling
222
+ sin = sin * self.attention_scaling
223
+
224
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
225
+
226
+
227
+ class DeciLMLinearScalingRotaryEmbedding(DeciLMRotaryEmbedding):
228
+ """DeciLMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
229
+
230
+ def __init__(self, *args, **kwargs):
231
+ logger.warning_once(
232
+ "`DeciLMLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.45. Please use "
233
+ "`DeciLMRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)."
234
+ )
235
+ kwargs["rope_type"] = "linear"
236
+ super().__init__(*args, **kwargs)
237
+
238
+
239
+ class DeciLMDynamicNTKScalingRotaryEmbedding(DeciLMRotaryEmbedding):
240
+ """DeciLMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
241
+
242
+ def __init__(self, *args, **kwargs):
243
+ logger.warning_once(
244
+ "`DeciLMDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.45. Please use "
245
+ "`DeciLMRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to "
246
+ "__init__)."
247
+ )
248
+ kwargs["rope_type"] = "dynamic"
249
+ super().__init__(*args, **kwargs)
250
+
251
+
252
+ def rotate_half(x):
253
+ """Rotates half the hidden dims of the input."""
254
+ x1 = x[..., : x.shape[-1] // 2]
255
+ x2 = x[..., x.shape[-1] // 2:]
256
+ return torch.cat((-x2, x1), dim=-1)
257
+
258
+
259
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
260
+ """Applies Rotary Position Embedding to the query and key tensors.
261
+
262
+ Args:
263
+ q (`torch.Tensor`): The query tensor.
264
+ k (`torch.Tensor`): The key tensor.
265
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
266
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
267
+ position_ids (`torch.Tensor`, *optional*):
268
+ Deprecated and unused.
269
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
270
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
271
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
272
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
273
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
274
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
275
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
276
+ Returns:
277
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
278
+ """
279
+ cos = cos.unsqueeze(unsqueeze_dim)
280
+ sin = sin.unsqueeze(unsqueeze_dim)
281
+ q_embed = (q * cos) + (rotate_half(q) * sin)
282
+ k_embed = (k * cos) + (rotate_half(k) * sin)
283
+ return q_embed, k_embed
284
+
285
+
286
+ class DeciLMMLP(nn.Module):
287
+ def __init__(self,
288
+ config: DeciLMConfig,
289
+ ffn_config: FFNConfig,
290
+ ):
291
+ super().__init__()
292
+ self.config = config
293
+ self.ffn_config = ffn_config
294
+ self.hidden_size = config.hidden_size
295
+ self.intermediate_size = _ffn_mult_to_intermediate_size(
296
+ ffn_config.ffn_mult, config.hidden_size) # DeciLM-specific code
297
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
298
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
299
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
300
+ self.act_fn = ACT2FN[config.hidden_act]
301
+
302
+ if ffn_config.sparsify is not None:
303
+ self.register_full_backward_hook(sparsity_backward_hook)
304
+
305
+ def forward(self, x):
306
+ if self.config.pretraining_tp > 1:
307
+ slice = self.intermediate_size // self.config.pretraining_tp
308
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
309
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
310
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
311
+
312
+ gate_proj = torch.cat(
313
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
314
+ )
315
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
316
+
317
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
318
+ down_proj = [
319
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
320
+ ]
321
+ down_proj = sum(down_proj)
322
+ else:
323
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
324
+
325
+ return down_proj
326
+
327
+
328
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
329
+ """
330
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
331
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
332
+ """
333
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
334
+ if n_rep == 1:
335
+ return hidden_states
336
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
337
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
338
+
339
+
340
+ class DeciLMAttention(nn.Module):
341
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
342
+
343
+ def __init__(self,
344
+ config: DeciLMConfig,
345
+ attention_config: AttentionConfig,
346
+ layer_idx: Optional[int] = None,
347
+ ):
348
+ super().__init__()
349
+ self.config = config
350
+ self.attention_config = attention_config
351
+ self.layer_idx = layer_idx
352
+ if layer_idx is None:
353
+ logger.warning_once(
354
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
355
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
356
+ "when creating this class."
357
+ )
358
+
359
+ self.attention_dropout = config.attention_dropout
360
+ self.hidden_size = config.hidden_size
361
+ self.num_heads = config.num_attention_heads
362
+ self.head_dim = self.hidden_size // self.num_heads
363
+ self.num_key_value_groups = attention_config.n_heads_in_group # DeciLM-specific code
364
+ self.num_key_value_heads = self.num_heads // self.num_key_value_groups # DeciLM-specific code
365
+ self.max_position_embeddings = config.max_position_embeddings
366
+ self.rope_theta = config.rope_theta
367
+ self.is_causal = True
368
+
369
+ if (self.head_dim * self.num_heads) != self.hidden_size:
370
+ raise ValueError(
371
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
372
+ f" and `num_heads`: {self.num_heads})."
373
+ )
374
+
375
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
376
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
377
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
378
+ self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
379
+
380
+ # TODO (joao): remove in v4.45 (RoPE is computed in the model, not in the decoder layers)
381
+ self.rotary_emb = DeciLMRotaryEmbedding(config=self.config)
382
+
383
+ if attention_config.sparsify is not None:
384
+ self.register_full_backward_hook(sparsity_backward_hook)
385
+
386
+ def forward(
387
+ self,
388
+ hidden_states: torch.Tensor,
389
+ attention_mask: Optional[torch.Tensor] = None,
390
+ position_ids: Optional[torch.LongTensor] = None,
391
+ past_key_value: Optional[Cache] = None,
392
+ output_attentions: bool = False,
393
+ use_cache: bool = False,
394
+ cache_position: Optional[torch.LongTensor] = None,
395
+ position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
396
+ **kwargs,
397
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
398
+ bsz, q_len, _ = hidden_states.size()
399
+ if self.config.pretraining_tp > 1:
400
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
401
+ query_slices = self.q_proj.weight.split(
402
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
403
+ )
404
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
405
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
406
+
407
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
408
+ query_states = torch.cat(query_states, dim=-1)
409
+
410
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
411
+ key_states = torch.cat(key_states, dim=-1)
412
+
413
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
414
+ value_states = torch.cat(value_states, dim=-1)
415
+
416
+ else:
417
+ query_states = self.q_proj(hidden_states)
418
+ key_states = self.k_proj(hidden_states)
419
+ value_states = self.v_proj(hidden_states)
420
+
421
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
422
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
423
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
424
+
425
+ if position_embeddings is None:
426
+ logger.warning_once(
427
+ "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
428
+ "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
429
+ "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.45 `position_ids` will be "
430
+ "removed and `position_embeddings` will be mandatory."
431
+ )
432
+ cos, sin = self.rotary_emb(value_states, position_ids)
433
+ else:
434
+ cos, sin = position_embeddings
435
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
436
+
437
+ if past_key_value is not None:
438
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
439
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
440
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
441
+
442
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
443
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
444
+
445
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
446
+
447
+ if attention_mask is not None: # no matter the length, we just slice it
448
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
449
+ attn_weights = attn_weights + causal_mask
450
+
451
+ # upcast attention to fp32
452
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
453
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
454
+ attn_output = torch.matmul(attn_weights, value_states)
455
+
456
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
457
+ raise ValueError(
458
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
459
+ f" {attn_output.size()}"
460
+ )
461
+
462
+ attn_output = attn_output.transpose(1, 2).contiguous()
463
+
464
+ attn_output = attn_output.reshape(bsz, q_len, -1)
465
+
466
+ if self.config.pretraining_tp > 1:
467
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
468
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
469
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
470
+ else:
471
+ attn_output = self.o_proj(attn_output)
472
+
473
+ if not output_attentions:
474
+ attn_weights = None
475
+
476
+ return attn_output, attn_weights, past_key_value
477
+
478
+
479
+ class DeciLMFlashAttention2(DeciLMAttention):
480
+ """
481
+ DeciLM flash attention module. This module inherits from `DeciLMAttention` as the weights of the module stays
482
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
483
+ flash attention and deal with padding tokens in case the input contains any of them.
484
+ """
485
+
486
+ def __init__(self, *args, **kwargs):
487
+ super().__init__(*args, **kwargs)
488
+
489
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
490
+ # 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.
491
+ # 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).
492
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
493
+
494
+ self.sliding_window = self.attention_config.prefill_sliding_window
495
+
496
+ def forward(
497
+ self,
498
+ hidden_states: torch.Tensor,
499
+ attention_mask: Optional[torch.LongTensor] = None,
500
+ position_ids: Optional[torch.LongTensor] = None,
501
+ past_key_value: Optional[Cache] = None,
502
+ output_attentions: bool = False,
503
+ use_cache: bool = False,
504
+ cache_position: Optional[torch.LongTensor] = None,
505
+ position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
506
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
507
+ output_attentions = False
508
+
509
+ bsz, q_len, _ = hidden_states.size()
510
+
511
+ query_states = self.q_proj(hidden_states)
512
+ key_states = self.k_proj(hidden_states)
513
+ value_states = self.v_proj(hidden_states)
514
+
515
+ # Flash attention requires the input to have the shape
516
+ # batch_size x seq_length x head_dim x hidden_dim
517
+ # therefore we just need to keep the original shape
518
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
519
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
520
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
521
+
522
+ if position_embeddings is None:
523
+ logger.warning_once(
524
+ "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
525
+ "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
526
+ "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.45 `position_ids` will be "
527
+ "removed and `position_embeddings` will be mandatory."
528
+ )
529
+ cos, sin = self.rotary_emb(value_states, position_ids)
530
+ else:
531
+ cos, sin = position_embeddings
532
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
533
+
534
+ if past_key_value is not None:
535
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
536
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
537
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
538
+
539
+ # 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
540
+ # to be able to avoid many of these transpose/reshape/view.
541
+ query_states = query_states.transpose(1, 2)
542
+ key_states = key_states.transpose(1, 2)
543
+ value_states = value_states.transpose(1, 2)
544
+
545
+ dropout_rate = self.attention_dropout if self.training else 0.0
546
+
547
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
548
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
549
+ # cast them back in the correct dtype just to be sure everything works as expected.
550
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
551
+ # in fp32. (DeciLMRMSNorm handles it correctly)
552
+
553
+ input_dtype = query_states.dtype
554
+ if input_dtype == torch.float32:
555
+ if torch.is_autocast_enabled():
556
+ target_dtype = torch.get_autocast_gpu_dtype()
557
+ # Handle the case where the model is quantized
558
+ elif hasattr(self.config, "_pre_quantization_dtype"):
559
+ target_dtype = self.config._pre_quantization_dtype
560
+ else:
561
+ target_dtype = self.q_proj.weight.dtype
562
+
563
+ logger.warning_once(
564
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
565
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
566
+ f" {target_dtype}."
567
+ )
568
+
569
+ query_states = query_states.to(target_dtype)
570
+ key_states = key_states.to(target_dtype)
571
+ value_states = value_states.to(target_dtype)
572
+
573
+ attn_output = _flash_attention_forward(
574
+ query_states,
575
+ key_states,
576
+ value_states,
577
+ attention_mask,
578
+ q_len,
579
+ position_ids=position_ids,
580
+ dropout=dropout_rate,
581
+ sliding_window=self.sliding_window,
582
+ use_top_left_mask=self._flash_attn_uses_top_left_mask,
583
+ is_causal=self.is_causal,
584
+ )
585
+
586
+ attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
587
+ attn_output = self.o_proj(attn_output)
588
+
589
+ if not output_attentions:
590
+ attn_weights = None
591
+
592
+ return attn_output, attn_weights, past_key_value
593
+
594
+
595
+ DECILM_ATTENTION_CLASSES = {
596
+ "eager": DeciLMAttention,
597
+ "flash_attention_2": DeciLMFlashAttention2,
598
+ }
599
+
600
+
601
+ class DeciLMDecoderLayer(nn.Module):
602
+ # DeciLM-specific code
603
+ def __init__(self, config: DeciLMConfig, layer_idx: int):
604
+ super().__init__()
605
+ self.config = config
606
+ self.hidden_size = config.hidden_size
607
+ self.block_config = config.block_configs[layer_idx]
608
+ self.attention_config = self.block_config.attention
609
+ self.ffn_config = self.block_config.ffn
610
+
611
+ if not self.attention_config.no_op:
612
+ self.input_layernorm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
613
+ if not self.attention_config.replace_with_linear:
614
+ self.self_attn = DECILM_ATTENTION_CLASSES[config._attn_implementation](
615
+ config=config, attention_config=self.attention_config, layer_idx=layer_idx)
616
+ else:
617
+ self.self_attn = DeciLMLinearAttention(config)
618
+
619
+ if not self.ffn_config.no_op:
620
+ self.post_attention_layernorm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
621
+ if not self.ffn_config.replace_with_linear:
622
+ self.mlp = DeciLMMLP(config, self.ffn_config)
623
+ else:
624
+ self.mlp = DeciLMLinearMLP(config)
625
+
626
+ self.is_sliding = self.attention_config.is_sliding
627
+ self.sliding_window = self.attention_config.prefill_sliding_window
628
+
629
+ def forward(
630
+ self,
631
+ hidden_states: torch.Tensor,
632
+ attention_mask: Optional[torch.Tensor] = None,
633
+ position_ids: Optional[torch.LongTensor] = None,
634
+ past_key_value: Optional[Cache] = None,
635
+ output_attentions: Optional[bool] = False,
636
+ use_cache: Optional[bool] = False,
637
+ cache_position: Optional[torch.LongTensor] = None,
638
+ position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.45
639
+ **kwargs,
640
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
641
+ """
642
+ Args:
643
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
644
+ attention_mask (`torch.FloatTensor`, *optional*):
645
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
646
+ query_sequence_length, key_sequence_length)` if default attention is used.
647
+ output_attentions (`bool`, *optional*):
648
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
649
+ returned tensors for more detail.
650
+ use_cache (`bool`, *optional*):
651
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
652
+ (see `past_key_values`).
653
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
654
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
655
+ Indices depicting the position of the input sequence tokens in the sequence
656
+ position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
657
+ Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
658
+ with `head_dim` being the embedding dimension of each attention head.
659
+ kwargs (`dict`, *optional*):
660
+ Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
661
+ into the model
662
+ """
663
+ if self.attention_config.unshifted_sink and self.attention_config.is_sink:
664
+ attention_mask = self._unshifted_sink_mask(
665
+ attention_mask, hidden_states,
666
+ self.attention_config.window_length, self.attention_config.num_sink_tokens)
667
+ else:
668
+ attention_mask = self._gemma2_window_mask(attention_mask, hidden_states, past_key_value)
669
+
670
+ self_attn_weights = None
671
+ present_key_value = past_key_value
672
+ if self.attention_config.no_op:
673
+ pass
674
+ elif self.attention_config.replace_with_linear:
675
+ residual = hidden_states
676
+ hidden_states = self.input_layernorm(hidden_states)
677
+ hidden_states = self.self_attn(hidden_states)
678
+ hidden_states = residual + hidden_states
679
+ else:
680
+ residual = hidden_states
681
+ hidden_states = self.input_layernorm(hidden_states)
682
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
683
+ hidden_states=hidden_states,
684
+ attention_mask=attention_mask,
685
+ position_ids=position_ids,
686
+ past_key_value=past_key_value,
687
+ output_attentions=output_attentions,
688
+ use_cache=use_cache,
689
+ cache_position=cache_position,
690
+ position_embeddings=position_embeddings,
691
+ **kwargs,
692
+ )
693
+ hidden_states = residual + hidden_states
694
+
695
+ if not self.ffn_config.no_op:
696
+ residual = hidden_states
697
+ hidden_states = self.post_attention_layernorm(hidden_states)
698
+ hidden_states = self.mlp(hidden_states)
699
+ hidden_states = residual + hidden_states
700
+
701
+ outputs = (hidden_states,)
702
+
703
+ if output_attentions:
704
+ outputs += (self_attn_weights,)
705
+
706
+ if use_cache:
707
+ outputs += (present_key_value,)
708
+
709
+ return outputs
710
+
711
+ def _gemma2_window_mask(self,
712
+ attention_mask: Optional[torch.Tensor],
713
+ hidden_states: torch.Tensor,
714
+ past_key_value: Optional[VariableCache],
715
+ ) -> Optional[torch.Tensor]:
716
+ if self.is_sliding and attention_mask is not None: # efficient SDPA and no padding
717
+ # Flash-attn is a 2D tensor
718
+ if self.config._attn_implementation == "flash_attention_2":
719
+ if past_key_value is not None: # when decoding
720
+ attention_mask = attention_mask[:, -self.sliding_window:]
721
+ else:
722
+ min_dtype = torch.finfo(hidden_states.dtype).min
723
+ sliding_window_mask = torch.tril(
724
+ torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
725
+ )
726
+ attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
727
+ if attention_mask.shape[-1] <= 1: # when decoding
728
+ attention_mask = attention_mask[:, :, :, -self.sliding_window:]
729
+ return attention_mask
730
+
731
+ def _unshifted_sink_mask(self,
732
+ attention_mask: torch.Tensor,
733
+ hidden_states: torch.Tensor,
734
+ window_length: int,
735
+ num_sink_tokens: Optional[int],
736
+ ) -> torch.Tensor:
737
+ assert self.config._attn_implementation == "eager", "Unshifted sink is only supported in 'eager' mode."
738
+ assert attention_mask is not None, "The attention mask seems to not be prepared"
739
+
740
+ attention_mask = attention_mask.clone()
741
+ min_dtype = torch.finfo(hidden_states.dtype).min
742
+
743
+ if window_length == 0:
744
+ attention_mask = torch.full_like(attention_mask, fill_value=min_dtype)
745
+ else:
746
+ query_length = attention_mask.shape[-2]
747
+ is_decode = (query_length == 1)
748
+ if is_decode:
749
+ attention_mask[:, :, :, :-window_length] = min_dtype
750
+ else:
751
+ sliding_window_mask = torch.tril(
752
+ torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-window_length
753
+ )
754
+ attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
755
+
756
+ attention_mask[:, :, :, :num_sink_tokens] = 0
757
+ return attention_mask
758
+
759
+
760
+ DECILM_START_DOCSTRING = r"""
761
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
762
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
763
+ etc.)
764
+
765
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
766
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
767
+ and behavior.
768
+
769
+ Parameters:
770
+ config ([`DeciLMConfig`]):
771
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
772
+ load the weights associated with the model, only the configuration. Check out the
773
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
774
+ """
775
+
776
+
777
+ @add_start_docstrings(
778
+ "The bare DeciLM Model outputting raw hidden-states without any specific head on top.",
779
+ DECILM_START_DOCSTRING,
780
+ )
781
+ class DeciLMPreTrainedModel(PreTrainedModel):
782
+ config_class = DeciLMConfig
783
+ base_model_prefix = "model"
784
+ supports_gradient_checkpointing = True
785
+ _no_split_modules = ["DeciLMDecoderLayer"]
786
+ _skip_keys_device_placement = ["past_key_values"]
787
+ _supports_flash_attn_2 = True
788
+ _supports_sdpa = False
789
+ _supports_cache_class = True
790
+ _supports_quantized_cache = False
791
+ _supports_static_cache = True
792
+
793
+ def _init_weights(self, module):
794
+ std = self.config.initializer_range
795
+ if isinstance(module, nn.Linear):
796
+ module.weight.data.normal_(mean=0.0, std=std)
797
+ if module.bias is not None:
798
+ module.bias.data.zero_()
799
+ elif isinstance(module, nn.Embedding):
800
+ module.weight.data.normal_(mean=0.0, std=std)
801
+ if module.padding_idx is not None:
802
+ module.weight.data[module.padding_idx].zero_()
803
+
804
+ def _prepare_generation_config(
805
+ self, generation_config: Optional[GenerationConfig], **kwargs: dict
806
+ ) -> tuple[GenerationConfig, dict]:
807
+ # DeciLM-specific code
808
+ generation_config, model_kwargs = super()._prepare_generation_config(generation_config, **kwargs)
809
+ generation_config.cache_implementation = "variable"
810
+ NEED_SETUP_CACHE_CLASSES_MAPPING["variable"] = VariableCache
811
+ return generation_config, model_kwargs
812
+
813
+
814
+ DECILM_INPUTS_DOCSTRING = r"""
815
+ Args:
816
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
817
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
818
+ it.
819
+
820
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
821
+ [`PreTrainedTokenizer.__call__`] for details.
822
+
823
+ [What are input IDs?](../glossary#input-ids)
824
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
825
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
826
+
827
+ - 1 for tokens that are **not masked**,
828
+ - 0 for tokens that are **masked**.
829
+
830
+ [What are attention masks?](../glossary#attention-mask)
831
+
832
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
833
+ [`PreTrainedTokenizer.__call__`] for details.
834
+
835
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
836
+ `past_key_values`).
837
+
838
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
839
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
840
+ information on the default strategy.
841
+
842
+ - 1 indicates the head is **not masked**,
843
+ - 0 indicates the head is **masked**.
844
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
845
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
846
+ config.n_positions - 1]`.
847
+
848
+ [What are position IDs?](../glossary#position-ids)
849
+ past_key_values (`VariableCache`, *optional*):
850
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
851
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
852
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
853
+
854
+ If passed to the forward function, past_key_values must be a VariableCache object (see imports).
855
+ For generation purposes, this is already handled inside model.generate().
856
+
857
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
858
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
859
+ of shape `(batch_size, sequence_length)`.
860
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
861
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
862
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
863
+ model's internal embedding lookup matrix.
864
+ use_cache (`bool`, *optional*):
865
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
866
+ `past_key_values`).
867
+ output_attentions (`bool`, *optional*):
868
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
869
+ tensors for more detail.
870
+ output_hidden_states (`bool`, *optional*):
871
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
872
+ more detail.
873
+ return_dict (`bool`, *optional*):
874
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
875
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
876
+ Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
877
+ this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
878
+ the complete sequence length.
879
+ """
880
+
881
+
882
+ @add_start_docstrings(
883
+ "The bare DeciLM Model outputting raw hidden-states without any specific head on top.",
884
+ DECILM_START_DOCSTRING,
885
+ )
886
+ class DeciLMModel(DeciLMPreTrainedModel):
887
+ """
888
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeciLMDecoderLayer`]
889
+
890
+ Args:
891
+ config: DeciLMConfig
892
+ """
893
+
894
+ def __init__(self, config: DeciLMConfig):
895
+ super().__init__(config)
896
+ self.padding_idx = config.pad_token_id
897
+ self.vocab_size = config.vocab_size
898
+
899
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
900
+ self.layers = nn.ModuleList(
901
+ [DeciLMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
902
+ )
903
+ self.norm = DeciLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
904
+ self.rotary_emb = DeciLMRotaryEmbedding(config=config)
905
+ self.gradient_checkpointing = False
906
+
907
+ # Initialize weights and apply final processing
908
+ self.post_init()
909
+
910
+ def get_input_embeddings(self):
911
+ return self.embed_tokens
912
+
913
+ def set_input_embeddings(self, value):
914
+ self.embed_tokens = value
915
+
916
+ @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
917
+ def forward(
918
+ self,
919
+ input_ids: torch.LongTensor = None,
920
+ attention_mask: Optional[torch.Tensor] = None,
921
+ position_ids: Optional[torch.LongTensor] = None,
922
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
923
+ inputs_embeds: Optional[torch.FloatTensor] = None,
924
+ use_cache: Optional[bool] = None,
925
+ output_attentions: Optional[bool] = None,
926
+ output_hidden_states: Optional[bool] = None,
927
+ return_dict: Optional[bool] = None,
928
+ cache_position: Optional[torch.LongTensor] = None,
929
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
930
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
931
+ output_hidden_states = (
932
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
933
+ )
934
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
935
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
936
+
937
+ if (input_ids is None) ^ (inputs_embeds is not None):
938
+ raise ValueError(
939
+ "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
940
+ )
941
+
942
+ if self.gradient_checkpointing and self.training and use_cache:
943
+ logger.warning_once(
944
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
945
+ )
946
+ use_cache = False
947
+
948
+ if inputs_embeds is None:
949
+ inputs_embeds = self.embed_tokens(input_ids)
950
+
951
+ is_legacy_cache_format = (past_key_values is not None) and not isinstance(past_key_values, Cache)
952
+ if is_legacy_cache_format:
953
+ raise NotImplementedError("DeciLMModel does not support legacy cache format, please use a newer "
954
+ "transformers version or use VariableCache explicitly (see import in this file).")
955
+
956
+ if cache_position is None:
957
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
958
+ cache_position = torch.arange(
959
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
960
+ )
961
+ if position_ids is None:
962
+ position_ids = cache_position.unsqueeze(0)
963
+
964
+ causal_mask = self._update_causal_mask(
965
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
966
+ )
967
+ hidden_states = inputs_embeds
968
+
969
+ # create position embeddings to be shared across the decoder layers
970
+ position_embeddings = self.rotary_emb(hidden_states, position_ids)
971
+
972
+ # decoder layers
973
+ all_hidden_states = () if output_hidden_states else None
974
+ all_self_attns = () if output_attentions else None
975
+ next_decoder_cache = None
976
+
977
+ for decoder_layer in self.layers:
978
+ if output_hidden_states:
979
+ all_hidden_states += (hidden_states,)
980
+
981
+ if self.gradient_checkpointing and self.training:
982
+ layer_outputs = self._gradient_checkpointing_func(
983
+ decoder_layer.__call__,
984
+ hidden_states,
985
+ causal_mask,
986
+ position_ids,
987
+ past_key_values,
988
+ output_attentions,
989
+ use_cache,
990
+ cache_position,
991
+ position_embeddings,
992
+ )
993
+ else:
994
+ layer_outputs = decoder_layer(
995
+ hidden_states,
996
+ attention_mask=causal_mask,
997
+ position_ids=position_ids,
998
+ past_key_value=past_key_values,
999
+ output_attentions=output_attentions,
1000
+ use_cache=use_cache,
1001
+ cache_position=cache_position,
1002
+ position_embeddings=position_embeddings,
1003
+ )
1004
+
1005
+ hidden_states = layer_outputs[0]
1006
+
1007
+ if use_cache:
1008
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1009
+
1010
+ if output_attentions:
1011
+ all_self_attns += (layer_outputs[1],)
1012
+
1013
+ hidden_states = self.norm(hidden_states)
1014
+
1015
+ # add hidden states from the last decoder layer
1016
+ if output_hidden_states:
1017
+ all_hidden_states += (hidden_states,)
1018
+
1019
+ next_cache = next_decoder_cache if use_cache else None
1020
+
1021
+ if not return_dict:
1022
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
1023
+ return BaseModelOutputWithPast(
1024
+ last_hidden_state=hidden_states,
1025
+ past_key_values=next_cache,
1026
+ hidden_states=all_hidden_states,
1027
+ attentions=all_self_attns,
1028
+ )
1029
+
1030
+ def _update_causal_mask(
1031
+ self,
1032
+ attention_mask: torch.Tensor,
1033
+ input_tensor: torch.Tensor,
1034
+ cache_position: torch.Tensor,
1035
+ past_key_values: Cache,
1036
+ output_attentions: bool,
1037
+ ):
1038
+ # 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
1039
+ # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
1040
+ # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
1041
+ # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
1042
+
1043
+ if self.config._attn_implementation == "flash_attention_2":
1044
+ if attention_mask is not None and 0.0 in attention_mask:
1045
+ return attention_mask
1046
+ return None
1047
+
1048
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
1049
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
1050
+ # to infer the attention mask.
1051
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
1052
+ assert not isinstance(past_key_values, StaticCache), "DeciLM does not support StaticCache"
1053
+ using_static_cache = isinstance(past_key_values, StaticCache)
1054
+
1055
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
1056
+ if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
1057
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
1058
+ attention_mask,
1059
+ inputs_embeds=input_tensor,
1060
+ past_key_values_length=past_seen_tokens,
1061
+ is_training=self.training,
1062
+ ) and all([not layer.is_sliding for layer in self.layers]):
1063
+ return None
1064
+
1065
+ dtype, device = input_tensor.dtype, input_tensor.device
1066
+ min_dtype = torch.finfo(dtype).min
1067
+ sequence_length = input_tensor.shape[1]
1068
+ if using_static_cache:
1069
+ target_length = past_key_values.get_max_length()
1070
+ else:
1071
+ target_length = (
1072
+ attention_mask.shape[-1]
1073
+ if isinstance(attention_mask, torch.Tensor)
1074
+ else past_seen_tokens + sequence_length + 1
1075
+ )
1076
+
1077
+ # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
1078
+ causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
1079
+ attention_mask,
1080
+ sequence_length=sequence_length,
1081
+ target_length=target_length,
1082
+ dtype=dtype,
1083
+ device=device,
1084
+ min_dtype=min_dtype,
1085
+ cache_position=cache_position,
1086
+ batch_size=input_tensor.shape[0],
1087
+ )
1088
+
1089
+ if (
1090
+ self.config._attn_implementation == "sdpa"
1091
+ and attention_mask is not None
1092
+ and attention_mask.device.type == "cuda"
1093
+ and not output_attentions
1094
+ ):
1095
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
1096
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
1097
+ # Details: https://github.com/pytorch/pytorch/issues/110213
1098
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
1099
+
1100
+ return causal_mask
1101
+
1102
+
1103
+ class DeciLMForCausalLM(DeciLMPreTrainedModel, GenerationMixin):
1104
+ _tied_weights_keys = ["lm_head.weight"]
1105
+
1106
+ def __init__(self, config):
1107
+ super().__init__(config)
1108
+ self.model = DeciLMModel(config)
1109
+ self.vocab_size = config.vocab_size
1110
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1111
+
1112
+ # Initialize weights and apply final processing
1113
+ self.post_init()
1114
+
1115
+ def get_input_embeddings(self):
1116
+ return self.model.embed_tokens
1117
+
1118
+ def set_input_embeddings(self, value):
1119
+ self.model.embed_tokens = value
1120
+
1121
+ def get_output_embeddings(self):
1122
+ return self.lm_head
1123
+
1124
+ def set_output_embeddings(self, new_embeddings):
1125
+ self.lm_head = new_embeddings
1126
+
1127
+ def set_decoder(self, decoder):
1128
+ self.model = decoder
1129
+
1130
+ def get_decoder(self):
1131
+ return self.model
1132
+
1133
+ @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
1134
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1135
+ def forward(
1136
+ self,
1137
+ input_ids: torch.LongTensor = None,
1138
+ attention_mask: Optional[torch.Tensor] = None,
1139
+ position_ids: Optional[torch.LongTensor] = None,
1140
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1141
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1142
+ labels: Optional[torch.LongTensor] = None,
1143
+ use_cache: Optional[bool] = None,
1144
+ output_attentions: Optional[bool] = None,
1145
+ output_hidden_states: Optional[bool] = None,
1146
+ return_dict: Optional[bool] = None,
1147
+ cache_position: Optional[torch.LongTensor] = None,
1148
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1149
+ r"""
1150
+ Args:
1151
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1152
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1153
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1154
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1155
+
1156
+ Return:
1157
+ """
1158
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1159
+ output_hidden_states = (
1160
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1161
+ )
1162
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1163
+
1164
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1165
+ outputs = self.model(
1166
+ input_ids=input_ids,
1167
+ attention_mask=attention_mask,
1168
+ position_ids=position_ids,
1169
+ past_key_values=past_key_values,
1170
+ inputs_embeds=inputs_embeds,
1171
+ use_cache=use_cache,
1172
+ output_attentions=output_attentions,
1173
+ output_hidden_states=output_hidden_states,
1174
+ return_dict=return_dict,
1175
+ cache_position=cache_position,
1176
+ )
1177
+
1178
+ hidden_states = outputs[0]
1179
+ if self.config.pretraining_tp > 1:
1180
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
1181
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
1182
+ logits = torch.cat(logits, dim=-1)
1183
+ else:
1184
+ logits = self.lm_head(hidden_states)
1185
+ logits = logits.float()
1186
+
1187
+ loss = None
1188
+ if labels is not None:
1189
+ # Shift so that tokens < n predict n
1190
+ shift_logits = logits[..., :-1, :].contiguous()
1191
+ shift_labels = labels[..., 1:].contiguous()
1192
+ # Flatten the tokens
1193
+ loss_fct = CrossEntropyLoss()
1194
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1195
+ shift_labels = shift_labels.view(-1)
1196
+ # Enable model parallelism
1197
+ shift_labels = shift_labels.to(shift_logits.device)
1198
+ loss = loss_fct(shift_logits, shift_labels)
1199
+
1200
+ if not return_dict:
1201
+ output = (logits,) + outputs[1:]
1202
+ return (loss,) + output if loss is not None else output
1203
+
1204
+ return CausalLMOutputWithPast(
1205
+ loss=loss,
1206
+ logits=logits,
1207
+ past_key_values=outputs.past_key_values,
1208
+ hidden_states=outputs.hidden_states,
1209
+ attentions=outputs.attentions,
1210
+ )
1211
+
1212
+ def prepare_inputs_for_generation(
1213
+ self,
1214
+ input_ids,
1215
+ past_key_values=None,
1216
+ attention_mask=None,
1217
+ inputs_embeds=None,
1218
+ cache_position=None,
1219
+ position_ids=None,
1220
+ use_cache=True,
1221
+ **kwargs,
1222
+ ):
1223
+ # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
1224
+ # Exception 1: when passing input_embeds, input_ids may be missing entries
1225
+ # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
1226
+ if past_key_values is not None:
1227
+ if inputs_embeds is not None: # Exception 1
1228
+ input_ids = input_ids[:, -cache_position.shape[0]:]
1229
+ elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2)
1230
+ input_ids = input_ids[:, cache_position]
1231
+
1232
+ if attention_mask is not None and position_ids is None:
1233
+ # create position_ids on the fly for batch generation
1234
+ position_ids = attention_mask.long().cumsum(-1) - 1
1235
+ position_ids.masked_fill_(attention_mask == 0, 1)
1236
+ if past_key_values:
1237
+ position_ids = position_ids[:, -input_ids.shape[1]:]
1238
+
1239
+ # 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.
1240
+ position_ids = position_ids.clone(memory_format=torch.contiguous_format)
1241
+
1242
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1243
+ if inputs_embeds is not None and cache_position[0] == 0:
1244
+ model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
1245
+ else:
1246
+ # The clone here is for the same reason as for `position_ids`.
1247
+ model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
1248
+
1249
+ assert not isinstance(past_key_values, StaticCache), "DeciLM does not support StaticCache"
1250
+ if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
1251
+ if model_inputs["inputs_embeds"] is not None:
1252
+ batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
1253
+ device = model_inputs["inputs_embeds"].device
1254
+ else:
1255
+ batch_size, sequence_length = model_inputs["input_ids"].shape
1256
+ device = model_inputs["input_ids"].device
1257
+
1258
+ dtype = self.lm_head.weight.dtype
1259
+ min_dtype = torch.finfo(dtype).min
1260
+
1261
+ attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
1262
+ attention_mask,
1263
+ sequence_length=sequence_length,
1264
+ target_length=past_key_values.get_max_length(),
1265
+ dtype=dtype,
1266
+ device=device,
1267
+ min_dtype=min_dtype,
1268
+ cache_position=cache_position,
1269
+ batch_size=batch_size,
1270
+ )
1271
+
1272
+ model_inputs.update(
1273
+ {
1274
+ "position_ids": position_ids,
1275
+ "cache_position": cache_position,
1276
+ "past_key_values": past_key_values,
1277
+ "use_cache": use_cache,
1278
+ "attention_mask": attention_mask,
1279
+ }
1280
+ )
1281
+ return model_inputs
1282
+
1283
+ def _maybe_initialize_input_ids_for_generation(
1284
+ self,
1285
+ inputs: Optional[torch.Tensor] = None,
1286
+ bos_token_id: Optional[torch.Tensor] = None,
1287
+ model_kwargs: Optional[dict[str, torch.Tensor]] = None,
1288
+ ) -> torch.LongTensor:
1289
+ """
1290
+ Patching hf bug that creates wrong cache length if only inputs_embeds are passed to the model
1291
+ """
1292
+ input_ids = super()._maybe_initialize_input_ids_for_generation(
1293
+ inputs=inputs, bos_token_id=bos_token_id, model_kwargs=model_kwargs)
1294
+ if (
1295
+ "inputs_embeds" in model_kwargs
1296
+ and input_ids is not None
1297
+ and input_ids.shape[1] == 0
1298
+ ):
1299
+ batch_size, input_sequence_length = model_kwargs["inputs_embeds"].shape[:2]
1300
+ input_ids = torch.zeros((batch_size, input_sequence_length), dtype=torch.long, device=self.device)
1301
+ return input_ids
1302
+
1303
+ def generate(
1304
+ self,
1305
+ inputs: Optional[torch.Tensor] = None,
1306
+ *args,
1307
+ **kwargs,
1308
+ ) -> Union[GenerateOutput, torch.LongTensor]:
1309
+ """
1310
+ Patching hf bug that creates wrong cache length if only inputs_embeds are passed to the model
1311
+ """
1312
+ only_passed_inputs_embeds = (
1313
+ "inputs_embeds" in kwargs and
1314
+ "input_ids" not in kwargs and
1315
+ inputs is None
1316
+ )
1317
+ if only_passed_inputs_embeds:
1318
+ input_sequence_length = kwargs["inputs_embeds"].shape[1]
1319
+
1320
+ generation_output = super().generate(inputs=inputs, *args, **kwargs)
1321
+
1322
+ if only_passed_inputs_embeds and isinstance(generation_output, torch.Tensor):
1323
+ generation_output = generation_output[:, input_sequence_length:]
1324
+
1325
+ return generation_output
1326
+
1327
+
1328
+ @add_start_docstrings(
1329
+ """
1330
+ The DeciLM Model transformer with a sequence classification head on top (linear layer).
1331
+
1332
+ [`DeciLMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1333
+ (e.g. GPT-2) do.
1334
+
1335
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1336
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1337
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1338
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1339
+ each row of the batch).
1340
+ """,
1341
+ DECILM_START_DOCSTRING,
1342
+ )
1343
+ class DeciLMForSequenceClassification(DeciLMPreTrainedModel):
1344
+ def __init__(self, config):
1345
+ super().__init__(config)
1346
+ self.num_labels = config.num_labels
1347
+ self.model = DeciLMModel(config)
1348
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1349
+
1350
+ # Initialize weights and apply final processing
1351
+ self.post_init()
1352
+
1353
+ def get_input_embeddings(self):
1354
+ return self.model.embed_tokens
1355
+
1356
+ def set_input_embeddings(self, value):
1357
+ self.model.embed_tokens = value
1358
+
1359
+ @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
1360
+ def forward(
1361
+ self,
1362
+ input_ids: Optional[torch.LongTensor] = None,
1363
+ attention_mask: Optional[torch.Tensor] = None,
1364
+ position_ids: Optional[torch.LongTensor] = None,
1365
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1366
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1367
+ labels: Optional[torch.LongTensor] = None,
1368
+ use_cache: Optional[bool] = None,
1369
+ output_attentions: Optional[bool] = None,
1370
+ output_hidden_states: Optional[bool] = None,
1371
+ return_dict: Optional[bool] = None,
1372
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1373
+ r"""
1374
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1375
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1376
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1377
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1378
+ """
1379
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1380
+
1381
+ transformer_outputs = self.model(
1382
+ input_ids,
1383
+ attention_mask=attention_mask,
1384
+ position_ids=position_ids,
1385
+ past_key_values=past_key_values,
1386
+ inputs_embeds=inputs_embeds,
1387
+ use_cache=use_cache,
1388
+ output_attentions=output_attentions,
1389
+ output_hidden_states=output_hidden_states,
1390
+ return_dict=return_dict,
1391
+ )
1392
+ hidden_states = transformer_outputs[0]
1393
+ logits = self.score(hidden_states)
1394
+
1395
+ if input_ids is not None:
1396
+ batch_size = input_ids.shape[0]
1397
+ else:
1398
+ batch_size = inputs_embeds.shape[0]
1399
+
1400
+ if self.config.pad_token_id is None and batch_size != 1:
1401
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1402
+ if self.config.pad_token_id is None:
1403
+ sequence_lengths = -1
1404
+ else:
1405
+ if input_ids is not None:
1406
+ # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
1407
+ sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
1408
+ sequence_lengths = sequence_lengths % input_ids.shape[-1]
1409
+ sequence_lengths = sequence_lengths.to(logits.device)
1410
+ else:
1411
+ sequence_lengths = -1
1412
+
1413
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1414
+
1415
+ loss = None
1416
+ if labels is not None:
1417
+ labels = labels.to(logits.device)
1418
+ if self.config.problem_type is None:
1419
+ if self.num_labels == 1:
1420
+ self.config.problem_type = "regression"
1421
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1422
+ self.config.problem_type = "single_label_classification"
1423
+ else:
1424
+ self.config.problem_type = "multi_label_classification"
1425
+
1426
+ if self.config.problem_type == "regression":
1427
+ loss_fct = MSELoss()
1428
+ if self.num_labels == 1:
1429
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1430
+ else:
1431
+ loss = loss_fct(pooled_logits, labels)
1432
+ elif self.config.problem_type == "single_label_classification":
1433
+ loss_fct = CrossEntropyLoss()
1434
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1435
+ elif self.config.problem_type == "multi_label_classification":
1436
+ loss_fct = BCEWithLogitsLoss()
1437
+ loss = loss_fct(pooled_logits, labels)
1438
+ if not return_dict:
1439
+ output = (pooled_logits,) + transformer_outputs[1:]
1440
+ return ((loss,) + output) if loss is not None else output
1441
+
1442
+ return SequenceClassifierOutputWithPast(
1443
+ loss=loss,
1444
+ logits=pooled_logits,
1445
+ past_key_values=transformer_outputs.past_key_values,
1446
+ hidden_states=transformer_outputs.hidden_states,
1447
+ attentions=transformer_outputs.attentions,
1448
+ )
1449
+
1450
+
1451
+ @add_start_docstrings(
1452
+ """
1453
+ The DeciLM Model transformer with a span classification head on top for extractive question-answering tasks like
1454
+ SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
1455
+ """,
1456
+ DECILM_START_DOCSTRING,
1457
+ )
1458
+ class DeciLMForQuestionAnswering(DeciLMPreTrainedModel):
1459
+ base_model_prefix = "transformer"
1460
+
1461
+ # Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->DeciLM
1462
+ def __init__(self, config):
1463
+ super().__init__(config)
1464
+ self.transformer = DeciLMModel(config)
1465
+ self.qa_outputs = nn.Linear(config.hidden_size, 2)
1466
+
1467
+ # Initialize weights and apply final processing
1468
+ self.post_init()
1469
+
1470
+ def get_input_embeddings(self):
1471
+ return self.transformer.embed_tokens
1472
+
1473
+ def set_input_embeddings(self, value):
1474
+ self.transformer.embed_tokens = value
1475
+
1476
+ @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
1477
+ def forward(
1478
+ self,
1479
+ input_ids: Optional[torch.LongTensor] = None,
1480
+ attention_mask: Optional[torch.FloatTensor] = None,
1481
+ position_ids: Optional[torch.LongTensor] = None,
1482
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1483
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1484
+ start_positions: Optional[torch.LongTensor] = None,
1485
+ end_positions: Optional[torch.LongTensor] = None,
1486
+ output_attentions: Optional[bool] = None,
1487
+ output_hidden_states: Optional[bool] = None,
1488
+ return_dict: Optional[bool] = None,
1489
+ ) -> Union[Tuple, QuestionAnsweringModelOutput]:
1490
+ r"""
1491
+ start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1492
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
1493
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1494
+ are not taken into account for computing the loss.
1495
+ end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1496
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
1497
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1498
+ are not taken into account for computing the loss.
1499
+ """
1500
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1501
+
1502
+ outputs = self.transformer(
1503
+ input_ids,
1504
+ attention_mask=attention_mask,
1505
+ position_ids=position_ids,
1506
+ past_key_values=past_key_values,
1507
+ inputs_embeds=inputs_embeds,
1508
+ output_attentions=output_attentions,
1509
+ output_hidden_states=output_hidden_states,
1510
+ return_dict=return_dict,
1511
+ )
1512
+
1513
+ sequence_output = outputs[0]
1514
+
1515
+ logits = self.qa_outputs(sequence_output)
1516
+ start_logits, end_logits = logits.split(1, dim=-1)
1517
+ start_logits = start_logits.squeeze(-1).contiguous()
1518
+ end_logits = end_logits.squeeze(-1).contiguous()
1519
+
1520
+ total_loss = None
1521
+ if start_positions is not None and end_positions is not None:
1522
+ # If we are on multi-GPU, split add a dimension
1523
+ if len(start_positions.size()) > 1:
1524
+ start_positions = start_positions.squeeze(-1).to(start_logits.device)
1525
+ if len(end_positions.size()) > 1:
1526
+ end_positions = end_positions.squeeze(-1).to(end_logits.device)
1527
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
1528
+ ignored_index = start_logits.size(1)
1529
+ start_positions = start_positions.clamp(0, ignored_index)
1530
+ end_positions = end_positions.clamp(0, ignored_index)
1531
+
1532
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
1533
+ start_loss = loss_fct(start_logits, start_positions)
1534
+ end_loss = loss_fct(end_logits, end_positions)
1535
+ total_loss = (start_loss + end_loss) / 2
1536
+
1537
+ if not return_dict:
1538
+ output = (start_logits, end_logits) + outputs[2:]
1539
+ return ((total_loss,) + output) if total_loss is not None else output
1540
+
1541
+ return QuestionAnsweringModelOutput(
1542
+ loss=total_loss,
1543
+ start_logits=start_logits,
1544
+ end_logits=end_logits,
1545
+ hidden_states=outputs.hidden_states,
1546
+ attentions=outputs.attentions,
1547
+ )
1548
+
1549
+
1550
+ @add_start_docstrings(
1551
+ """
1552
+ The DeciLM Model transformer with a token classification head on top (a linear layer on top of the hidden-states
1553
+ output) e.g. for Named-Entity-Recognition (NER) tasks.
1554
+ """,
1555
+ DECILM_START_DOCSTRING,
1556
+ )
1557
+ class DeciLMForTokenClassification(DeciLMPreTrainedModel):
1558
+ def __init__(self, config):
1559
+ super().__init__(config)
1560
+ self.num_labels = config.num_labels
1561
+ self.model = DeciLMModel(config)
1562
+ if getattr(config, "classifier_dropout", None) is not None:
1563
+ classifier_dropout = config.classifier_dropout
1564
+ elif getattr(config, "hidden_dropout", None) is not None:
1565
+ classifier_dropout = config.hidden_dropout
1566
+ else:
1567
+ classifier_dropout = 0.1
1568
+ self.dropout = nn.Dropout(classifier_dropout)
1569
+ self.score = nn.Linear(config.hidden_size, config.num_labels)
1570
+
1571
+ # Initialize weights and apply final processing
1572
+ self.post_init()
1573
+
1574
+ def get_input_embeddings(self):
1575
+ return self.model.embed_tokens
1576
+
1577
+ def set_input_embeddings(self, value):
1578
+ self.model.embed_tokens = value
1579
+
1580
+ @add_start_docstrings_to_model_forward(DECILM_INPUTS_DOCSTRING)
1581
+ def forward(
1582
+ self,
1583
+ input_ids: Optional[torch.LongTensor] = None,
1584
+ attention_mask: Optional[torch.Tensor] = None,
1585
+ position_ids: Optional[torch.LongTensor] = None,
1586
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1587
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1588
+ labels: Optional[torch.LongTensor] = None,
1589
+ use_cache: Optional[bool] = None,
1590
+ output_attentions: Optional[bool] = None,
1591
+ output_hidden_states: Optional[bool] = None,
1592
+ return_dict: Optional[bool] = None,
1593
+ ) -> Union[Tuple, TokenClassifierOutput]:
1594
+ r"""
1595
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1596
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1597
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1598
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1599
+ """
1600
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1601
+
1602
+ outputs = self.model(
1603
+ input_ids,
1604
+ attention_mask=attention_mask,
1605
+ position_ids=position_ids,
1606
+ past_key_values=past_key_values,
1607
+ inputs_embeds=inputs_embeds,
1608
+ use_cache=use_cache,
1609
+ output_attentions=output_attentions,
1610
+ output_hidden_states=output_hidden_states,
1611
+ return_dict=return_dict,
1612
+ )
1613
+ sequence_output = outputs[0]
1614
+ sequence_output = self.dropout(sequence_output)
1615
+ logits = self.score(sequence_output)
1616
+
1617
+ loss = None
1618
+ if labels is not None:
1619
+ loss_fct = CrossEntropyLoss()
1620
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
1621
+
1622
+ if not return_dict:
1623
+ output = (logits,) + outputs[2:]
1624
+ return ((loss,) + output) if loss is not None else output
1625
+
1626
+ return TokenClassifierOutput(
1627
+ loss=loss,
1628
+ logits=logits,
1629
+ hidden_states=outputs.hidden_states,
1630
+ attentions=outputs.attentions,
1631
+ )
1632
+
1633
+
1634
+ ########################################################################
1635
+ # DeciLM-specific code
1636
+ ########################################################################
1637
+
1638
+
1639
+ def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
1640
+ # DeciLM-specific code
1641
+ intermediate_size = int(2 * ffn_mult * n_embd / 3)
1642
+ return _find_multiple(intermediate_size, 256)
1643
+
1644
+
1645
+ def _find_multiple(n: int, k: int) -> int:
1646
+ # DeciLM-specific code
1647
+ if n % k == 0:
1648
+ return n
1649
+ return n + k - (n % k)
1650
+
1651
+
1652
+ class DeciLMLinearMLP(nn.Module):
1653
+ # DeciLM-specific code
1654
+ def __init__(self,
1655
+ config: DeciLMConfig,
1656
+ ):
1657
+ super().__init__()
1658
+ self.linear_mlp = nn.Linear(in_features=config.hidden_size,
1659
+ out_features=config.hidden_size,
1660
+ bias=False)
1661
+
1662
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1663
+ return self.linear_mlp.forward(x)
1664
+
1665
+
1666
+ class DeciLMLinearAttention(nn.Module):
1667
+ # DeciLM-specific code
1668
+ def __init__(self,
1669
+ config: DeciLMConfig,
1670
+ ):
1671
+ super().__init__()
1672
+ self.linear_attn = nn.Linear(in_features=config.hidden_size,
1673
+ out_features=config.hidden_size,
1674
+ bias=False)
1675
+
1676
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
1677
+ return self.linear_attn.forward(x)
1678
+
1679
+
1680
+ def sparsity_backward_hook(*args, **kwargs):
1681
+ raise NotImplementedError("No support for sparsity when training HF DeciLM (inference is ok though)")
nemo_common.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {}
nemo_common.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e4e4090fa34d96307127606cccef3ae99aedae58279e8bdf1746d44d3bf7aa47
3
+ size 860
nemo_model_config.yaml ADDED
@@ -0,0 +1,215 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ restore_from_path: null
2
+ restore_from_ckpt: null
3
+ mcore_gpt: true
4
+ micro_batch_size: 1
5
+ global_batch_size: 256
6
+ tensor_model_parallel_size: 8
7
+ pipeline_model_parallel_size: 8
8
+ virtual_pipeline_model_parallel_size: null
9
+ encoder_seq_length: 14336
10
+ max_position_embeddings: 14336
11
+ num_layers: 80
12
+ hidden_size: 8192
13
+ ffn_hidden_size: 11008
14
+ num_attention_heads: 64
15
+ init_method_std: 0.02
16
+ use_scaled_init_method: true
17
+ hidden_dropout: 0.0
18
+ attention_dropout: 0.0
19
+ ffn_dropout: 0.0
20
+ kv_channels: null
21
+ apply_query_key_layer_scaling: true
22
+ normalization: rmsnorm
23
+ layernorm_epsilon: 1.0e-05
24
+ do_layer_norm_weight_decay: false
25
+ make_vocab_size_divisible_by: 128
26
+ pre_process: true
27
+ post_process: true
28
+ persist_layer_norm: true
29
+ bias: false
30
+ activation: fast-swiglu
31
+ headscale: false
32
+ transformer_block_type: pre_ln
33
+ openai_gelu: false
34
+ normalize_attention_scores: true
35
+ position_embedding_type: rope
36
+ rotary_percentage: 1.0
37
+ attention_type: multihead
38
+ share_embeddings_and_output_weights: false
39
+ overlap_p2p_comm: false
40
+ batch_p2p_comm: true
41
+ num_query_groups: 8
42
+ tokenizer:
43
+ library: huggingface
44
+ type: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
45
+ use_fast: true
46
+ native_amp_init_scale: 4294967296
47
+ native_amp_growth_interval: 1000
48
+ hysteresis: 2
49
+ fp32_residual_connection: false
50
+ fp16_lm_cross_entropy: false
51
+ megatron_amp_O2: true
52
+ grad_allreduce_chunk_size_mb: 125
53
+ grad_div_ar_fusion: true
54
+ gradient_accumulation_fusion: false
55
+ bias_activation_fusion: false
56
+ bias_dropout_add_fusion: false
57
+ masked_softmax_fusion: true
58
+ get_attention_mask_from_fusion: true
59
+ apply_rope_fusion: false
60
+ seed: 1234
61
+ resume_from_checkpoint: null
62
+ use_cpu_initialization: false
63
+ onnx_safe: false
64
+ apex_transformer_log_level: 30
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+ gradient_as_bucket_view: true
66
+ sync_batch_comm: false
67
+ activations_checkpoint_granularity: null
68
+ activations_checkpoint_method: null
69
+ activations_checkpoint_num_layers: null
70
+ num_micro_batches_with_partial_activation_checkpoints: null
71
+ activations_checkpoint_layers_per_pipeline: null
72
+ sequence_parallel: true
73
+ transformer_engine: true
74
+ fp8: false
75
+ fp8_e4m3: false
76
+ fp8_hybrid: true
77
+ fp8_margin: 0
78
+ fp8_interval: 1
79
+ fp8_amax_history_len: 1024
80
+ fp8_amax_compute_algo: max
81
+ reduce_amax: true
82
+ use_emha: false
83
+ data:
84
+ index_mapping_dir: null
85
+ data_impl: jsonl
86
+ splits_string: null
87
+ seq_length: 14336
88
+ skip_warmup: true
89
+ num_workers: 0
90
+ dataloader_type: single
91
+ reset_position_ids: false
92
+ reset_attention_mask: false
93
+ eod_mask_loss: false
94
+ validation_drop_last: true
95
+ no_seqlen_plus_one_input_tokens: false
96
+ pad_samples_to_global_batch_size: false
97
+ shuffle_documents: true
98
+ data_prefix:
99
+ train:
100
+ - /lustre/fsw/portfolios/llmservice/users/jiaqiz/data/reinforce/hs3_genrm/hf/multilingual.rl.sys2.jsonl
101
+ validation:
102
+ - /lustre/fsw/portfolios/llmservice/users/jiaqiz/data/reinforce/hs3_genrm/rlhf_2_3_val.rl.sys2.jsonl
103
+ test:
104
+ - /lustre/fsw/portfolios/llmservice/users/jiaqiz/data/reinforce/hs3_genrm/rlhf_2_3_val.rl.sys2.jsonl
105
+ apply_chat_template: true
106
+ prompt_file: null
107
+ system_prompt_file: null
108
+ shuffle_train_data: false
109
+ nsys_profile:
110
+ enabled: false
111
+ start_step: 10
112
+ end_step: 10
113
+ ranks:
114
+ - 0
115
+ gen_shape: false
116
+ optim:
117
+ name: distributed_fused_adam
118
+ lr: 3.0e-07
119
+ weight_decay: 0.1
120
+ betas:
121
+ - 0.9
122
+ - 0.98
123
+ sched:
124
+ name: CosineAnnealing
125
+ warmup_steps: 10
126
+ constant_steps: 1000
127
+ min_lr: 2.9999e-07
128
+ max_steps: 3458
129
+ bucket_cap_mb: 200
130
+ overlap_grad_sync: false
131
+ overlap_param_sync: false
132
+ contiguous_grad_buffer: true
133
+ rotary_base: 500000.0
134
+ scale_positional_embedding: true
135
+ seq_len_interpolation_factor: null
136
+ scale_factor: 8.0
137
+ heterogeneous_layers_config_path: /lustre/fsw/portfolios/coreai/projects/coreai_nvfm_llm/models/megatron_conversion/llama3_3-nemotron-super-49b-v1/NeMo/config.json
138
+ name: heterogeneous_gpt
139
+ precision: bf16
140
+ hf_model_name_or_configs_dir: /lustre/fsw/portfolios/llmservice/users/tkonuk/share/models/llama-nemotron/llama-nemotron-super-49b-reason-final-checkpoint
141
+ grpo:
142
+ share_dir: /dev/shm/checkpoints_2627851
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+ forward_micro_batch_size: 4
144
+ offload_adam_states: true
145
+ ratio_eps: 0.2
146
+ ratio_eps_low: 0.2
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+ ratio_eps_high: 0.28
148
+ sampling_params:
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+ use_greedy: false
150
+ temperature: 1
151
+ top_k: -1
152
+ top_p: 1.0
153
+ repetition_penalty: 1.0
154
+ add_BOS: false
155
+ all_probs: false
156
+ compute_logprob: false
157
+ end_strings:
158
+ - <|endoftext|>
159
+ - <extra_id_1>
160
+ length_params:
161
+ max_length: 12288
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+ min_length: 1
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+ generation_rollout_mbs: 8
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+ trt_model_dir: /tmp/trt_llm_model
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+ initial_policy_kl_penalty: 0.001
166
+ inference_backend:
167
+ type: vllm
168
+ enable: true
169
+ seed: 1234
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+ max_input_len: 2048
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+ reshard: true
172
+ config:
173
+ trt_llm:
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+ enable: false
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+ model_type: llama
176
+ unload_engine_train: false
177
+ vllm:
178
+ enable: true
179
+ port: 4321
180
+ ip: cw-dfw-h100-001-243-012
181
+ trt_llm_pytorch:
182
+ enable: false
183
+ port: 4321
184
+ ip: localhost
185
+ dapo:
186
+ token_loss: false
187
+ clip_higher: false
188
+ peft:
189
+ peft_scheme: none
190
+ restore_from_path: null
191
+ restore_from_ckpt:
192
+ checkpoint_dir: null
193
+ checkpoint_name: null
194
+ lora_tuning:
195
+ target_modules:
196
+ - attention_qkv
197
+ adapter_dim: 32
198
+ adapter_dropout: 0.0
199
+ column_init_method: xavier
200
+ row_init_method: zero
201
+ layer_selection: null
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+ weight_tying: false
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+ position_embedding_strategy: null
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+ context_parallel_size: 1
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+ dist_ckpt_format: torch_dist
206
+ dist_ckpt_load_on_device: true
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+ dist_ckpt_parallel_save: true
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+ dist_ckpt_parallel_save_within_dp: false
209
+ dist_ckpt_parallel_load: false
210
+ dist_ckpt_torch_dist_multiproc: 2
211
+ dist_ckpt_assume_constant_structure: false
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+ dist_ckpt_parallel_dist_opt: true
213
+ dist_ckpt_load_strictness: log_all
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+ target: nemo_aligner.experimental.grpo.models.nlp.gpt.megatron_gpt_grpo_actor.MegatronGPTActorModel
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+ nemo_version: 2.2.0rc0
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}{%- if messages[0]['role'] == 'system' %}{%- set system_message = messages[0]['content']|trim %}{%- set messages = messages[1:] %}{%- else %}{%- set system_message = \"\" %}{%- endif %}{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}{{- system_message }}{{- \"<|eot_id|>\" }}{%- for message in messages %}{%- if message['role'] == 'assistant' and '</think>' in message['content'] %}{%- set content = message['content'].split('</think>')[-1].lstrip() %}{%- else %}{%- set content = message['content'] %}{%- endif %}{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' + content | trim + '<|eot_id|>' }}{%- endfor %}{%- if add_generation_prompt %}{{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}{%- endif %}",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
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+ "extra_special_tokens": {},
2057
+ "model_input_names": [
2058
+ "input_ids",
2059
+ "attention_mask"
2060
+ ],
2061
+ "model_max_length": 131072,
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+ "tokenizer_class": "PreTrainedTokenizerFast"
2063
+ }
tokenizer_name.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
transformers_4_44_2__activations.py ADDED
@@ -0,0 +1,239 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 +1,1347 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 DynamicCache(Cache):
190
+ """
191
+ A cache that grows dynamically as more tokens are generated. This is the default for generative models.
192
+
193
+ It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
194
+ `[batch_size, num_heads, seq_len, head_dim]`.
195
+
196
+ Example:
197
+
198
+ ```python
199
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache
200
+
201
+ >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
202
+ >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
203
+
204
+ >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
205
+
206
+ >>> # Prepare a cache class and pass it to model's forward
207
+ >>> past_key_values = DynamicCache()
208
+ >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
209
+ >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
210
+ ```
211
+ """
212
+
213
+ def __init__(self) -> None:
214
+ super().__init__()
215
+ self.key_cache: List[torch.Tensor] = []
216
+ self.value_cache: List[torch.Tensor] = []
217
+ self._seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
218
+
219
+ def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
220
+ """
221
+ Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
222
+ sequence length.
223
+ """
224
+ if layer_idx < len(self):
225
+ return (self.key_cache[layer_idx], self.value_cache[layer_idx])
226
+ else:
227
+ raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
228
+
229
+ def __iter__(self):
230
+ """
231
+ Support for backwards-compatible `past_key_value` iteration, e.g. `for x in past_key_value:` to iterate over
232
+ keys and values
233
+ """
234
+ for layer_idx in range(len(self)):
235
+ yield (self.key_cache[layer_idx], self.value_cache[layer_idx])
236
+
237
+ def __len__(self):
238
+ """
239
+ Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
240
+ to the number of layers in the model.
241
+ """
242
+ return len(self.key_cache)
243
+
244
+ def update(
245
+ self,
246
+ key_states: torch.Tensor,
247
+ value_states: torch.Tensor,
248
+ layer_idx: int,
249
+ cache_kwargs: Optional[Dict[str, Any]] = None,
250
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
251
+ """
252
+ Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
253
+
254
+ Parameters:
255
+ key_states (`torch.Tensor`):
256
+ The new key states to cache.
257
+ value_states (`torch.Tensor`):
258
+ The new value states to cache.
259
+ layer_idx (`int`):
260
+ The index of the layer to cache the states for.
261
+ cache_kwargs (`Dict[str, Any]`, `optional`):
262
+ Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.
263
+
264
+ Return:
265
+ A tuple containing the updated key and value states.
266
+ """
267
+ # Update the number of seen tokens
268
+ if layer_idx == 0:
269
+ self._seen_tokens += key_states.shape[-2]
270
+
271
+ # Update the cache
272
+ if len(self.key_cache) <= layer_idx:
273
+ self.key_cache.append(key_states)
274
+ self.value_cache.append(value_states)
275
+ else:
276
+ self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
277
+ self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
278
+
279
+ return self.key_cache[layer_idx], self.value_cache[layer_idx]
280
+
281
+ def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
282
+ """Returns the sequence length of the cached states. A layer index can be optionally passed."""
283
+ # TODO: deprecate this function in favor of `cache_position`
284
+ if len(self.key_cache) <= layer_idx:
285
+ return 0
286
+ return self.key_cache[layer_idx].shape[-2]
287
+
288
+ def get_max_length(self) -> Optional[int]:
289
+ """Returns the maximum sequence length of the cached states. DynamicCache does not have a maximum length."""
290
+ return None
291
+
292
+ def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
293
+ """Converts the `DynamicCache` instance into the its equivalent in the legacy cache format. Used for
294
+ backward compatibility."""
295
+ legacy_cache = ()
296
+ for layer_idx in range(len(self)):
297
+ legacy_cache += ((self.key_cache[layer_idx], self.value_cache[layer_idx]),)
298
+ return legacy_cache
299
+
300
+ @classmethod
301
+ def from_legacy_cache(cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None) -> "DynamicCache":
302
+ """Converts a cache in the legacy cache format into an equivalent `DynamicCache`. Used for
303
+ backward compatibility."""
304
+ cache = cls()
305
+ if past_key_values is not None:
306
+ for layer_idx in range(len(past_key_values)):
307
+ key_states, value_states = past_key_values[layer_idx]
308
+ cache.update(key_states, value_states, layer_idx)
309
+ return cache
310
+
311
+ def crop(self, max_length: int):
312
+ """Crop the past key values up to a new `max_length` in terms of tokens. `max_length` can also be
313
+ negative to remove `max_length` tokens. This is used in assisted decoding and contrastive search."""
314
+ # In case it is negative
315
+ if max_length < 0:
316
+ max_length = self.get_seq_length() - abs(max_length)
317
+
318
+ if self.get_seq_length() <= max_length:
319
+ return
320
+
321
+ self._seen_tokens = max_length
322
+ for idx in range(len(self.key_cache)):
323
+ self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
324
+ self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]
325
+
326
+ def batch_split(self, full_batch_size: int, split_size: int) -> List["DynamicCache"]:
327
+ """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
328
+ `_split_model_inputs()` in `generation.utils`"""
329
+ out = []
330
+ for i in range(0, full_batch_size, split_size):
331
+ current_split = DynamicCache()
332
+ current_split._seen_tokens = self._seen_tokens
333
+ current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
334
+ current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
335
+ out.append(current_split)
336
+ return out
337
+
338
+ @classmethod
339
+ def from_batch_splits(cls, splits: List["DynamicCache"]) -> "DynamicCache":
340
+ """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
341
+ `generation.utils`"""
342
+ cache = cls()
343
+ for idx in range(len(splits[0])):
344
+ layer_keys = torch.cat([current.key_cache[idx] for current in splits], dim=0)
345
+ layer_values = torch.cat([current.value_cache[idx] for current in splits], dim=0)
346
+ cache.update(layer_keys, layer_values, idx)
347
+ return cache
348
+
349
+ def batch_repeat_interleave(self, repeats: int):
350
+ """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
351
+ for layer_idx in range(len(self)):
352
+ self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(repeats, dim=0)
353
+ self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(repeats, dim=0)
354
+
355
+ def batch_select_indices(self, indices: torch.Tensor):
356
+ """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
357
+ for layer_idx in range(len(self)):
358
+ self.key_cache[layer_idx] = self.key_cache[layer_idx][indices, ...]
359
+ self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
360
+
361
+
362
+ class OffloadedCache(DynamicCache):
363
+ """
364
+ A drop-in replacement for DynamicCache that conserves GPU memory at the expense of more CPU memory.
365
+ Useful for generating from models with very long context.
366
+
367
+ In addition to the default CUDA stream, where all forward() computations happen,
368
+ this class uses another stream, the prefetch stream, which it creates itself.
369
+ Since scheduling of operations on separate streams happens independently, this class uses
370
+ the prefetch stream to asynchronously prefetch the KV cache of layer k+1 when layer k is executing.
371
+ The movement of the layer k-1 cache to the CPU is handled by the default stream as a simple way to
372
+ ensure the eviction is scheduled after all computations on that cache are finished.
373
+ """
374
+
375
+ def __init__(self) -> None:
376
+ if not torch.cuda.is_available():
377
+ raise RuntimeError("OffloadedCache can only be used with a GPU")
378
+ super().__init__()
379
+ self.original_device = []
380
+ self.prefetch_stream = torch.cuda.Stream()
381
+ self.beam_idx = None # used to delay beam search operations
382
+
383
+ def prefetch_layer(self, layer_idx: int):
384
+ "Starts prefetching the next layer cache"
385
+ if layer_idx < len(self):
386
+ with torch.cuda.stream(self.prefetch_stream):
387
+ # Prefetch next layer tensors to GPU
388
+ device = self.original_device[layer_idx]
389
+ self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device, non_blocking=True)
390
+ self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device, non_blocking=True)
391
+
392
+ def evict_previous_layer(self, layer_idx: int):
393
+ "Moves the previous layer cache to the CPU"
394
+ if len(self) > 2:
395
+ # We do it on the default stream so it occurs after all earlier computations on these tensors are done
396
+ prev_layer_idx = (layer_idx - 1) % len(self)
397
+ self.key_cache[prev_layer_idx] = self.key_cache[prev_layer_idx].to("cpu", non_blocking=True)
398
+ self.value_cache[prev_layer_idx] = self.value_cache[prev_layer_idx].to("cpu", non_blocking=True)
399
+
400
+ def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
401
+ "Gets the cache for this layer to the device. Prefetches the next and evicts the previous layer."
402
+ if layer_idx < len(self):
403
+ # Evict the previous layer if necessary
404
+ torch.cuda.current_stream().synchronize()
405
+ self.evict_previous_layer(layer_idx)
406
+ # Load current layer cache to its original device if not already there
407
+ original_device = self.original_device[layer_idx]
408
+ self.prefetch_stream.synchronize()
409
+ key_tensor = self.key_cache[layer_idx]
410
+ value_tensor = self.value_cache[layer_idx]
411
+ # Now deal with beam search ops which were delayed
412
+ if self.beam_idx is not None:
413
+ self.beam_idx = self.beam_idx.to(original_device)
414
+ key_tensor = key_tensor.index_select(0, self.beam_idx)
415
+ value_tensor = value_tensor.index_select(0, self.beam_idx)
416
+ # Prefetch the next layer
417
+ self.prefetch_layer((layer_idx + 1) % len(self))
418
+ return (key_tensor, value_tensor)
419
+ else:
420
+ raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
421
+
422
+ def reorder_cache(self, beam_idx: torch.LongTensor):
423
+ """Saves the beam indices and reorders the cache when the tensor is back to its device."""
424
+ # We delay this operation until the tensors are back to their original
425
+ # device because performing torch.index_select on the CPU is very slow
426
+ del self.beam_idx
427
+ self.beam_idx = beam_idx.clone()
428
+
429
+ def update(
430
+ self,
431
+ key_states: torch.Tensor,
432
+ value_states: torch.Tensor,
433
+ layer_idx: int,
434
+ cache_kwargs: Optional[Dict[str, Any]] = None,
435
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
436
+ """
437
+ Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
438
+ Parameters:
439
+ key_states (`torch.Tensor`):
440
+ The new key states to cache.
441
+ value_states (`torch.Tensor`):
442
+ The new value states to cache.
443
+ layer_idx (`int`):
444
+ The index of the layer to cache the states for.
445
+ cache_kwargs (`Dict[str, Any]`, `optional`):
446
+ Additional arguments for the cache subclass. No additional arguments are used in `OffloadedCache`.
447
+ Return:
448
+ A tuple containing the updated key and value states.
449
+ """
450
+ # Update the number of seen tokens
451
+ if layer_idx == 0:
452
+ self._seen_tokens += key_states.shape[-2]
453
+
454
+ # Update the cache
455
+ if len(self.key_cache) <= layer_idx:
456
+ self.key_cache.append(key_states)
457
+ self.value_cache.append(value_states)
458
+ self.original_device.append(key_states.device)
459
+ self.evict_previous_layer(layer_idx)
460
+ else:
461
+ key_tensor, value_tensor = self[layer_idx]
462
+ self.key_cache[layer_idx] = torch.cat([key_tensor, key_states], dim=-2)
463
+ self.value_cache[layer_idx] = torch.cat([value_tensor, value_states], dim=-2)
464
+
465
+ return self.key_cache[layer_idx], self.value_cache[layer_idx]
466
+
467
+ # According to https://docs.python.org/3/library/exceptions.html#NotImplementedError
468
+ # if a method is not supposed to be supported in a subclass we should set it to None
469
+ from_legacy_cache = None
470
+
471
+ to_legacy_cache = None
472
+
473
+
474
+ class SinkCache(Cache):
475
+ """
476
+ A cache that as described in the [Attention Sinks paper](https://arxiv.org/abs/2309.17453). It allows the model to
477
+ generate beyond the length of its context window, without losing fluency in the conversation. As it discards past
478
+ tokens, the model will lose the ability to generate tokens that depend on the context that was discarded.
479
+
480
+ It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
481
+ `[batch_size, num_heads, seq_len, head_dim]`.
482
+
483
+ Parameters:
484
+ window_length (`int`):
485
+ The length of the context window.
486
+ num_sink_tokens (`int`):
487
+ The number of sink tokens. See the original paper for more information.
488
+
489
+ Example:
490
+
491
+ ```python
492
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM, SinkCache
493
+
494
+ >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
495
+ >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
496
+
497
+ >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
498
+
499
+ >>> # Prepare a cache class and pass it to model's forward
500
+ >>> past_key_values = SinkCache(window_length=256, num_sink_tokens=4)
501
+ >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
502
+ >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
503
+ ```
504
+ """
505
+
506
+ def __init__(self, window_length: int, num_sink_tokens: int) -> None:
507
+ super().__init__()
508
+ self.key_cache: List[torch.Tensor] = []
509
+ self.value_cache: List[torch.Tensor] = []
510
+ self.window_length = window_length
511
+ self.num_sink_tokens = num_sink_tokens
512
+ self.cos_sin_rerotation_cache = {}
513
+ self._cos_cache = None
514
+ self._sin_cache = None
515
+ self._seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
516
+
517
+ @staticmethod
518
+ def _rotate_half(x):
519
+ x1 = x[..., : x.shape[-1] // 2]
520
+ x2 = x[..., x.shape[-1] // 2 :]
521
+ return torch.cat((-x2, x1), dim=-1)
522
+
523
+ def _apply_key_rotary_pos_emb(
524
+ self, key_states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
525
+ ) -> torch.Tensor:
526
+ rotated_key_states = (key_states * cos) + (self._rotate_half(key_states) * sin)
527
+ return rotated_key_states
528
+
529
+ def _get_rerotation_cos_sin(
530
+ self, key_states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
531
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
532
+ if key_states.shape[-2] not in self.cos_sin_rerotation_cache:
533
+ # Upcast to float32 temporarily for better accuracy
534
+ cos = cos.to(torch.float32)
535
+ sin = sin.to(torch.float32)
536
+
537
+ # Compute the cos and sin required for back- and forward-rotating to one position earlier in the sequence
538
+ original_cos = cos[self.num_sink_tokens + key_states.shape[-2] :]
539
+ shifted_cos = cos[self.num_sink_tokens : -key_states.shape[-2]]
540
+ original_sin = sin[self.num_sink_tokens + key_states.shape[-2] :]
541
+ shifted_sin = sin[self.num_sink_tokens : -key_states.shape[-2]]
542
+ rerotation_cos = original_cos * shifted_cos + original_sin * shifted_sin
543
+ rerotation_sin = -original_sin * shifted_cos + original_cos * shifted_sin
544
+
545
+ self.cos_sin_rerotation_cache[key_states.shape[-2]] = (
546
+ rerotation_cos.to(key_states.dtype).unsqueeze(0),
547
+ rerotation_sin.to(key_states.dtype).unsqueeze(0),
548
+ )
549
+ return self.cos_sin_rerotation_cache[key_states.shape[-2]]
550
+
551
+ def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
552
+ """Returns the sequence length of the cached states. A layer index can be optionally passed."""
553
+ # TODO: deprecate this function in favor of `cache_position`
554
+ # Workaround to make 'key_states.shape[-2] + past_key_value.get_seq_length(self.layer_idx)' <= window_length
555
+ if len(self.key_cache) <= layer_idx:
556
+ return 0
557
+ return self.key_cache[layer_idx].shape[-2]
558
+
559
+ def get_max_length(self) -> Optional[int]:
560
+ """Returns the maximum sequence length of the cached states."""
561
+ return self.window_length
562
+
563
+ def update(
564
+ self,
565
+ key_states: torch.Tensor,
566
+ value_states: torch.Tensor,
567
+ layer_idx: int,
568
+ cache_kwargs: Optional[Dict[str, Any]] = None,
569
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
570
+ """
571
+ Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
572
+
573
+ Parameters:
574
+ key_states (`torch.Tensor`):
575
+ The new key states to cache.
576
+ value_states (`torch.Tensor`):
577
+ The new value states to cache.
578
+ layer_idx (`int`):
579
+ The index of the layer to cache the states for.
580
+ cache_kwargs (`Dict[str, Any]`, `optional`):
581
+ Additional arguments for the cache subclass. The following arguments can be used in `SinkCache`: `sin`,
582
+ `cos` and `partial_rotation_size`. These arguments are used with models using RoPE, to recompute the
583
+ rotation as the tokens are shifted.
584
+
585
+ Return:
586
+ A tuple containing the updated key and value states.
587
+ """
588
+ # Optional kwargs for `SinkCache` -- needed on models using RoPE. `partial_rotation_size` is used on models
589
+ # with partially rotated position embeddings, like Phi or Persimmon.
590
+ sin = cache_kwargs.get("sin")
591
+ cos = cache_kwargs.get("cos")
592
+ partial_rotation_size = cache_kwargs.get("partial_rotation_size")
593
+ using_rope = cos is not None and sin is not None
594
+
595
+ # Update the number of seen tokens
596
+ if layer_idx == 0:
597
+ self._seen_tokens += key_states.shape[-2]
598
+
599
+ # Update the sin/cos cache, which holds sin/cos values for all possible positions
600
+ if using_rope and layer_idx == 0:
601
+ # BC: some models still pass `sin`/`cos` with 2 dims. In those models, they are the full sin/cos. Remove
602
+ # after all RoPE models have a llama-like cache utilization.
603
+ if cos.dim() == 2:
604
+ self._cos_cache = cos
605
+ self._sin_cache = sin
606
+ else:
607
+ if self._cos_cache is None:
608
+ self._cos_cache = cos[0, ...]
609
+ self._sin_cache = sin[0, ...]
610
+ elif self._cos_cache.shape[0] < self.window_length:
611
+ self._cos_cache = torch.cat([self._cos_cache, cos[0, ...]], dim=0)
612
+ self._sin_cache = torch.cat([self._sin_cache, sin[0, ...]], dim=0)
613
+
614
+ # [bsz, num_heads, seq_len, head_dim]
615
+ if len(self.key_cache) <= layer_idx:
616
+ # Empty cache
617
+ self.key_cache.append(key_states)
618
+ self.value_cache.append(value_states)
619
+
620
+ elif key_states.shape[-2] + self.get_seq_length(layer_idx) < self.window_length:
621
+ # Growing cache
622
+ self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
623
+ self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
624
+
625
+ else:
626
+ # Shifting cache
627
+ keys_to_keep = self.key_cache[layer_idx][
628
+ :, :, -self.window_length + self.num_sink_tokens + key_states.shape[-2] :
629
+ ]
630
+
631
+ # On RoPE models, we need to recompute the Key rotation as the tokens are shifted
632
+ if using_rope:
633
+ rerotation_cos, rerotation_sin = self._get_rerotation_cos_sin(
634
+ key_states, self._cos_cache[: self.window_length], self._sin_cache[: self.window_length]
635
+ )
636
+ if partial_rotation_size is not None:
637
+ keys_to_keep, keys_pass = (
638
+ keys_to_keep[..., :partial_rotation_size],
639
+ keys_to_keep[..., partial_rotation_size:],
640
+ )
641
+ keys_to_keep = self._apply_key_rotary_pos_emb(keys_to_keep, rerotation_cos, rerotation_sin)
642
+ if partial_rotation_size is not None:
643
+ keys_to_keep = torch.cat((keys_to_keep, keys_pass), dim=-1)
644
+
645
+ # Concatenate sink tokens, shifted & rotated tokens (if needed), and new tokens
646
+ sink_keys = self.key_cache[layer_idx][:, :, : self.num_sink_tokens]
647
+ self.key_cache[layer_idx] = torch.cat([sink_keys, keys_to_keep, key_states], dim=-2)
648
+
649
+ sink_values = self.value_cache[layer_idx][:, :, : self.num_sink_tokens]
650
+ values_to_keep = self.value_cache[layer_idx][
651
+ :, :, -self.window_length + self.num_sink_tokens + value_states.shape[-2] :
652
+ ]
653
+ self.value_cache[layer_idx] = torch.cat([sink_values, values_to_keep, value_states], dim=-2)
654
+
655
+ return self.key_cache[layer_idx], self.value_cache[layer_idx]
656
+
657
+
658
+ class StaticCache(Cache):
659
+ """
660
+ Static Cache class to be used with `torch.compile(model)` and `torch.export()`.
661
+
662
+ Parameters:
663
+ config (`PretrainedConfig`):
664
+ The configuration file defining the shape-related attributes required to initialize the static cache.
665
+ max_batch_size (`int`):
666
+ The maximum batch size with which the model will be used.
667
+ max_cache_len (`int`):
668
+ The maximum sequence length with which the model will be used.
669
+ device (`torch.device`):
670
+ The device on which the cache should be initialized. Should be the same as the layer.
671
+ dtype (*optional*, defaults to `torch.float32`):
672
+ The default `dtype` to use when initializing the layer.
673
+
674
+ Example:
675
+
676
+ ```python
677
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM, StaticCache
678
+
679
+ >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
680
+ >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
681
+
682
+ >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
683
+
684
+ >>> # Prepare a cache class and pass it to model's forward
685
+ >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
686
+ >>> max_generated_length = inputs.input_ids.shape[1] + 10
687
+ >>> past_key_values = StaticCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
688
+ >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
689
+ >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
690
+ ```
691
+ """
692
+
693
+ def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device, dtype=None) -> None:
694
+ super().__init__()
695
+ self.max_batch_size = max_batch_size
696
+ self.max_cache_len = config.max_position_embeddings if max_cache_len is None else max_cache_len
697
+ # Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
698
+ self.head_dim = (
699
+ config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
700
+ )
701
+
702
+ self.dtype = dtype if dtype is not None else torch.float32
703
+ self.num_key_value_heads = (
704
+ config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
705
+ )
706
+
707
+ self.key_cache: List[torch.Tensor] = []
708
+ self.value_cache: List[torch.Tensor] = []
709
+ # Note: There will be significant perf decrease if switching to use 5D tensors instead.
710
+ cache_shape = (max_batch_size, self.num_key_value_heads, self.max_cache_len, self.head_dim)
711
+ for idx in range(config.num_hidden_layers):
712
+ new_layer_key_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
713
+ new_layer_value_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
714
+ # Notes:
715
+ # 1. `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
716
+ # breaks when updating the cache. It can't be used if the cache code is being compiled (but in that case
717
+ # it is not needed anyway)
718
+ # 2. `torch.export()` requires mutations to be registered as buffers.
719
+ if not is_torchdynamo_compiling():
720
+ self.register_buffer(f"key_cache_{idx}", torch.zeros(cache_shape, dtype=dtype, device=device))
721
+ self.register_buffer(f"value_cache_{idx}", torch.zeros(cache_shape, dtype=dtype, device=device))
722
+ new_layer_key_cache = getattr(self, f"key_cache_{idx}")
723
+ new_layer_value_cache = getattr(self, f"value_cache_{idx}")
724
+ torch._dynamo.mark_static_address(new_layer_key_cache)
725
+ torch._dynamo.mark_static_address(new_layer_value_cache)
726
+ self.key_cache.append(new_layer_key_cache)
727
+ self.value_cache.append(new_layer_value_cache)
728
+ self._seen_tokens = 0 # Used in `generate` to keep tally of how many tokens the cache has seen
729
+
730
+ def update(
731
+ self,
732
+ key_states: torch.Tensor,
733
+ value_states: torch.Tensor,
734
+ layer_idx: int,
735
+ cache_kwargs: Optional[Dict[str, Any]] = None,
736
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
737
+ """
738
+ Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
739
+ It is VERY important to index using a tensor, otherwise you introduce a copy to the device.
740
+
741
+ Parameters:
742
+ key_states (`torch.Tensor`):
743
+ The new key states to cache.
744
+ value_states (`torch.Tensor`):
745
+ The new value states to cache.
746
+ layer_idx (`int`):
747
+ The index of the layer to cache the states for.
748
+ cache_kwargs (`Dict[str, Any]`, `optional`):
749
+ Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
750
+ to know how where to write in the cache.
751
+
752
+ Return:
753
+ A tuple containing the updated key and value states.
754
+ """
755
+ # Update the number of seen tokens
756
+ if layer_idx == 0:
757
+ self._seen_tokens += key_states.shape[-2]
758
+
759
+ cache_position = cache_kwargs.get("cache_position")
760
+ self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device=key_states.device)
761
+ self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device=value_states.device)
762
+ k_out = self.key_cache[layer_idx]
763
+ v_out = self.value_cache[layer_idx]
764
+
765
+ if cache_position is None:
766
+ k_out.copy_(key_states)
767
+ v_out.copy_(value_states)
768
+ else:
769
+ # Note: here we use `tensor.index_copy_(dim, index, tensor)` that is equivalent to
770
+ # `tensor[:, :, index] = tensor`, but the first one is compile-friendly and it does explicitly an in-place
771
+ # operation, that avoids copies and uses less memory.
772
+ try:
773
+ k_out.index_copy_(2, cache_position, key_states)
774
+ v_out.index_copy_(2, cache_position, value_states)
775
+ except NotImplementedError:
776
+ # The operator 'aten::index_copy.out' is not currently implemented for the MPS device.
777
+ k_out[:, :, cache_position] = key_states
778
+ v_out[:, :, cache_position] = value_states
779
+
780
+ return k_out, v_out
781
+
782
+ def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
783
+ """Returns the sequence length of the cached states that were seen by the model."""
784
+ # Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
785
+ # limit the check to the first batch member and head dimension.
786
+ # TODO: deprecate this function in favor of `cache_position`
787
+ # return (self.key_cache[layer_idx][0, 0].any(dim=-1)).sum()
788
+ return self._seen_tokens
789
+
790
+ def get_max_length(self) -> Optional[int]:
791
+ """Returns the maximum sequence length of the cached states."""
792
+ return self.max_cache_len
793
+
794
+ def reset(self):
795
+ self._seen_tokens = 0
796
+ """Resets the cache values while preserving the objects"""
797
+ for layer_idx in range(len(self.key_cache)):
798
+ # In-place ops prevent breaking the static address
799
+ self.key_cache[layer_idx].zero_()
800
+ self.value_cache[layer_idx].zero_()
801
+
802
+
803
+ class SlidingWindowCache(StaticCache):
804
+ """
805
+ Sliding Window Cache class to be used with `torch.compile` for models like Mistral that support sliding window attention.
806
+ Every time when we try to update the cache, we compute the `indices` based on `cache_position >= self.config.sliding_window - 1`,
807
+ if true(which means the cache can not hold all the old key value states and new states together because of the sliding window constraint),
808
+ we need to do a cycle shift based on `indices` to replace the oldest states by the new key value states passed in.
809
+
810
+ The `to_shift` is only true once we are above sliding_window. Thus with `sliding_window==64`:
811
+
812
+ indices = (slicing + to_shift[-1].int()-1) % self.config.sliding_window
813
+ tensor([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
814
+ 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
815
+ 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
816
+ 55, 56, 57, 58, 59, 60, 61, 62, 63, 0])
817
+
818
+ We overwrite the cache using these, then we always write at cache_position (clamped to `sliding_window`)
819
+
820
+ Parameters:
821
+ config (`PretrainedConfig`):
822
+ The configuration file defining the shape-related attributes required to initialize the static cache.
823
+ max_batch_size (`int`):
824
+ The maximum batch size with which the model will be used.
825
+ max_cache_len (`int`):
826
+ The maximum sequence length with which the model will be used.
827
+ device (`torch.device`):
828
+ The device on which the cache should be initialized. Should be the same as the layer.
829
+ dtype (*optional*, defaults to `torch.float32`):
830
+ The default `dtype` to use when initializing the layer.
831
+
832
+ Example:
833
+
834
+ ```python
835
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM, SlidingWindowCache
836
+
837
+ >>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
838
+ >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
839
+
840
+ >>> inputs = tokenizer(text="My name is GPT2", return_tensors="pt")
841
+
842
+ >>> # Prepare a cache class and pass it to model's forward
843
+ >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
844
+ >>> max_generated_length = inputs.input_ids.shape[1] + 10
845
+ >>> past_key_values = SlidingWindowCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
846
+ >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
847
+ >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
848
+ ```
849
+ """
850
+
851
+ def __init__(self, config: PretrainedConfig, max_batch_size: int, max_cache_len: int, device, dtype=None) -> None:
852
+ super().__init__(config, max_batch_size, max_cache_len, device, dtype)
853
+ if not hasattr(config, "sliding_window") or config.sliding_window is None:
854
+ raise ValueError(
855
+ "Setting `cache_implementation` to 'sliding_window' requires the model config supporting "
856
+ "sliding window attention, please check if there is a `sliding_window` field in the model "
857
+ "config and it's not set to None."
858
+ )
859
+ max_cache_len = min(config.sliding_window, max_cache_len)
860
+ super().__init__(
861
+ config=config, max_batch_size=max_batch_size, max_cache_len=max_cache_len, device=device, dtype=dtype
862
+ )
863
+
864
+ def update(
865
+ self,
866
+ key_states: torch.Tensor,
867
+ value_states: torch.Tensor,
868
+ layer_idx: int,
869
+ cache_kwargs: Optional[Dict[str, Any]] = None,
870
+ ) -> Tuple[torch.Tensor]:
871
+ cache_position = cache_kwargs.get("cache_position")
872
+ k_out = self.key_cache[layer_idx]
873
+ v_out = self.value_cache[layer_idx]
874
+
875
+ # assume this only happens in prefill phase when prompt length > sliding_window_size (= max_cache_len)
876
+ if cache_position.shape[0] > self.max_cache_len:
877
+ k_out = key_states[:, :, -self.max_cache_len :, :]
878
+ v_out = value_states[:, :, -self.max_cache_len :, :]
879
+ # Assumption: caches are all zeros at this point, `+=` is equivalent to `=` but compile-friendly
880
+ self.key_cache[layer_idx] += k_out
881
+ self.value_cache[layer_idx] += v_out
882
+ # we should return the whole states instead of k_out, v_out to take the whole prompt
883
+ # into consideration when building kv cache instead of just throwing away tokens outside of the window
884
+ return key_states, value_states
885
+
886
+ slicing = torch.ones(self.max_cache_len, dtype=torch.long, device=value_states.device).cumsum(0)
887
+ cache_position = cache_position.clamp(0, self.max_cache_len - 1)
888
+ to_shift = cache_position >= self.max_cache_len - 1
889
+ indices = (slicing + to_shift[-1].int() - 1) % self.max_cache_len
890
+
891
+ k_out = k_out[:, :, indices]
892
+ v_out = v_out[:, :, indices]
893
+
894
+ try:
895
+ cache_position.to(device=k_out.device)
896
+ k_out.index_copy_(2, cache_position, key_states)
897
+ v_out.index_copy_(2, cache_position, value_states)
898
+ except NotImplementedError:
899
+ # The operator 'aten::index_copy.out' is not currently implemented for the MPS device.
900
+ k_out[:, :, cache_position] = key_states
901
+ v_out[:, :, cache_position] = value_states
902
+
903
+ # `_.zero()` followed by `+=` is equivalent `=`, but compile-friendly (without graph breaks due to assignment)
904
+ self.key_cache[layer_idx].zero_()
905
+ self.value_cache[layer_idx].zero_()
906
+
907
+ self.key_cache[layer_idx] += k_out
908
+ self.value_cache[layer_idx] += v_out
909
+
910
+ return k_out, v_out
911
+
912
+ def get_max_length(self) -> Optional[int]:
913
+ # in theory there is no limit because the sliding window size is fixed no matter how long the sentence is
914
+ return None
915
+
916
+ def reset(self):
917
+ for layer_idx in range(len(self.key_cache)):
918
+ # In-place ops prevent breaking the static address
919
+ self.key_cache[layer_idx].zero_()
920
+ self.value_cache[layer_idx].zero_()
921
+
922
+
923
+ class EncoderDecoderCache(Cache):
924
+ """
925
+ Base, abstract class for all encoder-decoder caches. Can be used to hold combinations of self-attention and
926
+ cross-attention caches.
927
+
928
+ Example:
929
+
930
+ ```python
931
+ >>> from transformers import AutoProcessor, AutoModelForCausalLM, DynamicCache, EncoderDecoderCache
932
+
933
+ >>> model = AutoModelForCausalLM.from_pretrained("openai/whisper-small")
934
+ >>> processor = AutoProcessor.from_pretrained("openai/whisper-small")
935
+
936
+ >>> inputs = processor(audio=YOUR-AUDIO, return_tensors="pt")
937
+
938
+ >>> # Prepare cache classes for encoder and decoder and pass it to model's forward
939
+ >>> self_attention_cache = DynamicCache()
940
+ >>> cross_attention_cache = DynamicCache()
941
+ >>> past_key_values = EncoderDecoderCache(self_attention_cache, cross_attention_cache)
942
+ >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
943
+ >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
944
+ ```
945
+
946
+ """
947
+
948
+ def __init__(self, self_attention_cache: Cache, cross_attention_cache: Cache):
949
+ super().__init__()
950
+ self.self_attention_cache = self_attention_cache
951
+ self.cross_attention_cache = cross_attention_cache
952
+
953
+ self.is_updated = {}
954
+ for layer_idx in range(len(cross_attention_cache.key_cache)):
955
+ self.is_updated[layer_idx] = bool(cross_attention_cache.get_seq_length(layer_idx) > 0)
956
+
957
+ def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
958
+ """
959
+ Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
960
+ sequence length.
961
+ """
962
+ if layer_idx < len(self):
963
+ return (
964
+ self.self_attention_cache.key_cache[layer_idx],
965
+ self.self_attention_cache.value_cache[layer_idx],
966
+ self.cross_attention_cache.key_cache[layer_idx],
967
+ self.cross_attention_cache.value_cache[layer_idx],
968
+ )
969
+ else:
970
+ raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
971
+
972
+ def __len__(self):
973
+ """
974
+ Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
975
+ to the number of layers in the model.
976
+ """
977
+ return len(self.self_attention_cache)
978
+
979
+ def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
980
+ """Converts the `EncoderDecoderCache` instance into its equivalent in the legacy cache format."""
981
+ legacy_cache = ()
982
+ if len(self.cross_attention_cache) > 0:
983
+ for self_attn, cross_attn in zip(
984
+ self.self_attention_cache.to_legacy_cache(), self.cross_attention_cache.to_legacy_cache()
985
+ ):
986
+ legacy_cache += (self_attn + cross_attn,)
987
+ else:
988
+ legacy_cache = self.self_attention_cache.to_legacy_cache()
989
+ return legacy_cache
990
+
991
+ @classmethod
992
+ def from_legacy_cache(
993
+ cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
994
+ ) -> "EncoderDecoderCache":
995
+ """Converts a cache in the legacy cache format into an equivalent `EncoderDecoderCache`."""
996
+ cache = cls(self_attention_cache=DynamicCache(), cross_attention_cache=DynamicCache())
997
+ if past_key_values is not None:
998
+ for layer_idx in range(len(past_key_values)):
999
+ key_states, value_states = past_key_values[layer_idx][:2]
1000
+ cache.self_attention_cache.update(key_states, value_states, layer_idx)
1001
+ if len(past_key_values[layer_idx]) > 2:
1002
+ key_states, value_states = past_key_values[layer_idx][2:]
1003
+ cache.cross_attention_cache.update(key_states, value_states, layer_idx)
1004
+ cache.is_updated[layer_idx] = True
1005
+ return cache
1006
+
1007
+ def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
1008
+ """Returns the sequence length of the cached states. A layer index can be optionally passed."""
1009
+ if len(self.self_attention_cache.key_cache) <= layer_idx:
1010
+ return 0
1011
+ return (self.self_attention_cache.key_cache[layer_idx][0, 0].any(dim=-1)).sum()
1012
+
1013
+ def reset(self):
1014
+ if hasattr(self.self_attention_cache, "reset"):
1015
+ self.self_attention_cache.reset()
1016
+ if hasattr(self.cross_attention_cache, "reset"):
1017
+ self.cross_attention_cache.reset()
1018
+ elif not hasattr(self.self_attention_cache, "reset") and not hasattr(self.cross_attention_cache, "reset"):
1019
+ raise ValueError(
1020
+ "Neither self nor cross-attention cache have valid `.reset()` methods. `.reset()` should "
1021
+ "only be called on compatible cache classes, such as `StaticCache` or `SlidingWindowCache`. "
1022
+ f"Got {self.self_attention_cache.__str__()} for the self attention cache and "
1023
+ f"{self.cross_attention_cache.__str__()} for the cross attention cache."
1024
+ )
1025
+ for layer_idx in self.is_updated:
1026
+ self.is_updated[layer_idx] = False
1027
+
1028
+ def reorder_cache(self, beam_idx: torch.LongTensor):
1029
+ """Reorders the cache for beam search, given the selected beam indices."""
1030
+ self.self_attention_cache.reorder_cache(beam_idx)
1031
+ self.cross_attention_cache.reorder_cache(beam_idx)
1032
+
1033
+ def check_dynamic_cache(self, method: str):
1034
+ if not (
1035
+ isinstance(self.self_attention_cache, DynamicCache)
1036
+ and isinstance(self.cross_attention_cache, DynamicCache)
1037
+ ):
1038
+ raise ValueError(
1039
+ f"`{method}` is only defined for dynamic cache, got {self.self_attention_cache.__str__()} for the self "
1040
+ f"attention cache and {self.cross_attention_cache.__str__()} for the cross attention cache."
1041
+ )
1042
+
1043
+ # TODO(gante, sanchit-gandhi): move following functionality into `.generate`
1044
+ def crop(self, maximum_length: int):
1045
+ """Crop the past key values up to a new `maximum_length` in terms of tokens. `maximum_length` can also be
1046
+ negative to remove `maximum_length` tokens. This is used in assisted decoding and contrastive search."""
1047
+ self.check_dynamic_cache(self.crop.__name__)
1048
+ self.self_attention_cache.crop(maximum_length)
1049
+
1050
+ def batch_split(self, full_batch_size: int, split_size: int) -> "List[EncoderDecoderCache]":
1051
+ """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
1052
+ `_split_model_inputs()` in `generation.utils`"""
1053
+ self.check_dynamic_cache(self.batch_split.__name__)
1054
+ self_attention_cache = self.self_attention_cache.batch_split(full_batch_size, split_size)
1055
+ cross_attention_cache = self.cross_attention_cache.batch_split(full_batch_size, split_size)
1056
+
1057
+ out = []
1058
+ for self_attn, cross_attn in zip(self_attention_cache, cross_attention_cache):
1059
+ out.append(EncoderDecoderCache(self_attn, cross_attn))
1060
+ return out
1061
+
1062
+ @classmethod
1063
+ def from_batch_splits(cls, splits: List["EncoderDecoderCache"]) -> "EncoderDecoderCache":
1064
+ """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
1065
+ `generation.utils`"""
1066
+ self_attention_cache = DynamicCache()
1067
+ cross_attention_cache = DynamicCache()
1068
+ for idx in range(len(splits[0])):
1069
+ layer_keys = torch.cat([current.self_attention_cache.key_cache[idx] for current in splits], dim=0)
1070
+ layer_values = torch.cat([current.self_attention_cache.value_cache[idx] for current in splits], dim=0)
1071
+ self_attention_cache.update(layer_keys, layer_values, idx)
1072
+
1073
+ layer_keys = torch.cat([current.cross_attention_cache.key_cache[idx] for current in splits], dim=0)
1074
+ layer_values = torch.cat([current.cross_attention_cache.value_cache[idx] for current in splits], dim=0)
1075
+ cross_attention_cache.update(layer_keys, layer_values, idx)
1076
+ return cls(self_attention_cache, cross_attention_cache)
1077
+
1078
+ def batch_repeat_interleave(self, repeats: int):
1079
+ """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
1080
+ self.check_dynamic_cache(self.batch_repeat_interleave.__name__)
1081
+ self.self_attention_cache.batch_repeat_interleave(repeats)
1082
+ self.cross_attention_cache.batch_repeat_interleave(repeats)
1083
+
1084
+ def batch_select_indices(self, indices: torch.Tensor):
1085
+ """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
1086
+ self.check_dynamic_cache(self.batch_select_indices.__name__)
1087
+ self.self_attention_cache.batch_select_indices(indices)
1088
+ self.cross_attention_cache.batch_select_indices(indices)
1089
+
1090
+
1091
+ class HybridCache(Cache):
1092
+ """
1093
+ Hybrid Cache class to be used with `torch.compile` for Gemma2 models that alternate between a local sliding window attention
1094
+ and global attention in every other layer. Under the hood, Hybrid Cache leverages ["SlidingWindowCache"] for sliding window attention
1095
+ and ["StaticCache"] for global attention. For more information, see the documentation of each subcomponeent cache class.
1096
+
1097
+ Parameters:
1098
+ config (`PretrainedConfig):
1099
+ The configuration file defining the shape-related attributes required to initialize the static cache.
1100
+ max_batch_size (`int`):
1101
+ The maximum batch size with which the model will be used.
1102
+ max_cache_len (`int`):
1103
+ The maximum sequence length with which the model will be used.
1104
+ device (`torch.device`, *optional*, defaults to `"cpu"`):
1105
+ The device on which the cache should be initialized. Should be the same as the layer.
1106
+ dtype (*optional*, defaults to `torch.float32`):
1107
+ The default `dtype` to use when initializing the layer.
1108
+
1109
+ Example:
1110
+
1111
+ ```python
1112
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM, HybridCache
1113
+
1114
+ >>> model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b")
1115
+ >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
1116
+
1117
+ >>> inputs = tokenizer(text="My name is Gemma", return_tensors="pt")
1118
+
1119
+ >>> # Prepare a cache class and pass it to model's forward
1120
+ >>> # Leave empty space for 10 new tokens, which can be used when calling forward iteratively 10 times to generate
1121
+ >>> max_generated_length = inputs.input_ids.shape[1] + 10
1122
+ >>> past_key_values = HybridCache(config=model.config, max_batch_size=1, max_cache_len=max_generated_length, device=model.device, dtype=model.dtype)
1123
+ >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
1124
+ >>> past_kv_length = outputs.past_key_values # access cache filled with key/values from generation
1125
+ ```
1126
+ """
1127
+
1128
+ def __init__(self, config: PretrainedConfig, max_batch_size, max_cache_len, device="cpu", dtype=None) -> None:
1129
+ super().__init__()
1130
+ if not hasattr(config, "sliding_window") or config.sliding_window is None:
1131
+ raise ValueError(
1132
+ "Setting `cache_implementation` to 'sliding_window' requires the model config supporting "
1133
+ "sliding window attention, please check if there is a `sliding_window` field in the model "
1134
+ "config and it's not set to None."
1135
+ )
1136
+ self.max_cache_len = max_cache_len
1137
+ self.max_batch_size = max_batch_size
1138
+ # Some model define a custom `head_dim` != config.hidden_size // config.num_attention_heads
1139
+ self.head_dim = (
1140
+ config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads
1141
+ )
1142
+
1143
+ self.dtype = dtype if dtype is not None else torch.float32
1144
+ self.num_key_value_heads = (
1145
+ config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
1146
+ )
1147
+ self.is_sliding = torch.tensor(
1148
+ [not bool(i % 2) for i in range(config.num_hidden_layers)], dtype=torch.bool, device=device
1149
+ )
1150
+ self.key_cache: List[torch.Tensor] = []
1151
+ self.value_cache: List[torch.Tensor] = []
1152
+ global_cache_shape = (max_batch_size, self.num_key_value_heads, max_cache_len, self.head_dim)
1153
+ sliding_cache_shape = (
1154
+ max_batch_size,
1155
+ self.num_key_value_heads,
1156
+ min(config.sliding_window, max_cache_len),
1157
+ self.head_dim,
1158
+ )
1159
+ for i in range(config.num_hidden_layers):
1160
+ # Note: `mark_static_address` is used to tag the cache as an fixed data pointer, preventing cuda graph
1161
+ # breaks when updating the cache.
1162
+ cache_shape = global_cache_shape if not self.is_sliding[i] else sliding_cache_shape
1163
+ new_layer_key_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
1164
+ new_layer_value_cache = torch.zeros(cache_shape, dtype=self.dtype, device=device)
1165
+ torch._dynamo.mark_static_address(new_layer_key_cache)
1166
+ torch._dynamo.mark_static_address(new_layer_value_cache)
1167
+ self.key_cache.append(new_layer_key_cache)
1168
+ self.value_cache.append(new_layer_value_cache)
1169
+
1170
+ def _sliding_update(self, cache_position, layer_idx, key_states, value_states, k_out, v_out, max_cache_len):
1171
+ if cache_position.shape[0] > max_cache_len:
1172
+ k_out = key_states[:, :, -max_cache_len:, :]
1173
+ v_out = value_states[:, :, -max_cache_len:, :]
1174
+ # Assumption: caches are all zeros at this point, `+=` is equivalent to `=` but compile-friendly
1175
+ self.key_cache[layer_idx] += k_out
1176
+ self.value_cache[layer_idx] += v_out
1177
+ # we should return the whole states instead of k_out, v_out to take the whole prompt
1178
+ # into consideration when building kv cache instead of just throwing away tokens outside of the window
1179
+ return key_states, value_states
1180
+
1181
+ slicing = torch.ones(max_cache_len, dtype=torch.long, device=value_states.device).cumsum(0)
1182
+ cache_position = cache_position.clamp(0, max_cache_len - 1)
1183
+ to_shift = cache_position >= max_cache_len - 1
1184
+ indices = (slicing + to_shift[-1].int() - 1) % max_cache_len
1185
+ k_out = k_out[:, :, indices]
1186
+ v_out = v_out[:, :, indices]
1187
+
1188
+ k_out[:, :, cache_position] = key_states
1189
+ v_out[:, :, cache_position] = value_states
1190
+ # `_.zero()` followed by `+=` is equivalent `=`, but compile-friendly (without graph breaks due to assignment)
1191
+ self.key_cache[layer_idx].zero_()
1192
+ self.value_cache[layer_idx].zero_()
1193
+
1194
+ self.key_cache[layer_idx] += k_out
1195
+ self.value_cache[layer_idx] += v_out
1196
+ return k_out, v_out
1197
+
1198
+ def _static_update(self, cache_position, layer_idx, key_states, value_states, k_out, v_out, max_cache_len):
1199
+ k_out[:, :, cache_position] = key_states
1200
+ v_out[:, :, cache_position] = value_states
1201
+
1202
+ self.key_cache[layer_idx] = k_out
1203
+ self.value_cache[layer_idx] = v_out
1204
+ return k_out, v_out
1205
+
1206
+ def update(
1207
+ self,
1208
+ key_states: torch.Tensor,
1209
+ value_states: torch.Tensor,
1210
+ layer_idx: int,
1211
+ cache_kwargs: Optional[Dict[str, Any]] = None,
1212
+ ) -> Tuple[torch.Tensor]:
1213
+ cache_position = cache_kwargs.get("cache_position")
1214
+ sliding_window = cache_kwargs.get("sliding_window")
1215
+ self.key_cache[layer_idx] = self.key_cache[layer_idx].to(device=key_states.device)
1216
+ self.value_cache[layer_idx] = self.value_cache[layer_idx].to(device=value_states.device)
1217
+ k_out = self.key_cache[layer_idx]
1218
+ v_out = self.value_cache[layer_idx]
1219
+ if sliding_window:
1220
+ update_fn = self._sliding_update
1221
+ else:
1222
+ update_fn = self._static_update
1223
+
1224
+ return update_fn(
1225
+ cache_position,
1226
+ layer_idx,
1227
+ key_states,
1228
+ value_states,
1229
+ k_out,
1230
+ v_out,
1231
+ k_out.shape[2],
1232
+ )
1233
+
1234
+ def get_max_length(self) -> Optional[int]:
1235
+ # in theory there is no limit because the sliding window size is fixed
1236
+ # no matter how long the sentence is
1237
+ return self.max_cache_len
1238
+
1239
+ def get_seq_length(self, layer_idx: Optional[int] = 0):
1240
+ return None
1241
+
1242
+ def reset(self):
1243
+ """Resets the cache values while preserving the objects"""
1244
+ for layer_idx in range(len(self.key_cache)):
1245
+ # In-place ops prevent breaking the static address
1246
+ self.key_cache[layer_idx].zero_()
1247
+ self.value_cache[layer_idx].zero_()
1248
+
1249
+
1250
+ class MambaCache:
1251
+ """
1252
+ Cache for mamba model which does not have attention mechanism and key value states.
1253
+
1254
+ Arguments:
1255
+ config (`PretrainedConfig):
1256
+ The configuration file defining the shape-related attributes required to initialize the static cache.
1257
+ max_batch_size (`int`):
1258
+ The maximum batch size with which the model will be used.
1259
+ dtype (*optional*, defaults to `torch.float16`):
1260
+ The default `dtype` to use when initializing the layer.
1261
+ device (`torch.device`, *optional*):
1262
+ The device on which the cache should be initialized. Should be the same as the layer.
1263
+
1264
+ Attributes:
1265
+ dtype: (`torch.dtype`):
1266
+ The default `dtype` used to initializing the cache.
1267
+ intermediate_size: (`int`):
1268
+ Model's intermediate_size taken from config.
1269
+ ssm_state_size: (`int`):
1270
+ Model's state_size taken from config.
1271
+ conv_kernel_size: (`int`):
1272
+ Model's convolution kernel size taken from config
1273
+ conv_states: (`torch.Tensor`):
1274
+ A tensor of shape `[layer_idx, batch_size, intermediate_size, conv_kernel_size]` that holds convolutional states.
1275
+ ssm_states: (`torch.Tensor`):
1276
+ A tensor of shape `[layer_idx, batch_size, intermediate_size, ssm_state_size]` that holds ssm states
1277
+
1278
+ Example:
1279
+
1280
+ ```python
1281
+ >>> from transformers import AutoTokenizer, MambaForCausalLM, MambaCache
1282
+
1283
+ >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")
1284
+ >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
1285
+
1286
+ >>> inputs = tokenizer(text="My name is Mamba", return_tensors="pt")
1287
+
1288
+ >>> # Prepare a cache class and pass it to model's forward
1289
+ >>> past_key_values = MambaCache(config=model.config, max_batch_size=1, device=model.device, dtype=model.dtype)
1290
+ >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
1291
+ >>> past_kv = outputs.past_key_values
1292
+ ```
1293
+ """
1294
+
1295
+ def __init__(
1296
+ self,
1297
+ config: PretrainedConfig,
1298
+ max_batch_size: int,
1299
+ dtype: torch.dtype = torch.float16,
1300
+ device: Optional[str] = None,
1301
+ **kwargs,
1302
+ ):
1303
+ self.dtype = dtype
1304
+ self.max_batch_size = max_batch_size
1305
+ self.intermediate_size = config.intermediate_size
1306
+ self.ssm_state_size = config.state_size
1307
+ self.conv_kernel_size = config.conv_kernel
1308
+
1309
+ self.conv_states: torch.Tensor = torch.zeros(
1310
+ config.num_hidden_layers,
1311
+ self.max_batch_size,
1312
+ self.intermediate_size,
1313
+ self.conv_kernel_size,
1314
+ device=device,
1315
+ dtype=dtype,
1316
+ )
1317
+ self.ssm_states: torch.Tensor = torch.zeros(
1318
+ config.num_hidden_layers,
1319
+ self.max_batch_size,
1320
+ self.intermediate_size,
1321
+ self.ssm_state_size,
1322
+ device=device,
1323
+ dtype=dtype,
1324
+ )
1325
+
1326
+ torch._dynamo.mark_static_address(self.conv_states)
1327
+ torch._dynamo.mark_static_address(self.ssm_states)
1328
+
1329
+ def update_conv_state(
1330
+ self, layer_idx: int, new_conv_state: torch.Tensor, cache_position: torch.LongTensor
1331
+ ) -> torch.Tensor:
1332
+ conv_state = self.conv_states[layer_idx]
1333
+ cache_position = cache_position.clamp(0, self.conv_kernel_size - 1)
1334
+
1335
+ conv_state = conv_state.roll(shifts=-1, dims=-1)
1336
+ conv_state[:, :, cache_position] = new_conv_state.to(conv_state.device)
1337
+ self.conv_states[layer_idx].zero_()
1338
+ self.conv_states[layer_idx] += conv_state
1339
+ return self.conv_states[layer_idx]
1340
+
1341
+ def update_ssm_state(self, layer_idx: int, new_ssm_state: torch.Tensor):
1342
+ self.ssm_states[layer_idx] = new_ssm_state.to(self.ssm_states.device)
1343
+ return self.ssm_states[layer_idx]
1344
+
1345
+ def reset(self):
1346
+ self.conv_states.zero_()
1347
+ self.ssm_states.zero_()
transformers_4_44_2__configuration_llama.py ADDED
@@ -0,0 +1,203 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ #
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
+ from dataclasses import dataclass
15
+ from typing import List, Optional, Tuple, Union
16
+
17
+ import torch
18
+
19
+
20
+ @dataclass
21
+ class AttentionMaskConverter:
22
+ """
23
+ A utility attention mask class that allows one to:
24
+ - Create a causal 4d mask
25
+ - Create a causal 4d mask with slided window
26
+ - Convert a 2d attention mask (batch_size, query_length) to a 4d attention mask (batch_size, 1, query_length,
27
+ key_value_length) that can be multiplied with attention scores
28
+
29
+ Examples:
30
+
31
+ ```python
32
+ >>> import torch
33
+ >>> from transformers.modeling_attn_mask_utils import AttentionMaskConverter
34
+
35
+ >>> converter = AttentionMaskConverter(True)
36
+ >>> converter.to_4d(torch.tensor([[0, 0, 0, 1, 1]]), 5, key_value_length=5, dtype=torch.float32)
37
+ tensor([[[[-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
38
+ [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
39
+ [-3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38, -3.4028e+38],
40
+ [-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, -3.4028e+38],
41
+ [-3.4028e+38, -3.4028e+38, -3.4028e+38, 0.0000e+00, 0.0000e+00]]]])
42
+ ```
43
+
44
+ Parameters:
45
+ is_causal (`bool`):
46
+ Whether the attention mask should be a uni-directional (causal) or bi-directional mask.
47
+
48
+ sliding_window (`int`, *optional*):
49
+ Optionally, the sliding window masks can be created if `sliding_window` is defined to a positive integer.
50
+ """
51
+
52
+ is_causal: bool
53
+ sliding_window: int
54
+
55
+ def __init__(self, is_causal: bool, sliding_window: Optional[int] = None):
56
+ self.is_causal = is_causal
57
+ self.sliding_window = sliding_window
58
+
59
+ if self.sliding_window is not None and self.sliding_window <= 0:
60
+ raise ValueError(
61
+ f"Make sure that when passing `sliding_window` that its value is a strictly positive integer, not `{self.sliding_window}`"
62
+ )
63
+
64
+ def to_causal_4d(
65
+ self,
66
+ batch_size: int,
67
+ query_length: int,
68
+ key_value_length: int,
69
+ dtype: torch.dtype,
70
+ device: Union[torch.device, "str"] = "cpu",
71
+ ) -> Optional[torch.Tensor]:
72
+ """
73
+ Creates a causal 4D mask of (bsz, head_dim=1, query_length, key_value_length) shape and adds large negative
74
+ bias to upper right hand triangular matrix (causal mask).
75
+ """
76
+ if not self.is_causal:
77
+ raise ValueError(f"Please use `to_causal_4d` only if {self.__class__} has `is_causal` set to True.")
78
+
79
+ # If shape is not cached, create a new causal mask and cache it
80
+ input_shape = (batch_size, query_length)
81
+ past_key_values_length = key_value_length - query_length
82
+
83
+ # create causal mask
84
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
85
+ causal_4d_mask = None
86
+ if input_shape[-1] > 1 or self.sliding_window is not None:
87
+ causal_4d_mask = self._make_causal_mask(
88
+ input_shape,
89
+ dtype,
90
+ device=device,
91
+ past_key_values_length=past_key_values_length,
92
+ sliding_window=self.sliding_window,
93
+ )
94
+
95
+ return causal_4d_mask
96
+
97
+ def to_4d(
98
+ self,
99
+ attention_mask_2d: torch.Tensor,
100
+ query_length: int,
101
+ dtype: torch.dtype,
102
+ key_value_length: Optional[int] = None,
103
+ ) -> torch.Tensor:
104
+ """
105
+ Converts 2D attention mask to 4D attention mask by expanding mask to (bsz, head_dim=1, query_length,
106
+ key_value_length) shape and by adding a large negative bias to not-attended positions. If attention_mask is
107
+ causal, a causal mask will be added.
108
+ """
109
+ input_shape = (attention_mask_2d.shape[0], query_length)
110
+
111
+ # create causal mask
112
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
113
+ causal_4d_mask = None
114
+ if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:
115
+ if key_value_length is None:
116
+ raise ValueError(
117
+ "This attention mask converter is causal. Make sure to pass `key_value_length` to correctly create a causal mask."
118
+ )
119
+
120
+ past_key_values_length = key_value_length - query_length
121
+ causal_4d_mask = self._make_causal_mask(
122
+ input_shape,
123
+ dtype,
124
+ device=attention_mask_2d.device,
125
+ past_key_values_length=past_key_values_length,
126
+ sliding_window=self.sliding_window,
127
+ )
128
+ elif self.sliding_window is not None:
129
+ raise NotImplementedError("Sliding window is currently only implemented for causal masking")
130
+
131
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
132
+ expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
133
+ attention_mask_2d.device
134
+ )
135
+
136
+ if causal_4d_mask is not None:
137
+ expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min)
138
+
139
+ # expanded_attn_mask + causal_4d_mask can cause some overflow
140
+ expanded_4d_mask = expanded_attn_mask
141
+
142
+ return expanded_4d_mask
143
+
144
+ @staticmethod
145
+ def _make_causal_mask(
146
+ input_ids_shape: torch.Size,
147
+ dtype: torch.dtype,
148
+ device: torch.device,
149
+ past_key_values_length: int = 0,
150
+ sliding_window: Optional[int] = None,
151
+ ):
152
+ """
153
+ Make causal mask used for bi-directional self-attention.
154
+ """
155
+ bsz, tgt_len = input_ids_shape
156
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
157
+ mask_cond = torch.arange(mask.size(-1), device=device)
158
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
159
+
160
+ mask = mask.to(dtype)
161
+
162
+ if past_key_values_length > 0:
163
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
164
+
165
+ # add lower triangular sliding window mask if necessary
166
+ if sliding_window is not None:
167
+ diagonal = past_key_values_length - sliding_window - 1
168
+
169
+ context_mask = torch.tril(torch.ones_like(mask, dtype=torch.bool), diagonal=diagonal)
170
+ mask.masked_fill_(context_mask, torch.finfo(dtype).min)
171
+
172
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
173
+
174
+ @staticmethod
175
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
176
+ """
177
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
178
+ """
179
+ bsz, src_len = mask.size()
180
+ tgt_len = tgt_len if tgt_len is not None else src_len
181
+
182
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
183
+
184
+ inverted_mask = 1.0 - expanded_mask
185
+
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,
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
+
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
+
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
+ ```
206
+ [[[[0, 0, 0],
207
+ [0, 0, 0],
208
+ [0, 0, 1]]],
209
+ [[[1, 0, 0],
210
+ [1, 1, 0],
211
+ [1, 1, 1]]],
212
+ [[[0, 0, 0],
213
+ [0, 1, 0],
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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all():
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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 +1,139 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
23
+ from .transformers_4_44_2__cache_utils import Cache as Cache_4_44_2, SinkCache, StaticCache, SlidingWindowCache
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,
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_4_44_2 | None] = [None] * config.num_hidden_layers
54
+ self.layer_devices: list[torch.device | None] = [None] * config.num_hidden_layers
55
+
56
+ def update(
57
+ self,
58
+ key_states: torch.Tensor,
59
+ value_states: torch.Tensor,
60
+ layer_idx: int,
61
+ cache_kwargs: Optional[Dict[str, Any]] = None,
62
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
63
+ if self.layer_caches[layer_idx] is None:
64
+ self.layer_devices[layer_idx] = key_states.device
65
+ self._init_layer_cache(layer_idx)
66
+
67
+ layer_cache = self.layer_caches[layer_idx]
68
+ assert layer_cache is not None, f"Trying to update the cache of a cache-less layer: {layer_idx=}"
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, layer_idx: int) -> None:
80
+ block_config = self.config.block_configs[layer_idx]
81
+ attention_config = block_config.attention
82
+
83
+ if attention_config.no_op or attention_config.replace_with_linear:
84
+ return None
85
+
86
+ device = self.layer_devices[layer_idx]
87
+ assert device is not None, f"Trying to init layer cache for {layer_idx=} without device"
88
+
89
+ config = deepcopy(self.config)
90
+ config.num_hidden_layers = 1
91
+ config.num_key_value_heads = self.config.num_attention_heads // attention_config.n_heads_in_group
92
+
93
+ if attention_config.window_length is not None:
94
+ if not attention_config.is_sink:
95
+ config.sliding_window = attention_config.window_length
96
+ self.layer_caches[layer_idx] = SlidingWindowCache(config=config,
97
+ max_batch_size=self.max_batch_size,
98
+ max_cache_len=self.max_cache_len,
99
+ device=device,
100
+ dtype=self.dtype)
101
+ return
102
+ elif not attention_config.unshifted_sink:
103
+ self.layer_caches[layer_idx] = SinkCache(window_length=attention_config.window_length,
104
+ num_sink_tokens=attention_config.num_sink_tokens)
105
+ return
106
+
107
+ self.layer_caches[layer_idx] = StaticCache(config=config,
108
+ max_batch_size=self.max_batch_size,
109
+ max_cache_len=self.max_cache_len,
110
+ device=device,
111
+ dtype=self.dtype)
112
+
113
+ def _get_first_real_cache(self) -> Cache:
114
+ for layer_cache in self.layer_caches:
115
+ if layer_cache is not None:
116
+ return layer_cache
117
+ raise ValueError(f"No real cache found, all layer caches are None.")
118
+
119
+ def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
120
+ if layer_idx == 0 and self.layer_caches[0] is None:
121
+ try:
122
+ layer_cache = self._get_first_real_cache()
123
+ except ValueError:
124
+ return 0
125
+ else:
126
+ layer_cache = self.layer_caches[layer_idx]
127
+ return layer_cache.get_seq_length()
128
+
129
+ def get_max_length(self) -> Optional[int]:
130
+ """Returns the maximum sequence length of the cached states."""
131
+ return self.max_cache_len
132
+
133
+ def reset(self):
134
+ for layer_idx in range(len(self.layer_caches)):
135
+ layer_cache = self.layer_caches[layer_idx]
136
+ if hasattr(layer_cache, "reset"):
137
+ layer_cache.reset()
138
+ else:
139
+ self._init_layer_cache(layer_idx)