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.gitattributes CHANGED
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33
  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00004-of-00008.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00005-of-00008.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00006-of-00008.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00007-of-00008.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00008-of-00008.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00001-of-00008.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00002-of-00008.bin filter=lfs diff=lfs merge=lfs -text
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+ pytorch_model-00003-of-00008.bin filter=lfs diff=lfs merge=lfs -text
config.json ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "../model/internlm2_5-7b-chat",
3
+ "architectures": [
4
+ "InternLM2ForCausalLM"
5
+ ],
6
+ "attn_implementation": "eager",
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_internlm2.InternLM2Config",
9
+ "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
10
+ "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM"
11
+ },
12
+ "bias": false,
13
+ "bos_token_id": 1,
14
+ "eos_token_id": 2,
15
+ "hidden_act": "silu",
16
+ "hidden_size": 4096,
17
+ "initializer_range": 0.02,
18
+ "intermediate_size": 14336,
19
+ "max_position_embeddings": 32768,
20
+ "model_type": "internlm2",
21
+ "num_attention_heads": 32,
22
+ "num_hidden_layers": 32,
23
+ "num_key_value_heads": 8,
24
+ "pad_token_id": 2,
25
+ "pretraining_tp": 1,
26
+ "rms_norm_eps": 1e-05,
27
+ "rope_scaling": {
28
+ "factor": 2.0,
29
+ "type": "dynamic"
30
+ },
31
+ "rope_theta": 1000000,
32
+ "tie_word_embeddings": false,
33
+ "torch_dtype": "float16",
34
+ "transformers_version": "4.39.3",
35
+ "use_cache": true,
36
+ "vocab_size": 92544
37
+ }
configuration_internlm2.py ADDED
@@ -0,0 +1,180 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on transformers/src/transformers/models/llama/configuration_llama.py
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+ """ InternLM2 model configuration"""
18
+
19
+ from transformers.configuration_utils import PretrainedConfig
20
+ from transformers.utils import logging
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+ INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
25
+
26
+
27
+ # Modified from transformers.model.llama.configuration_llama.LlamaConfig
28
+ class InternLM2Config(PretrainedConfig):
29
+ r"""
30
+ This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
31
+ an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
32
+ configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
33
+
34
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
35
+ documentation from [`PretrainedConfig`] for more information.
36
+
37
+
38
+ Args:
39
+ vocab_size (`int`, *optional*, defaults to 32000):
40
+ Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
41
+ `inputs_ids` passed when calling [`InternLM2Model`]
42
+ hidden_size (`int`, *optional*, defaults to 4096):
43
+ Dimension of the hidden representations.
44
+ intermediate_size (`int`, *optional*, defaults to 11008):
45
+ Dimension of the MLP representations.
46
+ num_hidden_layers (`int`, *optional*, defaults to 32):
47
+ Number of hidden layers in the Transformer decoder.
48
+ num_attention_heads (`int`, *optional*, defaults to 32):
49
+ Number of attention heads for each attention layer in the Transformer decoder.
50
+ num_key_value_heads (`int`, *optional*):
51
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
52
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
53
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
54
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
55
+ by meanpooling all the original heads within that group. For more details checkout [this
56
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
57
+ `num_attention_heads`.
58
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
59
+ The non-linear activation function (function or string) in the decoder.
60
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
61
+ The maximum sequence length that this model might ever be used with. InternLM2 supports up to 32768 tokens.
62
+ initializer_range (`float`, *optional*, defaults to 0.02):
63
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
64
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
65
+ The epsilon used by the rms normalization layers.
66
+ use_cache (`bool`, *optional*, defaults to `True`):
67
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
68
+ relevant if `config.is_decoder=True`.
69
+ pad_token_id (`int`, *optional*):
70
+ Padding token id.
71
+ bos_token_id (`int`, *optional*, defaults to 1):
72
+ Beginning of stream token id.
73
+ eos_token_id (`int`, *optional*, defaults to 2):
74
+ End of stream token id.
75
+ pretraining_tp (`int`, *optional*, defaults to 1):
76
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
77
+ document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism)
78
+ to understand more about it. This value is necessary to ensure exact reproducibility
79
+ of the pretraining results. Please refer to [this
80
+ issue](https://github.com/pytorch/pytorch/issues/76232).
81
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
82
+ Whether to tie weight embeddings
83
+ rope_theta (`float`, *optional*, defaults to 10000.0):
84
+ The base period of the RoPE embeddings.
85
+ rope_scaling (`Dict`, *optional*):
86
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
87
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
88
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
89
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
90
+ these scaling strategies behave:
91
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
92
+ experimental feature, subject to breaking API changes in future versions.
93
+ """
94
+ _auto_class = "AutoConfig"
95
+ model_type = "internlm2"
96
+ keys_to_ignore_at_inference = ["past_key_values"]
97
+
98
+ def __init__( # pylint: disable=W0102
99
+ self,
100
+ vocab_size=103168,
101
+ hidden_size=4096,
102
+ intermediate_size=11008,
103
+ num_hidden_layers=32,
104
+ num_attention_heads=32,
105
+ num_key_value_heads=None,
106
+ hidden_act="silu",
107
+ max_position_embeddings=2048,
108
+ initializer_range=0.02,
109
+ rms_norm_eps=1e-6,
110
+ use_cache=True,
111
+ pad_token_id=0,
112
+ bos_token_id=1,
113
+ eos_token_id=2,
114
+ pretraining_tp=1,
115
+ tie_word_embeddings=False,
116
+ bias=True,
117
+ rope_theta=10000,
118
+ rope_scaling=None,
119
+ attn_implementation=None,
120
+ **kwargs,
121
+ ):
122
+ self.vocab_size = vocab_size
123
+ self.max_position_embeddings = max_position_embeddings
124
+ self.hidden_size = hidden_size
125
+ self.intermediate_size = intermediate_size
126
+ self.num_hidden_layers = num_hidden_layers
127
+ self.num_attention_heads = num_attention_heads
128
+ self.bias = bias
129
+
130
+ if num_key_value_heads is None:
131
+ num_key_value_heads = num_attention_heads
132
+ self.num_key_value_heads = num_key_value_heads
133
+
134
+ self.hidden_act = hidden_act
135
+ self.initializer_range = initializer_range
136
+ self.rms_norm_eps = rms_norm_eps
137
+ self.pretraining_tp = pretraining_tp
138
+ self.use_cache = use_cache
139
+ self.rope_theta = rope_theta
140
+ self.rope_scaling = rope_scaling
141
+ self._rope_scaling_validation()
142
+ self.attn_implementation = attn_implementation
143
+ if self.attn_implementation is None:
144
+ self.attn_implementation = "eager"
145
+
146
+ super().__init__(
147
+ pad_token_id=pad_token_id,
148
+ bos_token_id=bos_token_id,
149
+ eos_token_id=eos_token_id,
150
+ tie_word_embeddings=tie_word_embeddings,
151
+ **kwargs,
152
+ )
153
+
154
+ def _rope_scaling_validation(self):
155
+ """
156
+ Validate the `rope_scaling` configuration.
157
+ """
158
+ if self.rope_scaling is None:
159
+ return
160
+
161
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
162
+ raise ValueError(
163
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
164
+ f"got {self.rope_scaling}"
165
+ )
166
+ rope_scaling_type = self.rope_scaling.get("type", None)
167
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
168
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
169
+ raise ValueError(
170
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
171
+ )
172
+ if (
173
+ rope_scaling_factor is None
174
+ or not isinstance(rope_scaling_factor, (float, int))
175
+ or rope_scaling_factor < 1.0
176
+ ):
177
+ raise ValueError(
178
+ f"`rope_scaling`'s factor field must be a number >= 1, got {rope_scaling_factor} "
179
+ f"of type {type(rope_scaling_factor)}"
180
+ )
generation_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token_id": 1,
3
+ "eos_token_id": [
4
+ 2,
5
+ 92542
6
+ ],
7
+ "pad_token_id": 2,
8
+ "transformers_version": "4.39.3"
9
+ }
modeling_internlm2.py ADDED
@@ -0,0 +1,1800 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
2
+ #
3
+ # This code is based on transformers/src/transformers/models/llama/modeling_llama.py
4
+ #
5
+ # Licensed under the Apache License, Version 2.0 (the "License");
6
+ # you may not use this file except in compliance with the License.
7
+ # You may obtain a copy of the License at
8
+ #
9
+ # http://www.apache.org/licenses/LICENSE-2.0
10
+ #
11
+ # Unless required by applicable law or agreed to in writing, software
12
+ # distributed under the License is distributed on an "AS IS" BASIS,
13
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
14
+ # See the License for the specific language governing permissions and
15
+ # limitations under the License.
16
+ """PyTorch InternLM2.5 model."""
17
+ import math
18
+ import queue
19
+ import threading
20
+ from typing import List, Optional, Tuple, Union
21
+
22
+ import torch
23
+ import torch.nn.functional as F
24
+ import torch.utils.checkpoint
25
+ from einops import rearrange
26
+ from torch import nn
27
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
28
+ from transformers.activations import ACT2FN
29
+ from transformers.cache_utils import Cache, DynamicCache, StaticCache
30
+ from transformers.modeling_attn_mask_utils import AttentionMaskConverter
31
+ from transformers.modeling_outputs import (
32
+ BaseModelOutputWithPast,
33
+ CausalLMOutputWithPast,
34
+ QuestionAnsweringModelOutput,
35
+ SequenceClassifierOutputWithPast,
36
+ TokenClassifierOutput,
37
+ )
38
+ from transformers.modeling_utils import PreTrainedModel
39
+ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
40
+ from transformers.utils import (
41
+ add_start_docstrings,
42
+ add_start_docstrings_to_model_forward,
43
+ is_flash_attn_greater_or_equal_2_10,
44
+ logging,
45
+ replace_return_docstrings,
46
+ )
47
+
48
+ try:
49
+ from transformers.generation.streamers import BaseStreamer
50
+ except Exception:
51
+ BaseStreamer = None
52
+
53
+ from .configuration_internlm2 import InternLM2Config
54
+
55
+
56
+ try:
57
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
58
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
59
+ except:
60
+ pass
61
+
62
+
63
+ logger = logging.get_logger(__name__)
64
+
65
+ _CONFIG_FOR_DOC = "InternLM2Config"
66
+
67
+
68
+ def _get_unpad_data(attention_mask):
69
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
70
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
71
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
72
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) # pylint: disable=E1102
73
+ return (
74
+ indices,
75
+ cu_seqlens,
76
+ max_seqlen_in_batch,
77
+ )
78
+
79
+
80
+ class InternLM2RMSNorm(nn.Module):
81
+ """InternLM2RMSNorm is equivalent to T5LayerNorm."""
82
+
83
+ def __init__(self, hidden_size, eps=1e-6):
84
+ super().__init__()
85
+ self.weight = nn.Parameter(torch.ones(hidden_size))
86
+ self.variance_epsilon = eps
87
+
88
+ def forward(self, hidden_states):
89
+ input_dtype = hidden_states.dtype
90
+ hidden_states = hidden_states.to(torch.float32)
91
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
92
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
93
+ return self.weight * hidden_states.to(input_dtype)
94
+
95
+
96
+ ALL_LAYERNORM_LAYERS.append(InternLM2RMSNorm)
97
+
98
+
99
+ class InternLM2RotaryEmbedding(nn.Module):
100
+ """Rotary Position Embedding for the InternLM2 model. Credits to the Reddit user /u/lucidrains."""
101
+
102
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
103
+ super().__init__()
104
+ self.scaling_factor = scaling_factor
105
+ self.dim = dim
106
+ self.max_position_embeddings = max_position_embeddings
107
+ self.base = base
108
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
109
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
110
+ # For BC we register cos and sin cached
111
+ self.max_seq_len_cached = max_position_embeddings
112
+
113
+ @torch.no_grad()
114
+ def forward(self, x, position_ids):
115
+ # x: [bs, num_attention_heads, seq_len, head_size]
116
+ inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
117
+ position_ids_expanded = position_ids[:, None, :].float()
118
+ # Force float32 since bfloat16 loses precision on long contexts
119
+ # See https://github.com/huggingface/transformers/pull/29285
120
+ device_type = x.device.type
121
+ device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
122
+ with torch.autocast(device_type=device_type, enabled=False):
123
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
124
+ emb = torch.cat((freqs, freqs), dim=-1)
125
+ cos = emb.cos()
126
+ sin = emb.sin()
127
+ return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
128
+
129
+
130
+ class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
131
+ """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
132
+
133
+ def forward(self, x, position_ids):
134
+ # difference to the original RoPE: a scaling factor is aplied to the position ids
135
+ position_ids = position_ids.float() / self.scaling_factor
136
+ cos, sin = super().forward(x, position_ids)
137
+ return cos, sin
138
+
139
+
140
+ class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
141
+ """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
142
+ Credits to the Reddit users /u/bloc97 and /u/emozilla"""
143
+
144
+ def forward(self, x, position_ids):
145
+ # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
146
+ seq_len = torch.max(position_ids) + 1
147
+ if seq_len > self.max_position_embeddings:
148
+ base = self.base * (
149
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
150
+ ) ** (self.dim / (self.dim - 2))
151
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim))
152
+ self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation
153
+
154
+ cos, sin = super().forward(x, position_ids)
155
+ return cos, sin
156
+
157
+
158
+ def rotate_half(x):
159
+ """Rotates half the hidden dims of the input."""
160
+ x1 = x[..., : x.shape[-1] // 2]
161
+ x2 = x[..., x.shape[-1] // 2 :]
162
+ return torch.cat((-x2, x1), dim=-1)
163
+
164
+
165
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): # pylint: disable=unused-argument
166
+ """Applies Rotary Position Embedding to the query and key tensors.
167
+
168
+ Args:
169
+ q (`torch.Tensor`): The query tensor.
170
+ k (`torch.Tensor`): The key tensor.
171
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
172
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
173
+ position_ids (`torch.Tensor`, *optional*):
174
+ Deprecated and unused.
175
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
176
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
177
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
178
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
179
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
180
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
181
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
182
+ Returns:
183
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
184
+ """
185
+ cos = cos.unsqueeze(unsqueeze_dim)
186
+ sin = sin.unsqueeze(unsqueeze_dim)
187
+ q_embed = (q * cos) + (rotate_half(q) * sin)
188
+ k_embed = (k * cos) + (rotate_half(k) * sin)
189
+ return q_embed, k_embed
190
+
191
+
192
+ class InternLM2MLP(nn.Module):
193
+ """MLP for InternLM2 model."""
194
+
195
+ def __init__(self, config):
196
+ super().__init__()
197
+ self.config = config
198
+ self.hidden_size = config.hidden_size
199
+ self.intermediate_size = config.intermediate_size
200
+ self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
201
+ self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
202
+ self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
203
+ self.act_fn = ACT2FN[config.hidden_act]
204
+
205
+ def forward(self, x):
206
+ down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
207
+
208
+ return down_proj
209
+
210
+
211
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
212
+ """
213
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
214
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
215
+ """
216
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
217
+ if n_rep == 1:
218
+ return hidden_states
219
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
220
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
221
+
222
+
223
+ class InternLM2Attention(nn.Module):
224
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
225
+
226
+ def __init__(self, config: InternLM2Config, layer_idx: Optional[int] = None):
227
+ super().__init__()
228
+ self.config = config
229
+ self.layer_idx = layer_idx
230
+ if layer_idx is None:
231
+ logger.warning_once(
232
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
233
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
234
+ "when creating this class."
235
+ )
236
+
237
+ self.hidden_size = config.hidden_size
238
+ self.num_heads = config.num_attention_heads
239
+ self.head_dim = self.hidden_size // self.num_heads
240
+ self.num_key_value_heads = config.num_key_value_heads
241
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
242
+ self.max_position_embeddings = config.max_position_embeddings
243
+ self.rope_theta = config.rope_theta
244
+ self.is_causal = True
245
+
246
+ if (self.head_dim * self.num_heads) != self.hidden_size:
247
+ raise ValueError(
248
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
249
+ f" and `num_heads`: {self.num_heads})."
250
+ )
251
+
252
+ self.wqkv = nn.Linear(
253
+ self.hidden_size,
254
+ (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
255
+ bias=config.bias,
256
+ )
257
+ self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
258
+
259
+ self._init_rope()
260
+
261
+ def _init_rope(self):
262
+ if self.config.rope_scaling is None:
263
+ self.rotary_emb = InternLM2RotaryEmbedding(
264
+ self.head_dim,
265
+ max_position_embeddings=self.max_position_embeddings,
266
+ base=self.rope_theta,
267
+ )
268
+ else:
269
+ scaling_type = self.config.rope_scaling["type"]
270
+ scaling_factor = self.config.rope_scaling["factor"]
271
+ if scaling_type == "linear":
272
+ self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
273
+ self.head_dim,
274
+ max_position_embeddings=self.max_position_embeddings,
275
+ scaling_factor=scaling_factor,
276
+ base=self.rope_theta,
277
+ )
278
+ elif scaling_type == "dynamic":
279
+ self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
280
+ self.head_dim,
281
+ max_position_embeddings=self.max_position_embeddings,
282
+ scaling_factor=scaling_factor,
283
+ base=self.rope_theta,
284
+ )
285
+ else:
286
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
287
+
288
+ def forward(
289
+ self,
290
+ hidden_states: torch.Tensor,
291
+ attention_mask: Optional[torch.Tensor] = None,
292
+ position_ids: Optional[torch.LongTensor] = None,
293
+ past_key_value: Optional[Cache] = None,
294
+ output_attentions: bool = False,
295
+ use_cache: bool = False, # pylint: disable=unused-argument
296
+ cache_position: Optional[torch.LongTensor] = None,
297
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
298
+ bsz, q_len, _ = hidden_states.size()
299
+
300
+ if self.config.pretraining_tp > 1:
301
+ # split qkv_states by tp size
302
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
303
+ qkv_slices = self.wqkv.weight.split(key_value_slicing, dim=0)
304
+ qkv_states = torch.cat(
305
+ [F.linear(hidden_states, qkv_slice) for qkv_slice in qkv_slices], dim=-1 # pylint: disable=E1102
306
+ )
307
+ else:
308
+ qkv_states = self.wqkv(hidden_states)
309
+
310
+ qkv_states = rearrange(
311
+ qkv_states,
312
+ "b q (h gs d) -> b q h gs d",
313
+ gs=2 + self.num_key_value_groups,
314
+ d=self.head_dim,
315
+ )
316
+
317
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
318
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d").transpose(1, 2)
319
+ key_states = qkv_states[..., -2, :].transpose(1, 2)
320
+ value_states = qkv_states[..., -1, :].transpose(1, 2)
321
+
322
+ cos, sin = self.rotary_emb(value_states, position_ids)
323
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
324
+
325
+ if past_key_value is not None:
326
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
327
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
328
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
329
+
330
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
331
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
332
+
333
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
334
+
335
+ if attention_mask is not None: # no matter the length, we just slice it
336
+ causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
337
+ attn_weights = attn_weights + causal_mask
338
+
339
+ # upcast attention to fp32
340
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
341
+ attn_output = torch.matmul(attn_weights, value_states)
342
+
343
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
344
+ raise ValueError(
345
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
346
+ f" {attn_output.size()}"
347
+ )
348
+
349
+ attn_output = attn_output.transpose(1, 2).contiguous()
350
+
351
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
352
+
353
+ if self.config.pretraining_tp > 1:
354
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
355
+ o_proj_slices = self.wo.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
356
+ attn_output = sum(
357
+ [
358
+ F.linear(attn_output[i], o_proj_slices[i]) # pylint: disable=E1102
359
+ for i in range(self.config.pretraining_tp)
360
+ ]
361
+ )
362
+ else:
363
+ attn_output = self.wo(attn_output)
364
+
365
+ if not output_attentions:
366
+ attn_weights = None
367
+
368
+ return attn_output, attn_weights, past_key_value
369
+
370
+
371
+ class InternLM2FlashAttention2(InternLM2Attention):
372
+ """
373
+ InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
374
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
375
+ flash attention and deal with padding tokens in case the input contains any of them.
376
+ """
377
+
378
+ def __init__(self, *args, **kwargs):
379
+ super().__init__(*args, **kwargs)
380
+
381
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
382
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement,
383
+ # that was made default for flash_attn>=2.1. This attribute is used to handle this difference.
384
+ # Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
385
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1)
386
+ # produces a wrong mask (top-left).
387
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
388
+
389
+ def forward(
390
+ self,
391
+ hidden_states: torch.Tensor,
392
+ attention_mask: Optional[torch.LongTensor] = None,
393
+ position_ids: Optional[torch.LongTensor] = None,
394
+ past_key_value: Optional[Cache] = None,
395
+ output_attentions: bool = False,
396
+ use_cache: bool = False,
397
+ cache_position: Optional[torch.LongTensor] = None,
398
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
399
+ if isinstance(past_key_value, StaticCache):
400
+ raise ValueError(
401
+ "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
402
+ "make sure to use `sdpa` in the mean time, and open an issue at "
403
+ "https://github.com/huggingface/transformers"
404
+ )
405
+
406
+ output_attentions = False
407
+
408
+ bsz, q_len, _ = hidden_states.size()
409
+
410
+ qkv_states = self.wqkv(hidden_states)
411
+
412
+ qkv_states = rearrange(
413
+ qkv_states,
414
+ "b q (h gs d) -> b q h gs d",
415
+ gs=2 + self.num_key_value_groups,
416
+ d=self.head_dim,
417
+ )
418
+
419
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
420
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
421
+ key_states = qkv_states[..., -2, :]
422
+ value_states = qkv_states[..., -1, :]
423
+
424
+ query_states = query_states.transpose(1, 2)
425
+ key_states = key_states.transpose(1, 2)
426
+ value_states = value_states.transpose(1, 2)
427
+
428
+ cos, sin = self.rotary_emb(value_states, position_ids)
429
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
430
+
431
+ if past_key_value is not None:
432
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
433
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
434
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
435
+
436
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout
437
+ # [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
438
+ # to be able to avoid many of these transpose/reshape/view.
439
+ query_states = query_states.transpose(1, 2)
440
+ key_states = key_states.transpose(1, 2)
441
+ value_states = value_states.transpose(1, 2)
442
+
443
+ # dropout_rate = self.attention_dropout if self.training else 0.0
444
+ dropout_rate = 0.0
445
+
446
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
447
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
448
+ # cast them back in the correct dtype just to be sure everything works as expected.
449
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
450
+ # in fp32. (InternLM2RMSNorm handles it correctly)
451
+
452
+ input_dtype = query_states.dtype
453
+ if input_dtype == torch.float32:
454
+ if torch.is_autocast_enabled():
455
+ target_dtype = torch.get_autocast_gpu_dtype()
456
+ # Handle the case where the model is quantized
457
+ elif hasattr(self.config, "_pre_quantization_dtype"):
458
+ target_dtype = self.config._pre_quantization_dtype
459
+ else:
460
+ target_dtype = self.wqkv.weight.dtype
461
+
462
+ logger.warning_once(
463
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
464
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
465
+ f" {target_dtype}."
466
+ )
467
+
468
+ query_states = query_states.to(target_dtype)
469
+ key_states = key_states.to(target_dtype)
470
+ value_states = value_states.to(target_dtype)
471
+
472
+ attn_output = self._flash_attention_forward(
473
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
474
+ )
475
+
476
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
477
+ attn_output = self.wo(attn_output)
478
+
479
+ if not output_attentions:
480
+ attn_weights = None
481
+
482
+ return attn_output, attn_weights, past_key_value # pylint: disable=E0606
483
+
484
+ def _flash_attention_forward(
485
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
486
+ ):
487
+ """
488
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
489
+ first unpad the input, then computes the attention scores and pad the final attention scores.
490
+
491
+ Args:
492
+ query_states (`torch.Tensor`):
493
+ Input query states to be passed to Flash Attention API
494
+ key_states (`torch.Tensor`):
495
+ Input key states to be passed to Flash Attention API
496
+ value_states (`torch.Tensor`):
497
+ Input value states to be passed to Flash Attention API
498
+ attention_mask (`torch.Tensor`):
499
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
500
+ position of padding tokens and 1 for the position of non-padding tokens.
501
+ dropout (`float`):
502
+ Attention dropout
503
+ softmax_scale (`float`, *optional*):
504
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
505
+ """
506
+ if not self._flash_attn_uses_top_left_mask:
507
+ causal = self.is_causal
508
+ else:
509
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1.
510
+ # For details, please see the comment in InternLM2FlashAttention2 __init__.
511
+ causal = self.is_causal and query_length != 1
512
+
513
+ # Contains at least one padding token in the sequence
514
+ if attention_mask is not None:
515
+ batch_size = query_states.shape[0]
516
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
517
+ query_states, key_states, value_states, attention_mask, query_length
518
+ )
519
+
520
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
521
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
522
+
523
+ attn_output_unpad = flash_attn_varlen_func( # pylint: disable=E0606
524
+ query_states,
525
+ key_states,
526
+ value_states,
527
+ cu_seqlens_q=cu_seqlens_q,
528
+ cu_seqlens_k=cu_seqlens_k,
529
+ max_seqlen_q=max_seqlen_in_batch_q,
530
+ max_seqlen_k=max_seqlen_in_batch_k,
531
+ dropout_p=dropout,
532
+ softmax_scale=softmax_scale,
533
+ causal=causal,
534
+ )
535
+
536
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) # pylint: disable=E0606
537
+ else:
538
+ attn_output = flash_attn_func( # pylint: disable=E0606
539
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
540
+ )
541
+
542
+ return attn_output
543
+
544
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
545
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
546
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
547
+
548
+ key_layer = index_first_axis( # pylint: disable=E0606
549
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
550
+ )
551
+ value_layer = index_first_axis( # pylint: disable=E0606
552
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
553
+ )
554
+ if query_length == kv_seq_len:
555
+ query_layer = index_first_axis( # pylint: disable=E0606
556
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
557
+ )
558
+ cu_seqlens_q = cu_seqlens_k
559
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
560
+ indices_q = indices_k
561
+ elif query_length == 1:
562
+ max_seqlen_in_batch_q = 1
563
+ cu_seqlens_q = torch.arange(
564
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
565
+ ) # There is a memcpy here, that is very bad.
566
+ indices_q = cu_seqlens_q[:-1]
567
+ query_layer = query_layer.squeeze(1)
568
+ else:
569
+ # The -q_len: slice assumes left padding.
570
+ attention_mask = attention_mask[:, -query_length:]
571
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( # pylint: disable=E0606
572
+ query_layer, attention_mask
573
+ )
574
+
575
+ return (
576
+ query_layer,
577
+ key_layer,
578
+ value_layer,
579
+ indices_q,
580
+ (cu_seqlens_q, cu_seqlens_k),
581
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
582
+ )
583
+
584
+
585
+ # Copied from transformers.models.llama.modeling_llama.LllamaSdpaAttention with Llama->InternLM2
586
+ class InternLM2SdpaAttention(InternLM2Attention):
587
+ """
588
+ InternLM2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
589
+ `InternLM2Attention` as the weights of the module stays untouched. The only changes are on the forward pass
590
+ to adapt to SDPA API.
591
+ """
592
+
593
+ # Adapted from InternLM2Attention.forward
594
+ def forward(
595
+ self,
596
+ hidden_states: torch.Tensor,
597
+ attention_mask: Optional[torch.Tensor] = None,
598
+ position_ids: Optional[torch.LongTensor] = None,
599
+ past_key_value: Optional[Cache] = None,
600
+ output_attentions: bool = False,
601
+ use_cache: bool = False,
602
+ cache_position: Optional[torch.LongTensor] = None,
603
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
604
+ if output_attentions:
605
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"`
606
+ # once this is implemented.
607
+ logger.warning_once(
608
+ "InternLM2Model uses InternLM2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` "
609
+ "does not support `output_attentions=True`. Falling back to the manual attention implementation, "
610
+ "but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. "
611
+ 'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
612
+ )
613
+ return super().forward(
614
+ hidden_states=hidden_states,
615
+ attention_mask=attention_mask,
616
+ position_ids=position_ids,
617
+ past_key_value=past_key_value,
618
+ output_attentions=output_attentions,
619
+ use_cache=use_cache,
620
+ cache_position=cache_position,
621
+ )
622
+
623
+ bsz, q_len, _ = hidden_states.size()
624
+
625
+ qkv_states = self.wqkv(hidden_states)
626
+
627
+ qkv_states = rearrange(
628
+ qkv_states,
629
+ "b q (h gs d) -> b q h gs d",
630
+ gs=2 + self.num_key_value_groups,
631
+ d=self.head_dim,
632
+ )
633
+
634
+ query_states = qkv_states[..., : self.num_key_value_groups, :]
635
+ query_states = rearrange(query_states, "b q h gs d -> b q (h gs) d")
636
+ key_states = qkv_states[..., -2, :]
637
+ value_states = qkv_states[..., -1, :]
638
+
639
+ query_states = query_states.transpose(1, 2)
640
+ key_states = key_states.transpose(1, 2)
641
+ value_states = value_states.transpose(1, 2)
642
+
643
+ cos, sin = self.rotary_emb(value_states, position_ids)
644
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
645
+
646
+ if past_key_value is not None:
647
+ # sin and cos are specific to RoPE models; cache_position needed for the static cache
648
+ cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
649
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
650
+
651
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
652
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
653
+
654
+ causal_mask = attention_mask
655
+ if attention_mask is not None:
656
+ causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
657
+
658
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
659
+ # custom attn_mask, Reference: https://github.com/pytorch/pytorch/issues/112577.
660
+ if query_states.device.type == "cuda" and causal_mask is not None:
661
+ query_states = query_states.contiguous()
662
+ key_states = key_states.contiguous()
663
+ value_states = value_states.contiguous()
664
+
665
+ # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of
666
+ # an inline conditional assignment in SDPA to support both torch.compile's dynamic shapes and full graph
667
+ # options. An inline conditional prevents dynamic shapes from compiling.
668
+ is_causal = bool(causal_mask is None and q_len > 1)
669
+
670
+ attn_output = torch.nn.functional.scaled_dot_product_attention( # pylint: disable=E1102
671
+ query_states,
672
+ key_states,
673
+ value_states,
674
+ attn_mask=causal_mask,
675
+ dropout_p=0.0,
676
+ is_causal=is_causal,
677
+ )
678
+
679
+ attn_output = attn_output.transpose(1, 2).contiguous()
680
+ attn_output = attn_output.view(bsz, q_len, self.hidden_size)
681
+
682
+ attn_output = self.wo(attn_output)
683
+
684
+ return attn_output, None, past_key_value
685
+
686
+
687
+ INTERNLM2_ATTENTION_CLASSES = {
688
+ "eager": InternLM2Attention,
689
+ "flash_attention_2": InternLM2FlashAttention2,
690
+ "sdpa": InternLM2SdpaAttention,
691
+ }
692
+
693
+
694
+ # Modified from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->InternLM2
695
+ class InternLM2DecoderLayer(nn.Module):
696
+ """InternLM2 Decoder Layer. This module is a single layer of the InternLM2 model."""
697
+
698
+ def __init__(self, config: InternLM2Config, layer_idx: int):
699
+ super().__init__()
700
+ self.hidden_size = config.hidden_size
701
+ self.layer_idx = layer_idx
702
+
703
+ self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config, layer_idx=layer_idx)
704
+
705
+ self.feed_forward = InternLM2MLP(config)
706
+ self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
707
+ self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
708
+
709
+ def forward(
710
+ self,
711
+ hidden_states: torch.Tensor,
712
+ attention_mask: Optional[torch.Tensor] = None,
713
+ position_ids: Optional[torch.LongTensor] = None,
714
+ past_key_value: Optional[Cache] = None,
715
+ output_attentions: Optional[bool] = False,
716
+ use_cache: Optional[bool] = False,
717
+ cache_position: Optional[torch.LongTensor] = None,
718
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
719
+ """
720
+ Args:
721
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
722
+ attention_mask (`torch.FloatTensor`, *optional*):
723
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
724
+ query_sequence_length, key_sequence_length)` if default attention is used.
725
+ output_attentions (`bool`, *optional*):
726
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
727
+ returned tensors for more detail.
728
+ use_cache (`bool`, *optional*):
729
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
730
+ (see `past_key_values`).
731
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
732
+ """
733
+ residual = hidden_states
734
+
735
+ hidden_states = self.attention_norm(hidden_states)
736
+
737
+ # Self Attention
738
+ hidden_states, self_attn_weights, present_key_value = self.attention(
739
+ hidden_states=hidden_states,
740
+ attention_mask=attention_mask,
741
+ position_ids=position_ids,
742
+ past_key_value=past_key_value,
743
+ output_attentions=output_attentions,
744
+ use_cache=use_cache,
745
+ cache_position=cache_position,
746
+ )
747
+ hidden_states = residual + hidden_states
748
+
749
+ # Fully Connected
750
+ residual = hidden_states
751
+ hidden_states = self.ffn_norm(hidden_states)
752
+ hidden_states = self.feed_forward(hidden_states)
753
+ hidden_states = residual + hidden_states
754
+
755
+ outputs = (hidden_states,)
756
+
757
+ if output_attentions:
758
+ outputs += (self_attn_weights,)
759
+
760
+ if use_cache:
761
+ outputs += (present_key_value,)
762
+
763
+ return outputs
764
+
765
+
766
+ InternLM2_START_DOCSTRING = r"""
767
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
768
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
769
+ etc.)
770
+
771
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
772
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
773
+ and behavior.
774
+
775
+ Parameters:
776
+ config ([`InternLM2Config`]):
777
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
778
+ load the weights associated with the model, only the configuration. Check out the
779
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
780
+ """
781
+
782
+
783
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
784
+ @add_start_docstrings(
785
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
786
+ InternLM2_START_DOCSTRING,
787
+ )
788
+ class InternLM2PreTrainedModel(PreTrainedModel):
789
+ """
790
+ InternLM2 pretraiend model's base class.
791
+ """
792
+
793
+ config_class = InternLM2Config
794
+ base_model_prefix = "model"
795
+ supports_gradient_checkpointing = True
796
+ _no_split_modules = ["InternLM2DecoderLayer"]
797
+ _skip_keys_device_placement = ["past_key_values"]
798
+ _supports_flash_attn_2 = True
799
+ _supports_sdpa = True
800
+ _supports_cache_class = True
801
+ _supports_quantized_cache = True
802
+ _supports_static_cache = True
803
+
804
+ def _init_weights(self, module):
805
+ std = self.config.initializer_range
806
+ if isinstance(module, nn.Linear):
807
+ module.weight.data.normal_(mean=0.0, std=std)
808
+ if module.bias is not None:
809
+ module.bias.data.zero_()
810
+ elif isinstance(module, nn.Embedding):
811
+ module.weight.data.normal_(mean=0.0, std=std)
812
+ if module.padding_idx is not None:
813
+ module.weight.data[module.padding_idx].zero_()
814
+
815
+
816
+ InternLM2_INPUTS_DOCSTRING = r"""
817
+ Args:
818
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
819
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
820
+ it.
821
+
822
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
823
+ [`PreTrainedTokenizer.__call__`] for details.
824
+
825
+ [What are input IDs?](../glossary#input-ids)
826
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
827
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
828
+
829
+ - 1 for tokens that are **not masked**,
830
+ - 0 for tokens that are **masked**.
831
+
832
+ [What are attention masks?](../glossary#attention-mask)
833
+
834
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
835
+ [`PreTrainedTokenizer.__call__`] for details.
836
+
837
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
838
+ `past_key_values`).
839
+
840
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
841
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
842
+ information on the default strategy.
843
+
844
+ - 1 indicates the head is **not masked**,
845
+ - 0 indicates the head is **masked**.
846
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
847
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
848
+ config.n_positions - 1]`.
849
+
850
+ [What are position IDs?](../glossary#position-ids)
851
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
852
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
853
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
854
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
855
+
856
+ Two formats are allowed:
857
+ - a [`~cache_utils.Cache`] instance;
858
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
859
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
860
+ cache format.
861
+
862
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
863
+ legacy cache format will be returned.
864
+
865
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
866
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
867
+ of shape `(batch_size, sequence_length)`.
868
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
869
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
870
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
871
+ model's internal embedding lookup matrix.
872
+ use_cache (`bool`, *optional*):
873
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
874
+ `past_key_values`).
875
+ output_attentions (`bool`, *optional*):
876
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
877
+ tensors for more detail.
878
+ output_hidden_states (`bool`, *optional*):
879
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
880
+ more detail.
881
+ return_dict (`bool`, *optional*):
882
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
883
+ cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
884
+ Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
885
+ this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
886
+ the complete sequence length.
887
+ """
888
+
889
+
890
+ # Modified from transformers.models.llama.modeling_llama.LlamaModel with Llama->InternLM2
891
+ @add_start_docstrings(
892
+ "The bare InternLM2 Model outputting raw hidden-states without any specific head on top.",
893
+ InternLM2_START_DOCSTRING,
894
+ )
895
+ class InternLM2Model(InternLM2PreTrainedModel):
896
+ """
897
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
898
+
899
+ Args:
900
+ config: InternLM2Config
901
+ """
902
+
903
+ _auto_class = "AutoModel"
904
+
905
+ def __init__(self, config: InternLM2Config):
906
+ super().__init__(config)
907
+ self.padding_idx = config.pad_token_id
908
+ self.vocab_size = config.vocab_size
909
+ self.config = config
910
+
911
+ self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
912
+
913
+ self.layers = nn.ModuleList(
914
+ [InternLM2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
915
+ )
916
+ self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
917
+
918
+ self.gradient_checkpointing = False
919
+ # Initialize weights and apply final processing
920
+ self.post_init()
921
+
922
+ def get_input_embeddings(self):
923
+ return self.tok_embeddings
924
+
925
+ def set_input_embeddings(self, value):
926
+ self.tok_embeddings = value
927
+
928
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
929
+ def forward(
930
+ self,
931
+ input_ids: torch.LongTensor = None,
932
+ attention_mask: Optional[torch.Tensor] = None,
933
+ position_ids: Optional[torch.LongTensor] = None,
934
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
935
+ inputs_embeds: Optional[torch.FloatTensor] = None,
936
+ use_cache: Optional[bool] = None,
937
+ output_attentions: Optional[bool] = None,
938
+ output_hidden_states: Optional[bool] = None,
939
+ return_dict: Optional[bool] = None,
940
+ cache_position: Optional[torch.LongTensor] = None,
941
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
942
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
943
+ output_hidden_states = (
944
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
945
+ )
946
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
947
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
948
+
949
+ if (input_ids is None) ^ (inputs_embeds is not None):
950
+ raise ValueError(
951
+ "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
952
+ )
953
+
954
+ if self.gradient_checkpointing and self.training and use_cache:
955
+ logger.warning_once(
956
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
957
+ )
958
+ use_cache = False
959
+
960
+ if inputs_embeds is None:
961
+ inputs_embeds = self.tok_embeddings(input_ids)
962
+
963
+ return_legacy_cache = False
964
+ if use_cache and not isinstance(past_key_values, Cache): # kept for BC (non `Cache` `past_key_values` inputs)
965
+ return_legacy_cache = True
966
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
967
+
968
+ if cache_position is None:
969
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
970
+ cache_position = torch.arange(
971
+ past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
972
+ )
973
+ if position_ids is None:
974
+ position_ids = cache_position.unsqueeze(0)
975
+
976
+ causal_mask = self._update_causal_mask(
977
+ attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
978
+ )
979
+
980
+ # embed positions
981
+ hidden_states = inputs_embeds
982
+
983
+ # decoder layers
984
+ all_hidden_states = () if output_hidden_states else None
985
+ all_self_attns = () if output_attentions else None
986
+ next_decoder_cache = None
987
+
988
+ for decoder_layer in self.layers:
989
+ if output_hidden_states:
990
+ all_hidden_states += (hidden_states,)
991
+
992
+ if self.gradient_checkpointing and self.training:
993
+ layer_outputs = self._gradient_checkpointing_func(
994
+ decoder_layer.__call__,
995
+ hidden_states,
996
+ causal_mask,
997
+ position_ids,
998
+ past_key_values,
999
+ output_attentions,
1000
+ use_cache,
1001
+ cache_position,
1002
+ )
1003
+ else:
1004
+ layer_outputs = decoder_layer(
1005
+ hidden_states,
1006
+ attention_mask=causal_mask,
1007
+ position_ids=position_ids,
1008
+ past_key_value=past_key_values,
1009
+ output_attentions=output_attentions,
1010
+ use_cache=use_cache,
1011
+ cache_position=cache_position,
1012
+ )
1013
+
1014
+ hidden_states = layer_outputs[0]
1015
+
1016
+ if use_cache:
1017
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1018
+
1019
+ if output_attentions:
1020
+ all_self_attns += (layer_outputs[1],)
1021
+
1022
+ hidden_states = self.norm(hidden_states)
1023
+
1024
+ # add hidden states from the last decoder layer
1025
+ if output_hidden_states:
1026
+ all_hidden_states += (hidden_states,)
1027
+
1028
+ next_cache = next_decoder_cache if use_cache else None
1029
+ if return_legacy_cache:
1030
+ next_cache = next_cache.to_legacy_cache()
1031
+
1032
+ if not return_dict:
1033
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
1034
+ return BaseModelOutputWithPast(
1035
+ last_hidden_state=hidden_states,
1036
+ past_key_values=next_cache,
1037
+ hidden_states=all_hidden_states,
1038
+ attentions=all_self_attns,
1039
+ )
1040
+
1041
+ def _update_causal_mask(
1042
+ self,
1043
+ attention_mask: torch.Tensor,
1044
+ input_tensor: torch.Tensor,
1045
+ cache_position: torch.Tensor,
1046
+ past_key_values: Cache,
1047
+ output_attentions: bool,
1048
+ ):
1049
+ # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length
1050
+ # even when the static KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at
1051
+ # each decode steps due to the dynamic shapes. (`recording cudagraph tree for symint key 13`, etc.), which is
1052
+ # VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using `fullgraph=True`.
1053
+ # See more context in https://github.com/huggingface/transformers/pull/29114
1054
+
1055
+ if self.config.attn_implementation == "flash_attention_2":
1056
+ if attention_mask is not None and 0.0 in attention_mask:
1057
+ return attention_mask
1058
+ return None
1059
+
1060
+ # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
1061
+ # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
1062
+ # to infer the attention mask.
1063
+ past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
1064
+ using_static_cache = isinstance(past_key_values, StaticCache)
1065
+
1066
+ # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
1067
+ if self.config.attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
1068
+ if AttentionMaskConverter._ignore_causal_mask_sdpa(
1069
+ attention_mask,
1070
+ inputs_embeds=input_tensor,
1071
+ past_key_values_length=past_seen_tokens,
1072
+ is_training=self.training,
1073
+ ):
1074
+ return None
1075
+
1076
+ dtype, device = input_tensor.dtype, input_tensor.device
1077
+ min_dtype = torch.finfo(dtype).min
1078
+ sequence_length = input_tensor.shape[1]
1079
+ if using_static_cache:
1080
+ target_length = past_key_values.get_max_length()
1081
+ else:
1082
+ target_length = (
1083
+ attention_mask.shape[-1]
1084
+ if isinstance(attention_mask, torch.Tensor)
1085
+ else past_seen_tokens + sequence_length + 1
1086
+ )
1087
+
1088
+ if attention_mask is not None and attention_mask.dim() == 4:
1089
+ # in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
1090
+ if attention_mask.max() != 0:
1091
+ raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
1092
+ causal_mask = attention_mask
1093
+ else:
1094
+ causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
1095
+ if sequence_length != 1:
1096
+ causal_mask = torch.triu(causal_mask, diagonal=1)
1097
+ causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
1098
+ causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
1099
+ if attention_mask is not None:
1100
+ causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
1101
+ mask_length = attention_mask.shape[-1]
1102
+ padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
1103
+ padding_mask = padding_mask == 0
1104
+ causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
1105
+ padding_mask, min_dtype
1106
+ )
1107
+ if (
1108
+ self.config.attn_implementation == "sdpa"
1109
+ and attention_mask is not None
1110
+ and attention_mask.device.type == "cuda"
1111
+ and not output_attentions
1112
+ ):
1113
+ # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
1114
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
1115
+ # Details: https://github.com/pytorch/pytorch/issues/110213
1116
+ causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) # pylint: disable=E1120
1117
+
1118
+ return causal_mask
1119
+
1120
+
1121
+ # Modified from transformers.models.llama.modeling_llama.LlamaForCausalLM
1122
+ class InternLM2ForCausalLM(InternLM2PreTrainedModel):
1123
+ """Causal language model (CLM) for InternLM2."""
1124
+
1125
+ _auto_class = "AutoModelForCausalLM"
1126
+ _tied_weights_keys = ["output.weight"]
1127
+
1128
+ def __init__(self, config):
1129
+ super().__init__(config)
1130
+ self.model = InternLM2Model(config)
1131
+ self.vocab_size = config.vocab_size
1132
+ self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1133
+
1134
+ # Initialize weights and apply final processing
1135
+ self.post_init()
1136
+
1137
+ def get_input_embeddings(self):
1138
+ return self.model.tok_embeddings
1139
+
1140
+ def set_input_embeddings(self, value):
1141
+ self.model.tok_embeddings = value
1142
+
1143
+ def get_output_embeddings(self):
1144
+ return self.output
1145
+
1146
+ def set_output_embeddings(self, new_embeddings):
1147
+ self.output = new_embeddings
1148
+
1149
+ def set_decoder(self, decoder):
1150
+ self.model = decoder
1151
+
1152
+ def get_decoder(self):
1153
+ return self.model
1154
+
1155
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1156
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1157
+ def forward(
1158
+ self,
1159
+ input_ids: torch.LongTensor = None,
1160
+ attention_mask: Optional[torch.Tensor] = None,
1161
+ position_ids: Optional[torch.LongTensor] = None,
1162
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1163
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1164
+ labels: Optional[torch.LongTensor] = None,
1165
+ use_cache: Optional[bool] = None,
1166
+ output_attentions: Optional[bool] = None,
1167
+ output_hidden_states: Optional[bool] = None,
1168
+ return_dict: Optional[bool] = None,
1169
+ cache_position: Optional[torch.LongTensor] = None,
1170
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1171
+ r"""
1172
+ Args:
1173
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1174
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1175
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1176
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1177
+
1178
+ Returns:
1179
+
1180
+ Example:
1181
+
1182
+ ```python
1183
+ >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
1184
+
1185
+ >>> model = InternLM2ForCausalLM.from_pretrained("meta-InternLM2/InternLM2-2-7b-hf")
1186
+ >>> tokenizer = AutoTokenizer.from_pretrained("meta-InternLM2/InternLM2-2-7b-hf")
1187
+
1188
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1189
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1190
+
1191
+ >>> # Generate
1192
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1193
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1194
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1195
+ ```"""
1196
+
1197
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1198
+ output_hidden_states = (
1199
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1200
+ )
1201
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1202
+
1203
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1204
+ outputs = self.model(
1205
+ input_ids=input_ids,
1206
+ attention_mask=attention_mask,
1207
+ position_ids=position_ids,
1208
+ past_key_values=past_key_values,
1209
+ inputs_embeds=inputs_embeds,
1210
+ use_cache=use_cache,
1211
+ output_attentions=output_attentions,
1212
+ output_hidden_states=output_hidden_states,
1213
+ return_dict=return_dict,
1214
+ cache_position=cache_position,
1215
+ )
1216
+
1217
+ hidden_states = outputs[0]
1218
+ if self.config.pretraining_tp > 1:
1219
+ output_slices = self.output.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
1220
+ logits = [
1221
+ F.linear(hidden_states, output_slices[i]) # pylint: disable=not-callable
1222
+ for i in range(self.config.pretraining_tp)
1223
+ ]
1224
+ logits = torch.cat(logits, dim=-1)
1225
+ else:
1226
+ logits = self.output(hidden_states)
1227
+ logits = logits.float()
1228
+
1229
+ loss = None
1230
+ if labels is not None:
1231
+ # Shift so that tokens < n predict n
1232
+ shift_logits = logits[..., :-1, :].contiguous()
1233
+ shift_labels = labels[..., 1:].contiguous()
1234
+ # Flatten the tokens
1235
+ loss_fct = CrossEntropyLoss()
1236
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1237
+ shift_labels = shift_labels.view(-1)
1238
+ # Enable model parallelism
1239
+ shift_labels = shift_labels.to(shift_logits.device)
1240
+ loss = loss_fct(shift_logits, shift_labels)
1241
+
1242
+ if not return_dict:
1243
+ output = (logits,) + outputs[1:]
1244
+ return (loss,) + output if loss is not None else output
1245
+
1246
+ return CausalLMOutputWithPast(
1247
+ loss=loss,
1248
+ logits=logits,
1249
+ past_key_values=outputs.past_key_values,
1250
+ hidden_states=outputs.hidden_states,
1251
+ attentions=outputs.attentions,
1252
+ )
1253
+
1254
+ def prepare_inputs_for_generation(
1255
+ self,
1256
+ input_ids,
1257
+ past_key_values=None,
1258
+ attention_mask=None,
1259
+ inputs_embeds=None,
1260
+ cache_position=None,
1261
+ use_cache=True,
1262
+ **kwargs,
1263
+ ):
1264
+ past_length = 0
1265
+ if past_key_values is not None:
1266
+ if isinstance(past_key_values, Cache):
1267
+ past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
1268
+ max_cache_length = (
1269
+ torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
1270
+ if past_key_values.get_max_length() is not None
1271
+ else None
1272
+ )
1273
+ cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
1274
+ # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects
1275
+ else:
1276
+ cache_length = past_length = past_key_values[0][0].shape[2]
1277
+ max_cache_length = None
1278
+
1279
+ # Keep only the unprocessed tokens:
1280
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1281
+ # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as input)
1282
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1283
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1284
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1285
+ # input_ids based on the past_length.
1286
+ elif past_length < input_ids.shape[1]:
1287
+ input_ids = input_ids[:, past_length:]
1288
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1289
+
1290
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1291
+ if (
1292
+ max_cache_length is not None
1293
+ and attention_mask is not None
1294
+ and cache_length + input_ids.shape[1] > max_cache_length
1295
+ ):
1296
+ attention_mask = attention_mask[:, -max_cache_length:] # pylint: disable=E1130
1297
+
1298
+ position_ids = kwargs.get("position_ids", None)
1299
+ if attention_mask is not None and position_ids is None:
1300
+ # create position_ids on the fly for batch generation
1301
+ position_ids = attention_mask.long().cumsum(-1) - 1
1302
+ position_ids.masked_fill_(attention_mask == 0, 1)
1303
+ if past_key_values:
1304
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1305
+
1306
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1307
+ if inputs_embeds is not None and past_key_values is None:
1308
+ model_inputs = {"inputs_embeds": inputs_embeds}
1309
+ else:
1310
+ # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
1311
+ # recompiles graphs as the stride of the inputs is a guard.
1312
+ # Ref: https://github.com/huggingface/transformers/pull/29114
1313
+ # TODO: use `next_tokens` directly instead.
1314
+ model_inputs = {"input_ids": input_ids.contiguous()}
1315
+
1316
+ input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
1317
+ if cache_position is None:
1318
+ cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
1319
+ elif use_cache:
1320
+ cache_position = cache_position[-input_length:]
1321
+
1322
+ model_inputs.update(
1323
+ {
1324
+ "position_ids": position_ids,
1325
+ "cache_position": cache_position,
1326
+ "past_key_values": past_key_values,
1327
+ "use_cache": use_cache,
1328
+ "attention_mask": attention_mask,
1329
+ }
1330
+ )
1331
+ return model_inputs
1332
+
1333
+ @staticmethod
1334
+ def _reorder_cache(past_key_values, beam_idx):
1335
+ reordered_past = ()
1336
+ for layer_past in past_key_values:
1337
+ reordered_past += (
1338
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1339
+ )
1340
+ return reordered_past
1341
+
1342
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, meta_instruction=""):
1343
+ if history is None:
1344
+ history = []
1345
+ if tokenizer.add_bos_token:
1346
+ prompt = ""
1347
+ else:
1348
+ prompt = tokenizer.bos_token
1349
+ if meta_instruction:
1350
+ prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
1351
+ for record in history:
1352
+ prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
1353
+ prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
1354
+ return tokenizer([prompt], return_tensors="pt")
1355
+
1356
+ @torch.no_grad()
1357
+ def chat(
1358
+ self,
1359
+ tokenizer,
1360
+ query: str,
1361
+ history: Optional[List[Tuple[str, str]]] = None,
1362
+ streamer: Optional[BaseStreamer] = None,
1363
+ max_new_tokens: int = 1024,
1364
+ do_sample: bool = True,
1365
+ temperature: float = 0.8,
1366
+ top_p: float = 0.8,
1367
+ meta_instruction: str = "You are an AI assistant whose name is InternLM (书生·浦语).\n"
1368
+ "- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory "
1369
+ "(上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
1370
+ "- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such "
1371
+ "as English and 中文.",
1372
+ **kwargs,
1373
+ ):
1374
+ if history is None:
1375
+ history = []
1376
+ inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
1377
+ inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
1378
+ # also add end-of-assistant token in eos token id to avoid unnecessary generation
1379
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(["<|im_end|>"])[0]]
1380
+ outputs = self.generate(
1381
+ **inputs,
1382
+ streamer=streamer,
1383
+ max_new_tokens=max_new_tokens,
1384
+ do_sample=do_sample,
1385
+ temperature=temperature,
1386
+ top_p=top_p,
1387
+ eos_token_id=eos_token_id,
1388
+ **kwargs,
1389
+ )
1390
+ outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
1391
+ response = tokenizer.decode(outputs, skip_special_tokens=True)
1392
+ response = response.split("<|im_end|>")[0]
1393
+ history = history + [(query, response)]
1394
+ return response, history
1395
+
1396
+ @torch.no_grad()
1397
+ def stream_chat(
1398
+ self,
1399
+ tokenizer,
1400
+ query: str,
1401
+ history: List[Tuple[str, str]] = None,
1402
+ max_new_tokens: int = 1024,
1403
+ do_sample: bool = True,
1404
+ temperature: float = 0.8,
1405
+ top_p: float = 0.8,
1406
+ **kwargs,
1407
+ ):
1408
+ if history is None:
1409
+ history = []
1410
+ """
1411
+ Return a generator in format: (response, history)
1412
+ Eg.
1413
+ ('你好,有什么可以帮助您的吗', [('你好', '你好,有什么可以帮助您的吗')])
1414
+ ('你好,有什么可以帮助您的吗?', [('你好', '你好,有什么可以帮助您的吗?')])
1415
+ """
1416
+ if BaseStreamer is None:
1417
+ raise ModuleNotFoundError(
1418
+ "The version of `transformers` is too low. Please make sure "
1419
+ "that you have installed `transformers>=4.28.0`."
1420
+ )
1421
+
1422
+ response_queue = queue.Queue(maxsize=20)
1423
+
1424
+ class ChatStreamer(BaseStreamer):
1425
+ """
1426
+ Streamer used in generate to print words one by one.
1427
+ """
1428
+
1429
+ def __init__(self, tokenizer) -> None:
1430
+ super().__init__()
1431
+ self.tokenizer = tokenizer
1432
+ self.queue = response_queue
1433
+ self.query = query
1434
+ self.history = history
1435
+ self.response = ""
1436
+ self.cache = []
1437
+ self.received_inputs = False
1438
+ self.queue.put((self.response, history + [(self.query, self.response)]))
1439
+
1440
+ def put(self, value):
1441
+ if len(value.shape) > 1 and value.shape[0] > 1:
1442
+ raise ValueError("ChatStreamer only supports batch size 1")
1443
+ elif len(value.shape) > 1:
1444
+ value = value[0]
1445
+
1446
+ if not self.received_inputs:
1447
+ # The first received value is input_ids, ignore here
1448
+ self.received_inputs = True
1449
+ return
1450
+
1451
+ self.cache.extend(value.tolist())
1452
+ token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
1453
+ if token.strip() != "<|im_end|>":
1454
+ self.response = self.response + token
1455
+ history = self.history + [(self.query, self.response)]
1456
+ self.queue.put((self.response, history))
1457
+ self.cache = []
1458
+ else:
1459
+ self.end()
1460
+
1461
+ def end(self):
1462
+ self.queue.put(None)
1463
+
1464
+ def stream_producer():
1465
+ return self.chat(
1466
+ tokenizer=tokenizer,
1467
+ query=query,
1468
+ streamer=ChatStreamer(tokenizer=tokenizer),
1469
+ history=history,
1470
+ max_new_tokens=max_new_tokens,
1471
+ do_sample=do_sample,
1472
+ temperature=temperature,
1473
+ top_p=top_p,
1474
+ **kwargs,
1475
+ )
1476
+
1477
+ def consumer():
1478
+ producer = threading.Thread(target=stream_producer)
1479
+ producer.start()
1480
+ while True:
1481
+ res = response_queue.get()
1482
+ if res is None:
1483
+ return
1484
+ yield res
1485
+
1486
+ return consumer()
1487
+
1488
+
1489
+ # Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
1490
+ @add_start_docstrings(
1491
+ """
1492
+ The InternLM2 Model transformer with a sequence classification head on top (linear layer).
1493
+
1494
+ [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1495
+ (e.g. GPT-2) do.
1496
+
1497
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1498
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1499
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1500
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1501
+ each row of the batch).
1502
+ """,
1503
+ InternLM2_START_DOCSTRING,
1504
+ )
1505
+ class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
1506
+ """Sequence Classification Head for InternLM2 Model."""
1507
+
1508
+ def __init__(self, config):
1509
+ super().__init__(config)
1510
+ self.num_labels = config.num_labels
1511
+ self.model = InternLM2Model(config)
1512
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1513
+
1514
+ # Initialize weights and apply final processing
1515
+ self.post_init()
1516
+
1517
+ def get_input_embeddings(self):
1518
+ return self.model.tok_embeddings
1519
+
1520
+ def set_input_embeddings(self, value):
1521
+ self.model.tok_embeddings = value
1522
+
1523
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1524
+ def forward(
1525
+ self,
1526
+ input_ids: torch.LongTensor = None,
1527
+ attention_mask: Optional[torch.Tensor] = None,
1528
+ position_ids: Optional[torch.LongTensor] = None,
1529
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1530
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1531
+ labels: Optional[torch.LongTensor] = None,
1532
+ use_cache: Optional[bool] = None,
1533
+ output_attentions: Optional[bool] = None,
1534
+ output_hidden_states: Optional[bool] = None,
1535
+ return_dict: Optional[bool] = None,
1536
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1537
+ r"""
1538
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1539
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1540
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1541
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1542
+ """
1543
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1544
+
1545
+ transformer_outputs = self.model(
1546
+ input_ids,
1547
+ attention_mask=attention_mask,
1548
+ position_ids=position_ids,
1549
+ past_key_values=past_key_values,
1550
+ inputs_embeds=inputs_embeds,
1551
+ use_cache=use_cache,
1552
+ output_attentions=output_attentions,
1553
+ output_hidden_states=output_hidden_states,
1554
+ return_dict=return_dict,
1555
+ )
1556
+ hidden_states = transformer_outputs[0]
1557
+ logits = self.score(hidden_states)
1558
+
1559
+ if input_ids is not None:
1560
+ batch_size = input_ids.shape[0]
1561
+ else:
1562
+ batch_size = inputs_embeds.shape[0]
1563
+
1564
+ if self.config.pad_token_id is None and batch_size != 1:
1565
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1566
+ if self.config.pad_token_id is None:
1567
+ sequence_lengths = -1
1568
+ else:
1569
+ if input_ids is not None:
1570
+ # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
1571
+ sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
1572
+ sequence_lengths = sequence_lengths % input_ids.shape[-1]
1573
+ sequence_lengths = sequence_lengths.to(logits.device)
1574
+ else:
1575
+ sequence_lengths = -1
1576
+
1577
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1578
+
1579
+ loss = None
1580
+ if labels is not None:
1581
+ labels = labels.to(logits.device)
1582
+ if self.config.problem_type is None:
1583
+ if self.num_labels == 1:
1584
+ self.config.problem_type = "regression"
1585
+ elif self.num_labels > 1 and (labels.dtype in (torch.long, torch.int)):
1586
+ self.config.problem_type = "single_label_classification"
1587
+ else:
1588
+ self.config.problem_type = "multi_label_classification"
1589
+
1590
+ if self.config.problem_type == "regression":
1591
+ loss_fct = MSELoss()
1592
+ if self.num_labels == 1:
1593
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1594
+ else:
1595
+ loss = loss_fct(pooled_logits, labels)
1596
+ elif self.config.problem_type == "single_label_classification":
1597
+ loss_fct = CrossEntropyLoss()
1598
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1599
+ elif self.config.problem_type == "multi_label_classification":
1600
+ loss_fct = BCEWithLogitsLoss()
1601
+ loss = loss_fct(pooled_logits, labels)
1602
+ if not return_dict:
1603
+ output = (pooled_logits,) + transformer_outputs[1:]
1604
+ return ((loss,) + output) if loss is not None else output
1605
+
1606
+ return SequenceClassifierOutputWithPast(
1607
+ loss=loss,
1608
+ logits=pooled_logits,
1609
+ past_key_values=transformer_outputs.past_key_values,
1610
+ hidden_states=transformer_outputs.hidden_states,
1611
+ attentions=transformer_outputs.attentions,
1612
+ )
1613
+
1614
+
1615
+ # Copied from transformers.models.llama.modeling_llama.LlamaForQuestionAnswering with Llama->InternLM2
1616
+ @add_start_docstrings(
1617
+ """
1618
+ The InternLM2 Model transformer with a span classification head on top for extractive question-answering tasks like
1619
+ SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
1620
+ """,
1621
+ InternLM2_START_DOCSTRING,
1622
+ )
1623
+ class InternLM2ForQuestionAnswering(InternLM2PreTrainedModel):
1624
+ """Question Answering model for InternLM2."""
1625
+
1626
+ base_model_prefix = "transformer"
1627
+
1628
+ def __init__(self, config):
1629
+ super().__init__(config)
1630
+ self.transformer = InternLM2Model(config)
1631
+ self.qa_outputs = nn.Linear(config.hidden_size, 2)
1632
+
1633
+ # Initialize weights and apply final processing
1634
+ self.post_init()
1635
+
1636
+ def get_input_embeddings(self):
1637
+ return self.transformer.tok_embeddings
1638
+
1639
+ def set_input_embeddings(self, value):
1640
+ self.transformer.tok_embeddings = value
1641
+
1642
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1643
+ def forward(
1644
+ self,
1645
+ input_ids: Optional[torch.LongTensor] = None,
1646
+ attention_mask: Optional[torch.FloatTensor] = None,
1647
+ position_ids: Optional[torch.LongTensor] = None,
1648
+ past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
1649
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1650
+ start_positions: Optional[torch.LongTensor] = None,
1651
+ end_positions: Optional[torch.LongTensor] = None,
1652
+ output_attentions: Optional[bool] = None,
1653
+ output_hidden_states: Optional[bool] = None,
1654
+ return_dict: Optional[bool] = None,
1655
+ ) -> Union[Tuple, QuestionAnsweringModelOutput]:
1656
+ r"""
1657
+ start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1658
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
1659
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1660
+ are not taken into account for computing the loss.
1661
+ end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1662
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
1663
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1664
+ are not taken into account for computing the loss.
1665
+ """
1666
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1667
+
1668
+ outputs = self.transformer(
1669
+ input_ids,
1670
+ attention_mask=attention_mask,
1671
+ position_ids=position_ids,
1672
+ past_key_values=past_key_values,
1673
+ inputs_embeds=inputs_embeds,
1674
+ output_attentions=output_attentions,
1675
+ output_hidden_states=output_hidden_states,
1676
+ return_dict=return_dict,
1677
+ )
1678
+
1679
+ sequence_output = outputs[0]
1680
+
1681
+ logits = self.qa_outputs(sequence_output)
1682
+ start_logits, end_logits = logits.split(1, dim=-1)
1683
+ start_logits = start_logits.squeeze(-1).contiguous()
1684
+ end_logits = end_logits.squeeze(-1).contiguous()
1685
+
1686
+ total_loss = None
1687
+ if start_positions is not None and end_positions is not None:
1688
+ # If we are on multi-GPU, split add a dimension
1689
+ if len(start_positions.size()) > 1:
1690
+ start_positions = start_positions.squeeze(-1).to(start_logits.device)
1691
+ if len(end_positions.size()) > 1:
1692
+ end_positions = end_positions.squeeze(-1).to(end_logits.device)
1693
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
1694
+ ignored_index = start_logits.size(1)
1695
+ start_positions = start_positions.clamp(0, ignored_index)
1696
+ end_positions = end_positions.clamp(0, ignored_index)
1697
+
1698
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
1699
+ start_loss = loss_fct(start_logits, start_positions)
1700
+ end_loss = loss_fct(end_logits, end_positions)
1701
+ total_loss = (start_loss + end_loss) / 2
1702
+
1703
+ if not return_dict:
1704
+ output = (start_logits, end_logits) + outputs[2:]
1705
+ return ((total_loss,) + output) if total_loss is not None else output
1706
+
1707
+ return QuestionAnsweringModelOutput(
1708
+ loss=total_loss,
1709
+ start_logits=start_logits,
1710
+ end_logits=end_logits,
1711
+ hidden_states=outputs.hidden_states,
1712
+ attentions=outputs.attentions,
1713
+ )
1714
+
1715
+
1716
+ # Copied from transformers.models.llama.modeling_llama.LlamaForTokenClassification with Llama->InternLM2
1717
+ @add_start_docstrings(
1718
+ """
1719
+ The InternLM2 Model transformer with a token classification head on top (a linear layer on top of the hidden-states
1720
+ output) e.g. for Named-Entity-Recognition (NER) tasks.
1721
+ """,
1722
+ InternLM2_START_DOCSTRING,
1723
+ )
1724
+ class InternLM2ForTokenClassification(InternLM2PreTrainedModel):
1725
+ """Token classification model for InternLM2."""
1726
+
1727
+ def __init__(self, config):
1728
+ super().__init__(config)
1729
+ self.num_labels = config.num_labels
1730
+ self.model = InternLM2Model(config)
1731
+ if getattr(config, "classifier_dropout", None) is not None:
1732
+ classifier_dropout = config.classifier_dropout
1733
+ elif getattr(config, "hidden_dropout", None) is not None:
1734
+ classifier_dropout = config.hidden_dropout
1735
+ else:
1736
+ classifier_dropout = 0.1
1737
+ self.dropout = nn.Dropout(classifier_dropout)
1738
+ self.score = nn.Linear(config.hidden_size, config.num_labels)
1739
+
1740
+ # Initialize weights and apply final processing
1741
+ self.post_init()
1742
+
1743
+ def get_input_embeddings(self):
1744
+ return self.model.tok_embeddings
1745
+
1746
+ def set_input_embeddings(self, value):
1747
+ self.model.tok_embeddings = value
1748
+
1749
+ @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
1750
+ def forward(
1751
+ self,
1752
+ input_ids: torch.LongTensor = None,
1753
+ attention_mask: Optional[torch.Tensor] = None,
1754
+ position_ids: Optional[torch.LongTensor] = None,
1755
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1756
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1757
+ labels: Optional[torch.LongTensor] = None,
1758
+ use_cache: Optional[bool] = None,
1759
+ output_attentions: Optional[bool] = None,
1760
+ output_hidden_states: Optional[bool] = None,
1761
+ return_dict: Optional[bool] = None,
1762
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1763
+ r"""
1764
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1765
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1766
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1767
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1768
+ """
1769
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1770
+
1771
+ outputs = self.model(
1772
+ input_ids,
1773
+ attention_mask=attention_mask,
1774
+ position_ids=position_ids,
1775
+ past_key_values=past_key_values,
1776
+ inputs_embeds=inputs_embeds,
1777
+ use_cache=use_cache,
1778
+ output_attentions=output_attentions,
1779
+ output_hidden_states=output_hidden_states,
1780
+ return_dict=return_dict,
1781
+ )
1782
+ sequence_output = outputs[0]
1783
+ sequence_output = self.dropout(sequence_output)
1784
+ logits = self.score(sequence_output)
1785
+
1786
+ loss = None
1787
+ if labels is not None:
1788
+ loss_fct = CrossEntropyLoss()
1789
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
1790
+
1791
+ if not return_dict:
1792
+ output = (logits,) + outputs[2:]
1793
+ return ((loss,) + output) if loss is not None else output
1794
+
1795
+ return TokenClassifierOutput(
1796
+ loss=loss,
1797
+ logits=logits,
1798
+ hidden_states=outputs.hidden_states,
1799
+ attentions=outputs.attentions,
1800
+ )
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tokenization_internlm2.py ADDED
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+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
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+ #
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+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+
18
+ """Tokenization classes for InternLM."""
19
+ import os
20
+ from shutil import copyfile
21
+ from typing import Any, Dict, List, Optional, Tuple
22
+
23
+ import sentencepiece as spm
24
+ from transformers.tokenization_utils import PreTrainedTokenizer
25
+ from transformers.utils import logging
26
+
27
+ logger = logging.get_logger(__name__)
28
+
29
+ VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
30
+
31
+ PRETRAINED_VOCAB_FILES_MAP = {}
32
+
33
+
34
+ # Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer
35
+ class InternLM2Tokenizer(PreTrainedTokenizer):
36
+ """
37
+ Construct a InternLM2 tokenizer. Based on byte-level Byte-Pair-Encoding.
38
+
39
+ Args:
40
+ vocab_file (`str`):
41
+ Path to the vocabulary file.
42
+ """
43
+
44
+ vocab_files_names = VOCAB_FILES_NAMES
45
+ pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
46
+ model_input_names = ["input_ids", "attention_mask"]
47
+ _auto_class = "AutoTokenizer"
48
+
49
+ def __init__(
50
+ self,
51
+ vocab_file,
52
+ unk_token="<unk>",
53
+ bos_token="<s>",
54
+ eos_token="</s>",
55
+ pad_token="</s>",
56
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
57
+ add_bos_token=True,
58
+ add_eos_token=False,
59
+ decode_with_prefix_space=False,
60
+ clean_up_tokenization_spaces=False,
61
+ **kwargs,
62
+ ):
63
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
64
+ self.vocab_file = vocab_file
65
+ self.add_bos_token = add_bos_token
66
+ self.add_eos_token = add_eos_token
67
+ self.decode_with_prefix_space = decode_with_prefix_space
68
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
69
+ self.sp_model.Load(vocab_file)
70
+ self._no_prefix_space_tokens = None
71
+ super().__init__(
72
+ bos_token=bos_token,
73
+ eos_token=eos_token,
74
+ unk_token=unk_token,
75
+ pad_token=pad_token,
76
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
77
+ **kwargs,
78
+ )
79
+
80
+ @property
81
+ def no_prefix_space_tokens(self):
82
+ if self._no_prefix_space_tokens is None:
83
+ vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
84
+ self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
85
+ return self._no_prefix_space_tokens
86
+
87
+ @property
88
+ def vocab_size(self):
89
+ """Returns vocab size"""
90
+ return self.sp_model.get_piece_size()
91
+
92
+ @property
93
+ def bos_token_id(self) -> Optional[int]:
94
+ return self.sp_model.bos_id()
95
+
96
+ @property
97
+ def eos_token_id(self) -> Optional[int]:
98
+ return self.sp_model.eos_id()
99
+
100
+ def get_vocab(self):
101
+ """Returns vocab as a dict"""
102
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
103
+ vocab.update(self.added_tokens_encoder)
104
+ return vocab
105
+
106
+ def _tokenize(self, text):
107
+ """Returns a tokenized string."""
108
+ return self.sp_model.encode(text, out_type=str)
109
+
110
+ def _convert_token_to_id(self, token):
111
+ """Converts a token (str) in an id using the vocab."""
112
+ return self.sp_model.piece_to_id(token)
113
+
114
+ def _convert_id_to_token(self, index):
115
+ """Converts an index (integer) in a token (str) using the vocab."""
116
+ token = self.sp_model.IdToPiece(index)
117
+ return token
118
+
119
+ def _maybe_add_prefix_space(self, tokens, decoded):
120
+ if tokens and tokens[0] not in self.no_prefix_space_tokens:
121
+ return " " + decoded
122
+ else:
123
+ return decoded
124
+
125
+ def convert_tokens_to_string(self, tokens):
126
+ """Converts a sequence of tokens (string) in a single string."""
127
+ current_sub_tokens = []
128
+ out_string = ""
129
+ prev_is_special = False
130
+ for token in tokens:
131
+ # make sure that special tokens are not decoded using sentencepiece model
132
+ if token in self.all_special_tokens:
133
+ if not prev_is_special:
134
+ out_string += " "
135
+ out_string += self.sp_model.decode(current_sub_tokens) + token
136
+ prev_is_special = True
137
+ current_sub_tokens = []
138
+ else:
139
+ current_sub_tokens.append(token)
140
+ prev_is_special = False
141
+ out_string += self.sp_model.decode(current_sub_tokens)
142
+ out_string = self.clean_up_tokenization(out_string)
143
+ out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
144
+ return out_string[1:]
145
+
146
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
147
+ """
148
+ Save the vocabulary and special tokens file to a directory.
149
+
150
+ Args:
151
+ save_directory (`str`):
152
+ The directory in which to save the vocabulary.
153
+
154
+ Returns:
155
+ `Tuple(str)`: Paths to the files saved.
156
+ """
157
+ if not os.path.isdir(save_directory):
158
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
159
+ return
160
+ out_vocab_file = os.path.join(
161
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
162
+ )
163
+
164
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
165
+ copyfile(self.vocab_file, out_vocab_file)
166
+ elif not os.path.isfile(self.vocab_file):
167
+ with open(out_vocab_file, "wb") as fi:
168
+ content_spiece_model = self.sp_model.serialized_model_proto()
169
+ fi.write(content_spiece_model)
170
+
171
+ return (out_vocab_file,)
172
+
173
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
174
+ if self.add_bos_token:
175
+ bos_token_ids = [self.bos_token_id]
176
+ else:
177
+ bos_token_ids = []
178
+
179
+ output = bos_token_ids + token_ids_0
180
+
181
+ if token_ids_1 is not None:
182
+ output = output + token_ids_1
183
+
184
+ if self.add_eos_token:
185
+ output = output + [self.eos_token_id]
186
+
187
+ return output
188
+
189
+ def get_special_tokens_mask(
190
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
191
+ ) -> List[int]:
192
+ """
193
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
194
+ special tokens using the tokenizer `prepare_for_model` method.
195
+
196
+ Args:
197
+ token_ids_0 (`List[int]`):
198
+ List of IDs.
199
+ token_ids_1 (`List[int]`, *optional*):
200
+ Optional second list of IDs for sequence pairs.
201
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
202
+ Whether or not the token list is already formatted with special tokens for the model.
203
+
204
+ Returns:
205
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
206
+ """
207
+ if already_has_special_tokens:
208
+ return super().get_special_tokens_mask(
209
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
210
+ )
211
+
212
+ if token_ids_1 is None:
213
+ return [1] + ([0] * len(token_ids_0)) + [1]
214
+ return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
215
+
216
+ def create_token_type_ids_from_sequences(
217
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
218
+ ) -> List[int]:
219
+ """
220
+ Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
221
+ use of token type ids, therefore a list of zeros is returned.
222
+
223
+ Args:
224
+ token_ids_0 (`List[int]`):
225
+ List of IDs.
226
+ token_ids_1 (`List[int]`, *optional*):
227
+ Optional second list of IDs for sequence pairs.
228
+
229
+ Returns:
230
+ `List[int]`: List of zeros.
231
+ """
232
+ eos = [self.eos_token_id]
233
+
234
+ if token_ids_1 is None:
235
+ return len(token_ids_0 + eos) * [0]
236
+ return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
tokenization_internlm2_fast.py ADDED
@@ -0,0 +1,214 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
5
+ #
6
+ # Licensed under the Apache License, Version 2.0 (the "License");
7
+ # you may not use this file except in compliance with the License.
8
+ # You may obtain a copy of the License at
9
+ #
10
+ # http://www.apache.org/licenses/LICENSE-2.0
11
+ #
12
+ # Unless required by applicable law or agreed to in writing, software
13
+ # distributed under the License is distributed on an "AS IS" BASIS,
14
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
15
+ # See the License for the specific language governing permissions and
16
+ # limitations under the License.
17
+
18
+ """Tokenization Fast class for InternLM."""
19
+ import os
20
+ from shutil import copyfile
21
+ from typing import Any, Dict, Optional, Tuple
22
+
23
+ from tokenizers import processors, decoders, Tokenizer, normalizers
24
+ from tokenizers.models import BPE
25
+
26
+ from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
27
+ from transformers.utils import logging
28
+
29
+ from transformers.convert_slow_tokenizer import (
30
+ SLOW_TO_FAST_CONVERTERS,
31
+ SpmConverter,
32
+ SentencePieceExtractor,
33
+ )
34
+
35
+ from .tokenization_internlm2 import InternLM2Tokenizer
36
+
37
+ logger = logging.get_logger(__name__)
38
+
39
+ VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
40
+
41
+ # Modified from transformers.convert_slow_tokenizer.LlamaConverter
42
+ class InternLM2Converter(SpmConverter):
43
+ handle_byte_fallback = True
44
+
45
+ def vocab(self, proto):
46
+ vocab = [
47
+ ("<unk>", 0.0),
48
+ ("<s>", 0.0),
49
+ ("</s>", 0.0),
50
+ ]
51
+ vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
52
+ return vocab
53
+
54
+ def unk_id(self, proto):
55
+ unk_id = 0
56
+ return unk_id
57
+
58
+ def decoder(self, replacement, add_prefix_space):
59
+ decoders_sequence = [
60
+ decoders.Replace("▁", " "),
61
+ decoders.ByteFallback(),
62
+ decoders.Fuse(),
63
+ ]
64
+ if self.proto.normalizer_spec.add_dummy_prefix:
65
+ decoders_sequence.append(decoders.Strip(content=" ", left=1))
66
+ return decoders.Sequence(decoders_sequence)
67
+
68
+ def tokenizer(self, proto):
69
+ model_type = proto.trainer_spec.model_type
70
+ vocab_scores = self.vocab(proto)
71
+ # special tokens
72
+ added_tokens = self.original_tokenizer.added_tokens_decoder
73
+ for i in range(len(vocab_scores)):
74
+ piece, score = vocab_scores[i]
75
+ if i in added_tokens:
76
+ vocab_scores[i] = (added_tokens[i].content, score)
77
+ if model_type == 1:
78
+ raise RuntimeError("InternLM2 is supposed to be a BPE model!")
79
+
80
+ elif model_type == 2:
81
+ _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
82
+ bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
83
+ tokenizer = Tokenizer(
84
+ BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
85
+ )
86
+ tokenizer.add_special_tokens(
87
+ [ added_token for index, added_token in added_tokens.items()]
88
+ )
89
+ else:
90
+ raise Exception(
91
+ "You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
92
+ )
93
+
94
+ return tokenizer
95
+
96
+ def normalizer(self, proto):
97
+ normalizers_list = []
98
+ if proto.normalizer_spec.add_dummy_prefix:
99
+ normalizers_list.append(normalizers.Prepend(prepend="▁"))
100
+ normalizers_list.append(normalizers.Replace(pattern=" ", content="▁"))
101
+ return normalizers.Sequence(normalizers_list)
102
+
103
+ def pre_tokenizer(self, replacement, add_prefix_space):
104
+ return None
105
+
106
+ SLOW_TO_FAST_CONVERTERS["InternLM2Tokenizer"] = InternLM2Converter
107
+
108
+
109
+ # Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
110
+ class InternLM2TokenizerFast(PreTrainedTokenizerFast):
111
+ vocab_files_names = VOCAB_FILES_NAMES
112
+ slow_tokenizer_class = InternLM2Tokenizer
113
+ padding_side = "left"
114
+ model_input_names = ["input_ids", "attention_mask"]
115
+ _auto_class = "AutoTokenizer"
116
+
117
+ def __init__(
118
+ self,
119
+ vocab_file,
120
+ unk_token="<unk>",
121
+ bos_token="<s>",
122
+ eos_token="</s>",
123
+ pad_token="</s>",
124
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
125
+ add_bos_token=True,
126
+ add_eos_token=False,
127
+ decode_with_prefix_space=False,
128
+ clean_up_tokenization_spaces=False,
129
+ **kwargs,
130
+ ):
131
+ super().__init__(
132
+ vocab_file=vocab_file,
133
+ unk_token=unk_token,
134
+ bos_token=bos_token,
135
+ eos_token=eos_token,
136
+ pad_token=pad_token,
137
+ sp_model_kwargs=sp_model_kwargs,
138
+ add_bos_token=add_bos_token,
139
+ add_eos_token=add_eos_token,
140
+ decode_with_prefix_space=decode_with_prefix_space,
141
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
142
+ **kwargs,
143
+ )
144
+ self._add_bos_token = add_bos_token
145
+ self._add_eos_token = add_eos_token
146
+ self.update_post_processor()
147
+ self.vocab_file = vocab_file
148
+
149
+ @property
150
+ def can_save_slow_tokenizer(self) -> bool:
151
+ return os.path.isfile(self.vocab_file) if self.vocab_file else False
152
+
153
+ def update_post_processor(self):
154
+ """
155
+ Updates the underlying post processor with the current `bos_token` and `eos_token`.
156
+ """
157
+ bos = self.bos_token
158
+ bos_token_id = self.bos_token_id
159
+ if bos is None and self.add_bos_token:
160
+ raise ValueError("add_bos_token = True but bos_token = None")
161
+
162
+ eos = self.eos_token
163
+ eos_token_id = self.eos_token_id
164
+ if eos is None and self.add_eos_token:
165
+ raise ValueError("add_eos_token = True but eos_token = None")
166
+
167
+ single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
168
+ pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
169
+
170
+ special_tokens = []
171
+ if self.add_bos_token:
172
+ special_tokens.append((bos, bos_token_id))
173
+ if self.add_eos_token:
174
+ special_tokens.append((eos, eos_token_id))
175
+ self._tokenizer.post_processor = processors.TemplateProcessing(
176
+ single=single, pair=pair, special_tokens=special_tokens
177
+ )
178
+
179
+ @property
180
+ def add_eos_token(self):
181
+ return self._add_eos_token
182
+
183
+ @property
184
+ def add_bos_token(self):
185
+ return self._add_bos_token
186
+
187
+ @add_eos_token.setter
188
+ def add_eos_token(self, value):
189
+ self._add_eos_token = value
190
+ self.update_post_processor()
191
+
192
+ @add_bos_token.setter
193
+ def add_bos_token(self, value):
194
+ self._add_bos_token = value
195
+ self.update_post_processor()
196
+
197
+ def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
198
+ if not self.can_save_slow_tokenizer:
199
+ raise ValueError(
200
+ "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
201
+ "tokenizer."
202
+ )
203
+
204
+ if not os.path.isdir(save_directory):
205
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
206
+ return
207
+ out_vocab_file = os.path.join(
208
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
209
+ )
210
+
211
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
212
+ copyfile(self.vocab_file, out_vocab_file)
213
+
214
+ return (out_vocab_file,)
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:f868398fc4e05ee1e8aeba95ddf18ddcc45b8bce55d5093bead5bbf80429b48b
3
+ size 1477754
tokenizer_config.json ADDED
@@ -0,0 +1,102 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_bos_token": true,
3
+ "add_eos_token": false,
4
+ "added_tokens_decoder": {
5
+ "0": {
6
+ "content": "<unk>",
7
+ "lstrip": false,
8
+ "normalized": false,
9
+ "rstrip": false,
10
+ "single_word": false,
11
+ "special": true
12
+ },
13
+ "1": {
14
+ "content": "<s>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false,
19
+ "special": true
20
+ },
21
+ "2": {
22
+ "content": "</s>",
23
+ "lstrip": false,
24
+ "normalized": false,
25
+ "rstrip": false,
26
+ "single_word": false,
27
+ "special": true
28
+ },
29
+ "92538": {
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+ "content": "<|plugin|>",
31
+ "lstrip": false,
32
+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
36
+ },
37
+ "92539": {
38
+ "content": "<|interpreter|>",
39
+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "92540": {
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+ "lstrip": false,
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+ "rstrip": false,
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+ },
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+ },
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+ "92542": {
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+ "rstrip": false,
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+ "special": true
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+ },
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+ "92543": {
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+ "content": "<|im_start|>",
71
+ "lstrip": false,
72
+ "normalized": false,
73
+ "rstrip": false,
74
+ "single_word": false,
75
+ "special": true
76
+ }
77
+ },
78
+ "additional_special_tokens": [
79
+ "<|im_start|>",
80
+ "<|im_end|>",
81
+ "<|action_start|>",
82
+ "<|action_end|>",
83
+ "<|interpreter|>",
84
+ "<|plugin|>"
85
+ ],
86
+ "auto_map": {
87
+ "AutoTokenizer": [
88
+ "tokenization_internlm2.InternLM2Tokenizer",
89
+ "tokenization_internlm2_fast.InternLM2TokenizerFast"
90
+ ]
91
+ },
92
+ "bos_token": "<s>",
93
+ "chat_template": "{{ bos_token }}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
94
+ "clean_up_tokenization_spaces": false,
95
+ "decode_with_prefix_space": false,
96
+ "eos_token": "</s>",
97
+ "model_max_length": 1000000000000000019884624838656,
98
+ "pad_token": "</s>",
99
+ "sp_model_kwargs": null,
100
+ "tokenizer_class": "InternLM2Tokenizer",
101
+ "unk_token": "<unk>"
102
+ }