sylwia-kuros commited on
Commit
873925e
1 Parent(s): f6cb413

Upload model files

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+ }
configuration_codegen.py ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ CodeGen model configuration"""
16
+ from collections import OrderedDict
17
+ from typing import Any, List, Mapping, Optional
18
+
19
+ from transformers import PreTrainedTokenizer, TensorType, is_torch_available
20
+ from transformers.configuration_utils import PretrainedConfig
21
+ from transformers.onnx import OnnxConfigWithPast, PatchingSpec
22
+ from transformers.utils import logging
23
+
24
+
25
+ logger = logging.get_logger(__name__)
26
+
27
+
28
+ CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
29
+ "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
30
+ "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
31
+ "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
32
+ "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
33
+ "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
34
+ "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
35
+ "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
36
+ "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
37
+ "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
38
+ "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
39
+ "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
40
+ "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
41
+ }
42
+
43
+
44
+ class CodeGenConfig(PretrainedConfig):
45
+ r"""
46
+ This is the configuration class to store the configuration of a [`CodeGenModel`]. It is used to instantiate a
47
+ CodeGen model according to the specified arguments, defining the model architecture. Instantiating a configuration
48
+ with the defaults will yield a similar configuration to that of the CodeGen
49
+ [Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono) architecture. Configuration objects
50
+ inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from
51
+ [`PretrainedConfig`] for more information.
52
+
53
+ Args:
54
+ vocab_size (`int`, *optional*, defaults to 50400):
55
+ Vocabulary size of the CodeGen model. Defines the number of different tokens that can be represented by the
56
+ `inputs_ids` passed when calling [`CodeGenModel`].
57
+ n_positions (`int`, *optional*, defaults to 2048):
58
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
59
+ just in case (e.g., 512 or 1024 or 2048).
60
+ n_embd (`int`, *optional*, defaults to 4096):
61
+ Dimensionality of the embeddings and hidden states.
62
+ n_layer (`int`, *optional*, defaults to 28):
63
+ Number of hidden layers in the Transformer encoder.
64
+ n_head (`int`, *optional*, defaults to 16):
65
+ Number of attention heads for each attention layer in the Transformer encoder.
66
+ rotary_dim (`int`, *optional*, defaults to 64):
67
+ Number of dimensions in the embedding that Rotary Position Embedding is applied to.
68
+ n_inner (`int`, *optional*, defaults to None):
69
+ Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
70
+ activation_function (`str`, *optional*, defaults to `"gelu_new"`):
71
+ Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
72
+ resid_pdrop (`float`, *optional*, defaults to 0.1):
73
+ The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
74
+ embd_pdrop (`int`, *optional*, defaults to 0.1):
75
+ The dropout ratio for the embeddings.
76
+ attn_pdrop (`float`, *optional*, defaults to 0.1):
77
+ The dropout ratio for the attention.
78
+ layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
79
+ The epsilon to use in the layer normalization layers.
80
+ initializer_range (`float`, *optional*, defaults to 0.02):
81
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
82
+ scale_attn_weights (`bool`, *optional*, defaults to `True`):
83
+ Scale attention weights by dividing by sqrt(hidden_size).
84
+ use_cache (`bool`, *optional*, defaults to `True`):
85
+ Whether or not the model should return the last key/values attentions (not used by all models).
86
+
87
+ Example:
88
+
89
+ ```python
90
+ >>> from transformers import CodeGenModel, CodeGenConfig
91
+
92
+ >>> # Initializing a CodeGen 6B configuration
93
+ >>> configuration = CodeGenConfig()
94
+
95
+ >>> # Initializing a model from the configuration
96
+ >>> model = CodeGenModel(configuration)
97
+
98
+ >>> # Accessing the model configuration
99
+ >>> configuration = model.config
100
+ ```"""
101
+ model_type = "codegen"
102
+ attribute_map = {
103
+ "max_position_embeddings": "n_positions",
104
+ "hidden_size": "n_embd",
105
+ "num_attention_heads": "n_head",
106
+ "num_hidden_layers": "n_layer",
107
+ }
108
+
109
+ def __init__(
110
+ self,
111
+ vocab_size=50400,
112
+ n_positions=2048,
113
+ n_ctx=2048,
114
+ n_embd=4096,
115
+ n_layer=28,
116
+ n_head=16,
117
+ rotary_dim=64,
118
+ n_inner=None,
119
+ activation_function="gelu_new",
120
+ resid_pdrop=0.0,
121
+ embd_pdrop=0.0,
122
+ attn_pdrop=0.0,
123
+ layer_norm_epsilon=1e-5,
124
+ initializer_range=0.02,
125
+ scale_attn_weights=True,
126
+ use_cache=True,
127
+ bos_token_id=50256,
128
+ eos_token_id=50256,
129
+ tie_word_embeddings=False,
130
+ **kwargs
131
+ ):
132
+ self.vocab_size = vocab_size
133
+ self.n_ctx = n_ctx
134
+ self.n_positions = n_positions
135
+ self.n_embd = n_embd
136
+ self.n_layer = n_layer
137
+ self.n_head = n_head
138
+ self.n_inner = n_inner
139
+ self.rotary_dim = rotary_dim
140
+ self.activation_function = activation_function
141
+ self.resid_pdrop = resid_pdrop
142
+ self.embd_pdrop = embd_pdrop
143
+ self.attn_pdrop = attn_pdrop
144
+ self.layer_norm_epsilon = layer_norm_epsilon
145
+ self.initializer_range = initializer_range
146
+ self.scale_attn_weights = scale_attn_weights
147
+ self.use_cache = use_cache
148
+
149
+ self.bos_token_id = bos_token_id
150
+ self.eos_token_id = eos_token_id
151
+
152
+ super().__init__(
153
+ bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
154
+ )
155
+
156
+
157
+ # Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig
158
+ class CodeGenOnnxConfig(OnnxConfigWithPast):
159
+ def __init__(
160
+ self,
161
+ config: PretrainedConfig,
162
+ task: str = "default",
163
+ patching_specs: List[PatchingSpec] = None,
164
+ use_past: bool = False,
165
+ ):
166
+ super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
167
+ if not getattr(self._config, "pad_token_id", None):
168
+ # TODO: how to do that better?
169
+ self._config.pad_token_id = 0
170
+
171
+ @property
172
+ def inputs(self) -> Mapping[str, Mapping[int, str]]:
173
+ common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
174
+ if self.use_past:
175
+ self.fill_with_past_key_values_(common_inputs, direction="inputs")
176
+ common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
177
+ else:
178
+ common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
179
+
180
+ return common_inputs
181
+
182
+ @property
183
+ def num_layers(self) -> int:
184
+ return self._config.n_layer
185
+
186
+ @property
187
+ def num_attention_heads(self) -> int:
188
+ return self._config.n_head
189
+
190
+ def generate_dummy_inputs(
191
+ self,
192
+ tokenizer: PreTrainedTokenizer,
193
+ batch_size: int = -1,
194
+ seq_length: int = -1,
195
+ is_pair: bool = False,
196
+ framework: Optional[TensorType] = None,
197
+ ) -> Mapping[str, Any]:
198
+ common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
199
+ tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
200
+ )
201
+
202
+ # We need to order the input in the way they appears in the forward()
203
+ ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
204
+
205
+ # Need to add the past_keys
206
+ if self.use_past:
207
+ if not is_torch_available():
208
+ raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
209
+ else:
210
+ import torch
211
+
212
+ batch, seqlen = common_inputs["input_ids"].shape
213
+ # Not using the same length for past_key_values
214
+ past_key_values_length = seqlen + 2
215
+ past_shape = (
216
+ batch,
217
+ self.num_attention_heads,
218
+ past_key_values_length,
219
+ self._config.hidden_size // self.num_attention_heads,
220
+ )
221
+ ordered_inputs["past_key_values"] = [
222
+ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
223
+ ]
224
+
225
+ ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
226
+ if self.use_past:
227
+ mask_dtype = ordered_inputs["attention_mask"].dtype
228
+ ordered_inputs["attention_mask"] = torch.cat(
229
+ [ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
230
+ )
231
+
232
+ return ordered_inputs
233
+
234
+ @property
235
+ def default_onnx_opset(self) -> int:
236
+ return 13
merges.txt ADDED
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+ "bos_token": {
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+ "content": "<|endoftext|>",
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+ "lstrip": false,
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+ },
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+ }
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+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
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vocab.json ADDED
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