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# coding=utf-8 | |
# Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" CodeGen model configuration""" | |
from collections import OrderedDict | |
from typing import Any, List, Mapping, Optional | |
from ... import PreTrainedTokenizer, TensorType, is_torch_available | |
from ...configuration_utils import PretrainedConfig | |
from ...onnx import OnnxConfigWithPast, PatchingSpec | |
from ...utils import logging | |
logger = logging.get_logger(__name__) | |
CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
"Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json", | |
"Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json", | |
"Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json", | |
"Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json", | |
"Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json", | |
"Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json", | |
"Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json", | |
"Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json", | |
"Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json", | |
"Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json", | |
"Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json", | |
"Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json", | |
} | |
class CodeGenConfig(PretrainedConfig): | |
r""" | |
This is the configuration class to store the configuration of a [`CodeGenModel`]. It is used to instantiate a | |
CodeGen model according to the specified arguments, defining the model architecture. Instantiating a configuration | |
with the defaults will yield a similar configuration to that of the CodeGen | |
[Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono) architecture. Configuration objects | |
inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from | |
[`PretrainedConfig`] for more information. | |
Args: | |
vocab_size (`int`, *optional*, defaults to 50400): | |
Vocabulary size of the CodeGen model. Defines the number of different tokens that can be represented by the | |
`inputs_ids` passed when calling [`CodeGenModel`]. | |
n_positions (`int`, *optional*, defaults to 2048): | |
The maximum sequence length that this model might ever be used with. Typically set this to something large | |
just in case (e.g., 512 or 1024 or 2048). | |
n_embd (`int`, *optional*, defaults to 4096): | |
Dimensionality of the embeddings and hidden states. | |
n_layer (`int`, *optional*, defaults to 28): | |
Number of hidden layers in the Transformer encoder. | |
n_head (`int`, *optional*, defaults to 16): | |
Number of attention heads for each attention layer in the Transformer encoder. | |
rotary_dim (`int`, *optional*, defaults to 64): | |
Number of dimensions in the embedding that Rotary Position Embedding is applied to. | |
n_inner (`int`, *optional*, defaults to None): | |
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd | |
activation_function (`str`, *optional*, defaults to `"gelu_new"`): | |
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. | |
resid_pdrop (`float`, *optional*, defaults to 0.1): | |
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
embd_pdrop (`int`, *optional*, defaults to 0.1): | |
The dropout ratio for the embeddings. | |
attn_pdrop (`float`, *optional*, defaults to 0.1): | |
The dropout ratio for the attention. | |
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): | |
The epsilon to use in the layer normalization layers. | |
initializer_range (`float`, *optional*, defaults to 0.02): | |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
use_cache (`bool`, *optional*, defaults to `True`): | |
Whether or not the model should return the last key/values attentions (not used by all models). | |
Example: | |
```python | |
>>> from transformers import CodeGenConfig, CodeGenModel | |
>>> # Initializing a CodeGen 6B configuration | |
>>> configuration = CodeGenConfig() | |
>>> # Initializing a model (with random weights) from the configuration | |
>>> model = CodeGenModel(configuration) | |
>>> # Accessing the model configuration | |
>>> configuration = model.config | |
```""" | |
model_type = "codegen" | |
attribute_map = { | |
"max_position_embeddings": "n_positions", | |
"hidden_size": "n_embd", | |
"num_attention_heads": "n_head", | |
"num_hidden_layers": "n_layer", | |
} | |
def __init__( | |
self, | |
vocab_size=50400, | |
n_positions=2048, | |
n_ctx=2048, | |
n_embd=4096, | |
n_layer=28, | |
n_head=16, | |
rotary_dim=64, | |
n_inner=None, | |
activation_function="gelu_new", | |
resid_pdrop=0.0, | |
embd_pdrop=0.0, | |
attn_pdrop=0.0, | |
layer_norm_epsilon=1e-5, | |
initializer_range=0.02, | |
use_cache=True, | |
bos_token_id=50256, | |
eos_token_id=50256, | |
tie_word_embeddings=False, | |
**kwargs, | |
): | |
self.vocab_size = vocab_size | |
self.n_ctx = n_ctx | |
self.n_positions = n_positions | |
self.n_embd = n_embd | |
self.n_layer = n_layer | |
self.n_head = n_head | |
self.n_inner = n_inner | |
self.rotary_dim = rotary_dim | |
self.activation_function = activation_function | |
self.resid_pdrop = resid_pdrop | |
self.embd_pdrop = embd_pdrop | |
self.attn_pdrop = attn_pdrop | |
self.layer_norm_epsilon = layer_norm_epsilon | |
self.initializer_range = initializer_range | |
self.use_cache = use_cache | |
self.bos_token_id = bos_token_id | |
self.eos_token_id = eos_token_id | |
super().__init__( | |
bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs | |
) | |
# Copied from transformers.models.gpt2.configuration_gpt2.GPT2OnnxConfig | |
class CodeGenOnnxConfig(OnnxConfigWithPast): | |
def __init__( | |
self, | |
config: PretrainedConfig, | |
task: str = "default", | |
patching_specs: List[PatchingSpec] = None, | |
use_past: bool = False, | |
): | |
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past) | |
if not getattr(self._config, "pad_token_id", None): | |
# TODO: how to do that better? | |
self._config.pad_token_id = 0 | |
def inputs(self) -> Mapping[str, Mapping[int, str]]: | |
common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}) | |
if self.use_past: | |
self.fill_with_past_key_values_(common_inputs, direction="inputs") | |
common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"} | |
else: | |
common_inputs["attention_mask"] = {0: "batch", 1: "sequence"} | |
return common_inputs | |
def num_layers(self) -> int: | |
return self._config.n_layer | |
def num_attention_heads(self) -> int: | |
return self._config.n_head | |
def generate_dummy_inputs( | |
self, | |
tokenizer: PreTrainedTokenizer, | |
batch_size: int = -1, | |
seq_length: int = -1, | |
is_pair: bool = False, | |
framework: Optional[TensorType] = None, | |
) -> Mapping[str, Any]: | |
common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs( | |
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework | |
) | |
# We need to order the input in the way they appears in the forward() | |
ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]}) | |
# Need to add the past_keys | |
if self.use_past: | |
if not is_torch_available(): | |
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.") | |
else: | |
import torch | |
batch, seqlen = common_inputs["input_ids"].shape | |
# Not using the same length for past_key_values | |
past_key_values_length = seqlen + 2 | |
past_shape = ( | |
batch, | |
self.num_attention_heads, | |
past_key_values_length, | |
self._config.hidden_size // self.num_attention_heads, | |
) | |
ordered_inputs["past_key_values"] = [ | |
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers) | |
] | |
ordered_inputs["attention_mask"] = common_inputs["attention_mask"] | |
if self.use_past: | |
mask_dtype = ordered_inputs["attention_mask"].dtype | |
ordered_inputs["attention_mask"] = torch.cat( | |
[ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1 | |
) | |
return ordered_inputs | |
def default_onnx_opset(self) -> int: | |
return 13 | |