codegen2-3_7B_P-int4-ov / configuration_codegen.py
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# coding=utf-8
# Copyright 2022 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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""" CodeGen model configuration"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from transformers import PreTrainedTokenizer, TensorType, is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxConfigWithPast, PatchingSpec
from transformers.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.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size).
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 CodeGenModel, CodeGenConfig
>>> # Initializing a CodeGen 6B configuration
>>> configuration = CodeGenConfig()
>>> # Initializing a model 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,
scale_attn_weights=True,
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.scale_attn_weights = scale_attn_weights
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
@property
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
@property
def num_layers(self) -> int:
return self._config.n_layer
@property
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
@property
def default_onnx_opset(self) -> int:
return 13