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configuration_gpt_neo.py
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configuration_gpt_neo.py
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. 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.
""" GPT Neo model configuration"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
logger = logging.get_logger(__name__)
GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class GPTNeoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTNeoModel`]. It is used to instantiate a GPT
Neo 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 GPTNeo
[EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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 50257):
Vocabulary size of the GPT Neo model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPTNeoModel`]. Vocabulary size of the model. Defines the different
tokens that can be represented by the *inputs_ids* passed to the forward method of [`GPTNeoModel`].
attention_types (`List`, *optional*, defaults to `[[["global", "local"], 12]]`):
The type of attention for each layer in a `List` of the following format `[[["attention_type"],
num_layerss]]` e.g. for a 24 layer model `[[["global"], 24]]` or `[[["global", "local"], 12]]` Choose the
value of `attention_type` from `["global", "local"]`
hidden_size (`int`, *optional*, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
num_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 8192):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu_new"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
embed_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
max_position_embeddings (`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).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`GPTNeoModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
Example:
```python
>>> from transformers import GPTNeoConfig, GPTNeoModel
>>> # Initializing a GPTNeo EleutherAI/gpt-neo-1.3B style configuration
>>> configuration = GPTNeoConfig()
>>> # Initializing a model (with random weights) from the EleutherAI/gpt-neo-1.3B style configuration
>>> model = GPTNeoModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "gpt_neo"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__(
self,
vocab_size=50257,
max_position_embeddings=2048,
hidden_size=2048,
num_layers=24,
attention_types=[[["global", "local"], 12]],
num_heads=16,
intermediate_size=None,
window_size=256,
activation_function="gelu_new",
resid_dropout=0.0,
embed_dropout=0.0,
attention_dropout=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_heads = num_heads
self.intermediate_size = intermediate_size
self.window_size = window_size
self.activation_function = activation_function
self.resid_dropout = resid_dropout
self.embed_dropout = embed_dropout
self.attention_dropout = attention_dropout
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
self.attention_types = attention_types
self.attention_layers = self.expand_attention_types_params(attention_types)
if len(self.attention_layers) != self.num_layers:
raise ValueError(
"Configuration for convolutional module is incorrect. "
"It is required that `len(config.attention_layers)` == `config.num_layers` "
f"but is `len(config.attention_layers) = {len(self.attention_layers)}`, "
f"`config.num_layers = {self.num_layers}`. "
"`config.attention_layers` is prepared using `config.attention_types`. "
"Please verify the value of `config.attention_types` argument."
)
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@staticmethod
def expand_attention_types_params(attention_types):
attentions = []
for item in attention_types:
for _ in range(item[1]):
attentions.extend(item[0])
return attentions
def custom_unfold(input, dimension, size, step):
"""Custom torch.Tensor.unfold implementation to enable the export to ONNX."""
import torch
shape = input.size()
rank = len(shape)
sizedim = shape[dimension]
low_indices = torch.arange(0, sizedim, step)
min_length = torch.div(sizedim - size, step, rounding_mode="floor") + 1
indices = torch.arange(size) + low_indices[:min_length][:, None]
s = [slice(None)] * rank
s[dimension] = indices
sliced = input[s]
perm = list(range(0, rank + 1))
perm.append(perm.pop(dimension + 1))
return sliced.permute(perm)
def custom_get_block_length_and_num_blocks(seq_length, window_size):
"""
Custom implementation for GPTNeoAttentionMixin._get_block_length_and_num_blocks to enable the export to ONNX as
original implementation uses Python variables and control flow.
"""
import torch
candidates = torch.arange(1, window_size)
remainders = torch.remainder(seq_length, candidates)
divisor_indices = remainders == 0
divisors = candidates[divisor_indices]
largest_divisor = torch.max(divisors)
return largest_divisor, torch.div(seq_length, largest_divisor, rounding_mode="floor")
class GPTNeoOnnxConfig(OnnxConfigWithPast):
@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_attention_heads(self) -> int:
return self._config.num_heads
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