testing modeling_gpt2.py overwrite

config.json

@@ -1,38 +1,42 @@
 {
- "_num_labels": 1,
- "activation_function": "gelu_new",
- "architectures": [
- "GPT2LMHeadModel"
- ],
- "attn_pdrop": 0.1,
- "bos_token_id": 50256,
- "embd_pdrop": 0.1,
- "eos_token_id": 50256,
- "id2label": {
- "0": "LABEL_0"
- },
- "initializer_range": 0.02,
- "label2id": {
- "LABEL_0": 0
- },
- "layer_norm_epsilon": 1e-05,
- "model_type": "gpt2",
- "n_ctx": 1024,
- "n_embd": 768,
- "n_head": 12,
- "n_layer": 6,
- "n_positions": 1024,
- "resid_pdrop": 0.1,
- "summary_activation": null,
- "summary_first_dropout": 0.1,
- "summary_proj_to_labels": true,
- "summary_type": "cls_index",
- "summary_use_proj": true,
- "task_specific_params": {
- "text-generation": {
- "do_sample": true,
- "max_length": 50
- }
- },
- "vocab_size": 50257
- }
+ "_num_labels": 1,
+ "activation_function": "gelu_new",
+ "architectures": [
+ "GPT2LMHeadModel"
+ ],
+ "attn_pdrop": 0.1,
+ "bos_token_id": 50256,
+ "embd_pdrop": 0.1,
+ "eos_token_id": 50256,
+ "id2label": {
+ "0": "LABEL_0"
+ },
+ "auto_map": {
+ "AutoConfig": "configuration_gpt2.GPT2Config",
+ "AutoModelForCausalLM": "modeling_gpt2.GPT2LMHeadModel"
+ },
+ "initializer_range": 0.02,
+ "label2id": {
+ "LABEL_0": 0
+ },
+ "layer_norm_epsilon": 1e-05,
+ "model_type": "gpt2",
+ "n_ctx": 1024,
+ "n_embd": 768,
+ "n_head": 12,
+ "n_layer": 6,
+ "n_positions": 1024,
+ "resid_pdrop": 0.1,
+ "summary_activation": null,
+ "summary_first_dropout": 0.1,
+ "summary_proj_to_labels": true,
+ "summary_type": "cls_index",
+ "summary_use_proj": true,
+ "task_specific_params": {
+ "text-generation": {
+ "do_sample": true,
+ "max_length": 50
+ }
+ },
+ "vocab_size": 50257
+}

configuration_gpt2.py

@@ -0,0 +1,277 @@
+# coding=utf-8
+# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION. 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.
+""" OpenAI GPT-2 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__)
+
+GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+ "gpt2": "https://huggingface.co/gpt2/resolve/main/config.json",
+ "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/config.json",
+ "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/config.json",
+ "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/config.json",
+ "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/config.json",
+}
+
+
+class GPT2Config(PretrainedConfig):
+ """
+ This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
+ instantiate a GPT-2 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 GPT-2
+ [gpt2](https://huggingface.co/gpt2) 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-2 model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`GPT2Model`] or [`TFGPT2Model`].
+ n_positions (`int`, *optional*, defaults to 1024):
+ 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 768):
+ Dimensionality of the embeddings and hidden states.
+ n_layer (`int`, *optional*, defaults to 12):
+ Number of hidden layers in the Transformer encoder.
+ n_head (`int`, *optional*, defaults to 12):
+ Number of attention heads for each attention layer in the Transformer encoder.
+ n_inner (`int`, *optional*):
+ 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 (`float`, *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-05):
+ 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.
+ summary_type (`string`, *optional*, defaults to `"cls_index"`):
+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
+ [`TFGPT2DoubleHeadsModel`].
+
+ Has to be one of the following options:
+
+ - `"last"`: Take the last token hidden state (like XLNet).
+ - `"first"`: Take the first token hidden state (like BERT).
+ - `"mean"`: Take the mean of all tokens hidden states.
+ - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
+ - `"attn"`: Not implemented now, use multi-head attention.
+ summary_use_proj (`bool`, *optional*, defaults to `True`):
+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
+ [`TFGPT2DoubleHeadsModel`].
+
+ Whether or not to add a projection after the vector extraction.
+ summary_activation (`str`, *optional*):
+ Argument used when doing sequence summary. Used in for the multiple choice head in
+ [`GPT2DoubleHeadsModel`].
+
+ Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
+ summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
+ [`TFGPT2DoubleHeadsModel`].
+
+ Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
+ summary_first_dropout (`float`, *optional*, defaults to 0.1):
+ Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and
+ [`TFGPT2DoubleHeadsModel`].
+
+ The dropout ratio to be used after the projection and activation.
+ 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).
+ bos_token_id (`int`, *optional*, defaults to 50256):
+ Id of the beginning of sentence token in the vocabulary.
+ eos_token_id (`int`, *optional*, defaults to 50256):
+ Id of the end of sentence token in the vocabulary.
+ scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
+ Whether to additionally scale attention weights by `1 / layer_idx + 1`.
+ reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
+ Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
+ dot-product/softmax to float() when training with mixed precision.
+
+ Example:
+
+ ```python
+ >>> from transformers import GPT2Config, GPT2Model
+
+ >>> # Initializing a GPT2 configuration
+ >>> configuration = GPT2Config()
+
+ >>> # Initializing a model (with random weights) from the configuration
+ >>> model = GPT2Model(configuration)
+
+ >>> # Accessing the model configuration
+ >>> configuration = model.config
+ ```"""
+
+ model_type = "gpt2"
+ keys_to_ignore_at_inference = ["past_key_values"]
+ attribute_map = {
+ "hidden_size": "n_embd",
+ "max_position_embeddings": "n_positions",
+ "num_attention_heads": "n_head",
+ "num_hidden_layers": "n_layer",
+ }
+
+ def __init__(
+ self,
+ vocab_size=50257,
+ n_positions=1024,
+ n_embd=768,
+ n_layer=12,
+ n_head=12,
+ n_inner=None,
+ activation_function="gelu_new",
+ resid_pdrop=0.1,
+ embd_pdrop=0.1,
+ attn_pdrop=0.1,
+ layer_norm_epsilon=1e-5,
+ initializer_range=0.02,
+ summary_type="cls_index",
+ summary_use_proj=True,
+ summary_activation=None,
+ summary_proj_to_labels=True,
+ summary_first_dropout=0.1,
+ scale_attn_weights=True,
+ use_cache=True,
+ bos_token_id=50256,
+ eos_token_id=50256,
+ scale_attn_by_inverse_layer_idx=False,
+ reorder_and_upcast_attn=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ 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.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.summary_type = summary_type
+ self.summary_use_proj = summary_use_proj
+ self.summary_activation = summary_activation
+ self.summary_first_dropout = summary_first_dropout
+ self.summary_proj_to_labels = summary_proj_to_labels
+ self.scale_attn_weights = scale_attn_weights
+ self.use_cache = use_cache
+ self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
+ self.reorder_and_upcast_attn = reorder_and_upcast_attn
+
+ 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, **kwargs)
+
+
+class GPT2OnnxConfig(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