402 lines
18 KiB
Python
402 lines
18 KiB
Python
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# coding=utf-8
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# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" BART model configuration"""
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import warnings
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from collections import OrderedDict
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from typing import Any, Mapping, Optional
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from ... import PreTrainedTokenizer
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
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from ...onnx.utils import compute_effective_axis_dimension
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from ...utils import TensorType, is_torch_available, logging
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logger = logging.get_logger(__name__)
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class BartConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the BART
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[facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50265):
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Vocabulary size of the BART model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`BartModel`] or [`TFBartModel`].
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d_model (`int`, *optional*, defaults to 1024):
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Dimensionality of the layers and the pooler layer.
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encoder_layers (`int`, *optional*, defaults to 12):
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Number of encoder layers.
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decoder_layers (`int`, *optional*, defaults to 12):
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Number of decoder layers.
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encoder_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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decoder_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer decoder.
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decoder_ffn_dim (`int`, *optional*, defaults to 4096):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
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encoder_ffn_dim (`int`, *optional*, defaults to 4096):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
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activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"silu"` and `"gelu_new"` are supported.
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dropout (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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classifier_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for classifier.
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max_position_embeddings (`int`, *optional*, defaults to 1024):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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init_std (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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encoder_layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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decoder_layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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scale_embedding (`bool`, *optional*, defaults to `False`):
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Scale embeddings by diving by sqrt(d_model).
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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num_labels (`int`, *optional*, defaults to 3):
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The number of labels to use in [`BartForSequenceClassification`].
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forced_eos_token_id (`int`, *optional*, defaults to 2):
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The id of the token to force as the last generated token when `max_length` is reached. Usually set to
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`eos_token_id`.
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Example:
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```python
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>>> from transformers import BartConfig, BartModel
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>>> # Initializing a BART facebook/bart-large style configuration
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>>> configuration = BartConfig()
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>>> # Initializing a model (with random weights) from the facebook/bart-large style configuration
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>>> model = BartModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "bart"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
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def __init__(
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self,
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vocab_size=50265,
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max_position_embeddings=1024,
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encoder_layers=12,
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encoder_ffn_dim=4096,
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encoder_attention_heads=16,
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decoder_layers=12,
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decoder_ffn_dim=4096,
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decoder_attention_heads=16,
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encoder_layerdrop=0.0,
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decoder_layerdrop=0.0,
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activation_function="gelu",
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d_model=1024,
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dropout=0.1,
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attention_dropout=0.0,
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activation_dropout=0.0,
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init_std=0.02,
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classifier_dropout=0.0,
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scale_embedding=False,
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use_cache=True,
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num_labels=3,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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is_encoder_decoder=True,
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decoder_start_token_id=2,
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forced_eos_token_id=2,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.d_model = d_model
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self.encoder_ffn_dim = encoder_ffn_dim
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self.encoder_layers = encoder_layers
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self.encoder_attention_heads = encoder_attention_heads
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self.decoder_ffn_dim = decoder_ffn_dim
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self.decoder_layers = decoder_layers
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self.decoder_attention_heads = decoder_attention_heads
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.activation_function = activation_function
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self.init_std = init_std
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self.encoder_layerdrop = encoder_layerdrop
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self.decoder_layerdrop = decoder_layerdrop
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self.classifier_dropout = classifier_dropout
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self.use_cache = use_cache
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self.num_hidden_layers = encoder_layers
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self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
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super().__init__(
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num_labels=num_labels,
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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is_encoder_decoder=is_encoder_decoder,
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decoder_start_token_id=decoder_start_token_id,
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forced_eos_token_id=forced_eos_token_id,
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**kwargs,
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)
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# ensure backward compatibility for BART CNN models
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if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
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self.forced_bos_token_id = self.bos_token_id
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warnings.warn(
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f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
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"The config can simply be saved and uploaded again to be fixed."
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)
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class BartOnnxConfig(OnnxSeq2SeqConfigWithPast):
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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if self.task in ["default", "seq2seq-lm"]:
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common_inputs = OrderedDict(
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[
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("input_ids", {0: "batch", 1: "encoder_sequence"}),
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("attention_mask", {0: "batch", 1: "encoder_sequence"}),
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]
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)
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if self.use_past:
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common_inputs["decoder_input_ids"] = {0: "batch"}
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common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
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else:
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common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
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common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
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if self.use_past:
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self.fill_with_past_key_values_(common_inputs, direction="inputs")
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elif self.task == "causal-lm":
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# TODO: figure this case out.
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common_inputs = OrderedDict(
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[
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("input_ids", {0: "batch", 1: "encoder_sequence"}),
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("attention_mask", {0: "batch", 1: "encoder_sequence"}),
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]
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)
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if self.use_past:
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num_encoder_layers, _ = self.num_layers
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for i in range(num_encoder_layers):
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common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
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common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
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else:
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common_inputs = OrderedDict(
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[
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("input_ids", {0: "batch", 1: "encoder_sequence"}),
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("attention_mask", {0: "batch", 1: "encoder_sequence"}),
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("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
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("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
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]
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)
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return common_inputs
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@property
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def outputs(self) -> Mapping[str, Mapping[int, str]]:
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if self.task in ["default", "seq2seq-lm"]:
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common_outputs = super().outputs
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else:
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common_outputs = super(OnnxConfigWithPast, self).outputs
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if self.use_past:
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num_encoder_layers, _ = self.num_layers
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for i in range(num_encoder_layers):
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common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
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common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
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return common_outputs
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def _generate_dummy_inputs_for_default_and_seq2seq_lm(
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self,
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tokenizer: PreTrainedTokenizer,
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batch_size: int = -1,
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seq_length: int = -1,
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is_pair: bool = False,
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framework: Optional[TensorType] = None,
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) -> Mapping[str, Any]:
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encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
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tokenizer, batch_size, seq_length, is_pair, framework
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)
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# Generate decoder inputs
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decoder_seq_length = seq_length if not self.use_past else 1
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decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
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tokenizer, batch_size, decoder_seq_length, is_pair, framework
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)
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decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
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common_inputs = dict(**encoder_inputs, **decoder_inputs)
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if self.use_past:
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if not is_torch_available():
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raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
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else:
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import torch
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batch, encoder_seq_length = common_inputs["input_ids"].shape
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decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
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num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
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encoder_shape = (
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batch,
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num_encoder_attention_heads,
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encoder_seq_length,
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self._config.hidden_size // num_encoder_attention_heads,
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)
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decoder_past_length = decoder_seq_length + 3
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decoder_shape = (
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batch,
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num_decoder_attention_heads,
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decoder_past_length,
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self._config.hidden_size // num_decoder_attention_heads,
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)
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common_inputs["decoder_attention_mask"] = torch.cat(
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[common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
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)
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common_inputs["past_key_values"] = []
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# If the number of encoder and decoder layers are present in the model configuration, both are considered
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num_encoder_layers, num_decoder_layers = self.num_layers
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min_num_layers = min(num_encoder_layers, num_decoder_layers)
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max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
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remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
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for _ in range(min_num_layers):
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common_inputs["past_key_values"].append(
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(
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torch.zeros(decoder_shape),
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torch.zeros(decoder_shape),
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torch.zeros(encoder_shape),
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torch.zeros(encoder_shape),
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)
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)
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# TODO: test this.
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shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
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for _ in range(min_num_layers, max_num_layers):
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common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
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return common_inputs
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def _generate_dummy_inputs_for_causal_lm(
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self,
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tokenizer: PreTrainedTokenizer,
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batch_size: int = -1,
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seq_length: int = -1,
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is_pair: bool = False,
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framework: Optional[TensorType] = None,
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) -> Mapping[str, Any]:
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common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
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tokenizer, batch_size, seq_length, is_pair, framework
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)
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if self.use_past:
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if not is_torch_available():
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raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
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else:
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import torch
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batch, seqlen = common_inputs["input_ids"].shape
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# Not using the same length for past_key_values
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past_key_values_length = seqlen + 2
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num_encoder_layers, _ = self.num_layers
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num_encoder_attention_heads, _ = self.num_attention_heads
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past_shape = (
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batch,
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num_encoder_attention_heads,
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past_key_values_length,
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self._config.hidden_size // num_encoder_attention_heads,
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)
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|
mask_dtype = common_inputs["attention_mask"].dtype
|
||
|
|
common_inputs["attention_mask"] = torch.cat(
|
||
|
|
[common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
|
||
|
|
)
|
||
|
|
common_inputs["past_key_values"] = [
|
||
|
|
(torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers)
|
||
|
|
]
|
||
|
|
return common_inputs
|
||
|
|
|
||
|
|
def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
|
||
|
|
self,
|
||
|
|
tokenizer: PreTrainedTokenizer,
|
||
|
|
batch_size: int = -1,
|
||
|
|
seq_length: int = -1,
|
||
|
|
is_pair: bool = False,
|
||
|
|
framework: Optional[TensorType] = None,
|
||
|
|
) -> Mapping[str, Any]:
|
||
|
|
# Copied from OnnxConfig.generate_dummy_inputs
|
||
|
|
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
|
||
|
|
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
|
||
|
|
batch_size = compute_effective_axis_dimension(
|
||
|
|
batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
|
||
|
|
)
|
||
|
|
|
||
|
|
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
|
||
|
|
token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
|
||
|
|
seq_length = compute_effective_axis_dimension(
|
||
|
|
seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
|
||
|
|
)
|
||
|
|
|
||
|
|
# Generate dummy inputs according to compute batch and sequence
|
||
|
|
dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
|
||
|
|
common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
|
||
|
|
return common_inputs
|
||
|
|
|
||
|
|
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]:
|
||
|
|
if self.task in ["default", "seq2seq-lm"]:
|
||
|
|
common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
|
||
|
|
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
|
||
|
|
)
|
||
|
|
|
||
|
|
elif self.task == "causal-lm":
|
||
|
|
common_inputs = self._generate_dummy_inputs_for_causal_lm(
|
||
|
|
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
|
||
|
|
tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
|
||
|
|
)
|
||
|
|
|
||
|
|
return common_inputs
|
||
|
|
|
||
|
|
def _flatten_past_key_values_(self, flattened_output, name, idx, t):
|
||
|
|
if self.task in ["default", "seq2seq-lm"]:
|
||
|
|
flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
|
||
|
|
else:
|
||
|
|
flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
|
||
|
|
flattened_output, name, idx, t
|
||
|
|
)
|