237 lines
10 KiB
Python
237 lines
10 KiB
Python
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# coding=utf-8
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# Copyright 2022 the Big Science Workshop and 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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""" Bloom configuration"""
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from collections import OrderedDict
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from typing import TYPE_CHECKING, Any, List, Mapping, Optional
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from packaging import version
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if TYPE_CHECKING:
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from ... import PreTrainedTokenizer, TensorType
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfigWithPast, PatchingSpec
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from ...utils import is_torch_available, logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class BloomConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`BloomModel`]. It is used to instantiate a Bloom
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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 the Bloom architecture
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[bigscience/bloom](https://huggingface.co/bigscience/bloom).
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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 250880):
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Vocabulary size of the Bloom model. Defines the maximum number of different tokens that can be represented
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by the `inputs_ids` passed when calling [`BloomModel`]. Check [this
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discussion](https://huggingface.co/bigscience/bloom/discussions/120#633d28389addb8530b406c2a) on how the
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`vocab_size` has been defined.
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hidden_size (`int`, *optional*, defaults to 64):
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Dimensionality of the embeddings and hidden states.
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n_layer (`int`, *optional*, defaults to 2):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
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The epsilon to use in the layer normalization layers.
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initializer_range (`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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apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`):
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If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
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hidden_dropout (`float`, *optional*, defaults to 0.1):
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Dropout rate of the dropout function on the bias dropout.
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attention_dropout (`float`, *optional*, defaults to 0.1):
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Dropout rate applied to the attention probs
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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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pretraining_tp (`int`, *optional*, defaults to `1`):
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Experimental feature. Tensor parallelism rank used during pretraining with Megatron. Please refer to [this
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document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
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necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
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issue](https://github.com/pytorch/pytorch/issues/76232). Note also that this is enabled only when
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`slow_but_exact=True`.
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slow_but_exact (`bool`, *optional*, defaults to `False`):
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Experimental feature. Whether to use slow but exact implementation of the attention mechanism. While
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merging the TP rank tensors, due to slicing operations the results may be slightly different between the
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model trained on Megatron and our model. Please refer to [this
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issue](https://github.com/pytorch/pytorch/issues/76232). A solution to obtain more accurate results is to
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enable this feature. Enabling this will hurt the computational time of the inference. Will be probably
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resolved in the future once the main model has been fine-tuned with TP_rank=1.
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Example:
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```python
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>>> from transformers import BloomConfig, BloomModel
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>>> # Initializing a Bloom configuration
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>>> configuration = BloomConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = BloomModel(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 = "bloom"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"num_hidden_layers": "n_layer",
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"num_attention_heads": "n_head",
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}
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def __init__(
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self,
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vocab_size=250880,
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hidden_size=64,
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n_layer=2,
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n_head=8,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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use_cache=True,
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bos_token_id=1,
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eos_token_id=2,
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apply_residual_connection_post_layernorm=False,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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pretraining_tp=1, # TP rank used when training with megatron
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slow_but_exact=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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# Backward compatibility with n_embed kwarg
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n_embed = kwargs.pop("n_embed", None)
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self.hidden_size = hidden_size if n_embed is None else n_embed
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self.n_layer = n_layer
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self.n_head = n_head
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.pretraining_tp = pretraining_tp
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self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.slow_but_exact = slow_but_exact
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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class BloomOnnxConfig(OnnxConfigWithPast):
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torch_onnx_minimum_version = version.parse("1.12")
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def __init__(
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self,
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config: PretrainedConfig,
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task: str = "default",
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patching_specs: List[PatchingSpec] = None,
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use_past: bool = False,
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):
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super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
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if not getattr(self._config, "pad_token_id", None):
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# TODO: how to do that better?
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self._config.pad_token_id = 0
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
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if self.use_past:
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# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
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self.fill_with_past_key_values_(common_inputs, direction="inputs", inverted_values_shape=True)
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common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
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else:
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common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
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return common_inputs
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@property
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def num_layers(self) -> int:
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return self._config.n_layer
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@property
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def num_attention_heads(self) -> int:
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return self._config.n_head
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@property
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def atol_for_validation(self) -> float:
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return 1e-3
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def generate_dummy_inputs(
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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 = super(OnnxConfigWithPast, self).generate_dummy_inputs(
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tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
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)
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# We need to order the input in the way they appears in the forward()
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ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
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# Need to add the past_keys
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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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head_dim = self._config.hidden_size // self.num_attention_heads
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past_key_shape = (
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batch * self.num_attention_heads,
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head_dim,
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past_key_values_length,
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)
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past_value_shape = (
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batch * self.num_attention_heads,
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past_key_values_length,
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head_dim,
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)
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ordered_inputs["past_key_values"] = [
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(torch.zeros(past_key_shape), torch.zeros(past_value_shape)) for _ in range(self.num_layers)
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]
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ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
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if self.use_past:
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mask_dtype = ordered_inputs["attention_mask"].dtype
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ordered_inputs["attention_mask"] = torch.cat(
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[ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
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)
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return ordered_inputs
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@property
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def default_onnx_opset(self) -> int:
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return 13
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