configuration_bloom.py

# coding=utf-8
# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team.  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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""" Bloom configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
    from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


logger = logging.get_logger(__name__)


from ..deprecated._archive_maps import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP  # noqa: F401, E402


class BloomConfig(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`BloomModel`]. It is used to instantiate a Bloom
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to the Bloom architecture
    [bigscience/bloom](https://huggingface.co/bigscience/bloom).

    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 250880):
            Vocabulary size of the Bloom model. Defines the maximum number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`BloomModel`]. Check [this
            discussion](https://huggingface.co/bigscience/bloom/discussions/120#633d28389addb8530b406c2a) on how the
            `vocab_size` has been defined.
        hidden_size (`int`, *optional*, defaults to 64):
            Dimensionality of the embeddings and hidden states.
        n_layer (`int`, *optional*, defaults to 2):
            Number of hidden layers in the Transformer encoder.
        n_head (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        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.
        apply_residual_connection_post_layernorm (`bool`, *optional*, defaults to `False`):
            If enabled, use the layer norm of the hidden states as the residual in the transformer blocks
        hidden_dropout (`float`, *optional*, defaults to 0.1):
            Dropout rate of the dropout function on the bias dropout.
        attention_dropout (`float`, *optional*, defaults to 0.1):
            Dropout rate applied to the attention probs
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        pretraining_tp (`int`, *optional*, defaults to `1`):
            Experimental feature. Tensor parallelism rank used during pretraining with Megatron. Please refer to [this
            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
            issue](https://github.com/pytorch/pytorch/issues/76232). Note also that this is enabled only when
            `slow_but_exact=True`.
        slow_but_exact (`bool`, *optional*, defaults to `False`):
            Experimental feature. Whether to use slow but exact implementation of the attention mechanism. While
            merging the TP rank tensors, due to slicing operations the results may be slightly different between the
            model trained on Megatron and our model. Please refer to [this
            issue](https://github.com/pytorch/pytorch/issues/76232). A solution to obtain more accurate results is to
            enable this feature. Enabling this will hurt the computational time of the inference. Will be probably
            resolved in the future once the main model has been fine-tuned with TP_rank=1.

    Example:

    ```python
    >>> from transformers import BloomConfig, BloomModel

    >>> # Initializing a Bloom configuration
    >>> configuration = BloomConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = BloomModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "bloom"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {
        "num_hidden_layers": "n_layer",
        "num_attention_heads": "n_head",
    }

    def __init__(
        self,
        vocab_size=250880,
        hidden_size=64,
        n_layer=2,
        n_head=8,
        layer_norm_epsilon=1e-5,
        initializer_range=0.02,
        use_cache=True,
        bos_token_id=1,
        eos_token_id=2,
        apply_residual_connection_post_layernorm=False,
        hidden_dropout=0.0,
        attention_dropout=0.0,
        pretraining_tp=1,  # TP rank used when training with megatron
        slow_but_exact=False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        # Backward compatibility with n_embed kwarg
        n_embed = kwargs.pop("n_embed", None)
        self.hidden_size = hidden_size if n_embed is None else n_embed
        self.n_layer = n_layer
        self.n_head = n_head
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range
        self.use_cache = use_cache
        self.pretraining_tp = pretraining_tp
        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
        self.hidden_dropout = hidden_dropout
        self.attention_dropout = attention_dropout

        self.bos_token_id = bos_token_id
        self.eos_token_id = eos_token_id
        self.slow_but_exact = slow_but_exact

        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)


class BloomOnnxConfig(OnnxConfigWithPast):
    torch_onnx_minimum_version = version.parse("1.12")

    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:
            # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
            self.fill_with_past_key_values_(common_inputs, direction="inputs", inverted_values_shape=True)
            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

    @property
    def atol_for_validation(self) -> float:
        return 1e-3

    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
                head_dim = self._config.hidden_size // self.num_attention_heads
                past_key_shape = (
                    batch * self.num_attention_heads,
                    head_dim,
                    past_key_values_length,
                )
                past_value_shape = (
                    batch * self.num_attention_heads,
                    past_key_values_length,
                    head_dim,
                )
                ordered_inputs["past_key_values"] = [
                    (torch.zeros(past_key_shape), torch.zeros(past_value_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