configuration_megatron_gpt.py

# coding=utf-8
# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX 
# and OPT implementation in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Nemo Framework
# 
# 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.
""" MegatronGPT model configuration"""

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


logger = logging.get_logger(__name__)

class MegatronGPTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`MegatronGPTModel`]. It is used to instantiate an
    MegatronGPT 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 MegatronGPT 1.4B parameter 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 50432):
            Vocabulary size of the MegatronGPT model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`MegatronGPTModel`].
        hidden_size (`int`, *optional*, defaults to 6144):
            Dimension of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 44):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 64):
            Number of attention heads for each attention layer in the Transformer encoder.
        intermediate_size (`int`, *optional*, defaults to 24576):
            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` are supported.
        bias (`bool`, *optional*, defaults to True)
            Whether or not to include a bias in every linear layer
        rotary_pct (`float`, *optional*, defaults to 0.25):
            percentage of hidden dimensions to allocate to rotary embeddings
        rotary_emb_base (`int`, *optional*, defaults to 10000)
            base for computing rotary embeddings frequency
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio probability of the attention score.
        hidden_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio of (1) the word embeddings, (2) the post-attention hidden states, and (3) the post-mlp
            hidden states.
        classifier_dropout (`float`, *optional*, defaults to 0.1):
            Argument used when doing token classification, used in the model [`MegatronGPTForTokenClassification`].            
            The dropout ratio for the hidden layer.
        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).
        normalize_attention_scores (`bool`, *optional*, defaults to `True`)
            Whether to scale the output Q * K^T by 1 / sqrt(hidden_size_per_head). 
        initializer_range (`float`, *optional*, defaults to 1e-5):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        normalization (`string`, *optional*, defaults to `layernorm1p`)
            The type of normalization to use for the LayerNorm layers, either `layernorm` or `layernorm1p`
        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`.
        self_attention_relative_position_bias (`bool`, *optional*, defaults to `True`):
            Whether to calculate and apply the relative position bias within the attention function.
            If this is False, then model.generate will require you to calculate the triangular attention
            mask and pass it through in the attention mask.
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.

    """
    model_type = "megatron_gpt"

    def __init__(
        self,
        vocab_size=56064,
        hidden_size=2048,
        num_hidden_layers=24,
        num_attention_heads=16,
        intermediate_size=5440,
        hidden_act="fast-swiglu",
        bias=True,
        rotary_pct=0.5,
        rotary_emb_base=10000,
        attention_dropout=0.0,
        hidden_dropout=0.0,
        classifier_dropout=0.0,
        normalize_attention_scores=True,
        max_position_embeddings=2048,
        initializer_range=0.01,
        layer_norm_eps=1e-5,
        normalization='layernorm1p',
        use_cache=True,
        self_attention_relative_position_bias=True,
        bos_token_id=0,
        eos_token_id=2,
        tie_word_embeddings=False,
        rope_scaling=None,
        **kwargs,
    ):
        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.bias = bias
        self.rotary_pct = rotary_pct
        self.rotary_emb_base = rotary_emb_base
        self.attention_dropout = attention_dropout
        self.hidden_dropout = hidden_dropout
        self.classifier_dropout = classifier_dropout
        self.normalize_attention_scores = normalize_attention_scores
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.normalization = normalization
        self.use_cache = use_cache
        self.self_attention_relative_position_bias = self_attention_relative_position_bias
        self.tie_word_embeddings = tie_word_embeddings
        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()

        if self.hidden_size % self.num_attention_heads != 0:
            raise ValueError(
                "The hidden size is not divisble by the number of attention heads! Make sure to update them!"
            )

    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
            raise ValueError(
                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                f"got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_factor = self.rope_scaling.get("factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
            raise ValueError(
                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
            )
        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")