From 8256a73c812b9fa9dd93bcf0e4ab6b767410bd40 Mon Sep 17 00:00:00 2001 From: Arthur Zucker Date: Mon, 27 May 2024 15:21:34 +0200 Subject: [PATCH] new status --- .../models/gemma/configuration_gemma.py | 149 ++---------------- src/transformers/models/gemma/diff_gemma.py | 53 ++++++- 2 files changed, 68 insertions(+), 134 deletions(-) diff --git a/src/transformers/models/gemma/configuration_gemma.py b/src/transformers/models/gemma/configuration_gemma.py index 911cde0f27..ec4110a904 100644 --- a/src/transformers/models/gemma/configuration_gemma.py +++ b/src/transformers/models/gemma/configuration_gemma.py @@ -29,120 +29,31 @@ if is_flash_attn_2_available(): if is_flash_attn_2_available(): from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa -from ...configuration_utils import PretrainedConfig - - -class GemmaConfig(PretrainedConfig): - r""" - This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma - 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 Gemma-7B. - - 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 32000): - Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the - `inputs_ids` passed when calling [`GemmaModel`] - hidden_size (`int`, *optional*, defaults to 4096): - Dimension of the hidden representations. - intermediate_size (`int`, *optional*, defaults to 11008): - Dimension of the MLP representations. - num_hidden_layers (`int`, *optional*, defaults to 32): - Number of hidden layers in the Transformer decoder. - num_attention_heads (`int`, *optional*, defaults to 32): - Number of attention heads for each attention layer in the Transformer decoder. - num_key_value_heads (`int`, *optional*): - This is the number of key_value heads that should be used to implement Grouped Query Attention. If - `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if - `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When - converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed - by meanpooling all the original heads within that group. For more details checkout [this - paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to - `num_attention_heads`. - hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): - The non-linear activation function (function or string) in the decoder. - max_position_embeddings (`int`, *optional*, defaults to 2048): - The maximum sequence length that this model might ever be used with. Gemma 1 supports up to 2048 tokens, - Gemma 2 up to 4096, CodeGemma up to 16384. - initializer_range (`float`, *optional*, defaults to 0.02): - The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - rms_norm_eps (`float`, *optional*, defaults to 1e-06): - The epsilon used by the rms normalization layers. - 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`. - pad_token_id (`int`, *optional*): - Padding token id. - bos_token_id (`int`, *optional*, defaults to 1): - Beginning of stream token id. - eos_token_id (`int`, *optional*, defaults to 2): - End of stream token id. - pretraining_tp (`int`, *optional*, defaults to 1): - Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this - document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-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). - tie_word_embeddings (`bool`, *optional*, defaults to `False`): - Whether to tie weight embeddings - rope_theta (`float`, *optional*, defaults to 10000.0): - The base period of the RoPE embeddings. - rope_scaling (`Dict`, *optional*): - Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling - strategies: linear and dynamic. Their scaling factor must be a 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/LocalGemma/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an - experimental feature, subject to breaking API changes in future versions. - attention_bias (`bool`, *optional*, defaults to `False`): - Whether to use a bias in the query, key, value and output projection layers during self-attention. - attention_dropout (`float`, *optional*, defaults to 0.0): - The dropout ratio for the attention probabilities. - mlp_bias (`bool`, *optional*, defaults to `False`): - Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. - - ```python - >>> from transformers import GemmaModel, GemmaConfig - - >>> # Initializing a Gemma gemma-7b style configuration - >>> configuration = GemmaConfig() - - >>> # Initializing a model from the gemma-7b style configuration - >>> model = GemmaModel(configuration) - - >>> # Accessing the model configuration - >>> configuration = model.config - ```""" +class GemmaConfig(PreTrainedConfig): model_type = "gemma" - keys_to_ignore_at_inference = ["past_key_values"] - def __init__( self, - vocab_size=32000, - hidden_size=4096, - intermediate_size=11008, - num_hidden_layers=32, - num_attention_heads=32, - num_key_value_heads=None, - hidden_act="silu", - max_position_embeddings=2048, + vocab_size=256000, + hidden_size=3072, + intermediate_size=24576, + num_hidden_layers=28, + num_attention_heads=16, + num_key_value_heads=16, + head_dim=256, + hidden_act="gelu_pytorch_tanh", + hidden_activation=None, + max_position_embeddings=8192, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, - pad_token_id=None, - bos_token_id=1, - eos_token_id=2, - pretraining_tp=1, - tie_word_embeddings=False, + pad_token_id=0, + eos_token_id=1, + bos_token_id=2, + tie_word_embeddings=True, rope_theta=10000.0, - rope_scaling=None, attention_bias=False, attention_dropout=0.0, - mlp_bias=False, **kwargs, ): self.vocab_size = vocab_size @@ -151,23 +62,16 @@ class GemmaConfig(PretrainedConfig): self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads - - # for backward compatibility - if num_key_value_heads is None: - num_key_value_heads = num_attention_heads - + self.head_dim = head_dim self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act + self.hidden_activation = hidden_activation self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps - self.pretraining_tp = pretraining_tp self.use_cache = use_cache self.rope_theta = rope_theta - self.rope_scaling = rope_scaling - self._rope_scaling_validation() self.attention_bias = attention_bias self.attention_dropout = attention_dropout - self.mlp_bias = mlp_bias super().__init__( pad_token_id=pad_token_id, @@ -177,22 +81,3 @@ class GemmaConfig(PretrainedConfig): **kwargs, ) - 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 two fields, `type` 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 type 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 a float > 1, got {rope_scaling_factor}") diff --git a/src/transformers/models/gemma/diff_gemma.py b/src/transformers/models/gemma/diff_gemma.py index a823849162..4253546ae5 100644 --- a/src/transformers/models/gemma/diff_gemma.py +++ b/src/transformers/models/gemma/diff_gemma.py @@ -30,7 +30,7 @@ from transformers.models.llama.modeling_llama import ( apply_rotary_pos_emb, repeat_kv, ) -from transformers.models.llama.configuration_llama import LlamaConfig +from transformers import PreTrainedConfig from ...activations import ACT2FN from ...cache_utils import Cache from ...modeling_outputs import CausalLMOutputWithPast @@ -41,8 +41,57 @@ from ...utils import logging logger = logging.get_logger(__name__) -class GemmaConfig(LlamaConfig): +class GemmaConfig(PreTrainedConfig): model_type = "gemma" + def __init__( + self, + vocab_size=256000, + hidden_size=3072, + intermediate_size=24576, + num_hidden_layers=28, + num_attention_heads=16, + num_key_value_heads=16, + head_dim=256, + hidden_act="gelu_pytorch_tanh", + hidden_activation=None, + max_position_embeddings=8192, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + pad_token_id=0, + eos_token_id=1, + bos_token_id=2, + tie_word_embeddings=True, + rope_theta=10000.0, + attention_bias=False, + attention_dropout=0.0, + **kwargs, + ): + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.head_dim = head_dim + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.hidden_activation = hidden_activation + self.initializer_range = initializer_range + self.rms_norm_eps = rms_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.attention_bias = attention_bias + self.attention_dropout = attention_dropout + + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + class GemmaRMSNorm(nn.Module): def __init__(self, dim: int, eps: float = 1e-6):