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Arthur Zucker 2024-05-27 15:15:13 +02:00
parent 40c5e6da5e
commit 49656b30e8
2 changed files with 96 additions and 48 deletions
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138
src/transformers/models/gemma/configuration_gemma.py
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@ -1,5 +1,6 @@
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -12,13 +13,23 @@
# 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.
""" Gemma model configuration"""
from ...utils import (
is_flash_attn_2_available,
)
from .configuration_gemma import GemmaConfig
if is_flash_attn_2_available():
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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
from ...utils import logging
logger = logging.get_logger(__name__)
class GemmaConfig(PretrainedConfig):
@ -27,25 +38,23 @@ class GemmaConfig(PretrainedConfig):
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.
e.g. [google/gemma-7b](https://huggingface.co/google/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 256000):
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 3072):
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 24576):
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 28):
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 16):
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*, defaults to 16):
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
@ -53,15 +62,11 @@ class GemmaConfig(PretrainedConfig):
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`.
head_dim (`int`, *optional*, defaults to 256):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The legacy activation function. It is overwritten by the `hidden_activation`.
hidden_activation (`str` or `function`, *optional*):
The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
max_position_embeddings (`int`, *optional*, defaults to 8192):
The maximum sequence length that this model might ever be used with.
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):
@ -69,20 +74,35 @@ class GemmaConfig(PretrainedConfig):
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*, defaults to 0):
pad_token_id (`int`, *optional*):
Padding token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
bos_token_id (`int`, *optional*, defaults to 2):
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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
@ -102,26 +122,27 @@ class GemmaConfig(PretrainedConfig):
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,
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,
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,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
**kwargs,
):
self.vocab_size = vocab_size
@ -130,16 +151,23 @@ class GemmaConfig(PretrainedConfig):
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
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,
@ -148,3 +176,23 @@ class GemmaConfig(PretrainedConfig):
tie_word_embeddings=tie_word_embeddings,
**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}")

6
utils/diff_model_converter.py
View File

@ -409,9 +409,9 @@ def convert_file(diff_file):
with open(diff_file.replace("diff_", "modeling_"), "w") as f:
f.write(ruffed_code)
if hasattr(transformers, "config_bod"):
config_module = cst.Module(body = [*transformers.config_body])
with open(diff_file.replace("diff_", "config_"), "w") as f:
if hasattr(transformers, "config_body"):
config_module = cst.Module(body = [*transformers.config_body], header=new_mod.header)
with open(diff_file.replace("diff_", "configuration_"), "w") as f:
ruffed_code = fix_ruff(config_module.code)
f.write(ruffed_code)