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config.py
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config.py
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# coding=utf-8
# Copyright 2022 The FAIR team of Meta AI and The 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.
# 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.
""" LLaMA model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from typing import Optional, Any, Dict
import jax
import re
from jax.sharding import PartitionSpec as PS
logger = logging.get_logger(__name__)
LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class LLaMAConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`~LLaMAModel`]. It is used to instantiate an LLaMA
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 LLaMA-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 LLaMA model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`~LLaMAModel`] or [`~TFLLaMAModel`].
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 encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_sequence_length (`int`, *optional*, defaults to 2048):
Max sequence length for model (for RoPE computation)
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-12):
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`.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
Example:
```python
>>> from transformers import LLaMAModel, LLaMAConfig
>>> # Initializing a LLaMA llama-7b style configuration
>>> configuration = LLaMAConfig()
>>> # Initializing a model from the llama-7b style configuration
>>> model = LLaMAModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "llama"
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
max_sequence_length=2048,
rms_norm_eps=1e-6,
initializer_range=0.02,
use_cache=True,
pad_token_id=-1,
bos_token_id=1,
eos_token_id=2,
resid_pdrop=0.0,
embd_pdrop=0.0,
attn_pdrop=0.0,
tie_word_embeddings=False,
gradient_checkpointing=True,
gradient_checkpointing_policy='nothing_saveable',
unpadded_vocab_size=None,
mesh: Optional[jax.sharding.Mesh]=None,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.initializer_range = initializer_range
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_sequence_length = max_sequence_length
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.gradient_checkpointing = gradient_checkpointing
self.gradient_checkpointing_policy = gradient_checkpointing_policy
self.unpadded_vocab_size = unpadded_vocab_size
if self.unpadded_vocab_size is None:
self.unpadded_vocab_size = self.vocab_size
self.mesh = mesh
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,
)
@staticmethod
def get_partition_rules():
return [
# embeddings
(re.escape("['transformer']['wte']['embedding']"), PS("mp", "fsdp")),
# self atention
(''.join((re.escape("['attention']"), r"\['(wk|wq|wv)'\]", re.escape("['kernel']"))), PS("fsdp", "mp")),
(re.escape("['attention']['wo']['kernel']"), PS("mp", "fsdp")),
# mlp
(re.escape("['feed_forward']['w1']['kernel']"), PS("fsdp", "mp")),
(re.escape("['feed_forward']['w2']['kernel']"), PS("mp", "fsdp")),
(re.escape("['feed_forward']['w3']['kernel']"), PS("fsdp", "mp")),
# layer norms
(re.escape("['attention_norm']['kernel']"), PS()),
(re.escape("['ffn_norm']['kernel']"), PS()),
(re.escape("['transformer']['ln_f']['kernel']"), PS()),
# output head
(re.escape("['lm_head']['kernel']"), PS("fsdp", "mp")),
]
def to_dict(self) -> Dict[str, Any]:
if self.mesh is None:
return super().to_dict()
else:
new_conf = LLaMAConfig(**self.__dict__)
new_conf.mesh = None
return new_conf.to_dict()