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Unverified Commit 2ca5f82c authored by Cyrus Leung's avatar Cyrus Leung Committed by GitHub
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[Misc] Remove redundant config definitions (#21891) 


Signed-off-by: default avatarDarkLight1337 <tlleungac@connect.ust.hk>
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vllm/transformers_utils/configs/solar.py deleted 100644 → 0
View file @ 6f8d2618
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations 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 Meta AI team that trained the model.
#
# 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.
"""Solar model configuration"""
from transformers import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class SolarConfig(PretrainedConfig):
r"""
This is the configuration class to store
the configuration of a [`SolarModel`].
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 [`SolarModel`]
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.
Solar 1 supports up to 2048 tokens,
Solar 2 up to 4096, CodeSolar 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/LocalLLaMA/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.
sliding_window (`int`, *optional*, defaults to 2047):
Sliding window attention window size. If not specified,
will default to `2047`.
```python
>>> from transformers import SolarModel, SolarConfig
>>> # Initializing a Solar-pro style configuration
>>> configuration = SolarConfig()
>>> # Initializing a model from the Solar-pro style configuration
>>> model = SolarModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "solar"
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,
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,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
sliding_window=2047,
bskcn_1=None,
bskcn_2=None,
bskcn_3=None,
bskcn_4=None,
bskcn_tv=None,
**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
# 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.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
self.sliding_window = sliding_window
self.bskcn_1 = bskcn_1 if bskcn_1 is not None else [12, 20, 32, 44]
self.bskcn_2 = bskcn_2 if bskcn_2 is not None else [20, 32]
self.bskcn_3 = bskcn_3 if bskcn_3 is not None else [16, 24, 36, 48]
self.bskcn_4 = bskcn_4 if bskcn_4 is not None else [28, 40]
self.bskcn_tv = bskcn_tv if bskcn_tv is not None else [0.9, 0.8]
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,
)
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 "
f"['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,"
f" got {rope_scaling_factor}")
vllm/transformers_utils/configs/telechat2.py deleted 100644 → 0
View file @ 6f8d2618
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from https://www.modelscope.cn/models/TeleAI/TeleChat2-3B/resolve/master/configuration_telechat2.py
""" Telechat configuration compatible with LlamaConfig. """
from transformers.configuration_utils import PretrainedConfig
class Telechat2Config(PretrainedConfig):
model_type = "telechat"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_hidden_layers": "n_layer",
"num_attention_heads": "n_head",
"intermediate_size": "ffn_hidden_size",
"rms_norm_eps": "layer_norm_epsilon"
}
def __init__(
self,
vocab_size=160256,
hidden_size=4096,
n_layer=30,
n_head=32,
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,
ffn_hidden_size=12288,
training_seqlen=8192,
logn=True,
embed_layernorm=False,
hidden_act="silu",
**kwargs,
):
self.vocab_size = vocab_size
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.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.logn = logn
self.training_seqlen = training_seqlen
self.embed_layernorm = embed_layernorm
self.num_key_value_heads = kwargs.pop("num_key_value_heads", None)
self.ffn_hidden_size = ffn_hidden_size
self.hidden_act = hidden_act
super().__init__(bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs)
vllm/transformers_utils/processors/__init__.py
View file @ 2ca5f82c
# SPDX-License-Identifier: Apache-2.0 # SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""
Multi-modal processors may be defined in this directory for the following
reasons:
- There is no processing file defined by HF Hub or Transformers library.
- There is a need to override the existing processor to support vLLM.
"""
from vllm.transformers_utils.processors.deepseek_vl2 import ( from vllm.transformers_utils.processors.deepseek_vl2 import (
DeepseekVLV2Processor) DeepseekVLV2Processor)
......
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