mirrors/vllm-project--vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Deployments Models Agent Notes Packages Projects Releases Wiki Activity

chore: import upstream snapshot with attribution
pre-commit / pre-run-check (push) Has been cancelled
Details
pre-commit / pre-commit (push) Has been cancelled
Details

Browse Source
This commit is contained in:
wehub-resource-sync
2026-07-13 12:55:37 +08:00
commit 7ce4c8e27e
5900 changed files with 1668062 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
vllm/transformers_utils/configs/hyperclovax.py
+349
View File
@@ -0,0 +1,349 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Copyright 2025 NAVER Cloud HyperCLOVA team
#
# Copyright 2025 NAVER Cloud HyperCLOVA 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.
"""HyperCLOVA X model configuration."""
from transformers import AutoConfig
from transformers.configuration_utils import PretrainedConfig
class HyperCLOVAXConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a
[`HyperCLOVAXModel`]. It is used to instantiate a HyperCLOVAX model
according to the specified arguments, defining the model 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 32000):
Vocabulary size of the HyperCLOVAX model. Defines the number of
different tokens that can be represented by the `input_ids`
passed when calling [`HyperCLOVAXModel`]
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.
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. NOTE: if you apply new rope type and you expect the
model to work on longer `max_position_embeddings`, we recommend
you to update this value accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default',
'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with
'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling
factor to apply to the RoPE embeddings. In most scaling
types, a `factor` of x will enable the model to handle
sequences of length x * original maximum pre-trained
length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The
original max position embeddings used during pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be
applied on the attention computation. If unspecified, it
defaults to value recommended by the implementation, using
the `factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for
extrapolation (only) in the linear ramp function. If
unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for
interpolation (only) in the linear ramp function. If
unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be
applied to short contexts (<
`original_max_position_embeddings`). Must be a list of
numbers with the same length as the hidden size divided
by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be
applied to long contexts (<
`original_max_position_embeddings`). Must be a list of
numbers with the same length as the hidden size divided
by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low
frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high
frequency components of the RoPE
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.
head_dim (`int`, *optional*):
The attention head dimension. If None, it will default to
hidden_size // num_heads
embedding_multiplier (`float`, *optional*, defaults to `None`):
Multiplier applied to the embedding weights. If `None`, it is
equivalent to `1.0`.
logits_scaling (`float`, *optional*, defaults to `None`):
Scaling factor for logits. If `None`, it is equivalent to `1.0`.
attention_multiplier (`float`, *optional*, defaults to `None`):
Multiplier applied to the attention weights. If `None`, it is
equivalent to `self.head_dim ** -0.5`.
residual_multiplier (`float`, *optional*, defaults to `None`):
Scaling factor for residual connections. If `None`, it is
equivalent to `1.0`.
use_post_norm (`bool`, *optional*, defaults to `True`):
Determines whether to apply Peri-Layer Normalization. Set to
False to disable this feature.
rope_parameters (`dict`, *optional*):
Dictionary containing the RoPE parameters used by vLLM's
`get_rope`. When provided, takes precedence over `rope_theta`
and `rope_scaling`. If `None`, it is derived from `rope_theta`
and `rope_scaling` automatically.
"""
model_type = "hyperclovax"
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,
head_dim=None,
embedding_multiplier=None, # mup
logits_scaling=None, # mup
attention_multiplier=None, # mup
residual_multiplier=None, # mup
use_post_norm=True, # post-norm(peri-LN)
rope_parameters=None,
auto_map=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.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
self.head_dim = (
head_dim
if head_dim is not None
else self.hidden_size // self.num_attention_heads
)
# Derive rope_parameters for vLLM's get_rope() from rope_theta /
# rope_scaling, unless the caller already provided rope_parameters.
if rope_parameters is None:
if rope_scaling is not None:
# Shallow-copy to avoid mutating the caller's dict.
rope_parameters = dict(rope_scaling)
# BC: 'type' field -> 'rope_type', remove stale key.
if "type" in rope_parameters:
rope_parameters.setdefault("rope_type", rope_parameters.pop("type"))
else:
rope_parameters = {"rope_type": "default"}
if "rope_theta" not in rope_parameters:
rope_parameters["rope_theta"] = rope_theta
self.rope_parameters = rope_parameters
# BC: keep self.rope_scaling consistent for HF serialization.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
# mup
self.embedding_multiplier = (
embedding_multiplier if embedding_multiplier is not None else 1.0
)
self.logits_scaling = logits_scaling if logits_scaling is not None else 1.0
self.attention_multiplier = (
attention_multiplier
if attention_multiplier is not None
else self.head_dim**-0.5
)
self.residual_multiplier = (
residual_multiplier if residual_multiplier is not None else 1.0
)
# post-norm (Peri-LN)
self.use_post_norm = use_post_norm
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,
auto_map=auto_map,
**kwargs,
)
class HCXVisionConfig(PretrainedConfig):
"""Vendored HyperCLOVAX Vision config with transformers v5 fix.
The original remote code config does not handle empty initialization
(text_config=None), which breaks transformers v5's @strict validation.
TODO: Remove this class once HyperCLOVAX is upstreamed to transformers.
Tracking PR: https://github.com/huggingface/transformers/pull/44956
"""
model_type = "hyperclovax_vlm"
keys_to_ignore_at_inference = ["past_key_values"]
text_config_attribute_map = {
"n_embd": "hidden_size",
"n_positions": "max_position_embeddings",
"n_head": "num_attention_heads",
"n_layer": "num_hidden_layers",
}
def __init__(
self,
text_config=None,
vision_config=None,
use_nth_layer=-2,
img_start_id=100009,
decoder_max_length=4096,
anyres=False,
unpad=False,
max_num_grids=-1,
num_queries_vis_abstractor=-1,
ignore_index=-100,
proj_pos_emb=True,
proj_prenorm=False,
use_1x1_grid=False,
**kwargs,
):
for key, val in self.text_config_attribute_map.items():
if text_config is not None and key in text_config:
text_config[val] = text_config.pop(key)
self.text_config = None
if text_config is not None:
_text_config = AutoConfig.for_model(text_config["model_type"])
self.text_config = _text_config.from_dict(text_config)
self.hidden_size = self.text_config.hidden_size
self.vision_config = None
if vision_config is not None:
_vision_config = AutoConfig.for_model(vision_config["model_type"])
self.vision_config = _vision_config.from_dict(vision_config)
self.use_nth_layer = use_nth_layer
self.decoder_max_length = decoder_max_length
self.anyres = anyres
self.unpad = unpad
self.max_num_grids = max_num_grids
self.num_queries_vis_abstractor = num_queries_vis_abstractor
self.img_start_id = img_start_id
self.ignore_index = ignore_index
self.proj_pos_emb = proj_pos_emb
self.proj_prenorm = proj_prenorm
self.use_1x1_grid = use_1x1_grid
super().__init__(**kwargs)
def get_text_config(self, decoder=False):
if self.text_config is not None:
return self.text_config
return self