HunYuan opensource (#39606)
* merge opensource_hunyuan * add head_dim * fix assertion error * fix seen_tokens * ready_for_upstream (merge request !17) Squash merge branch 'ready_for_upstream' into 'main' * fix configuration type&docstring * fix style * ready_for_upstream (merge request !18) Squash merge branch 'ready_for_upstream' into 'main' * add doc * fix testcode * fix configuration type&docstring * rename base model * remove assert * update * remove tiktoken * update * fix moe and code style (#3) * update * fix format * update * revert makefile * fix moe config * fix numel() * remove prepare_inputs_for_generation * fix kv_seq_len * add docs/toctree * remove unused paramter&add licence * add licence * remove unused paramter * fix code * dense modular update import fix fix use mistralmodel fix qknorm add sliding_window make style fix dense done hunyuan moe fix import fix modular fixup fixup * update model path * fix mlp_bias * fix modular * Fix modeling (#5) * fix attention * use llamamodel * fix code * Fix qk (#6) * fix qk_norm * fix * fix modual * Fix moe (#7) * fix some moe code * fix einsum * try top1 * use top1 * Fix rotary (#8) * fix rotary * fix modeling * fix modular * fix testcode * remove A13B unit test * Fix moe v1 (#9) fix moe & gate * Fix gate norm (#10) * add norm_topk_prob * Fix testcase (#11) * fix&skip test * Fix testcase (#12) * skip testcase * Fix norm topk (#13) * hardcode norm_topk_prob * fix testcase --------- Co-authored-by: pridejcyang <pridejcyang@tencent.com> Co-authored-by: Mingji Han <mingjihan@tencent.com>
This commit is contained in:
yjc9696
@@ -529,6 +529,12 @@
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title: Helium
|
||||
- local: model_doc/herbert
|
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title: HerBERT
|
||||
- local: model_doc/hgnet_v2
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||||
title: HGNet-V2
|
||||
- local: model_doc/hunyuan_v1_dense
|
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title: HunYuanDenseV1
|
||||
- local: model_doc/hunyuan_v1_moe
|
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title: HunYuanMoEV1
|
||||
- local: model_doc/ibert
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||||
title: I-BERT
|
||||
- local: model_doc/jamba
|
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|
||||
50
docs/source/en/model_doc/hunyuan_v1_dense.md
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50
docs/source/en/model_doc/hunyuan_v1_dense.md
Normal file
@@ -0,0 +1,50 @@
|
||||
<!--Copyright (C) 2024 THL A29 Limited, a Tencent company 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.
|
||||
|
||||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
||||
rendered properly in your Markdown viewer.
|
||||
|
||||
-->
|
||||
|
||||
# HunYuanDenseV1
|
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|
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## Overview
|
||||
|
||||
To be released with the official model launch.
|
||||
|
||||
### Model Details
|
||||
|
||||
To be released with the official model launch.
|
||||
|
||||
|
||||
## Usage tips
|
||||
|
||||
To be released with the official model launch.
|
||||
|
||||
## HunYuanDenseV1Config
|
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|
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[[autodoc]] HunYuanDenseV1Config
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|
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## HunYuanModel
|
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|
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[[autodoc]] HunYuanDenseV1Model
|
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- forward
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||||
|
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## HunYuanDenseV1ForCausalLM
|
||||
|
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[[autodoc]] HunYuanDenseV1ForCausalLM
|
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- forward
|
||||
|
||||
## HunYuanDenseV1ForSequenceClassification
|
||||
|
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[[autodoc]] HunYuanDenseV1ForSequenceClassification
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- forward
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|
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50
docs/source/en/model_doc/hunyuan_v1_moe.md
Normal file
50
docs/source/en/model_doc/hunyuan_v1_moe.md
Normal file
@@ -0,0 +1,50 @@
|
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<!--Copyright (C) 2024 THL A29 Limited, a Tencent company 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.
|
||||
|
||||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
||||
rendered properly in your Markdown viewer.
|
||||
|
||||
-->
|
||||
|
||||
# HunYuanMoEV1
|
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|
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## Overview
|
||||
|
||||
To be released with the official model launch.
|
||||
|
||||
### Model Details
|
||||
|
||||
To be released with the official model launch.
|
||||
|
||||
|
||||
## Usage tips
|
||||
|
||||
To be released with the official model launch.
|
||||
|
||||
## HunYuanMoEV1Config
|
||||
|
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[[autodoc]] HunYuanMoEV1Config
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|
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## HunYuanMoEV1Model
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|
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[[autodoc]] HunYuanMoEV1Model
|
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- forward
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|
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## HunYuanMoEV1ForCausalLM
|
||||
|
||||
[[autodoc]] HunYuanMoEV1ForCausalLM
|
||||
- forward
|
||||
|
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## HunYuanMoEV1ForSequenceClassification
|
||||
|
||||
[[autodoc]] HunYuanMoEV1ForSequenceClassification
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||||
- forward
|
||||
|
||||
@@ -158,6 +158,8 @@ if TYPE_CHECKING:
|
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from .hgnet_v2 import *
|
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from .hiera import *
|
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from .hubert import *
|
||||
from .hunyuan_v1_dense import *
|
||||
from .hunyuan_v1_moe import *
|
||||
from .ibert import *
|
||||
from .idefics import *
|
||||
from .idefics2 import *
|
||||
|
||||
@@ -193,6 +193,8 @@ CONFIG_MAPPING_NAMES = OrderedDict[str, str](
|
||||
("hgnet_v2", "HGNetV2Config"),
|
||||
("hiera", "HieraConfig"),
|
||||
("hubert", "HubertConfig"),
|
||||
("hunyuan_v1_dense", "HunYuanDenseV1Config"),
|
||||
("hunyuan_v1_moe", "HunYuanMoEV1Config"),
|
||||
("ibert", "IBertConfig"),
|
||||
("idefics", "IdeficsConfig"),
|
||||
("idefics2", "Idefics2Config"),
|
||||
@@ -613,6 +615,8 @@ MODEL_NAMES_MAPPING = OrderedDict[str, str](
|
||||
("hgnet_v2", "HGNet-V2"),
|
||||
("hiera", "Hiera"),
|
||||
("hubert", "Hubert"),
|
||||
("hunyuan_v1_dense", "HunYuanDenseV1"),
|
||||
("hunyuan_v1_moe", "HunYuanMoeV1"),
|
||||
("ibert", "I-BERT"),
|
||||
("idefics", "IDEFICS"),
|
||||
("idefics2", "Idefics2"),
|
||||
|
||||
@@ -193,6 +193,8 @@ MODEL_MAPPING_NAMES = OrderedDict(
|
||||
("hgnet_v2", "HGNetV2Backbone"),
|
||||
("hiera", "HieraModel"),
|
||||
("hubert", "HubertModel"),
|
||||
("hunyuan_v1_dense", "HunYuanDenseV1Model"),
|
||||
("hunyuan_v1_moe", "HunYuanMoEV1Model"),
|
||||
("ibert", "IBertModel"),
|
||||
("idefics", "IdeficsModel"),
|
||||
("idefics2", "Idefics2Model"),
|
||||
@@ -664,6 +666,8 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
|
||||
("granitemoehybrid", "GraniteMoeHybridForCausalLM"),
|
||||
("granitemoeshared", "GraniteMoeSharedForCausalLM"),
|
||||
("helium", "HeliumForCausalLM"),
|
||||
("hunyuan_v1_dense", "HunYuanDenseV1ForCausalLM"),
|
||||
("hunyuan_v1_moe", "HunYuanMoEV1ForCausalLM"),
|
||||
("jamba", "JambaForCausalLM"),
|
||||
("jetmoe", "JetMoeForCausalLM"),
|
||||
("lfm2", "Lfm2ForCausalLM"),
|
||||
@@ -1209,6 +1213,8 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
|
||||
("gpt_oss", "GptOssForSequenceClassification"),
|
||||
("gptj", "GPTJForSequenceClassification"),
|
||||
("helium", "HeliumForSequenceClassification"),
|
||||
("hunyuan_v1_dense", "HunYuanDenseV1ForSequenceClassification"),
|
||||
("hunyuan_v1_moe", "HunYuanMoEV1ForSequenceClassification"),
|
||||
("ibert", "IBertForSequenceClassification"),
|
||||
("jamba", "JambaForSequenceClassification"),
|
||||
("jetmoe", "JetMoeForSequenceClassification"),
|
||||
|
||||
15
src/transformers/models/hunyuan_v1_dense/__init__.py
Normal file
15
src/transformers/models/hunyuan_v1_dense/__init__.py
Normal file
@@ -0,0 +1,15 @@
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from ...utils import _LazyModule
|
||||
from ...utils.import_utils import define_import_structure
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .configuration_hunyuan_v1_dense import *
|
||||
from .modeling_hunyuan_v1_dense import *
|
||||
from .tokenization_hy import *
|
||||
else:
|
||||
import sys
|
||||
|
||||
_file = globals()["__file__"]
|
||||
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
|
||||
@@ -0,0 +1,189 @@
|
||||
# coding=utf-8
|
||||
# Copyright (C) 2025 THL A29 Limited, a Tencent company 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.
|
||||
"""HunYuanDenseV1 model configuration"""
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class HunYuanDenseV1Config(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`HunYuanDenseV1Config`]. It is used to instantiate an
|
||||
HunYuan 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 HunYuan-7B.
|
||||
Hunyuan-7B-Instruct [tencent/Hunyuan-7B-Instruct](https://huggingface.co/tencent/Hunyuan-7B-Instruct).
|
||||
|
||||
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 290943):
|
||||
Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`HunYuanDenseV1Config`]
|
||||
hidden_size (`int`, *optional*, defaults to 4096):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 11008):
|
||||
Dimension of the MLP representations or shared 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-05):
|
||||
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*, defaults to 0):
|
||||
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.
|
||||
eod_token_id (int, *optional*, defaults to 3):
|
||||
Token ID representing the end-of-document marker. Used to indicate the termination of a text sequence.
|
||||
Example: In multi-document processing, this token helps the model distinguish between separate documents.
|
||||
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/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`, defaults to `False`, *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.
|
||||
head_dim (`int`, *optional*, defaults to 128):
|
||||
The attention head dimension.
|
||||
"""
|
||||
|
||||
model_type = "hunyuan_v1_dense"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=290943,
|
||||
hidden_size=4096,
|
||||
intermediate_size: int = 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-5,
|
||||
use_cache=True,
|
||||
pad_token_id=0,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
eod_token_id=3,
|
||||
pretraining_tp=1,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
attention_bias=False,
|
||||
attention_dropout=0.0,
|
||||
head_dim=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
|
||||
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.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() # TODO: Need validation?
|
||||
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,
|
||||
)
|
||||
|
||||
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 with two fields, `type` and `factor` or `type` and `alpha`, "
|
||||
f"got {self.rope_scaling}"
|
||||
)
|
||||
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
||||
rope_scaling_alpha = self.rope_scaling.get("alpha", 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 and rope_scaling_alpha is None:
|
||||
raise ValueError("`rope_scaling`'s factor or alpha field must be have one, got both of none")
|
||||
if rope_scaling_factor is not None:
|
||||
if not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
||||
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1.0, got {rope_scaling_factor}")
|
||||
if rope_scaling_alpha is not None:
|
||||
if not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0:
|
||||
raise ValueError(f"`rope_scaling`'s alpha field must be a float > 1.0, got {rope_scaling_alpha}")
|
||||
|
||||
|
||||
__all__ = ["HunYuanDenseV1Config"]
|
||||
@@ -0,0 +1,520 @@
|
||||
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
||||
# This file was automatically generated from src/transformers/models/hunyuan_v1_dense/modular_hunyuan_v1_dense.py.
|
||||
# Do NOT edit this file manually as any edits will be overwritten by the generation of
|
||||
# the file from the modular. If any change should be done, please apply the change to the
|
||||
# modular_hunyuan_v1_dense.py file directly. One of our CI enforces this.
|
||||
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
||||
# coding=utf-8
|
||||
# Copyright (C) 2025 THL A29 Limited, a Tencent company 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.
|
||||
|
||||
from typing import Callable, Optional, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
|
||||
from transformers.cache_utils import Cache
|
||||
|
||||
from ...activations import ACT2FN
|
||||
from ...cache_utils import DynamicCache
|
||||
from ...generation import GenerationMixin
|
||||
from ...integrations import use_kernel_forward_from_hub
|
||||
from ...masking_utils import create_causal_mask
|
||||
from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
|
||||
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
||||
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
||||
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
||||
from ...processing_utils import Unpack
|
||||
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
|
||||
from ...utils.deprecation import deprecate_kwarg
|
||||
from ...utils.generic import check_model_inputs
|
||||
from .configuration_hunyuan_v1_dense import HunYuanDenseV1Config
|
||||
|
||||
|
||||
@use_kernel_forward_from_hub("RMSNorm")
|
||||
class HunYuanDenseV1RMSNorm(nn.Module):
|
||||
def __init__(self, hidden_size, eps=1e-6):
|
||||
"""
|
||||
HunYuanDenseV1RMSNorm is equivalent to T5LayerNorm
|
||||
"""
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||||
self.variance_epsilon = eps
|
||||
|
||||
def forward(self, hidden_states):
|
||||
input_dtype = hidden_states.dtype
|
||||
hidden_states = hidden_states.to(torch.float32)
|
||||
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||||
return self.weight * hidden_states.to(input_dtype)
|
||||
|
||||
def extra_repr(self):
|
||||
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
||||
|
||||
|
||||
class HunYuanDenseV1MLP(nn.Module):
|
||||
def __init__(self, config: HunYuanDenseV1Config, layer_idx=None, is_shared_mlp=False):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.intermediate_size = config.intermediate_size
|
||||
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
||||
self.act_fn = ACT2FN[config.hidden_act]
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
def forward(self, x):
|
||||
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||||
return down_proj
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
"""Rotates half the hidden dims of the input."""
|
||||
x1 = x[..., : x.shape[-1] // 2]
|
||||
x2 = x[..., x.shape[-1] // 2 :]
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
|
||||
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
||||
"""Applies Rotary Position Embedding to the query and key tensors.
|
||||
|
||||
Args:
|
||||
q (`torch.Tensor`): The query tensor.
|
||||
k (`torch.Tensor`): The key tensor.
|
||||
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
||||
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
||||
position_ids (`torch.Tensor`, *optional*):
|
||||
Deprecated and unused.
|
||||
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
||||
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
||||
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
||||
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
||||
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
||||
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
||||
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
||||
Returns:
|
||||
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
||||
"""
|
||||
cos = cos.unsqueeze(unsqueeze_dim)
|
||||
sin = sin.unsqueeze(unsqueeze_dim)
|
||||
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
return q_embed, k_embed
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
"""
|
||||
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
||||
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
||||
"""
|
||||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||
if n_rep == 1:
|
||||
return hidden_states
|
||||
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
||||
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||
|
||||
|
||||
def eager_attention_forward(
|
||||
module: nn.Module,
|
||||
query: torch.Tensor,
|
||||
key: torch.Tensor,
|
||||
value: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
scaling: float,
|
||||
dropout: float = 0.0,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
):
|
||||
key_states = repeat_kv(key, module.num_key_value_groups)
|
||||
value_states = repeat_kv(value, module.num_key_value_groups)
|
||||
|
||||
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
|
||||
if attention_mask is not None:
|
||||
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
||||
attn_weights = attn_weights + causal_mask
|
||||
|
||||
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
||||
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class HunYuanDenseV1Attention(nn.Module):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
|
||||
def __init__(self, config: HunYuanDenseV1Config, layer_idx: int):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
||||
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
|
||||
self.scaling = self.head_dim**-0.5
|
||||
self.attention_dropout = config.attention_dropout
|
||||
self.is_causal = True
|
||||
|
||||
self.q_proj = nn.Linear(
|
||||
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
|
||||
)
|
||||
self.k_proj = nn.Linear(
|
||||
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
||||
)
|
||||
self.v_proj = nn.Linear(
|
||||
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
||||
)
|
||||
self.o_proj = nn.Linear(
|
||||
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
|
||||
)
|
||||
self.query_layernorm = HunYuanDenseV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||||
self.key_layernorm = HunYuanDenseV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||||
|
||||
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
past_key_values: Optional[Cache] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
input_shape = hidden_states.shape[:-1]
|
||||
hidden_shape = (*input_shape, -1, self.head_dim)
|
||||
|
||||
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
query_states = self.query_layernorm(query_states)
|
||||
key_states = self.key_layernorm(key_states)
|
||||
|
||||
if past_key_values is not None:
|
||||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
attention_interface: Callable = eager_attention_forward
|
||||
if self.config._attn_implementation != "eager":
|
||||
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
||||
|
||||
attn_output, attn_weights = attention_interface(
|
||||
self,
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attention_mask,
|
||||
dropout=0.0 if not self.training else self.attention_dropout,
|
||||
scaling=self.scaling,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
||||
attn_output = self.o_proj(attn_output)
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class HunYuanDenseV1DecoderLayer(GradientCheckpointingLayer):
|
||||
def __init__(self, config: HunYuanDenseV1Config, layer_idx: int):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
self.self_attn = HunYuanDenseV1Attention(config=config, layer_idx=layer_idx)
|
||||
|
||||
self.mlp = HunYuanDenseV1MLP(config)
|
||||
self.input_layernorm = HunYuanDenseV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = HunYuanDenseV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
use_cache: Optional[bool] = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> torch.Tensor:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
# Self Attention
|
||||
hidden_states, _ = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
return hidden_states
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class HunYuanDenseV1PreTrainedModel(PreTrainedModel):
|
||||
config: HunYuanDenseV1Config
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_no_split_modules = ["HunYuanDenseV1DecoderLayer"]
|
||||
_skip_keys_device_placement = ["past_key_values"]
|
||||
_supports_flash_attn = True
|
||||
_supports_sdpa = True
|
||||
_supports_flex_attn = True
|
||||
|
||||
_can_compile_fullgraph = True
|
||||
_supports_attention_backend = True
|
||||
_can_record_outputs = {
|
||||
"hidden_states": HunYuanDenseV1DecoderLayer,
|
||||
"attentions": HunYuanDenseV1Attention,
|
||||
}
|
||||
|
||||
def _init_weights(self, module):
|
||||
std = self.config.initializer_range
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.Embedding):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
|
||||
|
||||
class HunYuanDenseV1RotaryEmbedding(nn.Module):
|
||||
inv_freq: torch.Tensor # fix linting for `register_buffer`
|
||||
|
||||
def __init__(self, config: HunYuanDenseV1Config, device=None):
|
||||
super().__init__()
|
||||
# BC: "rope_type" was originally "type"
|
||||
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
||||
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
||||
else:
|
||||
self.rope_type = "default"
|
||||
self.max_seq_len_cached = config.max_position_embeddings
|
||||
self.original_max_seq_len = config.max_position_embeddings
|
||||
|
||||
self.config = config
|
||||
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
||||
if self.rope_type == "dynamic" and config.rope_scaling["alpha"]:
|
||||
# DynamicNTKAlphaRotary
|
||||
self.dim = config.head_dim
|
||||
base = config.rope_theta * config.rope_scaling.get("alpha") ** (self.dim / (self.dim - 2))
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
||||
self.attention_scaling = 1.0
|
||||
else:
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
||||
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self.original_inv_freq = self.inv_freq
|
||||
|
||||
@torch.no_grad()
|
||||
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
||||
def forward(self, x, position_ids):
|
||||
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
||||
position_ids_expanded = position_ids[:, None, :].float()
|
||||
|
||||
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos() * self.attention_scaling
|
||||
sin = emb.sin() * self.attention_scaling
|
||||
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class HunYuanDenseV1Model(HunYuanDenseV1PreTrainedModel):
|
||||
def __init__(self, config: HunYuanDenseV1Config):
|
||||
super().__init__(config)
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||||
self.layers = nn.ModuleList(
|
||||
[HunYuanDenseV1DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||||
)
|
||||
self.norm = HunYuanDenseV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.rotary_emb = HunYuanDenseV1RotaryEmbedding(config=config)
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
@check_model_inputs
|
||||
@auto_docstring
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> BaseModelOutputWithPast:
|
||||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
|
||||
|
||||
if use_cache and past_key_values is None:
|
||||
past_key_values = DynamicCache(config=self.config)
|
||||
|
||||
if cache_position is None:
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
cache_position: torch.Tensor = torch.arange(
|
||||
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||||
)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
causal_mask = create_causal_mask(
|
||||
config=self.config,
|
||||
input_embeds=inputs_embeds,
|
||||
attention_mask=attention_mask,
|
||||
cache_position=cache_position,
|
||||
past_key_values=past_key_values,
|
||||
position_ids=position_ids,
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||||
|
||||
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
||||
hidden_states = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=past_key_values,
|
||||
)
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class HunYuanDenseV1ForCausalLM(HunYuanDenseV1PreTrainedModel, GenerationMixin):
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
_tp_plan = {"lm_head": "colwise_rep"}
|
||||
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = HunYuanDenseV1Model(config)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model
|
||||
|
||||
@can_return_tuple
|
||||
@auto_docstring
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> CausalLMOutputWithPast:
|
||||
r"""
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, HunYuanDenseV1ForCausalLM
|
||||
|
||||
>>> model = HunYuanDenseV1ForCausalLM.from_pretrained("meta-hunyuan_v1_dense/HunYuanDenseV1-2-7b-hf")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("meta-hunyuan_v1_dense/HunYuanDenseV1-2-7b-hf")
|
||||
|
||||
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
||||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||||
|
||||
>>> # Generate
|
||||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||||
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
||||
```"""
|
||||
outputs: BaseModelOutputWithPast = self.model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = outputs.last_hidden_state
|
||||
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
||||
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||||
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
class HunYuanDenseV1ForSequenceClassification(GenericForSequenceClassification, HunYuanDenseV1PreTrainedModel):
|
||||
pass
|
||||
|
||||
|
||||
__all__ = [
|
||||
"HunYuanDenseV1ForCausalLM",
|
||||
"HunYuanDenseV1Model",
|
||||
"HunYuanDenseV1PreTrainedModel",
|
||||
"HunYuanDenseV1ForSequenceClassification",
|
||||
]
|
||||
@@ -0,0 +1,194 @@
|
||||
# coding=utf-8
|
||||
# Copyright (C) 2025 THL A29 Limited, a Tencent company 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.
|
||||
"""PyTorch HunYuanDenseV1 model."""
|
||||
|
||||
from typing import Callable, Optional
|
||||
|
||||
import torch
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
|
||||
from transformers.cache_utils import Cache
|
||||
from transformers.utils import (
|
||||
logging,
|
||||
)
|
||||
|
||||
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
||||
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
|
||||
from ...processing_utils import Unpack
|
||||
from ...utils import TransformersKwargs
|
||||
from ..llama.modeling_llama import (
|
||||
LlamaAttention,
|
||||
LlamaDecoderLayer,
|
||||
LlamaForCausalLM,
|
||||
LlamaForSequenceClassification,
|
||||
LlamaMLP,
|
||||
LlamaModel,
|
||||
LlamaPreTrainedModel,
|
||||
LlamaRMSNorm,
|
||||
apply_rotary_pos_emb,
|
||||
eager_attention_forward,
|
||||
)
|
||||
from .configuration_hunyuan_v1_dense import HunYuanDenseV1Config
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class HunYuanDenseV1RMSNorm(LlamaRMSNorm):
|
||||
pass
|
||||
|
||||
|
||||
class HunYuanDenseV1MLP(LlamaMLP):
|
||||
def __init__(self, config: HunYuanDenseV1Config, layer_idx=None, is_shared_mlp=False):
|
||||
super().__init__(config)
|
||||
self.layer_idx = layer_idx
|
||||
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
||||
|
||||
|
||||
class HunYuanDenseV1Attention(LlamaAttention):
|
||||
def __init__(self, config: HunYuanDenseV1Config, layer_idx: int):
|
||||
super().__init__(config, layer_idx)
|
||||
self.query_layernorm = HunYuanDenseV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||||
self.key_layernorm = HunYuanDenseV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
past_key_values: Optional[Cache] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
input_shape = hidden_states.shape[:-1]
|
||||
hidden_shape = (*input_shape, -1, self.head_dim)
|
||||
|
||||
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
query_states = self.query_layernorm(query_states)
|
||||
key_states = self.key_layernorm(key_states)
|
||||
|
||||
if past_key_values is not None:
|
||||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
attention_interface: Callable = eager_attention_forward
|
||||
if self.config._attn_implementation != "eager":
|
||||
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
||||
|
||||
attn_output, attn_weights = attention_interface(
|
||||
self,
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attention_mask,
|
||||
dropout=0.0 if not self.training else self.attention_dropout,
|
||||
scaling=self.scaling,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
||||
attn_output = self.o_proj(attn_output)
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class HunYuanDenseV1DecoderLayer(LlamaDecoderLayer):
|
||||
def __init__(self, config: HunYuanDenseV1Config, layer_idx: int):
|
||||
super().__init__()
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
|
||||
class HunYuanDenseV1PreTrainedModel(LlamaPreTrainedModel):
|
||||
def _init_weights(self, module):
|
||||
std = self.config.initializer_range
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.Embedding):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
|
||||
|
||||
class HunYuanDenseV1RotaryEmbedding(nn.Module):
|
||||
inv_freq: torch.Tensor # fix linting for `register_buffer`
|
||||
|
||||
def __init__(self, config: HunYuanDenseV1Config, device=None):
|
||||
super().__init__()
|
||||
# BC: "rope_type" was originally "type"
|
||||
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
||||
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
||||
else:
|
||||
self.rope_type = "default"
|
||||
self.max_seq_len_cached = config.max_position_embeddings
|
||||
self.original_max_seq_len = config.max_position_embeddings
|
||||
|
||||
self.config = config
|
||||
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
||||
if self.rope_type == "dynamic" and config.rope_scaling["alpha"]:
|
||||
# DynamicNTKAlphaRotary
|
||||
self.dim = config.head_dim
|
||||
base = config.rope_theta * config.rope_scaling.get("alpha") ** (self.dim / (self.dim - 2))
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
||||
self.attention_scaling = 1.0
|
||||
else:
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
||||
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self.original_inv_freq = self.inv_freq
|
||||
|
||||
@torch.no_grad()
|
||||
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
||||
def forward(self, x, position_ids):
|
||||
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
||||
position_ids_expanded = position_ids[:, None, :].float()
|
||||
|
||||
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos() * self.attention_scaling
|
||||
sin = emb.sin() * self.attention_scaling
|
||||
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
|
||||
class HunYuanDenseV1Model(LlamaModel):
|
||||
pass
|
||||
|
||||
|
||||
class HunYuanDenseV1ForCausalLM(LlamaForCausalLM):
|
||||
pass
|
||||
|
||||
|
||||
class HunYuanDenseV1ForSequenceClassification(LlamaForSequenceClassification):
|
||||
pass
|
||||
|
||||
|
||||
__all__ = [
|
||||
"HunYuanDenseV1ForCausalLM",
|
||||
"HunYuanDenseV1Model",
|
||||
"HunYuanDenseV1PreTrainedModel",
|
||||
"HunYuanDenseV1ForSequenceClassification",
|
||||
]
|
||||
14
src/transformers/models/hunyuan_v1_moe/__init__.py
Normal file
14
src/transformers/models/hunyuan_v1_moe/__init__.py
Normal file
@@ -0,0 +1,14 @@
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from ...utils import _LazyModule
|
||||
from ...utils.import_utils import define_import_structure
|
||||
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .configuration_hunyuan_v1_moe import *
|
||||
from .modeling_hunyuan import *
|
||||
else:
|
||||
import sys
|
||||
|
||||
_file = globals()["__file__"]
|
||||
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
|
||||
@@ -0,0 +1,204 @@
|
||||
# coding=utf-8
|
||||
# Copyright (C) 2025 THL A29 Limited, a Tencent company 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.
|
||||
"""HunYuanMoEV1 model configuration"""
|
||||
|
||||
from typing import Union
|
||||
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class HunYuanMoEV1Config(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`HunYuanMoEV1Model`]. It is used to instantiate an
|
||||
HunYuan 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 HunYuan-7B.
|
||||
Hunyuan-A13B-Instruct [tencent/Hunyuan-A13B-Instruct](https://huggingface.co/tencent/Hunyuan-A13B-Instruct).
|
||||
|
||||
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 290943):
|
||||
Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`HunYuanMoEV1Model`]
|
||||
hidden_size (`int`, *optional*, defaults to 4096):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 11008):
|
||||
Dimension of the MLP representations or shared 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-05):
|
||||
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*, defaults to 0):
|
||||
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.
|
||||
eod_token_id (int, *optional*, defaults to 3):
|
||||
Token ID representing the end-of-document marker. Used to indicate the termination of a text sequence.
|
||||
Example: In multi-document processing, this token helps the model distinguish between separate documents.
|
||||
sep_token_id (`int`, *optional*, defaults to 4):
|
||||
Token ID representing the separator token (`[SEP]`), used to demarcate boundaries between different text segments.
|
||||
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/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`, defaults to `False`, *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.
|
||||
num_experts (`int` or `List`, *optional*, defaults to 1):
|
||||
The number of experts for moe. If it is a list, it will be used as the number of experts for each layer.
|
||||
moe_topk (int or List, *optional*, defaults to 1):
|
||||
Number of experts selected per token (Top-K routing). List form enables layer-wise customization.
|
||||
head_dim (`int`, *optional*, defaults to 128):
|
||||
The attention head dimension.
|
||||
"""
|
||||
|
||||
model_type = "hunyuan_v1_moe"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=290943,
|
||||
hidden_size=4096,
|
||||
intermediate_size: int = 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-5,
|
||||
use_cache=True,
|
||||
pad_token_id=0,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
eod_token_id=3,
|
||||
sep_token_id=4,
|
||||
pretraining_tp=1,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
attention_bias=False,
|
||||
attention_dropout=0.0,
|
||||
num_experts: Union[int, list] = 1,
|
||||
moe_topk: Union[int, list] = 1,
|
||||
head_dim=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
|
||||
self.num_experts = num_experts
|
||||
self.moe_topk = moe_topk
|
||||
|
||||
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.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() # TODO: Need validation?
|
||||
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,
|
||||
sep_token_id=sep_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 with two fields, `type` and `factor` or `type` and `alpha`, "
|
||||
f"got {self.rope_scaling}"
|
||||
)
|
||||
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
||||
rope_scaling_alpha = self.rope_scaling.get("alpha", 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 and rope_scaling_alpha is None:
|
||||
raise ValueError("`rope_scaling`'s factor or alpha field must be have one, got both of none")
|
||||
if rope_scaling_factor is not None:
|
||||
if not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
||||
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1.0, got {rope_scaling_factor}")
|
||||
if rope_scaling_alpha is not None:
|
||||
if not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0:
|
||||
raise ValueError(f"`rope_scaling`'s alpha field must be a float > 1.0, got {rope_scaling_alpha}")
|
||||
|
||||
|
||||
__all__ = ["HunYuanMoEV1Config"]
|
||||
@@ -0,0 +1,591 @@
|
||||
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
||||
# This file was automatically generated from src/transformers/models/hunyuan_v1_moe/modular_hunyuan_v1_moe.py.
|
||||
# Do NOT edit this file manually as any edits will be overwritten by the generation of
|
||||
# the file from the modular. If any change should be done, please apply the change to the
|
||||
# modular_hunyuan_v1_moe.py file directly. One of our CI enforces this.
|
||||
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
|
||||
# coding=utf-8
|
||||
# Copyright (C) 2025 THL A29 Limited, a Tencent company 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.
|
||||
|
||||
from typing import Callable, Optional, Union
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
|
||||
from transformers.cache_utils import Cache
|
||||
|
||||
from ...activations import ACT2FN
|
||||
from ...cache_utils import DynamicCache
|
||||
from ...generation import GenerationMixin
|
||||
from ...integrations import use_kernel_forward_from_hub
|
||||
from ...masking_utils import create_causal_mask
|
||||
from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
|
||||
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
||||
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
||||
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
|
||||
from ...processing_utils import Unpack
|
||||
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
|
||||
from ...utils.deprecation import deprecate_kwarg
|
||||
from ...utils.generic import check_model_inputs
|
||||
from .configuration_hunyuan_v1_moe import HunYuanMoEV1Config
|
||||
|
||||
|
||||
@use_kernel_forward_from_hub("RMSNorm")
|
||||
class HunYuanMoEV1RMSNorm(nn.Module):
|
||||
def __init__(self, hidden_size, eps=1e-6):
|
||||
"""
|
||||
HunYuanMoEV1RMSNorm is equivalent to T5LayerNorm
|
||||
"""
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||||
self.variance_epsilon = eps
|
||||
|
||||
def forward(self, hidden_states):
|
||||
input_dtype = hidden_states.dtype
|
||||
hidden_states = hidden_states.to(torch.float32)
|
||||
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||||
return self.weight * hidden_states.to(input_dtype)
|
||||
|
||||
def extra_repr(self):
|
||||
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
||||
|
||||
|
||||
class HunYuanMoEV1MLP(nn.Module):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx=None, is_shared_mlp=False):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.intermediate_size = config.intermediate_size
|
||||
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
||||
self.act_fn = ACT2FN[config.hidden_act]
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
def forward(self, x):
|
||||
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||||
return down_proj
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
"""Rotates half the hidden dims of the input."""
|
||||
x1 = x[..., : x.shape[-1] // 2]
|
||||
x2 = x[..., x.shape[-1] // 2 :]
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
|
||||
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
|
||||
"""Applies Rotary Position Embedding to the query and key tensors.
|
||||
|
||||
Args:
|
||||
q (`torch.Tensor`): The query tensor.
|
||||
k (`torch.Tensor`): The key tensor.
|
||||
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
||||
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
||||
position_ids (`torch.Tensor`, *optional*):
|
||||
Deprecated and unused.
|
||||
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
||||
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
||||
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
||||
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
||||
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
||||
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
||||
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
||||
Returns:
|
||||
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
||||
"""
|
||||
cos = cos.unsqueeze(unsqueeze_dim)
|
||||
sin = sin.unsqueeze(unsqueeze_dim)
|
||||
q_embed = (q * cos) + (rotate_half(q) * sin)
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
return q_embed, k_embed
|
||||
|
||||
|
||||
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
||||
"""
|
||||
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
||||
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
||||
"""
|
||||
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
||||
if n_rep == 1:
|
||||
return hidden_states
|
||||
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
||||
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
||||
|
||||
|
||||
def eager_attention_forward(
|
||||
module: nn.Module,
|
||||
query: torch.Tensor,
|
||||
key: torch.Tensor,
|
||||
value: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
scaling: float,
|
||||
dropout: float = 0.0,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
):
|
||||
key_states = repeat_kv(key, module.num_key_value_groups)
|
||||
value_states = repeat_kv(value, module.num_key_value_groups)
|
||||
|
||||
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
|
||||
if attention_mask is not None:
|
||||
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
||||
attn_weights = attn_weights + causal_mask
|
||||
|
||||
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
||||
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
||||
attn_output = torch.matmul(attn_weights, value_states)
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class HunYuanMoEV1Attention(nn.Module):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx: int):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
||||
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
|
||||
self.scaling = self.head_dim**-0.5
|
||||
self.attention_dropout = config.attention_dropout
|
||||
self.is_causal = True
|
||||
|
||||
self.q_proj = nn.Linear(
|
||||
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
|
||||
)
|
||||
self.k_proj = nn.Linear(
|
||||
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
||||
)
|
||||
self.v_proj = nn.Linear(
|
||||
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
||||
)
|
||||
self.o_proj = nn.Linear(
|
||||
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
|
||||
)
|
||||
self.query_layernorm = HunYuanMoEV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||||
self.key_layernorm = HunYuanMoEV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||||
|
||||
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
past_key_values: Optional[Cache] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
input_shape = hidden_states.shape[:-1]
|
||||
hidden_shape = (*input_shape, -1, self.head_dim)
|
||||
|
||||
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
query_states = self.query_layernorm(query_states)
|
||||
key_states = self.key_layernorm(key_states)
|
||||
|
||||
if past_key_values is not None:
|
||||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
attention_interface: Callable = eager_attention_forward
|
||||
if self.config._attn_implementation != "eager":
|
||||
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
||||
|
||||
attn_output, attn_weights = attention_interface(
|
||||
self,
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attention_mask,
|
||||
dropout=0.0 if not self.training else self.attention_dropout,
|
||||
scaling=self.scaling,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
||||
attn_output = self.o_proj(attn_output)
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class HunYuanMoEV1Gate(nn.Module):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx: Optional[int] = None):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
num_experts = config.num_experts if isinstance(config.num_experts, int) else config.num_experts[layer_idx]
|
||||
self.wg = nn.Linear(config.hidden_size, num_experts, bias=False, dtype=torch.float32)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
bsz, seq_len, hidden_size = hidden_states.shape
|
||||
hidden_states = hidden_states.reshape(-1, hidden_size)
|
||||
if self.wg.weight.dtype == torch.float32:
|
||||
hidden_states = hidden_states.float()
|
||||
logits = self.wg(hidden_states)
|
||||
return logits
|
||||
|
||||
|
||||
class HunYuanMoEV1Moe(nn.Module):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx: Optional[int] = None):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
self.num_experts = config.num_experts if isinstance(config.num_experts, int) else config.num_experts[layer_idx]
|
||||
self.top_k = config.moe_topk if isinstance(config.moe_topk, int) else config.moe_topk[layer_idx]
|
||||
self.gate = HunYuanMoEV1Gate(config, layer_idx=layer_idx)
|
||||
# self.wg = nn.Linear(config.hidden_size, config.num_experts, bias=False, dtype=torch.float32)
|
||||
self.experts = nn.ModuleList(
|
||||
[HunYuanMoEV1MLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(self.num_experts)]
|
||||
)
|
||||
|
||||
self.shared_mlp = HunYuanMoEV1MLP(config, layer_idx=layer_idx, is_shared_mlp=True)
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
||||
hidden_states_mlp = self.shared_mlp(hidden_states)
|
||||
router_logits = self.gate(hidden_states)
|
||||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
# router_logits: (batch * sequence_length, n_experts)
|
||||
|
||||
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
||||
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
||||
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
||||
# we cast back to the input dtype
|
||||
routing_weights = routing_weights.to(hidden_states.dtype)
|
||||
|
||||
final_hidden_states = torch.zeros(
|
||||
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
||||
)
|
||||
|
||||
# One hot encode the selected experts to create an expert mask
|
||||
# this will be used to easily index which expert is going to be sollicitated
|
||||
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
||||
|
||||
# Loop over all available experts in the model and perform the computation on each expert
|
||||
expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
|
||||
for expert_idx in expert_hit:
|
||||
expert_layer = self.experts[expert_idx]
|
||||
idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
|
||||
|
||||
# Index the correct hidden states and compute the expert hidden state for
|
||||
# the current expert. We need to make sure to multiply the output hidden
|
||||
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
||||
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
|
||||
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
|
||||
|
||||
# However `index_add_` only support torch tensors for indexing so we'll use
|
||||
# the `top_x` tensor here.
|
||||
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
||||
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
||||
return final_hidden_states + hidden_states_mlp
|
||||
|
||||
|
||||
class HunYuanMoEV1DecoderLayer(GradientCheckpointingLayer):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx: int):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.self_attn = HunYuanMoEV1Attention(config=config, layer_idx=layer_idx)
|
||||
self.mlp = HunYuanMoEV1Moe(config, layer_idx=layer_idx)
|
||||
self.input_layernorm = HunYuanMoEV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = HunYuanMoEV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
use_cache: Optional[bool] = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> torch.Tensor:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
# Self Attention
|
||||
hidden_states, _ = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
return hidden_states
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class HunYuanMoEV1PreTrainedModel(PreTrainedModel):
|
||||
config: HunYuanMoEV1Config
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_no_split_modules = ["HunYuanMoEV1DecoderLayer"]
|
||||
_skip_keys_device_placement = ["past_key_values"]
|
||||
_supports_flash_attn = True
|
||||
_supports_sdpa = True
|
||||
_supports_flex_attn = True
|
||||
|
||||
_can_compile_fullgraph = True
|
||||
_supports_attention_backend = True
|
||||
_can_record_outputs = {
|
||||
"hidden_states": HunYuanMoEV1DecoderLayer,
|
||||
"attentions": HunYuanMoEV1Attention,
|
||||
}
|
||||
|
||||
def _init_weights(self, module):
|
||||
std = self.config.initializer_range
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.Embedding):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
|
||||
|
||||
class HunYuanMoEV1RotaryEmbedding(nn.Module):
|
||||
inv_freq: torch.Tensor # fix linting for `register_buffer`
|
||||
|
||||
def __init__(self, config: HunYuanMoEV1Config, device=None):
|
||||
super().__init__()
|
||||
# BC: "rope_type" was originally "type"
|
||||
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
||||
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
||||
else:
|
||||
self.rope_type = "default"
|
||||
self.max_seq_len_cached = config.max_position_embeddings
|
||||
self.original_max_seq_len = config.max_position_embeddings
|
||||
|
||||
self.config = config
|
||||
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
||||
if self.rope_type == "dynamic" and config.rope_scaling["alpha"]:
|
||||
# DynamicNTKAlphaRotary
|
||||
self.dim = config.head_dim
|
||||
base = config.rope_theta * config.rope_scaling.get("alpha") ** (self.dim / (self.dim - 2))
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
||||
self.attention_scaling = 1.0
|
||||
else:
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
||||
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self.original_inv_freq = self.inv_freq
|
||||
|
||||
@torch.no_grad()
|
||||
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
||||
def forward(self, x, position_ids):
|
||||
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
||||
position_ids_expanded = position_ids[:, None, :].float()
|
||||
|
||||
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos() * self.attention_scaling
|
||||
sin = emb.sin() * self.attention_scaling
|
||||
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class HunYuanMoEV1Model(HunYuanMoEV1PreTrainedModel):
|
||||
def __init__(self, config: HunYuanMoEV1Config):
|
||||
super().__init__(config)
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||||
self.layers = nn.ModuleList(
|
||||
[HunYuanMoEV1DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
||||
)
|
||||
self.norm = HunYuanMoEV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.rotary_emb = HunYuanMoEV1RotaryEmbedding(config=config)
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
@check_model_inputs
|
||||
@auto_docstring
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> BaseModelOutputWithPast:
|
||||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
|
||||
|
||||
if use_cache and past_key_values is None:
|
||||
past_key_values = DynamicCache(config=self.config)
|
||||
|
||||
if cache_position is None:
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
cache_position: torch.Tensor = torch.arange(
|
||||
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||||
)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
causal_mask = create_causal_mask(
|
||||
config=self.config,
|
||||
input_embeds=inputs_embeds,
|
||||
attention_mask=attention_mask,
|
||||
cache_position=cache_position,
|
||||
past_key_values=past_key_values,
|
||||
position_ids=position_ids,
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||||
|
||||
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
||||
hidden_states = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=past_key_values,
|
||||
)
|
||||
|
||||
|
||||
@auto_docstring
|
||||
class HunYuanMoEV1ForCausalLM(HunYuanMoEV1PreTrainedModel, GenerationMixin):
|
||||
_tied_weights_keys = ["lm_head.weight"]
|
||||
_tp_plan = {"lm_head": "colwise_rep"}
|
||||
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = HunYuanMoEV1Model(config)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model
|
||||
|
||||
@can_return_tuple
|
||||
@auto_docstring
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.LongTensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> CausalLMOutputWithPast:
|
||||
r"""
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, HunYuanMoEV1ForCausalLM
|
||||
|
||||
>>> model = HunYuanMoEV1ForCausalLM.from_pretrained("meta-hunyuan_v1_moe/HunYuanMoEV1-2-7b-hf")
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("meta-hunyuan_v1_moe/HunYuanMoEV1-2-7b-hf")
|
||||
|
||||
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
||||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||||
|
||||
>>> # Generate
|
||||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||||
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
||||
```"""
|
||||
outputs: BaseModelOutputWithPast = self.model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
hidden_states = outputs.last_hidden_state
|
||||
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
||||
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||||
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
|
||||
class HunYuanMoEV1ForSequenceClassification(GenericForSequenceClassification, HunYuanMoEV1PreTrainedModel):
|
||||
pass
|
||||
|
||||
|
||||
__all__ = [
|
||||
"HunYuanMoEV1ForCausalLM",
|
||||
"HunYuanMoEV1Model",
|
||||
"HunYuanMoEV1PreTrainedModel",
|
||||
"HunYuanMoEV1ForSequenceClassification",
|
||||
]
|
||||
272
src/transformers/models/hunyuan_v1_moe/modular_hunyuan_v1_moe.py
Normal file
272
src/transformers/models/hunyuan_v1_moe/modular_hunyuan_v1_moe.py
Normal file
@@ -0,0 +1,272 @@
|
||||
# coding=utf-8
|
||||
# Copyright (C) 2025 THL A29 Limited, a Tencent company 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.
|
||||
"""PyTorch HunYuanMoEV1 model."""
|
||||
|
||||
from typing import Callable, Optional
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
|
||||
from transformers.cache_utils import Cache
|
||||
from transformers.utils import (
|
||||
logging,
|
||||
)
|
||||
|
||||
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
|
||||
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
|
||||
from ...processing_utils import Unpack
|
||||
from ...utils import TransformersKwargs
|
||||
from ..llama.modeling_llama import (
|
||||
LlamaAttention,
|
||||
LlamaDecoderLayer,
|
||||
LlamaForCausalLM,
|
||||
LlamaForSequenceClassification,
|
||||
LlamaMLP,
|
||||
LlamaModel,
|
||||
LlamaPreTrainedModel,
|
||||
LlamaRMSNorm,
|
||||
apply_rotary_pos_emb,
|
||||
eager_attention_forward,
|
||||
)
|
||||
from .configuration_hunyuan_v1_moe import HunYuanMoEV1Config
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class HunYuanMoEV1RMSNorm(LlamaRMSNorm):
|
||||
pass
|
||||
|
||||
|
||||
class HunYuanMoEV1MLP(LlamaMLP):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx=None, is_shared_mlp=False):
|
||||
super().__init__(config)
|
||||
self.layer_idx = layer_idx
|
||||
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
||||
|
||||
|
||||
class HunYuanMoEV1Attention(LlamaAttention):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx: int):
|
||||
super().__init__(config, layer_idx)
|
||||
self.query_layernorm = HunYuanMoEV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||||
self.key_layernorm = HunYuanMoEV1RMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
||||
attention_mask: Optional[torch.Tensor],
|
||||
past_key_values: Optional[Cache] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**kwargs: Unpack[TransformersKwargs],
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
input_shape = hidden_states.shape[:-1]
|
||||
hidden_shape = (*input_shape, -1, self.head_dim)
|
||||
|
||||
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
||||
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
query_states = self.query_layernorm(query_states)
|
||||
key_states = self.key_layernorm(key_states)
|
||||
|
||||
if past_key_values is not None:
|
||||
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
||||
key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
attention_interface: Callable = eager_attention_forward
|
||||
if self.config._attn_implementation != "eager":
|
||||
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
||||
|
||||
attn_output, attn_weights = attention_interface(
|
||||
self,
|
||||
query_states,
|
||||
key_states,
|
||||
value_states,
|
||||
attention_mask,
|
||||
dropout=0.0 if not self.training else self.attention_dropout,
|
||||
scaling=self.scaling,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
||||
attn_output = self.o_proj(attn_output)
|
||||
return attn_output, attn_weights
|
||||
|
||||
|
||||
class HunYuanMoEV1Gate(nn.Module):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx: Optional[int] = None):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
num_experts = config.num_experts if isinstance(config.num_experts, int) else config.num_experts[layer_idx]
|
||||
self.wg = nn.Linear(config.hidden_size, num_experts, bias=False, dtype=torch.float32)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
bsz, seq_len, hidden_size = hidden_states.shape
|
||||
hidden_states = hidden_states.reshape(-1, hidden_size)
|
||||
if self.wg.weight.dtype == torch.float32:
|
||||
hidden_states = hidden_states.float()
|
||||
logits = self.wg(hidden_states)
|
||||
return logits
|
||||
|
||||
|
||||
class HunYuanMoEV1Moe(nn.Module):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx: Optional[int] = None):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.layer_idx = layer_idx
|
||||
self.num_experts = config.num_experts if isinstance(config.num_experts, int) else config.num_experts[layer_idx]
|
||||
self.top_k = config.moe_topk if isinstance(config.moe_topk, int) else config.moe_topk[layer_idx]
|
||||
self.gate = HunYuanMoEV1Gate(config, layer_idx=layer_idx)
|
||||
# self.wg = nn.Linear(config.hidden_size, config.num_experts, bias=False, dtype=torch.float32)
|
||||
self.experts = nn.ModuleList(
|
||||
[HunYuanMoEV1MLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(self.num_experts)]
|
||||
)
|
||||
|
||||
self.shared_mlp = HunYuanMoEV1MLP(config, layer_idx=layer_idx, is_shared_mlp=True)
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
||||
hidden_states_mlp = self.shared_mlp(hidden_states)
|
||||
router_logits = self.gate(hidden_states)
|
||||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
# router_logits: (batch * sequence_length, n_experts)
|
||||
|
||||
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
||||
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
|
||||
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
||||
# we cast back to the input dtype
|
||||
routing_weights = routing_weights.to(hidden_states.dtype)
|
||||
|
||||
final_hidden_states = torch.zeros(
|
||||
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
|
||||
)
|
||||
|
||||
# One hot encode the selected experts to create an expert mask
|
||||
# this will be used to easily index which expert is going to be sollicitated
|
||||
expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
|
||||
|
||||
# Loop over all available experts in the model and perform the computation on each expert
|
||||
expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
|
||||
for expert_idx in expert_hit:
|
||||
expert_layer = self.experts[expert_idx]
|
||||
idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0))
|
||||
|
||||
# Index the correct hidden states and compute the expert hidden state for
|
||||
# the current expert. We need to make sure to multiply the output hidden
|
||||
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
|
||||
current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
|
||||
current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
|
||||
|
||||
# However `index_add_` only support torch tensors for indexing so we'll use
|
||||
# the `top_x` tensor here.
|
||||
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
|
||||
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
|
||||
return final_hidden_states + hidden_states_mlp
|
||||
|
||||
|
||||
class HunYuanMoEV1DecoderLayer(LlamaDecoderLayer):
|
||||
def __init__(self, config: HunYuanMoEV1Config, layer_idx: int):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.self_attn = HunYuanMoEV1Attention(config=config, layer_idx=layer_idx)
|
||||
self.mlp = HunYuanMoEV1Moe(config, layer_idx=layer_idx)
|
||||
self.input_layernorm = HunYuanMoEV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = HunYuanMoEV1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.layer_idx = layer_idx
|
||||
|
||||
|
||||
class HunYuanMoEV1PreTrainedModel(LlamaPreTrainedModel):
|
||||
def _init_weights(self, module):
|
||||
std = self.config.initializer_range
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.Embedding):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
|
||||
|
||||
class HunYuanMoEV1RotaryEmbedding(nn.Module):
|
||||
inv_freq: torch.Tensor # fix linting for `register_buffer`
|
||||
|
||||
def __init__(self, config: HunYuanMoEV1Config, device=None):
|
||||
super().__init__()
|
||||
# BC: "rope_type" was originally "type"
|
||||
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
||||
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
||||
else:
|
||||
self.rope_type = "default"
|
||||
self.max_seq_len_cached = config.max_position_embeddings
|
||||
self.original_max_seq_len = config.max_position_embeddings
|
||||
|
||||
self.config = config
|
||||
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
||||
if self.rope_type == "dynamic" and config.rope_scaling["alpha"]:
|
||||
# DynamicNTKAlphaRotary
|
||||
self.dim = config.head_dim
|
||||
base = config.rope_theta * config.rope_scaling.get("alpha") ** (self.dim / (self.dim - 2))
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
||||
self.attention_scaling = 1.0
|
||||
else:
|
||||
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
||||
|
||||
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
||||
self.original_inv_freq = self.inv_freq
|
||||
|
||||
@torch.no_grad()
|
||||
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
||||
def forward(self, x, position_ids):
|
||||
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
||||
position_ids_expanded = position_ids[:, None, :].float()
|
||||
|
||||
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
||||
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
||||
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
cos = emb.cos() * self.attention_scaling
|
||||
sin = emb.sin() * self.attention_scaling
|
||||
|
||||
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
||||
|
||||
|
||||
class HunYuanMoEV1Model(LlamaModel):
|
||||
pass
|
||||
|
||||
|
||||
class HunYuanMoEV1ForCausalLM(LlamaForCausalLM):
|
||||
pass
|
||||
|
||||
|
||||
class HunYuanMoEV1ForSequenceClassification(LlamaForSequenceClassification):
|
||||
pass
|
||||
|
||||
|
||||
__all__ = [
|
||||
"HunYuanMoEV1ForCausalLM",
|
||||
"HunYuanMoEV1Model",
|
||||
"HunYuanMoEV1PreTrainedModel",
|
||||
"HunYuanMoEV1ForSequenceClassification",
|
||||
]
|
||||
0
tests/models/hunyuan_v1_dense/__init__.py
Normal file
0
tests/models/hunyuan_v1_dense/__init__.py
Normal file
@@ -0,0 +1,93 @@
|
||||
# Copyright (C) 2024 THL A29 Limited, a Tencent company 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.
|
||||
"""Testing suite for the PyTorch HunYuanDenseV1 model."""
|
||||
|
||||
import unittest
|
||||
|
||||
from transformers import HunYuanDenseV1Config, is_torch_available
|
||||
from transformers.testing_utils import (
|
||||
cleanup,
|
||||
require_torch,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
from transformers import (
|
||||
HunYuanDenseV1ForCausalLM,
|
||||
HunYuanDenseV1ForSequenceClassification,
|
||||
HunYuanDenseV1Model,
|
||||
)
|
||||
|
||||
from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
|
||||
|
||||
|
||||
class HunYuanDenseV1ModelTester(CausalLMModelTester):
|
||||
config_class = HunYuanDenseV1Config
|
||||
if is_torch_available():
|
||||
base_model_class = HunYuanDenseV1Model
|
||||
causal_lm_class = HunYuanDenseV1ForCausalLM
|
||||
sequence_class = HunYuanDenseV1ForSequenceClassification
|
||||
|
||||
|
||||
@require_torch
|
||||
class HunYuanDenseV1ModelTest(CausalLMModelTest, unittest.TestCase):
|
||||
all_model_classes = (
|
||||
(
|
||||
HunYuanDenseV1Model,
|
||||
HunYuanDenseV1ForCausalLM,
|
||||
HunYuanDenseV1ForSequenceClassification,
|
||||
)
|
||||
if is_torch_available()
|
||||
else ()
|
||||
)
|
||||
test_headmasking = False
|
||||
test_pruning = False
|
||||
model_tester_class = HunYuanDenseV1ModelTester
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
"feature-extraction": HunYuanDenseV1Model,
|
||||
"text-generation": HunYuanDenseV1ForCausalLM,
|
||||
"text-classification": HunYuanDenseV1ForSequenceClassification,
|
||||
}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
)
|
||||
|
||||
def is_pipeline_test_to_skip(
|
||||
self,
|
||||
pipeline_test_case_name,
|
||||
config_class,
|
||||
model_architecture,
|
||||
tokenizer_name,
|
||||
image_processor_name,
|
||||
feature_extractor_name,
|
||||
processor_name,
|
||||
):
|
||||
return True
|
||||
|
||||
|
||||
@require_torch
|
||||
class HunYuanDenseV1IntegrationTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
cleanup(torch_device, gc_collect=True)
|
||||
|
||||
def tearDown(self):
|
||||
cleanup(torch_device, gc_collect=True)
|
||||
|
||||
@slow
|
||||
def test_model_generation(self):
|
||||
# TODO Need new Dense Model
|
||||
return True
|
||||
0
tests/models/hunyuan_v1_moe/__init__.py
Normal file
0
tests/models/hunyuan_v1_moe/__init__.py
Normal file
132
tests/models/hunyuan_v1_moe/test_modeling_hunyuan_v1_moe.py
Normal file
132
tests/models/hunyuan_v1_moe/test_modeling_hunyuan_v1_moe.py
Normal file
@@ -0,0 +1,132 @@
|
||||
# Copyright (C) 2024 THL A29 Limited, a Tencent company 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.
|
||||
"""Testing suite for the PyTorch HunYuanMoEV1 model."""
|
||||
|
||||
import unittest
|
||||
|
||||
import pytest
|
||||
|
||||
from transformers import HunYuanMoEV1Config, is_torch_available
|
||||
from transformers.testing_utils import (
|
||||
cleanup,
|
||||
require_torch,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
from transformers import (
|
||||
AutoModelForCausalLM,
|
||||
AutoTokenizer,
|
||||
HunYuanMoEV1ForCausalLM,
|
||||
HunYuanMoEV1ForSequenceClassification,
|
||||
HunYuanMoEV1Model,
|
||||
)
|
||||
|
||||
from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
|
||||
|
||||
|
||||
class HunYuanMoEV1ModelTester(CausalLMModelTester):
|
||||
config_class = HunYuanMoEV1Config
|
||||
if is_torch_available():
|
||||
base_model_class = HunYuanMoEV1Model
|
||||
causal_lm_class = HunYuanMoEV1ForCausalLM
|
||||
sequence_class = HunYuanMoEV1ForSequenceClassification
|
||||
|
||||
|
||||
@require_torch
|
||||
class HunYuanMoEV1ModelTest(CausalLMModelTest, unittest.TestCase):
|
||||
all_model_classes = (
|
||||
(
|
||||
HunYuanMoEV1Model,
|
||||
HunYuanMoEV1ForCausalLM,
|
||||
HunYuanMoEV1ForSequenceClassification,
|
||||
)
|
||||
if is_torch_available()
|
||||
else ()
|
||||
)
|
||||
test_headmasking = False
|
||||
test_pruning = False
|
||||
model_tester_class = HunYuanMoEV1ModelTester
|
||||
pipeline_model_mapping = (
|
||||
{
|
||||
"feature-extraction": HunYuanMoEV1Model,
|
||||
"text-generation": HunYuanMoEV1ForCausalLM,
|
||||
"text-classification": HunYuanMoEV1ForSequenceClassification,
|
||||
}
|
||||
if is_torch_available()
|
||||
else {}
|
||||
)
|
||||
|
||||
def is_pipeline_test_to_skip(
|
||||
self,
|
||||
pipeline_test_case_name,
|
||||
config_class,
|
||||
model_architecture,
|
||||
tokenizer_name,
|
||||
image_processor_name,
|
||||
feature_extractor_name,
|
||||
processor_name,
|
||||
):
|
||||
return True
|
||||
|
||||
@unittest.skip("Hunyuan model Unsupported")
|
||||
@pytest.mark.torch_compile_test
|
||||
def test_generate_compilation_all_outputs(self):
|
||||
pass
|
||||
|
||||
@unittest.skip("Hunyuan model Unsupported")
|
||||
@pytest.mark.torch_compile_test
|
||||
def test_generate_compile_model_forward(self):
|
||||
pass
|
||||
|
||||
@unittest.skip("Hunyuan model Unsupported")
|
||||
def test_generate_from_inputs_embeds_with_static_cache(self):
|
||||
pass
|
||||
|
||||
@unittest.skip("Hunyuan model Unsupported")
|
||||
def test_generate_with_static_cache(self):
|
||||
pass
|
||||
|
||||
|
||||
@require_torch
|
||||
class HunYuanMoEV1IntegrationTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
cleanup(torch_device, gc_collect=True)
|
||||
|
||||
def tearDown(self):
|
||||
cleanup(torch_device, gc_collect=True)
|
||||
|
||||
@slow
|
||||
def test_model_generation(self):
|
||||
# we will compele this when model file change over
|
||||
# pass
|
||||
EXPECTED_ANSWER = "\nOkay, I need to write a short summary about the benefits of regular exercise. Let me start by recalling what I know. First,"
|
||||
prompt = "Write a short summary of the benefits of regular exercise"
|
||||
tokenizer = AutoTokenizer.from_pretrained("tencent/Hunyuan-A13B-Instruct")
|
||||
model = AutoModelForCausalLM.from_pretrained("tencent/Hunyuan-A13B-Instruct", device_map="auto")
|
||||
messages = [
|
||||
{"role": "user", "content": prompt},
|
||||
]
|
||||
tokenized_chat = tokenizer.apply_chat_template(
|
||||
messages,
|
||||
tokenize=True,
|
||||
add_generation_prompt=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
generated_ids = model.generate(tokenized_chat.to(model.device), max_new_tokens=30, top_k=1)
|
||||
text = tokenizer.decode(generated_ids[0])
|
||||
output = text.split("<think>")[1]
|
||||
self.assertEqual(EXPECTED_ANSWER, output)
|
||||
Reference in New Issue
Block a user