Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- config.json +119 -0
- configuration_hunyuan.py +319 -0
- generation_config.json +7 -0
- hunyuan.py +879 -0
- hy.tiktoken +0 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.gguf +3 -0
- model.safetensors.index.json +105 -0
- modeling_hunyuan.py +1756 -0
- test_hunyuan.py +40 -0
- tokenization_hy.py +298 -0
- tokenizer_config.json +18 -0
- vit_model.py +1083 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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model.gguf filter=lfs diff=lfs merge=lfs -text
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config.json
ADDED
@@ -0,0 +1,119 @@
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{
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"add_classification_head": false,
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+
"anyres_pooling_size": 2,
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4 |
+
"anyres_vit_max_image_size": null,
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5 |
+
"anyres_vit_two_views": false,
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+
"architectures": [
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"HunYuanMoEV1ForCausalLM"
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+
],
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+
"attention_bias": false,
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+
"attention_dropout": 0.1,
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"attention_head_dim": 128,
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"auto_map": {
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"AutoConfig": "configuration_hunyuan.HunYuanConfig",
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"AutoModel": "hunyuan.HunYuanModel",
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"AutoModelForCausalLM": "hunyuan.HunYuanMoEV1ForCausalLM"
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+
},
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+
"bos_token_id": 1,
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18 |
+
"cla_share_factor": 2,
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+
"class_num": 0,
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"dense_list": [
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4096,
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0
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],
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+
"eod_token_id": 127967,
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+
"eos_token_id": 127960,
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+
"group_limited_greedy": false,
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+
"hidden_act": "silu",
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+
"hidden_size": 4096,
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+
"im_end_id": 6,
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30 |
+
"im_newline_id": 12,
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+
"im_start_id": 5,
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+
"image_token_id": 9,
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+
"initializer_range": 0.02,
|
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+
"intermediate_size": 3072,
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35 |
+
"kv_lora_rank": null,
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+
"mask_init_id": 13,
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+
"max_position_embeddings": 32768,
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+
"mlp_bias": false,
|
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+
"model_type": "hunyuan",
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+
"moe_drop_tokens": false,
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+
"moe_intermediate_size": [
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+
3072,
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3072,
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+
3072,
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+
3072
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+
],
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"moe_layer_num_skipped": 0,
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+
"moe_random_routing_dropped_token": false,
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+
"moe_topk": [
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2,
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2,
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2,
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2
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],
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"n_group": null,
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"norm_topk_prob": true,
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"norm_type": "rms",
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"num_attention_heads": 32,
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"num_experts": 4,
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"num_hidden_layers": 4,
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"num_key_value_heads": 8,
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"num_media_embeds": 257,
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"num_shared_expert": [
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1,
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1,
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1,
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1
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],
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"org_vocab_size": 128167,
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"pad_id": 127961,
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"pad_token_id": 127961,
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+
"pool_type": "last",
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+
"position_embedding_xdrope": false,
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+
"pretraining_tp": 1,
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+
"q_lora_rank": null,
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"qk_nope_head_dim": null,
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"qk_rope_head_dim": null,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"alpha": 1000.0,
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"beta_fast": 32,
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"beta_slow": 1,
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"factor": 1.0,
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"mscale": 1.0,
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"mscale_all_dim": 1.0,
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"type": "dynamic"
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},
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"rope_theta": 10000.0,
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"routed_scaling_factor": 1.0,
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"sep_token_id": 127962,
|
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"skip_cls_token": false,
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"text_end_id": 8,
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"text_start_id": 7,
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"tie_word_embeddings": true,
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"topk_group": null,
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"torch_dtype": "float32",
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"transformers_version": "4.51.3",
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"use_cache": true,
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"use_cla": false,
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"use_mixed_mlp_moe": true,
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"use_mla": false,
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"use_qk_norm": true,
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"use_rotary_pos_emb": true,
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"v_head_dim": null,
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"video_end_id": 11,
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"video_start_id": 10,
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"vit_add_patchemb_bias": false,
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"vit_input_resolution": 224,
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"vit_mapping_type": "resampler",
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"vit_norm_type": "fused",
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"vit_patch": 1,
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"vit_path": null,
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"vit_remove_prenorm": false,
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"vit_token": 64,
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"vit_type": null,
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"vit_used_rms_norm": false,
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"vocab_size": 128167,
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"xdrope_section": null
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}
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configuration_hunyuan.py
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# coding=utf-8
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+
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
|
3 |
+
""" HunYuan model configuration"""
|
4 |
+
from torch import nn
|
5 |
+
from transformers.configuration_utils import PretrainedConfig
|
6 |
+
from transformers.utils import logging
|
7 |
+
from typing import List, Union, Optional
|
8 |
+
|
9 |
+
|
10 |
+
logger = logging.get_logger(__name__)
|
11 |
+
|
12 |
+
|
13 |
+
class HunYuanConfig(PretrainedConfig):
|
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+
r"""
|
15 |
+
This is the configuration class to store the configuration of a [`HunYuanModel`]. It is used to instantiate an
|
16 |
+
HunYuan model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
17 |
+
with the defaults will yield a similar configuration to that of the HunYuan-7B.
|
18 |
+
|
19 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
20 |
+
documentation from [`PretrainedConfig`] for more information.
|
21 |
+
|
22 |
+
|
23 |
+
Args:
|
24 |
+
vocab_size (`int`, *optional*, defaults to 32000):
|
25 |
+
Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
|
26 |
+
`inputs_ids` passed when calling [`HunYuanModel`]
|
27 |
+
hidden_size (`int`, *optional*, defaults to 4096):
|
28 |
+
Dimension of the hidden representations.
|
29 |
+
intermediate_size (`int`, *optional*, defaults to 11008):
|
30 |
+
Dimension of the MLP representations or shared MLP representations.
|
31 |
+
moe_intermediate_size (`int` or `List`, *optional*, defaults to 11008):
|
32 |
+
Dimension of the MLP representations in MoE. Use a list if you want a different size per layer.
|
33 |
+
num_hidden_layers (`int`, *optional*, defaults to 32):
|
34 |
+
Number of hidden layers in the Transformer decoder.
|
35 |
+
num_attention_heads (`int`, *optional*, defaults to 32):
|
36 |
+
Number of attention heads for each attention layer in the Transformer decoder.
|
37 |
+
num_key_value_heads (`int`, *optional*):
|
38 |
+
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
39 |
+
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
40 |
+
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
41 |
+
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
42 |
+
by meanpooling all the original heads within that group. For more details checkout [this
|
43 |
+
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
44 |
+
`num_attention_heads`.
|
45 |
+
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
46 |
+
The non-linear activation function (function or string) in the decoder.
|
47 |
+
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
48 |
+
The maximum sequence length that this model might ever be used with.
|
49 |
+
initializer_range (`float`, *optional*, defaults to 0.02):
|
50 |
+
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
51 |
+
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
52 |
+
The epsilon used by the rms normalization layers.
|
53 |
+
use_cache (`bool`, *optional*, defaults to `True`):
|
54 |
+
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
55 |
+
relevant if `config.is_decoder=True`.
|
56 |
+
pad_token_id (`int`, *optional*):
|
57 |
+
Padding token id.
|
58 |
+
bos_token_id (`int`, *optional*, defaults to 1):
|
59 |
+
Beginning of stream token id.
|
60 |
+
eos_token_id (`int`, *optional*, defaults to 2):
|
61 |
+
End of stream token id.
|
62 |
+
pretraining_tp (`int`, *optional*, defaults to 1):
|
63 |
+
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
64 |
+
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
65 |
+
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
66 |
+
issue](https://github.com/pytorch/pytorch/issues/76232).
|
67 |
+
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
68 |
+
Whether to tie weight embeddings
|
69 |
+
rope_theta (`float`, *optional*, defaults to 10000.0):
|
70 |
+
The base period of the RoPE embeddings.
|
71 |
+
rope_scaling (`Dict`, *optional*):
|
72 |
+
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
73 |
+
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
74 |
+
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
75 |
+
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
76 |
+
these scaling strategies behave:
|
77 |
+
https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
78 |
+
experimental feature, subject to breaking API changes in future versions.
|
79 |
+
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
80 |
+
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
81 |
+
attention_dropout (`float`, *optional*, defaults to 0.0):
|
82 |
+
The dropout ratio for the attention probabilities.
|
83 |
+
use_qk_norm (`bool`, *optional*, defaults to `False`):
|
84 |
+
Whether query and key in attention use norm
|
85 |
+
use_cla (`bool`, *optional*, defaults to `False`):
|
86 |
+
Whether to use CLA in attention
|
87 |
+
cla_share_factor (`int`, *optional*, defaults to 1):
|
88 |
+
The share factor of CLA
|
89 |
+
num_experts (`int` or `List`, *optional*, defaults to 1):
|
90 |
+
The number of experts for moe. If it is a list, it will be used as the number of experts for each layer.
|
91 |
+
num_shared_expert (`int` or `List`, *optional*, defaults to 1):
|
92 |
+
The number of shared experts for moe. If it is a list, it will be used as the number of shared experts for each layer.
|
93 |
+
moe_topk (`int` or `List`, *optional*, defaults to 1):
|
94 |
+
The topk value for moe. If it is a list, it will be used as the topk value for each layer.
|
95 |
+
capacity_factor (Not used) (`float` or `List`, *optional*, defaults to 1.0):
|
96 |
+
The capacity factor for moe. If it is a list, it will be used as the capacity factor for each layer.
|
97 |
+
moe_layer_num_skipped (`int`, *optional*, defaults to 0):
|
98 |
+
First moe_layer_num_skipped layers do not use MoE.
|
99 |
+
"""
|
100 |
+
|
101 |
+
model_type = "hunyuan"
|
102 |
+
keys_to_ignore_at_inference = ["past_key_values"]
|
103 |
+
|
104 |
+
def __init__(
|
105 |
+
self,
|
106 |
+
vocab_size=290943,
|
107 |
+
org_vocab_size=290943,
|
108 |
+
hidden_size=4096,
|
109 |
+
intermediate_size: int=11008,
|
110 |
+
moe_intermediate_size: Union[int, List]=None,
|
111 |
+
num_hidden_layers=32,
|
112 |
+
num_attention_heads=32,
|
113 |
+
num_key_value_heads=None,
|
114 |
+
attention_head_dim=None,
|
115 |
+
hidden_act="silu",
|
116 |
+
max_position_embeddings=2048,
|
117 |
+
initializer_range=0.02,
|
118 |
+
rms_norm_eps=1e-5,
|
119 |
+
use_cache=True,
|
120 |
+
pad_token_id=0,
|
121 |
+
bos_token_id=1,
|
122 |
+
eos_token_id=2,
|
123 |
+
eod_token_id=3,
|
124 |
+
sep_token_id=4,
|
125 |
+
im_start_id=5,
|
126 |
+
im_end_id=6,
|
127 |
+
text_start_id=7,
|
128 |
+
text_end_id=8,
|
129 |
+
image_token_id=9,
|
130 |
+
video_start_id=10,
|
131 |
+
video_end_id=11,
|
132 |
+
im_newline_id=12,
|
133 |
+
mask_init_id=13,
|
134 |
+
pretraining_tp=1,
|
135 |
+
tie_word_embeddings=False,
|
136 |
+
rope_theta=10000.0,
|
137 |
+
rope_scaling=None,
|
138 |
+
attention_bias=False,
|
139 |
+
mlp_bias=False,
|
140 |
+
attention_dropout=0.0,
|
141 |
+
use_qk_norm=False,
|
142 |
+
use_rotary_pos_emb=True,
|
143 |
+
use_cla=False,
|
144 |
+
cla_share_factor=1,
|
145 |
+
norm_type="hf_rms",
|
146 |
+
num_experts: Union[int, List]=1,
|
147 |
+
use_mixed_mlp_moe=False,
|
148 |
+
num_shared_expert: Union[int, List]=1,
|
149 |
+
moe_topk: Union[int, List]=1,
|
150 |
+
# capacity_factor: Union[int, List]=1.0,
|
151 |
+
moe_drop_tokens=False,
|
152 |
+
moe_random_routing_dropped_token=False,
|
153 |
+
use_mla=False,
|
154 |
+
kv_lora_rank=512,
|
155 |
+
q_lora_rank=1536,
|
156 |
+
qk_rope_head_dim=64,
|
157 |
+
v_head_dim=128,
|
158 |
+
qk_nope_head_dim=128,
|
159 |
+
moe_layer_num_skipped=0,
|
160 |
+
norm_topk_prob=True,
|
161 |
+
routed_scaling_factor=1.0,
|
162 |
+
group_limited_greedy=False,
|
163 |
+
n_group=None,
|
164 |
+
topk_group=None,
|
165 |
+
vit_path=None,
|
166 |
+
num_media_embeds=257,
|
167 |
+
vit_type="AnyResVit",
|
168 |
+
vit_input_resolution=224,
|
169 |
+
vit_token=64,
|
170 |
+
vit_patch=1,
|
171 |
+
vit_mapping_type="simple_conv_mlp",
|
172 |
+
vit_norm_type="fused",
|
173 |
+
vit_used_rms_norm=True,
|
174 |
+
vit_remove_prenorm=True,
|
175 |
+
vit_add_patchemb_bias=True,
|
176 |
+
anyres_vit_max_image_size=2048,
|
177 |
+
anyres_pooling_size=2,
|
178 |
+
anyres_vit_two_views=False,
|
179 |
+
skip_cls_token=False,
|
180 |
+
position_embedding_xdrope=False,
|
181 |
+
xdrope_section=None,
|
182 |
+
add_classification_head=False,
|
183 |
+
class_num=0,
|
184 |
+
pool_type="last",
|
185 |
+
pad_id=-1,
|
186 |
+
**kwargs,
|
187 |
+
):
|
188 |
+
self.vocab_size = vocab_size
|
189 |
+
self.org_vocab_size = org_vocab_size
|
190 |
+
self.max_position_embeddings = max_position_embeddings
|
191 |
+
self.hidden_size = hidden_size
|
192 |
+
self.intermediate_size = intermediate_size
|
193 |
+
self.moe_intermediate_size = moe_intermediate_size
|
194 |
+
self.num_hidden_layers = num_hidden_layers
|
195 |
+
self.num_attention_heads = num_attention_heads
|
196 |
+
self.num_experts = num_experts
|
197 |
+
self.use_mixed_mlp_moe = use_mixed_mlp_moe
|
198 |
+
self.num_shared_expert = num_shared_expert
|
199 |
+
self.moe_topk = moe_topk
|
200 |
+
# self.capacity_factor = capacity_factor
|
201 |
+
self.moe_drop_tokens = moe_drop_tokens
|
202 |
+
self.moe_random_routing_dropped_token = moe_random_routing_dropped_token
|
203 |
+
|
204 |
+
if attention_head_dim is not None:
|
205 |
+
self.attention_head_dim = attention_head_dim
|
206 |
+
else:
|
207 |
+
self.attention_head_dim = self.hidden_size // num_attention_heads
|
208 |
+
|
209 |
+
# for backward compatibility
|
210 |
+
if num_key_value_heads is None:
|
211 |
+
num_key_value_heads = num_attention_heads
|
212 |
+
|
213 |
+
self.num_key_value_heads = num_key_value_heads
|
214 |
+
self.hidden_act = hidden_act
|
215 |
+
self.initializer_range = initializer_range
|
216 |
+
self.rms_norm_eps = rms_norm_eps
|
217 |
+
self.pretraining_tp = pretraining_tp
|
218 |
+
self.use_cache = use_cache
|
219 |
+
self.rope_theta = rope_theta
|
220 |
+
self.rope_scaling = rope_scaling
|
221 |
+
# self._rope_scaling_validation() # TODO: Need validation?
|
222 |
+
self.attention_bias = attention_bias
|
223 |
+
self.mlp_bias = mlp_bias
|
224 |
+
self.attention_dropout = attention_dropout
|
225 |
+
self.use_qk_norm = use_qk_norm
|
226 |
+
self.use_rotary_pos_emb = use_rotary_pos_emb
|
227 |
+
self.use_cla = use_cla
|
228 |
+
self.cla_share_factor = cla_share_factor
|
229 |
+
self.norm_type = norm_type
|
230 |
+
# MLA args
|
231 |
+
self.use_mla = use_mla
|
232 |
+
self.kv_lora_rank = kv_lora_rank
|
233 |
+
self.q_lora_rank = q_lora_rank
|
234 |
+
self.qk_rope_head_dim = qk_rope_head_dim
|
235 |
+
self.qk_nope_head_dim = qk_nope_head_dim
|
236 |
+
self.v_head_dim = v_head_dim
|
237 |
+
|
238 |
+
# DeepSeek related args
|
239 |
+
self.moe_layer_num_skipped = moe_layer_num_skipped
|
240 |
+
self.norm_topk_prob = norm_topk_prob
|
241 |
+
self.routed_scaling_factor = routed_scaling_factor
|
242 |
+
self.group_limited_greedy = group_limited_greedy
|
243 |
+
self.n_group = n_group
|
244 |
+
self.topk_group = topk_group
|
245 |
+
self.add_classification_head = add_classification_head
|
246 |
+
self.class_num = class_num
|
247 |
+
self.pool_type = pool_type
|
248 |
+
self.pad_id = pad_id
|
249 |
+
|
250 |
+
if self.class_num is not None:
|
251 |
+
self.dense_list = [self.hidden_size, self.class_num]
|
252 |
+
|
253 |
+
# Vit args
|
254 |
+
self.vit_path = vit_path
|
255 |
+
self.num_media_embeds = num_media_embeds
|
256 |
+
self.vit_type = vit_type
|
257 |
+
self.vit_input_resolution = vit_input_resolution
|
258 |
+
self.vit_token = vit_token
|
259 |
+
self.vit_patch = vit_patch
|
260 |
+
self.vit_mapping_type = vit_mapping_type
|
261 |
+
self.vit_norm_type = vit_norm_type
|
262 |
+
self.vit_used_rms_norm = vit_used_rms_norm
|
263 |
+
self.vit_remove_prenorm = vit_remove_prenorm
|
264 |
+
self.vit_add_patchemb_bias = vit_add_patchemb_bias
|
265 |
+
self.anyres_vit_max_image_size = anyres_vit_max_image_size
|
266 |
+
self.anyres_pooling_size = anyres_pooling_size
|
267 |
+
self.anyres_vit_two_views = anyres_vit_two_views
|
268 |
+
self.skip_cls_token = skip_cls_token
|
269 |
+
self.position_embedding_xdrope = position_embedding_xdrope
|
270 |
+
self.xdrope_section = xdrope_section
|
271 |
+
|
272 |
+
# token id
|
273 |
+
self.eod_token_id = eod_token_id
|
274 |
+
self.im_start_id = im_start_id
|
275 |
+
self.im_end_id = im_end_id
|
276 |
+
self.text_start_id = text_start_id
|
277 |
+
self.text_end_id = text_end_id
|
278 |
+
self.image_token_id = image_token_id
|
279 |
+
self.video_start_id = video_start_id
|
280 |
+
self.video_end_id = video_end_id
|
281 |
+
self.im_newline_id = im_newline_id
|
282 |
+
self.mask_init_id = mask_init_id
|
283 |
+
|
284 |
+
super().__init__(
|
285 |
+
pad_token_id=pad_token_id,
|
286 |
+
bos_token_id=bos_token_id,
|
287 |
+
eos_token_id=eos_token_id,
|
288 |
+
sep_token_id=sep_token_id,
|
289 |
+
tie_word_embeddings=tie_word_embeddings,
|
290 |
+
**kwargs,
|
291 |
+
)
|
292 |
+
|
293 |
+
def _rope_scaling_validation(self):
|
294 |
+
"""
|
295 |
+
Validate the `rope_scaling` configuration.
|
296 |
+
"""
|
297 |
+
if self.rope_scaling is None:
|
298 |
+
return
|
299 |
+
|
300 |
+
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
301 |
+
raise ValueError(
|
302 |
+
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor` or `type` and `alpha`, "
|
303 |
+
f"got {self.rope_scaling}"
|
304 |
+
)
|
305 |
+
rope_scaling_type = self.rope_scaling.get("type", None)
|
306 |
+
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
307 |
+
rope_scaling_alpha = self.rope_scaling.get("alpha", None)
|
308 |
+
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
|
309 |
+
raise ValueError(
|
310 |
+
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
311 |
+
)
|
312 |
+
if rope_scaling_factor is None and rope_scaling_alpha is None:
|
313 |
+
raise ValueError("`rope_scaling`'s factor or alpha field must be have one, got both of none")
|
314 |
+
if rope_scaling_factor is not None:
|
315 |
+
if not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
316 |
+
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1.0, got {rope_scaling_factor}")
|
317 |
+
if rope_scaling_alpha is not None:
|
318 |
+
if not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0:
|
319 |
+
raise ValueError(f"`rope_scaling`'s alpha field must be a float > 1.0, got {rope_scaling_alpha}")
|
generation_config.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"bos_token_id": 1,
|
4 |
+
"eos_token_id": 127960,
|
5 |
+
"pad_token_id": 127961,
|
6 |
+
"transformers_version": "4.51.3"
|
7 |
+
}
|
hunyuan.py
ADDED
@@ -0,0 +1,879 @@
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|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
|
3 |
+
#
|
4 |
+
""" PyTorch HunYuan model."""
|
5 |
+
|
6 |
+
import math
|
7 |
+
import warnings
|
8 |
+
from typing import List, Optional, Tuple, Union
|
9 |
+
|
10 |
+
import torch
|
11 |
+
from torch import Tensor
|
12 |
+
import torch.nn.functional as F
|
13 |
+
import torch.utils.checkpoint
|
14 |
+
from torch import nn
|
15 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
16 |
+
|
17 |
+
from transformers.activations import ACT2FN
|
18 |
+
from transformers.cache_utils import Cache, DynamicCache
|
19 |
+
from transformers.modeling_attn_mask_utils import (
|
20 |
+
AttentionMaskConverter,
|
21 |
+
_prepare_4d_attention_mask,
|
22 |
+
_prepare_4d_causal_attention_mask,
|
23 |
+
_prepare_4d_causal_attention_mask_for_sdpa,
|
24 |
+
)
|
25 |
+
from transformers.modeling_outputs import (
|
26 |
+
BaseModelOutputWithPast,
|
27 |
+
CausalLMOutputWithPast,
|
28 |
+
SequenceClassifierOutputWithPast
|
29 |
+
)
|
30 |
+
from transformers.modeling_utils import PreTrainedModel
|
31 |
+
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
|
32 |
+
from transformers.utils import (
|
33 |
+
add_start_docstrings,
|
34 |
+
add_start_docstrings_to_model_forward,
|
35 |
+
is_flash_attn_2_available,
|
36 |
+
is_flash_attn_greater_or_equal_2_10,
|
37 |
+
logging,
|
38 |
+
replace_return_docstrings,
|
39 |
+
)
|
40 |
+
from transformers.utils.import_utils import is_torch_fx_available
|
41 |
+
from transformers.generation.utils import GenerateOutput
|
42 |
+
from .configuration_hunyuan import HunYuanConfig
|
43 |
+
from .modeling_hunyuan import HunYuanDecoderLayer, HunYuanRMSNorm
|
44 |
+
from .vit_model import NaVitForward, VitForward, Vit
|
45 |
+
|
46 |
+
|
47 |
+
if is_flash_attn_2_available():
|
48 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
49 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
50 |
+
|
51 |
+
|
52 |
+
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
|
53 |
+
# It means that the function will not be traced through and simply appear as a node in the graph.
|
54 |
+
if is_torch_fx_available():
|
55 |
+
if not is_torch_greater_or_equal_than_1_13:
|
56 |
+
import torch.fx
|
57 |
+
|
58 |
+
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
|
59 |
+
|
60 |
+
|
61 |
+
|
62 |
+
_CONFIG_FOR_DOC = "HunYuanConfig"
|
63 |
+
|
64 |
+
|
65 |
+
HUNYUAN_START_DOCSTRING = r"""
|
66 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
67 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
68 |
+
etc.)
|
69 |
+
|
70 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
71 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
72 |
+
and behavior.
|
73 |
+
|
74 |
+
Parameters:
|
75 |
+
config ([`HunYuanConfig`]):
|
76 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
77 |
+
load the weights associated with the model, only the configuration. Check out the
|
78 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
79 |
+
"""
|
80 |
+
|
81 |
+
|
82 |
+
@add_start_docstrings(
|
83 |
+
"The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
|
84 |
+
HUNYUAN_START_DOCSTRING,
|
85 |
+
)
|
86 |
+
class HunYuanPreTrainedModel(PreTrainedModel):
|
87 |
+
config_class = HunYuanConfig
|
88 |
+
base_model_prefix = "model"
|
89 |
+
supports_gradient_checkpointing = True
|
90 |
+
_no_split_modules = ["HunYuanDecoderLayer"]
|
91 |
+
_skip_keys_device_placement = "past_key_values"
|
92 |
+
_supports_flash_attn_2 = True
|
93 |
+
_supports_sdpa = True
|
94 |
+
_supports_cache_class = True
|
95 |
+
|
96 |
+
def _init_weights(self, module):
|
97 |
+
std = self.config.initializer_range
|
98 |
+
if isinstance(module, nn.Linear):
|
99 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
100 |
+
if module.bias is not None:
|
101 |
+
module.bias.data.zero_()
|
102 |
+
elif isinstance(module, nn.Embedding):
|
103 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
104 |
+
if module.padding_idx is not None:
|
105 |
+
module.weight.data[module.padding_idx].zero_()
|
106 |
+
|
107 |
+
|
108 |
+
HUNYUAN_INPUTS_DOCSTRING = r"""
|
109 |
+
Args:
|
110 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
111 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
112 |
+
it.
|
113 |
+
|
114 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
115 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
116 |
+
|
117 |
+
[What are input IDs?](../glossary#input-ids)
|
118 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
119 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
120 |
+
|
121 |
+
- 1 for tokens that are **not masked**,
|
122 |
+
- 0 for tokens that are **masked**.
|
123 |
+
|
124 |
+
[What are attention masks?](../glossary#attention-mask)
|
125 |
+
|
126 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
127 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
128 |
+
|
129 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
130 |
+
`past_key_values`).
|
131 |
+
|
132 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
133 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
134 |
+
information on the default strategy.
|
135 |
+
|
136 |
+
- 1 indicates the head is **not masked**,
|
137 |
+
- 0 indicates the head is **masked**.
|
138 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
139 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
140 |
+
config.n_positions - 1]`.
|
141 |
+
|
142 |
+
[What are position IDs?](../glossary#position-ids)
|
143 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
144 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
145 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
146 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
147 |
+
|
148 |
+
Two formats are allowed:
|
149 |
+
- a [`~cache_utils.Cache`] instance;
|
150 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
151 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
152 |
+
cache format.
|
153 |
+
|
154 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
155 |
+
legacy cache format will be returned.
|
156 |
+
|
157 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
158 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
159 |
+
of shape `(batch_size, sequence_length)`.
|
160 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
161 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
162 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
163 |
+
model's internal embedding lookup matrix.
|
164 |
+
use_cache (`bool`, *optional*):
|
165 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
166 |
+
`past_key_values`).
|
167 |
+
output_attentions (`bool`, *optional*):
|
168 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
169 |
+
tensors for more detail.
|
170 |
+
output_hidden_states (`bool`, *optional*):
|
171 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
172 |
+
more detail.
|
173 |
+
return_dict (`bool`, *optional*):
|
174 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
175 |
+
"""
|
176 |
+
|
177 |
+
|
178 |
+
@add_start_docstrings(
|
179 |
+
"The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
|
180 |
+
HUNYUAN_START_DOCSTRING,
|
181 |
+
)
|
182 |
+
class HunYuanModel(HunYuanPreTrainedModel):
|
183 |
+
"""
|
184 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HunYuanDecoderLayer`]
|
185 |
+
|
186 |
+
Args:
|
187 |
+
config: HunYuanConfig
|
188 |
+
"""
|
189 |
+
|
190 |
+
def __init__(self, config: HunYuanConfig):
|
191 |
+
super().__init__(config)
|
192 |
+
self.padding_idx = config.pad_token_id
|
193 |
+
self.vocab_size = config.vocab_size
|
194 |
+
self.add_classification_head = config.add_classification_head
|
195 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
196 |
+
self.layers = nn.ModuleList(
|
197 |
+
[HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
198 |
+
)
|
199 |
+
self._use_sdpa = config._attn_implementation == "sdpa"
|
200 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
201 |
+
if not config.add_classification_head:
|
202 |
+
self.norm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
203 |
+
|
204 |
+
self.cla = config.use_cla
|
205 |
+
self.cla_share_factor = config.cla_share_factor
|
206 |
+
|
207 |
+
self.gradient_checkpointing = False
|
208 |
+
# Initialize weights and apply final processing
|
209 |
+
self.post_init()
|
210 |
+
|
211 |
+
def get_input_embeddings(self):
|
212 |
+
return self.embed_tokens
|
213 |
+
|
214 |
+
def set_input_embeddings(self, value):
|
215 |
+
self.embed_tokens = value
|
216 |
+
|
217 |
+
@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
|
218 |
+
def forward(
|
219 |
+
self,
|
220 |
+
input_ids: torch.LongTensor = None,
|
221 |
+
attention_mask: Optional[torch.Tensor] = None,
|
222 |
+
position_ids: Optional[torch.LongTensor] = None,
|
223 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
224 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
225 |
+
use_cache: Optional[bool] = None,
|
226 |
+
output_attentions: Optional[bool] = None,
|
227 |
+
output_hidden_states: Optional[bool] = None,
|
228 |
+
return_dict: Optional[bool] = None,
|
229 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
230 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
231 |
+
output_hidden_states = (
|
232 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
233 |
+
)
|
234 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
235 |
+
|
236 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
237 |
+
|
238 |
+
# retrieve input_ids and inputs_embeds
|
239 |
+
# if input_ids is not None and inputs_embeds is not None:
|
240 |
+
# raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
241 |
+
if input_ids is not None:
|
242 |
+
batch_size, seq_length = input_ids.shape[:2]
|
243 |
+
elif inputs_embeds is not None:
|
244 |
+
batch_size, seq_length = inputs_embeds.shape[:2]
|
245 |
+
else:
|
246 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
247 |
+
|
248 |
+
if self.gradient_checkpointing and self.training:
|
249 |
+
if use_cache:
|
250 |
+
logger.warning_once(
|
251 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
252 |
+
)
|
253 |
+
use_cache = False
|
254 |
+
|
255 |
+
past_key_values_length = 0
|
256 |
+
if use_cache:
|
257 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
258 |
+
if use_legacy_cache:
|
259 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
260 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
261 |
+
|
262 |
+
if position_ids is None:
|
263 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
264 |
+
position_ids = torch.arange(
|
265 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
266 |
+
)
|
267 |
+
position_ids = position_ids.unsqueeze(0)
|
268 |
+
|
269 |
+
if inputs_embeds is None:
|
270 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
271 |
+
|
272 |
+
# Fix lora with gradient checkpointing training
|
273 |
+
if self.training and inputs_embeds.is_leaf:
|
274 |
+
inputs_embeds.requires_grad = True
|
275 |
+
|
276 |
+
if self._use_flash_attention_2:
|
277 |
+
# 2d mask is passed through the layers
|
278 |
+
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
279 |
+
elif self._use_sdpa and not output_attentions:
|
280 |
+
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
281 |
+
# the manual implementation that requires a 4D causal mask in all cases.
|
282 |
+
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
283 |
+
attention_mask,
|
284 |
+
(batch_size, seq_length),
|
285 |
+
inputs_embeds,
|
286 |
+
past_key_values_length,
|
287 |
+
)
|
288 |
+
else:
|
289 |
+
# 4d mask is passed through the layers
|
290 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
291 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
292 |
+
)
|
293 |
+
|
294 |
+
# embed positions
|
295 |
+
hidden_states = inputs_embeds
|
296 |
+
|
297 |
+
# decoder layers
|
298 |
+
all_hidden_states = () if output_hidden_states else None
|
299 |
+
all_self_attns = () if output_attentions else None
|
300 |
+
next_decoder_cache = None
|
301 |
+
|
302 |
+
prev_kv_states = None
|
303 |
+
for layer_idx, decoder_layer in enumerate(self.layers):
|
304 |
+
if output_hidden_states:
|
305 |
+
all_hidden_states += (hidden_states,)
|
306 |
+
|
307 |
+
if self.gradient_checkpointing and self.training:
|
308 |
+
layer_outputs = self._gradient_checkpointing_func(
|
309 |
+
decoder_layer.__call__,
|
310 |
+
hidden_states,
|
311 |
+
attention_mask,
|
312 |
+
position_ids,
|
313 |
+
past_key_values,
|
314 |
+
output_attentions,
|
315 |
+
use_cache,
|
316 |
+
prev_kv_states,
|
317 |
+
)
|
318 |
+
else:
|
319 |
+
layer_outputs = decoder_layer(
|
320 |
+
hidden_states,
|
321 |
+
attention_mask=attention_mask,
|
322 |
+
position_ids=position_ids,
|
323 |
+
past_key_value=past_key_values,
|
324 |
+
output_attentions=output_attentions,
|
325 |
+
use_cache=use_cache,
|
326 |
+
kv_states=prev_kv_states
|
327 |
+
)
|
328 |
+
|
329 |
+
hidden_states = layer_outputs[0]
|
330 |
+
|
331 |
+
if use_cache:
|
332 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
333 |
+
|
334 |
+
if output_attentions:
|
335 |
+
all_self_attns += (layer_outputs[1],)
|
336 |
+
|
337 |
+
kv_states = layer_outputs[-1]
|
338 |
+
|
339 |
+
if self.cla and layer_idx % self.cla_share_factor == 0:
|
340 |
+
prev_kv_states = kv_states
|
341 |
+
if not self.add_classification_head:
|
342 |
+
hidden_states = self.norm(hidden_states)
|
343 |
+
|
344 |
+
# add hidden states from the last decoder layer
|
345 |
+
if output_hidden_states:
|
346 |
+
all_hidden_states += (hidden_states,)
|
347 |
+
|
348 |
+
next_cache = None
|
349 |
+
if use_cache:
|
350 |
+
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
351 |
+
if not return_dict:
|
352 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
353 |
+
return BaseModelOutputWithPast(
|
354 |
+
last_hidden_state=hidden_states,
|
355 |
+
past_key_values=next_cache,
|
356 |
+
hidden_states=all_hidden_states,
|
357 |
+
attentions=all_self_attns,
|
358 |
+
)
|
359 |
+
|
360 |
+
|
361 |
+
class HunYuanMoEV1ForCausalLM(HunYuanPreTrainedModel):
|
362 |
+
_tied_weights_keys = ["lm_head.weight"]
|
363 |
+
|
364 |
+
def __init__(self, config: HunYuanConfig):
|
365 |
+
super().__init__(config)
|
366 |
+
if config.vit_path is not None:
|
367 |
+
if "-tp" in config.vit_type:
|
368 |
+
config.vit_type = config.vit_type.replace("-tp", "")
|
369 |
+
self.vit_type = config.vit_type
|
370 |
+
if self.vit_type not in ['NaVit', 'EvaVit']:
|
371 |
+
if config.vit_mapping_type == 'mlp':
|
372 |
+
self.vit_linear_encoder = torch.nn.Linear(config.hidden_size, config.hidden_size)
|
373 |
+
self.vit = Vit(config)
|
374 |
+
else:
|
375 |
+
self.vit = None
|
376 |
+
self.config = config
|
377 |
+
self.model = HunYuanModel(config)
|
378 |
+
self.add_classification_head = config.add_classification_head
|
379 |
+
self.pad_id = config.pad_id
|
380 |
+
self.vocab_size = config.vocab_size
|
381 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
382 |
+
if config.add_classification_head:
|
383 |
+
self.pool_head = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
|
384 |
+
self.pool_head2 = nn.Linear(config.hidden_size, config.class_num, bias=False)
|
385 |
+
# Initialize weights and apply final processing
|
386 |
+
self.post_init()
|
387 |
+
|
388 |
+
def get_input_embeddings(self):
|
389 |
+
return self.model.embed_tokens
|
390 |
+
|
391 |
+
def set_input_embeddings(self, value):
|
392 |
+
self.model.embed_tokens = value
|
393 |
+
|
394 |
+
def get_output_embeddings(self):
|
395 |
+
return self.lm_head
|
396 |
+
|
397 |
+
def set_output_embeddings(self, new_embeddings):
|
398 |
+
self.lm_head = new_embeddings
|
399 |
+
|
400 |
+
def set_decoder(self, decoder):
|
401 |
+
self.model = decoder
|
402 |
+
|
403 |
+
def get_decoder(self):
|
404 |
+
return self.model
|
405 |
+
|
406 |
+
@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
|
407 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
408 |
+
def forward(
|
409 |
+
self,
|
410 |
+
input_ids: torch.LongTensor = None,
|
411 |
+
attention_mask: Optional[torch.Tensor] = None,
|
412 |
+
position_ids: Optional[torch.LongTensor] = None,
|
413 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
414 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
415 |
+
labels: Optional[torch.LongTensor] = None,
|
416 |
+
use_cache: Optional[bool] = None,
|
417 |
+
output_attentions: Optional[bool] = None,
|
418 |
+
output_hidden_states: Optional[bool] = None,
|
419 |
+
return_dict: Optional[bool] = None,
|
420 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
421 |
+
r"""
|
422 |
+
Args:
|
423 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
424 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
425 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
426 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
427 |
+
|
428 |
+
Returns:
|
429 |
+
|
430 |
+
Example:
|
431 |
+
|
432 |
+
```python
|
433 |
+
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
|
434 |
+
|
435 |
+
>>> model = AutoModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
436 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
437 |
+
|
438 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
439 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
440 |
+
|
441 |
+
>>> # Generate
|
442 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
443 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
444 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
445 |
+
```"""
|
446 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
447 |
+
output_hidden_states = (
|
448 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
449 |
+
)
|
450 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
451 |
+
|
452 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
453 |
+
outputs = self.model(
|
454 |
+
input_ids=input_ids,
|
455 |
+
attention_mask=attention_mask,
|
456 |
+
position_ids=position_ids,
|
457 |
+
past_key_values=past_key_values,
|
458 |
+
inputs_embeds=inputs_embeds,
|
459 |
+
use_cache=use_cache,
|
460 |
+
output_attentions=output_attentions,
|
461 |
+
output_hidden_states=output_hidden_states,
|
462 |
+
return_dict=return_dict,
|
463 |
+
)
|
464 |
+
|
465 |
+
hidden_states = outputs[0]
|
466 |
+
|
467 |
+
if not self.add_classification_head:
|
468 |
+
if self.config.pretraining_tp > 1:
|
469 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
470 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
471 |
+
logits = torch.cat(logits, dim=-1)
|
472 |
+
else:
|
473 |
+
logits = self.lm_head(hidden_states)
|
474 |
+
logits = logits.float()
|
475 |
+
else:
|
476 |
+
logits = hidden_states
|
477 |
+
logits = logits.float()
|
478 |
+
pooled_output = self.pool_head(logits)
|
479 |
+
pooled_output = torch.tanh(pooled_output)
|
480 |
+
pooled_output = self.pool_head2(pooled_output).contiguous() # bs * class_num
|
481 |
+
if len(pooled_output.shape) < 2:
|
482 |
+
raise ValueError("pooled_output does not have enough dimensions for transpose")
|
483 |
+
|
484 |
+
if self.config.pool_type == "mean":
|
485 |
+
reward = pooled_output.mean(dim=1).squeeze(-1)
|
486 |
+
elif self.config.pool_type == "last":
|
487 |
+
# bs * hidden_size
|
488 |
+
seq_length = (input_ids != self.pad_id).long().sum(dim=1) - 1
|
489 |
+
batch_size = input_ids.size(0)
|
490 |
+
reward = pooled_output[torch.arange(batch_size, device=pooled_output.device), seq_length].squeeze(-1)
|
491 |
+
else:
|
492 |
+
reward = pooled_output[:, 0].squeeze(-1)
|
493 |
+
|
494 |
+
loss = None
|
495 |
+
if labels is not None:
|
496 |
+
# Shift so that tokens < n predict n
|
497 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
498 |
+
shift_labels = labels[..., 1:].contiguous()
|
499 |
+
# Flatten the tokens
|
500 |
+
loss_fct = CrossEntropyLoss()
|
501 |
+
shift_logits = shift_logits.reshape(-1, self.config.vocab_size)
|
502 |
+
shift_labels = shift_labels.reshape(-1)
|
503 |
+
# Enable model parallelism
|
504 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
505 |
+
loss = loss_fct(shift_logits, shift_labels)
|
506 |
+
|
507 |
+
if not return_dict:
|
508 |
+
output = (logits,) + outputs[1:]
|
509 |
+
return (loss,) + output if loss is not None else output
|
510 |
+
|
511 |
+
output = CausalLMOutputWithPast(
|
512 |
+
loss=loss,
|
513 |
+
logits=logits,
|
514 |
+
past_key_values=outputs.past_key_values,
|
515 |
+
hidden_states=outputs.hidden_states,
|
516 |
+
attentions=outputs.attentions,
|
517 |
+
)
|
518 |
+
if self.add_classification_head:
|
519 |
+
output['reward'] = reward
|
520 |
+
|
521 |
+
return output
|
522 |
+
|
523 |
+
def prepare_inputs_for_generation(
|
524 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
525 |
+
):
|
526 |
+
if past_key_values is not None:
|
527 |
+
if isinstance(past_key_values, Cache):
|
528 |
+
cache_length = past_key_values.get_seq_length()
|
529 |
+
past_length = past_key_values.seen_tokens
|
530 |
+
max_cache_length = past_key_values.get_max_cache_shape()
|
531 |
+
else:
|
532 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
533 |
+
max_cache_length = None
|
534 |
+
|
535 |
+
# Keep only the unprocessed tokens:
|
536 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
537 |
+
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
|
538 |
+
# input)
|
539 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
540 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
|
541 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
542 |
+
# input_ids based on the past_length.
|
543 |
+
elif past_length < input_ids.shape[1]:
|
544 |
+
input_ids = input_ids[:, past_length:]
|
545 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
546 |
+
|
547 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
548 |
+
if (
|
549 |
+
max_cache_length is not None
|
550 |
+
and attention_mask is not None
|
551 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
552 |
+
):
|
553 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
554 |
+
|
555 |
+
position_ids = kwargs.get("position_ids", None)
|
556 |
+
if attention_mask is not None and position_ids is None:
|
557 |
+
# create position_ids on the fly for batch generation
|
558 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
559 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
560 |
+
if past_key_values:
|
561 |
+
position_ids = position_ids[:, -input_ids.shape[1]:]
|
562 |
+
|
563 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
564 |
+
if inputs_embeds is not None and past_key_values is None:
|
565 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
566 |
+
else:
|
567 |
+
model_inputs = {"input_ids": input_ids}
|
568 |
+
|
569 |
+
model_inputs.update(
|
570 |
+
{
|
571 |
+
"position_ids": position_ids,
|
572 |
+
"past_key_values": past_key_values,
|
573 |
+
"use_cache": kwargs.get("use_cache"),
|
574 |
+
"attention_mask": attention_mask,
|
575 |
+
}
|
576 |
+
)
|
577 |
+
return model_inputs
|
578 |
+
|
579 |
+
@staticmethod
|
580 |
+
def _reorder_cache(past_key_values, beam_idx):
|
581 |
+
reordered_past = ()
|
582 |
+
for layer_past in past_key_values:
|
583 |
+
reordered_past += (
|
584 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
585 |
+
)
|
586 |
+
return reordered_past
|
587 |
+
|
588 |
+
|
589 |
+
class MultimodelHunYuanForCausalLM(HunYuanMoEV1ForCausalLM):
|
590 |
+
_tied_weights_keys = ["lm_head.weight"]
|
591 |
+
|
592 |
+
def __init__(self, config: HunYuanConfig):
|
593 |
+
super().__init__(config)
|
594 |
+
|
595 |
+
@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
|
596 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
597 |
+
def forward(
|
598 |
+
self,
|
599 |
+
input_ids: torch.LongTensor = None,
|
600 |
+
attention_mask: Optional[torch.Tensor] = None,
|
601 |
+
position_ids: Optional[torch.LongTensor] = None,
|
602 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
603 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
604 |
+
labels: Optional[torch.LongTensor] = None,
|
605 |
+
imgs: Optional[List[torch.FloatTensor]] = None,
|
606 |
+
imgs_pos: Optional[List[int]] = None,
|
607 |
+
use_cache: Optional[bool] = None,
|
608 |
+
output_attentions: Optional[bool] = None,
|
609 |
+
output_hidden_states: Optional[bool] = None,
|
610 |
+
return_dict: Optional[bool] = None,
|
611 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
612 |
+
r"""
|
613 |
+
Args:
|
614 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
615 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
616 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
617 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
618 |
+
|
619 |
+
Returns:
|
620 |
+
|
621 |
+
Example:
|
622 |
+
|
623 |
+
```python
|
624 |
+
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
|
625 |
+
|
626 |
+
>>> model = AutoModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
627 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
628 |
+
|
629 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
630 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
631 |
+
|
632 |
+
>>> # Generate
|
633 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
634 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
635 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
636 |
+
```"""
|
637 |
+
mask_init_id = self.config.mask_init_id
|
638 |
+
pad_id = self.config.pad_token_id
|
639 |
+
eod_id = self.config.eod_token_id
|
640 |
+
image_token_id = self.config.image_token_id
|
641 |
+
im_start_id = self.config.im_start_id
|
642 |
+
im_end_id = self.config.im_end_id
|
643 |
+
video_start_id = self.config.video_start_id
|
644 |
+
video_end_id = self.config.video_end_id
|
645 |
+
|
646 |
+
if self.vit is not None and imgs is not None:
|
647 |
+
encoder_input = self.model.embed_tokens(input_ids)
|
648 |
+
if self.vit_type in ['NaVit', 'EvaVit', 'AnyResVit']:
|
649 |
+
inputs_embeds, input_ids = NaVitForward(input_ids, encoder_input, self.vit, imgs, imgs_pos, self.config.vit_input_resolution, \
|
650 |
+
im_start_id, im_end_id, image_token_id, self.config.anyres_vit_two_views, self.config.torch_dtype)
|
651 |
+
else:
|
652 |
+
inputs_embeds, input_ids = VitForward(input_ids, encoder_input, self.vit, self.vit_linear_encoder, imgs, imgs_pos, \
|
653 |
+
self.config.vit_input_resolution, self.config.vit_mapping_type, self.config.vit_patch, self.config.vit_token)
|
654 |
+
|
655 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
656 |
+
output_hidden_states = (
|
657 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
658 |
+
)
|
659 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
660 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
661 |
+
|
662 |
+
outputs = self.model(
|
663 |
+
input_ids=input_ids,
|
664 |
+
attention_mask=attention_mask,
|
665 |
+
position_ids=position_ids,
|
666 |
+
past_key_values=past_key_values,
|
667 |
+
inputs_embeds=inputs_embeds,
|
668 |
+
use_cache=use_cache,
|
669 |
+
output_attentions=output_attentions,
|
670 |
+
output_hidden_states=output_hidden_states,
|
671 |
+
return_dict=return_dict,
|
672 |
+
)
|
673 |
+
|
674 |
+
hidden_states = outputs[0]
|
675 |
+
if self.config.pretraining_tp > 1:
|
676 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
677 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
678 |
+
logits = torch.cat(logits, dim=-1)
|
679 |
+
else:
|
680 |
+
logits = self.lm_head(hidden_states)
|
681 |
+
logits = logits.float()
|
682 |
+
|
683 |
+
loss = None
|
684 |
+
if labels is not None:
|
685 |
+
labels = labels.to(logits.device)
|
686 |
+
# Shift so that tokens < n predict n
|
687 |
+
shift_logits = logits
|
688 |
+
shift_labels = labels
|
689 |
+
# Flatten the tokens
|
690 |
+
loss_fct = CrossEntropyLoss()
|
691 |
+
shift_logits = shift_logits.reshape(-1, self.config.vocab_size)
|
692 |
+
shift_labels = shift_labels.reshape(-1)
|
693 |
+
shift_tokens = input_ids.reshape(-1)
|
694 |
+
# compute loss
|
695 |
+
mask = (shift_labels < mask_init_id) & (shift_labels != pad_id) & (shift_labels != image_token_id) & (shift_labels != im_start_id) \
|
696 |
+
& (shift_labels != im_end_id) & (shift_labels != video_start_id) & (shift_labels != video_end_id) & (shift_tokens != pad_id) & (shift_tokens != eod_id)
|
697 |
+
shift_logits = shift_logits[mask, :]
|
698 |
+
shift_labels = shift_labels[mask]
|
699 |
+
loss = loss_fct(shift_logits, shift_labels)
|
700 |
+
|
701 |
+
if not return_dict:
|
702 |
+
output = (logits,) + outputs[1:]
|
703 |
+
return (loss,) + output if loss is not None else output
|
704 |
+
|
705 |
+
return CausalLMOutputWithPast(
|
706 |
+
loss=loss,
|
707 |
+
logits=logits,
|
708 |
+
past_key_values=outputs.past_key_values,
|
709 |
+
hidden_states=outputs.hidden_states,
|
710 |
+
attentions=outputs.attentions,
|
711 |
+
)
|
712 |
+
|
713 |
+
def prepare_inputs_for_generation(
|
714 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
715 |
+
):
|
716 |
+
imgs = kwargs.pop("imgs", None)
|
717 |
+
imgs_pos = kwargs.pop("imgs_pos", None)
|
718 |
+
inputs = super().prepare_inputs_for_generation(
|
719 |
+
input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs
|
720 |
+
)
|
721 |
+
|
722 |
+
if imgs is not None:
|
723 |
+
inputs['imgs'] = imgs
|
724 |
+
if imgs_pos is not None:
|
725 |
+
inputs['imgs_pos'] = imgs_pos
|
726 |
+
return inputs
|
727 |
+
|
728 |
+
@torch.no_grad()
|
729 |
+
def generate(
|
730 |
+
self,
|
731 |
+
inputs: Optional[torch.Tensor] = None,
|
732 |
+
attention_mask: Optional[torch.Tensor] = None,
|
733 |
+
position_ids: Optional[torch.LongTensor] = None,
|
734 |
+
imgs: Optional[List[torch.FloatTensor]] = None,
|
735 |
+
imgs_pos: Optional[List[int]] = None,
|
736 |
+
**kwargs,
|
737 |
+
) -> Union[GenerateOutput, torch.LongTensor]:
|
738 |
+
if "inputs_embeds" in kwargs:
|
739 |
+
raise NotImplementedError("`inputs_embeds` is not supported")
|
740 |
+
|
741 |
+
if self.vit is not None:
|
742 |
+
encoder_input = self.model.embed_tokens(inputs)
|
743 |
+
if self.vit_type in ['NaVit', 'EvaVit', 'AnyResVit']:
|
744 |
+
inputs_embeds, input_ids = NaVitForward(inputs, encoder_input, self.vit, imgs, imgs_pos, self.config.vit_input_resolution, \
|
745 |
+
self.config.im_start_id, self.config.im_end_id, self.config.image_token_id, self.config.anyres_vit_two_views, self.config.torch_dtype)
|
746 |
+
else:
|
747 |
+
inputs_embeds, input_ids = VitForward(inputs, encoder_input, self.vit, self.vit_linear_encoder, imgs, imgs_pos, \
|
748 |
+
self.config.vit_input_resolution, self.config.vit_mapping_type, self.config.vit_patch, self.config.vit_token)
|
749 |
+
|
750 |
+
return super().generate(
|
751 |
+
inputs=input_ids,
|
752 |
+
position_ids=position_ids,
|
753 |
+
attention_mask=attention_mask,
|
754 |
+
inputs_embeds=inputs_embeds,
|
755 |
+
eos_token_id=self.config.eod_token_id,
|
756 |
+
**kwargs
|
757 |
+
)
|
758 |
+
|
759 |
+
|
760 |
+
@add_start_docstrings(
|
761 |
+
"""
|
762 |
+
The HunYuan Model transformer with a sequence classification head on top (linear layer).
|
763 |
+
|
764 |
+
[`HunYuanForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
765 |
+
(e.g. GPT-2) do.
|
766 |
+
|
767 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
768 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
769 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
770 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
771 |
+
each row of the batch).
|
772 |
+
""",
|
773 |
+
HUNYUAN_START_DOCSTRING,
|
774 |
+
)
|
775 |
+
class HunYuanForSequenceClassification(HunYuanPreTrainedModel):
|
776 |
+
def __init__(self, config):
|
777 |
+
super().__init__(config)
|
778 |
+
self.num_labels = config.num_labels
|
779 |
+
self.model = HunYuanModel(config)
|
780 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
781 |
+
|
782 |
+
# Initialize weights and apply final processing
|
783 |
+
self.post_init()
|
784 |
+
|
785 |
+
def get_input_embeddings(self):
|
786 |
+
return self.model.embed_tokens
|
787 |
+
|
788 |
+
def set_input_embeddings(self, value):
|
789 |
+
self.model.embed_tokens = value
|
790 |
+
|
791 |
+
@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
|
792 |
+
def forward(
|
793 |
+
self,
|
794 |
+
input_ids: torch.LongTensor = None,
|
795 |
+
attention_mask: Optional[torch.Tensor] = None,
|
796 |
+
position_ids: Optional[torch.LongTensor] = None,
|
797 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
798 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
799 |
+
labels: Optional[torch.LongTensor] = None,
|
800 |
+
use_cache: Optional[bool] = None,
|
801 |
+
output_attentions: Optional[bool] = None,
|
802 |
+
output_hidden_states: Optional[bool] = None,
|
803 |
+
return_dict: Optional[bool] = None,
|
804 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
805 |
+
r"""
|
806 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
807 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
808 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
809 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
810 |
+
"""
|
811 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
812 |
+
|
813 |
+
transformer_outputs = self.model(
|
814 |
+
input_ids,
|
815 |
+
attention_mask=attention_mask,
|
816 |
+
position_ids=position_ids,
|
817 |
+
past_key_values=past_key_values,
|
818 |
+
inputs_embeds=inputs_embeds,
|
819 |
+
use_cache=use_cache,
|
820 |
+
output_attentions=output_attentions,
|
821 |
+
output_hidden_states=output_hidden_states,
|
822 |
+
return_dict=return_dict,
|
823 |
+
)
|
824 |
+
hidden_states = transformer_outputs[0]
|
825 |
+
logits = self.score(hidden_states)
|
826 |
+
|
827 |
+
if input_ids is not None:
|
828 |
+
batch_size = input_ids.shape[0]
|
829 |
+
else:
|
830 |
+
batch_size = inputs_embeds.shape[0]
|
831 |
+
|
832 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
833 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
834 |
+
if self.config.pad_token_id is None:
|
835 |
+
sequence_lengths = -1
|
836 |
+
else:
|
837 |
+
if input_ids is not None:
|
838 |
+
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
|
839 |
+
logits.device
|
840 |
+
)
|
841 |
+
else:
|
842 |
+
sequence_lengths = -1
|
843 |
+
|
844 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
845 |
+
|
846 |
+
loss = None
|
847 |
+
if labels is not None:
|
848 |
+
labels = labels.to(logits.device)
|
849 |
+
if self.config.problem_type is None:
|
850 |
+
if self.num_labels == 1:
|
851 |
+
self.config.problem_type = "regression"
|
852 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
853 |
+
self.config.problem_type = "single_label_classification"
|
854 |
+
else:
|
855 |
+
self.config.problem_type = "multi_label_classification"
|
856 |
+
|
857 |
+
if self.config.problem_type == "regression":
|
858 |
+
loss_fct = MSELoss()
|
859 |
+
if self.num_labels == 1:
|
860 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
861 |
+
else:
|
862 |
+
loss = loss_fct(pooled_logits, labels)
|
863 |
+
elif self.config.problem_type == "single_label_classification":
|
864 |
+
loss_fct = CrossEntropyLoss()
|
865 |
+
loss = loss_fct(pooled_logits.reshape(-1, self.num_labels), labels.reshape(-1))
|
866 |
+
elif self.config.problem_type == "multi_label_classification":
|
867 |
+
loss_fct = BCEWithLogitsLoss()
|
868 |
+
loss = loss_fct(pooled_logits, labels)
|
869 |
+
if not return_dict:
|
870 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
871 |
+
return ((loss,) + output) if loss is not None else output
|
872 |
+
|
873 |
+
return SequenceClassifierOutputWithPast(
|
874 |
+
loss=loss,
|
875 |
+
logits=pooled_logits,
|
876 |
+
past_key_values=transformer_outputs.past_key_values,
|
877 |
+
hidden_states=transformer_outputs.hidden_states,
|
878 |
+
attentions=transformer_outputs.attentions,
|
879 |
+
)
|
hy.tiktoken
ADDED
The diff for this file is too large to render.
See raw diff
|
|
model-00001-of-00002.safetensors
ADDED
@@ -0,0 +1,3 @@
|
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|
|
|
|
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|
|
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+
version https://git-lfs.github.com/spec/v1
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oid sha256:0403737be8ae02a16247b7c8e74b06256d82e86393bea8934fd4d4c7ccccf360
|
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size 4969099160
|
model-00002-of-00002.safetensors
ADDED
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|
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|
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+
version https://git-lfs.github.com/spec/v1
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oid sha256:999509aca71a5ed94988cbd91253e125e4bcaaffed1b2119e268801ebee176b7
|
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+
size 822201896
|
model.gguf
ADDED
@@ -0,0 +1,3 @@
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|
|
|
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|
|
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+
version https://git-lfs.github.com/spec/v1
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oid sha256:29ea5aaff3b1c520c12042af37db7ae48cc413a741026d787f8c736e5920199c
|
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size 2900447776
|
model.safetensors.index.json
ADDED
@@ -0,0 +1,105 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"metadata": {
|
3 |
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"total_size": 5791289344
|
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|
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|
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|
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+
"model.layers.0.mlp.gate.wg.weight": "model-00001-of-00002.safetensors",
|
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+
"model.layers.0.mlp.shared_mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
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modeling_hunyuan.py
ADDED
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|
1 |
+
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
|
2 |
+
#
|
3 |
+
# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
|
4 |
+
# you may not use this file except in compliance with the License.
|
5 |
+
# You may obtain a copy of the License at
|
6 |
+
#
|
7 |
+
# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
|
8 |
+
#
|
9 |
+
# Unless required by applicable law or agreed to in writing, software
|
10 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
11 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
12 |
+
# See the License for the specific language governing permissions and
|
13 |
+
# limitations under the License.
|
14 |
+
#
|
15 |
+
""" PyTorch HunYuan model."""
|
16 |
+
|
17 |
+
import math
|
18 |
+
import warnings
|
19 |
+
from typing import List, Optional, Tuple, Union
|
20 |
+
|
21 |
+
import torch
|
22 |
+
from torch import Tensor
|
23 |
+
import torch.nn.functional as F
|
24 |
+
import torch.utils.checkpoint
|
25 |
+
from torch import nn
|
26 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
27 |
+
|
28 |
+
from transformers.activations import ACT2FN
|
29 |
+
from transformers.cache_utils import Cache, DynamicCache
|
30 |
+
from transformers.modeling_attn_mask_utils import (
|
31 |
+
AttentionMaskConverter,
|
32 |
+
_prepare_4d_attention_mask,
|
33 |
+
_prepare_4d_causal_attention_mask,
|
34 |
+
_prepare_4d_causal_attention_mask_for_sdpa,
|
35 |
+
)
|
36 |
+
from transformers.modeling_outputs import (
|
37 |
+
BaseModelOutputWithPast,
|
38 |
+
CausalLMOutputWithPast,
|
39 |
+
SequenceClassifierOutputWithPast
|
40 |
+
)
|
41 |
+
from transformers.modeling_utils import PreTrainedModel
|
42 |
+
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
|
43 |
+
from transformers.utils import (
|
44 |
+
add_start_docstrings,
|
45 |
+
add_start_docstrings_to_model_forward,
|
46 |
+
is_flash_attn_2_available,
|
47 |
+
is_flash_attn_greater_or_equal_2_10,
|
48 |
+
logging,
|
49 |
+
replace_return_docstrings,
|
50 |
+
)
|
51 |
+
from transformers.utils.import_utils import is_torch_fx_available
|
52 |
+
from .configuration_hunyuan import HunYuanConfig
|
53 |
+
|
54 |
+
|
55 |
+
if is_flash_attn_2_available():
|
56 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
57 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
58 |
+
|
59 |
+
|
60 |
+
# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
|
61 |
+
# It means that the function will not be traced through and simply appear as a node in the graph.
|
62 |
+
if is_torch_fx_available():
|
63 |
+
if not is_torch_greater_or_equal_than_1_13:
|
64 |
+
import torch.fx
|
65 |
+
|
66 |
+
_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
|
67 |
+
|
68 |
+
|
69 |
+
logger = logging.get_logger(__name__)
|
70 |
+
|
71 |
+
_CONFIG_FOR_DOC = "HunYuanConfig"
|
72 |
+
|
73 |
+
|
74 |
+
def topkgating(logits: Tensor, topk: int):
|
75 |
+
logits = logits.float()
|
76 |
+
gates = F.softmax(logits, dim=1)
|
77 |
+
print(f"logits\n", logits)
|
78 |
+
print(f"gates\n", gates)
|
79 |
+
# expert_capacity = topk * gates.shape[0]
|
80 |
+
expert_capacity = max(topk, topk * gates.shape[0] // gates.shape[1])
|
81 |
+
num_experts = int(gates.shape[1])
|
82 |
+
# Top-k router probability and corresponding expert indices for each token.
|
83 |
+
# Shape: [tokens_per_group, num_selected_experts].
|
84 |
+
expert_gate, expert_index = torch.topk(gates, topk)
|
85 |
+
expert_mask = F.one_hot(expert_index, num_experts)
|
86 |
+
# For a given token, determine if it was routed to a given expert.
|
87 |
+
# Shape: [tokens_per_group, num_experts]
|
88 |
+
expert_mask_aux = expert_mask.max(dim=-2)[0]
|
89 |
+
tokens_per_group_and_expert = torch.mean(expert_mask_aux.float(), dim=-2)
|
90 |
+
router_prob_per_group_and_expert = torch.mean(gates.float(), dim=-2)
|
91 |
+
l_aux = num_experts**2 * torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert)
|
92 |
+
print(f"l_aux\n", l_aux)
|
93 |
+
|
94 |
+
gates_s = torch.clamp(
|
95 |
+
torch.matmul(expert_mask.float(), gates.unsqueeze(-1)).sum(dim=1), min=torch.finfo(gates.dtype).eps
|
96 |
+
)
|
97 |
+
print(f"gates_s\n", gates_s)
|
98 |
+
router_probs = gates / gates_s
|
99 |
+
print(f"router_probs\n", router_probs)
|
100 |
+
# Make num_selected_experts the leading axis to ensure that top-1 choices
|
101 |
+
# have priority over top-2 choices, which have priority over top-3 choices,
|
102 |
+
# etc.
|
103 |
+
expert_index = torch.transpose(expert_index, 0, 1)
|
104 |
+
# Shape: [num_selected_experts * tokens_per_group]
|
105 |
+
expert_index = expert_index.reshape(-1)
|
106 |
+
|
107 |
+
# Create mask out of indices.
|
108 |
+
# Shape: [tokens_per_group * num_selected_experts, num_experts].
|
109 |
+
expert_mask = F.one_hot(expert_index, num_experts).to(torch.int32)
|
110 |
+
exp_counts = torch.sum(expert_mask, dim=0).detach()
|
111 |
+
|
112 |
+
# Experts have a fixed capacity that we cannot exceed. A token's priority
|
113 |
+
# within the expert's buffer is given by the masked, cumulative capacity of
|
114 |
+
# its target expert.
|
115 |
+
# Shape: [tokens_per_group * num_selected_experts, num_experts].
|
116 |
+
token_priority = torch.cumsum(expert_mask, dim=0) * expert_mask - 1
|
117 |
+
# Shape: [num_selected_experts, tokens_per_group, num_experts].
|
118 |
+
token_priority = token_priority.reshape((topk, -1, num_experts))
|
119 |
+
# Shape: [tokens_per_group, num_selected_experts, num_experts].
|
120 |
+
token_priority = torch.transpose(token_priority, 0, 1)
|
121 |
+
# For each token, across all selected experts, select the only non-negative
|
122 |
+
# (unmasked) priority. Now, for group G routing to expert E, token T has
|
123 |
+
# non-negative priority (i.e. token_priority[G,T,E] >= 0) if and only if E
|
124 |
+
# is its targeted expert.
|
125 |
+
# Shape: [tokens_per_group, num_experts].
|
126 |
+
token_priority = torch.max(token_priority, dim=1)[0]
|
127 |
+
print(f"token_priority\n", token_priority)
|
128 |
+
|
129 |
+
# Token T can only be routed to expert E if its priority is positive and
|
130 |
+
# less than the expert capacity. One-hot matrix will ignore indices outside
|
131 |
+
# the range [0, expert_capacity).
|
132 |
+
# Shape: [tokens_per_group, num_experts, expert_capacity].
|
133 |
+
valid_mask = torch.logical_and(token_priority >= 0, token_priority < expert_capacity)
|
134 |
+
token_priority = torch.masked_fill(token_priority, ~valid_mask, 0)
|
135 |
+
dispatch_mask = F.one_hot(token_priority, expert_capacity).to(torch.bool)
|
136 |
+
valid_mask = valid_mask.unsqueeze(-1).expand(-1, -1, expert_capacity)
|
137 |
+
dispatch_mask = torch.masked_fill(dispatch_mask, ~valid_mask, 0)
|
138 |
+
|
139 |
+
# The combine array will be used for combining expert outputs, scaled by the
|
140 |
+
# router probabilities. Shape: [num_groups, tokens_per_group, num_experts,
|
141 |
+
# expert_capacity].
|
142 |
+
combine_weights = torch.einsum("...te,...tec->...tec", router_probs, dispatch_mask)
|
143 |
+
exp_counts_capacity = torch.sum(dispatch_mask)
|
144 |
+
exp_capacity_rate = exp_counts_capacity / (logits.shape[0]*topk)
|
145 |
+
|
146 |
+
return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts
|
147 |
+
|
148 |
+
|
149 |
+
def top1gating(logits: Tensor, random_routing_dropped_token: bool = False):
|
150 |
+
"""Implements Top1Gating on logits."""
|
151 |
+
# everything is in fp32 in this function
|
152 |
+
logits = logits.float()
|
153 |
+
gates = F.softmax(logits, dim=1)
|
154 |
+
capacity = gates.shape[0]
|
155 |
+
|
156 |
+
# Create a mask for 1st's expert per token
|
157 |
+
# noisy gating
|
158 |
+
indices1_s = torch.argmax(gates, dim=1)
|
159 |
+
num_experts = int(gates.shape[1])
|
160 |
+
mask1 = F.one_hot(indices1_s, num_classes=num_experts)
|
161 |
+
|
162 |
+
# gating decisions
|
163 |
+
# exp_counts = torch.sum(mask1, dim=0).detach().to('cpu')
|
164 |
+
exp_counts = torch.sum(mask1, dim=0).detach()
|
165 |
+
|
166 |
+
# Compute l_aux
|
167 |
+
me = torch.mean(gates, dim=0)
|
168 |
+
ce = torch.mean(mask1.float(), dim=0)
|
169 |
+
l_aux = torch.sum(me * ce) * num_experts
|
170 |
+
mask1_rand = mask1
|
171 |
+
|
172 |
+
top_idx = torch.topk(mask1_rand, k=capacity, dim=0)[1]
|
173 |
+
|
174 |
+
new_mask1 = mask1 * torch.zeros_like(mask1).scatter_(0, top_idx, 1)
|
175 |
+
mask1 = new_mask1
|
176 |
+
mask1_bk = mask1
|
177 |
+
if random_routing_dropped_token:
|
178 |
+
not_full = capacity - new_mask1.sum(dim=0)
|
179 |
+
sorted_notfull, indices_notfull = torch.sort(not_full, descending=True)
|
180 |
+
sorted_notfull = sorted_notfull.to(torch.int64)
|
181 |
+
not_full_experts_ids = torch.repeat_interleave(indices_notfull, sorted_notfull)
|
182 |
+
shuffle_not_full_ids = torch.randperm(not_full_experts_ids.shape[0])
|
183 |
+
not_full_experts_ids = not_full_experts_ids[shuffle_not_full_ids]
|
184 |
+
indices1_s_after_drop = torch.argmax(new_mask1, dim=1)
|
185 |
+
# get drop idx
|
186 |
+
drop_mask = 1 - new_mask1.sum(dim=1)
|
187 |
+
drop_mask = drop_mask.bool()
|
188 |
+
drop_idx = drop_mask.nonzero().view(-1)
|
189 |
+
drop_num = drop_mask.sum().to(torch.int64)
|
190 |
+
indices1_s_after_drop.scatter_(0, drop_idx, not_full_experts_ids[:drop_num])
|
191 |
+
nodrop_mask1 = F.one_hot(indices1_s_after_drop, num_classes=num_experts)
|
192 |
+
mask1 = nodrop_mask1
|
193 |
+
|
194 |
+
# Compute locations in capacity buffer
|
195 |
+
locations1 = torch.cumsum(mask1, dim=0) - 1
|
196 |
+
|
197 |
+
# Store the capacity location for each token
|
198 |
+
locations1_s = torch.sum(locations1 * mask1, dim=1)
|
199 |
+
|
200 |
+
# Normalize gate probabilities
|
201 |
+
mask1_float = mask1.float()
|
202 |
+
gates = gates * mask1_float
|
203 |
+
|
204 |
+
locations1_sc = F.one_hot(locations1_s, num_classes=capacity).float() # one hot to float
|
205 |
+
combine_weights = torch.einsum("se,sc->sec", gates, locations1_sc)
|
206 |
+
|
207 |
+
dispatch_mask = combine_weights.bool()
|
208 |
+
|
209 |
+
exp_counts_capacity = torch.sum(mask1_bk)
|
210 |
+
exp_capacity_rate = exp_counts_capacity / (logits.shape[0])
|
211 |
+
return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts
|
212 |
+
|
213 |
+
|
214 |
+
def _get_unpad_data(attention_mask):
|
215 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
216 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
217 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
218 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
|
219 |
+
return (
|
220 |
+
indices,
|
221 |
+
cu_seqlens,
|
222 |
+
max_seqlen_in_batch,
|
223 |
+
)
|
224 |
+
|
225 |
+
|
226 |
+
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
227 |
+
warnings.warn(
|
228 |
+
"Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be "
|
229 |
+
"removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
|
230 |
+
)
|
231 |
+
return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
|
232 |
+
|
233 |
+
|
234 |
+
def _make_causal_mask(
|
235 |
+
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
236 |
+
):
|
237 |
+
warnings.warn(
|
238 |
+
"Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in "
|
239 |
+
"v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
|
240 |
+
)
|
241 |
+
return AttentionMaskConverter._make_causal_mask(
|
242 |
+
input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
|
243 |
+
)
|
244 |
+
|
245 |
+
|
246 |
+
class HunYuanRMSNorm(nn.Module):
|
247 |
+
def __init__(self, hidden_size, eps=1e-6):
|
248 |
+
"""
|
249 |
+
HunYuanRMSNorm is equivalent to T5LayerNorm
|
250 |
+
"""
|
251 |
+
super().__init__()
|
252 |
+
self.weight = nn.Parameter(torch.ones(hidden_size))
|
253 |
+
self.variance_epsilon = eps
|
254 |
+
|
255 |
+
def forward(self, hidden_states):
|
256 |
+
input_dtype = hidden_states.dtype
|
257 |
+
hidden_states = hidden_states.to(torch.float32)
|
258 |
+
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
259 |
+
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
260 |
+
return self.weight * hidden_states.to(input_dtype)
|
261 |
+
|
262 |
+
|
263 |
+
ALL_LAYERNORM_LAYERS.append(HunYuanRMSNorm)
|
264 |
+
|
265 |
+
|
266 |
+
class HunYuanRotaryEmbedding(nn.Module):
|
267 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
268 |
+
super().__init__()
|
269 |
+
|
270 |
+
self.dim = dim
|
271 |
+
self.max_position_embeddings = max_position_embeddings
|
272 |
+
self.base = base
|
273 |
+
inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
274 |
+
# inv_freq = inv_freq.bfloat16()
|
275 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
276 |
+
|
277 |
+
# Build here to make `torch.jit.trace` work.
|
278 |
+
self._set_cos_sin_cache(
|
279 |
+
seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
|
280 |
+
)
|
281 |
+
|
282 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
283 |
+
self.max_seq_len_cached = seq_len
|
284 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
|
285 |
+
|
286 |
+
self.inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
287 |
+
freqs = torch.outer(t, self.inv_freq)
|
288 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
289 |
+
emb = torch.cat((freqs, freqs), dim=-1).float()
|
290 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
291 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
292 |
+
|
293 |
+
def forward(self, x, seq_len=None):
|
294 |
+
# x: [bs, num_attention_heads, seq_len, head_size]
|
295 |
+
if seq_len > self.max_seq_len_cached or self.inv_freq.dtype != torch.float32:
|
296 |
+
self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
|
297 |
+
|
298 |
+
return (
|
299 |
+
self.cos_cached[:seq_len].to(dtype=x.dtype),
|
300 |
+
self.sin_cached[:seq_len].to(dtype=x.dtype),
|
301 |
+
)
|
302 |
+
|
303 |
+
|
304 |
+
class HunYuanLinearScalingRotaryEmbedding(HunYuanRotaryEmbedding):
|
305 |
+
"""HunYuanRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
|
306 |
+
|
307 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
308 |
+
self.scaling_factor = scaling_factor
|
309 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
310 |
+
|
311 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
312 |
+
self.max_seq_len_cached = seq_len
|
313 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
314 |
+
t = t / self.scaling_factor
|
315 |
+
|
316 |
+
freqs = torch.outer(t, self.inv_freq)
|
317 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
318 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
319 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
320 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
321 |
+
|
322 |
+
|
323 |
+
class HunYuanDynamicNTKScalingRotaryEmbedding(HunYuanRotaryEmbedding):
|
324 |
+
"""
|
325 |
+
HunYuanRotaryEmbedding extended with Dynamic NTK scaling.
|
326 |
+
Credits to the Reddit users /u/bloc97 and /u/emozilla
|
327 |
+
"""
|
328 |
+
|
329 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
|
330 |
+
self.scaling_factor = scaling_factor
|
331 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
332 |
+
|
333 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
334 |
+
self.max_seq_len_cached = seq_len
|
335 |
+
|
336 |
+
if seq_len > self.max_position_embeddings:
|
337 |
+
base = self.base * (
|
338 |
+
(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
|
339 |
+
) ** (self.dim / (self.dim - 2))
|
340 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
341 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
342 |
+
|
343 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
344 |
+
|
345 |
+
freqs = torch.outer(t, self.inv_freq)
|
346 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
347 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
348 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
349 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
350 |
+
|
351 |
+
|
352 |
+
class HunYuanDynamicNTKAlphaRotaryEmbedding(HunYuanRotaryEmbedding):
|
353 |
+
"""
|
354 |
+
HunYuanRotaryEmbedding extended with Dynamic NTK scaling.
|
355 |
+
Credits to the Reddit users /u/bloc97 and /u/emozilla
|
356 |
+
"""
|
357 |
+
|
358 |
+
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_alpha=1.0):
|
359 |
+
self.scaling_alpha = scaling_alpha
|
360 |
+
super().__init__(dim, max_position_embeddings, base, device)
|
361 |
+
|
362 |
+
def _set_cos_sin_cache(self, seq_len, device, dtype):
|
363 |
+
self.max_seq_len_cached = seq_len
|
364 |
+
base = self.base * self.scaling_alpha ** (self.dim / (self.dim-2))
|
365 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
|
366 |
+
|
367 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
368 |
+
|
369 |
+
t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
|
370 |
+
|
371 |
+
freqs = torch.outer(t, self.inv_freq)
|
372 |
+
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
373 |
+
emb = torch.cat((freqs, freqs), dim=-1)
|
374 |
+
self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
|
375 |
+
self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
|
376 |
+
|
377 |
+
|
378 |
+
def rotate_half(x):
|
379 |
+
"""Rotates half the hidden dims of the input."""
|
380 |
+
x1 = x[..., : x.shape[-1] // 2]
|
381 |
+
x2 = x[..., x.shape[-1] // 2:]
|
382 |
+
return torch.cat((-x2, x1), dim=-1)
|
383 |
+
|
384 |
+
|
385 |
+
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
|
386 |
+
"""Applies Rotary Position Embedding to the query and key tensors.
|
387 |
+
|
388 |
+
Args:
|
389 |
+
q (`torch.Tensor`): The query tensor.
|
390 |
+
k (`torch.Tensor`): The key tensor.
|
391 |
+
cos (`torch.Tensor`): The cosine part of the rotary embedding.
|
392 |
+
sin (`torch.Tensor`): The sine part of the rotary embedding.
|
393 |
+
position_ids (`torch.Tensor`):
|
394 |
+
The position indices of the tokens corresponding to the query and key tensors. For example, this can be
|
395 |
+
used to pass offsetted position ids when working with a KV-cache.
|
396 |
+
unsqueeze_dim (`int`, *optional*, defaults to 1):
|
397 |
+
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
|
398 |
+
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
|
399 |
+
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
|
400 |
+
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
|
401 |
+
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
402 |
+
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
403 |
+
Returns:
|
404 |
+
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
405 |
+
"""
|
406 |
+
cos = cos[position_ids].unsqueeze(unsqueeze_dim)
|
407 |
+
sin = sin[position_ids].unsqueeze(unsqueeze_dim)
|
408 |
+
q_embed = (q * cos) + (rotate_half(q) * sin)
|
409 |
+
k_embed = (k * cos) + (rotate_half(k) * sin)
|
410 |
+
return q_embed, k_embed
|
411 |
+
|
412 |
+
|
413 |
+
class HunYuanMLP(nn.Module):
|
414 |
+
def __init__(self, config: HunYuanConfig, layer_idx=None, is_shared_mlp=False):
|
415 |
+
super().__init__()
|
416 |
+
self.config = config
|
417 |
+
self.layer_idx = layer_idx
|
418 |
+
self.hidden_size = config.hidden_size
|
419 |
+
if is_shared_mlp:
|
420 |
+
self.intermediate_size = config.intermediate_size * config.num_shared_expert[0]
|
421 |
+
else:
|
422 |
+
self.intermediate_size = config.intermediate_size
|
423 |
+
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
424 |
+
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
|
425 |
+
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
|
426 |
+
self.act_fn = ACT2FN[config.hidden_act]
|
427 |
+
|
428 |
+
def forward(self, x):
|
429 |
+
if self.config.pretraining_tp > 1:
|
430 |
+
slice = self.intermediate_size // self.config.pretraining_tp
|
431 |
+
gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
|
432 |
+
up_proj_slices = self.up_proj.weight.split(slice, dim=0)
|
433 |
+
down_proj_slices = self.down_proj.weight.split(slice, dim=1)
|
434 |
+
|
435 |
+
gate_proj = torch.cat(
|
436 |
+
[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
|
437 |
+
)
|
438 |
+
up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
|
439 |
+
|
440 |
+
intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
|
441 |
+
down_proj = [
|
442 |
+
F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
|
443 |
+
]
|
444 |
+
down_proj = sum(down_proj)
|
445 |
+
else:
|
446 |
+
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
447 |
+
|
448 |
+
return down_proj
|
449 |
+
|
450 |
+
|
451 |
+
class HunYuanTopKGate(nn.Module):
|
452 |
+
def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
|
453 |
+
super().__init__()
|
454 |
+
self.config = config
|
455 |
+
self.layer_idx = layer_idx
|
456 |
+
self.moe_topk = config.moe_topk
|
457 |
+
self.drop_tokens = config.moe_drop_tokens
|
458 |
+
self.min_capacity = 8
|
459 |
+
self.random_routing_dropped_token = config.moe_random_routing_dropped_token
|
460 |
+
self.wg = nn.Linear(config.hidden_size, config.num_experts, bias=False, dtype=torch.float32)
|
461 |
+
|
462 |
+
def forward(self, hidden_states):
|
463 |
+
bsz, seq_len, hidden_size = hidden_states.shape
|
464 |
+
hidden_states = hidden_states.reshape(-1, hidden_size)
|
465 |
+
if self.wg.weight.dtype == torch.float32:
|
466 |
+
hidden_states = hidden_states.float()
|
467 |
+
logits = self.wg(hidden_states)
|
468 |
+
if self.moe_topk == 1:
|
469 |
+
gate_output = top1gating(logits, random_routing_dropped_token=self.random_routing_dropped_token)
|
470 |
+
else:
|
471 |
+
gate_output = topkgating(logits, self.moe_topk[0])
|
472 |
+
|
473 |
+
return gate_output
|
474 |
+
|
475 |
+
|
476 |
+
class HunYuanMoE(nn.Module):
|
477 |
+
def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
|
478 |
+
super().__init__()
|
479 |
+
self.config = config
|
480 |
+
self.layer_idx = layer_idx
|
481 |
+
self.moe_topk = config.moe_topk
|
482 |
+
self.num_experts = config.num_experts
|
483 |
+
if config.use_mixed_mlp_moe:
|
484 |
+
self.shared_mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=True)
|
485 |
+
self.gate = HunYuanTopKGate(config, layer_idx=layer_idx)
|
486 |
+
self.experts = nn.ModuleList(
|
487 |
+
[HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(config.num_experts)]
|
488 |
+
)
|
489 |
+
|
490 |
+
def forward(self, hidden_states):
|
491 |
+
bsz, seq_len, hidden_size = hidden_states.shape
|
492 |
+
|
493 |
+
if self.config.use_mixed_mlp_moe:
|
494 |
+
hidden_states_mlp = self.shared_mlp(hidden_states)
|
495 |
+
|
496 |
+
l_moe, combine_weights, dispatch_mask, exp_counts = self.gate(hidden_states)
|
497 |
+
|
498 |
+
reshaped_input = hidden_states.reshape(-1, hidden_size)
|
499 |
+
|
500 |
+
dispatched_input = torch.einsum("sec,sm->ecm", dispatch_mask.type_as(hidden_states), reshaped_input)
|
501 |
+
print(f"combine_weights\n", combine_weights)
|
502 |
+
print(f"dispatch_mask\n", dispatch_mask)
|
503 |
+
print(f"dispatched_input\n", dispatched_input)
|
504 |
+
|
505 |
+
chunks = dispatched_input.chunk(self.num_experts, dim=0)
|
506 |
+
expert_outputs = []
|
507 |
+
for chunk, expert in zip(chunks, self.experts):
|
508 |
+
expert_outputs.append(expert(chunk))
|
509 |
+
|
510 |
+
expert_output = torch.cat(expert_outputs, dim=0)
|
511 |
+
combined_output = torch.einsum("sec,ecm->sm", combine_weights.type_as(hidden_states), expert_output)
|
512 |
+
combined_output = combined_output.reshape(bsz, seq_len, hidden_size)
|
513 |
+
|
514 |
+
if self.config.use_mixed_mlp_moe:
|
515 |
+
print(f"mlp out\n", hidden_states_mlp)
|
516 |
+
print(f"moe out\n", combined_output)
|
517 |
+
output = hidden_states_mlp + combined_output
|
518 |
+
else:
|
519 |
+
output = combined_output
|
520 |
+
|
521 |
+
return output
|
522 |
+
|
523 |
+
|
524 |
+
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
525 |
+
"""
|
526 |
+
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
527 |
+
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
528 |
+
"""
|
529 |
+
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
530 |
+
if n_rep == 1:
|
531 |
+
return hidden_states
|
532 |
+
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
533 |
+
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
534 |
+
|
535 |
+
|
536 |
+
class HunYuanAttention(nn.Module):
|
537 |
+
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
538 |
+
|
539 |
+
def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
|
540 |
+
super().__init__()
|
541 |
+
self.config = config
|
542 |
+
self.layer_idx = layer_idx
|
543 |
+
# layer_idx 从 0 开始
|
544 |
+
self.attention_type = 'cross' if config.use_cla and layer_idx % config.cla_share_factor != 0 else 'self'
|
545 |
+
if layer_idx is None:
|
546 |
+
logger.warning_once(
|
547 |
+
f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
|
548 |
+
"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
|
549 |
+
"when creating this class."
|
550 |
+
)
|
551 |
+
|
552 |
+
self.attention_dropout = config.attention_dropout
|
553 |
+
self.hidden_size = config.hidden_size
|
554 |
+
self.num_heads = config.num_attention_heads
|
555 |
+
self.head_dim = self.hidden_size // self.num_heads
|
556 |
+
self.num_key_value_heads = config.num_key_value_heads
|
557 |
+
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
|
558 |
+
self.max_position_embeddings = config.max_position_embeddings
|
559 |
+
self.rope_theta = config.rope_theta
|
560 |
+
self.is_causal = True
|
561 |
+
self.use_qk_norm = config.use_qk_norm
|
562 |
+
|
563 |
+
if (self.head_dim * self.num_heads) != self.hidden_size:
|
564 |
+
raise ValueError(
|
565 |
+
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
566 |
+
f" and `num_heads`: {self.num_heads})."
|
567 |
+
)
|
568 |
+
|
569 |
+
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
|
570 |
+
if self.attention_type == 'self':
|
571 |
+
self.k_proj = nn.Linear(
|
572 |
+
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
573 |
+
)
|
574 |
+
self.v_proj = nn.Linear(
|
575 |
+
self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
576 |
+
)
|
577 |
+
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
|
578 |
+
if self.use_qk_norm:
|
579 |
+
self.query_layernorm = HunYuanRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
580 |
+
self.key_layernorm = HunYuanRMSNorm(self.head_dim, eps=config.rms_norm_eps)
|
581 |
+
self._init_rope()
|
582 |
+
|
583 |
+
def _init_rope(self):
|
584 |
+
if self.config.rope_scaling is None:
|
585 |
+
self.rotary_emb = HunYuanRotaryEmbedding(
|
586 |
+
self.head_dim,
|
587 |
+
max_position_embeddings=self.max_position_embeddings,
|
588 |
+
base=self.rope_theta,
|
589 |
+
)
|
590 |
+
else:
|
591 |
+
scaling_type = self.config.rope_scaling["type"]
|
592 |
+
scaling_factor = self.config.rope_scaling["factor"]
|
593 |
+
scaling_alpha = self.config.rope_scaling["alpha"]
|
594 |
+
if scaling_type == "linear":
|
595 |
+
self.rotary_emb = HunYuanLinearScalingRotaryEmbedding(
|
596 |
+
self.head_dim,
|
597 |
+
max_position_embeddings=self.max_position_embeddings,
|
598 |
+
scaling_factor=scaling_factor,
|
599 |
+
base=self.rope_theta,
|
600 |
+
)
|
601 |
+
elif scaling_type == "dynamic":
|
602 |
+
if scaling_alpha:
|
603 |
+
self.rotary_emb = HunYuanDynamicNTKAlphaRotaryEmbedding(
|
604 |
+
self.head_dim,
|
605 |
+
max_position_embeddings=self.max_position_embeddings,
|
606 |
+
scaling_alpha=scaling_alpha,
|
607 |
+
base=self.rope_theta,
|
608 |
+
)
|
609 |
+
else:
|
610 |
+
self.rotary_emb = HunYuanDynamicNTKScalingRotaryEmbedding(
|
611 |
+
self.head_dim,
|
612 |
+
max_position_embeddings=self.max_position_embeddings,
|
613 |
+
scaling_factor=scaling_factor,
|
614 |
+
base=self.rope_theta,
|
615 |
+
)
|
616 |
+
else:
|
617 |
+
raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
|
618 |
+
|
619 |
+
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
620 |
+
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
621 |
+
|
622 |
+
def forward(
|
623 |
+
self,
|
624 |
+
hidden_states: torch.Tensor,
|
625 |
+
attention_mask: Optional[torch.Tensor] = None,
|
626 |
+
position_ids: Optional[torch.LongTensor] = None,
|
627 |
+
past_key_value: Optional[Cache] = None,
|
628 |
+
output_attentions: bool = False,
|
629 |
+
use_cache: bool = False,
|
630 |
+
kv_states: torch.Tensor = None,
|
631 |
+
**kwargs,
|
632 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
633 |
+
if "padding_mask" in kwargs:
|
634 |
+
warnings.warn(
|
635 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
|
636 |
+
"`attention_mask` instead.`"
|
637 |
+
)
|
638 |
+
|
639 |
+
bsz, q_len, _ = hidden_states.size()
|
640 |
+
|
641 |
+
if self.config.pretraining_tp > 1:
|
642 |
+
query_slices = self.q_proj.weight.split(
|
643 |
+
(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
|
644 |
+
)
|
645 |
+
query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
|
646 |
+
query_states = torch.cat(query_states, dim=-1)
|
647 |
+
|
648 |
+
if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
|
649 |
+
orig_key_states, orig_value_states = kv_states
|
650 |
+
key_states, value_states = kv_states
|
651 |
+
else:
|
652 |
+
key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
|
653 |
+
key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
|
654 |
+
value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
|
655 |
+
|
656 |
+
key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
|
657 |
+
key_states = torch.cat(key_states, dim=-1)
|
658 |
+
|
659 |
+
value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
|
660 |
+
value_states = torch.cat(value_states, dim=-1)
|
661 |
+
orig_key_states, orig_value_states = key_states, value_states
|
662 |
+
|
663 |
+
else:
|
664 |
+
query_states = self.q_proj(hidden_states)
|
665 |
+
if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
|
666 |
+
orig_key_states, orig_value_states = kv_states
|
667 |
+
key_states, value_states = kv_states
|
668 |
+
else:
|
669 |
+
key_states = self.k_proj(hidden_states)
|
670 |
+
value_states = self.v_proj(hidden_states)
|
671 |
+
orig_key_states, orig_value_states = key_states, value_states
|
672 |
+
|
673 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
674 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
675 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
676 |
+
|
677 |
+
kv_seq_len = key_states.shape[-2]
|
678 |
+
if past_key_value is not None:
|
679 |
+
if self.layer_idx is None:
|
680 |
+
raise ValueError(
|
681 |
+
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
|
682 |
+
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
|
683 |
+
"with a layer index."
|
684 |
+
)
|
685 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
686 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
687 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
688 |
+
|
689 |
+
print(f"query_states\n", query_states)
|
690 |
+
print(f"key_states\n", key_states)
|
691 |
+
|
692 |
+
if self.use_qk_norm:
|
693 |
+
query_states = self.query_layernorm(query_states)
|
694 |
+
key_states = self.key_layernorm(key_states)
|
695 |
+
|
696 |
+
print(f"query_states_normed\n", query_states)
|
697 |
+
print(f"key_states_normed\n", key_states)
|
698 |
+
|
699 |
+
if past_key_value is not None:
|
700 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
701 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
702 |
+
|
703 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
704 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
705 |
+
|
706 |
+
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
|
707 |
+
|
708 |
+
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
|
709 |
+
raise ValueError(
|
710 |
+
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
|
711 |
+
f" {attn_weights.size()}"
|
712 |
+
)
|
713 |
+
|
714 |
+
if attention_mask is not None:
|
715 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
716 |
+
raise ValueError(
|
717 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
718 |
+
)
|
719 |
+
attn_weights = attn_weights + attention_mask
|
720 |
+
|
721 |
+
# upcast attention to fp32
|
722 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
723 |
+
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
724 |
+
attn_output = torch.matmul(attn_weights, value_states)
|
725 |
+
|
726 |
+
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
|
727 |
+
raise ValueError(
|
728 |
+
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
|
729 |
+
f" {attn_output.size()}"
|
730 |
+
)
|
731 |
+
|
732 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
733 |
+
|
734 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
735 |
+
|
736 |
+
if self.config.pretraining_tp > 1:
|
737 |
+
attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
|
738 |
+
o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
|
739 |
+
attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
|
740 |
+
else:
|
741 |
+
attn_output = self.o_proj(attn_output)
|
742 |
+
|
743 |
+
if not output_attentions:
|
744 |
+
attn_weights = None
|
745 |
+
|
746 |
+
return attn_output, attn_weights, past_key_value, (orig_key_states, orig_value_states)
|
747 |
+
|
748 |
+
|
749 |
+
class HunYuanFlashAttention2(HunYuanAttention):
|
750 |
+
"""
|
751 |
+
HunYuan flash attention module. This module inherits from `HunYuanAttention` as the weights of the module stays
|
752 |
+
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
|
753 |
+
flash attention and deal with padding tokens in case the input contains any of them.
|
754 |
+
"""
|
755 |
+
|
756 |
+
def __init__(self, *args, **kwargs):
|
757 |
+
super().__init__(*args, **kwargs)
|
758 |
+
|
759 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
760 |
+
|
761 |
+
def forward(
|
762 |
+
self,
|
763 |
+
hidden_states: torch.Tensor,
|
764 |
+
attention_mask: Optional[torch.LongTensor] = None,
|
765 |
+
position_ids: Optional[torch.LongTensor] = None,
|
766 |
+
past_key_value: Optional[Cache] = None,
|
767 |
+
output_attentions: bool = False,
|
768 |
+
use_cache: bool = False,
|
769 |
+
kv_states: torch.Tensor = None,
|
770 |
+
**kwargs,
|
771 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
772 |
+
# HunYuanFlashAttention2 attention does not support output_attentions
|
773 |
+
if "padding_mask" in kwargs:
|
774 |
+
warnings.warn(
|
775 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
|
776 |
+
"`attention_mask` instead.`"
|
777 |
+
)
|
778 |
+
|
779 |
+
# overwrite attention_mask with padding_mask
|
780 |
+
attention_mask = kwargs.pop("padding_mask")
|
781 |
+
|
782 |
+
bsz, q_len, _ = hidden_states.size()
|
783 |
+
|
784 |
+
query_states = self.q_proj(hidden_states)
|
785 |
+
if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
|
786 |
+
orig_key_states, orig_value_states = kv_states
|
787 |
+
key_states, value_states = kv_states
|
788 |
+
else:
|
789 |
+
key_states = self.k_proj(hidden_states)
|
790 |
+
value_states = self.v_proj(hidden_states)
|
791 |
+
orig_key_states, orig_value_states = key_states, value_states
|
792 |
+
|
793 |
+
# Flash attention requires the input to have the shape
|
794 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
795 |
+
# therefore we just need to keep the original shape
|
796 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
797 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
798 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
799 |
+
|
800 |
+
kv_seq_len = key_states.shape[-2]
|
801 |
+
if past_key_value is not None:
|
802 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
803 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
804 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
805 |
+
|
806 |
+
if self.use_qk_norm:
|
807 |
+
query_states = self.query_layernorm(query_states)
|
808 |
+
key_states = self.key_layernorm(key_states)
|
809 |
+
|
810 |
+
if past_key_value is not None:
|
811 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
812 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
813 |
+
|
814 |
+
query_states = query_states.transpose(1, 2)
|
815 |
+
key_states = key_states.transpose(1, 2)
|
816 |
+
value_states = value_states.transpose(1, 2)
|
817 |
+
|
818 |
+
dropout_rate = self.attention_dropout if self.training else 0.0
|
819 |
+
|
820 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
821 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
822 |
+
# cast them back in the correct dtype just to be sure everything works as expected.
|
823 |
+
# This might slowdown training & inference so it is recommended to not cast the LayerNorms
|
824 |
+
# in fp32. (HunYuanRMSNorm handles it correctly)
|
825 |
+
|
826 |
+
input_dtype = query_states.dtype
|
827 |
+
if input_dtype == torch.float32:
|
828 |
+
# Handle the case where the model is quantized
|
829 |
+
if hasattr(self.config, "_pre_quantization_dtype"):
|
830 |
+
target_dtype = self.config._pre_quantization_dtype
|
831 |
+
else:
|
832 |
+
target_dtype = self.q_proj.weight.dtype
|
833 |
+
|
834 |
+
logger.warning_once(
|
835 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
836 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
837 |
+
f" {target_dtype}."
|
838 |
+
)
|
839 |
+
|
840 |
+
query_states = query_states.to(target_dtype)
|
841 |
+
key_states = key_states.to(target_dtype)
|
842 |
+
value_states = value_states.to(target_dtype)
|
843 |
+
|
844 |
+
attn_output = self._flash_attention_forward(
|
845 |
+
query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
|
846 |
+
)
|
847 |
+
|
848 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
|
849 |
+
attn_output = self.o_proj(attn_output)
|
850 |
+
|
851 |
+
return attn_output, None, past_key_value, (orig_key_states, orig_value_states)
|
852 |
+
|
853 |
+
def _flash_attention_forward(
|
854 |
+
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
|
855 |
+
):
|
856 |
+
"""
|
857 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
858 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
859 |
+
|
860 |
+
Args:
|
861 |
+
query_states (`torch.Tensor`):
|
862 |
+
Input query states to be passed to Flash Attention API
|
863 |
+
key_states (`torch.Tensor`):
|
864 |
+
Input key states to be passed to Flash Attention API
|
865 |
+
value_states (`torch.Tensor`):
|
866 |
+
Input value states to be passed to Flash Attention API
|
867 |
+
attention_mask (`torch.Tensor`):
|
868 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
869 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
870 |
+
dropout (`int`, *optional*):
|
871 |
+
Attention dropout
|
872 |
+
softmax_scale (`float`, *optional*):
|
873 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
874 |
+
"""
|
875 |
+
if not self._flash_attn_uses_top_left_mask:
|
876 |
+
causal = self.is_causal
|
877 |
+
else:
|
878 |
+
causal = self.is_causal and query_length != 1
|
879 |
+
|
880 |
+
# Contains at least one padding token in the sequence
|
881 |
+
if attention_mask is not None:
|
882 |
+
batch_size = query_states.shape[0]
|
883 |
+
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
884 |
+
query_states, key_states, value_states, attention_mask, query_length
|
885 |
+
)
|
886 |
+
|
887 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
888 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
889 |
+
|
890 |
+
attn_output_unpad = flash_attn_varlen_func(
|
891 |
+
query_states,
|
892 |
+
key_states,
|
893 |
+
value_states,
|
894 |
+
cu_seqlens_q=cu_seqlens_q,
|
895 |
+
cu_seqlens_k=cu_seqlens_k,
|
896 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
897 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
898 |
+
dropout_p=dropout,
|
899 |
+
softmax_scale=softmax_scale,
|
900 |
+
causal=causal,
|
901 |
+
)
|
902 |
+
|
903 |
+
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
904 |
+
else:
|
905 |
+
attn_output = flash_attn_func(
|
906 |
+
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
907 |
+
)
|
908 |
+
|
909 |
+
return attn_output
|
910 |
+
|
911 |
+
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
912 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
913 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
914 |
+
|
915 |
+
key_layer = index_first_axis(
|
916 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
917 |
+
)
|
918 |
+
value_layer = index_first_axis(
|
919 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
920 |
+
)
|
921 |
+
if query_length == kv_seq_len:
|
922 |
+
query_layer = index_first_axis(
|
923 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
924 |
+
)
|
925 |
+
cu_seqlens_q = cu_seqlens_k
|
926 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
927 |
+
indices_q = indices_k
|
928 |
+
elif query_length == 1:
|
929 |
+
max_seqlen_in_batch_q = 1
|
930 |
+
cu_seqlens_q = torch.arange(
|
931 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
932 |
+
) # There is a memcpy here, that is very bad.
|
933 |
+
indices_q = cu_seqlens_q[:-1]
|
934 |
+
query_layer = query_layer.squeeze(1)
|
935 |
+
else:
|
936 |
+
# The -q_len: slice assumes left padding.
|
937 |
+
attention_mask = attention_mask[:, -query_length:]
|
938 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
939 |
+
|
940 |
+
return (
|
941 |
+
query_layer,
|
942 |
+
key_layer,
|
943 |
+
value_layer,
|
944 |
+
indices_q,
|
945 |
+
(cu_seqlens_q, cu_seqlens_k),
|
946 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
947 |
+
)
|
948 |
+
|
949 |
+
|
950 |
+
class HunYuanSdpaAttention(HunYuanAttention):
|
951 |
+
"""
|
952 |
+
HunYuan attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
953 |
+
`HunYuanAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt
|
954 |
+
to SDPA API.
|
955 |
+
"""
|
956 |
+
|
957 |
+
# Adapted from HunYuanAttention.forward
|
958 |
+
def forward(
|
959 |
+
self,
|
960 |
+
hidden_states: torch.Tensor,
|
961 |
+
attention_mask: Optional[torch.Tensor] = None,
|
962 |
+
position_ids: Optional[torch.LongTensor] = None,
|
963 |
+
past_key_value: Optional[Cache] = None,
|
964 |
+
output_attentions: bool = False,
|
965 |
+
use_cache: bool = False,
|
966 |
+
kv_states: torch.Tensor = None,
|
967 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
968 |
+
if output_attentions:
|
969 |
+
logger.warning_once(
|
970 |
+
'HunYuanModel is using HunYuanSdpaAttention,'
|
971 |
+
'but `torch.nn.functional.scaled_dot_product_attention`'
|
972 |
+
'does not support `output_attentions=True`. Falling back to the manual attention implementation, '
|
973 |
+
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. '
|
974 |
+
'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
975 |
+
)
|
976 |
+
return super().forward(
|
977 |
+
hidden_states=hidden_states,
|
978 |
+
attention_mask=attention_mask,
|
979 |
+
position_ids=position_ids,
|
980 |
+
past_key_value=past_key_value,
|
981 |
+
output_attentions=output_attentions,
|
982 |
+
use_cache=use_cache,
|
983 |
+
)
|
984 |
+
|
985 |
+
bsz, q_len, _ = hidden_states.size()
|
986 |
+
|
987 |
+
query_states = self.q_proj(hidden_states)
|
988 |
+
if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
|
989 |
+
orig_key_states, orig_value_states = kv_states
|
990 |
+
key_states, value_states = kv_states
|
991 |
+
else:
|
992 |
+
key_states = self.k_proj(hidden_states)
|
993 |
+
value_states = self.v_proj(hidden_states)
|
994 |
+
orig_key_states, orig_value_states = key_states, value_states
|
995 |
+
|
996 |
+
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
|
997 |
+
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
998 |
+
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
|
999 |
+
|
1000 |
+
kv_seq_len = key_states.shape[-2]
|
1001 |
+
if past_key_value is not None:
|
1002 |
+
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
|
1003 |
+
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
|
1004 |
+
|
1005 |
+
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
|
1006 |
+
|
1007 |
+
if self.use_qk_norm:
|
1008 |
+
query_states = self.query_layernorm(query_states)
|
1009 |
+
key_states = self.key_layernorm(key_states)
|
1010 |
+
|
1011 |
+
if past_key_value is not None:
|
1012 |
+
cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
|
1013 |
+
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
1014 |
+
|
1015 |
+
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
1016 |
+
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
1017 |
+
|
1018 |
+
if attention_mask is not None:
|
1019 |
+
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
|
1020 |
+
raise ValueError(
|
1021 |
+
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
|
1022 |
+
)
|
1023 |
+
|
1024 |
+
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
|
1025 |
+
# custom attn_mask,
|
1026 |
+
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
1027 |
+
if query_states.device.type == "cuda" and attention_mask is not None:
|
1028 |
+
query_states = query_states.contiguous()
|
1029 |
+
key_states = key_states.contiguous()
|
1030 |
+
value_states = value_states.contiguous()
|
1031 |
+
|
1032 |
+
attn_output = torch.nn.functional.scaled_dot_product_attention(
|
1033 |
+
query_states,
|
1034 |
+
key_states,
|
1035 |
+
value_states,
|
1036 |
+
attn_mask=attention_mask,
|
1037 |
+
dropout_p=self.attention_dropout if self.training else 0.0,
|
1038 |
+
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a
|
1039 |
+
# causal mask in case q_len == 1.
|
1040 |
+
is_causal=self.is_causal and attention_mask is None and q_len > 1,
|
1041 |
+
)
|
1042 |
+
|
1043 |
+
attn_output = attn_output.transpose(1, 2).contiguous()
|
1044 |
+
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
|
1045 |
+
|
1046 |
+
attn_output = self.o_proj(attn_output)
|
1047 |
+
|
1048 |
+
return attn_output, None, past_key_value, (orig_key_states, orig_value_states)
|
1049 |
+
|
1050 |
+
|
1051 |
+
HUNYUAN_ATTENTION_CLASSES = {
|
1052 |
+
"eager": HunYuanAttention,
|
1053 |
+
"flash_attention_2": HunYuanFlashAttention2,
|
1054 |
+
"sdpa": HunYuanSdpaAttention,
|
1055 |
+
}
|
1056 |
+
|
1057 |
+
|
1058 |
+
class HunYuanDecoderLayer(nn.Module):
|
1059 |
+
def __init__(self, config: HunYuanConfig, layer_idx: int):
|
1060 |
+
super().__init__()
|
1061 |
+
self.hidden_size = config.hidden_size
|
1062 |
+
self.layer_idx = layer_idx
|
1063 |
+
|
1064 |
+
#self.self_attn = HUNYUAN_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
1065 |
+
self.self_attn = HunYuanAttention(config=config, layer_idx=layer_idx)
|
1066 |
+
|
1067 |
+
if config.num_experts > 1:
|
1068 |
+
self.mlp = HunYuanMoE(config, layer_idx=layer_idx)
|
1069 |
+
else:
|
1070 |
+
self.mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False)
|
1071 |
+
self.input_layernorm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
1072 |
+
self.post_attention_layernorm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
1073 |
+
|
1074 |
+
def forward(
|
1075 |
+
self,
|
1076 |
+
hidden_states: torch.Tensor,
|
1077 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1078 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1079 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
1080 |
+
output_attentions: Optional[bool] = False,
|
1081 |
+
use_cache: Optional[bool] = False,
|
1082 |
+
kv_states: Optional[Tuple[torch.Tensor]] = None,
|
1083 |
+
**kwargs,
|
1084 |
+
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
1085 |
+
"""
|
1086 |
+
Args:
|
1087 |
+
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
1088 |
+
attention_mask (`torch.FloatTensor`, *optional*):
|
1089 |
+
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
1090 |
+
query_sequence_length, key_sequence_length)` if default attention is used.
|
1091 |
+
output_attentions (`bool`, *optional*):
|
1092 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
1093 |
+
returned tensors for more detail.
|
1094 |
+
use_cache (`bool`, *optional*):
|
1095 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
1096 |
+
(see `past_key_values`).
|
1097 |
+
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
1098 |
+
kv_states (`Tuple(torch.FloatTensor)`, *optional*): Used when CLA is enabled,
|
1099 |
+
key and value states from past attention blocks
|
1100 |
+
"""
|
1101 |
+
if "padding_mask" in kwargs:
|
1102 |
+
warnings.warn(
|
1103 |
+
"Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
|
1104 |
+
"`attention_mask` instead.`"
|
1105 |
+
)
|
1106 |
+
|
1107 |
+
residual = hidden_states
|
1108 |
+
|
1109 |
+
hidden_states = self.input_layernorm(hidden_states)
|
1110 |
+
|
1111 |
+
print(f"Layer {self.layer_idx}\n", hidden_states)
|
1112 |
+
|
1113 |
+
# Self Attention
|
1114 |
+
hidden_states, self_attn_weights, present_key_value, kv_states = self.self_attn(
|
1115 |
+
hidden_states=hidden_states,
|
1116 |
+
attention_mask=attention_mask,
|
1117 |
+
position_ids=position_ids,
|
1118 |
+
past_key_value=past_key_value,
|
1119 |
+
output_attentions=output_attentions,
|
1120 |
+
use_cache=use_cache,
|
1121 |
+
kv_states=kv_states,
|
1122 |
+
**kwargs,
|
1123 |
+
)
|
1124 |
+
print(f"Layer {self.layer_idx} self_attn output\n", hidden_states)
|
1125 |
+
hidden_states = residual + hidden_states
|
1126 |
+
|
1127 |
+
# Fully Connected
|
1128 |
+
residual = hidden_states
|
1129 |
+
hidden_states = self.post_attention_layernorm(hidden_states)
|
1130 |
+
hidden_states = self.mlp(hidden_states)
|
1131 |
+
print(f"Layer {self.layer_idx} mlp output\n", hidden_states)
|
1132 |
+
hidden_states = residual + hidden_states
|
1133 |
+
|
1134 |
+
print(f"Layer {self.layer_idx} final output\n", hidden_states)
|
1135 |
+
|
1136 |
+
exit(0)
|
1137 |
+
|
1138 |
+
outputs = (hidden_states,)
|
1139 |
+
|
1140 |
+
if output_attentions:
|
1141 |
+
outputs += (self_attn_weights,)
|
1142 |
+
|
1143 |
+
if use_cache:
|
1144 |
+
outputs += (present_key_value,)
|
1145 |
+
|
1146 |
+
outputs += (kv_states,)
|
1147 |
+
|
1148 |
+
return outputs
|
1149 |
+
|
1150 |
+
|
1151 |
+
HUNYUAN_START_DOCSTRING = r"""
|
1152 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
1153 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
1154 |
+
etc.)
|
1155 |
+
|
1156 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
1157 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
1158 |
+
and behavior.
|
1159 |
+
|
1160 |
+
Parameters:
|
1161 |
+
config ([`HunYuanConfig`]):
|
1162 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
1163 |
+
load the weights associated with the model, only the configuration. Check out the
|
1164 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
1165 |
+
"""
|
1166 |
+
|
1167 |
+
|
1168 |
+
@add_start_docstrings(
|
1169 |
+
"The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
|
1170 |
+
HUNYUAN_START_DOCSTRING,
|
1171 |
+
)
|
1172 |
+
class HunYuanPreTrainedModel(PreTrainedModel):
|
1173 |
+
config_class = HunYuanConfig
|
1174 |
+
base_model_prefix = "model"
|
1175 |
+
supports_gradient_checkpointing = True
|
1176 |
+
_no_split_modules = ["HunYuanDecoderLayer"]
|
1177 |
+
_skip_keys_device_placement = "past_key_values"
|
1178 |
+
_supports_flash_attn_2 = True
|
1179 |
+
_supports_sdpa = True
|
1180 |
+
_supports_cache_class = True
|
1181 |
+
|
1182 |
+
def _init_weights(self, module):
|
1183 |
+
std = self.config.initializer_range
|
1184 |
+
if isinstance(module, nn.Linear):
|
1185 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
1186 |
+
if module.bias is not None:
|
1187 |
+
module.bias.data.zero_()
|
1188 |
+
elif isinstance(module, nn.Embedding):
|
1189 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
1190 |
+
if module.padding_idx is not None:
|
1191 |
+
module.weight.data[module.padding_idx].zero_()
|
1192 |
+
|
1193 |
+
|
1194 |
+
HUNYUAN_INPUTS_DOCSTRING = r"""
|
1195 |
+
Args:
|
1196 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
1197 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
1198 |
+
it.
|
1199 |
+
|
1200 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
1201 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
1202 |
+
|
1203 |
+
[What are input IDs?](../glossary#input-ids)
|
1204 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1205 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
1206 |
+
|
1207 |
+
- 1 for tokens that are **not masked**,
|
1208 |
+
- 0 for tokens that are **masked**.
|
1209 |
+
|
1210 |
+
[What are attention masks?](../glossary#attention-mask)
|
1211 |
+
|
1212 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
1213 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
1214 |
+
|
1215 |
+
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
|
1216 |
+
`past_key_values`).
|
1217 |
+
|
1218 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
1219 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
1220 |
+
information on the default strategy.
|
1221 |
+
|
1222 |
+
- 1 indicates the head is **not masked**,
|
1223 |
+
- 0 indicates the head is **masked**.
|
1224 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1225 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
1226 |
+
config.n_positions - 1]`.
|
1227 |
+
|
1228 |
+
[What are position IDs?](../glossary#position-ids)
|
1229 |
+
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
|
1230 |
+
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
1231 |
+
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
|
1232 |
+
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
|
1233 |
+
|
1234 |
+
Two formats are allowed:
|
1235 |
+
- a [`~cache_utils.Cache`] instance;
|
1236 |
+
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
1237 |
+
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
|
1238 |
+
cache format.
|
1239 |
+
|
1240 |
+
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
|
1241 |
+
legacy cache format will be returned.
|
1242 |
+
|
1243 |
+
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
|
1244 |
+
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
|
1245 |
+
of shape `(batch_size, sequence_length)`.
|
1246 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
1247 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
1248 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
1249 |
+
model's internal embedding lookup matrix.
|
1250 |
+
use_cache (`bool`, *optional*):
|
1251 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
1252 |
+
`past_key_values`).
|
1253 |
+
output_attentions (`bool`, *optional*):
|
1254 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
1255 |
+
tensors for more detail.
|
1256 |
+
output_hidden_states (`bool`, *optional*):
|
1257 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
1258 |
+
more detail.
|
1259 |
+
return_dict (`bool`, *optional*):
|
1260 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
1261 |
+
"""
|
1262 |
+
|
1263 |
+
|
1264 |
+
@add_start_docstrings(
|
1265 |
+
"The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
|
1266 |
+
HUNYUAN_START_DOCSTRING,
|
1267 |
+
)
|
1268 |
+
class HunYuanModel(HunYuanPreTrainedModel):
|
1269 |
+
"""
|
1270 |
+
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HunYuanDecoderLayer`]
|
1271 |
+
|
1272 |
+
Args:
|
1273 |
+
config: HunYuanConfig
|
1274 |
+
"""
|
1275 |
+
|
1276 |
+
def __init__(self, config: HunYuanConfig):
|
1277 |
+
super().__init__(config)
|
1278 |
+
self.padding_idx = config.pad_token_id
|
1279 |
+
self.vocab_size = config.vocab_size
|
1280 |
+
|
1281 |
+
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
1282 |
+
self.layers = nn.ModuleList(
|
1283 |
+
[HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
1284 |
+
)
|
1285 |
+
self._use_sdpa = config._attn_implementation == "sdpa"
|
1286 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
1287 |
+
self.norm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
1288 |
+
|
1289 |
+
self.cla = config.use_cla
|
1290 |
+
self.cla_share_factor = config.cla_share_factor
|
1291 |
+
|
1292 |
+
self.gradient_checkpointing = False
|
1293 |
+
# Initialize weights and apply final processing
|
1294 |
+
self.post_init()
|
1295 |
+
|
1296 |
+
def get_input_embeddings(self):
|
1297 |
+
return self.embed_tokens
|
1298 |
+
|
1299 |
+
def set_input_embeddings(self, value):
|
1300 |
+
self.embed_tokens = value
|
1301 |
+
|
1302 |
+
@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
|
1303 |
+
def forward(
|
1304 |
+
self,
|
1305 |
+
input_ids: torch.LongTensor = None,
|
1306 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1307 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1308 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1309 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1310 |
+
use_cache: Optional[bool] = None,
|
1311 |
+
output_attentions: Optional[bool] = None,
|
1312 |
+
output_hidden_states: Optional[bool] = None,
|
1313 |
+
return_dict: Optional[bool] = None,
|
1314 |
+
) -> Union[Tuple, BaseModelOutputWithPast]:
|
1315 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1316 |
+
output_hidden_states = (
|
1317 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1318 |
+
)
|
1319 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
1320 |
+
|
1321 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1322 |
+
|
1323 |
+
# retrieve input_ids and inputs_embeds
|
1324 |
+
if input_ids is not None and inputs_embeds is not None:
|
1325 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
1326 |
+
elif input_ids is not None:
|
1327 |
+
batch_size, seq_length = input_ids.shape[:2]
|
1328 |
+
elif inputs_embeds is not None:
|
1329 |
+
batch_size, seq_length = inputs_embeds.shape[:2]
|
1330 |
+
else:
|
1331 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
1332 |
+
|
1333 |
+
if self.gradient_checkpointing and self.training:
|
1334 |
+
if use_cache:
|
1335 |
+
logger.warning_once(
|
1336 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
1337 |
+
)
|
1338 |
+
use_cache = False
|
1339 |
+
|
1340 |
+
past_key_values_length = 0
|
1341 |
+
if use_cache:
|
1342 |
+
use_legacy_cache = not isinstance(past_key_values, Cache)
|
1343 |
+
if use_legacy_cache:
|
1344 |
+
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
1345 |
+
past_key_values_length = past_key_values.get_usable_length(seq_length)
|
1346 |
+
|
1347 |
+
if position_ids is None:
|
1348 |
+
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
1349 |
+
position_ids = torch.arange(
|
1350 |
+
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
1351 |
+
)
|
1352 |
+
position_ids = position_ids.unsqueeze(0)
|
1353 |
+
|
1354 |
+
if inputs_embeds is None:
|
1355 |
+
inputs_embeds = self.embed_tokens(input_ids)
|
1356 |
+
|
1357 |
+
# Fix lora with gradient checkpointing training
|
1358 |
+
if self.training and inputs_embeds.is_leaf:
|
1359 |
+
inputs_embeds.requires_grad = True
|
1360 |
+
|
1361 |
+
if self._use_flash_attention_2:
|
1362 |
+
# 2d mask is passed through the layers
|
1363 |
+
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
1364 |
+
elif self._use_sdpa and not output_attentions:
|
1365 |
+
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
1366 |
+
# the manual implementation that requires a 4D causal mask in all cases.
|
1367 |
+
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
1368 |
+
attention_mask,
|
1369 |
+
(batch_size, seq_length),
|
1370 |
+
inputs_embeds,
|
1371 |
+
past_key_values_length,
|
1372 |
+
)
|
1373 |
+
else:
|
1374 |
+
# 4d mask is passed through the layers
|
1375 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
1376 |
+
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
1377 |
+
)
|
1378 |
+
|
1379 |
+
# embed positions
|
1380 |
+
hidden_states = inputs_embeds
|
1381 |
+
|
1382 |
+
# decoder layers
|
1383 |
+
all_hidden_states = () if output_hidden_states else None
|
1384 |
+
all_self_attns = () if output_attentions else None
|
1385 |
+
next_decoder_cache = None
|
1386 |
+
|
1387 |
+
prev_kv_states = None
|
1388 |
+
for layer_idx, decoder_layer in enumerate(self.layers):
|
1389 |
+
if output_hidden_states:
|
1390 |
+
all_hidden_states += (hidden_states,)
|
1391 |
+
|
1392 |
+
if self.gradient_checkpointing and self.training:
|
1393 |
+
layer_outputs = self._gradient_checkpointing_func(
|
1394 |
+
decoder_layer.__call__,
|
1395 |
+
hidden_states,
|
1396 |
+
attention_mask,
|
1397 |
+
position_ids,
|
1398 |
+
past_key_values,
|
1399 |
+
output_attentions,
|
1400 |
+
use_cache,
|
1401 |
+
prev_kv_states,
|
1402 |
+
)
|
1403 |
+
else:
|
1404 |
+
layer_outputs = decoder_layer(
|
1405 |
+
hidden_states,
|
1406 |
+
attention_mask=attention_mask,
|
1407 |
+
position_ids=position_ids,
|
1408 |
+
past_key_value=past_key_values,
|
1409 |
+
output_attentions=output_attentions,
|
1410 |
+
use_cache=use_cache,
|
1411 |
+
kv_states=prev_kv_states
|
1412 |
+
)
|
1413 |
+
|
1414 |
+
hidden_states = layer_outputs[0]
|
1415 |
+
|
1416 |
+
if use_cache:
|
1417 |
+
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
1418 |
+
|
1419 |
+
if output_attentions:
|
1420 |
+
all_self_attns += (layer_outputs[1],)
|
1421 |
+
|
1422 |
+
kv_states = layer_outputs[-1]
|
1423 |
+
|
1424 |
+
if self.cla and layer_idx % self.cla_share_factor == 0:
|
1425 |
+
prev_kv_states = kv_states
|
1426 |
+
|
1427 |
+
break # TEST
|
1428 |
+
|
1429 |
+
hidden_states = self.norm(hidden_states)
|
1430 |
+
|
1431 |
+
# add hidden states from the last decoder layer
|
1432 |
+
if output_hidden_states:
|
1433 |
+
all_hidden_states += (hidden_states,)
|
1434 |
+
|
1435 |
+
next_cache = None
|
1436 |
+
if use_cache:
|
1437 |
+
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
|
1438 |
+
if not return_dict:
|
1439 |
+
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
1440 |
+
return BaseModelOutputWithPast(
|
1441 |
+
last_hidden_state=hidden_states,
|
1442 |
+
past_key_values=next_cache,
|
1443 |
+
hidden_states=all_hidden_states,
|
1444 |
+
attentions=all_self_attns,
|
1445 |
+
)
|
1446 |
+
|
1447 |
+
|
1448 |
+
class HunYuanMoEV1ForCausalLM(HunYuanPreTrainedModel):
|
1449 |
+
_tied_weights_keys = ["lm_head.weight"]
|
1450 |
+
|
1451 |
+
def __init__(self, config: HunYuanConfig):
|
1452 |
+
super().__init__(config)
|
1453 |
+
self.model = HunYuanModel(config)
|
1454 |
+
self.vocab_size = config.vocab_size
|
1455 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
1456 |
+
|
1457 |
+
# Initialize weights and apply final processing
|
1458 |
+
self.post_init()
|
1459 |
+
|
1460 |
+
def get_input_embeddings(self):
|
1461 |
+
return self.model.embed_tokens
|
1462 |
+
|
1463 |
+
def set_input_embeddings(self, value):
|
1464 |
+
self.model.embed_tokens = value
|
1465 |
+
|
1466 |
+
def get_output_embeddings(self):
|
1467 |
+
return self.lm_head
|
1468 |
+
|
1469 |
+
def set_output_embeddings(self, new_embeddings):
|
1470 |
+
self.lm_head = new_embeddings
|
1471 |
+
|
1472 |
+
def set_decoder(self, decoder):
|
1473 |
+
self.model = decoder
|
1474 |
+
|
1475 |
+
def get_decoder(self):
|
1476 |
+
return self.model
|
1477 |
+
|
1478 |
+
@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
|
1479 |
+
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
1480 |
+
def forward(
|
1481 |
+
self,
|
1482 |
+
input_ids: torch.LongTensor = None,
|
1483 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1484 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1485 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1486 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1487 |
+
labels: Optional[torch.LongTensor] = None,
|
1488 |
+
use_cache: Optional[bool] = None,
|
1489 |
+
output_attentions: Optional[bool] = None,
|
1490 |
+
output_hidden_states: Optional[bool] = None,
|
1491 |
+
return_dict: Optional[bool] = None,
|
1492 |
+
) -> Union[Tuple, CausalLMOutputWithPast]:
|
1493 |
+
r"""
|
1494 |
+
Args:
|
1495 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1496 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
1497 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
1498 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
1499 |
+
|
1500 |
+
Returns:
|
1501 |
+
|
1502 |
+
Example:
|
1503 |
+
|
1504 |
+
```python
|
1505 |
+
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
|
1506 |
+
|
1507 |
+
>>> model = AutoModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
1508 |
+
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
1509 |
+
|
1510 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
1511 |
+
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
1512 |
+
|
1513 |
+
>>> # Generate
|
1514 |
+
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
1515 |
+
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
1516 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
|
1517 |
+
```"""
|
1518 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
1519 |
+
output_hidden_states = (
|
1520 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
1521 |
+
)
|
1522 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1523 |
+
|
1524 |
+
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1525 |
+
outputs = self.model(
|
1526 |
+
input_ids=input_ids,
|
1527 |
+
attention_mask=attention_mask,
|
1528 |
+
position_ids=position_ids,
|
1529 |
+
past_key_values=past_key_values,
|
1530 |
+
inputs_embeds=inputs_embeds,
|
1531 |
+
use_cache=use_cache,
|
1532 |
+
output_attentions=output_attentions,
|
1533 |
+
output_hidden_states=output_hidden_states,
|
1534 |
+
return_dict=return_dict,
|
1535 |
+
)
|
1536 |
+
|
1537 |
+
hidden_states = outputs[0]
|
1538 |
+
if self.config.pretraining_tp > 1:
|
1539 |
+
lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
|
1540 |
+
logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
|
1541 |
+
logits = torch.cat(logits, dim=-1)
|
1542 |
+
else:
|
1543 |
+
logits = self.lm_head(hidden_states)
|
1544 |
+
logits = logits.float()
|
1545 |
+
|
1546 |
+
loss = None
|
1547 |
+
if labels is not None:
|
1548 |
+
# Shift so that tokens < n predict n
|
1549 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
1550 |
+
shift_labels = labels[..., 1:].contiguous()
|
1551 |
+
# Flatten the tokens
|
1552 |
+
loss_fct = CrossEntropyLoss()
|
1553 |
+
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
1554 |
+
shift_labels = shift_labels.view(-1)
|
1555 |
+
# Enable model parallelism
|
1556 |
+
shift_labels = shift_labels.to(shift_logits.device)
|
1557 |
+
loss = loss_fct(shift_logits, shift_labels)
|
1558 |
+
|
1559 |
+
if not return_dict:
|
1560 |
+
output = (logits,) + outputs[1:]
|
1561 |
+
return (loss,) + output if loss is not None else output
|
1562 |
+
|
1563 |
+
return CausalLMOutputWithPast(
|
1564 |
+
loss=loss,
|
1565 |
+
logits=logits,
|
1566 |
+
past_key_values=outputs.past_key_values,
|
1567 |
+
hidden_states=outputs.hidden_states,
|
1568 |
+
attentions=outputs.attentions,
|
1569 |
+
)
|
1570 |
+
|
1571 |
+
def prepare_inputs_for_generation(
|
1572 |
+
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
1573 |
+
):
|
1574 |
+
if past_key_values is not None:
|
1575 |
+
if isinstance(past_key_values, Cache):
|
1576 |
+
cache_length = past_key_values.get_seq_length()
|
1577 |
+
past_length = past_key_values.seen_tokens
|
1578 |
+
max_cache_length = past_key_values.get_max_cache_shape()
|
1579 |
+
else:
|
1580 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
1581 |
+
max_cache_length = None
|
1582 |
+
|
1583 |
+
# Keep only the unprocessed tokens:
|
1584 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
1585 |
+
# some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
|
1586 |
+
# input)
|
1587 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
1588 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
|
1589 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
1590 |
+
# input_ids based on the past_length.
|
1591 |
+
elif past_length < input_ids.shape[1]:
|
1592 |
+
input_ids = input_ids[:, past_length:]
|
1593 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
1594 |
+
|
1595 |
+
# If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
|
1596 |
+
if (
|
1597 |
+
max_cache_length is not None
|
1598 |
+
and attention_mask is not None
|
1599 |
+
and cache_length + input_ids.shape[1] > max_cache_length
|
1600 |
+
):
|
1601 |
+
attention_mask = attention_mask[:, -max_cache_length:]
|
1602 |
+
|
1603 |
+
position_ids = kwargs.get("position_ids", None)
|
1604 |
+
if attention_mask is not None and position_ids is None:
|
1605 |
+
# create position_ids on the fly for batch generation
|
1606 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
1607 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
1608 |
+
if past_key_values:
|
1609 |
+
position_ids = position_ids[:, -input_ids.shape[1]:]
|
1610 |
+
|
1611 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1612 |
+
if inputs_embeds is not None and past_key_values is None:
|
1613 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
1614 |
+
else:
|
1615 |
+
model_inputs = {"input_ids": input_ids}
|
1616 |
+
|
1617 |
+
model_inputs.update(
|
1618 |
+
{
|
1619 |
+
"position_ids": position_ids,
|
1620 |
+
"past_key_values": past_key_values,
|
1621 |
+
"use_cache": kwargs.get("use_cache"),
|
1622 |
+
"attention_mask": attention_mask,
|
1623 |
+
}
|
1624 |
+
)
|
1625 |
+
return model_inputs
|
1626 |
+
|
1627 |
+
@staticmethod
|
1628 |
+
def _reorder_cache(past_key_values, beam_idx):
|
1629 |
+
reordered_past = ()
|
1630 |
+
for layer_past in past_key_values:
|
1631 |
+
reordered_past += (
|
1632 |
+
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
1633 |
+
)
|
1634 |
+
return reordered_past
|
1635 |
+
|
1636 |
+
|
1637 |
+
@add_start_docstrings(
|
1638 |
+
"""
|
1639 |
+
The HunYuan Model transformer with a sequence classification head on top (linear layer).
|
1640 |
+
|
1641 |
+
[`HunYuanForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
1642 |
+
(e.g. GPT-2) do.
|
1643 |
+
|
1644 |
+
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1645 |
+
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
1646 |
+
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
1647 |
+
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
1648 |
+
each row of the batch).
|
1649 |
+
""",
|
1650 |
+
HUNYUAN_START_DOCSTRING,
|
1651 |
+
)
|
1652 |
+
class HunYuanForSequenceClassification(HunYuanPreTrainedModel):
|
1653 |
+
def __init__(self, config):
|
1654 |
+
super().__init__(config)
|
1655 |
+
self.num_labels = config.num_labels
|
1656 |
+
self.model = HunYuanModel(config)
|
1657 |
+
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
1658 |
+
|
1659 |
+
# Initialize weights and apply final processing
|
1660 |
+
self.post_init()
|
1661 |
+
|
1662 |
+
def get_input_embeddings(self):
|
1663 |
+
return self.model.embed_tokens
|
1664 |
+
|
1665 |
+
def set_input_embeddings(self, value):
|
1666 |
+
self.model.embed_tokens = value
|
1667 |
+
|
1668 |
+
@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
|
1669 |
+
def forward(
|
1670 |
+
self,
|
1671 |
+
input_ids: torch.LongTensor = None,
|
1672 |
+
attention_mask: Optional[torch.Tensor] = None,
|
1673 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1674 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
1675 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1676 |
+
labels: Optional[torch.LongTensor] = None,
|
1677 |
+
use_cache: Optional[bool] = None,
|
1678 |
+
output_attentions: Optional[bool] = None,
|
1679 |
+
output_hidden_states: Optional[bool] = None,
|
1680 |
+
return_dict: Optional[bool] = None,
|
1681 |
+
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
1682 |
+
r"""
|
1683 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1684 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
1685 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1686 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1687 |
+
"""
|
1688 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1689 |
+
|
1690 |
+
transformer_outputs = self.model(
|
1691 |
+
input_ids,
|
1692 |
+
attention_mask=attention_mask,
|
1693 |
+
position_ids=position_ids,
|
1694 |
+
past_key_values=past_key_values,
|
1695 |
+
inputs_embeds=inputs_embeds,
|
1696 |
+
use_cache=use_cache,
|
1697 |
+
output_attentions=output_attentions,
|
1698 |
+
output_hidden_states=output_hidden_states,
|
1699 |
+
return_dict=return_dict,
|
1700 |
+
)
|
1701 |
+
hidden_states = transformer_outputs[0]
|
1702 |
+
logits = self.score(hidden_states)
|
1703 |
+
|
1704 |
+
if input_ids is not None:
|
1705 |
+
batch_size = input_ids.shape[0]
|
1706 |
+
else:
|
1707 |
+
batch_size = inputs_embeds.shape[0]
|
1708 |
+
|
1709 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
1710 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
1711 |
+
if self.config.pad_token_id is None:
|
1712 |
+
sequence_lengths = -1
|
1713 |
+
else:
|
1714 |
+
if input_ids is not None:
|
1715 |
+
sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
|
1716 |
+
logits.device
|
1717 |
+
)
|
1718 |
+
else:
|
1719 |
+
sequence_lengths = -1
|
1720 |
+
|
1721 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
1722 |
+
|
1723 |
+
loss = None
|
1724 |
+
if labels is not None:
|
1725 |
+
labels = labels.to(logits.device)
|
1726 |
+
if self.config.problem_type is None:
|
1727 |
+
if self.num_labels == 1:
|
1728 |
+
self.config.problem_type = "regression"
|
1729 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
1730 |
+
self.config.problem_type = "single_label_classification"
|
1731 |
+
else:
|
1732 |
+
self.config.problem_type = "multi_label_classification"
|
1733 |
+
|
1734 |
+
if self.config.problem_type == "regression":
|
1735 |
+
loss_fct = MSELoss()
|
1736 |
+
if self.num_labels == 1:
|
1737 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
1738 |
+
else:
|
1739 |
+
loss = loss_fct(pooled_logits, labels)
|
1740 |
+
elif self.config.problem_type == "single_label_classification":
|
1741 |
+
loss_fct = CrossEntropyLoss()
|
1742 |
+
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
1743 |
+
elif self.config.problem_type == "multi_label_classification":
|
1744 |
+
loss_fct = BCEWithLogitsLoss()
|
1745 |
+
loss = loss_fct(pooled_logits, labels)
|
1746 |
+
if not return_dict:
|
1747 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
1748 |
+
return ((loss,) + output) if loss is not None else output
|
1749 |
+
|
1750 |
+
return SequenceClassifierOutputWithPast(
|
1751 |
+
loss=loss,
|
1752 |
+
logits=pooled_logits,
|
1753 |
+
past_key_values=transformer_outputs.past_key_values,
|
1754 |
+
hidden_states=transformer_outputs.hidden_states,
|
1755 |
+
attentions=transformer_outputs.attentions,
|
1756 |
+
)
|
test_hunyuan.py
ADDED
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
2 |
+
import torch
|
3 |
+
|
4 |
+
model = AutoModelForCausalLM.from_pretrained("./.", trust_remote_code=True)
|
5 |
+
tokenizer = AutoTokenizer.from_pretrained("./.", trust_remote_code=True)
|
6 |
+
|
7 |
+
with torch.no_grad():
|
8 |
+
input_text = "Hi_"
|
9 |
+
inputs = tokenizer(text=input_text, return_tensors="pt")
|
10 |
+
del inputs["token_type_ids"]
|
11 |
+
print(inputs)
|
12 |
+
gen = model.generate(**inputs, max_new_tokens=1, do_sample=False)
|
13 |
+
|
14 |
+
decoded = tokenizer.batch_decode(gen, skip_special_tokens=True)
|
15 |
+
print(decoded)
|
16 |
+
|
17 |
+
|
18 |
+
"""
|
19 |
+
from hunyuan.configuration_hunyuan import HunYuanConfig
|
20 |
+
from hunyuan.modeling_hunyuan import HunYuanMoEV1ForCausalLM
|
21 |
+
import torch
|
22 |
+
|
23 |
+
config = HunYuanConfig.from_pretrained("./Hunyuan-A13B-Instruct", trust_remote_code=True)
|
24 |
+
config.moe_intermediate_size = [3072, 3072]
|
25 |
+
config.num_experts = 4
|
26 |
+
config.num_shared_expert = [1, 1]
|
27 |
+
config.moe_topk = [2, 2]
|
28 |
+
config.num_hidden_layers = 4
|
29 |
+
|
30 |
+
model = HunYuanMoEV1ForCausalLM(config)
|
31 |
+
print(model)
|
32 |
+
|
33 |
+
torch.manual_seed(0)
|
34 |
+
state_dict = model.state_dict()
|
35 |
+
for key in state_dict:
|
36 |
+
state_dict[key].uniform_(-0.2, 0.2)
|
37 |
+
model.load_state_dict(state_dict)
|
38 |
+
|
39 |
+
model.save_pretrained("./hunyuan-tiny")
|
40 |
+
"""
|
tokenization_hy.py
ADDED
@@ -0,0 +1,298 @@
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|
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|
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|
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|
|
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|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import base64
|
2 |
+
import logging
|
3 |
+
import os
|
4 |
+
import unicodedata
|
5 |
+
from typing import Collection, Dict, List, Set, Tuple, Union
|
6 |
+
|
7 |
+
import tiktoken
|
8 |
+
from transformers import PreTrainedTokenizer, AddedToken
|
9 |
+
|
10 |
+
logger = logging.getLogger(__name__)
|
11 |
+
|
12 |
+
|
13 |
+
VOCAB_FILES_NAMES = {"vocab_file": "hy.tiktoken"}
|
14 |
+
|
15 |
+
PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
|
16 |
+
# PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
|
17 |
+
ENDOFTEXT = "<|endoftext|>"
|
18 |
+
STARTOFTEXT = "<|startoftext|>"
|
19 |
+
BOSTOKEN = "<|bos|>"
|
20 |
+
EOSTOKEN = "<|eos|>"
|
21 |
+
PADTOKEN = "<|pad|>"
|
22 |
+
|
23 |
+
# as the default behavior is changed to allow special tokens in
|
24 |
+
# regular texts, the surface forms of special tokens need to be
|
25 |
+
# as different as possible to minimize the impact
|
26 |
+
EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
|
27 |
+
# changed to use actual index to avoid misconfiguration with vocabulary expansion
|
28 |
+
|
29 |
+
|
30 |
+
SPECIAL_START_ID = 127957
|
31 |
+
|
32 |
+
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
|
33 |
+
# with open(tiktoken_bpe_file, "rb", encoding="utf-8") as f:
|
34 |
+
# contents = f.read()
|
35 |
+
dic = {}
|
36 |
+
rank = 0
|
37 |
+
for line in open(tiktoken_bpe_file, "rb"):
|
38 |
+
if line:
|
39 |
+
token, _ = line.split()
|
40 |
+
if base64.b64decode(token) in dic:
|
41 |
+
continue
|
42 |
+
dic[base64.b64decode(token)] = int(rank)
|
43 |
+
rank += 1
|
44 |
+
global SPECIAL_START_ID
|
45 |
+
SPECIAL_START_ID=rank
|
46 |
+
return dic
|
47 |
+
|
48 |
+
# NOTE: Please use the code line to check `SPECIAL_START_ID` right, this will affect the SPECIAL_START_ID
|
49 |
+
# _load_tiktoken_bpe('/apdcephfs/share_1502809/shaneshu/tokenizer_exp/other_tokenizer_vocab/hy/' + VOCAB_FILES_NAMES['vocab_file'])
|
50 |
+
# print(SPECIAL_START_ID)
|
51 |
+
|
52 |
+
SPECIAL_TOKENS = tuple(
|
53 |
+
enumerate(
|
54 |
+
(
|
55 |
+
(
|
56 |
+
ENDOFTEXT,
|
57 |
+
STARTOFTEXT,
|
58 |
+
BOSTOKEN,
|
59 |
+
EOSTOKEN,
|
60 |
+
PADTOKEN,
|
61 |
+
)
|
62 |
+
+ EXTRAS
|
63 |
+
),
|
64 |
+
start=SPECIAL_START_ID,
|
65 |
+
)
|
66 |
+
)
|
67 |
+
# NOTE: Unused Token ID starts from 127962
|
68 |
+
SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
|
69 |
+
|
70 |
+
class HYTokenizer(PreTrainedTokenizer):
|
71 |
+
"""hunyuan tokenizer."""
|
72 |
+
|
73 |
+
vocab_files_names = VOCAB_FILES_NAMES
|
74 |
+
|
75 |
+
def __init__(
|
76 |
+
self,
|
77 |
+
vocab_file,
|
78 |
+
errors="replace",
|
79 |
+
extra_vocab_file=None,
|
80 |
+
**kwargs,
|
81 |
+
):
|
82 |
+
super().__init__(**kwargs)
|
83 |
+
|
84 |
+
# how to handle errors in decoding UTF-8 byte sequences
|
85 |
+
# use ignore if you are in streaming inference
|
86 |
+
self.errors = errors
|
87 |
+
|
88 |
+
self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
|
89 |
+
self.special_tokens = {
|
90 |
+
token: index
|
91 |
+
for index, token in SPECIAL_TOKENS
|
92 |
+
}
|
93 |
+
|
94 |
+
# try load extra vocab from file
|
95 |
+
if extra_vocab_file is not None:
|
96 |
+
used_ids = set(self.mergeable_ranks.values()) | set(self.special_tokens.values())
|
97 |
+
extra_mergeable_ranks = _load_tiktoken_bpe(extra_vocab_file)
|
98 |
+
for token, index in extra_mergeable_ranks.items():
|
99 |
+
if token in self.mergeable_ranks:
|
100 |
+
logger.info(f"extra token {token} exists, skipping")
|
101 |
+
continue
|
102 |
+
if index in used_ids:
|
103 |
+
logger.info(f'the index {index} for extra token {token} exists, skipping')
|
104 |
+
continue
|
105 |
+
self.mergeable_ranks[token] = index
|
106 |
+
# the index may be sparse after this, but don't worry tiktoken.Encoding will handle this
|
107 |
+
|
108 |
+
enc = tiktoken.Encoding(
|
109 |
+
"HunYuan",
|
110 |
+
pat_str=PAT_STR,
|
111 |
+
mergeable_ranks=self.mergeable_ranks,
|
112 |
+
special_tokens=self.special_tokens,
|
113 |
+
)
|
114 |
+
assert (
|
115 |
+
len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
|
116 |
+
), f"{len(self.mergeable_ranks)} + {len(self.special_tokens)} != {enc.n_vocab} in encoding"
|
117 |
+
|
118 |
+
self.decoder = {
|
119 |
+
v: k for k, v in self.mergeable_ranks.items()
|
120 |
+
} # type: dict[int, bytes|str]
|
121 |
+
self.decoder.update({v: k for k, v in self.special_tokens.items()})
|
122 |
+
|
123 |
+
self.tokenizer = enc # type: tiktoken.Encoding
|
124 |
+
|
125 |
+
self.eod_id = self.tokenizer.eot_token
|
126 |
+
self.bod_id = self.special_tokens[STARTOFTEXT]
|
127 |
+
self.bos_id = self.special_tokens[BOSTOKEN]
|
128 |
+
self.eos_id = self.special_tokens[EOSTOKEN]
|
129 |
+
self.pad_id = self.special_tokens[PADTOKEN]
|
130 |
+
|
131 |
+
def __getstate__(self):
|
132 |
+
# for pickle lovers
|
133 |
+
state = self.__dict__.copy()
|
134 |
+
del state["tokenizer"]
|
135 |
+
return state
|
136 |
+
|
137 |
+
def __setstate__(self, state):
|
138 |
+
# tokenizer is not python native; don't pass it; rebuild it
|
139 |
+
self.__dict__.update(state)
|
140 |
+
enc = tiktoken.Encoding(
|
141 |
+
"HunYuan",
|
142 |
+
pat_str=PAT_STR,
|
143 |
+
mergeable_ranks=self.mergeable_ranks,
|
144 |
+
special_tokens=self.special_tokens,
|
145 |
+
)
|
146 |
+
self.tokenizer = enc
|
147 |
+
|
148 |
+
def __len__(self) -> int:
|
149 |
+
return self.tokenizer.n_vocab
|
150 |
+
|
151 |
+
def get_vocab(self) -> Dict[bytes, int]:
|
152 |
+
return self.mergeable_ranks
|
153 |
+
|
154 |
+
def convert_tokens_to_ids(
|
155 |
+
self, tokens: Union[bytes, str, List[Union[bytes, str]]]
|
156 |
+
) -> List[int]:
|
157 |
+
ids = []
|
158 |
+
if isinstance(tokens, (str, bytes)):
|
159 |
+
if tokens in self.special_tokens:
|
160 |
+
return self.special_tokens[tokens]
|
161 |
+
else:
|
162 |
+
return self.mergeable_ranks.get(tokens)
|
163 |
+
for token in tokens:
|
164 |
+
if token in self.special_tokens:
|
165 |
+
ids.append(self.special_tokens[token])
|
166 |
+
else:
|
167 |
+
ids.append(self.mergeable_ranks.get(token))
|
168 |
+
return ids
|
169 |
+
|
170 |
+
def _add_tokens(
|
171 |
+
self,
|
172 |
+
new_tokens: Union[List[str], List[AddedToken]],
|
173 |
+
special_tokens: bool = False,
|
174 |
+
) -> int:
|
175 |
+
if not special_tokens and new_tokens:
|
176 |
+
raise ValueError("Adding regular tokens is not supported")
|
177 |
+
for token in new_tokens:
|
178 |
+
surface_form = token.content if isinstance(token, AddedToken) else token
|
179 |
+
if surface_form not in SPECIAL_TOKENS_SET:
|
180 |
+
raise ValueError("Adding unknown special tokens is not supported")
|
181 |
+
return 0
|
182 |
+
|
183 |
+
def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
|
184 |
+
"""
|
185 |
+
Save only the vocabulary of the tokenizer (vocabulary).
|
186 |
+
Returns:
|
187 |
+
`Tuple(str)`: Paths to the files saved.
|
188 |
+
"""
|
189 |
+
file_path = os.path.join(save_directory, "hunyuan.tiktoken")
|
190 |
+
with open(file_path, "w", encoding="utf-8") as w:
|
191 |
+
for k, v in self.mergeable_ranks.items():
|
192 |
+
line = base64.b64encode(k).decode("utf-8") + " " + str(v) + "\n"
|
193 |
+
w.write(line)
|
194 |
+
return (file_path,)
|
195 |
+
|
196 |
+
def tokenize(
|
197 |
+
self,
|
198 |
+
text: str,
|
199 |
+
allowed_special: Union[Set, str] = "all",
|
200 |
+
disallowed_special: Union[Collection, str] = (),
|
201 |
+
**kwargs,
|
202 |
+
) -> List[Union[bytes, str]]:
|
203 |
+
"""
|
204 |
+
Converts a string in a sequence of tokens.
|
205 |
+
Args:
|
206 |
+
text (`str`):
|
207 |
+
The sequence to be encoded.
|
208 |
+
allowed_special (`Literal["all"]` or `set`):
|
209 |
+
The surface forms of the tokens to be encoded as special tokens in regular texts.
|
210 |
+
Default to "all".
|
211 |
+
disallowed_special (`Literal["all"]` or `Collection`):
|
212 |
+
The surface forms of the tokens that should not be in regular texts and trigger errors.
|
213 |
+
Default to an empty tuple.
|
214 |
+
kwargs (additional keyword arguments, *optional*):
|
215 |
+
Will be passed to the underlying model specific encode method.
|
216 |
+
Returns:
|
217 |
+
`List[bytes|str]`: The list of tokens.
|
218 |
+
"""
|
219 |
+
tokens = []
|
220 |
+
text = unicodedata.normalize("NFC", text)
|
221 |
+
|
222 |
+
# this implementation takes a detour: text -> token id -> token surface forms
|
223 |
+
for t in self.tokenizer.encode(
|
224 |
+
text, allowed_special=allowed_special, disallowed_special=disallowed_special
|
225 |
+
):
|
226 |
+
tokens.append(self.decoder[t])
|
227 |
+
return tokens
|
228 |
+
|
229 |
+
def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
|
230 |
+
"""
|
231 |
+
Converts a sequence of tokens in a single string.
|
232 |
+
"""
|
233 |
+
text = ""
|
234 |
+
temp = b""
|
235 |
+
for t in tokens:
|
236 |
+
if isinstance(t, str):
|
237 |
+
if temp:
|
238 |
+
text += temp.decode("utf-8", errors=self.errors)
|
239 |
+
temp = b""
|
240 |
+
text += t
|
241 |
+
elif isinstance(t, bytes):
|
242 |
+
temp += t
|
243 |
+
else:
|
244 |
+
raise TypeError("token should only be of type types or str")
|
245 |
+
if temp:
|
246 |
+
text += temp.decode("utf-8", errors=self.errors)
|
247 |
+
return text
|
248 |
+
|
249 |
+
@property
|
250 |
+
def vocab_size(self):
|
251 |
+
return self.tokenizer.n_vocab
|
252 |
+
|
253 |
+
def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
|
254 |
+
"""Converts an id to a token, special tokens included"""
|
255 |
+
if index in self.decoder:
|
256 |
+
return self.decoder[index]
|
257 |
+
raise ValueError("unknown ids")
|
258 |
+
|
259 |
+
def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
|
260 |
+
"""Converts a token to an id using the vocab, special tokens included"""
|
261 |
+
if token in self.special_tokens:
|
262 |
+
return self.special_tokens[token]
|
263 |
+
if token in self.mergeable_ranks:
|
264 |
+
return self.mergeable_ranks[token]
|
265 |
+
raise ValueError("unknown token")
|
266 |
+
|
267 |
+
def _tokenize(self, text: str, **kwargs):
|
268 |
+
"""
|
269 |
+
Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
|
270 |
+
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
|
271 |
+
Do NOT take care of added tokens.
|
272 |
+
"""
|
273 |
+
raise NotImplementedError
|
274 |
+
|
275 |
+
def _decode(
|
276 |
+
self,
|
277 |
+
token_ids: Union[int, List[int]],
|
278 |
+
skip_special_tokens: bool = False,
|
279 |
+
errors: str = None,
|
280 |
+
**kwargs,
|
281 |
+
) -> str:
|
282 |
+
if isinstance(token_ids, int):
|
283 |
+
token_ids = [token_ids]
|
284 |
+
if skip_special_tokens:
|
285 |
+
token_ids = [i for i in token_ids if i < self.eod_id]
|
286 |
+
return self.tokenizer.decode(token_ids, errors=errors or self.errors)
|
287 |
+
|
288 |
+
# tests
|
289 |
+
if __name__ == "__main__":
|
290 |
+
tokenizer = HYTokenizer.from_pretrained('./hy')
|
291 |
+
text = '你好,世界'
|
292 |
+
tokens = tokenizer.tokenize(text)
|
293 |
+
print(tokens)
|
294 |
+
ids = tokenizer.convert_tokens_to_ids(tokens)
|
295 |
+
print(ids)
|
296 |
+
text2 = tokenizer.convert_tokens_to_string(tokens)
|
297 |
+
print(text2)
|
298 |
+
ids2 = tokenizer.convert_tokens_to_ids(tokens)
|
tokenizer_config.json
ADDED
@@ -0,0 +1,18 @@
|
|
|
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|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"GPT2LMHeadModel"
|
4 |
+
],
|
5 |
+
"model_max_length": 1048576,
|
6 |
+
"tokenizer_class": "HYTokenizer",
|
7 |
+
"auto_map": {
|
8 |
+
"AutoTokenizer": [
|
9 |
+
"tokenization_hy.HYTokenizer",
|
10 |
+
null
|
11 |
+
]
|
12 |
+
},
|
13 |
+
"eos_token": "<|eos|>",
|
14 |
+
"model_type": "gpt2",
|
15 |
+
"additional_special_tokens": ["<|startoftext|>", "<|extra_0|>", "<|extra_4|>", "<|extra_5|>", "<|eos|>"],
|
16 |
+
"pad_token": "<|pad|>",
|
17 |
+
"chat_template": "{% set loop_messages = messages %}\n{% if tools %}\n {% set weekday_map = {'Monday': '星期一', 'Tuesday': '星期二', 'Wednesday': '星期三', 'Thursday': '星期四', 'Friday': '星期五', 'Saturday': '星期六', 'Sunday': '星期日'} %}\n {% set weekday_cn = weekday_map[strftime_now('%A')] %}\n {% set datetime_str = strftime_now('%Y-%m-%d %H:%M:%S') %}\n {% set datetime_str = datetime_str + ' ' + weekday_cn %}\n {% for message in loop_messages %}\n {% if 'content' in message %}\n {% set content = message['content'] %}\n {% else %}\n {% set content = '' %}\n {% endif %}\n {% if loop.index0 == 0 %}\n {% set content_tmp = '你是一位函数组合专家。你会得到一个问题和一组可能的函数。根据问题,你需要进行一个或多个函数/工具调用以实现目的。\n如果没有一个函数可以使用,请直接使用自然语言回复用户,以助手:开头。\n如果给定的问题缺少函数所需的参数,请使用自然语言进行提问,向用户询问必要信息,以助手:开头。\n如果调用结果已经足够回答用户问题,请对历史结果进行总结,使用自然语言回复用户,以助手:开头。\n你应该只在工具调用部分返回函数调用。如果你决定调用任何函数,你必须将其格式化为<tool_calls>[{\"name\": \"func_name1\", \"arguments\": {\"argument1\": \"value1\", \"argument2\": \"value2\"}},...]</tool_calls>。你不应该在回复中包含任何其他文本。以下是你可以调用的函数列表,格式为JSON。\n' %}\n {% set content_tmp = content_tmp + '\n' + tools | tojson + '\n' %}\n {% if message['role'] == 'system' %}\n {% set content_tmp = content_tmp + '\n额外要求:\n' + content + '\n\n如果你决定返回函数调用,请将其格式化为<tool_calls>[{\"name\": \"func_name1\", \"arguments\": {\"argument1\": \"value1\", \"argument2\": \"value2\"}},...]</tool_calls>,不得包含其他文本。如果额外要求里有格式要求,请忽略,以此处为准。\n否则,请参考开头说的三种情况,以助手:开头进行回复。\n\n如果额外要求里有时间信息,就以额外要求里的时间为准,否则,参考当前时间:' + datetime_str %}\n {% set content = '<|startoftext|>' + content_tmp + '<|extra_4|>' %}\n {% elif message['role'] == 'user' %}\n {% set content_tmp = content_tmp + '\n如果你决定返回函数调用,请将其格式化为<tool_calls>[{\"name\": \"func_name1\", \"arguments\": {\"argument1\": \"value1\", \"argument2\": \"value2\"}},...]</tool_calls>,不得包含其他文本。\n否则,请参考开头说的三种情况,以助手:开头进行回复。\n\n当前时间:' + datetime_str %}\n {% set content_tmp = '<|startoftext|>' + content_tmp + '<|extra_4|>'%}\n {% set content = content_tmp + '用户:' + content + '<|extra_0|>' %}\n {% endif %}\n {% else %}\n {% if message['role'] == 'user' %}\n {% set content = '用户:' + content + '<|extra_0|>' %}\n {% elif message['role'] == 'assistant' %}\n {% if 'tool_calls' in message %}\n {% set tool_calls = message['tool_calls'] %}\n {% set ns = namespace(tool_calls=\"[\") %}\n {% for tool_call in tool_calls %}\n {% set function = tool_call['function'] %}\n {% set name = function['name'] %}\n {% set ns.tool_calls = ns.tool_calls + '{\"name\": \"' + name + '\", '%}\n {% set arguments = function['arguments'] %}\n {% if arguments is not string %}\n {% set arguments = arguments | tojson %}\n {% endif %}\n {% set ns.tool_calls = ns.tool_calls + '\"arguments\": ' + arguments + '}' %}\n {% if not loop.last %}\n {% set ns.tool_calls = ns.tool_calls + ', '%}\n {% endif %}\n {% endfor %}\n {% set ns.tool_calls = ns.tool_calls + ']' %}\n {% set content = content + '<tool_calls>' + ns.tool_calls + '</tool_calls>' %}\n {% else %}\n {% set content = '助手:' + content %}\n {% endif %}\n {% set content = content + '<|eos|>' %}\n {% elif message['role'] == 'tool' %}\n {% if content is not string %}\n {set content = content | tojson }\n {% endif %}\n {% set content = '<tool_response>' + content + '</tool_response>' %}\n {% set content = content + '<|extra_0|>' %}\n {% endif %}\n {% endif %}\n {{- content -}}\n {% endfor %}\n{% else %}\n {% set context = {'has_head': true} %}\n {% for message in loop_messages %}\n {% if 'content' in message %}\n {% set content = message['content'] %}\n {% else %}\n {% set content = '' %}\n {% endif %}\n {% if loop.index0 == 0 %}\n {% if content == '' %}\n {% set _ = context.update({'has_head': false}) %}\n {% elif message['role'] == 'system' %}\n {% set content = '<|startoftext|>' + content + '<|extra_4|>' %}\n {% endif %}\n {% endif %}\n {% if message['role'] == 'user' %}\n {% if loop.index0 == 1 and not context.has_head %}\n {% set content = '<|startoftext|>' + content %}\n {% endif %}\n {% if loop.index0 == 1 and context.has_head %}\n {% set content = content + '<|extra_0|>' %}\n {% else %}\n {% set content = '<|startoftext|>' + content + '<|extra_0|>' %}\n {% endif %}\n {% elif message['role'] == 'assistant' %}\n {% set content = content + '<|eos|>' %}\n {% elif message['role'] == 'tool' %}\n {% set content = content + '<|extra_0|>' %}\n {% endif %}\n {{- content -}}\n {% endfor %}\n{% endif %}\n{%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n' }}\n{%- endif %}"
|
18 |
+
}
|
vit_model.py
ADDED
@@ -0,0 +1,1083 @@
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|
1 |
+
import json
|
2 |
+
import types
|
3 |
+
import math
|
4 |
+
import torch
|
5 |
+
from torch import Tensor, nn
|
6 |
+
import torch.nn.functional as F
|
7 |
+
from typing import List, Tuple, Optional, Union
|
8 |
+
from contextlib import contextmanager
|
9 |
+
from transformers.modeling_attn_mask_utils import (
|
10 |
+
_prepare_4d_causal_attention_mask_for_sdpa,
|
11 |
+
_prepare_4d_causal_attention_mask_for_sdpa,
|
12 |
+
_prepare_4d_causal_attention_mask,
|
13 |
+
)
|
14 |
+
from transformers.models.clip.configuration_clip import CLIPVisionConfig
|
15 |
+
from transformers.modeling_outputs import BaseModelOutputWithPooling
|
16 |
+
from .modeling_hunyuan import HunYuanDecoderLayer, HunYuanRMSNorm
|
17 |
+
from .configuration_hunyuan import HunYuanConfig
|
18 |
+
|
19 |
+
|
20 |
+
def NaVitForward(input_ids, encoder_input, vit, image_tensors, images_pos, vit_input_resolution, im_start_id, im_end_id, image_token_id, anyres_vit_two_views, dtype):
|
21 |
+
# input_ids: (B, L)
|
22 |
+
# encoder_input: (L, B, E)
|
23 |
+
# image_tensors [[Tensor],...,[Tensor]]
|
24 |
+
# image_pos [[Tensor],...,[Tensor]]
|
25 |
+
# tokenizer = get_tokenizer()
|
26 |
+
b = len(input_ids)
|
27 |
+
img_embs = None
|
28 |
+
all_nums = sum([len(tensors) for tensors in image_tensors]) if image_tensors else 0
|
29 |
+
if all_nums != 0:
|
30 |
+
img_embs, img_batch_pos = vit(image_tensors)
|
31 |
+
else:
|
32 |
+
# when no input image, initialize a fake tensor
|
33 |
+
pad_nums = 1
|
34 |
+
image_tensors = [[torch.rand(3, vit_input_resolution, vit_input_resolution, dtype=dtype, device=torch.cuda.current_device()) for _ in range(pad_nums)]]
|
35 |
+
img_embs, img_batch_pos = vit(image_tensors)
|
36 |
+
|
37 |
+
encoder_input = encoder_input.clone()
|
38 |
+
if all_nums > 0:
|
39 |
+
assert len(images_pos) == len(img_batch_pos), \
|
40 |
+
(len(images_pos), len(img_batch_pos))
|
41 |
+
start_token_id = im_start_id
|
42 |
+
end_token_id = im_end_id
|
43 |
+
placeholder_id = image_token_id
|
44 |
+
for idx in range(len(images_pos)):
|
45 |
+
assert len(images_pos[idx]) == len(img_batch_pos[idx]), \
|
46 |
+
(len(images_pos[idx]), len(img_batch_pos[idx]))
|
47 |
+
for p_img_pos_in_batch, p_batch_img_pos in zip(img_batch_pos[idx], images_pos[idx]):
|
48 |
+
# the positions to be filled [s_start, s_end)
|
49 |
+
s_idx, s_start, s_end = p_img_pos_in_batch
|
50 |
+
current_embs = img_embs[s_idx, s_start:s_end]
|
51 |
+
im_s, im_e = p_batch_img_pos
|
52 |
+
assert len(current_embs) == im_e - im_s, \
|
53 |
+
(img_embs.shape, (s_start, s_end, s_idx), current_embs.shape, (im_s, im_e, idx))
|
54 |
+
if not anyres_vit_two_views:
|
55 |
+
assert input_ids[idx, im_s - 1] == start_token_id, \
|
56 |
+
input_ids[idx, im_s - 1]
|
57 |
+
assert input_ids[idx, im_e] == end_token_id, \
|
58 |
+
input_ids[idx, im_e]
|
59 |
+
assert (input_ids[idx, im_s:im_e] == placeholder_id).all(), \
|
60 |
+
f'The tokens to be filled are not the placeholder_id {placeholder_id}: {(input_ids[idx, im_s:im_e] == placeholder_id).sum()} vs {im_e - im_s}'
|
61 |
+
encoder_input[idx, im_s:im_e] = current_embs
|
62 |
+
else:
|
63 |
+
# when no input image, to mask vit value
|
64 |
+
vit_mask = torch.zeros([1, img_embs.shape[0]], device=torch.cuda.current_device())
|
65 |
+
current_embs = img_embs[0, :]
|
66 |
+
encoder_input[0, 1:img_embs.shape[0] + 1] = encoder_input[0, 1:img_embs.shape[0] + 1] * (1 - vit_mask) + current_embs * vit_mask
|
67 |
+
return encoder_input, input_ids
|
68 |
+
|
69 |
+
|
70 |
+
def VitForward(input_ids, encoder_input, vit, vit_linear_encoder, image_tensors, images_pos, vit_input_resolution, vit_mapping_type, vit_patch, vit_token):
|
71 |
+
vit_patch_mlp = (vit_patch > 1 and vit_mapping_type == 'mlp') or vit_patch == 0
|
72 |
+
|
73 |
+
b = len(input_ids)
|
74 |
+
if images_pos is None:
|
75 |
+
images_pos = torch.ones([len(input_ids), 1, 3])
|
76 |
+
images_pos[:, :, 1] = images_pos[:, :, 1]*(vit_token + 1)
|
77 |
+
images_pos = images_pos.long()
|
78 |
+
|
79 |
+
real_image_nums = []
|
80 |
+
image_tensors = image_tensors.view(b, -1, 3, vit_input_resolution, vit_input_resolution)
|
81 |
+
real_images = []
|
82 |
+
|
83 |
+
all_nums = 0
|
84 |
+
img_index = []
|
85 |
+
for s in range(len(images_pos)):
|
86 |
+
real_image_num = 0
|
87 |
+
for (im_s, im_e,index) in images_pos[s]:
|
88 |
+
if im_s == -1:
|
89 |
+
break
|
90 |
+
real_image_num += 1
|
91 |
+
all_nums += 1
|
92 |
+
img_index.append(index)
|
93 |
+
|
94 |
+
real_image_nums.append(real_image_num)
|
95 |
+
real_images.append(image_tensors[s][:real_image_num])
|
96 |
+
|
97 |
+
if vit_patch == 1:
|
98 |
+
img_index = None
|
99 |
+
|
100 |
+
if all_nums == 0:
|
101 |
+
# when no input image, initialize a fake tensor
|
102 |
+
img_input = torch.rand(b, 3, vit_input_resolution, vit_input_resolution).cuda().type(image_tensors.dtype)
|
103 |
+
img_embs = vit(img_input)
|
104 |
+
img_embs = vit_linear_encoder(img_embs)
|
105 |
+
else:
|
106 |
+
img_input = torch.cat(real_images)
|
107 |
+
img_embs = vit(img_input, img_index = img_index)
|
108 |
+
img_embs = vit_linear_encoder(img_embs)
|
109 |
+
|
110 |
+
encoder_input = encoder_input.clone()
|
111 |
+
start = 0
|
112 |
+
if all_nums > 0:
|
113 |
+
for s, real_image_len in enumerate(real_image_nums):
|
114 |
+
current_embs = img_embs[start:start + real_image_len, :] #[30, 256, 4096]
|
115 |
+
for ss in range(current_embs.shape[0]):
|
116 |
+
im_s, im_e, index = images_pos[s, ss]
|
117 |
+
# 子图特征更少
|
118 |
+
if index > 0 and vit_patch_mlp:
|
119 |
+
encoder_input[s, im_s:im_e,] = current_embs[ss, :(im_e-im_s)]
|
120 |
+
else:
|
121 |
+
encoder_input[s, im_s:im_e] = current_embs[ss, :]
|
122 |
+
start = start + real_image_len
|
123 |
+
else:
|
124 |
+
# when no input image, to mask vit value
|
125 |
+
for s in range(b):
|
126 |
+
vit_mask = torch.zeros([vit_token, 1]).cuda()
|
127 |
+
current_embs = img_embs[:, start:start + 1]
|
128 |
+
encoder_input[1:vit_token + 1, s] = encoder_input[1:vit_token + 1, s] * (1 - vit_mask) + current_embs[:, 0, :] * vit_mask
|
129 |
+
start = start + 1
|
130 |
+
return encoder_input, input_ids
|
131 |
+
|
132 |
+
|
133 |
+
def group_images_by_max_seq_len(
|
134 |
+
images: List[List[Tensor]], patch_size: int,
|
135 |
+
max_seq_len: int, adaptor_patch_size: int,
|
136 |
+
add_cls_token: bool = False) -> List[List[Tensor]]:
|
137 |
+
|
138 |
+
groups = []
|
139 |
+
group = []
|
140 |
+
pos_groups = []
|
141 |
+
seq_len = 0
|
142 |
+
num_images = 0
|
143 |
+
for image_list in images:
|
144 |
+
pos_group = []
|
145 |
+
for image in image_list:
|
146 |
+
num_images += 1
|
147 |
+
assert isinstance(image, Tensor)
|
148 |
+
|
149 |
+
image_dims = image.shape[-2:]
|
150 |
+
ph, pw = map(lambda t: t // patch_size, image_dims)
|
151 |
+
|
152 |
+
image_seq_len = (ph * pw)
|
153 |
+
new_image_seq_len = image_seq_len
|
154 |
+
grouped_len = seq_len + image_seq_len
|
155 |
+
if add_cls_token:
|
156 |
+
new_image_seq_len += 1
|
157 |
+
grouped_len += num_images
|
158 |
+
|
159 |
+
assert new_image_seq_len <= max_seq_len, f'image with dimensions {image_dims} exceeds maximum sequence length'
|
160 |
+
|
161 |
+
if grouped_len > max_seq_len:
|
162 |
+
groups.append(group)
|
163 |
+
group = []
|
164 |
+
seq_len = 0
|
165 |
+
num_images = 1
|
166 |
+
|
167 |
+
group.append(image)
|
168 |
+
start = seq_len // (adaptor_patch_size * adaptor_patch_size)
|
169 |
+
end = start + image_seq_len//(adaptor_patch_size * adaptor_patch_size)
|
170 |
+
batch_idx = len(groups)
|
171 |
+
pos_group.append([batch_idx, start, end])
|
172 |
+
seq_len += image_seq_len
|
173 |
+
pos_groups.append(pos_group)
|
174 |
+
|
175 |
+
if len(group) > 0:
|
176 |
+
groups.append(group)
|
177 |
+
|
178 |
+
return groups, pos_groups
|
179 |
+
|
180 |
+
|
181 |
+
class AnyResCLIPVisionEmbeddings(nn.Module):
|
182 |
+
def __init__(self, config: CLIPVisionConfig):
|
183 |
+
super().__init__()
|
184 |
+
|
185 |
+
self.config = config
|
186 |
+
# self.sparse_attn_mask = args.sparse_attn_mask
|
187 |
+
# self.use_flash_attn = args.use_flash_attn
|
188 |
+
self.embed_dim = config.hidden_size
|
189 |
+
self.image_size = config.max_image_size
|
190 |
+
self.patch_size = config.patch_size
|
191 |
+
self.max_seq_len = config.max_vit_seq_len
|
192 |
+
self.adaptor_patch_size = config.adaptor_patch_size
|
193 |
+
self.anyres_vit_two_views = config.anyres_vit_two_views
|
194 |
+
self.vit_add_patchemb_bias = config.vit_add_patchemb_bias
|
195 |
+
self.vit_remove_prenorm = config.vit_remove_prenorm
|
196 |
+
|
197 |
+
self.patch_embedding = nn.Conv2d(
|
198 |
+
in_channels=config.num_channels,
|
199 |
+
out_channels=self.embed_dim,
|
200 |
+
kernel_size=self.patch_size,
|
201 |
+
stride=self.patch_size,
|
202 |
+
bias=self.vit_add_patchemb_bias,
|
203 |
+
)
|
204 |
+
|
205 |
+
self.num_patches = (self.image_size // self.patch_size) ** 2
|
206 |
+
self.skip_cls_token = True
|
207 |
+
|
208 |
+
# add interpolate_pos_encoding
|
209 |
+
if self.anyres_vit_two_views:
|
210 |
+
self.num_positions = self.num_patches
|
211 |
+
self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim) * 0.02)
|
212 |
+
else:
|
213 |
+
self.num_positions = self.num_patches + 1
|
214 |
+
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)))
|
215 |
+
# self.position_ids = torch.arange(self.num_positions).expand((1, -1))
|
216 |
+
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
|
217 |
+
|
218 |
+
if not self.vit_remove_prenorm:
|
219 |
+
self.pre_layernorm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
220 |
+
|
221 |
+
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
|
222 |
+
"""
|
223 |
+
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
|
224 |
+
resolution images.
|
225 |
+
|
226 |
+
Source:
|
227 |
+
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
|
228 |
+
"""
|
229 |
+
num_patches = embeddings.shape[1]
|
230 |
+
position_embeddings = self.position_embedding(self.position_ids)
|
231 |
+
patch_pos_embed = position_embeddings[:, 1:]
|
232 |
+
num_positions = position_embeddings.shape[1] - 1
|
233 |
+
if num_patches == num_positions and height == width:
|
234 |
+
return patch_pos_embed
|
235 |
+
# class_pos_embed = position_embeddings[:, 0]
|
236 |
+
dim = embeddings.shape[-1]
|
237 |
+
h0 = height // self.patch_size
|
238 |
+
w0 = width // self.patch_size
|
239 |
+
# we add a small number to avoid floating point error in the interpolation
|
240 |
+
# see discussion at https://github.com/facebookresearch/dino/issues/8
|
241 |
+
h0, w0 = h0 + 0.1, w0 + 0.1
|
242 |
+
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
|
243 |
+
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
|
244 |
+
raw_type = patch_pos_embed.dtype
|
245 |
+
patch_pos_embed = nn.functional.interpolate(
|
246 |
+
patch_pos_embed.to(torch.float32, non_blocking=True),
|
247 |
+
scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
|
248 |
+
mode="bilinear",
|
249 |
+
align_corners=False,
|
250 |
+
)
|
251 |
+
patch_pos_embed = patch_pos_embed.to(raw_type, non_blocking=True)
|
252 |
+
assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
|
253 |
+
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
254 |
+
return patch_pos_embed
|
255 |
+
|
256 |
+
def rescale_positional_embedding(self, out_size):
|
257 |
+
h, w = out_size
|
258 |
+
pos_embed_shape = int((self.position_embedding.shape[1]) ** 0.5)
|
259 |
+
if (h, w) == (pos_embed_shape, pos_embed_shape):
|
260 |
+
return self.position_embedding
|
261 |
+
rescaled_positional_embedding = \
|
262 |
+
self.position_embedding.new_zeros(1, h*w, self.position_embedding.shape[2])
|
263 |
+
pe_2d = self.position_embedding[0].T.contiguous().view(1, -1, pos_embed_shape, pos_embed_shape)
|
264 |
+
pe_2d = F.interpolate(pe_2d, out_size, mode='bilinear', align_corners=False).view(-1, h*w)
|
265 |
+
rescaled_positional_embedding[0] = pe_2d.T.contiguous()
|
266 |
+
return rescaled_positional_embedding
|
267 |
+
|
268 |
+
def forward_single(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
|
269 |
+
if pixel_values.ndim == 3:
|
270 |
+
pixel_values = pixel_values[None]
|
271 |
+
batch_size, num_channels, height, width = pixel_values.shape
|
272 |
+
|
273 |
+
if self.anyres_vit_two_views:
|
274 |
+
# padding
|
275 |
+
pad_h = (self.patch_size - height % self.patch_size) % self.patch_size
|
276 |
+
pad_w = (self.patch_size - width % self.patch_size) % self.patch_size
|
277 |
+
pixel_values = F.pad(pixel_values, (0, pad_w, 0, pad_h))
|
278 |
+
|
279 |
+
patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
|
280 |
+
b, c, h, w = patch_embeds.shape
|
281 |
+
|
282 |
+
# (b, hw, c)
|
283 |
+
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
|
284 |
+
if self.anyres_vit_two_views:
|
285 |
+
embeddings = patch_embeds + self.rescale_positional_embedding(out_size=(h, w))
|
286 |
+
else:
|
287 |
+
embeddings = patch_embeds + self.interpolate_pos_encoding(patch_embeds, height, width)
|
288 |
+
if not self.vit_remove_prenorm:
|
289 |
+
embeddings = self.pre_layernorm(embeddings)
|
290 |
+
return embeddings, (h, w)
|
291 |
+
|
292 |
+
def forward(self, images: List[List[Tensor]]):
|
293 |
+
'''
|
294 |
+
Input:
|
295 |
+
images: List[List[Tensor]]
|
296 |
+
|
297 |
+
Return:
|
298 |
+
embeddings: Tensor (B, L, E)
|
299 |
+
attn_mask: Tensor (B, L, 2)
|
300 |
+
pos_groups: List[List[(batch_idx, start, end)]]
|
301 |
+
'''
|
302 |
+
batched_images, pos_groups = group_images_by_max_seq_len(
|
303 |
+
images, self.patch_size, self.max_seq_len, self.adaptor_patch_size, add_cls_token=not self.skip_cls_token)
|
304 |
+
max_seq_len = self.max_seq_len
|
305 |
+
|
306 |
+
# batched_images is a list of a list
|
307 |
+
B = len(batched_images)
|
308 |
+
L = max_seq_len
|
309 |
+
E = self.embed_dim
|
310 |
+
|
311 |
+
embeddings = torch.zeros(B, L, E, dtype=self.config.torch_dtype, requires_grad=True).cuda(non_blocking=True)
|
312 |
+
attn_mask = embeddings.new_full((B, 1, L, L), False, dtype=torch.bool) # True presents compute
|
313 |
+
assert len(images) == len(pos_groups), (len(images), len(pos_groups))
|
314 |
+
|
315 |
+
batch_images = []
|
316 |
+
batch_pos = []
|
317 |
+
for images_i, pos_group in zip(images, pos_groups):
|
318 |
+
assert len(images_i) == len(pos_group), (len(images_i), len(pos_group))
|
319 |
+
for image, pos in zip(images_i, pos_group):
|
320 |
+
batch_idx, start, end = pos
|
321 |
+
a2 = self.adaptor_patch_size ** 2
|
322 |
+
# recover the real number of the input image tokens
|
323 |
+
start *= a2
|
324 |
+
end *= a2
|
325 |
+
emb, _ = self.forward_single(image)
|
326 |
+
assert emb.ndim == 3, '(B, L, E)'
|
327 |
+
embeddings[batch_idx, start:end] = emb
|
328 |
+
attn_mask[batch_idx, :, start:end, start:end] = True
|
329 |
+
return embeddings, attn_mask, pos_groups
|
330 |
+
|
331 |
+
|
332 |
+
class CLIPVisionEmbeddings(nn.Module):
|
333 |
+
def __init__(self, config: CLIPVisionConfig, add_pre_layernorm=False, skip_cls_token=True, vit_patch=1):
|
334 |
+
super().__init__()
|
335 |
+
self.config = config
|
336 |
+
self.embed_dim = config.hidden_size
|
337 |
+
self.image_size = config.image_size
|
338 |
+
self.image_size = config.vit_input_resolution
|
339 |
+
self.patch_size = config.patch_size
|
340 |
+
|
341 |
+
self.class_embedding = nn.Parameter(torch.randn(self.embed_dim))
|
342 |
+
|
343 |
+
self.patch_embedding = nn.Conv2d(
|
344 |
+
in_channels=config.num_channels,
|
345 |
+
out_channels=self.embed_dim,
|
346 |
+
kernel_size=self.patch_size,
|
347 |
+
stride=self.patch_size,
|
348 |
+
bias=False,
|
349 |
+
)
|
350 |
+
|
351 |
+
self.num_patches = (self.image_size // self.patch_size) ** 2
|
352 |
+
|
353 |
+
self.skip_cls_token = skip_cls_token
|
354 |
+
|
355 |
+
self.num_positions = self.num_patches + 1
|
356 |
+
|
357 |
+
self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)))
|
358 |
+
if vit_patch > 1:
|
359 |
+
self.position_embedding = nn.Embedding(self.num_patches * (vit_patch ** 2 + 1) + 1, self.embed_dim)
|
360 |
+
# 0 支持最大16张图,目前写死了,如需其他的需要额外定义参数
|
361 |
+
elif vit_patch == 0:
|
362 |
+
self.position_embedding = nn.Embedding(self.num_patches * (16 ** 2 + 1) + 1, self.embed_dim)
|
363 |
+
else:
|
364 |
+
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
|
365 |
+
|
366 |
+
if add_pre_layernorm:
|
367 |
+
self.pre_layernorm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
|
368 |
+
else:
|
369 |
+
self.pre_layernorm = None
|
370 |
+
|
371 |
+
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
|
372 |
+
"""
|
373 |
+
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
|
374 |
+
resolution images.
|
375 |
+
|
376 |
+
Source:
|
377 |
+
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
|
378 |
+
"""
|
379 |
+
num_patches = embeddings.shape[1] - 1
|
380 |
+
position_embeddings = self.position_embedding(self.position_ids)
|
381 |
+
num_positions = position_embeddings.shape[1] - 1
|
382 |
+
if num_patches == num_positions and height == width:
|
383 |
+
return position_embeddings
|
384 |
+
class_pos_embed = position_embeddings[:, 0]
|
385 |
+
patch_pos_embed = position_embeddings[:, 1:]
|
386 |
+
dim = embeddings.shape[-1]
|
387 |
+
h0 = height // self.config.patch_size
|
388 |
+
w0 = width // self.config.patch_size
|
389 |
+
# we add a small number to avoid floating point error in the interpolation
|
390 |
+
# see discussion at https://github.com/facebookresearch/dino/issues/8
|
391 |
+
h0, w0 = h0 + 0.1, w0 + 0.1
|
392 |
+
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
|
393 |
+
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
|
394 |
+
raw_type = patch_pos_embed.dtype
|
395 |
+
patch_pos_embed = nn.functional.interpolate(
|
396 |
+
patch_pos_embed.float(),
|
397 |
+
scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
|
398 |
+
mode="bicubic",
|
399 |
+
align_corners=False,
|
400 |
+
)
|
401 |
+
# print(patch_pos_embed.shape)
|
402 |
+
patch_pos_embed = patch_pos_embed.to(raw_type)
|
403 |
+
assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
|
404 |
+
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
405 |
+
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
|
406 |
+
|
407 |
+
|
408 |
+
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False, img_index=None) -> torch.Tensor:
|
409 |
+
batch_size, num_channels, height, width = pixel_values.shape
|
410 |
+
patch_embeds = self.patch_embedding(pixel_values) # shape = [*, width, grid, grid]
|
411 |
+
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
|
412 |
+
if self.skip_cls_token:
|
413 |
+
embeddings = patch_embeds
|
414 |
+
if img_index is None:
|
415 |
+
position_ids = self.position_ids[:,1:]
|
416 |
+
embeddings = embeddings + self.position_embedding(position_ids)
|
417 |
+
else:
|
418 |
+
position_ids = (torch.tensor(img_index).cuda() * (self.num_positions - 1)).unsqueeze(1).repeat(1, self.num_positions - 1) \
|
419 |
+
+ self.position_ids.expand(batch_size, -1)[:, 1:]
|
420 |
+
embeddings = embeddings + self.position_embedding(position_ids)
|
421 |
+
else:
|
422 |
+
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
|
423 |
+
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
|
424 |
+
if interpolate_pos_encoding:
|
425 |
+
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
|
426 |
+
else:
|
427 |
+
if img_index is None:
|
428 |
+
embeddings = embeddings + self.position_embedding(self.position_ids)
|
429 |
+
else:
|
430 |
+
position_ids = self.position_ids.expand(batch_size,-1)[:,0].unsqueeze(1)
|
431 |
+
new_position = (torch.tensor(img_index).cuda() * (self.num_positions -1)).unsqueeze(1).repeat(1,self.num_positions-1) + self.position_ids.expand(batch_size,-1)[:,1:]
|
432 |
+
position_ids = torch.cat([position_ids,new_position],dim=1)
|
433 |
+
embeddings = embeddings + self.position_embedding(position_ids)
|
434 |
+
if self.pre_layernorm is not None:
|
435 |
+
embeddings = self.pre_layernorm(embeddings)
|
436 |
+
return embeddings
|
437 |
+
|
438 |
+
|
439 |
+
class NaVitTransformer(nn.Module):
|
440 |
+
def __init__(self, config: HunYuanConfig, vit_config: CLIPVisionConfig):
|
441 |
+
super().__init__()
|
442 |
+
self.config = config
|
443 |
+
self.vit_config = vit_config
|
444 |
+
with self.prepare_args(config, vit_config):
|
445 |
+
self._use_sdpa = config._attn_implementation == "sdpa"
|
446 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
447 |
+
self.layers = nn.ModuleList(
|
448 |
+
[HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
449 |
+
)
|
450 |
+
|
451 |
+
@contextmanager
|
452 |
+
def prepare_args(self, config, vit_config):
|
453 |
+
hidden_act = config.hidden_act
|
454 |
+
hidden_size = config.hidden_size
|
455 |
+
ffn_hidden_size = config.intermediate_size
|
456 |
+
num_attention_heads = config.num_attention_heads
|
457 |
+
num_key_value_heads = config.num_key_value_heads
|
458 |
+
attention_head_dim = config.attention_head_dim
|
459 |
+
use_qk_norm = config.use_qk_norm
|
460 |
+
use_rotary_pos_emb = config.use_rotary_pos_emb
|
461 |
+
num_hidden_layers = config.num_hidden_layers
|
462 |
+
rms_norm_eps = config.rms_norm_eps
|
463 |
+
attention_dropout = config.attention_dropout
|
464 |
+
# hidden_dropout = config.hidden_dropout
|
465 |
+
norm_type = config.norm_type
|
466 |
+
attention_bias = config.attention_bias
|
467 |
+
mlp_bias = config.mlp_bias
|
468 |
+
use_mla = config.use_mla
|
469 |
+
num_experts = config.num_experts
|
470 |
+
_attn_implementation = config._attn_implementation
|
471 |
+
|
472 |
+
config.hidden_act = vit_config.hidden_act
|
473 |
+
config.hidden_size = vit_config.hidden_size
|
474 |
+
config.intermediate_size = vit_config.intermediate_size
|
475 |
+
config.num_attention_heads = vit_config.num_attention_heads
|
476 |
+
config.num_key_value_heads = None
|
477 |
+
config.attention_head_dim = vit_config.hidden_size // vit_config.num_attention_heads
|
478 |
+
config.use_qk_norm = False
|
479 |
+
config.use_rotary_pos_emb = False
|
480 |
+
config.num_hidden_layers = vit_config.num_hidden_layers
|
481 |
+
config.rms_norm_eps = vit_config.layer_norm_eps
|
482 |
+
config.attention_dropout = vit_config.attention_dropout
|
483 |
+
# config.hidden_dropout = vit_config.hidden_dropout
|
484 |
+
config.norm_type = config.vit_norm_type
|
485 |
+
config.attention_bias = True
|
486 |
+
config.mlp_bias = True
|
487 |
+
config.use_mla = False
|
488 |
+
config.num_experts = 1
|
489 |
+
config._attn_implementation = "eager"
|
490 |
+
|
491 |
+
yield
|
492 |
+
config.hidden_act = hidden_act
|
493 |
+
config.hidden_size = hidden_size
|
494 |
+
config.intermediate_size = ffn_hidden_size
|
495 |
+
config.num_attention_heads = num_attention_heads
|
496 |
+
config.num_key_value_heads = num_key_value_heads
|
497 |
+
config.attention_head_dim = attention_head_dim
|
498 |
+
config.use_qk_norm = use_qk_norm
|
499 |
+
config.use_rotary_pos_emb = use_rotary_pos_emb
|
500 |
+
config.num_hidden_layers = num_hidden_layers
|
501 |
+
config.rms_norm_eps = rms_norm_eps
|
502 |
+
config.attention_dropout = attention_dropout
|
503 |
+
# config.hidden_dropout = hidden_dropout
|
504 |
+
config.attention_bias = attention_bias
|
505 |
+
config.mlp_bias = mlp_bias
|
506 |
+
config.norm_type = norm_type
|
507 |
+
config.use_mla = use_mla
|
508 |
+
config.num_experts = num_experts
|
509 |
+
config._attn_implementation = _attn_implementation
|
510 |
+
|
511 |
+
def forward(
|
512 |
+
self,
|
513 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
514 |
+
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
515 |
+
|
516 |
+
hidden_states, attention_mask, img_pos = self.embeddings(pixel_values)
|
517 |
+
attention_mask = attention_mask.int()
|
518 |
+
batch_size, seq_length, _ = hidden_states.shape
|
519 |
+
past_key_values_length = 0
|
520 |
+
|
521 |
+
if self._use_flash_attention_2:
|
522 |
+
# 2d mask is passed through the layers
|
523 |
+
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
524 |
+
elif self._use_sdpa:
|
525 |
+
# output_attentions=True can not be supported when using SDPA, and we fall back on
|
526 |
+
# the manual implementation that requires a 4D causal mask in all cases.
|
527 |
+
attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
|
528 |
+
attention_mask,
|
529 |
+
(batch_size, seq_length),
|
530 |
+
hidden_states,
|
531 |
+
past_key_values_length,
|
532 |
+
)
|
533 |
+
else:
|
534 |
+
attention_mask = _prepare_4d_causal_attention_mask(
|
535 |
+
attention_mask,
|
536 |
+
(batch_size, seq_length),
|
537 |
+
hidden_states,
|
538 |
+
past_key_values_length,
|
539 |
+
)
|
540 |
+
|
541 |
+
for layer_idx, decoder_layer in enumerate(self.layers):
|
542 |
+
layer_outputs = decoder_layer(
|
543 |
+
hidden_states,
|
544 |
+
attention_mask=attention_mask
|
545 |
+
)
|
546 |
+
hidden_states = layer_outputs[0]
|
547 |
+
|
548 |
+
return hidden_states, img_pos
|
549 |
+
|
550 |
+
|
551 |
+
class AnyResVitTransformer(NaVitTransformer):
|
552 |
+
def __init__(self, config: HunYuanConfig, vit_config: CLIPVisionConfig, anyres_vit_max_image_size):
|
553 |
+
super().__init__(config, vit_config)
|
554 |
+
old_anyres_vit_max_image_size = vit_config.max_image_size
|
555 |
+
anyres_vit_max_image_size = anyres_vit_max_image_size or old_anyres_vit_max_image_size
|
556 |
+
vit_config.max_image_size = anyres_vit_max_image_size
|
557 |
+
vit_config.torch_dtype = config.torch_dtype
|
558 |
+
vit_config.anyres_vit_two_views = config.anyres_vit_two_views
|
559 |
+
vit_config.vit_remove_prenorm = config.vit_remove_prenorm
|
560 |
+
vit_config.vit_add_patchemb_bias = config.vit_add_patchemb_bias
|
561 |
+
self.embeddings = AnyResCLIPVisionEmbeddings(vit_config)
|
562 |
+
vit_config.max_image_size = old_anyres_vit_max_image_size
|
563 |
+
|
564 |
+
def fix_embeddings_fn(self, pixel_values):
|
565 |
+
# (B, L, E)
|
566 |
+
embeddings, hw = self.embeddings.forward_single(pixel_values)
|
567 |
+
embeddings = self.embeddings.pre_layernorm(embeddings)
|
568 |
+
return embeddings
|
569 |
+
|
570 |
+
|
571 |
+
class CLIPVisionTransformer(nn.Module):
|
572 |
+
def __init__(self, config: HunYuanConfig, vit_config: CLIPVisionConfig):
|
573 |
+
super().__init__()
|
574 |
+
embed_dim = vit_config.hidden_size
|
575 |
+
|
576 |
+
self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=vit_config.layer_norm_eps)
|
577 |
+
self.embeddings = CLIPVisionEmbeddings(vit_config, skip_cls_token=config.skip_cls_token, vit_patch=config.vit_patch)
|
578 |
+
|
579 |
+
with self.prepare_args(config, vit_config):
|
580 |
+
self.layers = nn.ModuleList(
|
581 |
+
[HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
582 |
+
)
|
583 |
+
|
584 |
+
@contextmanager
|
585 |
+
def prepare_args(self, config, vit_config):
|
586 |
+
hidden_act = config.hidden_act
|
587 |
+
hidden_size = config.hidden_size
|
588 |
+
ffn_hidden_size = config.intermediate_size
|
589 |
+
num_attention_heads = config.num_attention_heads
|
590 |
+
num_key_value_heads = config.num_key_value_heads
|
591 |
+
attention_head_dim = config.attention_head_dim
|
592 |
+
use_qk_norm = config.use_qk_norm
|
593 |
+
use_rotary_pos_emb = config.use_rotary_pos_emb
|
594 |
+
num_hidden_layers = config.num_hidden_layers
|
595 |
+
rms_norm_eps = config.rms_norm_eps
|
596 |
+
attention_dropout = config.attention_dropout
|
597 |
+
# hidden_dropout = config.hidden_dropout
|
598 |
+
norm_type = config.norm_type
|
599 |
+
attention_bias = config.attention_bias
|
600 |
+
mlp_bias = config.mlp_bias
|
601 |
+
use_mla = config.use_mla
|
602 |
+
num_experts = config.num_experts
|
603 |
+
_attn_implementation = config._attn_implementation
|
604 |
+
|
605 |
+
config.hidden_act = vit_config.hidden_act
|
606 |
+
config.hidden_size = vit_config.hidden_size
|
607 |
+
config.intermediate_size = vit_config.intermediate_size
|
608 |
+
config.num_attention_heads = vit_config.num_attention_heads
|
609 |
+
config.num_key_value_heads = None
|
610 |
+
config.attention_head_dim = vit_config.hidden_size // vit_config.num_attention_heads
|
611 |
+
config.use_qk_norm = False
|
612 |
+
config.use_rotary_pos_emb = False
|
613 |
+
config.num_hidden_layers = vit_config.num_hidden_layers
|
614 |
+
config.rms_norm_eps = vit_config.layer_norm_eps
|
615 |
+
config.attention_dropout = vit_config.attention_dropout
|
616 |
+
# config.hidden_dropout = 0.0
|
617 |
+
config.norm_type = "fused"
|
618 |
+
config.attention_bias = True
|
619 |
+
config.mlp_bias = True
|
620 |
+
config.use_mla = False
|
621 |
+
config.num_experts = 1
|
622 |
+
config._attn_implementation = "eager"
|
623 |
+
|
624 |
+
yield
|
625 |
+
|
626 |
+
config.hidden_act = hidden_act
|
627 |
+
config.hidden_size = hidden_size
|
628 |
+
config.intermediate_size = ffn_hidden_size
|
629 |
+
config.num_attention_heads = num_attention_heads
|
630 |
+
config.num_key_value_heads = num_key_value_heads
|
631 |
+
config.attention_head_dim = attention_head_dim
|
632 |
+
config.use_qk_norm = use_qk_norm
|
633 |
+
config.use_rotary_pos_emb = use_rotary_pos_emb
|
634 |
+
config.num_hidden_layers = num_hidden_layers
|
635 |
+
config.rms_norm_eps = rms_norm_eps
|
636 |
+
config.attention_dropout = attention_dropout
|
637 |
+
# config.hidden_dropout = hidden_dropout
|
638 |
+
config.norm_type = norm_type
|
639 |
+
config.attention_bias = attention_bias
|
640 |
+
config.mlp_bias = mlp_bias
|
641 |
+
config.use_mla = use_mla
|
642 |
+
config.num_experts = num_experts
|
643 |
+
config._attn_implementation = _attn_implementation
|
644 |
+
|
645 |
+
def forward(
|
646 |
+
self,
|
647 |
+
pixel_values: Optional[torch.FloatTensor] = None,
|
648 |
+
interpolate_pos_encoding: Optional[bool] = None,
|
649 |
+
img_index=None
|
650 |
+
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
651 |
+
r"""
|
652 |
+
Returns:
|
653 |
+
|
654 |
+
"""
|
655 |
+
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, img_index=img_index)
|
656 |
+
hidden_states = self.pre_layrnorm(hidden_states)
|
657 |
+
batch = hidden_states.shape[0]
|
658 |
+
seq_len = hidden_states.shape[1]
|
659 |
+
device = hidden_states.device
|
660 |
+
attention_mask = torch.ones(batch, 1, seq_len, seq_len, dtype=torch.float32, device=device)
|
661 |
+
|
662 |
+
for layer_idx, decoder_layer in enumerate(self.layers):
|
663 |
+
layer_outputs = decoder_layer(
|
664 |
+
hidden_states,
|
665 |
+
attention_mask=attention_mask
|
666 |
+
)
|
667 |
+
hidden_states = layer_outputs[0]
|
668 |
+
|
669 |
+
return hidden_states
|
670 |
+
|
671 |
+
|
672 |
+
class Vit(torch.nn.Module):
|
673 |
+
def __init__(self, config, resampler_token=64, pool_rate=2):
|
674 |
+
super().__init__()
|
675 |
+
self.config = config
|
676 |
+
self.vit_mapping_type = config.vit_mapping_type
|
677 |
+
self.anyres_vit_max_image_size = config.anyres_vit_max_image_size
|
678 |
+
self.skip_cls_token = config.skip_cls_token
|
679 |
+
self.pool_rate = pool_rate
|
680 |
+
self.vit_type = self.config.vit_type
|
681 |
+
self.anyres_vit_two_views = self.config.anyres_vit_two_views
|
682 |
+
if self.vit_type in ['Vit-g', 'Vit-bigG', 'NaVit', 'EvaVit', 'AnyResVit']:
|
683 |
+
self.img_init(resampler_token, config.vit_input_resolution, config.vit_mapping_type, pool_rate)
|
684 |
+
else:
|
685 |
+
raise NotImplementedError(f"unsupported vit type: {self.vit_type}")
|
686 |
+
|
687 |
+
def img_init(self, resampler_token=64, vit_input_resolution=224, vit_mapping_type='resampler', pool_rate=2):
|
688 |
+
if self.vit_type == 'AnyResVit':
|
689 |
+
vit_config = json.load(open(f"{self.config.vit_path}/config.json"))
|
690 |
+
self.vit_config = types.SimpleNamespace(**vit_config["vision_config"])
|
691 |
+
self.vit_config.image_size = vit_input_resolution
|
692 |
+
self.vit = AnyResVitTransformer(self.config, self.vit_config, self.anyres_vit_max_image_size)
|
693 |
+
elif self.vit_type == 'Vit-g':
|
694 |
+
vit_config = json.load(open(f"{self.config.vit_path}/config.json"))
|
695 |
+
self.vit_config = types.SimpleNamespace(**{**vit_config["vision_config_dict"],**vit_config["vision_config"]})
|
696 |
+
self.vit_config.vit_input_resolution = vit_input_resolution
|
697 |
+
self.vit = CLIPVisionTransformer(self.config, self.vit_config)
|
698 |
+
else:
|
699 |
+
assert False, "other vit_types are not supported"
|
700 |
+
|
701 |
+
if self.vit_mapping_type == 'simple_conv_mlp':
|
702 |
+
self.perceive = SimpleConvMlp(self.vit_config.hidden_size, self.config.hidden_size, self.config.anyres_pooling_size, \
|
703 |
+
self.config.vit_used_rms_norm, self.config.rms_norm_eps, poolmlp=False, twoview=True)
|
704 |
+
elif self.vit_mapping_type == 'oryx_mlp':
|
705 |
+
self.perceive = OryxMLPv2(self.vit_config.hidden_size, self.config.hidden_size, twoview=True, use_pe=False)
|
706 |
+
elif self.vit_mapping_type == 'mlp':
|
707 |
+
self.mlp_depth = 2
|
708 |
+
# one mlp layer already in gpt_model.py
|
709 |
+
mlp_hidden_size = self.vit_config.hidden_size
|
710 |
+
if self.vit_type in ['NaVit', 'EvaVit']:
|
711 |
+
mlp_hidden_size *= self.vit_config.adaptor_patch_size **2
|
712 |
+
if self.mlp_depth > 1:
|
713 |
+
mlp_modules = [torch.nn.Linear(mlp_hidden_size, self.config.hidden_size), torch.nn.GELU()]
|
714 |
+
if self.vit_type in ['NaVit', 'EvaVit']:
|
715 |
+
for _ in range(1, self.mlp_depth):
|
716 |
+
mlp_modules.append(torch.nn.Linear(self.config.hidden_size, self.config.hidden_size))
|
717 |
+
mlp_modules.append(torch.nn.GELU())
|
718 |
+
self.perceive = torch.nn.Sequential(*mlp_modules)
|
719 |
+
else:
|
720 |
+
assert False, "other vit_mapping_types are not supported"
|
721 |
+
|
722 |
+
self.vit_patch_mlp = (self.config.vit_patch > 1 and self.vit_mapping_type == 'mlp') or self.config.vit_patch == 0
|
723 |
+
for name, param in self.named_parameters():
|
724 |
+
setattr(param, "is_vit_param", True)
|
725 |
+
|
726 |
+
def forward(self, images, img_index=None):
|
727 |
+
if self.vit_type in ['AnyResVit']:
|
728 |
+
dtype = self.config.torch_dtype
|
729 |
+
device = torch.cuda.current_device()
|
730 |
+
|
731 |
+
images_size = []
|
732 |
+
for i in range(len(images)):
|
733 |
+
images_size.append([])
|
734 |
+
for j in range(len(images[i])):
|
735 |
+
images_size[i].append((images[i][j].size()[1] // self.vit_config.patch_size, images[i][j].size()[2] // self.vit_config.patch_size))
|
736 |
+
|
737 |
+
images_feats, img_batch_pos = self.vit(pixel_values=images)
|
738 |
+
a2 = self.vit_config.adaptor_patch_size ** 2
|
739 |
+
|
740 |
+
if self.anyres_vit_two_views:
|
741 |
+
step = 2
|
742 |
+
else:
|
743 |
+
step = 1
|
744 |
+
perceive_fn = lambda x, img_size, is_video: self.perceive(x, img_size, is_video=is_video)
|
745 |
+
images_list = []
|
746 |
+
images_fix_i = 0
|
747 |
+
num_img_batch_pos = len(img_batch_pos)
|
748 |
+
for i in range(num_img_batch_pos): # batch_id
|
749 |
+
for j in range(0, len(img_batch_pos[i]), step):
|
750 |
+
if self.anyres_vit_two_views:
|
751 |
+
lower_idx, lower_begin, lower_end = img_batch_pos[i][j]
|
752 |
+
lower_begin = lower_begin * a2
|
753 |
+
lower_end = lower_end * a2
|
754 |
+
higher_idx, higher_begin, higher_end = img_batch_pos[i][j + 1]
|
755 |
+
higher_begin = higher_begin * a2
|
756 |
+
higher_end = higher_end * a2
|
757 |
+
lower_res_feat = images_feats[lower_idx, lower_begin:lower_end].unsqueeze(0)
|
758 |
+
higher_res_feat = images_feats[higher_idx, higher_begin:higher_end].unsqueeze(0)
|
759 |
+
lower_images_size = images_size[i][j]
|
760 |
+
higher_images_size = images_size[i][j + 1]
|
761 |
+
images_list.append(self.perceive(lower_res_feat, lower_images_size, higher_res_feat, higher_images_size))
|
762 |
+
else:
|
763 |
+
idx, begin, end = img_batch_pos[i][j]
|
764 |
+
begin = begin * a2
|
765 |
+
end = end * a2
|
766 |
+
is_video = hasattr(images[i][j],'_is_video') and images[i][j]._is_video
|
767 |
+
images_list.append(perceive_fn(images_feats[idx, begin:end].unsqueeze(0), images_size[i][j], is_video=is_video))
|
768 |
+
|
769 |
+
images = torch.cat(images_list, dim=1)
|
770 |
+
|
771 |
+
new_batch_pos = []
|
772 |
+
k = 0; cur_len = 0
|
773 |
+
for i in range(len(images_size)):
|
774 |
+
new_batch_pos.append([])
|
775 |
+
for j in range(0, len(images_size[i]), step):
|
776 |
+
new_pos = [0, cur_len, cur_len + images_list[k].size(1)]
|
777 |
+
cur_len += images_list[k].size(1)
|
778 |
+
k += 1
|
779 |
+
new_batch_pos[i].append(new_pos)
|
780 |
+
return images, new_batch_pos
|
781 |
+
elif self.vit_type == 'Vit-g':
|
782 |
+
images = self.vit(pixel_values=images, interpolate_pos_encoding=False, img_index=img_index)
|
783 |
+
else:
|
784 |
+
assert False, "other vit_types are not supported"
|
785 |
+
|
786 |
+
if self.vit_mapping_type == 'mlp':
|
787 |
+
if self.vit_type in ['Vit-g'] and not self.skip_cls_token:
|
788 |
+
images = images[:,1:,:]
|
789 |
+
b, v, d = images.shape
|
790 |
+
s = int(math.sqrt(v))
|
791 |
+
images = images.reshape(b, s, s, d)
|
792 |
+
|
793 |
+
|
794 |
+
if self.vit_patch_mlp and img_index is not None:
|
795 |
+
L_tensor = torch.tensor(img_index)
|
796 |
+
device = images.device
|
797 |
+
# 获取子图位置
|
798 |
+
nonzero_indices = torch.nonzero(L_tensor).squeeze().to(device)
|
799 |
+
# 获取主图位置
|
800 |
+
zero_indices = torch.nonzero(L_tensor == 0).squeeze().to(device)
|
801 |
+
|
802 |
+
|
803 |
+
images_nonzero = torch.index_select(images,0, nonzero_indices).to(device)
|
804 |
+
images_zero = torch.index_select(images, 0, zero_indices).to(device)
|
805 |
+
|
806 |
+
# 子图额外多pool一次
|
807 |
+
pool_rate = self.pool_rate * 2
|
808 |
+
images_nonzero = images_nonzero.reshape(-1, s // pool_rate, pool_rate, s // pool_rate, pool_rate, d)
|
809 |
+
images_nonzero = images_nonzero.permute(0, 1, 3, 5, 2, 4).reshape(-1, (s // pool_rate) * (s // pool_rate), d,
|
810 |
+
pool_rate*pool_rate).mean(-1)
|
811 |
+
|
812 |
+
# 为了组batch折衷方案
|
813 |
+
images_nonzero = F.pad(images_nonzero, (0, 0, 0, (s // self.pool_rate) * (s // self.pool_rate)- (s // pool_rate) * (s // pool_rate)))
|
814 |
+
images_zero = images_zero.reshape(-1, s // self.pool_rate, self.pool_rate, s // self.pool_rate, self.pool_rate, d)
|
815 |
+
images_zero = images_zero.permute(0, 1, 3, 5, 2, 4).reshape(-1, (s // self.pool_rate) * (s // self.pool_rate), d,
|
816 |
+
self.pool_rate*self.pool_rate).mean(-1)
|
817 |
+
# 组batch
|
818 |
+
images = torch.zeros(b, (s // self.pool_rate) * (s // self.pool_rate), d).to(device).to(images.dtype)
|
819 |
+
images.index_copy_(0, nonzero_indices, images_nonzero)
|
820 |
+
images.index_copy_(0, zero_indices, images_zero)
|
821 |
+
|
822 |
+
if self.mlp_depth >= 2:
|
823 |
+
images = self.perceive(images)
|
824 |
+
else:
|
825 |
+
if s % self.pool_rate == 0:
|
826 |
+
images = images.reshape(b, s//self.pool_rate, self.pool_rate, s//self.pool_rate, self.pool_rate, d)
|
827 |
+
images = images.permute(0, 1, 3, 5, 2, 4).reshape(b, (s//self.pool_rate) * (s//self.pool_rate), d, -1).mean(-1)
|
828 |
+
if self.mlp_depth >= 2:
|
829 |
+
images = self.perceive(images)
|
830 |
+
else:
|
831 |
+
raise ValueError
|
832 |
+
return images
|
833 |
+
|
834 |
+
|
835 |
+
class SimpleConvMlp(nn.Module):
|
836 |
+
def __init__(self, in_channels, out_channels, anyres_pooling_size, vit_used_rms_norm, rms_norm_eps, twoview=False, poolmlp=True, cat_extra_token=True):
|
837 |
+
super().__init__()
|
838 |
+
|
839 |
+
embed_std = 1 / math.sqrt(out_channels)
|
840 |
+
if poolmlp:
|
841 |
+
# if args.learnable_mlp_pooling_size is not None:
|
842 |
+
# in_channels *= args.learnable_mlp_pooling_size ** 2
|
843 |
+
self.proj = nn.Sequential(
|
844 |
+
nn.Linear(in_channels, out_channels),
|
845 |
+
nn.GELU()
|
846 |
+
)
|
847 |
+
self.vit_linear_encoder = nn.Linear(out_channels, out_channels)
|
848 |
+
self.image_newline = nn.Parameter(
|
849 |
+
torch.randn(out_channels) * embed_std
|
850 |
+
)
|
851 |
+
else:
|
852 |
+
self.proj = nn.Sequential(
|
853 |
+
nn.Conv2d(in_channels, in_channels * 2, kernel_size=anyres_pooling_size, stride=anyres_pooling_size),
|
854 |
+
nn.GELU(),
|
855 |
+
nn.Conv2d(in_channels * 2, in_channels * 4, kernel_size=1),
|
856 |
+
)
|
857 |
+
self.mlp = nn.Linear(in_channels * 4, out_channels)
|
858 |
+
self.image_newline = nn.Parameter(
|
859 |
+
torch.randn(in_channels * 4) * embed_std
|
860 |
+
)
|
861 |
+
self.poolmlp = poolmlp
|
862 |
+
|
863 |
+
self.image_begin = nn.Parameter(
|
864 |
+
torch.randn(out_channels) * embed_std
|
865 |
+
)
|
866 |
+
self.image_end = nn.Parameter(
|
867 |
+
torch.randn(out_channels) * embed_std
|
868 |
+
)
|
869 |
+
|
870 |
+
if twoview:
|
871 |
+
self.image_sep = nn.Parameter(
|
872 |
+
torch.randn(out_channels) * embed_std
|
873 |
+
)
|
874 |
+
|
875 |
+
self.cat_extra_token = cat_extra_token
|
876 |
+
self.use_rms_norm = vit_used_rms_norm
|
877 |
+
if self.use_rms_norm:
|
878 |
+
self.before_rms = HunYuanRMSNorm(in_channels, eps=rms_norm_eps)
|
879 |
+
self.after_rms = HunYuanRMSNorm(out_channels, eps=rms_norm_eps)
|
880 |
+
|
881 |
+
def forward(self, x, size=(16,16), x2=None, size2=(16, 16), is_video=False):
|
882 |
+
return self.single_forward(x=x, size=size, x2=x2, size2=size2, is_video=is_video)
|
883 |
+
|
884 |
+
def single_forward(self, x, size=(16,16), x2=None, size2=(16, 16), is_video=False):
|
885 |
+
remove_vit_special_tokens = False
|
886 |
+
learnable_mlp_pooling_size = None
|
887 |
+
if self.use_rms_norm:
|
888 |
+
x = self.before_rms(x)
|
889 |
+
h, w = size
|
890 |
+
dtype = x.dtype
|
891 |
+
x = x.permute(0, 2, 1).reshape(x.shape[0], -1, h, w)
|
892 |
+
if self.poolmlp:
|
893 |
+
if learnable_mlp_pooling_size is None:
|
894 |
+
x = F.avg_pool2d(x, anyres_pooling_size)
|
895 |
+
x = self.proj(x.permute(0, 2, 3, 1)) # b, h, w, c
|
896 |
+
else:
|
897 |
+
x = x.permute(0, 2, 3, 1) # b, h, w, c
|
898 |
+
x = x.reshape(x.shape[0], h // learnable_mlp_pooling_size, learnable_mlp_pooling_size,
|
899 |
+
w // learnable_mlp_pooling_size, learnable_mlp_pooling_size, -1)
|
900 |
+
x = x.permute(0, 1, 3, 2, 4, 5).reshape(x.shape[0], h // learnable_mlp_pooling_size, w // learnable_mlp_pooling_size, -1)
|
901 |
+
x = self.proj(x)
|
902 |
+
x = self.vit_linear_encoder(x)
|
903 |
+
b, h, w, c = x.shape
|
904 |
+
if not remove_vit_special_tokens:
|
905 |
+
x = torch.cat([
|
906 |
+
x,
|
907 |
+
self.image_newline.reshape(1, 1, 1, c).expand(b, h, 1, c).to(dtype, non_blocking=True)
|
908 |
+
], dim=2)
|
909 |
+
x = x.reshape(b, -1, c)
|
910 |
+
else:
|
911 |
+
x = self.proj(x) #b,c,h,w
|
912 |
+
if is_video:
|
913 |
+
video_avgpool_size = 2
|
914 |
+
stride = 2
|
915 |
+
x = F.avg_pool2d(x, kernel_size = video_avgpool_size, stride = stride)
|
916 |
+
b, c, h, w = x.shape
|
917 |
+
if not remove_vit_special_tokens:
|
918 |
+
x = torch.cat([
|
919 |
+
x,
|
920 |
+
self.image_newline.reshape(1, c, 1, 1).expand(b, c, h, 1).to(dtype, non_blocking=True)
|
921 |
+
], dim=-1)
|
922 |
+
x = x.reshape(b, c, -1).permute(0, 2, 1)
|
923 |
+
x = self.mlp(x)
|
924 |
+
|
925 |
+
|
926 |
+
if x2 is not None:
|
927 |
+
h2, w2 = size2
|
928 |
+
x2 = x2.permute(0, 2, 1).reshape(x2.shape[0], -1, h2, w2)
|
929 |
+
if self.poolmlp:
|
930 |
+
x2 = F.avg_pool2d(x2, 2)
|
931 |
+
x2 = self.proj(x2.permute(0, 2, 3, 1)) # b, h, w, c
|
932 |
+
x2 = self.vit_linear_encoder(x2)
|
933 |
+
b2, h2, w2, c2 = x2.shape
|
934 |
+
if not remove_vit_special_tokens:
|
935 |
+
x2 = torch.cat([
|
936 |
+
x2,
|
937 |
+
self.image_newline.reshape(1, 1, 1, c2).expand(b2, h2, 1, c2).to(dtype, non_blocking=True)
|
938 |
+
], dim=2)
|
939 |
+
x2 = x2.reshape(b2, -1, c2)
|
940 |
+
else:
|
941 |
+
x2 = self.proj(x2)
|
942 |
+
b2, c2, h2, w2 = x2.shape
|
943 |
+
if not remove_vit_special_tokens:
|
944 |
+
x2 = torch.cat([
|
945 |
+
x2,
|
946 |
+
self.image_newline.reshape(1, c2, 1, 1).expand(b2, c2, h2, 1).to(dtype, non_blocking=True)
|
947 |
+
], dim=-1)
|
948 |
+
x2 = x2.reshape(b2, c2, -1).permute(0, 2, 1) #b,n,c
|
949 |
+
x2 = self.mlp(x2)
|
950 |
+
|
951 |
+
sep = self.image_sep.reshape(1, 1, -1).expand(b2, 1, x2.shape[-1]).to(dtype, non_blocking=True)
|
952 |
+
|
953 |
+
x = torch.cat([x, sep, x2], dim=1)
|
954 |
+
|
955 |
+
if self.cat_extra_token:
|
956 |
+
begin = self.image_begin.reshape(1, 1, -1).expand(b, 1, x.shape[-1]).to(dtype, non_blocking=True)
|
957 |
+
end = self.image_end.reshape(1, 1, -1).expand(b, 1, x.shape[-1]).to(dtype, non_blocking=True)
|
958 |
+
x = torch.cat([begin, x, end], dim=1)
|
959 |
+
|
960 |
+
if self.use_rms_norm:
|
961 |
+
return self.after_rms(x)
|
962 |
+
else:
|
963 |
+
return x
|
964 |
+
|
965 |
+
|
966 |
+
class NormalizedDwPooler(nn.Module):
|
967 |
+
def __init__(self, dim):
|
968 |
+
super().__init__()
|
969 |
+
self.dim = dim
|
970 |
+
self.predictor = nn.Sequential(
|
971 |
+
nn.Linear(dim*2, dim),
|
972 |
+
nn.GELU(),
|
973 |
+
nn.Linear(dim, dim),
|
974 |
+
)
|
975 |
+
|
976 |
+
def forward(self, x, forward_type='2x'):
|
977 |
+
B, H, W, C = x.shape
|
978 |
+
|
979 |
+
if forward_type == '2x':
|
980 |
+
new_x = x.reshape(B, H//2, 2, W//2, 2, C).permute(0, 1, 3, 2, 4, 5).reshape(B, H//2, W//2, 4, C)
|
981 |
+
pooled_x = new_x.mean(-2, keepdim=True).expand(-1, -1, -1, 4, -1)
|
982 |
+
fused_x = torch.cat([new_x, pooled_x], dim=-1)
|
983 |
+
elif forward_type == '1x':
|
984 |
+
new_x = x.reshape(B, H, W, 1, C)
|
985 |
+
fused_x = torch.cat([new_x, new_x], dim=-1)
|
986 |
+
elif forward_type == '4x':
|
987 |
+
new_x = x.reshape(B, H//4, 4, W//4, 4, C).permute(0, 1, 3, 2, 4, 5).reshape(B, H//4, W//4, 16, C)
|
988 |
+
pooled_x = new_x.mean(-2, keepdim=True).expand(-1, -1, -1, 16, -1)
|
989 |
+
fused_x = torch.cat([new_x, pooled_x], dim=-1)
|
990 |
+
|
991 |
+
score = self.predictor(fused_x)
|
992 |
+
normalized_score = F.softmax(score, dim=-2)
|
993 |
+
new_x = (new_x * normalized_score).sum(dim=-2)
|
994 |
+
return new_x
|
995 |
+
|
996 |
+
|
997 |
+
class OryxMLPv2(nn.Module):
|
998 |
+
def __init__(self, in_channels, out_channels, twoview=False, use_pe=False):
|
999 |
+
super().__init__()
|
1000 |
+
|
1001 |
+
self.proj1 = nn.Linear(in_channels, out_channels)
|
1002 |
+
self.proj2 = nn.Linear(out_channels, out_channels)
|
1003 |
+
self.act = nn.GELU()
|
1004 |
+
self.pooler = NormalizedDwPooler(out_channels)
|
1005 |
+
embed_std = 1 / math.sqrt(out_channels)
|
1006 |
+
|
1007 |
+
self.use_pe = use_pe
|
1008 |
+
if not use_pe:
|
1009 |
+
self.image_newline = nn.Parameter(
|
1010 |
+
torch.randn(out_channels) * embed_std
|
1011 |
+
)
|
1012 |
+
self.image_begin = nn.Parameter(
|
1013 |
+
torch.randn(out_channels) * embed_std
|
1014 |
+
)
|
1015 |
+
self.image_end = nn.Parameter(
|
1016 |
+
torch.randn(out_channels) * embed_std
|
1017 |
+
)
|
1018 |
+
|
1019 |
+
if twoview:
|
1020 |
+
self.image_sep = nn.Parameter(
|
1021 |
+
torch.randn(out_channels) * embed_std
|
1022 |
+
)
|
1023 |
+
|
1024 |
+
def forward(self, x, size=(16,16), x2=None, size2=(16, 16), is_video=False):
|
1025 |
+
h, w = size
|
1026 |
+
dtype = x.dtype
|
1027 |
+
x = x.reshape(x.shape[0], h, w, -1)
|
1028 |
+
# x = self.pooler(x, forward_type=REGIONAL_POOL)
|
1029 |
+
# x = self.proj(x) #b,h,w, c
|
1030 |
+
x = self.proj1(x)
|
1031 |
+
x = self.pooler(x, forward_type='2x')
|
1032 |
+
x = self.act(x)
|
1033 |
+
x = self.proj2(x)
|
1034 |
+
|
1035 |
+
|
1036 |
+
b, h, w, c = x.shape
|
1037 |
+
if not self.use_pe:
|
1038 |
+
x = torch.cat([
|
1039 |
+
x,
|
1040 |
+
self.image_newline.reshape(1, 1, 1, c).expand(b, h, 1, c).to(dtype)
|
1041 |
+
], dim=2)
|
1042 |
+
else:
|
1043 |
+
pe_h = torch.arange(h, dtype=torch.long, device=x.device).reshape(1, h, 1, 1).expand(b, h, w, 1).reshape(b, h*w, 1)
|
1044 |
+
pe_w = torch.arange(w, dtype=torch.long, device=x.device).reshape(1, 1, w, 1).expand(b, h, w, 1).reshape(b, h*w, 1)
|
1045 |
+
pe = torch.cat([pe_h, pe_w], dim=-1)
|
1046 |
+
|
1047 |
+
x = x.reshape(b, -1, c)
|
1048 |
+
|
1049 |
+
if x2 is not None:
|
1050 |
+
h2, w2 = size2
|
1051 |
+
x2 = x2.reshape(x2.shape[0], h2, w2, -1)
|
1052 |
+
# x2 = self.pooler(x2, forward_type=REGIONAL_POOL)
|
1053 |
+
## x2 = self.proj(x2) #b,h,w, c
|
1054 |
+
x2 = self.proj1(x2)
|
1055 |
+
x2 = self.pooler(x2, forward_type='2x')
|
1056 |
+
x2 = self.act(x2)
|
1057 |
+
x2 = self.proj2(x2)
|
1058 |
+
|
1059 |
+
b2, h2, w2, c2 = x2.shape
|
1060 |
+
if not self.use_pe:
|
1061 |
+
x2 = torch.cat([
|
1062 |
+
x2,
|
1063 |
+
self.image_newline.reshape(1, 1, 1, c).expand(b, h2, 1, c).to(dtype)
|
1064 |
+
], dim=2)
|
1065 |
+
x2 = x2.reshape(b, -1, c)
|
1066 |
+
sep = self.image_sep.reshape(1, 1, -1).expand(b, 1, c2).to(dtype)
|
1067 |
+
x = torch.cat([x, sep, x2], dim=1)
|
1068 |
+
|
1069 |
+
begin = self.image_begin.reshape(1, 1, -1).expand(b, 1, c).to(dtype)
|
1070 |
+
end = self.image_end.reshape(1, 1, -1).expand(b, 1, c).to(dtype)
|
1071 |
+
x = torch.cat([begin, x, end], dim=1)
|
1072 |
+
# print(x.shape, x2.shape, h, w, h2, w2)
|
1073 |
+
# print("vit rank = " + str(torch.distributed.get_rank()) +" x = " + str(x))
|
1074 |
+
if self.use_pe:
|
1075 |
+
zero_pad = torch.zeros(b, 1, 2, device=x.device, dtype=torch.long)
|
1076 |
+
pe = torch.cat([zero_pad, pe, zero_pad], dim=1)
|
1077 |
+
assert pe.shape[1] == x.shape[1]
|
1078 |
+
return x, pe
|
1079 |
+
else:
|
1080 |
+
nseq = x.shape[1]
|
1081 |
+
fake_pe = torch.zeros(b, nseq, 2, device=x.device, dtype=torch.long)
|
1082 |
+
return x #, fake_pe
|
1083 |
+
|