diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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diff --git a/Hunyuan-A52B-Instruct-FP8/config.json b/Hunyuan-A52B-Instruct-FP8/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..521466789c9fd194478cf05284582e1d9c5a1b9a
--- /dev/null
+++ b/Hunyuan-A52B-Instruct-FP8/config.json
@@ -0,0 +1,118 @@
+{
+ "architectures": [
+ "HunYuanForCausalLM"
+ ],
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "auto_map": {
+ "AutoConfig": "configuration_hunyuan.HunYuanConfig",
+ "AutoModel": "modeling_hunyuan.HunyuanModel",
+ "AutoModelForCausalLM": "modeling_hunyuan.HunYuanForCausalLM"
+ },
+ "bos_token_id": 1,
+ "capacity_factor": 1.0,
+ "cla_share_factor": 2,
+ "eos_token_id": 2,
+ "hidden_act": "silu",
+ "hidden_size": 6400,
+ "initializer_range": 0.02,
+ "intermediate_size": 18304,
+ "max_position_embeddings": 131072,
+ "model_type": "hunyuan",
+ "moe_drop_tokens": false,
+ "moe_random_routing_dropped_token": false,
+ "moe_topk": 1,
+ "num_attention_heads": 80,
+ "num_experts": 16,
+ "num_hidden_layers": 64,
+ "num_key_value_heads": 8,
+ "num_shared_expert": 1,
+ "pad_token_id": 0,
+ "pretraining_tp": 1,
+ "quantization_config": {
+ "activation_scheme": "static",
+ "ignored_layers": [
+ "model.layers.38.mlp.gate.wg",
+ "model.layers.60.mlp.gate.wg",
+ "model.layers.20.mlp.gate.wg",
+ "model.layers.11.mlp.gate.wg",
+ "model.layers.37.mlp.gate.wg",
+ "model.layers.51.mlp.gate.wg",
+ "model.layers.54.mlp.gate.wg",
+ "model.layers.49.mlp.gate.wg",
+ "model.layers.59.mlp.gate.wg",
+ "lm_head",
+ "model.layers.61.mlp.gate.wg",
+ "model.layers.50.mlp.gate.wg",
+ "model.layers.26.mlp.gate.wg",
+ "model.layers.34.mlp.gate.wg",
+ "model.layers.29.mlp.gate.wg",
+ "model.layers.2.mlp.gate.wg",
+ "model.layers.58.mlp.gate.wg",
+ "model.layers.23.mlp.gate.wg",
+ "model.layers.16.mlp.gate.wg",
+ "model.layers.4.mlp.gate.wg",
+ "model.layers.31.mlp.gate.wg",
+ "model.layers.45.mlp.gate.wg",
+ "model.layers.56.mlp.gate.wg",
+ "model.layers.3.mlp.gate.wg",
+ "model.layers.19.mlp.gate.wg",
+ "model.layers.14.mlp.gate.wg",
+ "model.layers.8.mlp.gate.wg",
+ "model.layers.41.mlp.gate.wg",
+ "model.layers.53.mlp.gate.wg",
+ "model.layers.46.mlp.gate.wg",
+ "model.layers.7.mlp.gate.wg",
+ "model.layers.25.mlp.gate.wg",
+ "model.layers.6.mlp.gate.wg",
+ "model.layers.1.mlp.gate.wg",
+ "model.layers.10.mlp.gate.wg",
+ "model.layers.12.mlp.gate.wg",
+ "model.layers.15.mlp.gate.wg",
+ "model.layers.18.mlp.gate.wg",
+ "model.layers.57.mlp.gate.wg",
+ "model.layers.63.mlp.gate.wg",
+ "model.layers.55.mlp.gate.wg",
+ "model.layers.40.mlp.gate.wg",
+ "model.layers.32.mlp.gate.wg",
+ "model.layers.39.mlp.gate.wg",
+ "model.layers.62.mlp.gate.wg",
+ "model.layers.28.mlp.gate.wg",
+ "model.layers.30.mlp.gate.wg",
+ "model.layers.27.mlp.gate.wg",
+ "model.layers.22.mlp.gate.wg",
+ "model.layers.5.mlp.gate.wg",
+ "model.layers.21.mlp.gate.wg",
+ "model.layers.36.mlp.gate.wg",
+ "model.layers.48.mlp.gate.wg",
+ "model.layers.42.mlp.gate.wg",
+ "model.layers.44.mlp.gate.wg",
+ "model.layers.52.mlp.gate.wg",
+ "model.layers.0.mlp.gate.wg",
+ "model.layers.13.mlp.gate.wg",
+ "model.layers.17.mlp.gate.wg",
+ "model.layers.24.mlp.gate.wg",
+ "model.layers.33.mlp.gate.wg",
+ "model.layers.35.mlp.gate.wg",
+ "model.layers.43.mlp.gate.wg",
+ "model.layers.47.mlp.gate.wg",
+ "model.layers.9.mlp.gate.wg"
+ ],
+ "quant_method": "fp8"
+ },
+ "rms_norm_eps": 1e-05,
+ "rope_scaling": {
+ "alpha": 1000.0,
+ "factor": 1.0,
+ "type": "dynamic"
+ },
+ "rope_theta": 10000.0,
+ "tie_word_embeddings": true,
+ "torch_dtype": "bfloat16",
+ "transformers_version": "4.44.2",
+ "use_cache": true,
+ "use_cla": true,
+ "use_mixed_mlp_moe": true,
+ "use_qk_norm": true,
+ "vocab_size": 129024
+}
diff --git a/Hunyuan-A52B-Instruct-FP8/configuration_hunyuan.py b/Hunyuan-A52B-Instruct-FP8/configuration_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4d438853b0073c0328f2ee94b86e1f9c7a48f03
--- /dev/null
+++ b/Hunyuan-A52B-Instruct-FP8/configuration_hunyuan.py
@@ -0,0 +1,206 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" HunYuan model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class HunYuanConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`HunYuanModel`]. It is used to instantiate an
+ HunYuan model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the HunYuan-7B.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`HunYuanModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details checkout [this
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ End of stream token id.
+ pretraining_tp (`int`, *optional*, defaults to 1):
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+ issue](https://github.com/pytorch/pytorch/issues/76232).
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ use_qk_norm (`bool`, *optional*, defaults to `False`):
+ Whether query and key in attention use norm
+ use_cla (`bool`, *optional*, defaults to `False`):
+ Whether to use CLA in attention
+ cla_share_factor (`int`, *optional*, defaults to 1):
+ The share factor of CLA
+ """
+
+ model_type = "hunyuan"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=290943,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ rms_norm_eps=1e-5,
+ use_cache=True,
+ pad_token_id=0,
+ bos_token_id=1,
+ eos_token_id=2,
+ pretraining_tp=1,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ use_qk_norm=False,
+ use_cla=False,
+ cla_share_factor=1,
+ num_experts=1,
+ use_mixed_mlp_moe=False,
+ num_shared_expert=1,
+ moe_topk=1,
+ capacity_factor=1.0,
+ moe_drop_tokens=False,
+ moe_random_routing_dropped_token=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_experts = num_experts
+ self.use_mixed_mlp_moe = use_mixed_mlp_moe
+ self.num_shared_expert = num_shared_expert
+ self.moe_topk = moe_topk
+ self.capacity_factor = capacity_factor
+ self.moe_drop_tokens = moe_drop_tokens
+ self.moe_random_routing_dropped_token = moe_random_routing_dropped_token
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.pretraining_tp = pretraining_tp
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ # self._rope_scaling_validation() # TODO: Need validation?
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.use_qk_norm = use_qk_norm
+ self.use_cla = use_cla
+ self.cla_share_factor = cla_share_factor
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+ def _rope_scaling_validation(self):
+ """
+ Validate the `rope_scaling` configuration.
+ """
+ if self.rope_scaling is None:
+ return
+
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+ raise ValueError(
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor` or `type` and `alpha`, "
+ f"got {self.rope_scaling}"
+ )
+ rope_scaling_type = self.rope_scaling.get("type", None)
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
+ rope_scaling_alpha = self.rope_scaling.get("alpha", None)
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+ raise ValueError(
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+ )
+ if rope_scaling_factor is None and rope_scaling_alpha is None:
+ raise ValueError(f"`rope_scaling`'s factor or alpha field must be have one, got both of none")
+ if rope_scaling_factor is not None:
+ if not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1.0, got {rope_scaling_factor}")
+ if rope_scaling_alpha is not None:
+ if not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0:
+ raise ValueError(f"`rope_scaling`'s alpha field must be a float > 1.0, got {rope_scaling_alpha}")
+
+
+
diff --git a/Hunyuan-A52B-Instruct-FP8/generation_config.json b/Hunyuan-A52B-Instruct-FP8/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..3f80eeedd5c968afeee8a9b5fbec7e53249cdae4
--- /dev/null
+++ b/Hunyuan-A52B-Instruct-FP8/generation_config.json
@@ -0,0 +1,13 @@
+{
+ "do_sample": true,
+ "eos_token_id": [
+ 127960,
+ 127967
+ ],
+ "pad_token_id": 127961,
+ "repetition_penalty": 1.05,
+ "temperature": 0.7,
+ "top_k": 20,
+ "top_p": 0.6,
+ "transformers_version": "4.44.2"
+}
diff --git a/Hunyuan-A52B-Instruct-FP8/hy.tiktoken b/Hunyuan-A52B-Instruct-FP8/hy.tiktoken
new file mode 100644
index 0000000000000000000000000000000000000000..cedd95a03ff75f517446cdaeb03b20aadd97c410
--- /dev/null
+++ b/Hunyuan-A52B-Instruct-FP8/hy.tiktoken
@@ -0,0 +1,127957 @@
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+4paBbWU= 104643
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+4paBd2E= 104657
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+4paB5Zyo5q2k 120604
+5omn5Yuk 120605
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+4paBb3Q= 120849
+4paBb3RoZXI= 120850
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+4paBZXQ= 122679
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+5oiQ6ZW/5Li6 122735
+5pWZ6K+y 122736
+4paBTXk= 122737
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+57c= 122762
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+4paBQXI= 122796
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+KeOAiyw= 122831
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+4paBcHI= 123863
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+6ISC6IKq6IKd 123962
+6L6T5Ye655qE 123963
+4paBZmlyc3Q= 123964
+5Y6755qu 123965
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+6auY6ZuF 124049
+4paBY291bGQ= 124050
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+6L+Y6KGo56S6 124108
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+4paBR28= 124110
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+5YiR5LqL6K+J6K685rOV 124140
+iLQ= 124141
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+5pys55Sz6K+3 124145
+6Ls= 124146
+6aQ= 124147
+6Y0= 124148
+6Y8= 124149
+7oA= 124150
+57g= 124151
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+6ZE= 124232
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+44Q= 124363
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+6oY= 124514
+6bi9 124515
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+5ZSR 124551
+5qWt 124552
+5bCJ 124553
+6ZyW 124554
+7ps= 124555
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+4L4= 126491
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+8J+RiQ== 126514
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+44Of 126526
+6JOL 126527
+66s= 126528
+6aOb 126529
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+5Zu/ 126559
+5oG/ 126560
+5oiy 126561
+5Zik 126562
+6aSo 126563
+w5g= 126564
+542S 126565
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+4q0= 126587
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+55CN 126648
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+7Z8= 126650
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+6IaR 126675
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+6LCE 126687
+6aKm 126688
+0ok= 126689
+6LG6 126690
+5Zuh 126691
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+6KyC 126696
+5rKP 126697
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+5qiT 126702
+6YWK 126703
+5rGo 126704
+65s= 126705
+6KOP 126706
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+6JyN 126708
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+5YqH 126775
+24c= 126776
+6Z6R 126777
+64SI 126778
+4pmm 126779
+4oM= 126780
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+y4s= 126860
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+8Ks= 126948
+8KuU 126949
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+zIU= 127145
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+5YOu 127310
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+0og= 127312
+5b6R 127313
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+6a6u 127315
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+5oe3 127321
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+54Kd 127336
+6YKw 127337
+56Ga 127338
+6IWz 127339
+7Ys= 127340
+7YuU 127341
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+ }
+}
diff --git a/Hunyuan-A52B-Instruct-FP8/modeling_hunyuan.py b/Hunyuan-A52B-Instruct-FP8/modeling_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8849c6f4253563e232dfa46cb556a11f1285137
--- /dev/null
+++ b/Hunyuan-A52B-Instruct-FP8/modeling_hunyuan.py
@@ -0,0 +1,1726 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+""" PyTorch HunYuan model."""
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+ AttentionMaskConverter,
+ _prepare_4d_attention_mask,
+ _prepare_4d_causal_attention_mask,
+ _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+ SequenceClassifierOutputWithPast
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_flash_attn_2_available,
+ is_flash_attn_greater_or_equal_2_10,
+ logging,
+ replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_hunyuan import HunYuanConfig
+
+
+if is_flash_attn_2_available():
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+ if not is_torch_greater_or_equal_than_1_13:
+ import torch.fx
+
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "HunYuanConfig"
+
+
+def topkgating(logits: Tensor, topk: int):
+ logits = logits.float()
+ gates = F.softmax(logits, dim=1)
+ expert_capacity = topk * gates.shape[0]
+ num_experts = int(gates.shape[1])
+ # Top-k router probability and corresponding expert indices for each token.
+ # Shape: [tokens_per_group, num_selected_experts].
+ expert_gate, expert_index = torch.topk(gates, topk)
+ expert_mask = F.one_hot(expert_index, num_experts)
+ # For a given token, determine if it was routed to a given expert.
+ # Shape: [tokens_per_group, num_experts]
+ expert_mask_aux = expert_mask.max(dim=-2)[0]
+ tokens_per_group_and_expert = torch.mean(expert_mask_aux.float(), dim=-2)
+ router_prob_per_group_and_expert = torch.mean(gates.float(), dim=-2)
+ l_aux = num_experts**2 * torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert)
+
+ gates_s = torch.clamp(
+ torch.matmul(expert_mask.float(), gates.unsqueeze(-1)).sum(dim=1), min=torch.finfo(gates.dtype).eps
+ )
+ router_probs = gates / gates_s
+ # Make num_selected_experts the leading axis to ensure that top-1 choices
+ # have priority over top-2 choices, which have priority over top-3 choices,
+ # etc.
+ expert_index = torch.transpose(expert_index, 0, 1)
+ # Shape: [num_selected_experts * tokens_per_group]
+ expert_index = expert_index.reshape(-1)
+
+ # Create mask out of indices.
+ # Shape: [tokens_per_group * num_selected_experts, num_experts].
+ expert_mask = F.one_hot(expert_index, num_experts).to(torch.int32)
+ exp_counts = torch.sum(expert_mask, dim=0).detach()
+
+ # Experts have a fixed capacity that we cannot exceed. A token's priority
+ # within the expert's buffer is given by the masked, cumulative capacity of
+ # its target expert.
+ # Shape: [tokens_per_group * num_selected_experts, num_experts].
+ token_priority = torch.cumsum(expert_mask, dim=0) * expert_mask - 1
+ # Shape: [num_selected_experts, tokens_per_group, num_experts].
+ token_priority = token_priority.reshape((topk, -1, num_experts))
+ # Shape: [tokens_per_group, num_selected_experts, num_experts].
+ token_priority = torch.transpose(token_priority, 0, 1)
+ # For each token, across all selected experts, select the only non-negative
+ # (unmasked) priority. Now, for group G routing to expert E, token T has
+ # non-negative priority (i.e. token_priority[G,T,E] >= 0) if and only if E
+ # is its targeted expert.
+ # Shape: [tokens_per_group, num_experts].
+ token_priority = torch.max(token_priority, dim=1)[0]
+
+ # Token T can only be routed to expert E if its priority is positive and
+ # less than the expert capacity. One-hot matrix will ignore indices outside
+ # the range [0, expert_capacity).
+ # Shape: [tokens_per_group, num_experts, expert_capacity].
+ valid_mask = torch.logical_and(token_priority >= 0, token_priority < expert_capacity)
+ token_priority = torch.masked_fill(token_priority, ~valid_mask, 0)
+ dispatch_mask = F.one_hot(token_priority, expert_capacity).to(torch.bool)
+ valid_mask = valid_mask.unsqueeze(-1).expand(-1, -1, expert_capacity)
+ dispatch_mask = torch.masked_fill(dispatch_mask, ~valid_mask, 0)
+
+ # The combine array will be used for combining expert outputs, scaled by the
+ # router probabilities. Shape: [num_groups, tokens_per_group, num_experts,
+ # expert_capacity].
+ combine_weights = torch.einsum("...te,...tec->...tec", router_probs, dispatch_mask)
+ exp_counts_capacity = torch.sum(dispatch_mask)
+ exp_capacity_rate = exp_counts_capacity / (logits.shape[0]*topk)
+
+ return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts
+
+
+def top1gating(logits: Tensor, random_routing_dropped_token: bool = False):
+ """Implements Top1Gating on logits."""
+ # everything is in fp32 in this function
+ logits = logits.float()
+ gates = F.softmax(logits, dim=1)
+ capacity = gates.shape[0]
+
+ # Create a mask for 1st's expert per token
+ # noisy gating
+ indices1_s = torch.argmax(gates, dim=1)
+ num_experts = int(gates.shape[1])
+ mask1 = F.one_hot(indices1_s, num_classes=num_experts)
+
+ # gating decisions
+ # exp_counts = torch.sum(mask1, dim=0).detach().to('cpu')
+ exp_counts = torch.sum(mask1, dim=0).detach()
+
+ # Compute l_aux
+ me = torch.mean(gates, dim=0)
+ ce = torch.mean(mask1.float(), dim=0)
+ l_aux = torch.sum(me * ce) * num_experts
+ mask1_rand = mask1
+
+ top_idx = torch.topk(mask1_rand, k=capacity, dim=0)[1]
+
+ new_mask1 = mask1 * torch.zeros_like(mask1).scatter_(0, top_idx, 1)
+ mask1 = new_mask1
+ mask1_bk = mask1
+ if random_routing_dropped_token:
+ not_full = capacity - new_mask1.sum(dim=0)
+ sorted_notfull, indices_notfull = torch.sort(not_full, descending=True)
+ sorted_notfull = sorted_notfull.to(torch.int64)
+ not_full_experts_ids = torch.repeat_interleave(indices_notfull, sorted_notfull)
+ shuffle_not_full_ids = torch.randperm(not_full_experts_ids.shape[0])
+ not_full_experts_ids = not_full_experts_ids[shuffle_not_full_ids]
+ indices1_s_after_drop = torch.argmax(new_mask1, dim=1)
+ # get drop idx
+ drop_mask = 1 - new_mask1.sum(dim=1)
+ drop_mask = drop_mask.bool()
+ drop_idx = drop_mask.nonzero().view(-1)
+ drop_num = drop_mask.sum().to(torch.int64)
+ indices1_s_after_drop.scatter_(0, drop_idx, not_full_experts_ids[:drop_num])
+ nodrop_mask1 = F.one_hot(indices1_s_after_drop, num_classes=num_experts)
+ mask1 = nodrop_mask1
+
+ # Compute locations in capacity buffer
+ locations1 = torch.cumsum(mask1, dim=0) - 1
+
+ # Store the capacity location for each token
+ locations1_s = torch.sum(locations1 * mask1, dim=1)
+
+ # Normalize gate probabilities
+ mask1_float = mask1.float()
+ gates = gates * mask1_float
+
+ locations1_sc = F.one_hot(locations1_s, num_classes=capacity).float() # one hot to float
+ combine_weights = torch.einsum("se,sc->sec", gates, locations1_sc)
+
+ dispatch_mask = combine_weights.bool()
+
+ exp_counts_capacity = torch.sum(mask1_bk)
+ exp_capacity_rate = exp_counts_capacity / (logits.shape[0])
+ return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts
+
+
+def _get_unpad_data(attention_mask):
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+ return (
+ indices,
+ cu_seqlens,
+ max_seqlen_in_batch,
+ )
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ warnings.warn(
+ "Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be "
+ "removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+ )
+ return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _make_causal_mask(
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+ warnings.warn(
+ "Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in "
+ "v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
+ )
+ return AttentionMaskConverter._make_causal_mask(
+ input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+ )
+
+
+class HunYuanRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ HunYuanRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(HunYuanRMSNorm)
+
+
+class HunYuanRotaryEmbedding(nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ inv_freq = inv_freq.bfloat16()
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ # Build here to make `torch.jit.trace` work.
+ self._set_cos_sin_cache(
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+ )
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1).float()
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+ def forward(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ if seq_len > self.max_seq_len_cached:
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+ return (
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
+ )
+
+
+class HunYuanLinearScalingRotaryEmbedding(HunYuanRotaryEmbedding):
+ """HunYuanRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+ t = t / self.scaling_factor
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class HunYuanDynamicNTKScalingRotaryEmbedding(HunYuanRotaryEmbedding):
+ """
+ HunYuanRotaryEmbedding extended with Dynamic NTK scaling.
+ Credits to the Reddit users /u/bloc97 and /u/emozilla
+ """
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+
+ if seq_len > self.max_position_embeddings:
+ base = self.base * (
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+ ) ** (self.dim / (self.dim - 2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class HunYuanDynamicNTKAlphaRotaryEmbedding(HunYuanRotaryEmbedding):
+ """
+ HunYuanRotaryEmbedding extended with Dynamic NTK scaling.
+ Credits to the Reddit users /u/bloc97 and /u/emozilla
+ """
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_alpha=1.0):
+ self.scaling_alpha = scaling_alpha
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ base = self.base * self.scaling_alpha ** (self.dim / (self.dim-2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2:]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`):
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+ used to pass offsetted position ids when working with a KV-cache.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class HunYuanMLP(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx=None, is_shared_mlp=False):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.hidden_size = config.hidden_size
+ if is_shared_mlp:
+ self.intermediate_size = config.intermediate_size * config.num_shared_expert
+ else:
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ if self.config.pretraining_tp > 1:
+ slice = self.intermediate_size // self.config.pretraining_tp
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+ gate_proj = torch.cat(
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+ )
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+ down_proj = [
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+ ]
+ down_proj = sum(down_proj)
+ else:
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+ return down_proj
+
+
+class HunYuanTopKGate(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.moe_topk = config.moe_topk
+ self.drop_tokens = config.moe_drop_tokens
+ self.min_capacity = 8
+ self.random_routing_dropped_token = config.moe_random_routing_dropped_token
+ self.wg = nn.Linear(config.hidden_size, config.num_experts, bias=False, dtype=torch.float32)
+
+ def forward(self, hidden_states):
+ bsz, seq_len, hidden_size = hidden_states.shape
+ hidden_states = hidden_states.reshape(-1, hidden_size)
+ if self.wg.weight.dtype == torch.float32:
+ hidden_states = hidden_states.float()
+ logits = self.wg(hidden_states)
+ if self.moe_topk == 1:
+ gate_output = top1gating(logits, random_routing_dropped_token=self.random_routing_dropped_token)
+ else:
+ gate_output = topkgating(logits, self.moe_topk)
+
+ return gate_output
+
+
+class HunYuanMoE(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.moe_topk = config.moe_topk
+ self.num_experts = config.num_experts
+ if config.use_mixed_mlp_moe:
+ self.shared_mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=True)
+ self.gate = HunYuanTopKGate(config, layer_idx=layer_idx)
+ self.experts = nn.ModuleList(
+ [HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(config.num_experts)]
+ )
+
+ def forward(self, hidden_states):
+ bsz, seq_len, hidden_size = hidden_states.shape
+
+ if self.config.use_mixed_mlp_moe:
+ hidden_states_mlp = self.shared_mlp(hidden_states)
+
+ l_moe, combine_weights, dispatch_mask, exp_counts = self.gate(hidden_states)
+
+ reshaped_input = hidden_states.reshape(-1, hidden_size)
+
+ dispatched_input = torch.einsum("sec,sm->ecm", dispatch_mask.type_as(hidden_states), reshaped_input)
+
+ chunks = dispatched_input.chunk(self.num_experts, dim=0)
+ expert_outputs = []
+ for chunk, expert in zip(chunks, self.experts):
+ expert_outputs.append(expert(chunk))
+
+ expert_output = torch.cat(expert_outputs, dim=0)
+ combined_output = torch.einsum("sec,ecm->sm", combine_weights.type_as(hidden_states), expert_output)
+ combined_output = combined_output.reshape(bsz, seq_len, hidden_size)
+
+ if self.config.use_mixed_mlp_moe:
+ output = hidden_states_mlp + combined_output
+ else:
+ output = combined_output
+
+ return output
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class HunYuanAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ # layer_idx 从 0 开始
+ self.attention_type = 'cross' if config.use_cla and layer_idx % config.cla_share_factor != 0 else 'self'
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.is_causal = True
+ self.use_qk_norm = config.use_qk_norm
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+ if self.attention_type == 'self':
+ self.k_proj = nn.Linear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+ if self.use_qk_norm:
+ self.query_layernorm = HunYuanRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+ self.key_layernorm = HunYuanRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+ self._init_rope()
+
+ def _init_rope(self):
+ if self.config.rope_scaling is None:
+ self.rotary_emb = HunYuanRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ )
+ else:
+ scaling_type = self.config.rope_scaling["type"]
+ scaling_factor = self.config.rope_scaling["factor"]
+ scaling_alpha = self.config.rope_scaling["alpha"]
+ if scaling_type == "linear":
+ self.rotary_emb = HunYuanLinearScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "dynamic":
+ if scaling_alpha:
+ self.rotary_emb = HunYuanDynamicNTKAlphaRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_alpha=scaling_alpha,
+ base=self.rope_theta,
+ )
+ else:
+ self.rotary_emb = HunYuanDynamicNTKScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ else:
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ if self.config.pretraining_tp > 1:
+ query_slices = self.q_proj.weight.split(
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+ )
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+ query_states = torch.cat(query_states, dim=-1)
+
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+ key_states = torch.cat(key_states, dim=-1)
+
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+ value_states = torch.cat(value_states, dim=-1)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ else:
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ if self.config.pretraining_tp > 1:
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+ else:
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value, (orig_key_states, orig_value_states)
+
+
+class HunYuanFlashAttention2(HunYuanAttention):
+ """
+ HunYuan flash attention module. This module inherits from `HunYuanAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ # HunYuanFlashAttention2 attention does not support output_attentions
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ # overwrite attention_mask with padding_mask
+ attention_mask = kwargs.pop("padding_mask")
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (HunYuanRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ if input_dtype == torch.float32:
+ # Handle the case where the model is quantized
+ if hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = self._flash_attention_forward(
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None, past_key_value, (orig_key_states, orig_value_states)
+
+ def _flash_attention_forward(
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+ ):
+ """
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+ first unpad the input, then computes the attention scores and pad the final attention scores.
+
+ Args:
+ query_states (`torch.Tensor`):
+ Input query states to be passed to Flash Attention API
+ key_states (`torch.Tensor`):
+ Input key states to be passed to Flash Attention API
+ value_states (`torch.Tensor`):
+ Input value states to be passed to Flash Attention API
+ attention_mask (`torch.Tensor`):
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+ position of padding tokens and 1 for the position of non-padding tokens.
+ dropout (`int`, *optional*):
+ Attention dropout
+ softmax_scale (`float`, *optional*):
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+ """
+ if not self._flash_attn_uses_top_left_mask:
+ causal = self.is_causal
+ else:
+ causal = self.is_causal and query_length != 1
+
+ # Contains at least one padding token in the sequence
+ if attention_mask is not None:
+ batch_size = query_states.shape[0]
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+ query_states, key_states, value_states, attention_mask, query_length
+ )
+
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+ else:
+ attn_output = flash_attn_func(
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+ )
+
+ return attn_output
+
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+ key_layer = index_first_axis(
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+ )
+ value_layer = index_first_axis(
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+ )
+ if query_length == kv_seq_len:
+ query_layer = index_first_axis(
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+ )
+ cu_seqlens_q = cu_seqlens_k
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
+ indices_q = indices_k
+ elif query_length == 1:
+ max_seqlen_in_batch_q = 1
+ cu_seqlens_q = torch.arange(
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
+ ) # There is a memcpy here, that is very bad.
+ indices_q = cu_seqlens_q[:-1]
+ query_layer = query_layer.squeeze(1)
+ else:
+ # The -q_len: slice assumes left padding.
+ attention_mask = attention_mask[:, -query_length:]
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+ return (
+ query_layer,
+ key_layer,
+ value_layer,
+ indices_q,
+ (cu_seqlens_q, cu_seqlens_k),
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+ )
+
+
+class HunYuanSdpaAttention(HunYuanAttention):
+ """
+ HunYuan attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `HunYuanAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt
+ to SDPA API.
+ """
+
+ # Adapted from HunYuanAttention.forward
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if output_attentions:
+ logger.warning_once(
+ 'HunYuanModel is using HunYuanSdpaAttention,'
+ 'but `torch.nn.functional.scaled_dot_product_attention`'
+ 'does not support `output_attentions=True`. Falling back to the manual attention implementation, '
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. '
+ 'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
+ # custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and attention_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=attention_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a
+ # causal mask in case q_len == 1.
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None, past_key_value, (orig_key_states, orig_value_states)
+
+
+HUNYUAN_ATTENTION_CLASSES = {
+ "eager": HunYuanAttention,
+ "flash_attention_2": HunYuanFlashAttention2,
+ "sdpa": HunYuanSdpaAttention,
+}
+
+
+class HunYuanDecoderLayer(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.layer_idx = layer_idx
+
+ self.self_attn = HUNYUAN_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+ if config.num_experts > 1:
+ self.mlp = HunYuanMoE(config, layer_idx=layer_idx)
+ else:
+ self.mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False)
+ self.input_layernorm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ kv_states: Optional[Tuple[torch.Tensor]] = None,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ kv_states (`Tuple(torch.FloatTensor)`, *optional*): Used when CLA is enabled,
+ key and value states from past attention blocks
+ """
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value, kv_states = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ kv_states=kv_states,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ outputs += (kv_states,)
+
+ return outputs
+
+
+HUNYUAN_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`HunYuanConfig`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanPreTrainedModel(PreTrainedModel):
+ config_class = HunYuanConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["HunYuanDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = True
+ _supports_sdpa = True
+ _supports_cache_class = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+HUNYUAN_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanModel(HunYuanPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HunYuanDecoderLayer`]
+
+ Args:
+ config: HunYuanConfig
+ """
+
+ def __init__(self, config: HunYuanConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._use_sdpa = config._attn_implementation == "sdpa"
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+ self.norm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.cla = config.use_cla
+ self.cla_share_factor = config.cla_share_factor
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape[:2]
+ elif inputs_embeds is not None:
+ batch_size, seq_length = inputs_embeds.shape[:2]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ past_key_values_length = 0
+ if use_cache:
+ use_legacy_cache = not isinstance(past_key_values, Cache)
+ if use_legacy_cache:
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ # Fix lora with gradient checkpointing training
+ if self.training and inputs_embeds.is_leaf:
+ inputs_embeds.requires_grad = True
+
+ if self._use_flash_attention_2:
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ elif self._use_sdpa and not output_attentions:
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = None
+
+ prev_kv_states = None
+ for layer_idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ decoder_layer.__call__,
+ hidden_states,
+ attention_mask,
+ position_ids,
+ past_key_values,
+ output_attentions,
+ use_cache,
+ prev_kv_states,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ kv_states=prev_kv_states
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ kv_states = layer_outputs[-1]
+
+ if self.cla and layer_idx % self.cla_share_factor == 0:
+ prev_kv_states = kv_states
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = None
+ if use_cache:
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class HunYuanForCausalLM(HunYuanPreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: HunYuanConfig):
+ super().__init__(config)
+ self.model = HunYuanModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+ >>> model = AutoModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ if self.config.pretraining_tp > 1:
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+ logits = torch.cat(logits, dim=-1)
+ else:
+ logits = self.lm_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values is not None:
+ if isinstance(past_key_values, Cache):
+ cache_length = past_key_values.get_seq_length()
+ past_length = past_key_values.seen_tokens
+ max_cache_length = past_key_values.get_max_length()
+ else:
+ cache_length = past_length = past_key_values[0][0].shape[2]
+ max_cache_length = None
+
+ # Keep only the unprocessed tokens:
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+ # input)
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+ # input_ids based on the past_length.
+ elif past_length < input_ids.shape[1]:
+ input_ids = input_ids[:, past_length:]
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+ if (
+ max_cache_length is not None
+ and attention_mask is not None
+ and cache_length + input_ids.shape[1] > max_cache_length
+ ):
+ attention_mask = attention_mask[:, -max_cache_length:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -input_ids.shape[1]:]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+ )
+ return reordered_past
+
+
+@add_start_docstrings(
+ """
+ The HunYuan Model transformer with a sequence classification head on top (linear layer).
+
+ [`HunYuanForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanForSequenceClassification(HunYuanPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = HunYuanModel(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ sequence_lengths = -1
+ else:
+ if input_ids is not None:
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+ logits.device
+ )
+ else:
+ sequence_lengths = -1
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
diff --git a/Hunyuan-A52B-Instruct-FP8/special_tokens_map.json b/Hunyuan-A52B-Instruct-FP8/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..ff881491feee344d50baa2f97587980fded869bd
--- /dev/null
+++ b/Hunyuan-A52B-Instruct-FP8/special_tokens_map.json
@@ -0,0 +1,12 @@
+{
+ "additional_special_tokens": [
+ "<|startoftext|>",
+ "<|extra_0|>",
+ "<|extra_4|>",
+ "<|extra_5|>",
+ "<|eos|>"
+ ],
+ "bos_token": "<|startoftext|>",
+ "eos_token": "<|endoftext|>",
+ "pad_token": "<|pad|>"
+}
diff --git a/Hunyuan-A52B-Instruct-FP8/tokenization_hy.py b/Hunyuan-A52B-Instruct-FP8/tokenization_hy.py
new file mode 100644
index 0000000000000000000000000000000000000000..19c564ecfdf68fdb01c9b8ff4676b69ed2408a9b
--- /dev/null
+++ b/Hunyuan-A52B-Instruct-FP8/tokenization_hy.py
@@ -0,0 +1,353 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import base64
+import logging
+import tiktoken
+import unicodedata
+from transformers import PreTrainedTokenizer, AddedToken
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+
+logger = logging.getLogger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "hy.tiktoken"}
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|""" \
+ r"""[^\r\n\p{L}\p{N}]?\p{L}+|""" \
+ r"""\p{N}|""" \
+ r""" ?[^\s\p{L}\p{N}]+[\r\n]*|""" \
+ r"""\s*[\r\n]+|""" \
+ r"""\s+(?!\S)|""" \
+ r"""\s+"""
+# default eod_token and bod_token of our base model
+ENDOFTEXT = "<|endoftext|>"
+STARTOFTEXT = "<|startoftext|>"
+
+# extra flag token for other training
+BOSTOKEN = "<|bos|>"
+EOSTOKEN = "<|eos|>"
+
+PADTOKEN = "<|pad|>"
+
+# extra special tokens for the tokenizer
+# as the default behavior is changed to allow special tokens in
+# regular texts, the surface forms of special tokens need to be
+# as different as possible to minimize the impact
+EXTRAS = tuple((f"<|extra_{i}|>" for i in range(204)))
+
+SPECIAL_START_ID = 127957
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+ dic = {}
+ rank = 0
+ for i, line in enumerate(open(tiktoken_bpe_file, "rb")):
+ if line:
+ token, _ = line.split()
+ # skip duplicated tokens, this should not happen
+ if base64.b64decode(token) in dic:
+ raise ValueError(f"!ERROR: duplicated token {token} in your vocab file")
+ dic[base64.b64decode(token)] = int(rank)
+ rank += 1
+ return dic
+
+
+# special tokens for pretrain and finetune models
+SPECIAL_TOKENS = tuple(
+ enumerate(
+ (
+ (
+ ENDOFTEXT,
+ STARTOFTEXT,
+ BOSTOKEN,
+ EOSTOKEN,
+ PADTOKEN,
+ )
+ + EXTRAS
+ ),
+ start=SPECIAL_START_ID,
+ )
+)
+
+SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
+
+
+class HYTokenizer(PreTrainedTokenizer):
+ """
+ HunYuan Tokenizer Initialization. We extend `tiktoken` vocab and
+ the default EOD & BOD special tokens are used for base model.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+
+ errors (`str`):
+ How to handle errors in decoding UTF-8 byte sequences.
+ use ignore if you are in streaming inference
+
+ bod_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""<|startoftext|>""`):
+ The beginning of document token that was used for training. can be modified by your task.
+ default to be `<|startoftext|>` for released base model.
+
+ eod_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""<|endoftext|>""`):
+ The end of document token that was used for training. can be modified by your task.
+ default to be `<|endoftext|>` for released base model.
+
+ bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `None`):
+ The start or sep special token that was used for some training tasks.
+ default to be `<|startoftext|>` for released base model.
+ It can be set to `<|bos|>` when you training for some other task
+
+ eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `None`):
+ The end or sep special token that was used for some training tasks.
+ default to be `<|endoftext|>` for released base model.
+ It can be set to `<|eos|>` when you training for some other task
+
+ pad_token (`str` or `tokenizers.AddedToken`, *optional*):
+ A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+ attention mechanisms or loss computation.
+
+ special_vocab_file (str, *optional*):
+ Customed special extra vocab file, same format with hy.tiktoken.
+ **Be careful** to use the extra special vocab, it will may cause the model loading collapse.
+ The data line be like:
+ `PHxhYmN8Pg== 0`
+ the id followed `base64.encode(str)` is unused, we will reset them in case of collision
+
+ add_bod_token (`bool`, *optional*, defaults to `True`):
+ Whether or not to add an `bos_token` at the start of documents.
+ This will effect `build_inputs_with_special_tokens` method
+
+ add_eod_token (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an `eos_token` at the end of documents.
+ This will effect `build_inputs_with_special_tokens` method
+
+ """
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ errors="replace",
+ bod_token="<|startoftext|>",
+ eod_token="<|endoftext|>",
+ bos_token="<|startoftext|>",
+ eos_token="<|endoftext|>",
+ pad_token="<|pad|>",
+ add_bod_token=True,
+ add_eod_token=True,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.errors = errors
+ self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
+ self.special_tokens = {
+ token: index
+ for index, token in SPECIAL_TOKENS
+ }
+
+ enc = tiktoken.Encoding(
+ "HunYuan",
+ pat_str=PAT_STR,
+ mergeable_ranks=self.mergeable_ranks,
+ special_tokens=self.special_tokens,
+ )
+ assert (
+ len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
+ ), f"{len(self.mergeable_ranks)} + {len(self.special_tokens)} != {enc.n_vocab} in encoding"
+
+ self.decoder = {
+ v: k for k, v in self.mergeable_ranks.items()
+ } # type: dict[int, bytes|str]
+ self.decoder.update({v: k for k, v in self.special_tokens.items()})
+
+ self.tokenizer = enc
+
+ self.bod_token, self.bod_id = bod_token, self.special_tokens[bod_token]
+ self.eod_token, self.eod_id = eod_token, self.special_tokens[eod_token]
+ self.bos_token, self.bos_id = bos_token, self.special_tokens[bos_token]
+ self.eos_token, self.eos_id = eos_token, self.special_tokens[eos_token]
+ self.pad_token, self.pad_id = pad_token, self.special_tokens[pad_token]
+
+ self._num_special_token = len(self.special_tokens)
+
+ self.add_bod_token = add_bod_token
+ self.add_eod_token = add_eod_token
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ del state["tokenizer"]
+ return state
+
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+ enc = tiktoken.Encoding(
+ "HunYuan",
+ pat_str=PAT_STR,
+ mergeable_ranks=self.mergeable_ranks,
+ special_tokens=self.special_tokens,
+ )
+ self.tokenizer = enc
+
+ def __len__(self) -> int:
+ return self.tokenizer.n_vocab
+
+ def get_vocab(self) -> Dict[bytes, int]:
+ """Return the vocabulary as a dictionary, without special tokens."""
+ return self.mergeable_ranks
+
+ def convert_tokens_to_ids(
+ self, tokens: Union[bytes, str, List[Union[bytes, str]]]
+ ) -> List[int]:
+ ids = []
+ if isinstance(tokens, (str, bytes)):
+ if tokens in self.special_tokens:
+ return self.special_tokens[tokens]
+ else:
+ return self.mergeable_ranks.get(tokens)
+ for token in tokens:
+ if token in self.special_tokens:
+ ids.append(self.special_tokens[token])
+ else:
+ ids.append(self.mergeable_ranks.get(token))
+ return ids
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ bod_token_id = [self.bod_id] if self.add_bod_token else []
+ eod_token_id = [self.eod_id] if self.add_eod_token else []
+ output = bod_token_id + token_ids_0 + eod_token_id
+ if token_ids_1 is not None:
+ output = output + bod_token_id + token_ids_1 + eod_token_id
+ return output
+
+ def _add_tokens(
+ self,
+ new_tokens: Union[List[str], List[AddedToken]],
+ special_tokens: bool = False,
+ ) -> List[Tuple[int, str]]:
+ """do not support adding tokens"""
+ if not special_tokens and new_tokens:
+ raise ValueError("Adding regular tokens is not supported")
+ for token in new_tokens:
+ surface_form = token.content if isinstance(token, AddedToken) else token
+ if surface_form not in SPECIAL_TOKENS_SET:
+ raise ValueError("Adding unknown special tokens is not supported")
+ return 0
+
+ def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+ """
+ Save only the vocabulary of the tokenizer (vocabulary).
+ Returns:
+ `Tuple(str)`: Paths to the files saved.
+ """
+ file_path = os.path.join(save_directory, "hy.tiktoken")
+ with open(file_path, "w", encoding="utf8") as w:
+ for k, v in self.mergeable_ranks.items():
+ line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+ w.write(line)
+ return (file_path,)
+
+ def tokenize(
+ self,
+ text: str,
+ allowed_special: Union[Set, str] = "all",
+ disallowed_special: Union[Collection, str] = (),
+ **kwargs,
+ ) -> List[Union[bytes, str]]:
+ """
+ Converts a string in a sequence of tokens.
+ Args:
+ text (`str`):
+ The sequence to be encoded.
+ allowed_special (`Literal["all"]` or `set`):
+ The surface forms of the tokens to be encoded as special tokens in regular texts.
+ Default to "all".
+ disallowed_special (`Literal["all"]` or `Collection`):
+ The surface forms of the tokens that should not be in regular texts and trigger errors.
+ Default to an empty tuple.
+ kwargs (additional keyword arguments, *optional*):
+ Will be passed to the underlying model specific encode method.
+ Returns:
+ `List[bytes|str]`: The list of tokens.
+ """
+ tokens = []
+ text = unicodedata.normalize("NFC", text)
+
+ # this implementation takes a detour: text -> token id -> token surface forms
+ for t in self.tokenizer.encode(
+ text, allowed_special=allowed_special, disallowed_special=disallowed_special
+ ):
+ tokens.append(self.decoder[t])
+ return tokens
+
+ def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+ """
+ Converts a sequence of tokens in a single string.
+ """
+ text = ""
+ temp = b""
+ for t in tokens:
+ if isinstance(t, str):
+ if temp:
+ text += temp.decode("utf-8", errors=self.errors)
+ temp = b""
+ text += t
+ elif isinstance(t, bytes):
+ temp += t
+ else:
+ raise TypeError("token should only be of type types or str")
+ if temp:
+ text += temp.decode("utf-8", errors=self.errors)
+ return text
+
+ @property
+ def vocab_size(self):
+ return self.tokenizer.n_vocab
+
+ def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+ """Converts an id to a token, special tokens included"""
+ if index in self.decoder:
+ return self.decoder[index]
+ raise ValueError("unknown ids")
+
+ def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+ """Converts a token to an id using the vocab, special tokens included"""
+ if token in self.special_tokens:
+ return self.special_tokens[token]
+ if token in self.mergeable_ranks:
+ return self.mergeable_ranks[token]
+ raise ValueError("unknown token")
+
+ def _tokenize(self, text: str, **kwargs):
+ """
+ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+ vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+ Do NOT take care of added tokens.
+ """
+ raise NotImplementedError
+
+ def _decode(
+ self,
+ token_ids: Union[int, List[int]],
+ skip_special_tokens: bool = False,
+ errors: str = None,
+ **kwargs,
+ ) -> str:
+ if isinstance(token_ids, int):
+ token_ids = [token_ids]
+ if skip_special_tokens:
+ token_ids = [i for i in token_ids if i < self.eod_id]
+ return self.tokenizer.decode(token_ids, errors=errors or self.errors)
diff --git a/Hunyuan-A52B-Instruct-FP8/tokenizer_config.json b/Hunyuan-A52B-Instruct-FP8/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1ab9e47e91550802046143e73e6530e352817531
--- /dev/null
+++ b/Hunyuan-A52B-Instruct-FP8/tokenizer_config.json
@@ -0,0 +1,23 @@
+{
+ "added_tokens_decoder": {},
+ "additional_special_tokens": [
+ "<|startoftext|>",
+ "<|extra_0|>",
+ "<|extra_4|>",
+ "<|extra_5|>",
+ "<|eos|>"
+ ],
+ "auto_map": {
+ "AutoTokenizer": [
+ "tokenization_hy.HYTokenizer",
+ null
+ ]
+ },
+ "bos_token": "<|startoftext|>",
+ "chat_template": "{% set context = {'has_head': true} %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = message['content'] %}{% if loop.index0 == 0 %}{% if content == '' %}{% set _ = context.update({'has_head': false}) %}{% else %}{% set content = '<|startoftext|>' + content + '<|extra_4|>' %}{% endif %}{% endif %}{% if message['role'] == 'user' %}{% if loop.index0 == 1 and not context.has_head %}{% set content = '<|startoftext|>' + content %}{% endif %}{% if loop.index0 == 1 and context.has_head %}{% set content = content + '<|extra_0|>' %}{% else %}{% set content = '<|startoftext|>' + content + '<|extra_0|>' %}{% endif %}{% elif message['role'] == 'assistant' %}{% set content = content + '<|eos|>' %}{% endif %}{{ content }}{% endfor %}",
+ "clean_up_tokenization_spaces": true,
+ "eos_token": "<|endoftext|>",
+ "model_max_length": 1048576,
+ "pad_token": "<|pad|>",
+ "tokenizer_class": "HYTokenizer"
+}
diff --git a/Hunyuan-A52B-Instruct/config.json b/Hunyuan-A52B-Instruct/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..007a06d23d83ad047d85f9faf411823216daec04
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/config.json
@@ -0,0 +1,47 @@
+{
+ "architectures": [
+ "HunYuanForCausalLM"
+ ],
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "auto_map": {
+ "AutoConfig": "configuration_hunyuan.HunYuanConfig",
+ "AutoModel": "modeling_hunyuan.HunyuanModel",
+ "AutoModelForCausalLM": "modeling_hunyuan.HunYuanForCausalLM"
+ },
+ "bos_token_id": 1,
+ "capacity_factor": 1.0,
+ "cla_share_factor": 2,
+ "eos_token_id": 2,
+ "hidden_act": "silu",
+ "hidden_size": 6400,
+ "initializer_range": 0.02,
+ "intermediate_size": 18304,
+ "max_position_embeddings": 131072,
+ "model_type": "hunyuan",
+ "moe_drop_tokens": false,
+ "moe_random_routing_dropped_token": false,
+ "moe_topk": 1,
+ "num_attention_heads": 80,
+ "num_experts": 16,
+ "num_hidden_layers": 64,
+ "num_key_value_heads": 8,
+ "num_shared_expert": 1,
+ "pad_token_id": 0,
+ "pretraining_tp": 1,
+ "rms_norm_eps": 1e-05,
+ "rope_scaling": {
+ "alpha": 1000.0,
+ "factor": 1.0,
+ "type": "dynamic"
+ },
+ "rope_theta": 10000.0,
+ "tie_word_embeddings": true,
+ "torch_dtype": "bfloat16",
+ "transformers_version": "4.41.2",
+ "use_cache": true,
+ "use_cla": true,
+ "use_mixed_mlp_moe": true,
+ "use_qk_norm": true,
+ "vocab_size": 129024
+}
diff --git a/Hunyuan-A52B-Instruct/configuration_hunyuan.py b/Hunyuan-A52B-Instruct/configuration_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4d438853b0073c0328f2ee94b86e1f9c7a48f03
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/configuration_hunyuan.py
@@ -0,0 +1,206 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" HunYuan model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class HunYuanConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`HunYuanModel`]. It is used to instantiate an
+ HunYuan model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the HunYuan-7B.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`HunYuanModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details checkout [this
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ End of stream token id.
+ pretraining_tp (`int`, *optional*, defaults to 1):
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+ issue](https://github.com/pytorch/pytorch/issues/76232).
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ use_qk_norm (`bool`, *optional*, defaults to `False`):
+ Whether query and key in attention use norm
+ use_cla (`bool`, *optional*, defaults to `False`):
+ Whether to use CLA in attention
+ cla_share_factor (`int`, *optional*, defaults to 1):
+ The share factor of CLA
+ """
+
+ model_type = "hunyuan"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=290943,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ rms_norm_eps=1e-5,
+ use_cache=True,
+ pad_token_id=0,
+ bos_token_id=1,
+ eos_token_id=2,
+ pretraining_tp=1,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ use_qk_norm=False,
+ use_cla=False,
+ cla_share_factor=1,
+ num_experts=1,
+ use_mixed_mlp_moe=False,
+ num_shared_expert=1,
+ moe_topk=1,
+ capacity_factor=1.0,
+ moe_drop_tokens=False,
+ moe_random_routing_dropped_token=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_experts = num_experts
+ self.use_mixed_mlp_moe = use_mixed_mlp_moe
+ self.num_shared_expert = num_shared_expert
+ self.moe_topk = moe_topk
+ self.capacity_factor = capacity_factor
+ self.moe_drop_tokens = moe_drop_tokens
+ self.moe_random_routing_dropped_token = moe_random_routing_dropped_token
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.pretraining_tp = pretraining_tp
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ # self._rope_scaling_validation() # TODO: Need validation?
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.use_qk_norm = use_qk_norm
+ self.use_cla = use_cla
+ self.cla_share_factor = cla_share_factor
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+ def _rope_scaling_validation(self):
+ """
+ Validate the `rope_scaling` configuration.
+ """
+ if self.rope_scaling is None:
+ return
+
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+ raise ValueError(
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor` or `type` and `alpha`, "
+ f"got {self.rope_scaling}"
+ )
+ rope_scaling_type = self.rope_scaling.get("type", None)
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
+ rope_scaling_alpha = self.rope_scaling.get("alpha", None)
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+ raise ValueError(
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+ )
+ if rope_scaling_factor is None and rope_scaling_alpha is None:
+ raise ValueError(f"`rope_scaling`'s factor or alpha field must be have one, got both of none")
+ if rope_scaling_factor is not None:
+ if not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1.0, got {rope_scaling_factor}")
+ if rope_scaling_alpha is not None:
+ if not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0:
+ raise ValueError(f"`rope_scaling`'s alpha field must be a float > 1.0, got {rope_scaling_alpha}")
+
+
+
diff --git a/Hunyuan-A52B-Instruct/generation_config.json b/Hunyuan-A52B-Instruct/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b99f428da0092596e95f17a97af46f35520727a6
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/generation_config.json
@@ -0,0 +1,10 @@
+{
+ "eos_token_id": [127960, 127967],
+ "pad_token_id": 127961,
+ "do_sample": true,
+ "top_k": 20,
+ "top_p": 0.6,
+ "repetition_penalty": 1.05,
+ "temperature": 0.7,
+ "transformers_version": "4.31.0"
+}
diff --git a/Hunyuan-A52B-Instruct/hy.tiktoken b/Hunyuan-A52B-Instruct/hy.tiktoken
new file mode 100644
index 0000000000000000000000000000000000000000..cedd95a03ff75f517446cdaeb03b20aadd97c410
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/hy.tiktoken
@@ -0,0 +1,127957 @@
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+4paBX19fXw== 104903
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+5oyH5a+85oSP6KeB 104954
+54A= 104955
+57yF 104956
+4paB5Y+l5a2Q 104957
+4paBb2Y= 104958
+6KA= 104959
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+4paBY2w= 104999
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+4paBdG8= 105001
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+4paBeHg= 105100
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+PyM= 105283
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+4paB5byg 105381
+6K665paH 105382
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+6bM= 105384
+4paB5bm0 105385
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+4paBdW4= 105412
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+4paBaXM= 105420
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+4paBTw== 105436
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+55aj 105438
+55u45bqU55qE 105439
+5Lik5bm0 105440
+6Jg= 105441
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+4paBdGU= 108134
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+4paBb3Q= 120849
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+4paBaW50ZXI= 120874
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+6YWK 126703
+5rGo 126704
+65s= 126705
+6KOP 126706
+6Y+I 126707
+6JyN 126708
+5oO0 126709
+6Lyq 126710
+6JaF 126711
+6ZK6 126712
+5aGs 126713
+5r6N 126714
+64u6 126715
+6IqD 126716
+6LWt 126717
+4oCW 126718
+6Zex 126719
+5bmr 126720
+6buJ 126721
+4pif 126722
+5bOS 126723
+5Y2F 126724
+6Y21 126725
+5ZmM 126726
+6IaY 126727
+6aqI 126728
+56CA 126729
+6I+h 126730
+6LeO 126731
+5pOH 126732
+5rW8 126733
+6J28 126734
+5rui 126735
+4L2j 126736
+6ZWV 126737
+5ZGy 126738
+7Juz 126739
+5aKS 126740
+4oil 126741
+5b2I 126742
+44G5 126743
+5raU 126744
+6LCy 126745
+5Za6 126746
+57Ky 126747
+6Jit 126748
+5pK1 126749
+57mH 126750
+6Iuc 126751
+5Yud 126752
+6I27 126753
+57ON 126754
+5q2y 126755
+56KB 126756
+6YGR 126757
+5ae5 126758
+7KuH 126759
+5YCc 126760
+5YqJ 126761
+6LK0 126762
+5pOU 126763
+6Jmi 126764
+5YmQ 126765
+6ZC1 126766
+6YOF 126767
+5o64 126768
+6JuG 126769
+5aCD 126770
+5bWH 126771
+6YOv 126772
+5p+p 126773
+57+m 126774
+5YqH 126775
+24c= 126776
+6Z6R 126777
+64SI 126778
+4pmm 126779
+4oM= 126780
+4oOj 126781
+5pCh 126782
+6Ke4 126783
+6YKo 126784
+6YOT 126785
+6ZqO 126786
+6JOR 126787
+6Y6u 126788
+6Y6P 126789
+6Z2b 126790
+56Kj 126791
+5ZSi 126792
+6Zq7 126793
+5bCV 126794
+56WX 126795
+5oK7 126796
+5Yue 126797
+5p2I 126798
+6ZuO 126799
+7ZuQ 126800
+6Kiq 126801
+6aOv 126802
+6ZWe 126803
+5Z+t 126804
+5Z6a 126805
+5qeO 126806
+5rWD 126807
+55az 126808
+6Ieo 126809
+6Kme 126810
+57m8 126811
+6Iyt 126812
+6Ku4 126813
+6IuB 126814
+6Kqs 126815
+66uN 126816
+57G8 126817
+6LmC 126818
+5rGc 126819
+6JO/ 126820
+542g 126821
+5L2e 126822
+5pSd 126823
+6Ki8 126824
+44Ok 126825
+6ZSX 126826
+6I+A 126827
+44So 126828
+5Z27 126829
+5a2s 126830
+6Kqw 126831
+57yB 126832
+6LOj 126833
+5bKA 126834
+6Jel 126835
+44GQ 126836
+5Z2p 126837
+55KO 126838
+6JuQ 126839
+6Jms 126840
+6KGb 126841
+56Os 126842
+542O 126843
+5ruY 126844
+55u5 126845
+6JyK 126846
+57um 126847
+6J6f 126848
+5ZaP 126849
+5amK 126850
+5qSB 126851
+5pup 126852
+5L6q 126853
+6Zyw 126854
+5YK3 126855
+5ZWV 126856
+5a+p 126857
+5oCG 126858
+6I6q 126859
+y4s= 126860
+4paM 126861
+5bG+ 126862
+66e6 126863
+6I24 126864
+6YOX 126865
+55Ok 126866
+7ZM= 126867
+7ZOa 126868
+6aWs 126869
+5oG4 126870
+7pux 126871
+5ZWW 126872
+6Jyx 126873
+55KB 126874
+6JKh 126875
+5Lud 126876
+6aSY 126877
+6IW0 126878
+6aaX 126879
+6L6H 126880
+6JiH 126881
+6YGS 126882
+6I+f 126883
+56mp 126884
+6IC3 126885
+6Yyv 126886
+5pOK 126887
+5riR 126888
+6aCG 126889
+5ZKC 126890
+5ZOa 126891
+57SN 126892
+6LWf 126893
+5ZCy 126894
+55e8 126895
+5bSn 126896
+6LqP 126897
+6JKv 126898
+5q24 126899
+6Yed 126900
+6bKb 126901
+6Iu7 126902
+56+E 126903
+5aGK 126904
+56+m 126905
+44GO 126906
+7Y+Q 126907
+6Kqy 126908
+6bmC 126909
+6bug 126910
+6IyP 126911
+5qWr 126912
+5Yqt 126913
+4L6x 126914
+6LGH 126915
+5bSD 126916
+5q2p 126917
+5ruC 126918
+5q66 126919
+5oWc 126920
+5bK/ 126921
+6ZOf 126922
+6aKb 126923
+5p2p 126924
+6ZiV 126925
+6aW0 126926
+5LyJ 126927
+6Iib 126928
+6Ii4 126929
+6aqg 126930
+5bO2 126931
+2q0= 126932
+6YGV 126933
+5aiJ 126934
+55eo 126935
+6Jqx 126936
+5r24 126937
+57ej 126938
+5ruf 126939
+5r+b 126940
+6aKi 126941
+6am3 126942
+5p+e 126943
+7IOt 126944
+56u2 126945
+5YKp 126946
+6JO8 126947
+8Ks= 126948
+8KuU 126949
+8KuUhA== 126950
+5aeS 126951
+5qm8 126952
+6KiT 126953
+5Ymb 126954
+5L+O 126955
+6Zuc 126956
+6Zmf 126957
+6JKM 126958
+6Ki0 126959
+5aC/ 126960
+6LCh 126961
+6auB 126962
+44Ov 126963
+5ZKy 126964
+6IuV 126965
+556R 126966
+6ZOE 126967
+5L2k 126968
+5aaz 126969
+6KiC 126970
+6LWF 126971
+6Zm4 126972
+67o= 126973
+67qt 126974
+55uk 126975
+6JG6 126976
+6ZKk 126977
+5ZWy 126978
+5qWK 126979
+54en 126980
+6LKn 126981
+44G2 126982
+6IGW 126983
+5ZSB 126984
+5ZWf 126985
+5qS5 126986
+5pyQ 126987
+56SO 126988
+6YaM 126989
+6Z2I 126990
+54yX 126991
+57K/ 126992
+5q6B 126993
+56K0 126994
+6ZOR 126995
+6Z2l 126996
+5YOt 126997
+7Z+K 126998
+44OP 126999
+56Ct 127000
+6ZOx 127001
+5L24 127002
+57ys 127003
+44Ks 127004
+5Zey 127005
+5o+p 127006
+57OX 127007
+5oCP 127008
+7Lo= 127009
+7Lqq 127010
+6JeB 127011
+6JCL 127012
+6aOy 127013
+5baZ 127014
+6KGE 127015
+5L2l 127016
+55WI 127017
+6IKf 127018
+6LCA 127019
+5qW4 127020
+5rem 127021
+656i 127022
+6ayj 127023
+5o+u 127024
+5bmj 127025
+54iw 127026
+57yi 127027
+5ZyT 127028
+6aaU 127029
+6IWm 127030
+5qO5 127031
+6aaV 127032
+4pyF 127033
+6ISr 127034
+44Sl 127035
+6o6u 127036
+44Oi 127037
+5q2Q 127038
+6Y2m 127039
+5LuD 127040
+6aC7 127041
+57qw 127042
+54+u 127043
+6L+z 127044
+y4o= 127045
+6ZWj 127046
+6YaN 127047
+6ZK9 127048
+6bK3 127049
+5aKJ 127050
+6YKX 127051
+74Gu 127052
+5a+n 127053
+54K3 127054
+56Cc 127055
+57iu 127056
+5bKZ 127057
+4piR 127058
+56et 127059
+4paN 127060
+6JGz 127061
+5a2x 127062
+6Zez 127063
+44Ce 127064
+55ul 127065
+5reZ 127066
+7Yqq 127067
+5Zmk 127068
+6Jao 127069
+6YeL 127070
+6ZK/ 127071
+5427 127072
+566T 127073
+6JK9 127074
+5b6t 127075
+6IWn 127076
+6ZCY 127077
+6JOW 127078
+7oCD 127079
+5qS9 127080
+6ICE 127081
+65uU 127082
+5aSJ 127083
+5a6f 127084
+5r2L 127085
+56qg 127086
+6Y+C 127087
+5Z+V 127088
+5q6T 127089
+44Gs 127090
+6IiK 127091
+57uJ 127092
+6LWd 127093
+5aq9 127094
+6KG/ 127095
+44Gl 127096
+5pe4 127097
+4p2W 127098
+5oCZ 127099
+6Zmw 127100
+6KSH 127101
+24s= 127102
+6bme 127103
+6bmz 127104
+6LGi 127105
+6LWJ 127106
+56i5 127107
+4oql 127108
+5pCm 127109
+5p6L 127110
+4LyN 127111
+6LC2 127112
+6IKT 127113
+5YOW 127114
+5qm5 127115
+5oG9 127116
+54qN 127117
+7YOQ 127118
+6KST 127119
+5YiO 127120
+6KC5 127121
+5rSE 127122
+5YOz 127123
+5b+k 127124
+5ZCW 127125
+6ZOh 127126
+5YOV 127127
+6Kyd 127128
+5ZOQ 127129
+54KB 127130
+57uM 127131
+5bqr 127132
+56qo 127133
+6Iq+ 127134
+5ayX 127135
+6bqC 127136
+664= 127137
+666v 127138
+4oiV 127139
+57S5 127140
+64q7 127141
+6ZOn 127142
+6ZSf 127143
+6amF 127144
+zIU= 127145
+5ba3 127146
+57u7 127147
+6L24 127148
+6bKF 127149
+5bSG 127150
+6LK8 127151
+6Iqr 127152
+5r+J 127153
+6KGp 127154
+6aCC 127155
+5b6z 127156
+6ZmJ 127157
+5oa2 127158
+5aaq 127159
+5bug 127160
+6JCc 127161
+5Zyc 127162
+5buq 127163
+6Kqg 127164
+5rOr 127165
+54eI 127166
+57Sd 127167
+5qWd 127168
+6KOf 127169
+56ej 127170
+5YKV 127171
+5piJ 127172
+56mw 127173
+44CT 127174
+5qaJ 127175
+6IqX 127176
+44OE 127177
+5bSu 127178
+6I6z 127179
+5ZWr 127180
+6JK0 127181
+5Zuf 127182
+55Wi 127183
+44Ci 127184
+5Zy7 127185
+6K+k 127186
+6ZKs 127187
+4p2l 127188
+55q0 127189
+6aq6 127190
+5oim 127191
+57uh 127192
+5red 127193
+5ZW1 127194
+5bGG 127195
+5qqX 127196
+6KiO 127197
+6aGn 127198
+6JiF 127199
+6YOP 127200
+6Yaq 127201
+6ZS6 127202
+6LGQ 127203
+57ag 127204
+5byB 127205
+5bG8 127206
+6Jqn 127207
+5pGI 127208
+55qy 127209
+57SZ 127210
+6KWB 127211
+5YSy 127212
+57yx 127213
+6LiU 127214
+5qW9 127215
+5rmv 127216
+576w 127217
+5qOC 127218
+55mC 127219
+6Y+E 127220
+5Zes 127221
+6Lu4 127222
+5oeR 127223
+57ep 127224
+6ZKP 127225
+6aG8 127226
+6auZ 127227
+5YK+ 127228
+5pO0 127229
+56ab 127230
+6LKr 127231
+55mA 127232
+6569 127233
+5664 127234
+5o+G 127235
+56+R 127236
+5qau 127237
+44ON 127238
+6LWz 127239
+6bK1 127240
+57Wh 127241
+5oyy 127242
+6KKI 127243
+5ZOd 127244
+5Z2c 127245
+6bqX 127246
+6L6L 127247
+5ZKj 127248
+5oGB 127249
+5ZmZ 127250
+5bOk 127251
+6ZS2 127252
+5rGG 127253
+5pi0 127254
+56iX 127255
+5a6u 127256
+5Y+V 127257
+5ry4 127258
+6KqK 127259
+6ICL 127260
+5rCh 127261
+4pyo 127262
+5YeH 127263
+5pC0 127264
+4paE 127265
+6Lmp 127266
+4pmC 127267
+6Jmb 127268
+5Zqf 127269
+6KyH 127270
+54q4 127271
+55Sj 127272
+6by5 127273
+6b2K 127274
+5be/ 127275
+5pWX 127276
+6J2I 127277
+6Z+r 127278
+56ye 127279
+54+Z 127280
+6amf 127281
+6b6M 127282
+66eI 127283
+5L2I 127284
+56+q 127285
+56GQ 127286
+5bWs 127287
+5bev 127288
+6LC1 127289
+7JeG 127290
+6ZWb 127291
+44Om 127292
+5buz 127293
+5qGg 127294
+6Iqo 127295
+6IGp 127296
+5oW2 127297
+5qeY 127298
+542+ 127299
+6bip 127300
+5Z6T 127301
+54uS 127302
+5pCN 127303
+6JOl 127304
+55eN 127305
+56+B 127306
+6aKP 127307
+5q6Y 127308
+5L2U 127309
+5YOu 127310
+6bmY 127311
+0og= 127312
+5b6R 127313
+6a4= 127314
+6a6u 127315
+56u9 127316
+5a++ 127317
+6KG+ 127318
+6YaQ 127319
+4piO 127320
+5oe3 127321
+57Wy 127322
+5a+s 127323
+56K8 127324
+6I6c 127325
+6K6n 127326
+4pSD 127327
+6KSb 127328
+6ama 127329
+6IuO 127330
+5Yi9 127331
+5aWn 127332
+6ZWS 127333
+6JuP 127334
+5pid 127335
+54Kd 127336
+6YKw 127337
+56Ga 127338
+6IWz 127339
+7Ys= 127340
+7YuU 127341
+5LqT 127342
+77+8 127343
+5rWQ 127344
+5bKs 127345
+6ZOz 127346
+6bKG 127347
+55Sv 127348
+6Jq1 127349
+7Y+r 127350
+5Ymc 127351
+6b6K 127352
+66ec 127353
+5qmL 127354
+5Z6F 127355
+6Ies 127356
+5rKU 127357
+55SR 127358
+5qqp 127359
+44K9 127360
+6ZaS 127361
+6aGU 127362
+5Yer 127363
+5q6a 127364
+6YG6 127365
+5Y+7 127366
+54ST 127367
+5pqd 127368
+6YeG 127369
+5YKI 127370
+5aar 127371
+566s 127372
+6aqn 127373
+5r+s 127374
+6J6t 127375
+5YGJ 127376
+5Z+X 127377
+5pOB 127378
+55m6 127379
+6Zep 127380
+6L6O 127381
+6Kqk 127382
+57e0 127383
+7J6I 127384
+5LqC 127385
+6KGd 127386
+6ZKW 127387
+y4c= 127388
+5bCL 127389
+7pex 127390
+6K6j 127391
+6IGS 127392
+6bmu 127393
+5bOB 127394
+5YSG 127395
+5oyI 127396
+5oy5 127397
+5rKi 127398
+5b65 127399
+65+W 127400
+7Y+D 127401
+6aKA 127402
+5pO6 127403
+6YWp 127404
+5p2y 127405
+562u 127406
+44Kx 127407
+5Lyi 127408
+5qy4 127409
+6KS0 127410
+4pmk 127411
+6ZWm 127412
+5L26 127413
+5bqg 127414
+44CD 127415
+6IKE 127416
+6ZqX 127417
+5qqU 127418
+5rCQ 127419
+6YKh 127420
+6ZCi 127421
+55Ca 127422
+6Jyi 127423
+6L2r 127424
+7KM= 127425
+7KOL 127426
+74M= 127427
+74OY 127428
+5Lmc 127429
+6Kel 127430
+5be7 127431
+5Yur 127432
+6ZS0 127433
+5o6D 127434
+6Z28 127435
+4L2U 127436
+55CH 127437
+6Kmi 127438
+6bOf 127439
+5oC1 127440
+5o+E 127441
+6LK/ 127442
+5Zij 127443
+5Yuw 127444
+5Yyv 127445
+6ISI 127446
+5amm 127447
+65Oz 127448
+2KY= 127449
+6ZOM 127450
+57i+ 127451
+6La1 127452
+5b+q 127453
+6ZON 127454
+5L6R 127455
+54qE 127456
+54+j 127457
+6LGV 127458
+6Ze/ 127459
+6ZWd 127460
+6buS 127461
+5ZW7 127462
+5o6+ 127463
+5peu 127464
+5p+7 127465
+6IiC 127466
+6Jam 127467
+5ZSz 127468
+55OY 127469
+57C9 127470
+6bOc 127471
+6Kmz 127472
+yZQ= 127473
+562a 127474
+5qml 127475
+5a+k 127476
+572Y 127477
+66eY 127478
+7pg= 127479
+7pi8 127480
+4p6V 127481
+57ac 127482
+6JOu 127483
+6L6C 127484
+8Kw= 127485
+8KyY 127486
+8KyYrQ== 127487
+5oWu 127488
+56CS 127489
+56qj 127490
+56++ 127491
+5oOh 127492
+6Z2c 127493
+66ak 127494
+4YC8 127495
+67c= 127496
+67e4 127497
+64+J 127498
+5a2r 127499
+5Lq7 127500
+5Zii 127501
+6a2a 127502
+546m 127503
+6JWI 127504
+5L22 127505
+54qH 127506
+55aD 127507
+6IOq 127508
+zIc= 127509
+5Zu5 127510
+56+Z 127511
+5YCM 127512
+5pKQ 127513
+6Iy8 127514
+7KuQ 127515
+6LCU 127516
+54WZ 127517
+6IyM 127518
+6Y2X 127519
+6I68 127520
+6byQ 127521
+5ayF 127522
+56yE 127523
+6JK5 127524
+6Zmj 127525
+4L6y 127526
+5qSk 127527
+5Y+1 127528
+5Y2f 127529
+5Y6T 127530
+5p+i 127531
+6ZWn 127532
+6aSN 127533
+6Luf 127534
+5Z+8 127535
+5rKk 127536
+6LOi 127537
+6ZOW 127538
+5Zaf 127539
+6Y60 127540
+5qeB 127541
+6Lyv 127542
+7qA= 127543
+7qCA 127544
+546z 127545
+7ZY= 127546
+7Za5 127547
+5ZOZ 127548
+57SU 127549
+6Iu0 127550
+6YSE 127551
+yoM= 127552
+5oSg 127553
+5rmc 127554
+5oqf 127555
+6J29 127556
+5L25 127557
+5rS6 127558
+zIg= 127559
+5qyk 127560
+6L6U 127561
+7IK0 127562
+7ZuG 127563
+6IiQ 127564
+6LS9 127565
+6Leh 127566
+6JWk 127567
+56u0 127568
+6K+C 127569
+7Ke6 127570
+67OD 127571
+6Lyb 127572
+65uI 127573
+6LKJ 127574
+5p2T 127575
+6Iur 127576
+5o+a 127577
+6Yau 127578
+5o+2 127579
+5rmO 127580
+4pyq 127581
+5ZGT 127582
+57ql 127583
+6Le5 127584
+6Z+T 127585
+7Ic= 127586
+7IeL 127587
+44Oo 127588
+5rC9 127589
+6ZWr 127590
+6Zax 127591
+6LiQ 127592
+5ZCz 127593
+6I+9 127594
+64+M 127595
+5Z64 127596
+5par 127597
+6aaK 127598
+5Z+H 127599
+5a2b 127600
+7Z+3 127601
+5Zmr 127602
+6IuM 127603
+5aWu 127604
+5bOE 127605
+44Cd 127606
+44Ge 127607
+5Zm0 127608
+552l 127609
+56yP 127610
+5bWv 127611
+6ZOI 127612
+4ouF 127613
+57me 127614
+4pWx 127615
+6b6H 127616
+5Yu7 127617
+5bWL 127618
+55a0 127619
+6Zib 127620
+7JM= 127621
+7JOc 127622
+44Kh 127623
+5Zyq 127624
+6Jq0 127625
+8J+M 127626
+8J+Mnw== 127627
+5puJ 127628
+5rmq 127629
+6ZiT 127630
+5oid 127631
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+7Iam 127663
+5aiM 127664
+6I6K 127665
+5Yu1 127666
+7LM= 127667
+7LOl 127668
+5rKW 127669
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+64Y= 127671
+64a+ 127672
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+6Jau 127676
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+6bOZ 127702
+66eQ 127703
+6KGy 127704
+6Ja5 127705
+64Kc 127706
+zKc= 127707
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+6YqF 127711
+5bW0 127712
+5rGK 127713
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+57qu 127761
+5ZuN 127762
+5oSN 127763
+6Zqx 127764
+64Kg 127765
+y5A= 127766
+56Cm 127767
+56yz 127768
+66O2 127769
+57+A 127770
+56C7 127771
+5YKb 127772
+6KWe 127773
+4p2X 127774
+6IeJ 127775
+zpw= 127776
+5Yus 127777
+6bKU 127778
+44G8 127779
+5aeY 127780
+6Le2 127781
+6byL 127782
+44K0 127783
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+6aO+ 127787
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+57eS 127795
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+6IOX 127797
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+5LmC 127799
+5rag 127800
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+5rSn 127803
+44Gc 127804
+6ZiP 127805
+44Ky 127806
+6J6I 127807
+55WA 127808
+6L68 127809
+6Zug 127810
+65o= 127811
+65qo 127812
+6buf 127813
+7Ko= 127814
+6ZKH 127815
+5riM 127816
+5oGC 127817
+66+h 127818
+44K2 127819
+5Lyd 127820
+6aCB 127821
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+5oiZ 127825
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+6L6t 127829
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+7Kq1 127833
+5r+D 127834
+5qet 127835
+6JyJ 127836
+6YWi 127837
+6bmX 127838
+y4w= 127839
+5qS0 127840
+6YSv 127841
+5rC1 127842
+8J+P 127843
+8J+Puw== 127844
+5Yq8 127845
+5rWX 127846
+5rmr 127847
+6Ka9 127848
+6ZKM 127849
+6LWn 127850
+64+2 127851
+5qWu 127852
+6Lma 127853
+6ZyI 127854
+8Kg= 127855
+8Kix 127856
+8Kixkg== 127857
+55+N 127858
+6KO+ 127859
+yZE= 127860
+6Jm7 127861
+6IqY 127862
+5baF 127863
+54y2 127864
+6IyU 127865
+5q6E 127866
+6ZSo 127867
+8J+Ygg== 127868
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+6Ku+ 127871
+6ZKa 127872
+6a2N 127873
+6bKC 127874
+5ZSb 127875
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+6bi3 127900
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+5rOF 127902
+5peD 127903
+4pyO 127904
+4qw= 127905
+4qyH 127906
+44OS 127907
+55Kg 127908
+6Ys= 127909
+6Yu8 127910
+5Lu1 127911
+6a2J 127912
+54mW 127913
+7Jq3 127914
+55Wb 127915
+6o66 127916
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+6Zqw 127919
+5Luh 127920
+6JuE 127921
+7Ku9 127922
+7YOU 127923
+4pSc 127924
+6bKH 127925
+66w= 127926
+66y0 127927
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+7Ia/ 127929
+6ZSc 127930
+44Ku 127931
+6Y2R 127932
+57yD 127933
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+6K+z 127936
+6Lm8 127937
+7Kq9 127938
+5pqr 127939
+6Jap 127940
+6b8= 127941
+6b+U 127942
+5rCY 127943
+7ZuY 127944
+5YuW 127945
+6YKZ 127946
+6b6F 127947
+7Y4= 127948
+7Y6m 127949
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diff --git a/Hunyuan-A52B-Instruct/modeling_hunyuan.py b/Hunyuan-A52B-Instruct/modeling_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8849c6f4253563e232dfa46cb556a11f1285137
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/modeling_hunyuan.py
@@ -0,0 +1,1726 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+""" PyTorch HunYuan model."""
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+ AttentionMaskConverter,
+ _prepare_4d_attention_mask,
+ _prepare_4d_causal_attention_mask,
+ _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+ SequenceClassifierOutputWithPast
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_flash_attn_2_available,
+ is_flash_attn_greater_or_equal_2_10,
+ logging,
+ replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_hunyuan import HunYuanConfig
+
+
+if is_flash_attn_2_available():
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+ if not is_torch_greater_or_equal_than_1_13:
+ import torch.fx
+
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "HunYuanConfig"
+
+
+def topkgating(logits: Tensor, topk: int):
+ logits = logits.float()
+ gates = F.softmax(logits, dim=1)
+ expert_capacity = topk * gates.shape[0]
+ num_experts = int(gates.shape[1])
+ # Top-k router probability and corresponding expert indices for each token.
+ # Shape: [tokens_per_group, num_selected_experts].
+ expert_gate, expert_index = torch.topk(gates, topk)
+ expert_mask = F.one_hot(expert_index, num_experts)
+ # For a given token, determine if it was routed to a given expert.
+ # Shape: [tokens_per_group, num_experts]
+ expert_mask_aux = expert_mask.max(dim=-2)[0]
+ tokens_per_group_and_expert = torch.mean(expert_mask_aux.float(), dim=-2)
+ router_prob_per_group_and_expert = torch.mean(gates.float(), dim=-2)
+ l_aux = num_experts**2 * torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert)
+
+ gates_s = torch.clamp(
+ torch.matmul(expert_mask.float(), gates.unsqueeze(-1)).sum(dim=1), min=torch.finfo(gates.dtype).eps
+ )
+ router_probs = gates / gates_s
+ # Make num_selected_experts the leading axis to ensure that top-1 choices
+ # have priority over top-2 choices, which have priority over top-3 choices,
+ # etc.
+ expert_index = torch.transpose(expert_index, 0, 1)
+ # Shape: [num_selected_experts * tokens_per_group]
+ expert_index = expert_index.reshape(-1)
+
+ # Create mask out of indices.
+ # Shape: [tokens_per_group * num_selected_experts, num_experts].
+ expert_mask = F.one_hot(expert_index, num_experts).to(torch.int32)
+ exp_counts = torch.sum(expert_mask, dim=0).detach()
+
+ # Experts have a fixed capacity that we cannot exceed. A token's priority
+ # within the expert's buffer is given by the masked, cumulative capacity of
+ # its target expert.
+ # Shape: [tokens_per_group * num_selected_experts, num_experts].
+ token_priority = torch.cumsum(expert_mask, dim=0) * expert_mask - 1
+ # Shape: [num_selected_experts, tokens_per_group, num_experts].
+ token_priority = token_priority.reshape((topk, -1, num_experts))
+ # Shape: [tokens_per_group, num_selected_experts, num_experts].
+ token_priority = torch.transpose(token_priority, 0, 1)
+ # For each token, across all selected experts, select the only non-negative
+ # (unmasked) priority. Now, for group G routing to expert E, token T has
+ # non-negative priority (i.e. token_priority[G,T,E] >= 0) if and only if E
+ # is its targeted expert.
+ # Shape: [tokens_per_group, num_experts].
+ token_priority = torch.max(token_priority, dim=1)[0]
+
+ # Token T can only be routed to expert E if its priority is positive and
+ # less than the expert capacity. One-hot matrix will ignore indices outside
+ # the range [0, expert_capacity).
+ # Shape: [tokens_per_group, num_experts, expert_capacity].
+ valid_mask = torch.logical_and(token_priority >= 0, token_priority < expert_capacity)
+ token_priority = torch.masked_fill(token_priority, ~valid_mask, 0)
+ dispatch_mask = F.one_hot(token_priority, expert_capacity).to(torch.bool)
+ valid_mask = valid_mask.unsqueeze(-1).expand(-1, -1, expert_capacity)
+ dispatch_mask = torch.masked_fill(dispatch_mask, ~valid_mask, 0)
+
+ # The combine array will be used for combining expert outputs, scaled by the
+ # router probabilities. Shape: [num_groups, tokens_per_group, num_experts,
+ # expert_capacity].
+ combine_weights = torch.einsum("...te,...tec->...tec", router_probs, dispatch_mask)
+ exp_counts_capacity = torch.sum(dispatch_mask)
+ exp_capacity_rate = exp_counts_capacity / (logits.shape[0]*topk)
+
+ return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts
+
+
+def top1gating(logits: Tensor, random_routing_dropped_token: bool = False):
+ """Implements Top1Gating on logits."""
+ # everything is in fp32 in this function
+ logits = logits.float()
+ gates = F.softmax(logits, dim=1)
+ capacity = gates.shape[0]
+
+ # Create a mask for 1st's expert per token
+ # noisy gating
+ indices1_s = torch.argmax(gates, dim=1)
+ num_experts = int(gates.shape[1])
+ mask1 = F.one_hot(indices1_s, num_classes=num_experts)
+
+ # gating decisions
+ # exp_counts = torch.sum(mask1, dim=0).detach().to('cpu')
+ exp_counts = torch.sum(mask1, dim=0).detach()
+
+ # Compute l_aux
+ me = torch.mean(gates, dim=0)
+ ce = torch.mean(mask1.float(), dim=0)
+ l_aux = torch.sum(me * ce) * num_experts
+ mask1_rand = mask1
+
+ top_idx = torch.topk(mask1_rand, k=capacity, dim=0)[1]
+
+ new_mask1 = mask1 * torch.zeros_like(mask1).scatter_(0, top_idx, 1)
+ mask1 = new_mask1
+ mask1_bk = mask1
+ if random_routing_dropped_token:
+ not_full = capacity - new_mask1.sum(dim=0)
+ sorted_notfull, indices_notfull = torch.sort(not_full, descending=True)
+ sorted_notfull = sorted_notfull.to(torch.int64)
+ not_full_experts_ids = torch.repeat_interleave(indices_notfull, sorted_notfull)
+ shuffle_not_full_ids = torch.randperm(not_full_experts_ids.shape[0])
+ not_full_experts_ids = not_full_experts_ids[shuffle_not_full_ids]
+ indices1_s_after_drop = torch.argmax(new_mask1, dim=1)
+ # get drop idx
+ drop_mask = 1 - new_mask1.sum(dim=1)
+ drop_mask = drop_mask.bool()
+ drop_idx = drop_mask.nonzero().view(-1)
+ drop_num = drop_mask.sum().to(torch.int64)
+ indices1_s_after_drop.scatter_(0, drop_idx, not_full_experts_ids[:drop_num])
+ nodrop_mask1 = F.one_hot(indices1_s_after_drop, num_classes=num_experts)
+ mask1 = nodrop_mask1
+
+ # Compute locations in capacity buffer
+ locations1 = torch.cumsum(mask1, dim=0) - 1
+
+ # Store the capacity location for each token
+ locations1_s = torch.sum(locations1 * mask1, dim=1)
+
+ # Normalize gate probabilities
+ mask1_float = mask1.float()
+ gates = gates * mask1_float
+
+ locations1_sc = F.one_hot(locations1_s, num_classes=capacity).float() # one hot to float
+ combine_weights = torch.einsum("se,sc->sec", gates, locations1_sc)
+
+ dispatch_mask = combine_weights.bool()
+
+ exp_counts_capacity = torch.sum(mask1_bk)
+ exp_capacity_rate = exp_counts_capacity / (logits.shape[0])
+ return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts
+
+
+def _get_unpad_data(attention_mask):
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+ return (
+ indices,
+ cu_seqlens,
+ max_seqlen_in_batch,
+ )
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ warnings.warn(
+ "Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be "
+ "removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+ )
+ return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _make_causal_mask(
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+ warnings.warn(
+ "Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in "
+ "v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
+ )
+ return AttentionMaskConverter._make_causal_mask(
+ input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+ )
+
+
+class HunYuanRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ HunYuanRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(HunYuanRMSNorm)
+
+
+class HunYuanRotaryEmbedding(nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ inv_freq = inv_freq.bfloat16()
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ # Build here to make `torch.jit.trace` work.
+ self._set_cos_sin_cache(
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+ )
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1).float()
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+ def forward(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ if seq_len > self.max_seq_len_cached:
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+ return (
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
+ )
+
+
+class HunYuanLinearScalingRotaryEmbedding(HunYuanRotaryEmbedding):
+ """HunYuanRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+ t = t / self.scaling_factor
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class HunYuanDynamicNTKScalingRotaryEmbedding(HunYuanRotaryEmbedding):
+ """
+ HunYuanRotaryEmbedding extended with Dynamic NTK scaling.
+ Credits to the Reddit users /u/bloc97 and /u/emozilla
+ """
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+
+ if seq_len > self.max_position_embeddings:
+ base = self.base * (
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+ ) ** (self.dim / (self.dim - 2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class HunYuanDynamicNTKAlphaRotaryEmbedding(HunYuanRotaryEmbedding):
+ """
+ HunYuanRotaryEmbedding extended with Dynamic NTK scaling.
+ Credits to the Reddit users /u/bloc97 and /u/emozilla
+ """
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_alpha=1.0):
+ self.scaling_alpha = scaling_alpha
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ base = self.base * self.scaling_alpha ** (self.dim / (self.dim-2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2:]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`):
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+ used to pass offsetted position ids when working with a KV-cache.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class HunYuanMLP(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx=None, is_shared_mlp=False):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.hidden_size = config.hidden_size
+ if is_shared_mlp:
+ self.intermediate_size = config.intermediate_size * config.num_shared_expert
+ else:
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ if self.config.pretraining_tp > 1:
+ slice = self.intermediate_size // self.config.pretraining_tp
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+ gate_proj = torch.cat(
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+ )
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+ down_proj = [
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+ ]
+ down_proj = sum(down_proj)
+ else:
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+ return down_proj
+
+
+class HunYuanTopKGate(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.moe_topk = config.moe_topk
+ self.drop_tokens = config.moe_drop_tokens
+ self.min_capacity = 8
+ self.random_routing_dropped_token = config.moe_random_routing_dropped_token
+ self.wg = nn.Linear(config.hidden_size, config.num_experts, bias=False, dtype=torch.float32)
+
+ def forward(self, hidden_states):
+ bsz, seq_len, hidden_size = hidden_states.shape
+ hidden_states = hidden_states.reshape(-1, hidden_size)
+ if self.wg.weight.dtype == torch.float32:
+ hidden_states = hidden_states.float()
+ logits = self.wg(hidden_states)
+ if self.moe_topk == 1:
+ gate_output = top1gating(logits, random_routing_dropped_token=self.random_routing_dropped_token)
+ else:
+ gate_output = topkgating(logits, self.moe_topk)
+
+ return gate_output
+
+
+class HunYuanMoE(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.moe_topk = config.moe_topk
+ self.num_experts = config.num_experts
+ if config.use_mixed_mlp_moe:
+ self.shared_mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=True)
+ self.gate = HunYuanTopKGate(config, layer_idx=layer_idx)
+ self.experts = nn.ModuleList(
+ [HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(config.num_experts)]
+ )
+
+ def forward(self, hidden_states):
+ bsz, seq_len, hidden_size = hidden_states.shape
+
+ if self.config.use_mixed_mlp_moe:
+ hidden_states_mlp = self.shared_mlp(hidden_states)
+
+ l_moe, combine_weights, dispatch_mask, exp_counts = self.gate(hidden_states)
+
+ reshaped_input = hidden_states.reshape(-1, hidden_size)
+
+ dispatched_input = torch.einsum("sec,sm->ecm", dispatch_mask.type_as(hidden_states), reshaped_input)
+
+ chunks = dispatched_input.chunk(self.num_experts, dim=0)
+ expert_outputs = []
+ for chunk, expert in zip(chunks, self.experts):
+ expert_outputs.append(expert(chunk))
+
+ expert_output = torch.cat(expert_outputs, dim=0)
+ combined_output = torch.einsum("sec,ecm->sm", combine_weights.type_as(hidden_states), expert_output)
+ combined_output = combined_output.reshape(bsz, seq_len, hidden_size)
+
+ if self.config.use_mixed_mlp_moe:
+ output = hidden_states_mlp + combined_output
+ else:
+ output = combined_output
+
+ return output
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class HunYuanAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ # layer_idx 从 0 开始
+ self.attention_type = 'cross' if config.use_cla and layer_idx % config.cla_share_factor != 0 else 'self'
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.is_causal = True
+ self.use_qk_norm = config.use_qk_norm
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+ if self.attention_type == 'self':
+ self.k_proj = nn.Linear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+ if self.use_qk_norm:
+ self.query_layernorm = HunYuanRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+ self.key_layernorm = HunYuanRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+ self._init_rope()
+
+ def _init_rope(self):
+ if self.config.rope_scaling is None:
+ self.rotary_emb = HunYuanRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ )
+ else:
+ scaling_type = self.config.rope_scaling["type"]
+ scaling_factor = self.config.rope_scaling["factor"]
+ scaling_alpha = self.config.rope_scaling["alpha"]
+ if scaling_type == "linear":
+ self.rotary_emb = HunYuanLinearScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "dynamic":
+ if scaling_alpha:
+ self.rotary_emb = HunYuanDynamicNTKAlphaRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_alpha=scaling_alpha,
+ base=self.rope_theta,
+ )
+ else:
+ self.rotary_emb = HunYuanDynamicNTKScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ else:
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ if self.config.pretraining_tp > 1:
+ query_slices = self.q_proj.weight.split(
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+ )
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+ query_states = torch.cat(query_states, dim=-1)
+
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+ key_states = torch.cat(key_states, dim=-1)
+
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+ value_states = torch.cat(value_states, dim=-1)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ else:
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ if self.config.pretraining_tp > 1:
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+ else:
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value, (orig_key_states, orig_value_states)
+
+
+class HunYuanFlashAttention2(HunYuanAttention):
+ """
+ HunYuan flash attention module. This module inherits from `HunYuanAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ # HunYuanFlashAttention2 attention does not support output_attentions
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ # overwrite attention_mask with padding_mask
+ attention_mask = kwargs.pop("padding_mask")
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (HunYuanRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ if input_dtype == torch.float32:
+ # Handle the case where the model is quantized
+ if hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = self._flash_attention_forward(
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None, past_key_value, (orig_key_states, orig_value_states)
+
+ def _flash_attention_forward(
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+ ):
+ """
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+ first unpad the input, then computes the attention scores and pad the final attention scores.
+
+ Args:
+ query_states (`torch.Tensor`):
+ Input query states to be passed to Flash Attention API
+ key_states (`torch.Tensor`):
+ Input key states to be passed to Flash Attention API
+ value_states (`torch.Tensor`):
+ Input value states to be passed to Flash Attention API
+ attention_mask (`torch.Tensor`):
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+ position of padding tokens and 1 for the position of non-padding tokens.
+ dropout (`int`, *optional*):
+ Attention dropout
+ softmax_scale (`float`, *optional*):
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+ """
+ if not self._flash_attn_uses_top_left_mask:
+ causal = self.is_causal
+ else:
+ causal = self.is_causal and query_length != 1
+
+ # Contains at least one padding token in the sequence
+ if attention_mask is not None:
+ batch_size = query_states.shape[0]
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+ query_states, key_states, value_states, attention_mask, query_length
+ )
+
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+ else:
+ attn_output = flash_attn_func(
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+ )
+
+ return attn_output
+
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+ key_layer = index_first_axis(
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+ )
+ value_layer = index_first_axis(
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+ )
+ if query_length == kv_seq_len:
+ query_layer = index_first_axis(
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+ )
+ cu_seqlens_q = cu_seqlens_k
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
+ indices_q = indices_k
+ elif query_length == 1:
+ max_seqlen_in_batch_q = 1
+ cu_seqlens_q = torch.arange(
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
+ ) # There is a memcpy here, that is very bad.
+ indices_q = cu_seqlens_q[:-1]
+ query_layer = query_layer.squeeze(1)
+ else:
+ # The -q_len: slice assumes left padding.
+ attention_mask = attention_mask[:, -query_length:]
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+ return (
+ query_layer,
+ key_layer,
+ value_layer,
+ indices_q,
+ (cu_seqlens_q, cu_seqlens_k),
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+ )
+
+
+class HunYuanSdpaAttention(HunYuanAttention):
+ """
+ HunYuan attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `HunYuanAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt
+ to SDPA API.
+ """
+
+ # Adapted from HunYuanAttention.forward
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if output_attentions:
+ logger.warning_once(
+ 'HunYuanModel is using HunYuanSdpaAttention,'
+ 'but `torch.nn.functional.scaled_dot_product_attention`'
+ 'does not support `output_attentions=True`. Falling back to the manual attention implementation, '
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. '
+ 'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
+ # custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and attention_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=attention_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a
+ # causal mask in case q_len == 1.
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None, past_key_value, (orig_key_states, orig_value_states)
+
+
+HUNYUAN_ATTENTION_CLASSES = {
+ "eager": HunYuanAttention,
+ "flash_attention_2": HunYuanFlashAttention2,
+ "sdpa": HunYuanSdpaAttention,
+}
+
+
+class HunYuanDecoderLayer(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.layer_idx = layer_idx
+
+ self.self_attn = HUNYUAN_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+ if config.num_experts > 1:
+ self.mlp = HunYuanMoE(config, layer_idx=layer_idx)
+ else:
+ self.mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False)
+ self.input_layernorm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ kv_states: Optional[Tuple[torch.Tensor]] = None,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ kv_states (`Tuple(torch.FloatTensor)`, *optional*): Used when CLA is enabled,
+ key and value states from past attention blocks
+ """
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value, kv_states = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ kv_states=kv_states,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ outputs += (kv_states,)
+
+ return outputs
+
+
+HUNYUAN_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`HunYuanConfig`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanPreTrainedModel(PreTrainedModel):
+ config_class = HunYuanConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["HunYuanDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = True
+ _supports_sdpa = True
+ _supports_cache_class = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+HUNYUAN_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanModel(HunYuanPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HunYuanDecoderLayer`]
+
+ Args:
+ config: HunYuanConfig
+ """
+
+ def __init__(self, config: HunYuanConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._use_sdpa = config._attn_implementation == "sdpa"
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+ self.norm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.cla = config.use_cla
+ self.cla_share_factor = config.cla_share_factor
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape[:2]
+ elif inputs_embeds is not None:
+ batch_size, seq_length = inputs_embeds.shape[:2]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ past_key_values_length = 0
+ if use_cache:
+ use_legacy_cache = not isinstance(past_key_values, Cache)
+ if use_legacy_cache:
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ # Fix lora with gradient checkpointing training
+ if self.training and inputs_embeds.is_leaf:
+ inputs_embeds.requires_grad = True
+
+ if self._use_flash_attention_2:
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ elif self._use_sdpa and not output_attentions:
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = None
+
+ prev_kv_states = None
+ for layer_idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ decoder_layer.__call__,
+ hidden_states,
+ attention_mask,
+ position_ids,
+ past_key_values,
+ output_attentions,
+ use_cache,
+ prev_kv_states,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ kv_states=prev_kv_states
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ kv_states = layer_outputs[-1]
+
+ if self.cla and layer_idx % self.cla_share_factor == 0:
+ prev_kv_states = kv_states
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = None
+ if use_cache:
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class HunYuanForCausalLM(HunYuanPreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: HunYuanConfig):
+ super().__init__(config)
+ self.model = HunYuanModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+ >>> model = AutoModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ if self.config.pretraining_tp > 1:
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+ logits = torch.cat(logits, dim=-1)
+ else:
+ logits = self.lm_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values is not None:
+ if isinstance(past_key_values, Cache):
+ cache_length = past_key_values.get_seq_length()
+ past_length = past_key_values.seen_tokens
+ max_cache_length = past_key_values.get_max_length()
+ else:
+ cache_length = past_length = past_key_values[0][0].shape[2]
+ max_cache_length = None
+
+ # Keep only the unprocessed tokens:
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+ # input)
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+ # input_ids based on the past_length.
+ elif past_length < input_ids.shape[1]:
+ input_ids = input_ids[:, past_length:]
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+ if (
+ max_cache_length is not None
+ and attention_mask is not None
+ and cache_length + input_ids.shape[1] > max_cache_length
+ ):
+ attention_mask = attention_mask[:, -max_cache_length:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -input_ids.shape[1]:]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+ )
+ return reordered_past
+
+
+@add_start_docstrings(
+ """
+ The HunYuan Model transformer with a sequence classification head on top (linear layer).
+
+ [`HunYuanForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanForSequenceClassification(HunYuanPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = HunYuanModel(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ sequence_lengths = -1
+ else:
+ if input_ids is not None:
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+ logits.device
+ )
+ else:
+ sequence_lengths = -1
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
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+ "model.layers.9.mlp.experts.9.down_proj.bias": "pytorch_model-00020-of-00080.bin",
+ "model.layers.9.mlp.experts.9.down_proj.weight": "pytorch_model-00006-of-00080.bin",
+ "model.layers.9.mlp.experts.9.gate_proj.bias": "pytorch_model-00020-of-00080.bin",
+ "model.layers.9.mlp.experts.9.gate_proj.weight": "pytorch_model-00006-of-00080.bin",
+ "model.layers.9.mlp.experts.9.up_proj.bias": "pytorch_model-00020-of-00080.bin",
+ "model.layers.9.mlp.experts.9.up_proj.weight": "pytorch_model-00006-of-00080.bin",
+ "model.layers.9.mlp.gate.wg.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.mlp.shared_mlp.down_proj.bias": "pytorch_model-00002-of-00080.bin",
+ "model.layers.9.mlp.shared_mlp.down_proj.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.mlp.shared_mlp.gate_proj.bias": "pytorch_model-00002-of-00080.bin",
+ "model.layers.9.mlp.shared_mlp.gate_proj.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.mlp.shared_mlp.up_proj.bias": "pytorch_model-00002-of-00080.bin",
+ "model.layers.9.mlp.shared_mlp.up_proj.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.self_attn.key_layernorm.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.self_attn.o_proj.bias": "pytorch_model-00002-of-00080.bin",
+ "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.self_attn.q_proj.bias": "pytorch_model-00002-of-00080.bin",
+ "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.self_attn.query_layernorm.weight": "pytorch_model-00001-of-00080.bin",
+ "model.norm.weight": "pytorch_model-00062-of-00080.bin"
+ }
+}
diff --git a/Hunyuan-A52B-Instruct/test.py b/Hunyuan-A52B-Instruct/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ccf4c79ac8d806f21ec08223ab7c347443d54b8
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/test.py
@@ -0,0 +1,49 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from tokenizers import ByteLevelBPETokenizer
+from transformers import AutoTokenizer
+
+# Step 1: Initialize ByteLevelBPETokenizer
+#tokenizer = ByteLevelBPETokenizer(
+# "vocab.json",
+# "merges.txt"
+#)
+
+# Step 2: Save the tokenizer configuration
+#tokenizer.save_model("auto_model")
+
+# Step 3: Load the tokenizer using AutoTokenizer
+auto_tokenizer = AutoTokenizer.from_pretrained("./", use_fast=False, trust_remote_code=True)
+
+# Test the tokenizer
+text = "Hello, world!"
+encoded = auto_tokenizer.encode(text)
+decoded = auto_tokenizer.decode(encoded)
+
+print("Encoded:", encoded)
+print("Decoded:", decoded)
+
+messages = [
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Hello, how are you?"},
+ {"role": "assistant", "content": "I'm good, thank you! How can I help you today?"},
+ {"role": "user", "content": "Nothing"},
+]
+
+print('messages:', messages)
+ids = auto_tokenizer.apply_chat_template(messages)
+print(f"input_ids:\t{ids}")
+text = auto_tokenizer.decode(ids)
+print(f"input_text:\t[{text}]")
diff --git a/Hunyuan-A52B-Instruct/test4consistent.py b/Hunyuan-A52B-Instruct/test4consistent.py
new file mode 100644
index 0000000000000000000000000000000000000000..85e624383201cbb8680285a6e2cdb9081df393db
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/test4consistent.py
@@ -0,0 +1,31 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# test tokenizer encode & decode consistency
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained('/tokenizer_exp/other_tokenizer_vocab/hy', local_files_only=True, trust_remote_code=True)
+
+test_data = [line.strip() for line in open('/tokenizer_exp/data/test.txt', 'r').readlines()]
+
+num_origi_len = 0
+num_token_len = 0
+
+for d in test_data:
+ a = tokenizer.encode(d)
+ num_origi_len += len(d)
+ num_token_len += len(a)
+ b = tokenizer.decode(a)
+ assert b == d, f"encode & decode not consistent: {d} vs {b}"
+
+print(f" original length: {num_origi_len}")
+print(f" token length: {num_token_len}")
diff --git a/Hunyuan-A52B-Instruct/tokenization_hy.py b/Hunyuan-A52B-Instruct/tokenization_hy.py
new file mode 100644
index 0000000000000000000000000000000000000000..91c651bdf7e67bf7787ac52f86bcb7e59070adea
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/tokenization_hy.py
@@ -0,0 +1,354 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import base64
+import logging
+import tiktoken
+import unicodedata
+from transformers import PreTrainedTokenizer, AddedToken
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+
+logger = logging.getLogger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "hy.tiktoken"}
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|""" \
+ r"""[^\r\n\p{L}\p{N}]?\p{L}+|""" \
+ r"""\p{N}|""" \
+ r""" ?[^\s\p{L}\p{N}]+[\r\n]*|""" \
+ r"""\s*[\r\n]+|""" \
+ r"""\s+(?!\S)|""" \
+ r"""\s+"""
+# default eod_token and bod_token of our base model
+ENDOFTEXT = "<|endoftext|>"
+STARTOFTEXT = "<|startoftext|>"
+
+# extra flag token for other training
+BOSTOKEN = "<|bos|>"
+EOSTOKEN = "<|eos|>"
+
+PADTOKEN = "<|pad|>"
+
+# extra special tokens for the tokenizer
+# as the default behavior is changed to allow special tokens in
+# regular texts, the surface forms of special tokens need to be
+# as different as possible to minimize the impact
+EXTRAS = tuple((f"<|extra_{i}|>" for i in range(204)))
+
+SPECIAL_START_ID = 127957
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+ dic = {}
+ rank = 0
+ for i, line in enumerate(open(tiktoken_bpe_file, "rb")):
+ if line:
+ token, _ = line.split()
+ # skip duplicated tokens, this should not happen
+ if base64.b64decode(token) in dic:
+ raise ValueError(f"!ERROR: duplicated token {token} in your vocab file")
+ dic[base64.b64decode(token)] = int(rank)
+ rank += 1
+ return dic
+
+
+# special tokens for pretrain and finetune models
+SPECIAL_TOKENS = tuple(
+ enumerate(
+ (
+ (
+ ENDOFTEXT,
+ STARTOFTEXT,
+ BOSTOKEN,
+ EOSTOKEN,
+ PADTOKEN,
+ )
+ + EXTRAS
+ ),
+ start=SPECIAL_START_ID,
+ )
+)
+
+SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
+
+
+class HYTokenizer(PreTrainedTokenizer):
+ """
+ HunYuan Tokenizer Initialization. We extend `tiktoken` vocab and
+ the default EOD & BOD special tokens are used for base model.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+
+ errors (`str`):
+ How to handle errors in decoding UTF-8 byte sequences.
+ use ignore if you are in streaming inference
+
+ bod_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""<|startoftext|>""`):
+ The beginning of document token that was used for training. can be modified by your task.
+ default to be `<|startoftext|>` for released base model.
+
+ eod_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""<|endoftext|>""`):
+ The end of document token that was used for training. can be modified by your task.
+ default to be `<|endoftext|>` for released base model.
+
+ bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `None`):
+ The start or sep special token that was used for some training tasks.
+ default to be `<|startoftext|>` for released base model.
+ It can be set to `<|bos|>` when you training for some other task
+
+ eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `None`):
+ The end or sep special token that was used for some training tasks.
+ default to be `<|endoftext|>` for released base model.
+ It can be set to `<|eos|>` when you training for some other task
+
+ pad_token (`str` or `tokenizers.AddedToken`, *optional*):
+ A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+ attention mechanisms or loss computation.
+
+ special_vocab_file (str, *optional*):
+ Customed special extra vocab file, same format with hy.tiktoken.
+ **Be careful** to use the extra special vocab, it will may cause the model loading collapse.
+ The data line be like:
+ `PHxhYmN8Pg== 0`
+ the id followed `base64.encode(str)` is unused, we will reset them in case of collision
+
+ add_bod_token (`bool`, *optional*, defaults to `True`):
+ Whether or not to add an `bos_token` at the start of documents.
+ This will effect `build_inputs_with_special_tokens` method
+
+ add_eod_token (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an `eos_token` at the end of documents.
+ This will effect `build_inputs_with_special_tokens` method
+
+ """
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ errors="replace",
+ bod_token="<|startoftext|>",
+ eod_token="<|endoftext|>",
+ bos_token="<|startoftext|>",
+ eos_token="<|endoftext|>",
+ pad_token="<|pad|>",
+ add_bod_token=True,
+ add_eod_token=True,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.errors = errors
+ self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
+ self.special_tokens = {
+ token: index
+ for index, token in SPECIAL_TOKENS
+ }
+
+ enc = tiktoken.Encoding(
+ "HunYuan",
+ pat_str=PAT_STR,
+ mergeable_ranks=self.mergeable_ranks,
+ special_tokens=self.special_tokens,
+ )
+ assert (
+ len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
+ ), f"{len(self.mergeable_ranks)} + {len(self.special_tokens)} != {enc.n_vocab} in encoding"
+
+ self.decoder = {
+ v: k for k, v in self.mergeable_ranks.items()
+ } # type: dict[int, bytes|str]
+ self.decoder.update({v: k for k, v in self.special_tokens.items()})
+
+ self.tokenizer = enc
+
+ self.bod_token, self.bod_id = bod_token, self.special_tokens[bod_token]
+ self.eod_token, self.eod_id = eod_token, self.special_tokens[eod_token]
+ self.bos_token, self.bos_id = bos_token, self.special_tokens[bos_token]
+ self.eos_token, self.eos_id = eos_token, self.special_tokens[eos_token]
+ self.pad_token, self.pad_id = pad_token, self.special_tokens[pad_token]
+
+ self._num_special_token = len(self.special_tokens)
+
+ self.add_bod_token = add_bod_token
+ self.add_eod_token = add_eod_token
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ del state["tokenizer"]
+ return state
+
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+ enc = tiktoken.Encoding(
+ "HunYuan",
+ pat_str=PAT_STR,
+ mergeable_ranks=self.mergeable_ranks,
+ special_tokens=self.special_tokens,
+ )
+ self.tokenizer = enc
+
+ def __len__(self) -> int:
+ return self.tokenizer.n_vocab
+
+ def get_vocab(self) -> Dict[bytes, int]:
+ """Return the vocabulary as a dictionary, without special tokens."""
+ return self.mergeable_ranks
+
+ def convert_tokens_to_ids(
+ self, tokens: Union[bytes, str, List[Union[bytes, str]]]
+ ) -> List[int]:
+ ids = []
+ if isinstance(tokens, (str, bytes)):
+ if tokens in self.special_tokens:
+ return self.special_tokens[tokens]
+ else:
+ return self.mergeable_ranks.get(tokens)
+ for token in tokens:
+ if token in self.special_tokens:
+ ids.append(self.special_tokens[token])
+ else:
+ ids.append(self.mergeable_ranks.get(token))
+ return ids
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ bod_token_id = [self.bod_id] if self.add_bod_token else []
+ eod_token_id = [self.eod_id] if self.add_eod_token else []
+ output = bod_token_id + token_ids_0 + eod_token_id
+ if token_ids_1 is not None:
+ output = output + bod_token_id + token_ids_1 + eod_token_id
+ return output
+
+ def _add_tokens(
+ self,
+ new_tokens: Union[List[str], List[AddedToken]],
+ special_tokens: bool = False,
+ ) -> List[Tuple[int, str]]:
+ """do not support adding tokens"""
+ if not special_tokens and new_tokens:
+ raise ValueError("Adding regular tokens is not supported")
+ for token in new_tokens:
+ surface_form = token.content if isinstance(token, AddedToken) else token
+ if surface_form not in SPECIAL_TOKENS_SET:
+ raise ValueError("Adding unknown special tokens is not supported")
+ return 0
+
+ def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+ """
+ Save only the vocabulary of the tokenizer (vocabulary).
+ Returns:
+ `Tuple(str)`: Paths to the files saved.
+ """
+ file_path = os.path.join(save_directory, "hy.tiktoken")
+ with open(file_path, "w", encoding="utf8") as w:
+ for k, v in self.mergeable_ranks.items():
+ line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+ w.write(line)
+ return (file_path,)
+
+ def tokenize(
+ self,
+ text: str,
+ allowed_special: Union[Set, str] = "all",
+ disallowed_special: Union[Collection, str] = (),
+ **kwargs,
+ ) -> List[Union[bytes, str]]:
+ """
+ Converts a string in a sequence of tokens.
+ Args:
+ text (`str`):
+ The sequence to be encoded.
+ allowed_special (`Literal["all"]` or `set`):
+ The surface forms of the tokens to be encoded as special tokens in regular texts.
+ Default to "all".
+ disallowed_special (`Literal["all"]` or `Collection`):
+ The surface forms of the tokens that should not be in regular texts and trigger errors.
+ Default to an empty tuple.
+ kwargs (additional keyword arguments, *optional*):
+ Will be passed to the underlying model specific encode method.
+ Returns:
+ `List[bytes|str]`: The list of tokens.
+ """
+ tokens = []
+ text = unicodedata.normalize("NFC", text)
+
+ # this implementation takes a detour: text -> token id -> token surface forms
+ for t in self.tokenizer.encode(
+ text, allowed_special=allowed_special, disallowed_special=disallowed_special
+ ):
+ tokens.append(self.decoder[t])
+ return tokens
+
+ def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+ """
+ Converts a sequence of tokens in a single string.
+ """
+ text = ""
+ temp = b""
+ for t in tokens:
+ if isinstance(t, str):
+ if temp:
+ text += temp.decode("utf-8", errors=self.errors)
+ temp = b""
+ text += t
+ elif isinstance(t, bytes):
+ temp += t
+ else:
+ raise TypeError("token should only be of type types or str")
+ if temp:
+ text += temp.decode("utf-8", errors=self.errors)
+ return text
+
+ @property
+ def vocab_size(self):
+ return self.tokenizer.n_vocab
+
+ def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+ """Converts an id to a token, special tokens included"""
+ if index in self.decoder:
+ return self.decoder[index]
+ raise ValueError("unknown ids")
+
+ def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+ """Converts a token to an id using the vocab, special tokens included"""
+ if token in self.special_tokens:
+ return self.special_tokens[token]
+ if token in self.mergeable_ranks:
+ return self.mergeable_ranks[token]
+ raise ValueError("unknown token")
+
+ def _tokenize(self, text: str, **kwargs):
+ """
+ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+ vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+ Do NOT take care of added tokens.
+ """
+ raise NotImplementedError
+
+ def _decode(
+ self,
+ token_ids: Union[int, List[int]],
+ skip_special_tokens: bool = False,
+ errors: str = None,
+ **kwargs,
+ ) -> str:
+ if isinstance(token_ids, int):
+ token_ids = [token_ids]
+ if skip_special_tokens:
+ token_ids = [i for i in token_ids if i < self.eod_id]
+ return self.tokenizer.decode(token_ids, errors=errors or self.errors)
diff --git a/Hunyuan-A52B-Instruct/tokenizer_config.json b/Hunyuan-A52B-Instruct/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c239bd7995f53018fc0ad0089ecd014a23727b0
--- /dev/null
+++ b/Hunyuan-A52B-Instruct/tokenizer_config.json
@@ -0,0 +1,13 @@
+{
+ "model_max_length": 1048576,
+ "tokenizer_class": "HYTokenizer",
+ "auto_map": {
+ "AutoTokenizer": [
+ "tokenization_hy.HYTokenizer",
+ null
+ ]
+ },
+ "additional_special_tokens": ["<|startoftext|>", "<|extra_0|>", "<|extra_4|>", "<|extra_5|>", "<|eos|>"],
+ "chat_template": "{% set context = {'has_head': true} %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = message['content'] %}{% if loop.index0 == 0 %}{% if content == '' %}{% set _ = context.update({'has_head': false}) %}{% else %}{% set content = '<|startoftext|>' + content + '<|extra_4|>' %}{% endif %}{% endif %}{% if message['role'] == 'user' %}{% if loop.index0 == 1 and not context.has_head %}{% set content = '<|startoftext|>' + content %}{% endif %}{% if loop.index0 == 1 and context.has_head %}{% set content = content + '<|extra_0|>' %}{% else %}{% set content = '<|startoftext|>' + content + '<|extra_0|>' %}{% endif %}{% elif message['role'] == 'assistant' %}{% set content = content + '<|eos|>' %}{% endif %}{{ content }}{% endfor %}"
+}
+
diff --git a/Hunyuan-A52B-Pretrain/__init__.py b/Hunyuan-A52B-Pretrain/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Hunyuan-A52B-Pretrain/config.json b/Hunyuan-A52B-Pretrain/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c6839ac08e0f02b386e668091fdf0161cb1396f9
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/config.json
@@ -0,0 +1,47 @@
+{
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "architectures": [
+ "HunYuanForCausalLM"
+ ],
+ "auto_map": {
+ "AutoConfig": "configuration_hunyuan.HunYuanConfig",
+ "AutoModel": "modeling_hunyuan.HunyuanModel",
+ "AutoModelForCausalLM": "modeling_hunyuan.HunYuanForCausalLM"
+ },
+ "bos_token_id": 1,
+ "capacity_factor": 1.0,
+ "cla_share_factor": 2,
+ "eos_token_id": 2,
+ "hidden_act": "silu",
+ "hidden_size": 6400,
+ "initializer_range": 0.02,
+ "intermediate_size": 18304,
+ "max_position_embeddings": 262144,
+ "model_type": "hunyuan",
+ "moe_drop_tokens": false,
+ "moe_random_routing_dropped_token": false,
+ "moe_topk": 1,
+ "num_attention_heads": 80,
+ "num_experts": 16,
+ "num_hidden_layers": 64,
+ "num_key_value_heads": 8,
+ "num_shared_expert": 1,
+ "pad_token_id": 0,
+ "pretraining_tp": 1,
+ "rms_norm_eps": 1e-05,
+ "rope_scaling": {
+ "alpha": 100000.0,
+ "factor": 1.0,
+ "type": "dynamic"
+ },
+ "rope_theta": 10000.0,
+ "tie_word_embeddings": true,
+ "transformers_version": "4.41.2",
+ "use_cache": true,
+ "use_cla": true,
+ "use_mixed_mlp_moe": true,
+ "use_qk_norm": true,
+ "vocab_size": 128512,
+ "torch_dtype": "bfloat16"
+}
diff --git a/Hunyuan-A52B-Pretrain/configuration_hunyuan.py b/Hunyuan-A52B-Pretrain/configuration_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4d438853b0073c0328f2ee94b86e1f9c7a48f03
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/configuration_hunyuan.py
@@ -0,0 +1,206 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" HunYuan model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class HunYuanConfig(PretrainedConfig):
+ r"""
+ This is the configuration class to store the configuration of a [`HunYuanModel`]. It is used to instantiate an
+ HunYuan model according to the specified arguments, defining the model architecture. Instantiating a configuration
+ with the defaults will yield a similar configuration to that of the HunYuan-7B.
+
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+ documentation from [`PretrainedConfig`] for more information.
+
+
+ Args:
+ vocab_size (`int`, *optional*, defaults to 32000):
+ Vocabulary size of the HunYuan model. Defines the number of different tokens that can be represented by the
+ `inputs_ids` passed when calling [`HunYuanModel`]
+ hidden_size (`int`, *optional*, defaults to 4096):
+ Dimension of the hidden representations.
+ intermediate_size (`int`, *optional*, defaults to 11008):
+ Dimension of the MLP representations.
+ num_hidden_layers (`int`, *optional*, defaults to 32):
+ Number of hidden layers in the Transformer decoder.
+ num_attention_heads (`int`, *optional*, defaults to 32):
+ Number of attention heads for each attention layer in the Transformer decoder.
+ num_key_value_heads (`int`, *optional*):
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+ by meanpooling all the original heads within that group. For more details checkout [this
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+ `num_attention_heads`.
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+ The non-linear activation function (function or string) in the decoder.
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
+ The maximum sequence length that this model might ever be used with.
+ initializer_range (`float`, *optional*, defaults to 0.02):
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+ The epsilon used by the rms normalization layers.
+ use_cache (`bool`, *optional*, defaults to `True`):
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
+ relevant if `config.is_decoder=True`.
+ pad_token_id (`int`, *optional*):
+ Padding token id.
+ bos_token_id (`int`, *optional*, defaults to 1):
+ Beginning of stream token id.
+ eos_token_id (`int`, *optional*, defaults to 2):
+ End of stream token id.
+ pretraining_tp (`int`, *optional*, defaults to 1):
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+ issue](https://github.com/pytorch/pytorch/issues/76232).
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+ Whether to tie weight embeddings
+ rope_theta (`float`, *optional*, defaults to 10000.0):
+ The base period of the RoPE embeddings.
+ rope_scaling (`Dict`, *optional*):
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+ these scaling strategies behave:
+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+ experimental feature, subject to breaking API changes in future versions.
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
+ attention_dropout (`float`, *optional*, defaults to 0.0):
+ The dropout ratio for the attention probabilities.
+ use_qk_norm (`bool`, *optional*, defaults to `False`):
+ Whether query and key in attention use norm
+ use_cla (`bool`, *optional*, defaults to `False`):
+ Whether to use CLA in attention
+ cla_share_factor (`int`, *optional*, defaults to 1):
+ The share factor of CLA
+ """
+
+ model_type = "hunyuan"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=290943,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ num_key_value_heads=None,
+ hidden_act="silu",
+ max_position_embeddings=2048,
+ initializer_range=0.02,
+ rms_norm_eps=1e-5,
+ use_cache=True,
+ pad_token_id=0,
+ bos_token_id=1,
+ eos_token_id=2,
+ pretraining_tp=1,
+ tie_word_embeddings=False,
+ rope_theta=10000.0,
+ rope_scaling=None,
+ attention_bias=False,
+ attention_dropout=0.0,
+ use_qk_norm=False,
+ use_cla=False,
+ cla_share_factor=1,
+ num_experts=1,
+ use_mixed_mlp_moe=False,
+ num_shared_expert=1,
+ moe_topk=1,
+ capacity_factor=1.0,
+ moe_drop_tokens=False,
+ moe_random_routing_dropped_token=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.num_experts = num_experts
+ self.use_mixed_mlp_moe = use_mixed_mlp_moe
+ self.num_shared_expert = num_shared_expert
+ self.moe_topk = moe_topk
+ self.capacity_factor = capacity_factor
+ self.moe_drop_tokens = moe_drop_tokens
+ self.moe_random_routing_dropped_token = moe_random_routing_dropped_token
+
+ # for backward compatibility
+ if num_key_value_heads is None:
+ num_key_value_heads = num_attention_heads
+
+ self.num_key_value_heads = num_key_value_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.pretraining_tp = pretraining_tp
+ self.use_cache = use_cache
+ self.rope_theta = rope_theta
+ self.rope_scaling = rope_scaling
+ # self._rope_scaling_validation() # TODO: Need validation?
+ self.attention_bias = attention_bias
+ self.attention_dropout = attention_dropout
+ self.use_qk_norm = use_qk_norm
+ self.use_cla = use_cla
+ self.cla_share_factor = cla_share_factor
+
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )
+
+ def _rope_scaling_validation(self):
+ """
+ Validate the `rope_scaling` configuration.
+ """
+ if self.rope_scaling is None:
+ return
+
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+ raise ValueError(
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor` or `type` and `alpha`, "
+ f"got {self.rope_scaling}"
+ )
+ rope_scaling_type = self.rope_scaling.get("type", None)
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
+ rope_scaling_alpha = self.rope_scaling.get("alpha", None)
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+ raise ValueError(
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+ )
+ if rope_scaling_factor is None and rope_scaling_alpha is None:
+ raise ValueError(f"`rope_scaling`'s factor or alpha field must be have one, got both of none")
+ if rope_scaling_factor is not None:
+ if not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1.0, got {rope_scaling_factor}")
+ if rope_scaling_alpha is not None:
+ if not isinstance(rope_scaling_alpha, float) or rope_scaling_alpha <= 1.0:
+ raise ValueError(f"`rope_scaling`'s alpha field must be a float > 1.0, got {rope_scaling_alpha}")
+
+
+
diff --git a/Hunyuan-A52B-Pretrain/generation_config.json b/Hunyuan-A52B-Pretrain/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..c5e0cea6e41caba8398e6aa13cb9cf038b3a1fb7
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/generation_config.json
@@ -0,0 +1,10 @@
+{
+ "eos_token_id": 127957,
+ "pad_token_id": 127961,
+ "do_sample": true,
+ "top_k": 20,
+ "top_p": 0.6,
+ "repetition_penalty": 1.05,
+ "transformers_version": "4.31.0",
+ "temperature": 0.7
+}
diff --git a/Hunyuan-A52B-Pretrain/hy.tiktoken b/Hunyuan-A52B-Pretrain/hy.tiktoken
new file mode 100644
index 0000000000000000000000000000000000000000..cedd95a03ff75f517446cdaeb03b20aadd97c410
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/hy.tiktoken
@@ -0,0 +1,127957 @@
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+57Cn 104951
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+5Z2a5a6e 104953
+5oyH5a+85oSP6KeB 104954
+54A= 104955
+57yF 104956
+4paB5Y+l5a2Q 104957
+4paBb2Y= 104958
+6KA= 104959
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+6IWK 104996
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+6LW1 104998
+4paBY2w= 104999
+4oCd44CB4oCc 105000
+4paBdG8= 105001
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+5piv5oyH 105099
+4paBeHg= 105100
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+5Lia57up 105102
+55qE5pa55rOV 105103
+5YyF6KOF 105104
+5bm/5bee 105105
+LOOAig== 105106
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+5LiK5LqG 105110
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+5piv5LiN 105113
+6YO95Zyo 105114
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+4paBRg== 105117
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+5rig6YGT 105120
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+5LiA5L2N 105122
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+44CLKA== 105135
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+6L+Y5Lya 105280
+6K6p5L2g 105281
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+PyM= 105283
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+5Lmf5bCx 105380
+4paB5byg 105381
+6K665paH 105382
+5b6I5pyJ 105383
+6bM= 105384
+4paB5bm0 105385
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+4paBdW4= 105412
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+4paBaXM= 105420
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+55aj 105438
+55u45bqU55qE 105439
+5Lik5bm0 105440
+6Jg= 105441
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+4paBdGU= 108134
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+4paB5Zyo5q2k 120604
+5omn5Yuk 120605
+4paBd2hpY2g= 120606
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+4paBb3Q= 120849
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+4paBaW50ZXI= 120874
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+4paBTXk= 122737
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+4paBQXI= 122796
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+KeOAiyw= 122831
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+a1c= 122876
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+6Ls= 124146
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+5aCD 126770
+5bWH 126771
+6YOv 126772
+5p+p 126773
+57+m 126774
+5YqH 126775
+24c= 126776
+6Z6R 126777
+64SI 126778
+4pmm 126779
+4oM= 126780
+4oOj 126781
+5pCh 126782
+6Ke4 126783
+6YKo 126784
+6YOT 126785
+6ZqO 126786
+6JOR 126787
+6Y6u 126788
+6Y6P 126789
+6Z2b 126790
+56Kj 126791
+5ZSi 126792
+6Zq7 126793
+5bCV 126794
+56WX 126795
+5oK7 126796
+5Yue 126797
+5p2I 126798
+6ZuO 126799
+7ZuQ 126800
+6Kiq 126801
+6aOv 126802
+6ZWe 126803
+5Z+t 126804
+5Z6a 126805
+5qeO 126806
+5rWD 126807
+55az 126808
+6Ieo 126809
+6Kme 126810
+57m8 126811
+6Iyt 126812
+6Ku4 126813
+6IuB 126814
+6Kqs 126815
+66uN 126816
+57G8 126817
+6LmC 126818
+5rGc 126819
+6JO/ 126820
+542g 126821
+5L2e 126822
+5pSd 126823
+6Ki8 126824
+44Ok 126825
+6ZSX 126826
+6I+A 126827
+44So 126828
+5Z27 126829
+5a2s 126830
+6Kqw 126831
+57yB 126832
+6LOj 126833
+5bKA 126834
+6Jel 126835
+44GQ 126836
+5Z2p 126837
+55KO 126838
+6JuQ 126839
+6Jms 126840
+6KGb 126841
+56Os 126842
+542O 126843
+5ruY 126844
+55u5 126845
+6JyK 126846
+57um 126847
+6J6f 126848
+5ZaP 126849
+5amK 126850
+5qSB 126851
+5pup 126852
+5L6q 126853
+6Zyw 126854
+5YK3 126855
+5ZWV 126856
+5a+p 126857
+5oCG 126858
+6I6q 126859
+y4s= 126860
+4paM 126861
+5bG+ 126862
+66e6 126863
+6I24 126864
+6YOX 126865
+55Ok 126866
+7ZM= 126867
+7ZOa 126868
+6aWs 126869
+5oG4 126870
+7pux 126871
+5ZWW 126872
+6Jyx 126873
+55KB 126874
+6JKh 126875
+5Lud 126876
+6aSY 126877
+6IW0 126878
+6aaX 126879
+6L6H 126880
+6JiH 126881
+6YGS 126882
+6I+f 126883
+56mp 126884
+6IC3 126885
+6Yyv 126886
+5pOK 126887
+5riR 126888
+6aCG 126889
+5ZKC 126890
+5ZOa 126891
+57SN 126892
+6LWf 126893
+5ZCy 126894
+55e8 126895
+5bSn 126896
+6LqP 126897
+6JKv 126898
+5q24 126899
+6Yed 126900
+6bKb 126901
+6Iu7 126902
+56+E 126903
+5aGK 126904
+56+m 126905
+44GO 126906
+7Y+Q 126907
+6Kqy 126908
+6bmC 126909
+6bug 126910
+6IyP 126911
+5qWr 126912
+5Yqt 126913
+4L6x 126914
+6LGH 126915
+5bSD 126916
+5q2p 126917
+5ruC 126918
+5q66 126919
+5oWc 126920
+5bK/ 126921
+6ZOf 126922
+6aKb 126923
+5p2p 126924
+6ZiV 126925
+6aW0 126926
+5LyJ 126927
+6Iib 126928
+6Ii4 126929
+6aqg 126930
+5bO2 126931
+2q0= 126932
+6YGV 126933
+5aiJ 126934
+55eo 126935
+6Jqx 126936
+5r24 126937
+57ej 126938
+5ruf 126939
+5r+b 126940
+6aKi 126941
+6am3 126942
+5p+e 126943
+7IOt 126944
+56u2 126945
+5YKp 126946
+6JO8 126947
+8Ks= 126948
+8KuU 126949
+8KuUhA== 126950
+5aeS 126951
+5qm8 126952
+6KiT 126953
+5Ymb 126954
+5L+O 126955
+6Zuc 126956
+6Zmf 126957
+6JKM 126958
+6Ki0 126959
+5aC/ 126960
+6LCh 126961
+6auB 126962
+44Ov 126963
+5ZKy 126964
+6IuV 126965
+556R 126966
+6ZOE 126967
+5L2k 126968
+5aaz 126969
+6KiC 126970
+6LWF 126971
+6Zm4 126972
+67o= 126973
+67qt 126974
+55uk 126975
+6JG6 126976
+6ZKk 126977
+5ZWy 126978
+5qWK 126979
+54en 126980
+6LKn 126981
+44G2 126982
+6IGW 126983
+5ZSB 126984
+5ZWf 126985
+5qS5 126986
+5pyQ 126987
+56SO 126988
+6YaM 126989
+6Z2I 126990
+54yX 126991
+57K/ 126992
+5q6B 126993
+56K0 126994
+6ZOR 126995
+6Z2l 126996
+5YOt 126997
+7Z+K 126998
+44OP 126999
+56Ct 127000
+6ZOx 127001
+5L24 127002
+57ys 127003
+44Ks 127004
+5Zey 127005
+5o+p 127006
+57OX 127007
+5oCP 127008
+7Lo= 127009
+7Lqq 127010
+6JeB 127011
+6JCL 127012
+6aOy 127013
+5baZ 127014
+6KGE 127015
+5L2l 127016
+55WI 127017
+6IKf 127018
+6LCA 127019
+5qW4 127020
+5rem 127021
+656i 127022
+6ayj 127023
+5o+u 127024
+5bmj 127025
+54iw 127026
+57yi 127027
+5ZyT 127028
+6aaU 127029
+6IWm 127030
+5qO5 127031
+6aaV 127032
+4pyF 127033
+6ISr 127034
+44Sl 127035
+6o6u 127036
+44Oi 127037
+5q2Q 127038
+6Y2m 127039
+5LuD 127040
+6aC7 127041
+57qw 127042
+54+u 127043
+6L+z 127044
+y4o= 127045
+6ZWj 127046
+6YaN 127047
+6ZK9 127048
+6bK3 127049
+5aKJ 127050
+6YKX 127051
+74Gu 127052
+5a+n 127053
+54K3 127054
+56Cc 127055
+57iu 127056
+5bKZ 127057
+4piR 127058
+56et 127059
+4paN 127060
+6JGz 127061
+5a2x 127062
+6Zez 127063
+44Ce 127064
+55ul 127065
+5reZ 127066
+7Yqq 127067
+5Zmk 127068
+6Jao 127069
+6YeL 127070
+6ZK/ 127071
+5427 127072
+566T 127073
+6JK9 127074
+5b6t 127075
+6IWn 127076
+6ZCY 127077
+6JOW 127078
+7oCD 127079
+5qS9 127080
+6ICE 127081
+65uU 127082
+5aSJ 127083
+5a6f 127084
+5r2L 127085
+56qg 127086
+6Y+C 127087
+5Z+V 127088
+5q6T 127089
+44Gs 127090
+6IiK 127091
+57uJ 127092
+6LWd 127093
+5aq9 127094
+6KG/ 127095
+44Gl 127096
+5pe4 127097
+4p2W 127098
+5oCZ 127099
+6Zmw 127100
+6KSH 127101
+24s= 127102
+6bme 127103
+6bmz 127104
+6LGi 127105
+6LWJ 127106
+56i5 127107
+4oql 127108
+5pCm 127109
+5p6L 127110
+4LyN 127111
+6LC2 127112
+6IKT 127113
+5YOW 127114
+5qm5 127115
+5oG9 127116
+54qN 127117
+7YOQ 127118
+6KST 127119
+5YiO 127120
+6KC5 127121
+5rSE 127122
+5YOz 127123
+5b+k 127124
+5ZCW 127125
+6ZOh 127126
+5YOV 127127
+6Kyd 127128
+5ZOQ 127129
+54KB 127130
+57uM 127131
+5bqr 127132
+56qo 127133
+6Iq+ 127134
+5ayX 127135
+6bqC 127136
+664= 127137
+666v 127138
+4oiV 127139
+57S5 127140
+64q7 127141
+6ZOn 127142
+6ZSf 127143
+6amF 127144
+zIU= 127145
+5ba3 127146
+57u7 127147
+6L24 127148
+6bKF 127149
+5bSG 127150
+6LK8 127151
+6Iqr 127152
+5r+J 127153
+6KGp 127154
+6aCC 127155
+5b6z 127156
+6ZmJ 127157
+5oa2 127158
+5aaq 127159
+5bug 127160
+6JCc 127161
+5Zyc 127162
+5buq 127163
+6Kqg 127164
+5rOr 127165
+54eI 127166
+57Sd 127167
+5qWd 127168
+6KOf 127169
+56ej 127170
+5YKV 127171
+5piJ 127172
+56mw 127173
+44CT 127174
+5qaJ 127175
+6IqX 127176
+44OE 127177
+5bSu 127178
+6I6z 127179
+5ZWr 127180
+6JK0 127181
+5Zuf 127182
+55Wi 127183
+44Ci 127184
+5Zy7 127185
+6K+k 127186
+6ZKs 127187
+4p2l 127188
+55q0 127189
+6aq6 127190
+5oim 127191
+57uh 127192
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+5ZW1 127194
+5bGG 127195
+5qqX 127196
+6KiO 127197
+6aGn 127198
+6JiF 127199
+6YOP 127200
+6Yaq 127201
+6ZS6 127202
+6LGQ 127203
+57ag 127204
+5byB 127205
+5bG8 127206
+6Jqn 127207
+5pGI 127208
+55qy 127209
+57SZ 127210
+6KWB 127211
+5YSy 127212
+57yx 127213
+6LiU 127214
+5qW9 127215
+5rmv 127216
+576w 127217
+5qOC 127218
+55mC 127219
+6Y+E 127220
+5Zes 127221
+6Lu4 127222
+5oeR 127223
+57ep 127224
+6ZKP 127225
+6aG8 127226
+6auZ 127227
+5YK+ 127228
+5pO0 127229
+56ab 127230
+6LKr 127231
+55mA 127232
+6569 127233
+5664 127234
+5o+G 127235
+56+R 127236
+5qau 127237
+44ON 127238
+6LWz 127239
+6bK1 127240
+57Wh 127241
+5oyy 127242
+6KKI 127243
+5ZOd 127244
+5Z2c 127245
+6bqX 127246
+6L6L 127247
+5ZKj 127248
+5oGB 127249
+5ZmZ 127250
+5bOk 127251
+6ZS2 127252
+5rGG 127253
+5pi0 127254
+56iX 127255
+5a6u 127256
+5Y+V 127257
+5ry4 127258
+6KqK 127259
+6ICL 127260
+5rCh 127261
+4pyo 127262
+5YeH 127263
+5pC0 127264
+4paE 127265
+6Lmp 127266
+4pmC 127267
+6Jmb 127268
+5Zqf 127269
+6KyH 127270
+54q4 127271
+55Sj 127272
+6by5 127273
+6b2K 127274
+5be/ 127275
+5pWX 127276
+6J2I 127277
+6Z+r 127278
+56ye 127279
+54+Z 127280
+6amf 127281
+6b6M 127282
+66eI 127283
+5L2I 127284
+56+q 127285
+56GQ 127286
+5bWs 127287
+5bev 127288
+6LC1 127289
+7JeG 127290
+6ZWb 127291
+44Om 127292
+5buz 127293
+5qGg 127294
+6Iqo 127295
+6IGp 127296
+5oW2 127297
+5qeY 127298
+542+ 127299
+6bip 127300
+5Z6T 127301
+54uS 127302
+5pCN 127303
+6JOl 127304
+55eN 127305
+56+B 127306
+6aKP 127307
+5q6Y 127308
+5L2U 127309
+5YOu 127310
+6bmY 127311
+0og= 127312
+5b6R 127313
+6a4= 127314
+6a6u 127315
+56u9 127316
+5a++ 127317
+6KG+ 127318
+6YaQ 127319
+4piO 127320
+5oe3 127321
+57Wy 127322
+5a+s 127323
+56K8 127324
+6I6c 127325
+6K6n 127326
+4pSD 127327
+6KSb 127328
+6ama 127329
+6IuO 127330
+5Yi9 127331
+5aWn 127332
+6ZWS 127333
+6JuP 127334
+5pid 127335
+54Kd 127336
+6YKw 127337
+56Ga 127338
+6IWz 127339
+7Ys= 127340
+7YuU 127341
+5LqT 127342
+77+8 127343
+5rWQ 127344
+5bKs 127345
+6ZOz 127346
+6bKG 127347
+55Sv 127348
+6Jq1 127349
+7Y+r 127350
+5Ymc 127351
+6b6K 127352
+66ec 127353
+5qmL 127354
+5Z6F 127355
+6Ies 127356
+5rKU 127357
+55SR 127358
+5qqp 127359
+44K9 127360
+6ZaS 127361
+6aGU 127362
+5Yer 127363
+5q6a 127364
+6YG6 127365
+5Y+7 127366
+54ST 127367
+5pqd 127368
+6YeG 127369
+5YKI 127370
+5aar 127371
+566s 127372
+6aqn 127373
+5r+s 127374
+6J6t 127375
+5YGJ 127376
+5Z+X 127377
+5pOB 127378
+55m6 127379
+6Zep 127380
+6L6O 127381
+6Kqk 127382
+57e0 127383
+7J6I 127384
+5LqC 127385
+6KGd 127386
+6ZKW 127387
+y4c= 127388
+5bCL 127389
+7pex 127390
+6K6j 127391
+6IGS 127392
+6bmu 127393
+5bOB 127394
+5YSG 127395
+5oyI 127396
+5oy5 127397
+5rKi 127398
+5b65 127399
+65+W 127400
+7Y+D 127401
+6aKA 127402
+5pO6 127403
+6YWp 127404
+5p2y 127405
+562u 127406
+44Kx 127407
+5Lyi 127408
+5qy4 127409
+6KS0 127410
+4pmk 127411
+6ZWm 127412
+5L26 127413
+5bqg 127414
+44CD 127415
+6IKE 127416
+6ZqX 127417
+5qqU 127418
+5rCQ 127419
+6YKh 127420
+6ZCi 127421
+55Ca 127422
+6Jyi 127423
+6L2r 127424
+7KM= 127425
+7KOL 127426
+74M= 127427
+74OY 127428
+5Lmc 127429
+6Kel 127430
+5be7 127431
+5Yur 127432
+6ZS0 127433
+5o6D 127434
+6Z28 127435
+4L2U 127436
+55CH 127437
+6Kmi 127438
+6bOf 127439
+5oC1 127440
+5o+E 127441
+6LK/ 127442
+5Zij 127443
+5Yuw 127444
+5Yyv 127445
+6ISI 127446
+5amm 127447
+65Oz 127448
+2KY= 127449
+6ZOM 127450
+57i+ 127451
+6La1 127452
+5b+q 127453
+6ZON 127454
+5L6R 127455
+54qE 127456
+54+j 127457
+6LGV 127458
+6Ze/ 127459
+6ZWd 127460
+6buS 127461
+5ZW7 127462
+5o6+ 127463
+5peu 127464
+5p+7 127465
+6IiC 127466
+6Jam 127467
+5ZSz 127468
+55OY 127469
+57C9 127470
+6bOc 127471
+6Kmz 127472
+yZQ= 127473
+562a 127474
+5qml 127475
+5a+k 127476
+572Y 127477
+66eY 127478
+7pg= 127479
+7pi8 127480
+4p6V 127481
+57ac 127482
+6JOu 127483
+6L6C 127484
+8Kw= 127485
+8KyY 127486
+8KyYrQ== 127487
+5oWu 127488
+56CS 127489
+56qj 127490
+56++ 127491
+5oOh 127492
+6Z2c 127493
+66ak 127494
+4YC8 127495
+67c= 127496
+67e4 127497
+64+J 127498
+5a2r 127499
+5Lq7 127500
+5Zii 127501
+6a2a 127502
+546m 127503
+6JWI 127504
+5L22 127505
+54qH 127506
+55aD 127507
+6IOq 127508
+zIc= 127509
+5Zu5 127510
+56+Z 127511
+5YCM 127512
+5pKQ 127513
+6Iy8 127514
+7KuQ 127515
+6LCU 127516
+54WZ 127517
+6IyM 127518
+6Y2X 127519
+6I68 127520
+6byQ 127521
+5ayF 127522
+56yE 127523
+6JK5 127524
+6Zmj 127525
+4L6y 127526
+5qSk 127527
+5Y+1 127528
+5Y2f 127529
+5Y6T 127530
+5p+i 127531
+6ZWn 127532
+6aSN 127533
+6Luf 127534
+5Z+8 127535
+5rKk 127536
+6LOi 127537
+6ZOW 127538
+5Zaf 127539
+6Y60 127540
+5qeB 127541
+6Lyv 127542
+7qA= 127543
+7qCA 127544
+546z 127545
+7ZY= 127546
+7Za5 127547
+5ZOZ 127548
+57SU 127549
+6Iu0 127550
+6YSE 127551
+yoM= 127552
+5oSg 127553
+5rmc 127554
+5oqf 127555
+6J29 127556
+5L25 127557
+5rS6 127558
+zIg= 127559
+5qyk 127560
+6L6U 127561
+7IK0 127562
+7ZuG 127563
+6IiQ 127564
+6LS9 127565
+6Leh 127566
+6JWk 127567
+56u0 127568
+6K+C 127569
+7Ke6 127570
+67OD 127571
+6Lyb 127572
+65uI 127573
+6LKJ 127574
+5p2T 127575
+6Iur 127576
+5o+a 127577
+6Yau 127578
+5o+2 127579
+5rmO 127580
+4pyq 127581
+5ZGT 127582
+57ql 127583
+6Le5 127584
+6Z+T 127585
+7Ic= 127586
+7IeL 127587
+44Oo 127588
+5rC9 127589
+6ZWr 127590
+6Zax 127591
+6LiQ 127592
+5ZCz 127593
+6I+9 127594
+64+M 127595
+5Z64 127596
+5par 127597
+6aaK 127598
+5Z+H 127599
+5a2b 127600
+7Z+3 127601
+5Zmr 127602
+6IuM 127603
+5aWu 127604
+5bOE 127605
+44Cd 127606
+44Ge 127607
+5Zm0 127608
+552l 127609
+56yP 127610
+5bWv 127611
+6ZOI 127612
+4ouF 127613
+57me 127614
+4pWx 127615
+6b6H 127616
+5Yu7 127617
+5bWL 127618
+55a0 127619
+6Zib 127620
+7JM= 127621
+7JOc 127622
+44Kh 127623
+5Zyq 127624
+6Jq0 127625
+8J+M 127626
+8J+Mnw== 127627
+5puJ 127628
+5rmq 127629
+6ZiT 127630
+5oid 127631
+6LeX 127632
+56Kb 127633
+6I+T 127634
+6JK6 127635
+5o+3 127636
+7L8= 127637
+7L+W 127638
+5YSA 127639
+5pev 127640
+54qf 127641
+6bKL 127642
+5YGC 127643
+44OZ 127644
+5L27 127645
+5aua 127646
+5723 127647
+5p28 127648
+5Y+h 127649
+44KQ 127650
+5o60 127651
+7KeP 127652
+5r2U 127653
+6YC2 127654
+566c 127655
+55GX 127656
+5L23 127657
+5p6T 127658
+5bGQ 127659
+5oSs 127660
+66uP 127661
+5p+w 127662
+7Iam 127663
+5aiM 127664
+6I6K 127665
+5Yu1 127666
+7LM= 127667
+7LOl 127668
+5rKW 127669
+5ZeX 127670
+64Y= 127671
+64a+ 127672
+5oq7 127673
+5amz 127674
+57qW 127675
+6Jau 127676
+5ZyE 127677
+5rCW 127678
+54+l 127679
+6IWg 127680
+6ay7 127681
+7Y+a 127682
+57+V 127683
+6YCx 127684
+7oCR 127685
+5rOU 127686
+55aU 127687
+6I6o 127688
+6JeN 127689
+5Li8 127690
+5raZ 127691
+44KF 127692
+6I29 127693
+5aKX 127694
+5rqA 127695
+4oaY 127696
+5YCr 127697
+5rqx 127698
+5ryy 127699
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+zKc= 127707
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+zpw= 127776
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+44G8 127779
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+44Ky 127806
+6J6I 127807
+55WA 127808
+6L68 127809
+6Zug 127810
+65o= 127811
+65qo 127812
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+7Ko= 127814
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+66+h 127818
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+5r+D 127834
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+6JyJ 127836
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+6bmX 127838
+y4w= 127839
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+5rC1 127842
+8J+P 127843
+8J+Puw== 127844
+5Yq8 127845
+5rWX 127846
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+6LWn 127850
+64+2 127851
+5qWu 127852
+6Lma 127853
+6ZyI 127854
+8Kg= 127855
+8Kix 127856
+8Kixkg== 127857
+55+N 127858
+6KO+ 127859
+yZE= 127860
+6Jm7 127861
+6IqY 127862
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+54y2 127864
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+5q6E 127866
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+4qw= 127905
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+6Ys= 127909
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+66w= 127926
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+44Ku 127931
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+5pqr 127939
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+6b+U 127942
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+7ZuY 127944
+5YuW 127945
+6YKZ 127946
+6b6F 127947
+7Y4= 127948
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diff --git a/Hunyuan-A52B-Pretrain/modeling_hunyuan.py b/Hunyuan-A52B-Pretrain/modeling_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8849c6f4253563e232dfa46cb556a11f1285137
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/modeling_hunyuan.py
@@ -0,0 +1,1726 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+""" PyTorch HunYuan model."""
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+ AttentionMaskConverter,
+ _prepare_4d_attention_mask,
+ _prepare_4d_causal_attention_mask,
+ _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPast,
+ CausalLMOutputWithPast,
+ SequenceClassifierOutputWithPast
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_flash_attn_2_available,
+ is_flash_attn_greater_or_equal_2_10,
+ logging,
+ replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_hunyuan import HunYuanConfig
+
+
+if is_flash_attn_2_available():
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+ if not is_torch_greater_or_equal_than_1_13:
+ import torch.fx
+
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "HunYuanConfig"
+
+
+def topkgating(logits: Tensor, topk: int):
+ logits = logits.float()
+ gates = F.softmax(logits, dim=1)
+ expert_capacity = topk * gates.shape[0]
+ num_experts = int(gates.shape[1])
+ # Top-k router probability and corresponding expert indices for each token.
+ # Shape: [tokens_per_group, num_selected_experts].
+ expert_gate, expert_index = torch.topk(gates, topk)
+ expert_mask = F.one_hot(expert_index, num_experts)
+ # For a given token, determine if it was routed to a given expert.
+ # Shape: [tokens_per_group, num_experts]
+ expert_mask_aux = expert_mask.max(dim=-2)[0]
+ tokens_per_group_and_expert = torch.mean(expert_mask_aux.float(), dim=-2)
+ router_prob_per_group_and_expert = torch.mean(gates.float(), dim=-2)
+ l_aux = num_experts**2 * torch.mean(tokens_per_group_and_expert * router_prob_per_group_and_expert)
+
+ gates_s = torch.clamp(
+ torch.matmul(expert_mask.float(), gates.unsqueeze(-1)).sum(dim=1), min=torch.finfo(gates.dtype).eps
+ )
+ router_probs = gates / gates_s
+ # Make num_selected_experts the leading axis to ensure that top-1 choices
+ # have priority over top-2 choices, which have priority over top-3 choices,
+ # etc.
+ expert_index = torch.transpose(expert_index, 0, 1)
+ # Shape: [num_selected_experts * tokens_per_group]
+ expert_index = expert_index.reshape(-1)
+
+ # Create mask out of indices.
+ # Shape: [tokens_per_group * num_selected_experts, num_experts].
+ expert_mask = F.one_hot(expert_index, num_experts).to(torch.int32)
+ exp_counts = torch.sum(expert_mask, dim=0).detach()
+
+ # Experts have a fixed capacity that we cannot exceed. A token's priority
+ # within the expert's buffer is given by the masked, cumulative capacity of
+ # its target expert.
+ # Shape: [tokens_per_group * num_selected_experts, num_experts].
+ token_priority = torch.cumsum(expert_mask, dim=0) * expert_mask - 1
+ # Shape: [num_selected_experts, tokens_per_group, num_experts].
+ token_priority = token_priority.reshape((topk, -1, num_experts))
+ # Shape: [tokens_per_group, num_selected_experts, num_experts].
+ token_priority = torch.transpose(token_priority, 0, 1)
+ # For each token, across all selected experts, select the only non-negative
+ # (unmasked) priority. Now, for group G routing to expert E, token T has
+ # non-negative priority (i.e. token_priority[G,T,E] >= 0) if and only if E
+ # is its targeted expert.
+ # Shape: [tokens_per_group, num_experts].
+ token_priority = torch.max(token_priority, dim=1)[0]
+
+ # Token T can only be routed to expert E if its priority is positive and
+ # less than the expert capacity. One-hot matrix will ignore indices outside
+ # the range [0, expert_capacity).
+ # Shape: [tokens_per_group, num_experts, expert_capacity].
+ valid_mask = torch.logical_and(token_priority >= 0, token_priority < expert_capacity)
+ token_priority = torch.masked_fill(token_priority, ~valid_mask, 0)
+ dispatch_mask = F.one_hot(token_priority, expert_capacity).to(torch.bool)
+ valid_mask = valid_mask.unsqueeze(-1).expand(-1, -1, expert_capacity)
+ dispatch_mask = torch.masked_fill(dispatch_mask, ~valid_mask, 0)
+
+ # The combine array will be used for combining expert outputs, scaled by the
+ # router probabilities. Shape: [num_groups, tokens_per_group, num_experts,
+ # expert_capacity].
+ combine_weights = torch.einsum("...te,...tec->...tec", router_probs, dispatch_mask)
+ exp_counts_capacity = torch.sum(dispatch_mask)
+ exp_capacity_rate = exp_counts_capacity / (logits.shape[0]*topk)
+
+ return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts
+
+
+def top1gating(logits: Tensor, random_routing_dropped_token: bool = False):
+ """Implements Top1Gating on logits."""
+ # everything is in fp32 in this function
+ logits = logits.float()
+ gates = F.softmax(logits, dim=1)
+ capacity = gates.shape[0]
+
+ # Create a mask for 1st's expert per token
+ # noisy gating
+ indices1_s = torch.argmax(gates, dim=1)
+ num_experts = int(gates.shape[1])
+ mask1 = F.one_hot(indices1_s, num_classes=num_experts)
+
+ # gating decisions
+ # exp_counts = torch.sum(mask1, dim=0).detach().to('cpu')
+ exp_counts = torch.sum(mask1, dim=0).detach()
+
+ # Compute l_aux
+ me = torch.mean(gates, dim=0)
+ ce = torch.mean(mask1.float(), dim=0)
+ l_aux = torch.sum(me * ce) * num_experts
+ mask1_rand = mask1
+
+ top_idx = torch.topk(mask1_rand, k=capacity, dim=0)[1]
+
+ new_mask1 = mask1 * torch.zeros_like(mask1).scatter_(0, top_idx, 1)
+ mask1 = new_mask1
+ mask1_bk = mask1
+ if random_routing_dropped_token:
+ not_full = capacity - new_mask1.sum(dim=0)
+ sorted_notfull, indices_notfull = torch.sort(not_full, descending=True)
+ sorted_notfull = sorted_notfull.to(torch.int64)
+ not_full_experts_ids = torch.repeat_interleave(indices_notfull, sorted_notfull)
+ shuffle_not_full_ids = torch.randperm(not_full_experts_ids.shape[0])
+ not_full_experts_ids = not_full_experts_ids[shuffle_not_full_ids]
+ indices1_s_after_drop = torch.argmax(new_mask1, dim=1)
+ # get drop idx
+ drop_mask = 1 - new_mask1.sum(dim=1)
+ drop_mask = drop_mask.bool()
+ drop_idx = drop_mask.nonzero().view(-1)
+ drop_num = drop_mask.sum().to(torch.int64)
+ indices1_s_after_drop.scatter_(0, drop_idx, not_full_experts_ids[:drop_num])
+ nodrop_mask1 = F.one_hot(indices1_s_after_drop, num_classes=num_experts)
+ mask1 = nodrop_mask1
+
+ # Compute locations in capacity buffer
+ locations1 = torch.cumsum(mask1, dim=0) - 1
+
+ # Store the capacity location for each token
+ locations1_s = torch.sum(locations1 * mask1, dim=1)
+
+ # Normalize gate probabilities
+ mask1_float = mask1.float()
+ gates = gates * mask1_float
+
+ locations1_sc = F.one_hot(locations1_s, num_classes=capacity).float() # one hot to float
+ combine_weights = torch.einsum("se,sc->sec", gates, locations1_sc)
+
+ dispatch_mask = combine_weights.bool()
+
+ exp_counts_capacity = torch.sum(mask1_bk)
+ exp_capacity_rate = exp_counts_capacity / (logits.shape[0])
+ return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts
+
+
+def _get_unpad_data(attention_mask):
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+ return (
+ indices,
+ cu_seqlens,
+ max_seqlen_in_batch,
+ )
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ warnings.warn(
+ "Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be "
+ "removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+ )
+ return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _make_causal_mask(
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+ warnings.warn(
+ "Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in "
+ "v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
+ )
+ return AttentionMaskConverter._make_causal_mask(
+ input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+ )
+
+
+class HunYuanRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ HunYuanRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+ALL_LAYERNORM_LAYERS.append(HunYuanRMSNorm)
+
+
+class HunYuanRotaryEmbedding(nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+
+ self.dim = dim
+ self.max_position_embeddings = max_position_embeddings
+ self.base = base
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ inv_freq = inv_freq.bfloat16()
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ # Build here to make `torch.jit.trace` work.
+ self._set_cos_sin_cache(
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+ )
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1).float()
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+ def forward(self, x, seq_len=None):
+ # x: [bs, num_attention_heads, seq_len, head_size]
+ if seq_len > self.max_seq_len_cached:
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+ return (
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
+ )
+
+
+class HunYuanLinearScalingRotaryEmbedding(HunYuanRotaryEmbedding):
+ """HunYuanRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+ t = t / self.scaling_factor
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class HunYuanDynamicNTKScalingRotaryEmbedding(HunYuanRotaryEmbedding):
+ """
+ HunYuanRotaryEmbedding extended with Dynamic NTK scaling.
+ Credits to the Reddit users /u/bloc97 and /u/emozilla
+ """
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+ self.scaling_factor = scaling_factor
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+
+ if seq_len > self.max_position_embeddings:
+ base = self.base * (
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+ ) ** (self.dim / (self.dim - 2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class HunYuanDynamicNTKAlphaRotaryEmbedding(HunYuanRotaryEmbedding):
+ """
+ HunYuanRotaryEmbedding extended with Dynamic NTK scaling.
+ Credits to the Reddit users /u/bloc97 and /u/emozilla
+ """
+
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_alpha=1.0):
+ self.scaling_alpha = scaling_alpha
+ super().__init__(dim, max_position_embeddings, base, device)
+
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
+ self.max_seq_len_cached = seq_len
+ base = self.base * self.scaling_alpha ** (self.dim / (self.dim-2))
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+ freqs = torch.outer(t, self.inv_freq)
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
+ emb = torch.cat((freqs, freqs), dim=-1)
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2:]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+ """Applies Rotary Position Embedding to the query and key tensors.
+
+ Args:
+ q (`torch.Tensor`): The query tensor.
+ k (`torch.Tensor`): The key tensor.
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
+ position_ids (`torch.Tensor`):
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+ used to pass offsetted position ids when working with a KV-cache.
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+ Returns:
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+ """
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class HunYuanMLP(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx=None, is_shared_mlp=False):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.hidden_size = config.hidden_size
+ if is_shared_mlp:
+ self.intermediate_size = config.intermediate_size * config.num_shared_expert
+ else:
+ self.intermediate_size = config.intermediate_size
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ if self.config.pretraining_tp > 1:
+ slice = self.intermediate_size // self.config.pretraining_tp
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+ gate_proj = torch.cat(
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+ )
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+ down_proj = [
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+ ]
+ down_proj = sum(down_proj)
+ else:
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+ return down_proj
+
+
+class HunYuanTopKGate(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.moe_topk = config.moe_topk
+ self.drop_tokens = config.moe_drop_tokens
+ self.min_capacity = 8
+ self.random_routing_dropped_token = config.moe_random_routing_dropped_token
+ self.wg = nn.Linear(config.hidden_size, config.num_experts, bias=False, dtype=torch.float32)
+
+ def forward(self, hidden_states):
+ bsz, seq_len, hidden_size = hidden_states.shape
+ hidden_states = hidden_states.reshape(-1, hidden_size)
+ if self.wg.weight.dtype == torch.float32:
+ hidden_states = hidden_states.float()
+ logits = self.wg(hidden_states)
+ if self.moe_topk == 1:
+ gate_output = top1gating(logits, random_routing_dropped_token=self.random_routing_dropped_token)
+ else:
+ gate_output = topkgating(logits, self.moe_topk)
+
+ return gate_output
+
+
+class HunYuanMoE(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.moe_topk = config.moe_topk
+ self.num_experts = config.num_experts
+ if config.use_mixed_mlp_moe:
+ self.shared_mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=True)
+ self.gate = HunYuanTopKGate(config, layer_idx=layer_idx)
+ self.experts = nn.ModuleList(
+ [HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(config.num_experts)]
+ )
+
+ def forward(self, hidden_states):
+ bsz, seq_len, hidden_size = hidden_states.shape
+
+ if self.config.use_mixed_mlp_moe:
+ hidden_states_mlp = self.shared_mlp(hidden_states)
+
+ l_moe, combine_weights, dispatch_mask, exp_counts = self.gate(hidden_states)
+
+ reshaped_input = hidden_states.reshape(-1, hidden_size)
+
+ dispatched_input = torch.einsum("sec,sm->ecm", dispatch_mask.type_as(hidden_states), reshaped_input)
+
+ chunks = dispatched_input.chunk(self.num_experts, dim=0)
+ expert_outputs = []
+ for chunk, expert in zip(chunks, self.experts):
+ expert_outputs.append(expert(chunk))
+
+ expert_output = torch.cat(expert_outputs, dim=0)
+ combined_output = torch.einsum("sec,ecm->sm", combine_weights.type_as(hidden_states), expert_output)
+ combined_output = combined_output.reshape(bsz, seq_len, hidden_size)
+
+ if self.config.use_mixed_mlp_moe:
+ output = hidden_states_mlp + combined_output
+ else:
+ output = combined_output
+
+ return output
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class HunYuanAttention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ # layer_idx 从 0 开始
+ self.attention_type = 'cross' if config.use_cla and layer_idx % config.cla_share_factor != 0 else 'self'
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.attention_dropout = config.attention_dropout
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.max_position_embeddings = config.max_position_embeddings
+ self.rope_theta = config.rope_theta
+ self.is_causal = True
+ self.use_qk_norm = config.use_qk_norm
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+ if self.attention_type == 'self':
+ self.k_proj = nn.Linear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.v_proj = nn.Linear(
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias
+ )
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+ if self.use_qk_norm:
+ self.query_layernorm = HunYuanRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+ self.key_layernorm = HunYuanRMSNorm(self.head_dim, eps=config.rms_norm_eps)
+ self._init_rope()
+
+ def _init_rope(self):
+ if self.config.rope_scaling is None:
+ self.rotary_emb = HunYuanRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ base=self.rope_theta,
+ )
+ else:
+ scaling_type = self.config.rope_scaling["type"]
+ scaling_factor = self.config.rope_scaling["factor"]
+ scaling_alpha = self.config.rope_scaling["alpha"]
+ if scaling_type == "linear":
+ self.rotary_emb = HunYuanLinearScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ elif scaling_type == "dynamic":
+ if scaling_alpha:
+ self.rotary_emb = HunYuanDynamicNTKAlphaRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_alpha=scaling_alpha,
+ base=self.rope_theta,
+ )
+ else:
+ self.rotary_emb = HunYuanDynamicNTKScalingRotaryEmbedding(
+ self.head_dim,
+ max_position_embeddings=self.max_position_embeddings,
+ scaling_factor=scaling_factor,
+ base=self.rope_theta,
+ )
+ else:
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ if self.config.pretraining_tp > 1:
+ query_slices = self.q_proj.weight.split(
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+ )
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+ query_states = torch.cat(query_states, dim=-1)
+
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+ key_states = torch.cat(key_states, dim=-1)
+
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+ value_states = torch.cat(value_states, dim=-1)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ else:
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ if self.config.pretraining_tp > 1:
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+ else:
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value, (orig_key_states, orig_value_states)
+
+
+class HunYuanFlashAttention2(HunYuanAttention):
+ """
+ HunYuan flash attention module. This module inherits from `HunYuanAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.LongTensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ # HunYuanFlashAttention2 attention does not support output_attentions
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ # overwrite attention_mask with padding_mask
+ attention_mask = kwargs.pop("padding_mask")
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ # Flash attention requires the input to have the shape
+ # batch_size x seq_length x head_dim x hidden_dim
+ # therefore we just need to keep the original shape
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ dropout_rate = self.attention_dropout if self.training else 0.0
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in the correct dtype just to be sure everything works as expected.
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+ # in fp32. (HunYuanRMSNorm handles it correctly)
+
+ input_dtype = query_states.dtype
+ if input_dtype == torch.float32:
+ # Handle the case where the model is quantized
+ if hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ attn_output = self._flash_attention_forward(
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None, past_key_value, (orig_key_states, orig_value_states)
+
+ def _flash_attention_forward(
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+ ):
+ """
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+ first unpad the input, then computes the attention scores and pad the final attention scores.
+
+ Args:
+ query_states (`torch.Tensor`):
+ Input query states to be passed to Flash Attention API
+ key_states (`torch.Tensor`):
+ Input key states to be passed to Flash Attention API
+ value_states (`torch.Tensor`):
+ Input value states to be passed to Flash Attention API
+ attention_mask (`torch.Tensor`):
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+ position of padding tokens and 1 for the position of non-padding tokens.
+ dropout (`int`, *optional*):
+ Attention dropout
+ softmax_scale (`float`, *optional*):
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+ """
+ if not self._flash_attn_uses_top_left_mask:
+ causal = self.is_causal
+ else:
+ causal = self.is_causal and query_length != 1
+
+ # Contains at least one padding token in the sequence
+ if attention_mask is not None:
+ batch_size = query_states.shape[0]
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+ query_states, key_states, value_states, attention_mask, query_length
+ )
+
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+ else:
+ attn_output = flash_attn_func(
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+ )
+
+ return attn_output
+
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+ key_layer = index_first_axis(
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+ )
+ value_layer = index_first_axis(
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+ )
+ if query_length == kv_seq_len:
+ query_layer = index_first_axis(
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+ )
+ cu_seqlens_q = cu_seqlens_k
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
+ indices_q = indices_k
+ elif query_length == 1:
+ max_seqlen_in_batch_q = 1
+ cu_seqlens_q = torch.arange(
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
+ ) # There is a memcpy here, that is very bad.
+ indices_q = cu_seqlens_q[:-1]
+ query_layer = query_layer.squeeze(1)
+ else:
+ # The -q_len: slice assumes left padding.
+ attention_mask = attention_mask[:, -query_length:]
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+ return (
+ query_layer,
+ key_layer,
+ value_layer,
+ indices_q,
+ (cu_seqlens_q, cu_seqlens_k),
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+ )
+
+
+class HunYuanSdpaAttention(HunYuanAttention):
+ """
+ HunYuan attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `HunYuanAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt
+ to SDPA API.
+ """
+
+ # Adapted from HunYuanAttention.forward
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ kv_states: torch.Tensor = None,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if output_attentions:
+ logger.warning_once(
+ 'HunYuanModel is using HunYuanSdpaAttention,'
+ 'but `torch.nn.functional.scaled_dot_product_attention`'
+ 'does not support `output_attentions=True`. Falling back to the manual attention implementation, '
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. '
+ 'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ if self.attention_type == "cross" and kv_states is not None and isinstance(kv_states, tuple):
+ orig_key_states, orig_value_states = kv_states
+ key_states, value_states = kv_states
+ else:
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+ orig_key_states, orig_value_states = key_states, value_states
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+ if self.use_qk_norm:
+ query_states = self.query_layernorm(query_states)
+ key_states = self.key_layernorm(key_states)
+
+ if past_key_value is not None:
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with
+ # custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and attention_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=attention_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a
+ # causal mask in case q_len == 1.
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None, past_key_value, (orig_key_states, orig_value_states)
+
+
+HUNYUAN_ATTENTION_CLASSES = {
+ "eager": HunYuanAttention,
+ "flash_attention_2": HunYuanFlashAttention2,
+ "sdpa": HunYuanSdpaAttention,
+}
+
+
+class HunYuanDecoderLayer(nn.Module):
+ def __init__(self, config: HunYuanConfig, layer_idx: int):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.layer_idx = layer_idx
+
+ self.self_attn = HUNYUAN_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+ if config.num_experts > 1:
+ self.mlp = HunYuanMoE(config, layer_idx=layer_idx)
+ else:
+ self.mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False)
+ self.input_layernorm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ kv_states: Optional[Tuple[torch.Tensor]] = None,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*):
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+ query_sequence_length, key_sequence_length)` if default attention is used.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ kv_states (`Tuple(torch.FloatTensor)`, *optional*): Used when CLA is enabled,
+ key and value states from past attention blocks
+ """
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use "
+ "`attention_mask` instead.`"
+ )
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value, kv_states = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ kv_states=kv_states,
+ **kwargs,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ outputs += (kv_states,)
+
+ return outputs
+
+
+HUNYUAN_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`HunYuanConfig`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanPreTrainedModel(PreTrainedModel):
+ config_class = HunYuanConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["HunYuanDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = True
+ _supports_sdpa = True
+ _supports_cache_class = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+
+HUNYUAN_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+ Two formats are allowed:
+ - a [`~cache_utils.Cache`] instance;
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+ cache format.
+
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+ legacy cache format will be returned.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+ of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanModel(HunYuanPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HunYuanDecoderLayer`]
+
+ Args:
+ config: HunYuanConfig
+ """
+
+ def __init__(self, config: HunYuanConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList(
+ [HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+ )
+ self._use_sdpa = config._attn_implementation == "sdpa"
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+ self.norm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.cla = config.use_cla
+ self.cla_share_factor = config.cla_share_factor
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape[:2]
+ elif inputs_embeds is not None:
+ batch_size, seq_length = inputs_embeds.shape[:2]
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ past_key_values_length = 0
+ if use_cache:
+ use_legacy_cache = not isinstance(past_key_values, Cache)
+ if use_legacy_cache:
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0)
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ # Fix lora with gradient checkpointing training
+ if self.training and inputs_embeds.is_leaf:
+ inputs_embeds.requires_grad = True
+
+ if self._use_flash_attention_2:
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ elif self._use_sdpa and not output_attentions:
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+
+ # embed positions
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = None
+
+ prev_kv_states = None
+ for layer_idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ decoder_layer.__call__,
+ hidden_states,
+ attention_mask,
+ position_ids,
+ past_key_values,
+ output_attentions,
+ use_cache,
+ prev_kv_states,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ kv_states=prev_kv_states
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ kv_states = layer_outputs[-1]
+
+ if self.cla and layer_idx % self.cla_share_factor == 0:
+ prev_kv_states = kv_states
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = None
+ if use_cache:
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class HunYuanForCausalLM(HunYuanPreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: HunYuanConfig):
+ super().__init__(config)
+ self.model = HunYuanModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ Returns:
+
+ Example:
+
+ ```python
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+ >>> model = AutoModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+ ```"""
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ if self.config.pretraining_tp > 1:
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+ logits = torch.cat(logits, dim=-1)
+ else:
+ logits = self.lm_head(hidden_states)
+ logits = logits.float()
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values is not None:
+ if isinstance(past_key_values, Cache):
+ cache_length = past_key_values.get_seq_length()
+ past_length = past_key_values.seen_tokens
+ max_cache_length = past_key_values.get_max_length()
+ else:
+ cache_length = past_length = past_key_values[0][0].shape[2]
+ max_cache_length = None
+
+ # Keep only the unprocessed tokens:
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+ # input)
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+ # input_ids based on the past_length.
+ elif past_length < input_ids.shape[1]:
+ input_ids = input_ids[:, past_length:]
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+ if (
+ max_cache_length is not None
+ and attention_mask is not None
+ and cache_length + input_ids.shape[1] > max_cache_length
+ ):
+ attention_mask = attention_mask[:, -max_cache_length:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -input_ids.shape[1]:]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+ )
+ return reordered_past
+
+
+@add_start_docstrings(
+ """
+ The HunYuan Model transformer with a sequence classification head on top (linear layer).
+
+ [`HunYuanForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ HUNYUAN_START_DOCSTRING,
+)
+class HunYuanForSequenceClassification(HunYuanPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = HunYuanModel(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ sequence_lengths = -1
+ else:
+ if input_ids is not None:
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+ logits.device
+ )
+ else:
+ sequence_lengths = -1
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )
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+ "model.layers.9.self_attn.key_layernorm.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.self_attn.o_proj.bias": "pytorch_model-00002-of-00080.bin",
+ "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.self_attn.q_proj.bias": "pytorch_model-00002-of-00080.bin",
+ "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00080.bin",
+ "model.layers.9.self_attn.query_layernorm.weight": "pytorch_model-00001-of-00080.bin",
+ "model.norm.weight": "pytorch_model-00062-of-00080.bin"
+ }
+}
diff --git a/Hunyuan-A52B-Pretrain/test.py b/Hunyuan-A52B-Pretrain/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ccf4c79ac8d806f21ec08223ab7c347443d54b8
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/test.py
@@ -0,0 +1,49 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from tokenizers import ByteLevelBPETokenizer
+from transformers import AutoTokenizer
+
+# Step 1: Initialize ByteLevelBPETokenizer
+#tokenizer = ByteLevelBPETokenizer(
+# "vocab.json",
+# "merges.txt"
+#)
+
+# Step 2: Save the tokenizer configuration
+#tokenizer.save_model("auto_model")
+
+# Step 3: Load the tokenizer using AutoTokenizer
+auto_tokenizer = AutoTokenizer.from_pretrained("./", use_fast=False, trust_remote_code=True)
+
+# Test the tokenizer
+text = "Hello, world!"
+encoded = auto_tokenizer.encode(text)
+decoded = auto_tokenizer.decode(encoded)
+
+print("Encoded:", encoded)
+print("Decoded:", decoded)
+
+messages = [
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Hello, how are you?"},
+ {"role": "assistant", "content": "I'm good, thank you! How can I help you today?"},
+ {"role": "user", "content": "Nothing"},
+]
+
+print('messages:', messages)
+ids = auto_tokenizer.apply_chat_template(messages)
+print(f"input_ids:\t{ids}")
+text = auto_tokenizer.decode(ids)
+print(f"input_text:\t[{text}]")
diff --git a/Hunyuan-A52B-Pretrain/test4consistent.py b/Hunyuan-A52B-Pretrain/test4consistent.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cdc6d2c339a002430e3e7f489ceb73751815267
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/test4consistent.py
@@ -0,0 +1,32 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# test tokenizer encode & decode consistency
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained('/tokenizer_exp/other_tokenizer_vocab/hy', local_files_only=True, trust_remote_code=True)
+
+test_data = [line.strip() for line in open('/tokenizer_exp/data/test.txt', 'r').readlines()]
+
+num_origi_len = 0
+num_token_len = 0
+
+for d in test_data:
+ a = tokenizer.encode(d)
+ num_origi_len += len(d)
+ num_token_len += len(a)
+ b = tokenizer.decode(a)
+ assert b == d, f"encode & decode not consistent: {d} vs {b}"
+
+print(f" original length: {num_origi_len}")
+print(f" token length: {num_token_len}")
diff --git a/Hunyuan-A52B-Pretrain/tokenization_hy.py b/Hunyuan-A52B-Pretrain/tokenization_hy.py
new file mode 100644
index 0000000000000000000000000000000000000000..91c651bdf7e67bf7787ac52f86bcb7e59070adea
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/tokenization_hy.py
@@ -0,0 +1,354 @@
+# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+#
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import base64
+import logging
+import tiktoken
+import unicodedata
+from transformers import PreTrainedTokenizer, AddedToken
+from typing import Collection, Dict, List, Set, Tuple, Union
+
+
+logger = logging.getLogger(__name__)
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "hy.tiktoken"}
+PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|""" \
+ r"""[^\r\n\p{L}\p{N}]?\p{L}+|""" \
+ r"""\p{N}|""" \
+ r""" ?[^\s\p{L}\p{N}]+[\r\n]*|""" \
+ r"""\s*[\r\n]+|""" \
+ r"""\s+(?!\S)|""" \
+ r"""\s+"""
+# default eod_token and bod_token of our base model
+ENDOFTEXT = "<|endoftext|>"
+STARTOFTEXT = "<|startoftext|>"
+
+# extra flag token for other training
+BOSTOKEN = "<|bos|>"
+EOSTOKEN = "<|eos|>"
+
+PADTOKEN = "<|pad|>"
+
+# extra special tokens for the tokenizer
+# as the default behavior is changed to allow special tokens in
+# regular texts, the surface forms of special tokens need to be
+# as different as possible to minimize the impact
+EXTRAS = tuple((f"<|extra_{i}|>" for i in range(204)))
+
+SPECIAL_START_ID = 127957
+
+
+def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
+ dic = {}
+ rank = 0
+ for i, line in enumerate(open(tiktoken_bpe_file, "rb")):
+ if line:
+ token, _ = line.split()
+ # skip duplicated tokens, this should not happen
+ if base64.b64decode(token) in dic:
+ raise ValueError(f"!ERROR: duplicated token {token} in your vocab file")
+ dic[base64.b64decode(token)] = int(rank)
+ rank += 1
+ return dic
+
+
+# special tokens for pretrain and finetune models
+SPECIAL_TOKENS = tuple(
+ enumerate(
+ (
+ (
+ ENDOFTEXT,
+ STARTOFTEXT,
+ BOSTOKEN,
+ EOSTOKEN,
+ PADTOKEN,
+ )
+ + EXTRAS
+ ),
+ start=SPECIAL_START_ID,
+ )
+)
+
+SPECIAL_TOKENS_SET = set(t for i, t in SPECIAL_TOKENS)
+
+
+class HYTokenizer(PreTrainedTokenizer):
+ """
+ HunYuan Tokenizer Initialization. We extend `tiktoken` vocab and
+ the default EOD & BOD special tokens are used for base model.
+
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+
+ errors (`str`):
+ How to handle errors in decoding UTF-8 byte sequences.
+ use ignore if you are in streaming inference
+
+ bod_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""<|startoftext|>""`):
+ The beginning of document token that was used for training. can be modified by your task.
+ default to be `<|startoftext|>` for released base model.
+
+ eod_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `""<|endoftext|>""`):
+ The end of document token that was used for training. can be modified by your task.
+ default to be `<|endoftext|>` for released base model.
+
+ bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `None`):
+ The start or sep special token that was used for some training tasks.
+ default to be `<|startoftext|>` for released base model.
+ It can be set to `<|bos|>` when you training for some other task
+
+ eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `None`):
+ The end or sep special token that was used for some training tasks.
+ default to be `<|endoftext|>` for released base model.
+ It can be set to `<|eos|>` when you training for some other task
+
+ pad_token (`str` or `tokenizers.AddedToken`, *optional*):
+ A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
+ attention mechanisms or loss computation.
+
+ special_vocab_file (str, *optional*):
+ Customed special extra vocab file, same format with hy.tiktoken.
+ **Be careful** to use the extra special vocab, it will may cause the model loading collapse.
+ The data line be like:
+ `PHxhYmN8Pg== 0`
+ the id followed `base64.encode(str)` is unused, we will reset them in case of collision
+
+ add_bod_token (`bool`, *optional*, defaults to `True`):
+ Whether or not to add an `bos_token` at the start of documents.
+ This will effect `build_inputs_with_special_tokens` method
+
+ add_eod_token (`bool`, *optional*, defaults to `False`):
+ Whether or not to add an `eos_token` at the end of documents.
+ This will effect `build_inputs_with_special_tokens` method
+
+ """
+ vocab_files_names = VOCAB_FILES_NAMES
+
+ def __init__(
+ self,
+ vocab_file,
+ errors="replace",
+ bod_token="<|startoftext|>",
+ eod_token="<|endoftext|>",
+ bos_token="<|startoftext|>",
+ eos_token="<|endoftext|>",
+ pad_token="<|pad|>",
+ add_bod_token=True,
+ add_eod_token=True,
+ **kwargs,
+ ):
+ super().__init__(**kwargs)
+
+ self.errors = errors
+ self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: Dict[bytes, int]
+ self.special_tokens = {
+ token: index
+ for index, token in SPECIAL_TOKENS
+ }
+
+ enc = tiktoken.Encoding(
+ "HunYuan",
+ pat_str=PAT_STR,
+ mergeable_ranks=self.mergeable_ranks,
+ special_tokens=self.special_tokens,
+ )
+ assert (
+ len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
+ ), f"{len(self.mergeable_ranks)} + {len(self.special_tokens)} != {enc.n_vocab} in encoding"
+
+ self.decoder = {
+ v: k for k, v in self.mergeable_ranks.items()
+ } # type: dict[int, bytes|str]
+ self.decoder.update({v: k for k, v in self.special_tokens.items()})
+
+ self.tokenizer = enc
+
+ self.bod_token, self.bod_id = bod_token, self.special_tokens[bod_token]
+ self.eod_token, self.eod_id = eod_token, self.special_tokens[eod_token]
+ self.bos_token, self.bos_id = bos_token, self.special_tokens[bos_token]
+ self.eos_token, self.eos_id = eos_token, self.special_tokens[eos_token]
+ self.pad_token, self.pad_id = pad_token, self.special_tokens[pad_token]
+
+ self._num_special_token = len(self.special_tokens)
+
+ self.add_bod_token = add_bod_token
+ self.add_eod_token = add_eod_token
+
+ def __getstate__(self):
+ state = self.__dict__.copy()
+ del state["tokenizer"]
+ return state
+
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+ enc = tiktoken.Encoding(
+ "HunYuan",
+ pat_str=PAT_STR,
+ mergeable_ranks=self.mergeable_ranks,
+ special_tokens=self.special_tokens,
+ )
+ self.tokenizer = enc
+
+ def __len__(self) -> int:
+ return self.tokenizer.n_vocab
+
+ def get_vocab(self) -> Dict[bytes, int]:
+ """Return the vocabulary as a dictionary, without special tokens."""
+ return self.mergeable_ranks
+
+ def convert_tokens_to_ids(
+ self, tokens: Union[bytes, str, List[Union[bytes, str]]]
+ ) -> List[int]:
+ ids = []
+ if isinstance(tokens, (str, bytes)):
+ if tokens in self.special_tokens:
+ return self.special_tokens[tokens]
+ else:
+ return self.mergeable_ranks.get(tokens)
+ for token in tokens:
+ if token in self.special_tokens:
+ ids.append(self.special_tokens[token])
+ else:
+ ids.append(self.mergeable_ranks.get(token))
+ return ids
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ bod_token_id = [self.bod_id] if self.add_bod_token else []
+ eod_token_id = [self.eod_id] if self.add_eod_token else []
+ output = bod_token_id + token_ids_0 + eod_token_id
+ if token_ids_1 is not None:
+ output = output + bod_token_id + token_ids_1 + eod_token_id
+ return output
+
+ def _add_tokens(
+ self,
+ new_tokens: Union[List[str], List[AddedToken]],
+ special_tokens: bool = False,
+ ) -> List[Tuple[int, str]]:
+ """do not support adding tokens"""
+ if not special_tokens and new_tokens:
+ raise ValueError("Adding regular tokens is not supported")
+ for token in new_tokens:
+ surface_form = token.content if isinstance(token, AddedToken) else token
+ if surface_form not in SPECIAL_TOKENS_SET:
+ raise ValueError("Adding unknown special tokens is not supported")
+ return 0
+
+ def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
+ """
+ Save only the vocabulary of the tokenizer (vocabulary).
+ Returns:
+ `Tuple(str)`: Paths to the files saved.
+ """
+ file_path = os.path.join(save_directory, "hy.tiktoken")
+ with open(file_path, "w", encoding="utf8") as w:
+ for k, v in self.mergeable_ranks.items():
+ line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
+ w.write(line)
+ return (file_path,)
+
+ def tokenize(
+ self,
+ text: str,
+ allowed_special: Union[Set, str] = "all",
+ disallowed_special: Union[Collection, str] = (),
+ **kwargs,
+ ) -> List[Union[bytes, str]]:
+ """
+ Converts a string in a sequence of tokens.
+ Args:
+ text (`str`):
+ The sequence to be encoded.
+ allowed_special (`Literal["all"]` or `set`):
+ The surface forms of the tokens to be encoded as special tokens in regular texts.
+ Default to "all".
+ disallowed_special (`Literal["all"]` or `Collection`):
+ The surface forms of the tokens that should not be in regular texts and trigger errors.
+ Default to an empty tuple.
+ kwargs (additional keyword arguments, *optional*):
+ Will be passed to the underlying model specific encode method.
+ Returns:
+ `List[bytes|str]`: The list of tokens.
+ """
+ tokens = []
+ text = unicodedata.normalize("NFC", text)
+
+ # this implementation takes a detour: text -> token id -> token surface forms
+ for t in self.tokenizer.encode(
+ text, allowed_special=allowed_special, disallowed_special=disallowed_special
+ ):
+ tokens.append(self.decoder[t])
+ return tokens
+
+ def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
+ """
+ Converts a sequence of tokens in a single string.
+ """
+ text = ""
+ temp = b""
+ for t in tokens:
+ if isinstance(t, str):
+ if temp:
+ text += temp.decode("utf-8", errors=self.errors)
+ temp = b""
+ text += t
+ elif isinstance(t, bytes):
+ temp += t
+ else:
+ raise TypeError("token should only be of type types or str")
+ if temp:
+ text += temp.decode("utf-8", errors=self.errors)
+ return text
+
+ @property
+ def vocab_size(self):
+ return self.tokenizer.n_vocab
+
+ def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
+ """Converts an id to a token, special tokens included"""
+ if index in self.decoder:
+ return self.decoder[index]
+ raise ValueError("unknown ids")
+
+ def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
+ """Converts a token to an id using the vocab, special tokens included"""
+ if token in self.special_tokens:
+ return self.special_tokens[token]
+ if token in self.mergeable_ranks:
+ return self.mergeable_ranks[token]
+ raise ValueError("unknown token")
+
+ def _tokenize(self, text: str, **kwargs):
+ """
+ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+ vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+ Do NOT take care of added tokens.
+ """
+ raise NotImplementedError
+
+ def _decode(
+ self,
+ token_ids: Union[int, List[int]],
+ skip_special_tokens: bool = False,
+ errors: str = None,
+ **kwargs,
+ ) -> str:
+ if isinstance(token_ids, int):
+ token_ids = [token_ids]
+ if skip_special_tokens:
+ token_ids = [i for i in token_ids if i < self.eod_id]
+ return self.tokenizer.decode(token_ids, errors=errors or self.errors)
diff --git a/Hunyuan-A52B-Pretrain/tokenizer_config.json b/Hunyuan-A52B-Pretrain/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c239bd7995f53018fc0ad0089ecd014a23727b0
--- /dev/null
+++ b/Hunyuan-A52B-Pretrain/tokenizer_config.json
@@ -0,0 +1,13 @@
+{
+ "model_max_length": 1048576,
+ "tokenizer_class": "HYTokenizer",
+ "auto_map": {
+ "AutoTokenizer": [
+ "tokenization_hy.HYTokenizer",
+ null
+ ]
+ },
+ "additional_special_tokens": ["<|startoftext|>", "<|extra_0|>", "<|extra_4|>", "<|extra_5|>", "<|eos|>"],
+ "chat_template": "{% set context = {'has_head': true} %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = message['content'] %}{% if loop.index0 == 0 %}{% if content == '' %}{% set _ = context.update({'has_head': false}) %}{% else %}{% set content = '<|startoftext|>' + content + '<|extra_4|>' %}{% endif %}{% endif %}{% if message['role'] == 'user' %}{% if loop.index0 == 1 and not context.has_head %}{% set content = '<|startoftext|>' + content %}{% endif %}{% if loop.index0 == 1 and context.has_head %}{% set content = content + '<|extra_0|>' %}{% else %}{% set content = '<|startoftext|>' + content + '<|extra_0|>' %}{% endif %}{% elif message['role'] == 'assistant' %}{% set content = content + '<|eos|>' %}{% endif %}{{ content }}{% endfor %}"
+}
+
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d99b3a26b5891ef91843b2cd71696909c5e1c597
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,77 @@
+TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
+Tencent-Hunyuan-Large Release Date: November 5, 2024
+THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW.
+By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+1. DEFINITIONS.
+a. “Acceptable Use Policy” shall mean the policy made available by Tencent as set forth in the Exhibit A.
+b. “Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of Tencent Hunyuan Works or any portion or element thereof set forth herein.
+c. “Documentation” shall mean the specifications, manuals and documentation for Tencent Hunyuan made publicly available by Tencent.
+d. “Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means.
+e. “Licensee,” “You” or “Your” shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Tencent Hunyuan Works for any purpose and in any field of use.
+f. “Materials” shall mean, collectively, Tencent’s proprietary Tencent Hunyuan and Documentation (and any portion thereof) as made available by Tencent under this Agreement.
+g. “Model Derivatives” shall mean all: (i) modifications to Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; (ii) works based on Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Tencent Hunyuan or any Model Derivative of Tencent Hunyuan, to that model in order to cause that model to perform similarly to Tencent Hunyuan or a Model Derivative of Tencent Hunyuan, including distillation methods, methods that use intermediate data representations, or methods based on the generation of synthetic data Outputs by Tencent Hunyuan or a Model Derivative of Tencent Hunyuan for training that model. For clarity, Outputs by themselves are not deemed Model Derivatives.
+h. “Output” shall mean the information and/or content output of Tencent Hunyuan or a Model Derivative that results from operating or otherwise using Tencent Hunyuan or a Model Derivative, including via a Hosted Service.
+i. “Tencent,” “We” or “Us” shall mean THL A29 Limited.
+j. “Tencent Hunyuan” shall mean the large language models, text/image/video/audio/3D generation models, and multimodal large language models and their software and algorithms, including trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing made publicly available by Us, including, without limitation to, Tencent-Hunyuan-Large released at https://github.com/Tencent/Tencent-Hunyuan-Large, https://huggingface.co/tencent/Tencent-Hunyuan-Large.
+k. “Tencent Hunyuan Works” shall mean: (i) the Materials; (ii) Model Derivatives; and (iii) all derivative works thereof.
+l. “Territory” shall mean the worldwide territory, excluding the territory of the European Union.
+m. “Third Party” or “Third Parties” shall mean individuals or legal entities that are not under common control with Us or You.
+n. “including” shall mean including but not limited to.
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+We grant You, for the Territory only, a non-exclusive, non-transferable and royalty-free limited license under Tencent’s intellectual property or other rights owned by Us embodied in or utilized by the Materials to use, reproduce, distribute, create derivative works of (including Model Derivatives), and make modifications to the Materials, only in accordance with the terms of this Agreement and the Acceptable Use Policy, and You must not violate (or encourage or permit anyone else to violate) any term of this Agreement or the Acceptable Use Policy.
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+d. All distributions to Third Parties (other than through a Hosted Service) must be accompanied by a “Notice” text file that contains the following notice: “Tencent Hunyuan is licensed under the Tencent Hunyuan Community License Agreement, Copyright © 2024 Tencent. All Rights Reserved. The trademark rights of “Tencent Hunyuan” are owned by Tencent or its affiliate.”
+You may add Your own copyright statement to Your modifications and, except as set forth in this Section and in Section 5, may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Model Derivatives as a whole, provided Your use, reproduction, modification, distribution, performance and display of the work otherwise complies with the terms and conditions of this Agreement (including as regards the Territory). If You receive Tencent Hunyuan Works from a Licensee as part of an integrated end user product, then this Section 3 of this Agreement will not apply to You.
+4. ADDITIONAL COMMERCIAL TERMS.
+If, on the Tencent Hunyuan version release date, the monthly active users of all products or services made available by or for Licensee is greater than 100 million monthly active users in the preceding calendar month, You must request a license from Tencent, which Tencent may grant to You in its sole discretion, and You are not authorized to exercise any of the rights under this Agreement unless or until Tencent otherwise expressly grants You such rights.
+5. RULES OF USE.
+a. Your use of the Tencent Hunyuan Works must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Tencent Hunyuan Works, which is hereby incorporated by reference into this Agreement. You must include the use restrictions referenced in these Sections 5(a) and 5(b) as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Tencent Hunyuan Works and You must provide notice to subsequent users to whom You distribute that Tencent Hunyuan Works are subject to the use restrictions in these Sections 5(a) and 5(b).
+b. You must not use the Tencent Hunyuan Works or any Output or results of the Tencent Hunyuan Works to improve any other large language model (other than Tencent Hunyuan or Model Derivatives thereof).
+c. You must not use, reproduce, modify, distribute, or display the Tencent Hunyuan Works, Output or results of the Tencent Hunyuan Works outside the Territory. Any such use outside the Territory is unlicensed and unauthorized under this Agreement.
+6. INTELLECTUAL PROPERTY.
+a. Subject to Tencent’s ownership of Tencent Hunyuan Works made by or for Tencent and intellectual property rights therein, conditioned upon Your compliance with the terms and conditions of this Agreement, as between You and Tencent, You will be the owner of any derivative works and modifications of the Materials and any Model Derivatives that are made by or for You.
+b. No trademark licenses are granted under this Agreement, and in connection with the Tencent Hunyuan Works, Licensee may not use any name or mark owned by or associated with Tencent or any of its affiliates, except as required for reasonable and customary use in describing and distributing the Tencent Hunyuan Works. Tencent hereby grants You a license to use “Tencent Hunyuan” (the “Mark”) in the Territory solely as required to comply with the provisions of Section 3(c), provided that You comply with any applicable laws related to trademark protection. All goodwill arising out of Your use of the Mark will inure to the benefit of Tencent.
+c. If You commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any person or entity alleging that the Materials or any Output, or any portion of any of the foregoing, infringe any intellectual property or other right owned or licensable by You, then all licenses granted to You under this Agreement shall terminate as of the date such lawsuit or other proceeding is filed. You will defend, indemnify and hold harmless Us from and against any claim by any Third Party arising out of or related to Your or the Third Party’s use or distribution of the Tencent Hunyuan Works.
+d. Tencent claims no rights in Outputs You generate. You and Your users are solely responsible for Outputs and their subsequent uses.
+7. DISCLAIMERS OF WARRANTY AND LIMITATIONS OF LIABILITY.
+a. We are not obligated to support, update, provide training for, or develop any further version of the Tencent Hunyuan Works or to grant any license thereto.
+b. UNLESS AND ONLY TO THE EXTENT REQUIRED BY APPLICABLE LAW, THE TENCENT HUNYUAN WORKS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED “AS IS” WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES OF ANY KIND INCLUDING ANY WARRANTIES OF TITLE, MERCHANTABILITY, NONINFRINGEMENT, COURSE OF DEALING, USAGE OF TRADE, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING, REPRODUCING, MODIFYING, PERFORMING, DISPLAYING OR DISTRIBUTING ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR OR A THIRD PARTY’S USE OR DISTRIBUTION OF ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS AND YOUR EXERCISE OF RIGHTS AND PERMISSIONS UNDER THIS AGREEMENT.
+c. TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT SHALL TENCENT OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, FOR ANY DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, CONSEQUENTIAL OR PUNITIVE DAMAGES, OR LOST PROFITS OF ANY KIND ARISING FROM THIS AGREEMENT OR RELATED TO ANY OF THE TENCENT HUNYUAN WORKS OR OUTPUTS, EVEN IF TENCENT OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+8. SURVIVAL AND TERMINATION.
+a. The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
+b. We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent Hunyuan Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement.
+9. GOVERNING LAW AND JURISDICTION.
+a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
+b. Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute.
+
+EXHIBIT A
+ACCEPTABLE USE POLICY
+
+Tencent reserves the right to update this Acceptable Use Policy from time to time.
+Last modified: November 5, 2024
+
+Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan. You agree not to use Tencent Hunyuan or Model Derivatives:
+1. Outside the Territory;
+2. In any way that violates any applicable national, federal, state, local, international or any other law or regulation;
+3. To harm Yourself or others;
+4. To repurpose or distribute output from Tencent Hunyuan or any Model Derivatives to harm Yourself or others;
+5. To override or circumvent the safety guardrails and safeguards We have put in place;
+6. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+7. To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections;
+8. To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement;
+9. To intentionally defame, disparage or otherwise harass others;
+10. To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
+11. To generate or disseminate personal identifiable information with the purpose of harming others;
+12. To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
+13. To impersonate another individual without consent, authorization, or legal right;
+14. To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
+15. In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
+16. To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
+17. For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
+18. To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+19. For military purposes;
+20. To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
diff --git a/Notice b/Notice
new file mode 100644
index 0000000000000000000000000000000000000000..4d8d765b8f456c9176e93770abd5d2767b6e312c
--- /dev/null
+++ b/Notice
@@ -0,0 +1,78 @@
+Usage and Legal Notices:
+
+Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. The below software and/or models in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+Tencent-Hunyuan-Large is licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT except for the third-party components listed below. Tencent-Hunyuan-Large does not impose any additional limitations beyond what is outlined in the respective licenses of these third-party components. Users must comply with all terms and conditions of original licenses of these third-party components and must ensure that the usage of the third party components adheres to all relevant laws and regulations.
+
+For avoidance of doubts, Tencent-Hunyuan-Large means the large language models and their software and algorithms, including trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing made publicly available by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
+
+
+
+Other dependencies and licenses:
+
+Open Source Software Licensed under the Apache License Version 2.0:
+The below software in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2023 THL A29 Limited.
+--------------------------------------------------------------------
+1. VLLM
+Copyright (c) VLLM original author and authors
+Source code of this software can be obtained from: https://github.com/vllm-project/vllm/tree/main
+
+
+Terms of the Apache License Version 2.0:
+--------------------------------------------------------------------
+Apache License
+
+Version 2.0, January 2004
+
+http://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
+
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+
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+
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
+
+5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
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+6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.
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+7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4abc455ea014603532d5bdeaa5b630a977c688c8
--- /dev/null
+++ b/README.md
@@ -0,0 +1,163 @@
+---
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+
+license: other
+license_name: tencent-license
+license_link: https://huggingface.co/tencent/Tencent-Hunyuan-Large/blob/main/LICENSE.txt
+---
+
+
+ 
+
+
+
+ GITHUB | 🖥️ official website ｜ 🕖 HunyuanAPI｜ 🐳 Gitee
+
+ Technical Report ｜ Demo ｜ Tencent Cloud TI
+
+
+
+
+
+
+
+
+### Model Introduction
+
+With the rapid development of artificial intelligence technology, large language models (LLMs) have made significant progress in fields such as natural language processing, computer vision, and scientific tasks. However, as the scale of these models increases, optimizing resource consumption while maintaining high performance has become a key challenge. To address this challenge, we have explored Mixture of Experts (MoE) models. The currently unveiled Hunyuan-Large (Hunyuan-MoE-A52B) model is the largest open-source Transformer-based MoE model in the industry, featuring a total of 389 billion parameters and 52 billion active parameters. This is currently the largest open-source Transformer-based MoE model in the industry, featuring a total of 389 billion parameters and 52 billion active parameters.
+
+By open-sourcing the Hunyuan-Large model and revealing related technical details, we hope to inspire more researchers with innovative ideas and collectively advance the progress and application of AI technology. We welcome you to join our open-source community to explore and optimize future AI models together!
+
+### Introduction to Model Technical Advantages
+
+#### Model
+- **High-Quality Synthetic Data**: By enhancing training with synthetic data, Hunyuan-Large can learn richer representations, handle long-context inputs, and generalize better to unseen data.
+
+- **KV Cache Compression**: Utilizes Grouped Query Attention (GQA) and Cross-Layer Attention (CLA) strategies to significantly reduce memory usage and computational overhead of KV caches, improving inference throughput.
+
+- **Expert-Specific Learning Rate Scaling**: Sets different learning rates for different experts to ensure each sub-model effectively learns from the data and contributes to overall performance.
+
+- **Long-Context Processing Capability**: The pre-trained model supports text sequences up to 256K, and the Instruct model supports up to 128K, significantly enhancing the ability to handle long-context tasks.
+
+- **Extensive Benchmarking**: Conducts extensive experiments across various languages and tasks to validate the practical effectiveness and safety of Hunyuan-Large.
+
+
+
+
+## Benchmark Evaluation
+
+**Hunyuan-Large pre-trained model** achieves the best overall performance compared to both Dense and MoE based
+competitors having similar activated parameter sizes. For aggregated benchmarks such as MMLU, MMLU-Pro, and CMMLU,
+Hunyuan-Large consistently achieves the best performance, confirming its comprehensive abilities on aggregated tasks.
+Hunyuan-Large also shows superior performance in commonsense understanding and reasoning, and classical NLP tasks
+such as QA and reading comprehension tasks (e.g., CommonsenseQA, PIQA and TriviaQA).
+For the mathematics capability, Hunyuan-Large outperforms all baselines in math datasets of GSM8K and MATH,
+and also gains the best results on CMATH in Chinese.We also observe that Hunyuan-Large achieves the overall
+best performance in all Chinese tasks (e.g., CMMLU, C-Eval).
+
+| Model | LLama3.1-405B | LLama3.1-70B | Mixtral-8x22B | DeepSeek-V2 | Hunyuan-Large |
+|------------------|---------------|--------------|---------------|-------------|---------------|
+| MMLU | 85.2 | 79.3 | 77.8 | 78.5 | **88.4** |
+| MMLU-Pro | **61.6** | 53.8 | 49.5 | - | 60.2 |
+| BBH | 85.9 | 81.6 | 78.9 | 78.9 | **86.3** |
+| HellaSwag | - | - | **88.7** | 87.8 | 86.8 |
+| CommonsenseQA | 85.8 | 84.1 | 82.4 | - | **92.9** |
+| WinoGrande | 86.7 | 85.3 | 85.0 | 84.9 | **88.7** |
+| PIQA | - | - | 83.6 | 83.7 | **88.3** |
+| NaturalQuestions | - | - | 39.6 | 38.7 | **52.8** |
+| DROP | 84.8 | 79.6 | 80.4 | 80.1 | **88.9** |
+| ARC-C | **96.1** | 92.9 | 91.2 | 92.4 | 95.0 |
+| TriviaQA | - | - | 82.1 | 79.9 | **89.2** |
+| CMMLU | - | - | 60.0 | 84.0 | **90.2** |
+| C-Eval | - | - | 59.6 | 81.7 | **91.9** |
+| C3 | - | - | 71.4 | 77.4 | **82.3** |
+| GSM8K | 89.0 | 83.7 | 83.7 | 79.2 | **92.8** |
+| MATH | 53.8 | 41.4 | 42.5 | 43.6 | **69.8** |
+| CMATH | - | - | 72.3 | 78.7 | **91.3** |
+| HumanEval | 61.0 | 58.5 | 53.1 | 48.8 | **71.4** |
+| MBPP | **73.4** | 68.6 | 64.2 | 66.6 | 72.6 |
+
+**Hunyuan-Large-Instruct** achieves consistent improvements on most types of tasks compared to LLMs having similar
+activated parameters, indicating the effectiveness of our post-training. Delving into the model performance
+in different categories of benchmarks, we find that our instruct model achieves the best performance on MMLU and MATH dataset.
+Notably, on the MMLU dataset, our model demonstrates a significant improvement, outperforming the LLama3.1-405B model by 2.6%.
+This enhancement is not just marginal but indicative of the Hunyuan-Large-Instruct’s superior understanding and reasoning
+capabilities across a wide array of language understanding tasks. The model’s prowess is further underscored in its performance
+on the MATH dataset, where it surpasses the LLama3.1-405B by a notable margin of 3.6%.
+Remarkably, this leap in accuracy is achieved with only 52 billion activated parameters, underscoring the efficiency of our model.
+
+| Model | LLama3.1 405B Inst. | LLama3.1 70B Inst. | Mixtral 8x22B Inst. | DeepSeekV2.5 Chat | Hunyuan-Large Inst. |
+|----------------------|---------------------|--------------------|---------------------|-------------------|---------------------|
+| MMLU | 87.3 | 83.6 | 77.8 | 80.4 | **89.9** |
+| CMMLU | - | - | 61.0 | - | **90.4** |
+| C-Eval | - | - | 60.0 | - | **88.6** |
+| BBH | - | - | 78.4 | 84.3 | **89.5** |
+| HellaSwag | - | - | 86.0 | **90.3** | 88.5 |
+| ARC-C | **96.9** | 94.8 | 90.0 | - | 94.6 |
+| GPQA_diamond | **51.1** | 46.7 | - | - | 42.4 |
+| MATH | 73.8 | 68.0 | 49.8 | 74.7 | **77.4** |
+| HumanEval | 89.0 | 80.5 | 75.0 | 89.0 | **90.0** |
+| AlignBench | 6.0 | 5.9 | 6.2 | 8.0 | **8.3** |
+| MT-Bench | 9.1 | 8.8 | 8.1 | 9.0 | **9.4** |
+| IFEval strict-prompt | **86.0** | 83.6 | 71.2 | - | 85.0 |
+| Arena-Hard | 69.3 | 55.7 | - | 76.2 | **81.8** |
+| AlpacaEval-2.0 | 39.3 | 34.3 | 30.9 | 50.5 | **51.8** |
+
+
+## Quick Start
+
+You can quickly get started by referring to the content in the Quick Start Guide.
+
+
+## Inference and Deployment
+
+HunyuanLLM uses TRT-LLM and vLLM for deployment. We are open sourcing the vLLM deployment (see Reasoning with vLLM), and the TRT-LLM deployment (see Reasoning with TRT-LLM) will be available in the near future.
+
+Learn More at Tencent-Hunyuan-Large.
+
+
+### Citation
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{sun2024hunyuanlargeopensourcemoemodel,
+ title={Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent},
+ author={Xingwu Sun and Yanfeng Chen and Yiqing Huang and Ruobing Xie and Jiaqi Zhu and Kai Zhang and Shuaipeng Li and Zhen Yang and Jonny Han and Xiaobo Shu and Jiahao Bu and Zhongzhi Chen and Xuemeng Huang and Fengzong Lian and Saiyong Yang and Jianfeng Yan and Yuyuan Zeng and Xiaoqin Ren and Chao Yu and Lulu Wu and Yue Mao and Tao Yang and Suncong Zheng and Kan Wu and Dian Jiao and Jinbao Xue and Xipeng Zhang and Decheng Wu and Kai Liu and Dengpeng Wu and Guanghui Xu and Shaohua Chen and Shuang Chen and Xiao Feng and Yigeng Hong and Junqiang Zheng and Chengcheng Xu and Zongwei Li and Xiong Kuang and Jianglu Hu and Yiqi Chen and Yuchi Deng and Guiyang Li and Ao Liu and Chenchen Zhang and Shihui Hu and Zilong Zhao and Zifan Wu and Yao Ding and Weichao Wang and Han Liu and Roberts Wang and Hao Fei and Peijie She and Ze Zhao and Xun Cao and Hai Wang and Fusheng Xiang and Mengyuan Huang and Zhiyuan Xiong and Bin Hu and Xuebin Hou and Lei Jiang and Jiajia Wu and Yaping Deng and Yi Shen and Qian Wang and Weijie Liu and Jie Liu and Meng Chen and Liang Dong and Weiwen Jia and Hu Chen and Feifei Liu and Rui Yuan and Huilin Xu and Zhenxiang Yan and Tengfei Cao and Zhichao Hu and Xinhua Feng and Dong Du and Tinghao She and Yangyu Tao and Feng Zhang and Jianchen Zhu and Chengzhong Xu and Xirui Li and Chong Zha and Wen Ouyang and Yinben Xia and Xiang Li and Zekun He and Rongpeng Chen and Jiawei Song and Ruibin Chen and Fan Jiang and Chongqing Zhao and Bo Wang and Hao Gong and Rong Gan and Winston Hu and Zhanhui Kang and Yong Yang and Yuhong Liu and Di Wang and Jie Jiang},
+ year={2024},
+ eprint={2411.02265},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL},
+ url={https://arxiv.org/abs/2411.02265},
+}
+```
+
+
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..438b179deced68df9e8b0e0c246210850d1ca004
--- /dev/null
+++ b/config.json
@@ -0,0 +1 @@
+downloads counted
\ No newline at end of file