diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..007a06d23d83ad047d85f9faf411823216daec04
--- /dev/null
+++ b/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/configuration_hunyuan.py b/configuration_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b5d75860202b3f915120d230063b620e627d078
--- /dev/null
+++ b/configuration_hunyuan.py
@@ -0,0 +1,212 @@
+# coding=utf-8
+# Copyright 2024 Tencent Inc. All Rights Reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" 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/generation_config.json b/generation_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b99f428da0092596e95f17a97af46f35520727a6
--- /dev/null
+++ b/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/hy.tiktoken b/hy.tiktoken
new file mode 100644
index 0000000000000000000000000000000000000000..cedd95a03ff75f517446cdaeb03b20aadd97c410
--- /dev/null
+++ b/hy.tiktoken
@@ -0,0 +1,127957 @@
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+ICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICA= 1961
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+5Y+X5qyi6L+O 104851
+5a6Z 104852
+5oq95Y+W5LiL5YiX5paH5pys5Lit 104853
+4paB5ZA= 104854
+5Yqp5omL 104855
+T1NT 104856
+6aKE56S6 104857
+5pud 104858
+5p+/ 104859
+6L6Q 104860
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+54m6 104881
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+6L+q5aOr 104886
+5b636YeM 104887
+54m55bCU 104888
+5rS9 104889
+57uT5p6E5oCn 104890
+5Zo= 104891
+5paw6auY 104892
+55Wc54mn 104893
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+5pal 104895
+5YKF 104896
+5Yy755aX5L+d6Zmp 104897
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+4paB4paB4paB4paB4paB4paB4paB4paB4paB4paB4paB4paB4paB4paB4paB4paB 104902
+4paBX19fXw== 104903
+54mb5LuU 104904
+5pyJ6Imy 104905
+4paB5Lqa 104906
+4paBKA== 104907
+5bCP5p2/ 104908
+5Y+35rCR5LqL 104909
+5LiA56+H5paH56ug 104910
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+566t 104912
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+5LiN5oeI 104919
+6YeH6K6/5pe2 104920
+ZWNobg== 104921
+5oCd6K6u 104922
+5Z2q 104923
+5oqk6Iiq 104924
+6KGs 104925
+5LiA5qC3 104926
+5bel5L+h 104927
+5YWl5aKD 104928
+6aW/ 104929
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+562U5qGI5Li6 104931
+4paB44CK 104932
+5pSv6YWN 104933
+546L6ICF 104934
+JeOAgg== 104935
+5b+F5LiN5Y+v 104936
+6L6w 104937
+57uT5qC4 104938
+5byY 104939
+5LmU 104940
+5rih 104941
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+57uP6L+H 104949
+5bm25oyJ5a+55pa55b2T5LqL5Lq6 104950
+57Cn 104951
+5b+D6IKM 104952
+5Z2a5a6e 104953
+5oyH5a+85oSP6KeB 104954
+54A= 104955
+57yF 104956
+4paB5Y+l5a2Q 104957
+4paBb2Y= 104958
+6KA= 104959
+5Zui6Zif 104960
+ZXZlbG9w 104961
+5Lul5ZCO 104962
+5rGw 104963
+5bim5p2l 104964
+4paB5paH56ug 104965
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+55qE5paw 104974
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+6L+Q6JCl 104987
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+5q2k5aSW 104990
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+5pS55ZaE 104994
+6ICM5piv 104995
+6IWK 104996
+5a6D5Lus 104997
+6LW1 104998
+4paBY2w= 104999
+4oCd44CB4oCc 105000
+4paBdG8= 105001
+4paBYW5k 105002
+6K+t6KiA 105003
+5LuW5Lus55qE 105004
+55qE5pe26Ze0 105005
+5bCP5LyZ5Ly0 105006
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+6L+e57ut 105020
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+4paBLA== 105027
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+6K6p5Lq6 105031
+6L+Q55So 105032
+4paBLQ== 105033
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+5LuN54S2 105097
+5oCn5ZKM 105098
+5piv5oyH 105099
+4paBeHg= 105100
+4paBbWFu 105101
+5Lia57up 105102
+55qE5pa55rOV 105103
+5YyF6KOF 105104
+5bm/5bee 105105
+LOOAig== 105106
+5Yew 105107
+5LiA5p2h 105108
+5Lq655Sf 105109
+5LiK5LqG 105110
+4paB5pyJ 105111
+6K6p5oiR 105112
+5piv5LiN 105113
+6YO95Zyo 105114
+5pac 105115
+5omT5byA 105116
+4paBRg== 105117
+44CL44CC 105118
+562W55Wl 105119
+5rig6YGT 105120
+54m55Yir5piv 105121
+5LiA5L2N 105122
+5q+P5bm0 105123
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+4paB5Y+l5a2Q5LiA 105126
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+6aqo6LSo 105130
+6L2/ 105131
+6LCD6IqC 105132
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+5bm25rKh5pyJ 105134
+44CLKA== 105135
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+5q+U5LiK5bm0 105137
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+5L6d54S2 105139
+5Yy75a2m 105140
+55qE5L2c55So 105141
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+5bmz5pe2 105186
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+5Luk5Lq6 105188
+4paBXA== 105189
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+5pS75Ye7 105278
+57u05L+u 105279
+6L+Y5Lya 105280
+6K6p5L2g 105281
+5a6Y572R 105282
+PyM= 105283
+5Lik5Lq6 105284
+5bKB55qE 105285
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+6IiG 105287
+6L+Y6IO9 105288
+5pWZ56eR 105289
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+6K6k6K+B 105302
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+5aW55Lus 105364
+5p2R5rCR 105365
+5LiN5aSf 105366
+5Y+v5Lul5Zyo 105367
+4paBWw== 105368
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+5oyq 105370
+5a6e5aSE 105371
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+5pS25Yiw 105373
+56a75ama 105374
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+55qE5Lqn5ZOB 105378
+44CC44CK 105379
+5Lmf5bCx 105380
+4paB5byg 105381
+6K665paH 105382
+5b6I5pyJ 105383
+6bM= 105384
+4paB5bm0 105385
+56ys5LqU 105386
+6ZKl 105387
+4paB5LqU 105388
+5pys5Lq6 105389
+4paB5L2G 105390
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+4paB5a+55LqO 105397
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+6aG+5a6i 105402
+5a6k5YaF 105403
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+6L6F5Yqp 105407
+5pW055CG 105408
+5ouN5pGE 105409
+5LiA6L65 105410
+6ICD5a+f 105411
+4paBdW4= 105412
+5oiR5bCx 105413
+55qE5Z+65pys 105414
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+5Y2w5bqm 105416
+5Zyw55qE 105417
+5rCR6L+b 105418
+6K665Z2b 105419
+4paBaXM= 105420
+57uZ5L2g 105421
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+5Y2z5bCG 105425
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+5aaC5p6c5L2g 105432
+55qE5Lic6KW/ 105433
+5Lmf6IO9 105434
+5pSv5Ye6 105435
+4paBTw== 105436
+5rO7 105437
+55aj 105438
+55u45bqU55qE 105439
+5Lik5bm0 105440
+6Jg= 105441
+5Y+R5Ye6 105442
+5bCP5bq356S+5Lya 105443
+5bC65a+4 105444
+5bel5Y6C 105445
+5Y6f5paZ 105446
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+5oiR5piv 105458
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+5Yia5Yia 105460
+5Y6f55CG 105461
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+5ZGV 105470
+5riF5rSX 105471
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+6Ieq6LS4 105486
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+5ZKM5bmz 105488
+5YiG5Yir5Li6 105489
+5pyJ5b6I5aSa 105490
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+55Sz5oql 105503
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+55+/5Lqn 105507
+5oSk 105508
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+55qE5oi/5a2Q 120521
+4paBZXZlcnk= 120522
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+4paB5Zyo5q2k 120604
+5omn5Yuk 120605
+4paBd2hpY2g= 120606
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+5YWo5L2T5YWa5ZGY 120847
+6YO95pyJ5LiA5Liq 120848
+4paBb3Q= 120849
+4paBb3RoZXI= 120850
+6LWw6L+R 120851
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+5LmW5LmW 120873
+4paBaW50ZXI= 120874
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+4paBQmw= 121049
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+6Iac55qE 123377
+5paw5pe25pyf 123378
+6ams55qE 123379
+5a+56KKr 123380
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+6K+055qE6K+d 123382
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+5aSH5b+Y5b2V 123384
+55uK55Sf6I+M 123385
+5rex5Yqg5bel 123386
+5pu05aSa55qE5Lq6 123387
+5by65oKN 123388
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+5Yqf5Yqz 123391
+5aSN5pa5 123392
+5a2m57GN 123393
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+5Yed6KGA 123395
+5bel5L2c5oOF5Ya1 123396
+6IOM56a7 123397
+6aOO5bCa 123398
+6YeH6K6/5pe26KGo56S6 123399
+4paBd291bGQ= 123400
+54Ot54Gr 123401
+5pWw5qyh 123402
+56iN56iN 123403
+6L+35oOR 123404
+55S16ZWA 123405
+56eS5p2A 123406
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+55qE55yL5rOV 123442
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+5biu5Yqp5LiL 123468
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+5pys5Zy65q+U6LWb 123470
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+5LqO5b+D 123473
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+55qE5o2f5aSx 123476
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+5bey5bCG 123510
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+5pi+6JGX5oCn 123526
+5LmL5pel6LW35Y2B5pel5YaF 123527
+55qu55qE 123528
+4paBdGVhY2g= 123529
+55m96Zuq 123530
+5Zyo5biC5Zy6 123531
+556p55uu55qE 123532
+6ICB5bmy6YOo 123533
+5bC/6aKR 123534
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+6K+X5oSP 123536
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+5Y6f5qCH6aKY 123540
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+5oiR5Lmf5piv 123544
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+5Y2O5bGx 123546
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+5omA5L2c55qE 123548
+5Lmf5q2j5piv 123549
+6Kej5Yaz5Yqe5rOV 123550
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+5qCH5b+X5oCn 123553
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+5a655piT6YCg5oiQ 123555
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+5a+55oiR5Lus55qE 123559
+5a+G5pav 123560
+54ix55qE5Lq6 123561
+4paBdGhpbms= 123562
+5pm26I65 123563
+6IGK6IGK 123564
+5paw6Zu25ZSu 123565
+4paB5b2p 123566
+5r+A6L+b 123567
+576K5rC0 123568
+5bm05pe26Ze0 123569
+5LiN5ZKM 123570
+6YW456Kx 123571
+4paBTWU= 123572
+5bqU5oCl5aSE572u 123573
+4paB5YiZ 123574
+5aWX562S 123575
+5YmR5qGl 123576
+55uY55qE 123577
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+4paB5L2c5ZOB55uu5b2V 123588
+5Y+M6K+t 123589
+6aG65Liw 123590
+6K+l5pa55rOV 123591
+5b+D6KGA 123592
+4paBQ28= 123593
+5ZGK6K+r 123594
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+5rKz6L65 123650
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+5Zyo5a6h55CG 123666
+6IuP5qC85YWw 123667
+5byA5bel5bu66K6+ 123668
+6Ks= 123669
+5b2i5L2T 123670
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+5LiA5q2l5LiA5q2l 123672
+56S+5Lya5L+h55So 123673
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+5oiQ5Yqf5Li+5Yqe 123691
+5rKZ5ouJ 123692
+5oiR5LiN6IO9 123693
+dGVybmF0aW9uYWw= 123694
+5pWy6Zeo 123695
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+4paBbWV0aG9k 123730
+5Y2x5oCl 123731
+5oqA5pyv5Zyo 123732
+Y2RvdHM= 123733
+4paB5LqL5Lu2 123734
+5bu65oiQ5ZCO 123735
+5Zyw5Yq/ 123736
+56eY5Lmm5aSE 123737
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+4paBcHI= 123863
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+4paBZmlyc3Q= 123964
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+4L2g 126642
+542t 126643
+556g 126644
+6aOV 126645
+5ZeO 126646
+5q+9 126647
+55CN 126648
+5a2R 126649
+7Z8= 126650
+5b2K 126651
+6ZWE 126652
+6KeQ 126653
+4L2T 126654
+55me 126655
+6ZOk 126656
+4pS8 126657
+5bOL 126658
+54qS 126659
+4L20 126660
+5Y6p 126661
+54C5 126662
+6JOB 126663
+55aW 126664
+5ois 126665
+6La4 126666
+57eK 126667
+562G 126668
+57uU 126669
+6bKi 126670
+5b+W 126671
+5764 126672
+6LmH 126673
+6IeK 126674
+6IaR 126675
+55ym 126676
+5Luo 126677
+5bGM 126678
+542Q 126679
+6JiW 126680
+4L26 126681
+5a6T 126682
+5ZWc 126683
+55uh 126684
+552o 126685
+6KuH 126686
+6LCE 126687
+6aKm 126688
+0ok= 126689
+6LG6 126690
+5Zuh 126691
+55yZ 126692
+6IK8 126693
+7Yq7 126694
+5pKF 126695
+6KyC 126696
+5rKP 126697
+44CF 126698
+5ZOL 126699
+5bKY 126700
+44KA 126701
+5qiT 126702
+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
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+7LM= 127667
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+zKc= 127707
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+55WA 127808
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+6Zug 127810
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+7Ko= 127814
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+y4w= 127839
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+8J+P 127843
+8J+Puw== 127844
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+64+2 127851
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+6Lma 127853
+6ZyI 127854
+8Kg= 127855
+8Kix 127856
+8Kixkg== 127857
+55+N 127858
+6KO+ 127859
+yZE= 127860
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+4qw= 127905
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+6Ys= 127909
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+5YuW 127945
+6YKZ 127946
+6b6F 127947
+7Y4= 127948
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diff --git a/modeling_hunyuan.py b/modeling_hunyuan.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2e78653c702d1a56e8a1955465f375b417bafde
--- /dev/null
+++ b/modeling_hunyuan.py
@@ -0,0 +1,1715 @@
+# coding=utf-8
+# Copyright 2024 Tencent Inc. All Rights Reserved.
+#
+""" 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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diff --git a/test.py b/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f709b4379eaa882aab79ae9539a8cd940ad5a07
--- /dev/null
+++ b/test.py
@@ -0,0 +1,35 @@
+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}]")
\ No newline at end of file
diff --git a/test4consistent.py b/test4consistent.py
new file mode 100644
index 0000000000000000000000000000000000000000..8abccf17957fc6ab84fd19a61a6a5718491a5ecc
--- /dev/null
+++ b/test4consistent.py
@@ -0,0 +1,19 @@
+
+# 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/tokenization_hy.py b/tokenization_hy.py
new file mode 100644
index 0000000000000000000000000000000000000000..f10af8f9c53b300e43a29ab1c4024535f34470fe
--- /dev/null
+++ b/tokenization_hy.py
@@ -0,0 +1,354 @@
+# coding=utf-8
+# Copyright 2024 The Tencent Inc. HunYuan Team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+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/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1c239bd7995f53018fc0ad0089ecd014a23727b0
--- /dev/null
+++ b/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 %}"
+}
+