change

Hunyuan-A50B-Pretrain/config.json

@@ -1,46 +0,0 @@
-{
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "architectures": [
-    "HunYuanForCausalLM"
-  ],
-  "auto_map": {
-    "AutoConfig": "configuration_hunyuan.HunYuanConfig",
-    "AutoModel": "modeling_hunyuan.HunyuanModel",
-    "AutoModelForCausalLM": "modeling_hunyuan.HunYuanForCausalLM"
-  },
-  "bos_token_id": 1,
-  "capacity_factor": 1.0,
-  "cla_share_factor": 2,
-  "eos_token_id": 2,
-  "hidden_act": "silu",
-  "hidden_size": 6400,
-  "initializer_range": 0.02,
-  "intermediate_size": 18304,
-  "max_position_embeddings": 262144,
-  "model_type": "hunyuan",
-  "moe_drop_tokens": false,
-  "moe_random_routing_dropped_token": false,
-  "moe_topk": 1,
-  "num_attention_heads": 80,
-  "num_experts": 16,
-  "num_hidden_layers": 64,
-  "num_key_value_heads": 8,
-  "num_shared_expert": 1,
-  "pad_token_id": 0,
-  "pretraining_tp": 1,
-  "rms_norm_eps": 1e-05,
-  "rope_scaling": {
-    "alpha": 100000.0,
-    "factor": 1.0,
-    "type": "dynamic"
-  },
-  "rope_theta": 10000.0,
-  "tie_word_embeddings": true,
-  "transformers_version": "4.41.2",
-  "use_cache": true,
-  "use_cla": true,
-  "use_mixed_mlp_moe": true,
-  "use_qk_norm": true,
-  "vocab_size": 128512
-}

Hunyuan-A50B-Pretrain/configuration_hunyuan.py

@@ -1,192 +0,0 @@
-# coding=utf-8
-# Copyright 2024 Tencent Inc. All Rights Reserved.
-""" 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("`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}")

Hunyuan-A50B-Pretrain/generation_config.json

@@ -1,12 +0,0 @@
-{
-  "chat_format": "chatml",
-  "eos_token_id": 127957,
-  "pad_token_id": 127961,
-  "max_window_size": 6144,
-  "max_new_tokens": 512,
-  "do_sample": true,
-  "top_k": 20,
-  "top_p": 0.7,
-  "repetition_penalty": 1.0,
-  "transformers_version": "4.31.0"
-}

Hunyuan-A50B-Pretrain/modeling_hunyuan.py

@@ -1,1715 +0,0 @@
-# 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,
-        )

Hunyuan-A50B-Pretrain/pytorch_model-00001-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1dc37634e1930d3963535d0523cbde0931d525e02b64236c4530636ec9ffe462
-size 9986983328

Hunyuan-A50B-Pretrain/pytorch_model-00002-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e7f84a7d2b2e83f5dc870fe552e1cea957098dafa369ddbb48fa46c9b7dbdbfb
-size 9795604562

Hunyuan-A50B-Pretrain/pytorch_model-00003-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:77df873923eca055614f350e743ac306787c7124670861544e045f0aeb3fee75
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00004-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:da2c940c518e6df50bd6e7fdd8eccb2a6b16f2c1c54d8bf8a2e07b7fd00c92ff
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00005-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5f38ab3eb61069daf554e65c1f3c8e7fe94b3d2239c4bd1d8c1776b8ed5d2b4e
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00006-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1c7e0281e28338c2ff2e932eb922d49a61fb94a412b9c76b7f69a72c389a84ef
-size 9840245586

Hunyuan-A50B-Pretrain/pytorch_model-00007-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a80abb7484aa22de026bbd6ae71e9844cac6b6479811d33bb3e9dfefcf28d046
-size 9841638038

Hunyuan-A50B-Pretrain/pytorch_model-00008-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1a17737adc5f5a050042fcb41a286bdfa1588dd01202f589b6424d300802c453
-size 9841638014

Hunyuan-A50B-Pretrain/pytorch_model-00009-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:823505c864ad5ca1bd092edecfae7e68c7e8d151a00bc4eac7fe537318fe722a
-size 9841638002

Hunyuan-A50B-Pretrain/pytorch_model-00010-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:58ca1562400bf4e4694dc75fe06b93b6bc312996b44c7364c2570412e25c4b92
-size 9841638002

Hunyuan-A50B-Pretrain/pytorch_model-00011-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d1a31c8de2af3903bc35845a321c444ccec248d03b6b4a30b69e69a737c253dc
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00012-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9ec068b1e2d1ec16e58c4fb138e07a9636daa6e53678daf5e9a0e417942f4a2c
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00013-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:15a892ef2f91e3593bb9fc1e62619f8a1c39bf25142304bfce89f583cad78138
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00014-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:720c59aad36342abd7a2d4120b27796cb0ce81d40b1042f84ee6c482964bf26b
-size 9840245650

Hunyuan-A50B-Pretrain/pytorch_model-00015-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f5988b51d791d6449e1cb076333726196984d8ce15a0c5aaf38a1b1feadc1e7f
-size 9841638102

Hunyuan-A50B-Pretrain/pytorch_model-00016-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:54189fab48ac121ddcb3bc743f3e58868f9cde784dd35829ac71d128702690c6
-size 9841638078

Hunyuan-A50B-Pretrain/pytorch_model-00017-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1413c9aeb730b6651d8e07e66b29f18a5705cad143a08dbc0eeabdfcc89803f2
-size 9841638066

Hunyuan-A50B-Pretrain/pytorch_model-00018-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:21534e10706dd1674d716e1d2c727a62341bd24fb71bb7863afb849a35dbcb45
-size 9841638066

Hunyuan-A50B-Pretrain/pytorch_model-00019-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e89ad4534f00c5b80057e0b7f5c09dc5c3250c76277f7c70753254b41e647da5
-size 9841638642

Hunyuan-A50B-Pretrain/pytorch_model-00020-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:06acd5300df80e3a19fa81b6a1f07fb4341f6d236a9abdf5c6355460169dcd81
-size 8519585885

Hunyuan-A50B-Pretrain/pytorch_model-00021-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a3fd4a4f6d849cc3b382701c0b02185b7185be1480861c7e094b10881f0ae59e
-size 9994004482

Hunyuan-A50B-Pretrain/pytorch_model-00022-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0273795375f0fe9a3f0a6023d4f5136b77ba9a7e7d01f3fc9e8f977d05f1f1b1
-size 9783670286

Hunyuan-A50B-Pretrain/pytorch_model-00023-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:214860620cd4e3f2a76a6ecf3342fd1951c9dff727e48690f037fc4537c54e09
-size 9841638002

Hunyuan-A50B-Pretrain/pytorch_model-00024-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5ed0326cf5f651d68b1d44a5a4a620db6b8b183b6141794a45fd17cf1135476f
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00025-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:229a4c360e77740c43765d0be8bdf0d8f6c7f7b49cbed5e82b3aaa69048985ec
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00026-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1b7e0b3ee6220ea4f878d3cda3f986149811d6b2168d98a834820603c1d00d1c
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00027-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0c0d50f8905298b5f0029101d9b88dcb146e10b47298a0691b6830f7fd9d5a73
-size 9840245586

Hunyuan-A50B-Pretrain/pytorch_model-00028-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:383a2fd3e8844aeffa9d8301a0e086fe85aaa52635042d84ede002c1ad215baa
-size 9841638038

Hunyuan-A50B-Pretrain/pytorch_model-00029-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:c06d6664acc53e8ee2377e4c9d24aec1ce936ca3a16aa91352322f6fe73ff1bc
-size 9841638026

Hunyuan-A50B-Pretrain/pytorch_model-00030-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:70ccdfb8d25df5178468f42a2174d5bf1e6d57b0b1521e96c478355a8d243ac8
-size 9841638002

Hunyuan-A50B-Pretrain/pytorch_model-00031-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ba397be8edb97cdc6fb49dd26b757c341ec66461b3d9aa68989f6177fcc02f75
-size 9841638002

Hunyuan-A50B-Pretrain/pytorch_model-00032-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e2eab171a16d48be2bdd9db1fb8359a38a29063365b98f84134607cbec1dc1ad
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00033-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3f94d0ce0b5a0ffdc5a2420bd8fa9f038a39b11591b61d7542678d5bd4f53150
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00034-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:08668f2c0aa326e8a8af24f8e871de10915b075bbcf6b7b80fb5263179e735aa
-size 9841638642

Hunyuan-A50B-Pretrain/pytorch_model-00035-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:45ac7951c0f6ef9f05648d2ff0035d0d113ee78426155392c46a4ca71fb39b85
-size 9840245650

Hunyuan-A50B-Pretrain/pytorch_model-00036-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:18db40e0f0ec40a813550a523c6adbcc8d8315d0120547070aa41f27f8b5c7e1
-size 9841638102

Hunyuan-A50B-Pretrain/pytorch_model-00037-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:db95c6d4e8d3f2413a1d5db18d81461e0fc89dea4fd01a0eac1de38f289cad30
-size 9841638090

Hunyuan-A50B-Pretrain/pytorch_model-00038-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:851f0befef1a17e1fef76317e565705ede8756592a7b1a580631456d6f7c1b63
-size 9841638066

Hunyuan-A50B-Pretrain/pytorch_model-00039-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9bfe76cd734b20e4b1fb511d875833c8bbd38411c0e5559710800e19c2d58289
-size 9841638066

Hunyuan-A50B-Pretrain/pytorch_model-00040-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:03a909e37057bfa93ab98b4f10cfe92ab48f0ce69d24c6f60dd54d7943d96c78
-size 6879545036

Hunyuan-A50B-Pretrain/pytorch_model-00041-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3961e57e3b417e36eb02ce367329357ea24743400f452edb8ca94376edbcc90e
-size 9994004482

Hunyuan-A50B-Pretrain/pytorch_model-00042-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d5ad4ee06f8b166405c00dbc716de71c7e6537d0afb8aa20802b43d7f87fe4e9
-size 9783670286

Hunyuan-A50B-Pretrain/pytorch_model-00043-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1a26f8d57918f36ee78021ca36f4d64d4c2cd7fbbd52e19a44c70ecc761d1d83
-size 9841638002

Hunyuan-A50B-Pretrain/pytorch_model-00044-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2d6e96797eb4e4875e4b10f3cb34d7db993db411c0be8259ad4dc94faea75126
-size 9841638578

Hunyuan-A50B-Pretrain/pytorch_model-00045-of-00080.bin

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:18f94ff9c64842699f2b163a7eb24f008c5f5c3861fb3f28f92b8140b79ad689
-size 9841638578