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Hunyuan.py vs hunyuan_modeling.py

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# coding=utf-8

# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

""" PyTorch HunYuan model."""

import math

import warnings

from typing import List, Optional, Tuple, Union

import torch

from torch import Tensor

import torch.nn.functional as F

import torch.utils.checkpoint

from torch import nn

from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN

from transformers.cache_utils import Cache, DynamicCache

from transformers.modeling_attn_mask_utils import (

AttentionMaskConverter,

_prepare_4d_attention_mask,

_prepare_4d_causal_attention_mask,

_prepare_4d_causal_attention_mask_for_sdpa,

)

from transformers.modeling_outputs import (

BaseModelOutputWithPast,

CausalLMOutputWithPast,

SequenceClassifierOutputWithPast

)

from transformers.modeling_utils import PreTrainedModel

from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13

from transformers.utils import (

add_start_docstrings,

add_start_docstrings_to_model_forward,

is_flash_attn_2_available,

is_flash_attn_greater_or_equal_2_10,

logging,

replace_return_docstrings,

)

from transformers.utils.import_utils import is_torch_fx_available

from transformers.generation.utils import GenerateOutput

from .configuration_hunyuan import HunYuanConfig

from .modeling_hunyuan import HunYuanDecoderLayer, HunYuanRMSNorm

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"

HUNYUAN_START_DOCSTRING = r"""

def topkgating(logits: Tensor, topk: int):

This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the

logits = logits.float()

library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads

gates = F.softmax(logits, dim=1)

etc.)

# expert_capacity = topk * gates.shape[0]

expert_capacity = max(topk, topk * gates.shape[0] // gates.shape[1])

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)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.

gates_s = torch.clamp(

Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage

torch.matmul(expert_mask.float(), gates.unsqueeze(-1)).sum(dim=1), min=torch.finfo(gates.dtype).eps

and behavior.

)

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)

Parameters:

# Create mask out of indices.

config ([`HunYuanConfig`]):

# Shape: [tokens_per_group * num_selected_experts, num_experts].

Model configuration class with all the parameters of the model. Initializing with a config file does not

expert_mask = F.one_hot(expert_index, num_experts).to(torch.int32)

load the weights associated with the model, only the configuration. Check out the

exp_counts = torch.sum(expert_mask, dim=0).detach()

[`~PreTrainedModel.from_pretrained`] method to load the model weights.

"""

# 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]

@add_start_docstrings(

# Token T can only be routed to expert E if its priority is positive and

"The bare HunYuan Model outputting raw hidden-states without any specific head on top.",

# less than the expert capacity. One-hot matrix will ignore indices outside

HUNYUAN_START_DOCSTRING,

# the range [0, expert_capacity).

)

# Shape: [tokens_per_group, num_experts, expert_capacity].

class HunYuanPreTrainedModel(PreTrainedModel):

valid_mask = torch.logical_and(token_priority >= 0, token_priority < expert_capacity)

config_class = HunYuanConfig

token_priority = torch.masked_fill(token_priority, ~valid_mask, 0)

base_model_prefix = "model"

dispatch_mask = F.one_hot(token_priority, expert_capacity).to(torch.bool)

supports_gradient_checkpointing = True

valid_mask = valid_mask.unsqueeze(-1).expand(-1, -1, expert_capacity)

_no_split_modules = ["HunYuanDecoderLayer"]

dispatch_mask = torch.masked_fill(dispatch_mask, ~valid_mask, 0)

_skip_keys_device_placement = "past_key_values"

_supports_flash_attn_2 = True

_supports_sdpa = True

_supports_cache_class = True

def _init_weights(self, module):

# The combine array will be used for combining expert outputs, scaled by the

std = self.config.initializer_range

# router probabilities. Shape: [num_groups, tokens_per_group, num_experts,

if isinstance(module, nn.Linear):

# expert_capacity].

module.weight.data.normal_(mean=0.0, std=std)

combine_weights = torch.einsum("...te,...tec->...tec", router_probs, dispatch_mask)

if module.bias is not None:

exp_counts_capacity = torch.sum(dispatch_mask)

module.bias.data.zero_()

exp_capacity_rate = exp_counts_capacity / (logits.shape[0]*topk)

elif isinstance(module, nn.Embedding):

module.weight.data.normal_(mean=0.0, std=std)

return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts

if module.padding_idx is not None:

module.weight.data[module.padding_idx].zero_()

HUNYUAN_INPUTS_DOCSTRING = r"""

def top1gating(logits: Tensor, random_routing_dropped_token: bool = False):

Args:

"""Implements Top1Gating on logits."""

input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):

# everything is in fp32 in this function

Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide

logits = logits.float()

it.

gates = F.softmax(logits, dim=1)

capacity = gates.shape[0]

Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and

# Create a mask for 1st's expert per token

[`PreTrainedTokenizer.__call__`] for details.

# 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)

[What are input IDs?](../glossary#input-ids)

# gating decisions

attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):

# exp_counts = torch.sum(mask1, dim=0).detach().to('cpu')

Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

exp_counts = torch.sum(mask1, dim=0).detach()

- 1 for tokens that are **not masked**,

# Compute l_aux

- 0 for tokens that are **masked**.

me = torch.mean(gates, dim=0)

ce = torch.mean(mask1.float(), dim=0)

l_aux = torch.sum(me * ce) * num_experts

mask1_rand = mask1

[What are attention masks?](../glossary#attention-mask)

top_idx = torch.topk(mask1_rand, k=capacity, dim=0)[1]

Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and

new_mask1 = mask1 * torch.zeros_like(mask1).scatter_(0, top_idx, 1)

[`PreTrainedTokenizer.__call__`] for details.

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

If `past_key_values` is used, optionally only the last `input_ids` have to be input (see

# Compute locations in capacity buffer

`past_key_values`).

locations1 = torch.cumsum(mask1, dim=0) - 1

If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]

# Store the capacity location for each token

and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more

locations1_s = torch.sum(locations1 * mask1, dim=1)

information on the default strategy.

- 1 indicates the head is **not masked**,

# Normalize gate probabilities

- 0 indicates the head is **masked**.

mask1_float = mask1.float()

position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):

gates = gates * mask1_float

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)

locations1_sc = F.one_hot(locations1_s, num_classes=capacity).float() # one hot to float

past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):

combine_weights = torch.einsum("se,sc->sec", gates, locations1_sc)

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:

dispatch_mask = combine_weights.bool()

- 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

exp_counts_capacity = torch.sum(mask1_bk)

legacy cache format will be returned.

exp_capacity_rate = exp_counts_capacity / (logits.shape[0])

return [l_aux, exp_capacity_rate], combine_weights, dispatch_mask, exp_counts

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`

def _get_unpad_data(attention_mask):

of shape `(batch_size, sequence_length)`.

seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)

inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):

indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()

Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This

max_seqlen_in_batch = seqlens_in_batch.max().item()

is useful if you want more control over how to convert `input_ids` indices into associated vectors than the

cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))

model's internal embedding lookup matrix.

return (

use_cache (`bool`, *optional*):

indices,

If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see

cu_seqlens,

`past_key_values`).

max_seqlen_in_batch,

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(

def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):

"The bare HunYuan Model outputting raw hidden-states without any specific head on top.",

warnings.warn(

HUNYUAN_START_DOCSTRING,

"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"

class HunYuanModel(HunYuanPreTrainedModel):

)

"""

return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)

Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HunYuanDecoderLayer`]

Args:

config: HunYuanConfig

"""

def __init__(self, config: HunYuanConfig):

def _make_causal_mask(

super().__init__(config)

input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0

self.padding_idx = config.pad_token_id

self.vocab_size = config.vocab_size

warnings.warn(

self.add_classification_head = config.add_classification_head

"Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in "

self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)

"v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"

self.layers = nn.ModuleList(

)

[HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]

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()

)

self._use_sdpa = config._attn_implementation == "sdpa"

self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"

if not config.add_classification_head:

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

def _set_cos_sin_cache(self, seq_len, device, dtype):

# Initialize weights and apply final processing

self.max_seq_len_cached = seq_len

self.post_init()

t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)

def get_input_embeddings(self):

self.inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))

return self.embed_tokens

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 set_input_embeddings(self, value):

def forward(self, x, seq_len=None):

self.embed_tokens = value

# x: [bs, num_attention_heads, seq_len, head_size]

if seq_len > self.max_seq_len_cached or self.inv_freq.dtype != torch.float32:

self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)

return (

def forward(

self.cos_cached[:seq_len].to(dtype=x.dtype),

self,

self.sin_cached[:seq_len].to(dtype=x.dtype),

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

class HunYuanLinearScalingRotaryEmbedding(HunYuanRotaryEmbedding):

# if input_ids is not None and inputs_embeds is not None:

"""HunYuanRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

# raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")

if 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:

def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):

if use_cache:

self.scaling_factor = scaling_factor

logger.warning_once(

super().__init__(dim, max_position_embeddings, base, device)

"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."

)

use_cache = False

past_key_values_length = 0

def _set_cos_sin_cache(self, seq_len, device, dtype):

if use_cache:

self.max_seq_len_cached = seq_len

use_legacy_cache = not isinstance(past_key_values, Cache)

t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

if use_legacy_cache:

t = t / self.scaling_factor

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:

freqs = torch.outer(t, self.inv_freq)

device = input_ids.device if input_ids is not None else inputs_embeds.device

# Different from paper, but it uses a different permutation in order to obtain the same calculation

position_ids = torch.arange(

emb = torch.cat((freqs, freqs), dim=-1)

past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device

self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)

)

self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

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:

class HunYuanDynamicNTKScalingRotaryEmbedding(HunYuanRotaryEmbedding):

# 2d mask is passed through the layers

"""

attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None

HunYuanRotaryEmbedding extended with Dynamic NTK scaling.

elif self._use_sdpa and not output_attentions:

Credits to the Reddit users /u/bloc97 and /u/emozilla

# 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

def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):

hidden_states = inputs_embeds

self.scaling_factor = scaling_factor

super().__init__(dim, max_position_embeddings, base, device)

# decoder layers

def _set_cos_sin_cache(self, seq_len, device, dtype):

all_hidden_states = () if output_hidden_states else None

self.max_seq_len_cached = seq_len

all_self_attns = () if output_attentions else None

next_decoder_cache = None

prev_kv_states = None

if seq_len > self.max_position_embeddings:

for layer_idx, decoder_layer in enumerate(self.layers):

base = self.base * (

if output_hidden_states:

(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)

all_hidden_states += (hidden_states,)

) ** (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)

if self.gradient_checkpointing and self.training:

t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

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]

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)

if use_cache:

next_decoder_cache = layer_outputs[2 if output_attentions else 1]

if output_attentions:

class HunYuanDynamicNTKAlphaRotaryEmbedding(HunYuanRotaryEmbedding):

all_self_attns += (layer_outputs[1],)

"""

HunYuanRotaryEmbedding extended with Dynamic NTK scaling.

Credits to the Reddit users /u/bloc97 and /u/emozilla

"""

kv_states = layer_outputs[-1]

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)

if self.cla and layer_idx % self.cla_share_factor == 0:

def _set_cos_sin_cache(self, seq_len, device, dtype):

prev_kv_states = kv_states

self.max_seq_len_cached = seq_len

if not self.add_classification_head:

base = self.base * self.scaling_alpha ** (self.dim / (self.dim-2))

hidden_states = self.norm(hidden_states)

inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))

# add hidden states from the last decoder layer

self.register_buffer("inv_freq", inv_freq, persistent=False)

if output_hidden_states:

all_hidden_states += (hidden_states,)

next_cache = None

t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)

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,

)

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 HunYuanMoEV1ForCausalLM(HunYuanPreTrainedModel):

_tied_weights_keys = ["lm_head.weight"]

def __init__(self, config: HunYuanConfig):

def rotate_half(x):

super().__init__(config)

"""Rotates half the hidden dims of the input."""

x1 = x[..., : x.shape[-1] // 2]

self.config = config

x2 = x[..., x.shape[-1] // 2:]

self.model = HunYuanModel(config)

return torch.cat((-x2, x1), dim=-1)

self.add_classification_head = config.add_classification_head

self.pad_id = config.pad_id

self.vocab_size = config.vocab_size

self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

if config.add_classification_head:

self.pool_head = nn.Linear(config.hidden_size, config.hidden_size, bias=False)

self.pool_head2 = nn.Linear(config.hidden_size, config.class_num, 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):

def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):

self.model.embed_tokens = value

"""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

def get_output_embeddings(self):

return self.lm_head

def set_output_embeddings(self, new_embeddings):

class HunYuanMLP(nn.Module):

self.lm_head = new_embeddings

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[0]

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 set_decoder(self, decoder):

def forward(self, x):

self.model = decoder

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)

def get_decoder(self):

gate_proj = torch.cat(

return self.model

[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)

@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)

intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)

@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)

down_proj = [

def forward(

F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)

self,

]

input_ids: torch.LongTensor = None,

down_proj = sum(down_proj)

attention_mask: Optional[torch.Tensor] = None,

else:

position_ids: Optional[torch.LongTensor] = None,

down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))

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:

return down_proj

Example:

```python

class HunYuanTopKGate(nn.Module):

>>> from transformers import AutoTokenizer, AutoModelForCausalLM

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)

>>> model = AutoModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)

def forward(self, hidden_states):

>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

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[0])

>>> prompt = "Hey, are you conscious? Can you talk to me?"

return gate_output

>>> 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)

class HunYuanMoE(nn.Module):

outputs = self.model(

def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):

input_ids=input_ids,

super().__init__()

attention_mask=attention_mask,

self.config = config

position_ids=position_ids,

self.layer_idx = layer_idx

past_key_values=past_key_values,

self.moe_topk = config.moe_topk

inputs_embeds=inputs_embeds,

self.num_experts = config.num_experts

use_cache=use_cache,

if config.use_mixed_mlp_moe:

output_attentions=output_attentions,

self.shared_mlp = HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=True)

output_hidden_states=output_hidden_states,

self.gate = HunYuanTopKGate(config, layer_idx=layer_idx)

return_dict=return_dict,

self.experts = nn.ModuleList(

[HunYuanMLP(config, layer_idx=layer_idx, is_shared_mlp=False) for _ in range(config.num_experts)]

)

hidden_states = outputs[0]

def forward(self, hidden_states):

bsz, seq_len, hidden_size = hidden_states.shape

if not self.add_classification_head:

if self.config.use_mixed_mlp_moe:

if self.config.pretraining_tp > 1:

hidden_states_mlp = self.shared_mlp(hidden_states)

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()

else:

logits = hidden_states

logits = logits.float()

pooled_output = self.pool_head(logits)

pooled_output = torch.tanh(pooled_output)

pooled_output = self.pool_head2(pooled_output).contiguous() # bs * class_num

if len(pooled_output.shape) < 2:

raise ValueError("pooled_output does not have enough dimensions for transpose")

if self.config.pool_type == "mean":

l_moe, combine_weights, dispatch_mask, exp_counts = self.gate(hidden_states)

reward = pooled_output.mean(dim=1).squeeze(-1)

elif self.config.pool_type == "last":

# bs * hidden_size

seq_length = (input_ids != self.pad_id).long().sum(dim=1) - 1

batch_size = input_ids.size(0)

reward = pooled_output[torch.arange(batch_size, device=pooled_output.device), seq_length].squeeze(-1)

else:

reward = pooled_output[:, 0].squeeze(-1)

loss = None

reshaped_input = hidden_states.reshape(-1, hidden_size)

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.reshape(-1, self.config.vocab_size)

shift_labels = shift_labels.reshape(-1)

# Enable model parallelism

shift_labels = shift_labels.to(shift_logits.device)

loss = loss_fct(shift_logits, shift_labels)

if not return_dict:

dispatched_input = torch.einsum("sec,sm->ecm", dispatch_mask.type_as(hidden_states), reshaped_input)

output = (logits,) + outputs[1:]

return (loss,) + output if loss is not None else output

output = CausalLMOutputWithPast(

chunks = dispatched_input.chunk(self.num_experts, dim=0)

loss=loss,

expert_outputs = []

logits=logits,

for chunk, expert in zip(chunks, self.experts):

past_key_values=outputs.past_key_values,

expert_outputs.append(expert(chunk))

hidden_states=outputs.hidden_states,

attentions=outputs.attentions,

expert_output = torch.cat(expert_outputs, dim=0)

)

combined_output = torch.einsum("sec,ecm->sm", combine_weights.type_as(hidden_states), expert_output)

if self.add_classification_head:

combined_output = combined_output.reshape(bsz, seq_len, hidden_size)

output['reward'] = reward

if self.config.use_mixed_mlp_moe:

output = hidden_states_mlp + combined_output

else:

output = combined_output

return output

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_cache_shape()

else:

cache_length = past_length = past_key_values[0][0].shape[2]

max_cache_length = None

# Keep only the unprocessed tokens:

def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:

# 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

This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,

# input)

num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)

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):]

batch, num_key_value_heads, slen, head_dim = hidden_states.shape

# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard

if n_rep == 1:

# input_ids based on the past_length.

return hidden_states

elif past_length < input_ids.shape[1]:

hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)

input_ids = input_ids[:, past_length:]

return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)

# 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)

class HunYuanAttention(nn.Module):

if attention_mask is not None and position_ids is None:

"""Multi-headed attention from 'Attention Is All You Need' paper"""

# 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

def __init__(self, config: HunYuanConfig, layer_idx: Optional[int] = None):

if inputs_embeds is not None and past_key_values is None:

super().__init__()

model_inputs = {"inputs_embeds": inputs_embeds}

self.config = config

else:

self.layer_idx = layer_idx

model_inputs = {"input_ids": input_ids}

# 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."

)

model_inputs.update(

self.attention_dropout = config.attention_dropout

{

self.hidden_size = config.hidden_size

"position_ids": position_ids,

self.num_heads = config.num_attention_heads

"past_key_values": past_key_values,

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_he

Diff salvati

Testo originale

Apri file

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 transformers.generation.utils import GenerateOutput
from .configuration_hunyuan import HunYuanConfig
from .modeling_hunyuan import HunYuanDecoderLayer, HunYuanRMSNorm

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)

_CONFIG_FOR_DOC = "HunYuanConfig"

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_()

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.add_classification_head = config.add_classification_head
        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"
        if not config.add_classification_head:
            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")
        if 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
        if not self.add_classification_head:
            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 HunYuanMoEV1ForCausalLM(HunYuanPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

def __init__(self, config: HunYuanConfig):
        super().__init__(config)
        
        self.config = config
        self.model = HunYuanModel(config)
        self.add_classification_head = config.add_classification_head
        self.pad_id = config.pad_id
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        if config.add_classification_head:
            self.pool_head = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
            self.pool_head2 = nn.Linear(config.hidden_size, config.class_num, 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

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 not self.add_classification_head:
            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()
        else:
            logits = hidden_states
            logits = logits.float()
            pooled_output = self.pool_head(logits)
            pooled_output = torch.tanh(pooled_output)
            pooled_output = self.pool_head2(pooled_output).contiguous()  # bs * class_num
            if len(pooled_output.shape) < 2:
                raise ValueError("pooled_output does not have enough dimensions for transpose")

if self.config.pool_type == "mean":
                reward = pooled_output.mean(dim=1).squeeze(-1)
            elif self.config.pool_type == "last":
                # bs * hidden_size
                seq_length = (input_ids != self.pad_id).long().sum(dim=1) - 1
                batch_size = input_ids.size(0)
                reward = pooled_output[torch.arange(batch_size, device=pooled_output.device), seq_length].squeeze(-1)
            else:
                reward = pooled_output[:, 0].squeeze(-1)

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.reshape(-1, self.config.vocab_size)
            shift_labels = shift_labels.reshape(-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

output = CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
        if self.add_classification_head:
            output['reward'] = reward

return output

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_cache_shape()
            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

class MultimodelHunYuanForCausalLM(HunYuanMoEV1ForCausalLM):
    _tied_weights_keys = ["lm_head.weight"]

def __init__(self, config: HunYuanConfig):
        super().__init__(config)

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."
        ```"""
        mask_init_id = self.config.mask_init_id
        pad_id = self.config.pad_token_id
        eod_id = self.config.eod_token_id
        image_token_id = self.config.image_token_id
        im_start_id = self.config.im_start_id
        im_end_id = self.config.im_end_id
        video_start_id = self.config.video_start_id
        video_end_id = self.config.video_end_id

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:
            labels = labels.to(logits.device)
            # Shift so that tokens < n predict n
            shift_logits = logits
            shift_labels = labels
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.reshape(-1, self.config.vocab_size)
            shift_labels = shift_labels.reshape(-1)
            shift_tokens = input_ids.reshape(-1)
            # compute loss
            mask = (shift_labels < mask_init_id) & (shift_labels != pad_id) & (shift_labels != image_token_id) & (shift_labels != im_start_id) \
                    & (shift_labels != im_end_id) & (shift_labels != video_start_id) & (shift_labels != video_end_id) & (shift_tokens != pad_id) & (shift_tokens != eod_id)
            shift_logits = shift_logits[mask, :]
            shift_labels = shift_labels[mask]
            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
    ):
        imgs = kwargs.pop("imgs", None)
        imgs_pos = kwargs.pop("imgs_pos", None)
        inputs = super().prepare_inputs_for_generation(
            input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, **kwargs
        )

if imgs is not None:
            inputs['imgs'] = imgs
        if imgs_pos is not None:
            inputs['imgs_pos'] = imgs_pos
        return inputs
    
    @torch.no_grad()
    def generate(
        self,
        inputs: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        imgs: Optional[List[torch.FloatTensor]] = None,
        imgs_pos: Optional[List[int]] = None,
        **kwargs,
    ) -> Union[GenerateOutput, torch.LongTensor]:
        if "inputs_embeds" in kwargs:
            raise NotImplementedError("`inputs_embeds` is not supported")
        
        return super().generate(
            inputs=input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            eos_token_id=self.config.eod_token_id,
            **kwargs
        )

@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.reshape(-1, self.num_labels), labels.reshape(-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,
        )

Testo modificato

Apri file

# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
#
# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://github.com/Tencent/Tencent-Hunyuan-Large/blob/main/License.docx
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
""" PyTorch HunYuan model."""

import math
import warnings
from typing import List, Optional, Tuple, Union

import torch
from torch import Tensor
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

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

_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]
    expert_capacity = max(topk, topk * gates.shape[0] // gates.shape[1])
    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)

self.inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
        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 or self.inv_freq.dtype != torch.float32:
            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 _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)

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)

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[0]
        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[0])

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)

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, 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)

key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

# 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.
"""

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.
"""

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

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")

# 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,)

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,)

class HunYuanMoEV1ForCausalLM(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

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

@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

if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

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