テキスト比較

2 つのテキストファイルの違いを見つける

Real-time diff

Unified diff

Collapse lines

Highlight change

Syntax highlighting

ツール

Diffchecker Desktop The most secure way to run Diffchecker. Get the Diffchecker Desktop app: your diffs never leave your computer!Get Desktop

Hunyuan Left A13B Right Large

Created a day agoDiff expires in 29 days

Lines
Total
Removed

Words
Total
Removed

To continue using this feature, upgrade to Diffchecker Pro View Pricing

558 lines

Lines
Total
Added

Words
Total
Added

To continue using this feature, upgrade to Diffchecker Pro View Pricing

559 lines

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

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

# You may obtain a copy of the License at

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

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

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

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

# See the License for the specific language governing permissions and

# limitations under the License.

""" PyTorch HunYuan model."""

import math

import warnings

from typing import List, Optional, Tuple, Union

import torch

from torch import Tensor

import torch.nn.functional as F

import torch.utils.checkpoint

from torch import nn

from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from transformers.activations import ACT2FN

from transformers.cache_utils import Cache, DynamicCache

from transformers.modeling_attn_mask_utils import (

AttentionMaskConverter,

_prepare_4d_attention_mask,

_prepare_4d_causal_attention_mask,

_prepare_4d_causal_attention_mask_for_sdpa,

)

from transformers.modeling_outputs import (

BaseModelOutputWithPast,

CausalLMOutputWithPast,

SequenceClassifierOutputWithPast

)

from transformers.modeling_utils import PreTrainedModel

from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13

from transformers.utils import (

add_start_docstrings,

add_start_docstrings_to_model_forward,

is_flash_attn_2_available,

is_flash_attn_greater_or_equal_2_10,

logging,

replace_return_docstrings,

)

from transformers.utils.import_utils import is_torch_fx_available

from .configuration_hunyuan import HunYuanConfig

if is_flash_attn_2_available():

from flash_attn import flash_attn_func, flash_attn_varlen_func

from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa

# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.

# It means that the function will not be traced through and simply appear as a node in the graph.

if is_torch_fx_available():

if not is_torch_greater_or_equal_than_1_13:

import torch.fx

_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "HunYuanConfig"

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

logits = logits.float()

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

# expert_capacity = topk * gates.shape[0]

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()

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:

if seq_len > self.max_seq_len_cached:

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

return (

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

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

)

class HunYuanLinearScalingRotaryEmbedding(HunYuanRotaryEmbedding):

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

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

self.scaling_factor = scaling_factor

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

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

self.max_seq_len_cached = seq_len

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

t = t / self.scaling_factor

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

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

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

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

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

class HunYuanDynamicNTKScalingRotaryEmbedding(HunYuanRotaryEmbedding):

"""

HunYuanRotaryEmbedding extended with Dynamic NTK scaling.

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

"""

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

self.scaling_factor = scaling_factor

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

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

self.max_seq_len_cached = seq_len

if seq_len > self.max_position_embeddings:

base = self.base * (

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

) ** (self.dim / (self.dim - 2))

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

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

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

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

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

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

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

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

class HunYuanDynamicNTKAlphaRotaryEmbedding(HunYuanRotaryEmbedding):

"""

HunYuanRotaryEmbedding extended with Dynamic NTK scaling.

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

"""

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

self.scaling_alpha = scaling_alpha

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

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

self.max_seq_len_cached = seq_len

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

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

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

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

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

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

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

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

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

def rotate_half(x):

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

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

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

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

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

"""Applies Rotary Position Embedding to the query and key tensors.

Args:

q (`torch.Tensor`): The query tensor.

k (`torch.Tensor`): The key tensor.

cos (`torch.Tensor`): The cosine part of the rotary embedding.

sin (`torch.Tensor`): The sine part of the rotary embedding.

position_ids (`torch.Tensor`):

The position indices of the tokens corresponding to the query and key tensors. For example, this can be

used to pass offsetted position ids when working with a KV-cache.

unsqueeze_dim (`int`, *optional*, defaults to 1):

The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and

sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note

that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and

k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes

cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have

the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.

Returns:

`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.

"""

cos = cos[position_ids].unsqueeze(unsqueeze_dim)

sin = sin[position_ids].unsqueeze(unsqueeze_dim)

q_embed = (q * cos) + (rotate_half(q) * sin)

k_embed = (k * cos) + (rotate_half(k) * sin)

return q_embed, k_embed

class HunYuanMLP(nn.Module):

def __init__(self, config: HunYuanConfig, layer_idx=None, is_shared_mlp=False):

super().__init__()

self.config = config

self.layer_idx = layer_idx

self.hidden_size = config.hidden_size

if is_shared_mlp:

self.intermediate_size = config.intermediate_size * config.num_shared_expert[0]

self.intermediate_size = config.intermediate_size * config.num_shared_expert

else:

self.intermediate_size = config.intermediate_size

self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)

self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)

self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)

self.act_fn = ACT2FN[config.hidden_act]

def forward(self, x):

if self.config.pretraining_tp > 1:

slice = self.intermediate_size // self.config.pretraining_tp

gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)

up_proj_slices = self.up_proj.weight.split(slice, dim=0)

down_proj_slices = self.down_proj.weight.split(slice, dim=1)

gate_proj = torch.cat(

[F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1

)

up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)

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

down_proj = [

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

]

down_proj = sum(down_proj)

else:

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

return down_proj

class HunYuanTopKGate(nn.Module):

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

super().__init__()

self.config = config

self.layer_idx = layer_idx

self.moe_topk = config.moe_topk

self.drop_tokens = config.moe_drop_tokens

self.min_capacity = 8

self.random_routing_dropped_token = config.moe_random_routing_dropped_token

self.wg = nn.Linear(config.hidden_size, config.num_experts, bias=False, dtype=torch.float32)

def forward(self, hidden_states):

bsz, seq_len, hidden_size = hidden_states.shape

hidden_states = hidden_states.reshape(-1, hidden_size)

if self.wg.weight.dtype == torch.float32:

hidden_states = hidden_states.float()

logits = self.wg(hidden_states)

if self.moe_topk == 1:

gate_output = top1gating(logits, random_routing_dropped_token=self.random_routing_dropped_token)

else:

gate_output = topkgating(logits, self.moe_topk[0])

gate_output = topkgating(logits, self.moe_topk)

return gate_output

class HunYuanMoE(nn.Module):

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

super().__init__()

self.config = config

self.layer_idx = layer_idx

self.moe_topk = config.moe_topk

self.num_experts = config.num_experts

if config.use_mixed_mlp_moe:

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

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

self.experts = nn.ModuleList(

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

)

def forward(self, hidden_states):

bsz, seq_len, hidden_size = hidden_states.shape

if self.config.use_mixed_mlp_moe:

hidden_states_mlp = self.shared_mlp(hidden_states)

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

reshaped_input = hidden_states.reshape(-1, hidden_size)

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

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

expert_outputs = []

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

expert_outputs.append(expert(chunk))

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

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

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

if self.config.use_mixed_mlp_moe:

output = hidden_states_mlp + combined_output

else:

output = combined_output

return output

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

"""

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

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

"""

batch, num_key_value_heads, slen, head_dim = hidden_states.shape

if n_rep == 1:

return hidden_states

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

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

class HunYuanAttention(nn.Module):

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

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

super().__init__()

self.config = config

self.layer_idx = layer_idx

# layer_idx 从 0 开始

self.attention_type = 'cross' if config.use_cla and layer_idx % config.cla_share_factor != 0 else 'self'

if layer_idx is None:

logger.warning_once(

f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "

"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "

"when creating this class."

)

self.attention_dropout = config.attention_dropout

self.hidden_size = config.hidden_size

self.num_heads = config.num_attention_heads

self.head_dim = self.hidden_size // self.num_heads

self.num_key_value_heads = config.num_key_value_heads

self.num_key_value_groups = self.num_heads // self.num_key_value_heads

self.max_position_embeddings = config.max_position_embeddings

self.rope_theta = config.rope_theta

self.is_causal = True

self.use_qk_norm = config.use_qk_norm

if (self.head_dim * self.num_heads) != self.hidden_size:

raise ValueError(

f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"

f" and `num_heads`: {self.num_heads})."

)

self.q_proj = nn.Linear(self.hidden_size, self.num_h

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_bi

保存された差分

原文

ファイルを開く

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

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

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

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import (
    AttentionMaskConverter,
    _prepare_4d_attention_mask,
    _prepare_4d_causal_attention_mask,
    _prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    SequenceClassifierOutputWithPast
)
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)
from transformers.utils.import_utils import is_torch_fx_available
from .configuration_hunyuan import HunYuanConfig

if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa

# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
# It means that the function will not be traced through and simply appear as a node in the graph.
if is_torch_fx_available():
    if not is_torch_greater_or_equal_than_1_13:
        import torch.fx

_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "HunYuanConfig"

def topkgating(logits: Tensor, topk: int):
    logits = logits.float()
    gates = F.softmax(logits, dim=1)
    # expert_capacity = topk * gates.shape[0]
    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.
"""

@add_start_docstrings(
    "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
    HUNYUAN_START_DOCSTRING,
)
class HunYuanPreTrainedModel(PreTrainedModel):
    config_class = HunYuanConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["HunYuanDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True

def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

HUNYUAN_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

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

[What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

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

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

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

If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

- 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

[What are position IDs?](../glossary#position-ids)
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

Two formats are allowed:
            - a [`~cache_utils.Cache`] instance;
            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
            cache format.

The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
            legacy cache format will be returned.

If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
            of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    "The bare HunYuan Model outputting raw hidden-states without any specific head on top.",
    HUNYUAN_START_DOCSTRING,
)
class HunYuanModel(HunYuanPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`HunYuanDecoderLayer`]

Args:
        config: HunYuanConfig
    """

def __init__(self, config: HunYuanConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [HunYuanDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self._use_sdpa = config._attn_implementation == "sdpa"
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
        self.norm = HunYuanRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

self.cla = config.use_cla
        self.cla_share_factor = config.cla_share_factor

self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

def get_input_embeddings(self):
        return self.embed_tokens

def set_input_embeddings(self, value):
        self.embed_tokens = value

@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

return_dict = return_dict if return_dict is not None else self.config.use_return_dict

# retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape[:2]
        elif inputs_embeds is not None:
            batch_size, seq_length = inputs_embeds.shape[:2]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

past_key_values_length = 0
        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_length)

if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0)

if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        
        # Fix lora with gradient checkpointing training
        if self.training and inputs_embeds.is_leaf:
            inputs_embeds.requires_grad = True

if self._use_flash_attention_2:
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        elif self._use_sdpa and not output_attentions:
            # output_attentions=True can not be supported when using SDPA, and we fall back on
            # the manual implementation that requires a 4D causal mask in all cases.
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
            )

# embed positions
        hidden_states = inputs_embeds

# decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

prev_kv_states = None
        for layer_idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    prev_kv_states,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    kv_states=prev_kv_states
                )

hidden_states = layer_outputs[0]

if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

if output_attentions:
                all_self_attns += (layer_outputs[1],)

kv_states = layer_outputs[-1]

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

hidden_states = self.norm(hidden_states)

# add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

next_cache = None
        if use_cache:
            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )

class 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

# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

hidden_states = outputs[0]
        if self.config.pretraining_tp > 1:
            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
            logits = torch.cat(logits, dim=-1)
        else:
            logits = self.lm_head(hidden_states)
        logits = logits.float()

loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_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

@add_start_docstrings(
    """
    The HunYuan Model transformer with a sequence classification head on top (linear layer).

[`HunYuanForSequenceClassification`] uses the last token in order to do the classification, as other causal models
    (e.g. GPT-2) do.

Since it does classification on the last token, it requires to know the position of the last token. If a
    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
    each row of the batch).
    """,
    HUNYUAN_START_DOCSTRING,
)
class HunYuanForSequenceClassification(HunYuanPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = HunYuanModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

# Initialize weights and apply final processing
        self.post_init()

def get_input_embeddings(self):
        return self.model.embed_tokens

def set_input_embeddings(self, value):
        self.model.embed_tokens = value

@add_start_docstrings_to_model_forward(HUNYUAN_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

transformer_outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

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

if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
                    logits.device
                )
            else:
                sequence_lengths = -1

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

loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

変更されたテキスト

ファイルを開く

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]
    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)

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

# 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]

# 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

ALL_LAYERNORM_LAYERS.append(HunYuanRMSNorm)

class HunYuanRotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
        inv_freq = inv_freq.bfloat16()
        self.register_buffer("inv_freq", inv_freq, persistent=False)

# Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )

def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)

freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1).float()
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)