# This file is modified based on https://github.com/huggingface/transformers/blob/v4.52.4/src/transformers/models/qwen3/modeling_qwen3.py. # # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/qwen3/modular_qwen3.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_qwen3.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable, Optional, Tuple, Union, List import torch from torch import nn from einops import rearrange from transformers.activations import ACT2FN from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache from transformers.generation import GenerationMixin from transformers.integrations import use_kernel_forward_from_hub from transformers.modeling_attn_mask_utils import AttentionMaskConverter from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_layers import GradientCheckpointingLayer from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging from configuration_sdar import SDARConfig from fused_linear_diffusion_cross_entropy import FusedLinearDiffusionCrossEntropyLoss from flash_attn.ops.triton.layer_norm import rms_norm_fn as flash_rms_norm import torch.nn.functional as F try: from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input except: pass try: from liger_kernel.ops.swiglu import LigerSiLUMulFunction # noqa: F401 liger_kernel_is_available = True except ImportError: liger_kernel_is_available = False if is_torch_flex_attn_available(): from torch.nn.attention.flex_attention import BlockMask, create_block_mask, flex_attention from transformers.integrations.flex_attention import make_flex_block_causal_mask logger = logging.get_logger(__name__) def modify_padded_position_ids_2d(position_ids: torch.LongTensor) -> torch.LongTensor: """ 使用完全向量化的 PyTorch 操作修改一个 batch 的 packed position_ids。 这个函数假设输入是一个 2D Tensor，形状为 (batch_size, sequence_length)。 它会独立地处理 batch 中的每一行。 Args: position_ids: 二维 PyTorch Tensor, shape (batch_size, sequence_length). Returns: 修改后的 position_ids Tensor, shape (batch_size, sequence_length). """ if position_ids.dim() != 2: raise ValueError(f"Input tensor must be 2D, but got {position_ids.dim()} dimensions.") batch_size, seq_len = position_ids.shape device = position_ids.device col_indices = torch.arange(seq_len, device=device, dtype=position_ids.dtype).expand(batch_size, -1) mask = (position_ids != 0) masked_indices = col_indices * mask last_nonzero_idx = torch.max(masked_indices, dim=1).values has_nonzero = torch.any(mask, dim=1) pad_start_idx = torch.where(has_nonzero, last_nonzero_idx + 1, torch.tensor(0, device=device, dtype=position_ids.dtype)) padding_mask = col_indices >= pad_start_idx.unsqueeze(1) new_pad_values = col_indices - pad_start_idx.unsqueeze(1) position_ids = torch.where(padding_mask, new_pad_values, position_ids) return position_ids def calculate_token_nums(position_ids: torch.Tensor): """ 使用 PyTorch 高效计算一个批次中每个打包序列的长度。 Args: position_ids (torch.Tensor): 一个 2D Tensor，形状为 (batch_size, sequence_length)。 例如：tensor([[0,1,2,3,4,0,1,2,3,4,5,0,1,2,3,0,0,0]]) Returns: list[list[int]]: 一个嵌套列表，包含每个批次项中各个序列的长度。 例如：[[5, 6, 4, 1, 1, 1]] """ # 检查输入是否为 2D Tensor if position_ids.dim() != 2: raise ValueError(f"输入必须是 2D Tensor，但得到了 {position_ids.dim()}D") all_lengths = [] # 我们按批次逐行处理。因为每行的序列长度数量不同（ragged）， # 所以 Python 循环在批次维度上是最高效且最清晰的写法。 # 循环内部的操作是完全向量化的。 for pids_row in position_ids: # 获取当前行的总长度 seq_len = pids_row.shape[0] # 1. 找到所有值为 0 的元素的索引 # pids_row == 0 会返回一个布尔 Tensor: [True, False, ..., True, ...] # torch.nonzero 会返回这些 True 值的索引 # .flatten() 将其从 (N, 1) 形状的 Tensor 变为 (N,) 形状 zero_indices = torch.nonzero(pids_row == 0).flatten() # 2. 将序列的总长度作为一个额外的切分点添加到末尾 # 这对于计算最后一个序列的长度至关重要 # 注意：要确保新创建的 tensor 和原始 tensor 在同一个设备上 (cpu/cuda) split_points = torch.cat([ zero_indices, torch.tensor([seq_len], device=pids_row.device, dtype=zero_indices.dtype) ]) # 3. 计算相邻切分点之间的差值，这就是我们想要的长度 # torch.diff([a, b, c, d]) 会返回 [b-a, c-b, d-c] lengths = torch.diff(split_points) all_lengths.append(lengths) return all_lengths def forward_add_noise_packed( inputs_ids: torch.Tensor, num_tokens_list: List[torch.Tensor], prompt_mask: torch.Tensor, mask_id: int, eps: float = 1e-3, max_tries: int = 10, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ 为一批打包（packed）序列的 token ID 添加噪声。 此函数保留了为每个逻辑样本（在每个批次项内拼接）生成独立随机噪声率的逻辑。 它会随机将一部分 token 的 ID 替换为 mask_id。 这个过程会避开被 prompt_mask 标记的位置。 Args: inputs_ids (torch.Tensor): 输入的 token ID 张量，形状为 (bsz, total_tokens)。 num_tokens_list (List[torch.Tensor]): 一个张量列表，长度为 bsz。列表中的每个张量记录了对应批次项中 每个逻辑样本的长度。例如: [tensor([len1, len2]), tensor([len3, len4, len5])]. prompt_mask (torch.Tensor): 布尔型张量，形状为 (bsz, total_tokens)，值为 True 的位置表示是 prompt， 不应添加噪声。 mask_id (int): 用于替换的 mask token 的 ID。 eps (float): 微小值，用于防止噪声率 t 恰好为 0，确保 p_mask > 0。 max_tries (int): 为确保至少一个非 prompt token 被 mask，对每个批次项尝试的最大次数。 Returns: Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - noisy_input_ids (torch.Tensor): 添加噪声后的 token ID 张量，形状为 (bsz, total_tokens)。 - final_masked_indices (torch.Tensor): 布尔型张量，标记了哪些位置被实际 mask 了，形状为 (bsz, total_tokens)。 - p_masks (torch.Tensor): 一个一维张量，包含了被 mask 的 token 对应的实际噪声率。 """ # 1. 验证和获取形状 bsz, total_tokens = inputs_ids.shape device = inputs_ids.device # 检查输入的一致性 assert len(num_tokens_list) == bsz, f"num_tokens_list 的长度 ({len(num_tokens_list)}) 必须等于 bsz ({bsz})" assert prompt_mask.shape == (bsz, total_tokens), f"prompt_mask 形状不匹配, 期望 {(bsz, total_tokens)}, 得到 {prompt_mask.shape}" # 准备结果容器 noisy_ids_list = [] final_masked_indices_list = [] p_masks_per_token_list = [] # 2. 在批次维度上迭代 # 这是处理不同打包结构最直接有效的方法 for i in range(bsz): # 提取当前批次项的数据 current_ids = inputs_ids[i:i+1] # shape: (1, total_tokens) current_num_tokens = num_tokens_list[i] current_prompt_mask = prompt_mask[i:i+1] # shape: (1, total_tokens) num_samples_in_item = len(current_num_tokens) # 验证当前批次项的 token 总数是否匹配 assert total_tokens == torch.sum(current_num_tokens), \ f"批次项 {i} 的 num_tokens 之和 ({torch.sum(current_num_tokens)}) 与 total_tokens ({total_tokens}) 不匹配" eligible_for_masking = ~current_prompt_mask # 如果没有任何 token 可以被 mask，直接使用原始输入，并设置 p_mask 为 eps if not eligible_for_masking.any(): noisy_ids_list.append(current_ids) final_masked_indices_list.append(torch.zeros_like(current_prompt_mask, dtype=torch.bool)) # p_mask_per_token 的形状应为 (1, total_tokens) 以便后续拼接 p_masks_per_token_list.append(torch.full((1, total_tokens), eps, device=device, dtype=torch.float)) continue # --- 尝试生成 mask，确保至少 mask 一个 token --- final_masked_indices_item = torch.zeros_like(current_prompt_mask, dtype=torch.bool) p_mask_per_token = None for _ in range(max_tries): # 为每个逻辑样本生成一个独立的噪声率 t t = torch.rand(num_samples_in_item, device=device) p_mask_per_sample = (1 - eps) * t + eps # 将每个样本的噪声率扩展到其所有 token 上 p_mask_per_token_1d = torch.repeat_interleave(p_mask_per_sample, current_num_tokens) p_mask_per_token = p_mask_per_token_1d.unsqueeze(0) # shape: (1, total_tokens) # 根据噪声率生成随机 mask masked_indices = torch.rand_like(p_mask_per_token) < p_mask_per_token # 应用 prompt mask，确保 prompt 不被 mask final_masked_indices_item = masked_indices & eligible_for_masking # 如果成功 mask 了至少一个 token，则跳出尝试循环 if final_masked_indices_item.any(): break # 如果 max_tries 之后仍然没有 mask 任何 token (极小概率)，就强制 mask 一个可 mask 的 token if not final_masked_indices_item.any(): eligible_indices = torch.nonzero(eligible_for_masking.squeeze(0), as_tuple=True)[0] if len(eligible_indices) > 0: # 随机选择一个可 mask 的位置 random_choice = torch.randint(0, len(eligible_indices), (1,)).item() force_mask_idx = eligible_indices[random_choice] final_masked_indices_item[0, force_mask_idx] = True # --- 根据最终的 mask 生成带噪声的 IDs --- noisy_ids_item = torch.where( final_masked_indices_item, mask_id, current_ids ) # 保存这个批次项的结果 noisy_ids_list.append(noisy_ids_item) final_masked_indices_list.append(final_masked_indices_item) p_masks_per_token_list.append(p_mask_per_token) # 3. 将列表中的结果堆叠成最终的批处理张量 noisy_input_ids = torch.cat(noisy_ids_list, dim=0) final_masked_indices = torch.cat(final_masked_indices_list, dim=0) p_mask_full = torch.cat(p_masks_per_token_list, dim=0) # 4. 提取被 mask 位置对应的噪声率 p_masks = p_mask_full[final_masked_indices] return noisy_input_ids, final_masked_indices, p_masks def block_diff_mask(b, h, q_idx, kv_idx, block_size=None, n=None): """ Constructs the specialized block diffusion attention mask for training composed of three masks: - **Block Diagonal Mask (M_BD)**: Self-attention within noised blocks - **Offset Block Causal Mask (M_OBC)**: Cross-attention for conditional context - **Block Causal Mask (M_BC)**: Attention to update x0 Args: b, h: Batch and head indices (ignored for mask logic). q_idx, kv_idx: Query and Key indices. seq_len: Total sequence length. block_size: Defines the block structure. Returns: A boolean attention mask. """ # Indicate whether token belongs to xt or x0 x0_flag_q = q_idx >= n x0_flag_kv = kv_idx >= n # Compute block indices block_q = torch.where( x0_flag_q == 1, (q_idx - n) // block_size, q_idx // block_size ) block_kv = torch.where( x0_flag_kv == 1, (kv_idx - n) // block_size, kv_idx // block_size ) # **1. Block Diagonal Mask (M_BD) ** block_diagonal = (block_q == block_kv) & (x0_flag_q == x0_flag_kv) # **2. Offset Block-Causal Mask (M_OBC) ** offset_block_causal = (block_q > block_kv) & ( x0_flag_kv == 1) & (x0_flag_q == 0) # **3. Block-Causal Mask (M_BC) ** block_causal = (block_q >= block_kv) & (x0_flag_kv == 1) & (x0_flag_q == 1) # **4. Combine Masks ** return block_diagonal | offset_block_causal | block_causal def block_attn_mask(num_tokens, block_size, device): masks = [] for i in range(len(num_tokens)): cur_masks = [] for num in num_tokens[i]: # 全部返回 n*n 而非 2n*2n single_mask = block_diff_mask( b=None, h=None, q_idx=torch.arange(num * 2, device=device)[:, None], kv_idx=torch.arange(num * 2, device=device)[None, :], block_size=block_size, n=num, ) cur_masks.append(single_mask) masks.append(torch.block_diag(*cur_masks)) masks = torch.stack(masks, dim=0) return masks @torch.compile(fullgraph=True, mode="max-autotune-no-cudagraphs") def fused_flex_attention(query, key, value, attention_mask, **kwargs): return flex_attention(query, key, value, block_mask=attention_mask, **kwargs) @use_kernel_forward_from_hub("RMSNorm") class SDARRMSNorm(nn.Module): def __init__(self, hidden_size, eps=1e-6): """ SDARRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): return flash_rms_norm( hidden_states, weight=self.weight, bias=None, eps=self.variance_epsilon) ''' 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) ''' def extra_repr(self): return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" class SDARMLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size 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 liger_kernel_is_available: return self.down_proj(LigerSiLUMulFunction.apply(self.gate_proj(x), self.up_proj(x))) else: down_proj = self.down_proj(self.act_fn( self.gate_proj(x)) * self.up_proj(x)) return down_proj 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=None, 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`, *optional*): Deprecated and unused. 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.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = (q * cos) + (rotate_half(q) * sin) k_embed = (k * cos) + (rotate_half(k) * sin) return q_embed, k_embed def 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) def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs, ): key_states = repeat_kv(key, module.num_key_value_groups) value_states = repeat_kv(value, module.num_key_value_groups) attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling if attention_mask is not None: causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask attn_weights = nn.functional.softmax( attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout( attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value_states) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights class SDARAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: SDARConfig, layer_idx: int): super().__init__() self.config = config self.layer_idx = layer_idx self.head_dim = getattr( config, "head_dim", config.hidden_size // config.num_attention_heads) self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads self.scaling = self.head_dim**-0.5 self.attention_dropout = config.attention_dropout self.is_causal = True self.hidden_size = config.hidden_size self.num_attention_heads = config.num_attention_heads self.num_key_value_heads = config.num_key_value_heads self.q_proj = nn.Linear( config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias ) self.k_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.v_proj = nn.Linear( config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias ) self.o_proj = nn.Linear( config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias ) # unlike olmo, only on the head dim! self.q_norm = SDARRMSNorm(self.head_dim, eps=config.rms_norm_eps) # thus post q_norm does not need reshape self.k_norm = SDARRMSNorm(self.head_dim, eps=config.rms_norm_eps) self.sliding_window = config.sliding_window if not ( self.config.use_sliding_window and getattr(self.config, "sliding_window", None) is not None and self.layer_idx >= self.config.max_window_layers ): self.sliding_window = None def forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: input_shape = hidden_states.shape[:-1] bsz, q_len = input_shape hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_norm(self.q_proj( hidden_states).view(hidden_shape)).transpose(1, 2) key_states = self.k_norm(self.k_proj( hidden_states).view(hidden_shape)).transpose(1, 2) value_states = self.v_proj(hidden_states).view( hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb( query_states, key_states, cos, sin) if past_key_value is not None and kwargs.get("store_kv", False): # sin and cos are specific to RoPE models; cache_position needed for the static cache key_states, value_states = past_key_value.update( key_states, value_states, self.layer_idx) elif past_key_value is not None and not kwargs.get("store_kv", False) and len(past_key_value) > self.layer_idx: # only retrive, do not store kv past_key_states, past_value_states = past_key_value[self.layer_idx] key_states = torch.cat( [past_key_states, key_states], dim=-2) value_states = torch.cat( [past_value_states, value_states], dim=-2) if self.training: attn_output, attn_weights = fused_flex_attention( query=query_states, key=key_states, value=value_states, attention_mask=attention_mask, enable_gqa=True, scale=self.scaling, return_lse=True ) attn_weights = attn_weights.to( value_states.dtype) if attn_weights is not None else None attn_output = rearrange(attn_output, 'b h l d -> b l (h d)') else: attention_mask = attention_mask.bool() if attention_mask is not None else None attn_weights = None if torch.all(attention_mask): # decoding query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) attn_output = flash_attn_func( query_states, key_states, value_states, causal=False, softmax_scale=self.scaling ) attn_output = rearrange(attn_output, 'b l h d -> b l (h d)') else: # prefilling attn_output = F.scaled_dot_product_attention( query=query_states, key=key_states, value=value_states, attn_mask=attention_mask, is_causal=False, scale=self.scaling, enable_gqa=True ) attn_output = rearrange(attn_output, 'b h l d -> b l (h d)') attn_output = self.o_proj(attn_output) return attn_output, attn_weights # , attn_weights class SDARDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: SDARConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size self.self_attn = SDARAttention(config=config, layer_idx=layer_idx) self.mlp = SDARMLP(config) self.input_layernorm = SDARRMSNorm( config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = SDARRMSNorm( config.hidden_size, eps=config.rms_norm_eps) if ( config.sliding_window and config._attn_implementation != "flash_attention_2" ): # diff with Llama is this warning logger.warning_once( f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " "unexpected results may be encountered." ) 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: Optional[bool] = False, use_cache: Optional[bool] = False, store_kv: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, # necessary, but kept here for BC position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights = 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, store_kv=store_kv, cache_position=cache_position, position_embeddings=position_embeddings, **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,) return outputs @auto_docstring class SDARPreTrainedModel(PreTrainedModel): config_class = SDARConfig base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["SDARDecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn_2 = True _supports_sdpa = True _supports_flex_attn = True _supports_cache_class = True _supports_quantized_cache = True _supports_static_cache = True _supports_attention_backend = 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_() elif isinstance(module, SDARRMSNorm): module.weight.data.fill_(1.0) class SDARRotaryEmbedding(nn.Module): def __init__(self, config: SDARConfig, device=None): super().__init__() # BC: "rope_type" was originally "type" if hasattr(config, "rope_scaling") and config.rope_scaling is not None: self.rope_type = config.rope_scaling.get( "rope_type", config.rope_scaling.get("type")) else: self.rope_type = "default" self.max_seq_len_cached = config.max_position_embeddings self.original_max_seq_len = config.max_position_embeddings self.config = config self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] inv_freq, self.attention_scaling = self.rope_init_fn( self.config, device) self.register_buffer("inv_freq", inv_freq, persistent=False) self.original_inv_freq = self.inv_freq @torch.no_grad() # power user: used with advanced RoPE types (e.g. dynamic rope) @dynamic_rope_update def forward(self, x, position_ids): inv_freq_expanded = self.inv_freq[None, :, None].float().expand( position_ids.shape[0], -1, 1).to(x.device) position_ids_expanded = position_ids[:, None, :].float() device_type = x.device.type if isinstance( x.device.type, str) and x.device.type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): # Force float32 freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) cos = emb.cos() * self.attention_scaling sin = emb.sin() * self.attention_scaling return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) @auto_docstring class SDARModel(SDARPreTrainedModel): def __init__(self, config: SDARConfig): 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( [SDARDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = SDARRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = SDARRotaryEmbedding(config=config) 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 @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, store_kv: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> 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 if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError( "You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False # TODO (joao): remove this exception in v4.56 -- it exists for users that try to pass a legacy cache if not isinstance(past_key_values, (type(None), Cache)): raise ValueError( "The `past_key_values` should be either a `Cache` object or `None`.") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache() if cache_position is None: past_seen_tokens = past_key_values.get_seq_length( ) if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) # causal_mask = self._update_causal_mask( # attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions # ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers[: self.config.num_hidden_layers]: if output_hidden_states: all_hidden_states += (hidden_states,) 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, store_kv=store_kv, cache_position=cache_position, position_embeddings=position_embeddings, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, ) def _update_causal_mask( self, attention_mask: Union[torch.Tensor, "BlockMask"], input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: Cache, output_attentions: bool = False, ): if self.config._attn_implementation == "flash_attention_2": if attention_mask is not None and past_key_values is not None: is_padding_right = attention_mask[:, - 1].sum().item() != input_tensor.size()[0] if is_padding_right: raise ValueError( "You are attempting to perform batched generation with padding_side='right'" " this may lead to unexpected behaviour for Flash Attention version of Qwen3. Make sure to " " call `tokenizer.padding_side = 'left'` before tokenizing the input. " ) if attention_mask is not None and 0.0 in attention_mask: return attention_mask return None if self.config._attn_implementation == "flex_attention": if isinstance(attention_mask, torch.Tensor): seq_len_q, seq_len_kv = attention_mask.shape assert seq_len_q == seq_len_kv, f"got {attention_mask.shape=}" attention_mask = create_block_mask( # 2d bool tensor, shape: [2*seqlen, 2*seqlen] lambda b, h, q_idx, kv_idx: attention_mask[q_idx, kv_idx], B=None, H=None, Q_LEN=seq_len_q, KV_LEN=seq_len_kv, ) else: # Here we pass in flex mask computed externally assert isinstance(attention_mask, BlockMask) return attention_mask # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail # to infer the attention mask. past_seen_tokens = past_key_values.get_seq_length( ) if past_key_values is not None else 0 using_static_cache = isinstance(past_key_values, StaticCache) using_sliding_window_cache = isinstance( past_key_values, SlidingWindowCache) # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward if ( self.config._attn_implementation == "sdpa" and not (using_static_cache or using_sliding_window_cache) and not output_attentions ): if AttentionMaskConverter._ignore_causal_mask_sdpa( attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens, sliding_window=self.config.sliding_window, is_training=self.training, ): return None dtype = input_tensor.dtype min_dtype = torch.finfo(dtype).min sequence_length = input_tensor.shape[1] # SlidingWindowCache or StaticCache if using_sliding_window_cache or using_static_cache: target_length = past_key_values.get_max_cache_shape() # DynamicCache or no cache else: target_length = ( attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else past_seen_tokens + sequence_length + 1 ) # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, cache_position=cache_position, batch_size=input_tensor.shape[0], config=self.config, past_key_values=past_key_values, ) if ( self.config._attn_implementation == "sdpa" and attention_mask is not None and attention_mask.device.type in ["cuda", "xpu", "npu"] and not output_attentions ): # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. # Details: https://github.com/pytorch/pytorch/issues/110213 causal_mask = AttentionMaskConverter._unmask_unattended( causal_mask, min_dtype) return causal_mask @staticmethod def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, config: SDARConfig, past_key_values: Cache, ): """ Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. Args: attention_mask (`torch.Tensor`): A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. sequence_length (`int`): The sequence length being processed. target_length (`int`): The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. dtype (`torch.dtype`): The dtype to use for the 4D attention mask. cache_position (`torch.Tensor`): Indices depicting the position of the input sequence tokens in the sequence. batch_size (`torch.Tensor`): Batch size. config (`SDARConfig`): The model's configuration class past_key_values (`Cache`): The cache class that is being used currently to generate """ if attention_mask is not None and attention_mask.dim() == 4: # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. causal_mask = attention_mask else: min_dtype = torch.finfo(dtype).min causal_mask = torch.full( (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device ) diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape( -1, 1 ) text_config = config.get_text_config() if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None: # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also # the check is needed to verify is current checkpoint was trained with sliding window or not if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length: sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= ( cache_position.reshape(-1, 1) - text_config.sliding_window ) diagonal_attend_mask.bitwise_or_(sliding_attend_mask) causal_mask *= diagonal_attend_mask causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) if attention_mask is not None: causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit if attention_mask.shape[-1] > target_length: attention_mask = attention_mask[:, :target_length] mask_length = attention_mask.shape[-1] padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to( causal_mask.device ) padding_mask = padding_mask == 0 causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( padding_mask, min_dtype ) return causal_mask class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... @auto_docstring class SDARForCausalLM(SDARPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): super().__init__(config) self.model = SDARModel(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 def prepare_for_bd_training(self, inputs_ids, position_ids, prompt_mask, masked_indices=None, p_mask_input=None): bsz, seq_len = inputs_ids.shape num_tokens = calculate_token_nums(position_ids) # List[torch.Tensor] # 如果手动传入了 masked_indices，就直接用它 if masked_indices is not None: # 手动mask模式：用于RL训练或固定mask实验 # 注意：外部传入的masked_indices已经只在response部分（通过 & response_mask），不需要再次过滤 noisy_inputs_ids = torch.where(masked_indices, self.config.mask_token_id, inputs_ids) logits_to_keep_half = masked_indices # (B, L) bool # 生成默认的p_mask：扁平化后的噪声率，形状为(M,)，其中M=sum(masked_indices) # 默认值0.5表示中等噪声水平（用于扩散loss） M = masked_indices.sum().item() p_mask = torch.full((M,), 0.5, device=inputs_ids.device, dtype=torch.float) else: # 随机mask模式：用于Block Diffusion预训练 # 返回：noisy_inputs_ids (B, L), logits_to_keep_half (B, L) bool, p_mask (M,) float noisy_inputs_ids, logits_to_keep_half, p_mask = forward_add_noise_packed( inputs_ids=inputs_ids, num_tokens_list=num_tokens, prompt_mask=prompt_mask, mask_id=self.config.mask_token_id, ) # 确保两个分支返回的形状一致 # logits_to_keep_half: (B, L) bool - 标记哪些位置被mask # p_mask: (M,) float - 每个被mask位置的噪声率，其中M = sum(logits_to_keep_half) assert logits_to_keep_half.shape == (bsz, seq_len), f"logits_to_keep_half shape error: {logits_to_keep_half.shape}" assert p_mask.shape == (logits_to_keep_half.sum(),), f"p_mask shape error: {p_mask.shape}, expected ({logits_to_keep_half.sum()},)" # 如果提供了p_mask_input（用于RL训练），计算p_to_keep # p_to_keep表示从masked位置中选出p_mask=True的位置 p_to_keep = None if p_mask_input is not None: # 注意：外部传入的p_mask_input已经只在response部分（通过 & response_mask），不需要再次过滤 # p_mask_input (B, L), logits_to_keep_half (B, L) # p_to_keep (M,) bool，其中M=sum(logits_to_keep_half) p_to_keep = p_mask_input[logits_to_keep_half] router_noisy_part_list = [] for i in range(bsz): cur_router_noisy_part = (torch.arange(num_tokens[i].shape[0] *2) % 2 == 0).to(inputs_ids.device) cur_router_noisy_part = cur_router_noisy_part.repeat_interleave(num_tokens[i].repeat_interleave(2)) router_noisy_part_list.append(cur_router_noisy_part) router_noisy_part = torch.stack(router_noisy_part_list, dim=0) # concated inputs_ids: (bzs, seq_len x 2) concat_inputs_ids = inputs_ids.repeat(1, 2) # concated logits_to_keep: (bsz, seq_len x 2) logits_to_keep = torch.zeros( bsz, 2 * seq_len, dtype=torch.bool, device=inputs_ids.device) # concated position_ids: (bsz, seq_len x 2) concat_position_ids = torch.zeros( bsz, 2 * seq_len, dtype=position_ids.dtype, device=position_ids.device) for i in range(bsz): concat_inputs_ids[i][router_noisy_part[i]] = noisy_inputs_ids[i] concat_inputs_ids[i][~router_noisy_part[i]] = inputs_ids[i] logits_to_keep[i][router_noisy_part[i]] = logits_to_keep_half[i] concat_position_ids[i][router_noisy_part[i]] = position_ids[i] concat_position_ids[i][~router_noisy_part[i]] = position_ids[i] # create flex_attention mask attention_mask = block_attn_mask(num_tokens, self.config.block_size, inputs_ids.device) flex_attention_mask_3d = create_block_mask( lambda b, h, q_idx, kv_idx: attention_mask[b, q_idx, kv_idx], B=attention_mask.size(0), H=None, Q_LEN=attention_mask.size(1), KV_LEN=attention_mask.size(2), ) return concat_inputs_ids, concat_position_ids, flex_attention_mask_3d, logits_to_keep_half, logits_to_keep, p_mask, p_to_keep @can_return_tuple @auto_docstring def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = 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, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, masked_indices: Optional[torch.Tensor] = None, return_logits: bool = False, # RL training parameters compute_rl_loss: bool = False, p_mask: Optional[torch.Tensor] = None, adv: Optional[torch.Tensor] = None, adv_optimization: bool = False, logp_old_tok: Optional[torch.Tensor] = None, logp_ref_tok: Optional[torch.Tensor] = None, is_real: Optional[torch.Tensor] = None, ppo_eps: float = 0.2, kl_beta: float = 0.0, use_kl_estimator_k3: bool = True, return_entropy: bool = False, dynamic_threshold: Optional[float] = None, loss_mean: bool = True, **kwargs: Unpack[KwargsForCausalLM], ) -> CausalLMOutputWithPast: 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 ) if self.training: assert inputs_embeds is None, "only support input_ids during training" prompt_mask = (labels == -100) if labels is not None else None position_ids = modify_padded_position_ids_2d(position_ids) ( concat_inputs_ids, concat_position_ids, flex_attention_mask_3d, logits_to_keep_half, logits_to_keep, p_mask_out, p_to_keep, ) = self.prepare_for_bd_training( input_ids, position_ids, prompt_mask, masked_indices, p_mask_input=p_mask ) outputs = self.model( input_ids=concat_inputs_ids, attention_mask=flex_attention_mask_3d, position_ids=concat_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=True, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state hidden_states = hidden_states[logits_to_keep].contiguous() # 初始化 entropy entropy = torch.tensor(0.0, device=input_ids.device) # ====================== RL loss（PPO） ====================== if compute_rl_loss: assert p_to_keep is not None, "p_mask must be provided for RL loss computation." assert adv is not None, "adv must be provided for RL loss computation." assert is_real is not None, "is_real must be provided for RL loss computation." assert labels is not None, "labels must be provided for RL loss computation." assert masked_indices is not None, "masked_indices must be provided for RL loss computation." device = input_ids.device # logits (M, V) — 保持原样 logits = self.lm_head(hidden_states) # mask — 保持原样 is_real_tensor = ( is_real.to(device=device, dtype=torch.bool) if torch.is_tensor(is_real) else torch.tensor(is_real, dtype=torch.bool, device=device) ) p_mask_real = p_mask & is_real_tensor.unsqueeze(1) # (B, L) p_to_keep_real = p_mask_real[masked_indices] # (M,) bool # 选出 logits — 保持原样 logits_p = logits[p_to_keep_real] # (N, V) N = p_to_keep_real.sum().item() total_response_tokens = (labels != -100).sum().item() total_p_mask = p_mask.sum().item() total_masked_indices = masked_indices.sum().item() total_is_real = is_real_tensor.sum().item() if is_real_tensor.dim() > 0 else (1 if is_real_tensor.item() else 0) # log_softmax log_probs_p = torch.nn.functional.log_softmax(logits_p, dim=-1) # labels / logp — 保持原样 labels_p = labels[masked_indices][p_to_keep_real] # (N,) logp_p = log_probs_p.gather(dim=-1, index=labels_p.unsqueeze(-1)).squeeze(-1) # entropy（可选） if return_entropy: with torch.no_grad(): entropy_p = -(log_probs_p.exp() * log_probs_p).sum(dim=-1) entropy = entropy_p.mean() if entropy_p.numel() > 0 else torch.tensor(0.0, device=device) del entropy_p # advantage 处理 adv_tensor = adv.to(device) if torch.is_tensor(adv) else torch.tensor(adv, dtype=torch.float, device=device) adv_optimization=False if adv_optimization: # token级别优化：对相同前缀取最大advantage（剪枝优化版本） response_mask = (labels != -100) # (B, L) bsz, seq_len = input_ids.shape # 预计算每个样本的response起始位置 response_starts = torch.full((bsz,), seq_len, dtype=torch.long, device=device) for b in range(bsz): if response_mask[b].any(): response_starts[b] = response_mask[b].long().argmax() # 剪枝1: 找出已经是最大advantage的样本，直接填充不参与比较 max_adv_value = adv_tensor.max() is_max_adv = (adv_tensor == max_adv_value) # (B,) bool # 创建优化后的 advantage map (B, L)，确保dtype与adv_tensor一致 optimized_adv = torch.zeros_like(labels, dtype=adv_tensor.dtype) # 对于已是最大advantage的样本，直接填充 for b in range(bsz): if is_max_adv[b]: optimized_adv[b][response_mask[b]] = max_adv_value # 统计信息 total_response_tokens = 0 updated_tokens = 0 skipped_tokens = 0 original_adv_sum = 0.0 optimized_adv_sum = 0.0 # 按position处理，批量比较前缀 for pos in range(seq_len): valid_samples = response_mask[:, pos] # (B,) if not valid_samples.any(): continue # 剪枝2: 排除已是最大advantage的样本 valid_samples = valid_samples & ~is_max_adv if not valid_samples.any(): # 所有样本都是最大值，统计后跳过 max_count = (response_mask[:, pos] & is_max_adv).sum().item() total_response_tokens += max_count skipped_tokens += max_count original_adv_sum += max_adv_value.item() * max_count optimized_adv_sum += max_adv_value.item() * max_count continue # 获取所有需要处理的样本索引 valid_indices = valid_samples.nonzero(as_tuple=True)[0] # (N,) for b in valid_indices: b_item = b.item() response_start = response_starts[b_item].item() prefix_len = pos + 1 - response_start if prefix_len <= 0: optimized_adv[b_item, pos] = adv_tensor[b_item] continue # 找出所有response起始位置相同且在pos位置有效的样本（包括已是最大值的） same_start_mask = (response_starts == response_start) & response_mask[:, pos] same_start_indices = same_start_mask.nonzero(as_tuple=True)[0] if len(same_start_indices) == 1: # 只有自己，不需要比较 optimized_adv[b_item, pos] = adv_tensor[b_item] total_response_tokens += 1 original_adv_sum += adv_tensor[b_item].item() optimized_adv_sum += adv_tensor[b_item].item() continue # 剪枝3: 如果候选中有最大advantage样本，可以直接用最大值 has_max_in_candidates = (same_start_mask & is_max_adv).any() prefix_end = pos + 1 current_prefix = input_ids[b_item, response_start:prefix_end] # 批量比较：提取所有候选样本的前缀 prefixes = input_ids[same_start_indices, response_start:prefix_end] # (M, prefix_len) # 使用广播比较：(M, prefix_len) vs (prefix_len,) matches = (prefixes == current_prefix.unsqueeze(0)).all(dim=1) # (M,) # 找到匹配的样本 matching_indices = same_start_indices[matches] # 在相同前缀的样本中取最大 advantage original_adv_value = adv_tensor[b_item].item() if matching_indices.numel() > 0: # 剪枝4: 如果匹配中有最大值样本，直接用最大值 if has_max_in_candidates and is_max_adv[matching_indices].any(): max_adv = max_adv_value else: max_adv = adv_tensor[matching_indices].max() optimized_adv[b_item, pos] = max_adv # 统计 if abs(max_adv.item() - original_adv_value) > 1e-6: updated_tokens += 1 original_adv_sum += original_adv_value optimized_adv_sum += max_adv.item() else: optimized_adv[b_item, pos] = adv_tensor[b_item] original_adv_sum += original_adv_value optimized_adv_sum += original_adv_value total_response_tokens += 1 # 输出统计信息 if total_response_tokens > 0: update_ratio = updated_tokens / total_response_tokens skip_ratio = skipped_tokens / total_response_tokens avg_original = original_adv_sum / total_response_tokens avg_optimized = optimized_adv_sum / total_response_tokens print(f"[Adv Optimization] Total: {total_response_tokens}, " f"Updated: {updated_tokens} ({update_ratio:.2%}), " f"Skipped: {skipped_tokens} ({skip_ratio:.2%}), " f"Avg adv: {avg_original:.4f} -> {avg_optimized:.4f} " f"(+{avg_optimized - avg_original:.4f})") # 使用优化后的 advantage adv_expanded = optimized_adv else: # 不优化：直接使用原始 advantage adv_expanded = adv_tensor.unsqueeze(1).expand_as(p_mask) adv_p = adv_expanded[masked_indices][p_to_keep_real] # old logp if logp_old_tok is not None and logp_old_tok.numel() > 0: logp_old_p = logp_old_tok.to(device)[masked_indices][p_to_keep_real] else: logp_old_p = logp_p.detach() # ratio/exp ratio_p = (logp_p - logp_old_p).clamp(-10.0, 10.0).exp() clipped = ratio_p.clamp(1 - ppo_eps, 1 + ppo_eps+0.08) surrogate_p = torch.minimum(ratio_p * adv_p, clipped * adv_p) # 输出离1最远的ratio值 # if not torch.allclose(ratio_p, torch.ones_like(ratio_p)): furthest_value = ratio_p[torch.abs(ratio_p - 1).argmax()] # print(f"Furthest ratio from 1: {furthest_value.item()}") # Policy loss: use mean or sum based on loss_mean parameter num_masked = masked_indices.sum().item() num_loss_elements = surrogate_p.numel() print(f"masked_indices.sum()={num_masked}, surrogate_p.numel()={num_loss_elements}") if loss_mean: policy_loss = -surrogate_p.mean() else: policy_loss = -surrogate_p.sum() # KL（可选） kl_loss = torch.tensor(0.0, device=device) if kl_beta > 0 and logp_ref_tok is not None: logp_ref_p = logp_ref_tok.to(device)[masked_indices][p_to_keep_real] kl_seq_p = logp_p - logp_ref_p if use_kl_estimator_k3: kl_seq_p = (-kl_seq_p).clamp(-10.0, 10.0).exp() - 1.0 + kl_seq_p # KL loss: use mean or sum based on loss_mean parameter if loss_mean: kl_loss = kl_beta * kl_seq_p.mean() else: kl_loss = kl_beta * kl_seq_p.sum() del logp_ref_p, kl_seq_p loss = policy_loss + kl_loss kl_loss_value = kl_loss.detach().clone() # 清理 del logits, logits_p, log_probs_p, labels_p del is_real_tensor, p_mask_real, p_to_keep_real del adv_tensor, adv_expanded, adv_p del logp_p, logp_old_p, ratio_p, clipped, surrogate_p del policy_loss, kl_loss logits = None # ====================== GRPO / return logits ====================== elif return_logits: logits = self.lm_head(hidden_states) loss = None # ====================== Block Diffusion fused loss ====================== else: assert labels is not None, "Labels must be provided for training." answer_len = (labels != -100).sum() loss_fct = FusedLinearDiffusionCrossEntropyLoss(reduction="sum") loss = loss_fct( x=hidden_states, target=labels[logits_to_keep_half].contiguous(), weight=self.lm_head.weight, bias=self.lm_head.bias, p_mask=p_mask_out, ) loss = loss / answer_len logits = None # ====================== eval / inference ====================== else: outputs: BaseModelOutputWithPast = 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, cache_position=cache_position, **kwargs, ) hidden_states = outputs.last_hidden_state slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep hidden_states = hidden_states[:, slice_indices, :].contiguous() fuse_linear_and_cross_entropy = self.config.fuse_cross_entropy and self.training if fuse_linear_and_cross_entropy: logits = None else: logits = self.lm_head(hidden_states) loss = None if labels is not None: loss_fct = nn.CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) output = CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) if self.training and compute_rl_loss: output.entropy = entropy output.kl_loss = kl_loss_value if "kl_loss_value" in locals() else torch.tensor(0.0, device=input_ids.device) return output __all__ = [ "SDARForCausalLM", "SDARModel", "SDARPreTrainedModel", ]