# This file is modified based on https://github.com/huggingface/transformers/blob/v4.52.4/src/transformers/models/qwen3/modeling_qwen3.py. # 关闭所有 transformers 日志和警告（必须在所有导入前设��） import os import sys import io os.environ["TRANSFORMERS_VERBOSITY"] = "error" # 抑制 transformers 和 Triton 的警告日志 class OutputFilter: """输出过滤器，抑制特定日志输出，同时保持 tqdm 等工具正常工作""" def __init__(self, original_stream): self.original_stream = original_stream def write(self, text): # 过滤规则： # 1. 🚨 开头的警告 (transformers docstring) # 2. Config not found 消息 # 3. Triton autotune 日志 (BLOCKS_ARE_CONTIGUOUS, triton_flex_attention, AUTOTUNE benchmarking) filtered_keywords = [ "🚨", "Config not found for sdar", "BLOCKS_ARE_CONTIGUOUS", "triton_flex_attention", "AUTOTUNE benchmarking", ] should_filter = any(keyword in text for keyword in filtered_keywords) if not should_filter: self.original_stream.write(text) return len(text) if not should_filter else 0 def flush(self): self.original_stream.flush() # 代理 tqdm 需要的所有属性 def isatty(self): return self.original_stream.isatty() @property def encoding(self): return self.original_stream.encoding @property def mode(self): return self.original_stream.mode @property def name(self): return self.original_stream.name def fileno(self): return self.original_stream.fileno() def __getattr__(self, name): return getattr(self.original_stream, name) # 同时过滤 stdout 和 stderr (Triton 日志打印到 stdout) sys.stdout = OutputFilter(sys.stdout) sys.stderr = OutputFilter(sys.stderr) import logging import warnings warnings.filterwarnings("ignore", message=".*is part of.*") warnings.filterwarnings("ignore", message=".*docstring.*") # 关闭 torch._inductor autotune 日志 logging.getLogger("torch._inductor").setLevel(logging.CRITICAL) # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # 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 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, can_return_tuple, logging from .configuration_sdar import SDARConfig from flash_attn.ops.triton.layer_norm import rms_norm_fn as flash_rms_norm # ===== FusedLinearDiffusionCrossEntropyLoss ===== try: from .fused_linear_diffusion_cross_entropy import FusedLinearDiffusionCrossEntropyLoss fused_diffusion_loss_available = True except ImportError: fused_diffusion_loss_available = False # ===== End FusedLoss ===== import torch.nn.functional as F # Flash Attention for inference (optional - only required for rollout/eval) flash_attn_func = None flash_attn_varlen_func = None index_first_axis = None pad_input = None unpad_input = None 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 ImportError: pass try: from liger_kernel.ops.swiglu import LigerSiLUMulFunction # noqa: F401 liger_kernel_is_available = True except ImportError: liger_kernel_is_available = False # ===== Flex Attention ===== # ===== Flex Attention ===== # Direct import - will error if not available from torch.nn.attention.flex_attention import BlockMask, create_block_mask, flex_attention # ===== End Flex Attention ===== logger = logging.get_logger(__name__) @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 # ===== DiRL Block Diffusion Functions ===== def calculate_token_nums(position_ids: torch.Tensor): """ Calculate token counts per segment for block attention mask creation. Args: position_ids: (B, L) position ids Returns: list[list[torch.Tensor]]: nested list of segment lengths for each sample """ if position_ids.dim() != 2: raise ValueError(f"Input must be 2D Tensor, got {position_ids.dim()}D") all_lengths = [] for pids_row in position_ids: seq_len = pids_row.shape[0] # Find indices where value is 0 (segment boundaries) zero_indices = torch.nonzero(pids_row == 0).flatten() # Add total sequence length as final split point split_points = torch.cat([ zero_indices, torch.tensor([seq_len], device=pids_row.device, dtype=zero_indices.dtype) ]) # Calculate differences between adjacent split points lengths = torch.diff(split_points) all_lengths.append(lengths) return all_lengths # ===== End DiRL Block Diffusion Functions ===== # ===== Flex Attention Block Mask Functions ===== def block_diff_mask(b, h, q_idx, kv_idx, block_size=None, n=None): """Construct block diffusion attention mask for flex_attention. Three masks combined: - Block Diagonal (M_BD): Self-attention within blocks - Offset Block-Causal (M_OBC): Cross-attention for context - Block-Causal (M_BC): Causal attention within x0 Args: b, h: Batch and head indices (ignored for mask logic). q_idx, kv_idx: Query and Key indices. block_size: Defines the block structure. n: Boundary point for xt/x0 split. Returns: A boolean attention mask where True = attend, False = mask. """ x0_flag_q = q_idx >= n x0_flag_kv = kv_idx >= n 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 ) block_diagonal = (block_q == block_kv) & (x0_flag_q == x0_flag_kv) offset_block_causal = (block_q > block_kv) & (x0_flag_kv == 1) & (x0_flag_q == 0) block_causal = (block_q >= block_kv) & (x0_flag_kv == 1) & (x0_flag_q == 1) return block_diagonal | offset_block_causal | block_causal def block_attn_mask(num_tokens, block_size, device): """Create block attention mask for flex_attention. Args: num_tokens: list of lists, each inner list is token counts per sample block_size: block size for block attention device: torch device Returns: bool tensor (B, N, N) where True = attend """ masks = [] for i in range(len(num_tokens)): cur_masks = [] for num in num_tokens[i]: 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)) return torch.stack(masks, dim=0) @torch.compile(fullgraph=True, mode="max-autotune-no-cudagraphs") def fused_flex_attention(query, key, value, attention_mask, **kwargs): """Compiled flex_attention wrapper for performance.""" return flex_attention(query, key, value, block_mask=attention_mask, **kwargs) # ===== End Flex Attention Block Mask Functions ===== 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) ''' attention_mask = attention_mask.bool() if attention_mask is not None else 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 ) 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 = attn_output.transpose(1, 2).contiguous() ''' #print(query_states.shape, key_states.shape, value_states.shape) # --- After RoPE and KV-cache handling, expand KV to all heads --- key_states = repeat_kv(key_states, self.num_key_value_groups) # [B, H, K, D] value_states = repeat_kv(value_states, self.num_key_value_groups) # [B, H, K, D] bsz, q_len = input_shape # ===== Attention with Training/Inference Branch ===== attn_weights = None if self.training: # ===== Training Mode: Use flex_attention with BlockMask ===== if isinstance(attention_mask, BlockMask): 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: raise TypeError( f"Expected BlockMask during training, got {type(attention_mask)}. " "Ensure create_flex_block_mask() is called in training code." ) else: # ===== Inference Mode: Compatible with regular bool attention_mask ===== # Used for rollout/eval where tokenizer's attention_mask is passed attention_mask_bool = attention_mask.bool() if attention_mask is not None else None if attention_mask_bool is not None and torch.all(attention_mask_bool): # Full attention (decoding) - use Flash Attention if flash_attn_func is None: raise ImportError( "Flash Attention is required for inference. " "Please install flash-attn: pip install flash-attn --no-build-isolation" ) 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: # Partial attention (prefilling) - use SDPA attn_output = F.scaled_dot_product_attention( query=query_states, key=key_states, value=value_states, attn_mask=attention_mask_bool, is_causal=False, scale=self.scaling, enable_gqa=True ) attn_output = rearrange(attn_output, 'b h l d -> b l (h d)') # ===== End Attention ===== attn_output = self.o_proj(attn_output) return attn_output, 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 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) 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 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: r""" Args: store_kv (`bool`, *optional*): Whether to store key and value states in the cache. If `True`, the key and value states will be stored in `past_key_values`. If `False` and `past_key_values` exists, only retrieves cached states without updating the cache. """ 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 # 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): ... 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): """ Prepare block diffusion training data (aligned with DiRL). Args: inputs_ids: (B, L) input token ids position_ids: (B, L) position ids prompt_mask: (B, L) bool tensor, marks prompt positions masked_indices: (B, L) bool tensor, marks which positions are masked p_mask_input: (B, L) bool tensor, for RL training p_mask Returns: concat_inputs_ids: (B, 2*L) concatenated input ids concat_position_ids: (B, 2*L) concatenated position ids flex_attention_mask_3d: BlockMask for flex_attention logits_to_keep_half: (B, L) bool tensor logits_to_keep: (B, 2*L) bool tensor p_mask: (M,) float tensor p_to_keep: (M,) bool tensor or None """ bsz, seq_len = inputs_ids.shape num_tokens = calculate_token_nums(position_ids) # Get mask_token_id from config, or use default value (bos_token_id + 26, same as DiRL) mask_token_id = getattr(self.config, 'mask_token_id', None) if mask_token_id is None: mask_token_id = self.config.bos_token_id + 26 # Get block_size from config, or use default value (4, same as DiRL) block_size = getattr(self.config, 'block_size', 4) # Process masked_indices if masked_indices is not None: # RL training mode: use provided masked_indices noisy_inputs_ids = torch.where(masked_indices, mask_token_id, inputs_ids) logits_to_keep_half = masked_indices M = masked_indices.sum().item() p_mask = torch.full((M,), 0.5, device=inputs_ids.device, dtype=torch.float) else: # No masked_indices provided: create response_mask (labels != -100 positions) if prompt_mask is not None: response_mask = (prompt_mask == False) else: # If no prompt_mask, assume all are response response_mask = torch.ones(bsz, seq_len, dtype=torch.bool, device=inputs_ids.device) masked_indices = response_mask noisy_inputs_ids = torch.where(masked_indices, mask_token_id, inputs_ids) logits_to_keep_half = masked_indices M = masked_indices.sum().item() p_mask = torch.full((M,), 0.5, device=inputs_ids.device, dtype=torch.float) # 计算 p_to_keep - 用于将原始空间的 p_mask 映射到提取空间 p_to_keep = None if p_mask_input is not None: p_to_keep = p_mask_input[logits_to_keep_half] # Build router_noisy_part (marks which positions are noisy) # True = original token, False = noisy token, alternating pattern # 学习DiRL方式：基于每个样本的segment数量构建 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) concat_inputs_ids = inputs_ids.repeat(1, 2) logits_to_keep = torch.zeros(bsz, 2 * seq_len, dtype=torch.bool, device=inputs_ids.device) 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 (using existing block_attn_mask) attention_mask = block_attn_mask(num_tokens, 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 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, # ===== DiRL compatibility parameters ===== masked_indices: Optional[torch.Tensor] = None, # (B, L) bool tensor, marks masked positions return_logits: bool = False, # If True, return only logits for masked positions # ===== RL training parameters (compute_rl_loss mode) ===== compute_rl_loss: bool = False, rl_p_mask: Optional[torch.Tensor] = None, rl_adv: Optional[torch.Tensor] = None, rl_adv_clip_enabled: bool = False, rl_adv_clip_min: float = 0.0, rl_logp_old_tok: Optional[torch.Tensor] = None, rl_logp_ref_tok: Optional[torch.Tensor] = None, rl_logp_ref_mask: Optional[torch.Tensor] = None, # Mask for valid logp_ref positions rl_is_real: Optional[torch.Tensor] = None, rl_correctness: Optional[torch.Tensor] = None, # (B,) bool tensor for NLL loss rl_ppo_eps: float = 0.2, rl_ppo_eps_high: float = 0.28, # clip eps for advantage actions (adv > 0) rl_kl_beta: float = 0.0, # KL penalty coefficient rl_nll_weight: float = 1.0, # NLL loss coefficient rl_use_kl_estimator_k3: bool = True, rl_return_entropy: bool = False, rl_loss_reduction_mode: str = "token", # "token" or "sequence" **kwargs: Unpack[KwargsForCausalLM], ) -> CausalLMOutputWithPast: r""" 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]`. Example: ```python >>> from transformers import AutoTokenizer, SDARForCausalLM >>> model = SDARForCausalLM.from_pretrained("DiffuOpen/SDAR-1.7B-Chat") >>> tokenizer = AutoTokenizer.from_pretrained("DiffuOpen/SDAR-1.7B-Chat") >>> 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 ) # ===== DiRL Block Diffusion Training Mode ===== 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 ( 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=rl_p_mask ) outputs = self.model( input_ids=concat_inputs_ids, attention_mask=flex_attention_mask_3d, position_ids=concat_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 # Only keep positions marked by logits_to_keep (masked_indices positions) hidden_states = hidden_states[logits_to_keep].contiguous() # Initialize entropy (in training mode) entropy = torch.tensor(0.0, device=input_ids.device) # ===== Branch 1: compute_rl_loss (PPO training) ===== if compute_rl_loss: assert p_to_keep is not None, "p_to_keep must be provided for RL loss computation." assert rl_adv is not None, "rl_adv must be provided for RL loss computation." assert rl_is_real is not None, "rl_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) - M is the number of extracted positions after block diffusion logits = self.lm_head(hidden_states) # mask — is_real filtering is_real_tensor = ( rl_is_real.to(device=device, dtype=torch.bool) if torch.is_tensor(rl_is_real) else torch.tensor(rl_is_real, dtype=torch.bool, device=device) ) p_mask_real = rl_p_mask & is_real_tensor.unsqueeze(1) # (B, L) # Compute sequence lengths and batch indices for sequence-level reduction if rl_loss_reduction_mode == "sequence": # Count non-padding tokens per sample (B,) seq_lengths = (labels != -100).sum(dim=1) # Create batch index mapping for extracted tokens batch_indices = torch.arange(input_ids.size(0), device=device).unsqueeze(1).expand_as(rl_p_mask) # (B, L) # 正确的索引映射：将 (B, L) 空间的 p_mask_real 映射到提取空间 (M,) # logits_to_keep_half (即 masked_indices) 标记了原始空间中哪些位置被提取 # 在 prepare_for_bd_training 中，提取后的位置对应 logits_to_keep_half 中为 True 的位置 p_to_keep_real = logits_to_keep_half[p_mask_real] # (M,) bool # Select logits using p_to_keep (在正确的提取空间) logits_p = logits[p_to_keep_real] # (N, V) N = p_to_keep_real.sum().item() # log_softmax log_probs_p = torch.nn.functional.log_softmax(logits_p, dim=-1) # Compute entropy if requested entropy_value = None if rl_return_entropy: probs_p = log_probs_p.exp() # Convert log_probs to probs entropy_p = -(probs_p * log_probs_p).sum(dim=-1) # (N,) per-token entropy entropy_value = entropy_p.mean().detach().clone() # Scalar mean entropy del probs_p, entropy_p # Cleanup # labels / logp - 需要同样映射到提取空间 labels_p = labels[p_mask_real][logits_to_keep_half[p_mask_real]] # (N,) logp_p = log_probs_p.gather(dim=-1, index=labels_p.unsqueeze(-1)).squeeze(-1) # Extract batch indices for sequence-level grouping if rl_loss_reduction_mode == "sequence": batch_indices_p = batch_indices[p_mask_real][logits_to_keep_half[p_mask_real]] # (N,) # Valid token mask (TrajRL: no specific token filtering for now) valid_token_mask = torch.ones_like(labels_p, dtype=torch.bool) # advantage (TrajRL: per-token advantage, shape (B, L)) adv_tensor = rl_adv.to(device) if torch.is_tensor(rl_adv) else torch.tensor(rl_adv, dtype=torch.float, device=device) # Handle different adv shapes: # - (B, L): per-token advantage (TrajRL GAE), use directly # - (B,): per-sample advantage (DiRL-style), expand to (B, L) if adv_tensor.dim() == 2: adv_expanded = adv_tensor elif adv_tensor.dim() == 1: adv_expanded = adv_tensor.unsqueeze(1).expand_as(rl_p_mask) else: raise ValueError(f"Unexpected adv shape: {adv_tensor.shape}, expected (B, L) or (B,)") # 映射到提取空间 adv_p = adv_expanded[p_mask_real][logits_to_keep_half[p_mask_real]] # old logp - 需要映射到提取空间 if rl_logp_old_tok is not None and rl_logp_old_tok.numel() > 0: logp_old_p = rl_logp_old_tok.to(device)[p_mask_real][logits_to_keep_half[p_mask_real]] else: logp_old_p = logp_p.detach() # NLL Loss for correct samples (TrajRL VAPO) nll_loss = torch.tensor(0.0, device=device) if rl_correctness is not None and rl_correctness.any(): correctness_tensor = rl_correctness.to(device=device, dtype=torch.bool) # Expand (B,) to (B, L) following the same pattern as rl_adv (1D case) correctness_expanded = correctness_tensor.unsqueeze(1).expand_as(rl_p_mask) # (B, L) # 映射到提取空间 correct_p = correctness_expanded[p_mask_real][logits_to_keep_half[p_mask_real]] # (N,) if correct_p.any(): nll_loss = rl_nll_weight * (-logp_p[correct_p].mean()) # KL Loss (aligned with DiRL) # Only compute KL for tokens with valid logp_ref (masked out positions use no KL penalty) kl_loss = torch.tensor(0.0, device=device) if rl_kl_beta > 0 and rl_logp_ref_tok is not None: # 映射到提取空间 logp_ref_p = rl_logp_ref_tok.to(device)[p_mask_real][logits_to_keep_half[p_mask_real]] # Apply logp_ref_mask if provided: only compute KL where logp_ref is valid if rl_logp_ref_mask is not None: logp_ref_mask_p = rl_logp_ref_mask.to(device)[p_mask_real][logits_to_keep_half[p_mask_real]] valid_token_mask = valid_token_mask & logp_ref_mask_p # Filter to valid positions only logp_ref_p = logp_ref_p[valid_token_mask] logp_p_valid = logp_p[valid_token_mask] # Compute KL only on valid positions kl_seq_p = logp_p_valid - logp_ref_p if rl_use_kl_estimator_k3: kl_seq_p = (-kl_seq_p).clamp(-10.0, 10.0).exp() - 1.0 + kl_seq_p kl_loss = rl_kl_beta * kl_seq_p.mean() # PPO loss with asymmetric clipping (DiRL-style) # Use different eps for advantage (adv > 0) vs disadvantage (adv < 0) actions ratio_p = (logp_p - logp_old_p).clamp(-10.0, 10.0).exp() # Asymmetric clipping based on advantage sign # For adv >= 0: use eps_high (more conservative) # For adv < 0: use eps (more aggressive) clipped_low = ratio_p.clamp(1 - rl_ppo_eps, 1 + rl_ppo_eps) clipped_high = ratio_p.clamp(1 - rl_ppo_eps, 1 + rl_ppo_eps_high) clipped = torch.where(adv_p >= 0, clipped_high, clipped_low) surrogate_p = torch.minimum(ratio_p * adv_p, clipped * adv_p) if rl_loss_reduction_mode == "sequence": # Sequence-level reduction: average within each sequence, then average across sequences B = input_ids.size(0) seq_loss_sum = torch.zeros(B, device=device, dtype=surrogate_p.dtype) seq_loss_sum.scatter_add_(0, batch_indices_p, surrogate_p) # Normalize by sequence length seq_loss_mean = seq_loss_sum / seq_lengths.float() # Average across sequences (only non-zero entries) valid_seqs = (seq_lengths > 0) policy_loss = -seq_loss_mean[valid_seqs].mean() # Store sequence-level statistics for logging seq_loss_std = seq_loss_mean[valid_seqs].std() seq_loss_min = seq_loss_mean[valid_seqs].min() seq_loss_max = seq_loss_mean[valid_seqs].max() else: # Token-level reduction (default): average across all tokens policy_loss = -surrogate_p.mean() # Validation: ensure loss is finite if not torch.isfinite(policy_loss): logger.warning(f"Non-finite policy_loss detected: {policy_loss.item()}, mode={rl_loss_reduction_mode}") policy_loss = torch.tensor(0.0, device=device, requires_grad=True) # Total loss loss = policy_loss + kl_loss + nll_loss # Save loss values for output (before cleanup) nll_loss_value = nll_loss.detach().clone() kl_loss_value = kl_loss.detach().clone() # Save PPO statistics for logging (before cleanup) # ratio_mean: mean importance sampling ratio ratio_mean_value = ratio_p.mean().detach().clone() # clip_frac: fraction of samples that were clipped clip_frac_value = (ratio_p != clipped).float().mean().detach().clone() # Store entropy value (computed earlier) entropy_value_stored = entropy_value if entropy_value is not None else torch.tensor(0.0, device=device) # Cleanup 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, policy_loss, entropy_value logits = None # ===== Branch 2: return_logits (inference) ===== elif return_logits: logits = self.lm_head(hidden_states) # (M, V) loss = None return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # ===== Branch 3: block diffusion loss (pre-training) ===== else: assert labels is not None, "Labels must be provided for training." # Use FusedLinearDiffusionCrossEntropyLoss if available if fused_diffusion_loss_available and getattr(self.config, 'use_fused_diffusion_loss', False): 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=getattr(self.lm_head, 'bias', None), p_mask=p_mask_out, ) loss = loss / answer_len else: # Fallback: CrossEntropyLoss on full logits logits_full = self.lm_head(hidden_states) loss_fct = nn.CrossEntropyLoss(ignore_index=-100) loss = loss_fct(logits_full.view(-1, self.config.vocab_size), labels.view(-1)) logits = None # ===== Block Diffusion Training Mode: Construct output directly ===== # Construct output and return directly (don't execute Standard Mode code) output = CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Add RL loss attributes for training (DiRL-style) if self.training and compute_rl_loss: output.nll_loss = nll_loss_value if "nll_loss_value" in locals() else torch.tensor(0.0, device=input_ids.device) output.kl_loss = kl_loss_value if "kl_loss_value" in locals() else torch.tensor(0.0, device=input_ids.device) # Add PPO statistics for logging output.ratio_mean = ratio_mean_value if "ratio_mean_value" in locals() else torch.tensor(0.0, device=input_ids.device) output.clip_frac = clip_frac_value if "clip_frac_value" in locals() else torch.tensor(0.0, device=input_ids.device) output.entropy = entropy_value_stored if "entropy_value_stored" in locals() else torch.tensor(0.0, device=input_ids.device) output.seq_loss_std = seq_loss_std if "seq_loss_std" in locals() else torch.tensor(0.0, device=input_ids.device) output.seq_loss_min = seq_loss_min if "seq_loss_min" in locals() else torch.tensor(0.0, device=input_ids.device) output.seq_loss_max = seq_loss_max if "seq_loss_max" in locals() else torch.tensor(0.0, device=input_ids.device) return output else: # ===== Standard Mode (no block diffusion) ===== # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) 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 # Only compute necessary logits, and do not upcast them to float if we are not computing the loss 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 = getattr(self.config, 'fuse_cross_entropy', False) and self.training if fuse_linear_and_cross_entropy: # When using fused_linear_ce_loss, we do not compute the whole logits on HBM logits = None else: logits = self.lm_head(hidden_states) loss = None if labels is not None: # ===== FusedLinearDiffusionCrossEntropyLoss (if available) ===== use_fused_loss = ( fused_diffusion_loss_available and getattr(self.config, 'use_fused_diffusion_loss', False) and self.training ) if use_fused_loss: # FusedLoss directly computes from hidden_states, no need for logits p_mask = kwargs.get('p_mask', None) if p_mask is None: # Default: all ones (no noise scaling) # Match the shape of labels for correct reshaping in FusedLoss if labels.dim() >= 2: p_mask = torch.ones_like(labels, dtype=hidden_states.dtype, device=hidden_states.device) else: # labels is already 1D p_mask = torch.ones(labels.shape, dtype=hidden_states.dtype, device=hidden_states.device) num_chunks = getattr(self.config, 'fused_loss_num_chunks', 8) loss_fct = FusedLinearDiffusionCrossEntropyLoss( ignore_index=-100, label_smoothing=0.0, logit_scale=1.0, num_chunks=num_chunks, reduction="mean" ) loss = loss_fct( x=hidden_states, target=labels, weight=self.lm_head.weight, bias=getattr(self.lm_head, 'bias', None), p_mask=p_mask, ) else: # Standard CrossEntropyLoss (fallback) loss_fct = nn.CrossEntropyLoss(ignore_index=-100) loss = loss_fct( logits.view(-1, self.config.vocab_size), labels.view(-1)) # ===== End FusedLoss ===== output = CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) # Add RL loss attributes for training (DiRL-style) if self.training and compute_rl_loss: output.nll_loss = nll_loss_value if "nll_loss_value" in locals() else torch.tensor(0.0, device=input_ids.device) 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", ]