# Explicit dependency closure for Transformers dynamic-module copying. from .cache_ring_buffer import __name__ as _transport_cache_ring_buffer from .elementwise_fusion import __name__ as _transport_elementwise_fusion from .hierarchical_summary_attention import __name__ as _transport_hierarchical_summary_attention from .indexer_step import __name__ as _transport_indexer_step from .layer_attention import __name__ as _transport_layer_attention from .layer_decoder import __name__ as _transport_layer_decoder from .layer_norm_rope import __name__ as _transport_layer_norm_rope from .prefill_attention import __name__ as _transport_prefill_attention from .runtime_config import __name__ as _transport_runtime_config from .sksa_attention import __name__ as _transport_sksa_attention from .sparse_decode_gather import __name__ as _transport_sparse_decode_gather from .summary_context import __name__ as _transport_summary_context # 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 copy import copy as _copy_summary_context from typing import Any, Callable, Optional, Union import torch from torch import nn from transformers.cache_utils import Cache, DynamicCache from transformers.generation import GenerationMixin from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast from transformers.modeling_utils import PreTrainedModel from transformers.processing_utils import Unpack from transformers.utils import TransformersKwargs, auto_docstring, can_return_tuple from transformers.utils.generic import check_model_inputs from transformers.models.qwen3.configuration_qwen3 import Qwen3Config from .sksa_attention import ( SummaryBlockLightningIndexer, shared_indexer_step, shared_indexer_positions, aligned_summary_block_local_start, gather_summary_block_decode_kv, score_indexer_candidates, summary_chunk_attention_indexer_key_input, summary_block_sparse_prefill_attention, ) from .summary_context import SummaryBatchContext, build_summary_sliding_context from .runtime_config import _get_shared_batch_mask, _parse_config_pattern, _validate_sksa_runtime_config from .layer_norm_rope import Qwen3RMSNorm, Qwen3RotaryEmbedding, apply_rotary_pos_emb, rotate_half from .cache_ring_buffer import Qwen3RingBufferCache from .layer_attention import Qwen3Attention, Qwen3SummaryAttention, _sdpa_attention_forward, repeat_kv from .layer_decoder import Qwen3DecoderLayer, Qwen3MLP @auto_docstring class Qwen3PreTrainedModel(PreTrainedModel): config: Qwen3Config base_model_prefix = "model" supports_gradient_checkpointing = False _no_split_modules = ["Qwen3DecoderLayer"] _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn = True _supports_sdpa = True _supports_flex_attn = True _can_compile_fullgraph = True _supports_attention_backend = True _can_record_outputs = { "hidden_states": Qwen3DecoderLayer, "attentions": Qwen3Attention, } @classmethod def _supports_default_dynamic_cache(cls) -> bool: """The summary model initializes and owns its ring/meta cache.""" return False @auto_docstring class Qwen3Model(Qwen3PreTrainedModel): def __init__(self, config: Qwen3Config): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size partial_chunk_policy = getattr( config, "summary_partial_chunk_policy", "defer" ) if partial_chunk_policy != "defer": raise ValueError( "C-KSA runtime requires summary_partial_chunk_policy='defer' " "so prefill and cache decode share one chunk-boundary policy" ) if not getattr(config, "summary_layer_freq", False): if config.use_summary_attention: config.summary_layer_freq = [1]*config.num_hidden_layers else: config.summary_layer_freq = [0]*config.num_hidden_layers Warning(f'Please set config.summary_layer_freq, temp set summary_layer_freq = {config.num_hidden_layers}') else: config.summary_layer_freq = _parse_config_pattern(config.summary_layer_freq) _validate_sksa_runtime_config(config) self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = nn.ModuleList( [Qwen3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.rotary_emb = Qwen3RotaryEmbedding(config=config) self.gradient_checkpointing = False self.has_sliding_layers = "sliding_attention" in self.config.layer_types # Cache per-layer sliding_chunk_nums for KV cache eviction _sv = _parse_config_pattern(getattr(config, "summary_sliding_chunk_num", 0) or 0) if isinstance(_sv, list): self._sliding_chunk_nums = [int(v) for v in _sv] else: self._sliding_chunk_nums = [int(_sv)] * config.num_hidden_layers # Initialize weights and apply final processing self.post_init() def _expand_input_with_summary_tokens(self, input_ids): """Expand input_ids with summary tokens for prefill phase (vectorized). Returns: Tuple of (expanded_input_ids, position_ids, text_only_mask) """ summary_chunk = self.config.summary_chunk_size summary_num = self.config.summary_token_num summary_begin = self.config.summary_token_begin if summary_chunk == 0 or summary_num == 0: return input_ids, None, None batch_size, seq_len = input_ids.shape device = input_ids.device dtype = input_ids.dtype block = summary_chunk + summary_num # Number of full chunks and remainder n_full_chunks = seq_len // summary_chunk remainder = seq_len % summary_chunk has_remainder = remainder > 0 # Total expanded length: full_chunks * block + remainder expanded_len = n_full_chunks * block + (remainder if has_remainder else 0) # --- Build expanded_input_ids --- expanded_ids = torch.empty((batch_size, expanded_len), dtype=dtype, device=device) text_only_mask = torch.zeros((batch_size, expanded_len), dtype=torch.bool, device=device) # Compute text positions: for chunk i, text goes to [i*block, i*block+summary_chunk) # Summary positions: [i*block+summary_chunk, (i+1)*block) if n_full_chunks > 0: chunk_indices = torch.arange(n_full_chunks, device=device) # Text source positions in original input_ids text_src_offsets = (chunk_indices * summary_chunk).unsqueeze(1) + torch.arange(summary_chunk, device=device).unsqueeze(0) # [n_full_chunks, summary_chunk] # Text dest positions in expanded text_dst_offsets = (chunk_indices * block).unsqueeze(1) + torch.arange(summary_chunk, device=device).unsqueeze(0) # [n_full_chunks, summary_chunk] # Summary dest positions summary_dst_offsets = (chunk_indices * block + summary_chunk).unsqueeze(1) + torch.arange(summary_num, device=device).unsqueeze(0) # [n_full_chunks, summary_num] text_src_flat = text_src_offsets.reshape(-1) text_dst_flat = text_dst_offsets.reshape(-1) summary_dst_flat = summary_dst_offsets.reshape(-1) # Copy text tokens expanded_ids[:, text_dst_flat] = input_ids[:, text_src_flat] text_only_mask[:, text_dst_flat] = True # Fill summary tokens summary_ids_val = torch.arange(summary_num, device=device, dtype=dtype) + summary_begin expanded_ids[:, summary_dst_flat] = summary_ids_val.repeat(n_full_chunks).unsqueeze(0).expand(batch_size, -1) # Handle remainder (last partial chunk, no summary tokens) if has_remainder: rem_start_src = n_full_chunks * summary_chunk rem_start_dst = n_full_chunks * block rem_offsets = torch.arange(remainder, device=device) expanded_ids[:, rem_start_dst + rem_offsets] = input_ids[:, rem_start_src + rem_offsets] text_only_mask[:, rem_start_dst + rem_offsets] = True # --- Build position_ids --- position_ids = torch.empty((batch_size, expanded_len), dtype=torch.long, device=device) if n_full_chunks > 0: # Text position IDs if self.config.summary_chunk_position_ids_type == 'origin': text_pos = text_src_flat.unsqueeze(0).expand(batch_size, -1) elif self.config.summary_chunk_position_ids_type == 'inner_chunk': inner_pos = torch.arange(summary_chunk, device=device).repeat(n_full_chunks) text_pos = inner_pos.unsqueeze(0).expand(batch_size, -1) else: raise ValueError(f'Check config.summary_chunk_position_ids_type: {self.config.summary_chunk_position_ids_type}') position_ids[:, text_dst_flat] = text_pos # Summary position IDs if self.config.summary_token_position_ids_type == 'zeros': position_ids[:, summary_dst_flat] = 0 elif self.config.summary_token_position_ids_type in ('last_chunk_slice_left', 'last_chunk_slice_right'): # Vectorized slice_ends computation for all chunks at once if self.config.summary_token_position_ids_type == 'last_chunk_slice_left': idx = torch.arange(0, summary_num, device=device, dtype=torch.long) else: idx = torch.arange(1, summary_num + 1, device=device, dtype=torch.long) # For each chunk i: prev_text_end = i * summary_chunk prev_ends = (chunk_indices * summary_chunk).unsqueeze(1) # [n_full_chunks, 1] slice_ends = prev_ends + (idx.unsqueeze(0) * summary_chunk) // summary_num - 1 # [n_full_chunks, summary_num] slice_ends = slice_ends.clamp(min=0) # Clamp per-chunk: min is prev_text_end for that chunk slice_ends = torch.max(slice_ends, prev_ends) position_ids[:, summary_dst_flat] = slice_ends.reshape(-1).unsqueeze(0).expand(batch_size, -1) else: raise ValueError(f'Unknown summary_token_position_ids_type: {self.config.summary_token_position_ids_type}') # Remainder position IDs if has_remainder: if self.config.summary_chunk_position_ids_type == 'origin': rem_pos = (rem_start_src + rem_offsets).unsqueeze(0).expand(batch_size, -1) elif self.config.summary_chunk_position_ids_type == 'inner_chunk': rem_pos = rem_offsets.unsqueeze(0).expand(batch_size, -1) else: raise ValueError(f'Check config.summary_chunk_position_ids_type: {self.config.summary_chunk_position_ids_type}') position_ids[:, rem_start_dst + rem_offsets] = rem_pos return expanded_ids, position_ids, text_only_mask def _build_summary_context(self, input_ids, position_ids, is_prefill, use_cache): """Build summary context for attention layers.""" summary_chunk = self.config.summary_chunk_size summary_num = self.config.summary_token_num summary_begin = self.config.summary_token_begin if summary_chunk > 0 and summary_num > 0: return build_summary_sliding_context( input_ids=input_ids, position_ids=position_ids, summary_token_num=summary_num, summary_token_begin=summary_begin, ) return None def _filter_summary_tokens(self, hidden_states, text_only_mask, use_summary, is_decode): """Filter out summary tokens from output hidden states.""" if text_only_mask is not None: if text_only_mask.ndim == 2 and text_only_mask.shape[0] > 1: shared = text_only_mask[0] if not torch.equal(text_only_mask, shared.unsqueeze(0).expand_as(text_only_mask)): return hidden_states[text_only_mask.to(hidden_states.device)].unsqueeze(0) # Prefill synchronized batch: all rows share the same expanded layout. text_pos = _get_shared_batch_mask(text_only_mask, "text_only_mask").to(hidden_states.device) return hidden_states[:, text_pos, :] elif use_summary and is_decode and hidden_states.size(1) > 1: # Decode: if we have multiple tokens, only return the first (text token) return hidden_states[:, :1, :] return hidden_states @check_model_inputs() @auto_docstring @shared_indexer_step 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, cache_position: Optional[torch.LongTensor] = None, summary_ctx: Optional[SummaryBatchContext] = None, **kwargs: Unpack[TransformersKwargs], ) -> BaseModelOutputWithPast: r""" summary_ctx (`SummaryBatchContext`, *optional*): Precomputed summary-attention metadata. When omitted, the model builds it from the expanded input during summary-attention prefill. """ if self.training or torch.is_grad_enabled(): raise RuntimeError("Inference-only export: call eval() under torch.no_grad() or torch.inference_mode()") if inputs_embeds is not None or input_ids is None: raise ValueError("This inference runtime requires input_ids, not inputs_embeds") if input_ids.ndim != 2 or input_ids.shape[0] != 1 or input_ids.shape[1] == 0: raise ValueError("This inference runtime requires one nonempty request") if isinstance(attention_mask, torch.Tensor): if attention_mask.ndim != 2 or not bool(torch.all(attention_mask == 1)): raise ValueError("Padded inputs and custom attention masks are not supported") use_summary = getattr(self.config, "use_summary_attention", False) is_prefill = past_key_values is None or past_key_values.get_seq_length() == 0 # Prefill phase with summary attention: expand input_ids with summary tokens text_only_mask = None if use_summary and input_ids is not None and inputs_embeds is None and is_prefill: input_ids, position_ids, text_only_mask = self._expand_input_with_summary_tokens(input_ids) if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # Initialize cache if use_cache and past_key_values is None: if use_summary: past_key_values = Qwen3RingBufferCache( config=self.config, sliding_chunk_nums=self._sliding_chunk_nums) else: past_key_values = DynamicCache(config=self.config) 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) # Caller-owned decode contexts may be updated and reused across steps. # Preserve their lookup fields exactly; step-derived lookups belong to # a local shallow context, while the original mask normalization stays. provided_summary_ctx = summary_ctx # Build summary context if needed if use_summary and summary_ctx is None and input_ids is not None: summary_ctx = self._build_summary_context(input_ids, position_ids, is_prefill, use_cache) if use_summary and summary_ctx is not None: summary_ctx.summary_mask = _get_shared_batch_mask(summary_ctx.summary_mask, "summary_mask") if not is_prefill and summary_ctx is provided_summary_ctx: summary_ctx = _copy_summary_context(summary_ctx) if not torch.is_grad_enabled(): # Prefill historically rebuilds lookup positions. Decode may # receive explicit custom positions: preserve those inputs and # their original validation/consumers rather than repair them. if is_prefill or summary_ctx.text_positions is None: summary_ctx.text_positions = torch.nonzero(~summary_ctx.summary_mask, as_tuple=False).flatten() if is_prefill or summary_ctx.summary_positions is None: summary_ctx.summary_positions = torch.nonzero(summary_ctx.summary_mask, as_tuple=False).flatten() if is_prefill and isinstance(past_key_values, Qwen3RingBufferCache): past_key_values.prepare_prefill(summary_ctx.text_positions, summary_ctx.summary_positions) causal_mask_mapping = attention_mask if not isinstance(causal_mask_mapping, (dict, list)): if summary_ctx and summary_ctx.enabled: seq_len = inputs_embeds.shape[1] # During prefill, Qwen3SummaryAttention uses summary_attn_func # which does not need a dense mask. Skip expensive mask construction. # During decode, prepare_inputs_for_generation already computed # per-layer keep_indices and passed them as attention_mask (list). # If we reach here with a non-list, it means no mask is needed. causal_mask_mapping = None else: # Prepare mask arguments mask_kwargs = { "config": self.config, "input_embeds": inputs_embeds, "attention_mask": attention_mask, "cache_position": cache_position, "past_key_values": past_key_values, "position_ids": position_ids, } # Create the masks - disable causal mask when summary context is enabled causal_mask_mapping = { "full_attention": create_causal_mask(**mask_kwargs), } # The sliding window alternating layers are not always activated depending on the config if self.has_sliding_layers: causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) for layer_idx, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]): if causal_mask_mapping is None: layer_mask = None elif isinstance(causal_mask_mapping, list): layer_mask = causal_mask_mapping[layer_idx] else: layer_mask = causal_mask_mapping[decoder_layer.attention_type] hidden_states = decoder_layer( hidden_states, attention_mask=layer_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, summary_ctx=summary_ctx, **kwargs, ) hidden_states = self.norm(hidden_states) # After prefill: reorganize cache to ring buffer layout if use_cache and use_summary and past_key_values is not None and is_prefill: if hasattr(past_key_values, 'reorganize_after_prefill') and summary_ctx is not None: past_key_values.reorganize_after_prefill(summary_ctx.summary_mask) # After chunk boundary decode: reset chunk counters if use_cache and use_summary and past_key_values is not None and not is_prefill: if hasattr(past_key_values, 'reset_chunk_counter'): past_key_values.reset_chunk_counter() # Filter out summary tokens from output hidden_states = self._filter_summary_tokens(hidden_states, text_only_mask, use_summary, past_key_values is not None and past_key_values.get_seq_length() > 0) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, ) @auto_docstring class Qwen3ForCausalLM(Qwen3PreTrainedModel, GenerationMixin): _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} def __init__(self, config): super().__init__(config) self.model = Qwen3Model(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() @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, cache_position: Optional[torch.LongTensor] = None, logits_to_keep: Union[int, torch.Tensor] = 0, summary_ctx: Optional[SummaryBatchContext] = None, **kwargs: Unpack[TransformersKwargs], ) -> CausalLMOutputWithPast: r""" summary_ctx (`SummaryBatchContext`, *optional*): Precomputed summary positions and masks for this inference forward. labels (`torch.LongTensor`, *optional*): Unsupported by this inference export; passing labels raises an error. """ if self.training or torch.is_grad_enabled() or labels is not None: raise RuntimeError("Inference-only export: call eval() under torch.no_grad() or torch.inference_mode(), without labels") 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, cache_position=cache_position, summary_ctx=summary_ctx, **kwargs, ) hidden_states = outputs.last_hidden_state # Keep BF16 logits and the established last-token default. if isinstance(logits_to_keep, int) and logits_to_keep == 0: # Inference: only need last token's logits to avoid OOM from [seq_len, vocab_size] logits = self.lm_head(hidden_states[:, -1:, :]) else: slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(hidden_states[:, slice_indices, :]) truncate_n = getattr(self.config, "truncate_predict_nums", None) if truncate_n is None: truncate_n = self.config.vocab_size - getattr( self.config, "summary_token_num", 0 ) if truncate_n > 0: logits = logits[..., :truncate_n] loss = None return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def _build_summary_attention_mask_for_generation( self, *, input_ids: torch.LongTensor, past_key_values: Optional[Cache], attention_mask: Optional[torch.Tensor], ) -> Optional[torch.Tensor]: """Ring buffer cache handles attention internally — no mask needed for decode.""" if isinstance(past_key_values, Qwen3RingBufferCache): return None return attention_mask def prepare_inputs_for_generation( self, input_ids: torch.LongTensor, past_key_values: Optional[Cache] = None, attention_mask: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, cache_position: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, **kwargs, ): if inputs_embeds is not None: raise ValueError("This inference runtime requires input_ids, not inputs_embeds") use_summary = getattr(self.config, "use_summary_attention", False) # If not using summary attention, use standard behavior if not use_summary: return super().prepare_inputs_for_generation( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, inputs_embeds=inputs_embeds, cache_position=cache_position, position_ids=position_ids, **kwargs, ) # For summary attention: handle cache-based input slicing summary_chunk_size = getattr(self.config, "summary_chunk_size", 0) summary_token_num = getattr(self.config, "summary_token_num", 0) summary_token_begin = getattr(self.config, "summary_token_begin", 0) # Prefill phase: pass full sequence, forward() will handle summary token insertion if past_key_values is None or past_key_values.get_seq_length() == 0: if cache_position is None: cache_position = torch.arange(0, input_ids.shape[1], device=input_ids.device) return { "input_ids": input_ids, "attention_mask": attention_mask, "position_ids": position_ids, "past_key_values": past_key_values, "cache_position": cache_position, "use_cache": kwargs.get("use_cache"), } # Decode phase: only pass new tokens not in cache # Get current chunk size (number of text tokens in current chunk) cur_chunk = past_key_values.get_cur_chunk_size() if hasattr(past_key_values, "get_cur_chunk_size") else 0 true_token_num = past_key_values.get_true_token_num() # Only take the new tokens that haven't been processed if input_ids.shape[1] > 1: # Slice to get only new tokens new_token_count = input_ids.shape[1] - true_token_num assert new_token_count > 0, f'new_token_count={new_token_count} should be greater than 0' input_ids = input_ids[:, -new_token_count:] if input_ids.shape[1] != 1: raise ValueError("Cached generation supports one new text token at a time") device = input_ids.device # Check if we need to insert summary tokens # If cur_chunk >= summary_chunk_size, we need to generate summary tokens if cur_chunk == summary_chunk_size - 1: # Insert summary tokens batch_size = input_ids.shape[0] summary_ids = ( torch.arange(summary_token_num, device=device, dtype=input_ids.dtype) + summary_token_begin ).unsqueeze(0).repeat(batch_size, 1) # Concatenate: [text_token, summary_tokens] input_ids = torch.cat([input_ids, summary_ids], dim=1) # Position IDs: text token uses cur_chunk, summary tokens use 0 if self.config.summary_chunk_position_ids_type == 'origin': text_pos = torch.full((batch_size, 1), past_key_values.get_true_token_num(), device=device, dtype=torch.long) elif self.config.summary_chunk_position_ids_type == 'inner_chunk': text_pos = torch.full((batch_size, 1), cur_chunk, device=device, dtype=torch.long) else: raise ValueError(f'Check config.summary_chunk_position_ids_type: {self.config.summary_chunk_position_ids_type}') if self.config.summary_token_position_ids_type == 'zeros': summary_pos = torch.zeros((batch_size, summary_token_num), device=device, dtype=torch.long) elif self.config.summary_token_position_ids_type == 'last_chunk_slice_left': # 等分成 summary_num 份，每个 summary token 取对应 slice 的末尾 prev_text_end = past_key_values.get_true_token_num()+1-summary_chunk_size cur_text_end = past_key_values.get_true_token_num()+1 chunk_len = cur_text_end - prev_text_end idx = torch.arange(0, summary_token_num, device=device, dtype=torch.long,) # 每一份的末尾（全局 position） slice_ends = prev_text_end + (idx * chunk_len) // summary_token_num - 1 slice_ends = slice_ends.clamp(min=prev_text_end) summary_pos = slice_ends.to(dtype=torch.long, device=device).unsqueeze(0).expand(batch_size, -1) elif self.config.summary_token_position_ids_type == 'last_chunk_slice_right': # 等分成 summary_num 份，每个 summary token 取对应 slice 的末尾 prev_text_end = past_key_values.get_true_token_num()+1-summary_chunk_size cur_text_end = past_key_values.get_true_token_num()+1 chunk_len = cur_text_end - prev_text_end idx = torch.arange(1, summary_token_num + 1, device=device, dtype=torch.long,) # 每一份的末尾（全局 position） slice_ends = prev_text_end + (idx * chunk_len) // summary_token_num - 1 slice_ends = slice_ends.clamp(min=prev_text_end) summary_pos = slice_ends.to(dtype=torch.long, device=device).unsqueeze(0).expand(batch_size, -1) else: raise ValueError('') position_ids = torch.cat([text_pos, summary_pos], dim=1) else: # Normal decode: just the new text token with position = cur_chunk if position_ids is None: batch_size = input_ids.shape[0] if self.config.summary_chunk_position_ids_type == 'origin': position_ids = torch.full((batch_size, input_ids.shape[1]), past_key_values.get_true_token_num(), device=input_ids.device, dtype=torch.long) elif self.config.summary_chunk_position_ids_type == 'inner_chunk': position_ids = torch.full((batch_size, input_ids.shape[1]), cur_chunk, device=input_ids.device, dtype=torch.long) else: raise ValueError(f'Check config.summary_chunk_position_ids_type: {self.config.summary_chunk_position_ids_type}') return { "input_ids": input_ids, "attention_mask": self._build_summary_attention_mask_for_generation( input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, ), "position_ids": position_ids, "past_key_values": past_key_values, "cache_position": cache_position, "use_cache": kwargs.get("use_cache"), } __all__ = [ "Qwen3ForCausalLM", "Qwen3PreTrainedModel", "Qwen3Model", "Qwen3RingBufferCache", "Qwen3SummaryAttention", "SummaryBatchContext", "build_summary_sliding_context", ]