# SPDX-License-Identifier: Apache-2.0 from typing import Optional import numpy as np import torch import torch.nn as nn from vllm.attention.layer import Attention from vllm.config import (CompilationLevel, CUDAGraphMode, VllmConfig, get_layers_from_vllm_config) from vllm.distributed.parallel_state import get_pp_group from vllm.logger import logger from vllm.model_executor.model_loader import get_model from vllm.model_executor.models import supports_multimodal from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.sample.metadata import SamplingMetadata from vllm.v1.spec_decode.metadata import SpecDecodeMetadata from vllm_npu.ascend_forward_context import set_ascend_forward_context from vllm_npu.attention.attention_mask import AttentionMaskBuilder from vllm_npu.attention.attention_v1 import AscendAttentionState from vllm_npu.attention.utils import AscendCommonAttentionMetadata from vllm_npu.spec_decode.interface import Proposer, SpecDcodeType PADDING_SLOT_ID = -1 class EagleProposer(Proposer): def __init__(self, vllm_config: VllmConfig, device: torch.device, runner=None): self.name = SpecDcodeType.EAGLE if vllm_config.speculative_config.method == "eagle" else SpecDcodeType.EAGLE3 self.vllm_config = vllm_config self.device = device self.runner = runner self.block_size = vllm_config.cache_config.block_size # We need to get the hidden size from the draft model config because # the draft model's hidden size can be different from the target model's # hidden size (e.g., Llama 3.3 70B). self.hidden_size = vllm_config.speculative_config.draft_model_config.get_hidden_size( ) self.use_cuda_graph = (self.vllm_config.compilation_config.level == CompilationLevel.PIECEWISE and not self.vllm_config.model_config.enforce_eager) self.cudagraph_batch_sizes = list( reversed( self.vllm_config.compilation_config.cudagraph_capture_sizes)) # persistent buffers for cuda graph self.input_ids = torch.zeros( self.vllm_config.scheduler_config.max_num_batched_tokens, dtype=torch.int32, device=device) self.positions = torch.zeros( self.vllm_config.scheduler_config.max_num_batched_tokens, dtype=torch.int64, device=device) self.hidden_states = torch.zeros( (self.vllm_config.scheduler_config.max_num_batched_tokens, self.hidden_size), dtype=self.vllm_config.model_config.dtype, device=device) # We need +1 here because the arange is used to set query_start_loc, # which has one more element than batch_size. self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs + 1, device=device, dtype=torch.int32) attn_mask_len = self.vllm_config.model_config.max_model_len self.attn_mask_builder = AttentionMaskBuilder( attn_mask_len, self.vllm_config.model_config.dtype, device=device) def load_model(self, model: nn.Module) -> None: target_attn_layer_names = set( get_layers_from_vllm_config(self.vllm_config, Attention).keys()) self.model = get_model(vllm_config=self.vllm_config, model_config=self.vllm_config. speculative_config.draft_model_config) draft_attn_layer_names = ( get_layers_from_vllm_config(self.vllm_config, Attention).keys() - target_attn_layer_names) self.attn_layer_name = next(iter(draft_attn_layer_names)) # share embed_tokens with the target model if needed if get_pp_group().world_size == 1: logger.info( "The EAGLE head shares the same vocab embedding" \ " with the target model." ) self.model.model.embed_tokens = model.model.embed_tokens else: logger.info( "Since PP > 1, the EAGLE head loaded its own vocab embedding" \ " weights instead of sharing them with the target model." ) # share lm_head with the target model if needed # some model definition do not define lm_head explicitly # and reuse embed_tokens for lm_head, e.g., CohereForCausalLM if self.name == SpecDcodeType.EAGLE and hasattr(model, "lm_head"): logger.info("Loading EAGLE LM head weights from the target model.") if supports_multimodal(model): self.model.lm_head = model.get_language_model().lm_head else: self.model.lm_head = model.lm_head @torch.inference_mode() def dummy_run(self, num_tokens: int, with_prefill: bool = False, skip_attn: bool = False, num_reqs: int = 0, num_tokens_across_dp: Optional[torch.Tensor] = None, aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE, batch_descriptor=None, dummy_compute_logits=lambda hidden_states: None): moe_comm_type = self.runner._select_moe_comm_method( num_tokens, with_prefill) with set_ascend_forward_context(None, self.vllm_config, moe_comm_type=moe_comm_type, num_tokens=num_tokens): self.model( input_ids=self.input_ids[:num_tokens], positions=self.positions[:num_tokens], hidden_states=self.hidden_states[:num_tokens], ) dummy_compute_logits(self.hidden_states) def generate_token_ids(self, valid_sampled_token_ids: list[list[int]], sampling_metadata: SamplingMetadata = None, scheduler_output: SchedulerOutput = None, spec_decode_metadata: SpecDecodeMetadata = None, positions: torch.Tensor = None, num_scheduled_tokens: int = 0, hidden_states: torch.Tensor = None, attn_metadata=None, aux_hidden_states: torch.Tensor = None): attn_metadata = self._get_eagle_atten_dict(scheduler_output) next_token_ids: list[int] = [] for i, token_ids in enumerate(valid_sampled_token_ids): if token_ids: # Common case. next_token_id = token_ids[-1] else: # Partial prefill (rare case). # Get the next token id from the request state. req_id = self.runner.input_batch.req_ids[i] req_state = self.runner.requests[req_id] seq_len = (req_state.num_computed_tokens + scheduler_output.num_scheduled_tokens[req_id]) next_token_id = req_state.get_token_id(seq_len) next_token_ids.append(next_token_id) next_token_ids = torch.tensor(next_token_ids, dtype=torch.int32, device=self.device) eagle_attn_metadata = attn_metadata[self.attn_layer_name] if spec_decode_metadata is None: # input_ids can be None for multimodal models. target_token_ids = self.runner.input_ids[:num_scheduled_tokens] target_positions = positions[:num_scheduled_tokens] if self.name == SpecDcodeType.EAGLE3: target_hidden_states = torch.cat( [h[:num_scheduled_tokens] for h in aux_hidden_states], dim=-1) else: target_hidden_states = hidden_states[:num_scheduled_tokens] target_slot_mapping = eagle_attn_metadata.slot_mapping cu_num_tokens = eagle_attn_metadata.query_start_loc else: num_draft_tokens = spec_decode_metadata.num_draft_tokens num_rejected_tokens = [ n + 1 - len(valid_sampled_token_ids[i]) if n > 0 else 0 for i, n in enumerate(num_draft_tokens) ] num_rejected_tokens = torch.tensor( num_rejected_tokens, dtype=torch.int32, device=self.device, ) num_tokens = num_scheduled_tokens - sum(num_rejected_tokens) cu_num_tokens, token_indices = self._prepare_inputs( eagle_attn_metadata.query_start_loc, num_rejected_tokens, num_tokens) target_token_ids = self.runner.input_ids[token_indices] target_positions = positions[token_indices] if self.name == SpecDcodeType.EAGLE3: target_hidden_states = torch.cat( [h[token_indices] for h in aux_hidden_states], dim=-1) else: target_hidden_states = hidden_states[token_indices] target_slot_mapping = eagle_attn_metadata.slot_mapping[ token_indices] draft_token_ids = self._propose( target_token_ids=target_token_ids, target_positions=target_positions, target_hidden_states=target_hidden_states, target_slot_mapping=target_slot_mapping, next_token_ids=next_token_ids, cu_num_tokens=cu_num_tokens, block_table=eagle_attn_metadata.block_tables, sampling_metadata=sampling_metadata, ) spec_token_ids = draft_token_ids.tolist() return spec_token_ids def _get_eagle_atten_dict( self, scheduler_output: "SchedulerOutput", ): total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens assert total_num_scheduled_tokens > 0 num_reqs = self.runner.input_batch.num_reqs assert num_reqs > 0 # OPTIMIZATION: Start copying the block table first. # This way, we can overlap the copy with the following CPU operations. self.runner.input_batch.block_table.commit_block_table(num_reqs) # Get the number of scheduled tokens for each request. req_ids = self.runner.input_batch.req_ids tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids] num_scheduled_tokens = np.array(tokens, dtype=np.int32) max_num_scheduled_tokens = max(tokens) self.runner.query_lens = torch.from_numpy(num_scheduled_tokens) # Get request indices. # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2] req_indices = np.repeat(self.runner.arange_np[:num_reqs], num_scheduled_tokens) # cu_num_tokens: [2, 5, 3] -> [2, 7, 10] # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] cu_num_tokens, arange = self._get_cumsum_and_arange( num_scheduled_tokens) # Get positions. positions_np = self.runner.positions_np[:total_num_scheduled_tokens] np.add(self.runner.input_batch.num_computed_tokens_cpu[req_indices], arange, out=positions_np) # Calculate M-RoPE positions. # Only relevant for models using M-RoPE (e.g, Qwen2-VL) if self.runner.uses_mrope: self.runner._calc_mrope_positions(scheduler_output) # Get token indices. # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2] # where M is the max_model_len. token_indices = ( positions_np + req_indices * self.runner.input_batch.token_ids_cpu.shape[1]) # NOTE(woosuk): We use torch.index_select instead of np.take here # because torch.index_select is much faster than np.take for large # tensors. torch.index_select( self.runner.input_batch.token_ids_cpu_tensor.flatten(), 0, torch.from_numpy(token_indices), out=self.runner.input_ids_cpu[:total_num_scheduled_tokens]) # Prepare the attention metadata for each KV cache group and make layers # in the same group share the same metadata. # NOTE(Chen): there is exactly one KV cache group that contains all # attetnion layers in the model for now, so the current logic for # getting attn_metadata is not related to kv_cache_group information. # Will extend this part to support multiple KV cache groups later. for kv_cache_group_id, kv_cache_group_spec in enumerate( self.runner.kv_cache_config.kv_cache_groups): block_size = kv_cache_group_spec.kv_cache_spec.block_size block_table = self.runner.input_batch.block_table[ kv_cache_group_id] # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1] # where K is the max_num_blocks_per_req and the block size is 2. # NOTE(woosuk): We can't simply use `token_indices // block_size` # here because M (max_model_len) is not necessarily divisible by # block_size. block_table_indices = ( req_indices * block_table.max_num_blocks_per_req + positions_np // block_size) block_table_cpu = block_table.get_cpu_tensor() block_numbers = block_table_cpu.flatten( )[block_table_indices].numpy() block_offsets = positions_np % block_size np.add( block_numbers * block_size, block_offsets, out=block_table.slot_mapping_np[:total_num_scheduled_tokens]) # Prepare the attention metadata. self.runner.query_start_loc_np[0] = 0 self.runner.query_start_loc_np[1:num_reqs + 1] = cu_num_tokens self.runner.seq_lens_np[:num_reqs] = ( self.runner.input_batch.num_computed_tokens_cpu[:num_reqs] + num_scheduled_tokens) # Copy the tensors to the NPU. self.runner.input_ids[:total_num_scheduled_tokens].copy_( self.runner.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True) if self.runner.uses_mrope: # Only relevant for models using M-RoPE (e.g, Qwen2-VL) self.runner.mrope_positions[:, :total_num_scheduled_tokens].copy_( self.runner. mrope_positions_cpu[:, :total_num_scheduled_tokens], non_blocking=True) else: # Common case (1D positions) self.runner.positions[:total_num_scheduled_tokens].copy_( self.runner.positions_cpu[:total_num_scheduled_tokens], non_blocking=True) self.runner.query_start_loc[:num_reqs + 1].copy_( self.runner.query_start_loc_cpu[:num_reqs + 1], non_blocking=True) self.runner.seq_lens[:num_reqs].copy_( self.runner.seq_lens_cpu[:num_reqs], non_blocking=True) # Fill unused with -1. Needed for reshape_and_cache self.runner.seq_lens[num_reqs:].fill_(0) self.runner.query_start_loc[num_reqs + 1:].fill_(-1) attn_metadata = {} # Prepare the attention metadata for each KV cache group and make layers # in the same group share the same metadata. for kv_cache_group_id, kv_cache_group_spec in enumerate( self.runner.kv_cache_config.kv_cache_groups): common_attn_metadata = AscendCommonAttentionMetadata( query_start_loc=self.runner.query_start_loc[:num_reqs + 1], query_start_loc_cpu=self.runner.query_start_loc_cpu[:num_reqs + 1], seq_lens_cpu=self.runner.seq_lens_cpu, num_reqs=num_reqs, max_query_len=max_num_scheduled_tokens, num_actual_tokens=total_num_scheduled_tokens, actual_seq_lengths_q=self.runner.actual_seq_lengths_q, block_table_tensor=self.runner.input_batch.block_table[0]. get_device_tensor(), slot_mapping=self.runner.slot_mapping, positions=self.runner.positions, attn_mask=self.runner.attn_mask, spec_attn_mask=self.runner.spec_attn_mask, attn_state=self.runner.attn_state, decode_token_per_req=self.runner.decode_token_per_req, num_computed_tokens_cpu=None, seq_lens=None) builder = self.runner.attn_groups[0][0].get_metadata_builder() attn_metadata_i = builder.build(0, common_attn_metadata, self.runner.get_model()) for layer_name in kv_cache_group_spec.layer_names: attn_metadata[layer_name] = attn_metadata_i return attn_metadata def _get_cumsum_and_arange( self, num_tokens: np.ndarray, cumsum_dtype: Optional[np.dtype] = None, ) -> tuple[np.ndarray, np.ndarray]: """Get the cumulative sum and batched arange of the given array. # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]) # Equivalent to but faster than: # np.concatenate([np.arange(n) for n in num_tokens]) """ # Step 1. [2, 5, 3] -> [2, 7, 10] cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype) total_num_tokens = cu_num_tokens[-1] # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7] cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens) # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2] arange = self.runner.arange_np[:total_num_tokens] - cumsums_offsets return cu_num_tokens, arange def _propose( self, # [num_tokens] target_token_ids: torch.Tensor, # [num_tokens] target_positions: torch.Tensor, # [num_tokens, hidden_size] target_hidden_states: torch.Tensor, # [num_tokens] target_slot_mapping: torch.Tensor, # [batch_size] next_token_ids: torch.Tensor, # [batch_size + 1] starting with 0 cu_num_tokens: torch.Tensor, # [batch_size, max_num_blocks_per_req] block_table: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> torch.Tensor: device = cu_num_tokens.device cu_num_tokens = cu_num_tokens.cpu() block_table = block_table.cpu() num_tokens = target_token_ids.shape[0] batch_size = next_token_ids.shape[0] last_token_indices = cu_num_tokens[1:] - 1 target_positions = target_positions.cpu() if self.name == SpecDcodeType.EAGLE3: assert isinstance(self.model, Eagle3LlamaForCausalLM) target_hidden_states = self.model.combine_hidden_states( target_hidden_states) assert target_hidden_states.shape[-1] == self.hidden_size # Shift the input ids by one token. # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3] self.input_ids[:num_tokens - 1] = target_token_ids[1:] # Replace the last token with the next token. # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4] self.input_ids[last_token_indices] = next_token_ids seq_lens = (target_positions[last_token_indices] + 1).int() query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1] max_query_len = query_lens.max().item() attn_mask = self.runner.attn_mask common_attn_metadata = AscendCommonAttentionMetadata( query_start_loc=cu_num_tokens.to(device), query_start_loc_cpu=cu_num_tokens, seq_lens_cpu=seq_lens.cpu(), max_query_len=max_query_len, num_reqs=batch_size, num_actual_tokens=num_tokens, actual_seq_lengths_q=self.runner.actual_seq_lengths_q, block_table_tensor=self.runner.input_batch.block_table[0]. get_device_tensor(), slot_mapping=target_slot_mapping, positions=target_positions, attn_mask=attn_mask, spec_attn_mask=self.runner.spec_attn_mask, attn_state=self.runner.attn_state, decode_token_per_req=self.runner.decode_token_per_req, num_computed_tokens_cpu=None, seq_lens=None) # FIXME(woosuk): The below two ops cause synchronization. Optimize. builder = self.runner.attn_groups[0][0].get_metadata_builder() attn_metadata = builder.build(0, common_attn_metadata, self.runner.get_model()) if self.use_cuda_graph and \ num_tokens <= self.cudagraph_batch_sizes[-1]: num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens) else: num_input_tokens = num_tokens with_prefill = attn_metadata.attn_state not in [ AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding ] moe_comm_type = self.runner._select_moe_comm_method( num_input_tokens, with_prefill) # copy inputs to buffer for cudagraph self.positions[:num_tokens] = target_positions.to(device) self.hidden_states[:num_tokens] = target_hidden_states attn_metadata.block_tables = block_table.to(device) with set_ascend_forward_context(attn_metadata, self.vllm_config, moe_comm_type=moe_comm_type, num_tokens=num_input_tokens): last_hidden_states, hidden_states = self.model( input_ids=self.input_ids[:num_input_tokens], positions=self.positions[:num_input_tokens], hidden_states=self.hidden_states[:num_input_tokens], ) sample_hidden_states = last_hidden_states[last_token_indices] logits = self.model.compute_logits(sample_hidden_states) draft_token_ids = logits.argmax(dim=-1) # Early exit if there is only one draft token to be generated. if self.vllm_config.speculative_config.num_speculative_tokens == 1: # [batch_size, 1] return draft_token_ids.view(-1, 1) # Generate the remaining draft tokens. draft_token_ids_tensor = torch.zeros( (self.vllm_config.speculative_config.num_speculative_tokens, *draft_token_ids.shape), dtype=draft_token_ids.dtype) draft_token_ids_tensor[0] = draft_token_ids positions_cpu = target_positions[last_token_indices].cpu().to( torch.int64) hidden_states = hidden_states[last_token_indices] if self.use_cuda_graph and \ batch_size <= self.cudagraph_batch_sizes[-1]: input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size) else: input_batch_size = batch_size moe_comm_type = self.runner._select_moe_comm_method( input_batch_size, False) attn_metadata.num_actual_tokens = batch_size attn_metadata.max_query_len = 1 attn_metadata.query_start_loc = self.arange[:batch_size + 1] attn_metadata.query_start_loc_list = attn_metadata.query_start_loc[ 1:].tolist() attn_metadata.num_decodes, attn_metadata.num_prefills, attn_metadata.num_decode_tokens, attn_metadata.num_prefill_tokens = 0, batch_size, 0, batch_size attn_metadata.num_actual_tokens_pcp_padded = attn_metadata.num_decode_tokens + attn_metadata.num_prefill_tokens query_lens.fill_(1) attn_metadata.query_lens = query_lens attn_metadata.actual_seq_lengths_q = [1 + i for i in range(batch_size)] attn_metadata.seq_lens_list = seq_lens.tolist() attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill for now_speculative in range( self.vllm_config.speculative_config.num_speculative_tokens - 1): # Update the inputs. # cast to int32 is crucial when eagle model is compiled. # tensor.argmax() returns int64 by default. input_ids = draft_token_ids_tensor[now_speculative].to(device) positions_cpu += 1 # NOTE(woosuk): We should handle the case where the draft model # generates tokens beyond the max model length. Since it is complex # to remove such requests from the batch, we keep them in the batch # but adjust the position ids and slot mappings to avoid the # out-of-range access during the model execution. The draft tokens # generated with this adjustment should be ignored. exceeds_max_model_len = positions_cpu >= self.vllm_config.model_config.max_model_len # Mask out the position ids that exceed the max model length. # Otherwise, we may get out-of-range error in RoPE. clamped_positions_cpu = torch.where(exceeds_max_model_len, 0, positions_cpu) clamped_positions = clamped_positions_cpu.to(device) # TODO: Increment the sequence lengths. attn_metadata.seq_lens += 1 attn_metadata.seq_lens_list = [ _ + 1 for _ in attn_metadata.seq_lens_list ] # TODO: Consider max model length. # attn_metadata.max_seq_len = min(attn_metadata.max_seq_len, # self.max_model_len) # For the requests that exceed the max model length, we set the # TODO: sequence length to 1 to minimize their overheads in attention. # Compute the slot mapping. block_numbers = (clamped_positions_cpu // self.block_size) block_ids = block_table.gather(dim=1, index=block_numbers.view(-1, 1)) block_ids = block_ids.view(-1) slot_mapping_cpu = ( block_ids * self.vllm_config.cache_config.block_size + clamped_positions_cpu % self.block_size) # Mask out the slot mappings that exceed the max model length. # Otherwise, the KV cache will be inadvertently updated with the # padding tokens. slot_mapping_cpu.masked_fill_(exceeds_max_model_len, PADDING_SLOT_ID) # NOTE: ASCEND slot_mapping must on cpu attn_metadata.slot_mapping = slot_mapping_cpu.to( torch.int32).to(device) # copy inputs to buffer for cudagraph self.input_ids[:batch_size] = input_ids self.positions[:batch_size] = clamped_positions self.hidden_states[:batch_size] = hidden_states attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask( attn_metadata.seq_lens, positions_cpu, self.vllm_config.model_config.dtype, self.device) attn_metadata.attn_mask = attn_mask attn_metadata.block_tables = block_table.to(device) # Run the model. with set_ascend_forward_context(attn_metadata, self.vllm_config, moe_comm_type=moe_comm_type, num_tokens=input_batch_size): last_hidden_states, hidden_states = self.model( input_ids=self.input_ids[:input_batch_size], positions=self.positions[:input_batch_size], hidden_states=self.hidden_states[:input_batch_size], ) hidden_states = hidden_states[:batch_size] logits = self.model.compute_logits(last_hidden_states[:batch_size]) # TODO(wenlong): get more than one token for tree attention draft_token_ids = logits.argmax(dim=-1) draft_token_ids_tensor[now_speculative + 1] = draft_token_ids.cpu() # [batch_size, num_speculative_tokens] draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1) return draft_token_ids def _prepare_inputs( self, # [batch_size + 1] cu_target_query_lens: torch.Tensor, # [batch_size] num_rejected_tokens: torch.Tensor, num_tokens: int, ) -> tuple[torch.Tensor, torch.Tensor]: # cu_target_query_lens: [0, a, a + b, a + b + c] # num_rejected_tokens: [n1, n2, n3] # num_tokens_per_req: [a - n1, b - n2, c - n3] # cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3] # token_indices: [0, 1, ..., a - n1 - 1, # a, a + 1, ..., a + b - n2 - 1, # a + b, a + b + 1, ..., a + b + c - n3 - 1] # [0, a, a + b, a + b + c] -> [a, b, c] query_len_per_req = (cu_target_query_lens[1:] - cu_target_query_lens[:-1]) # [a, b, c] -> [a - n1, b - n2, c - n3] num_tokens_per_req = query_len_per_req - num_rejected_tokens # [a - n1, b - n2, c - n3] -> # [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3] cu_num_tokens = torch.zeros_like(cu_target_query_lens) torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:]) token_indices = torch.empty( num_tokens, dtype=torch.int32, device=cu_target_query_lens.device, ) BLOCK_SIZE = 1024 self._prepare_eagle_input_sequential( token_indices, cu_target_query_lens, cu_num_tokens, block_size=BLOCK_SIZE, ) return cu_num_tokens, token_indices def _prepare_eagle_input_sequential(self, out_tensor: torch.Tensor, cu_query_lens: torch.Tensor, cu_num_tokens: torch.Tensor, block_size: int): num_programs = len(cu_num_tokens) - 1 for pid in range(num_programs): start_pos = cu_num_tokens[pid].item() end_pos = cu_num_tokens[pid + 1].item() num_tokens = end_pos - start_pos index_start = cu_query_lens[pid].item() num_blocks = int( torch.ceil(torch.tensor(num_tokens / block_size)).item()) for i in range(num_blocks): offset_tensor = torch.arange(0, block_size, dtype=torch.int32, device=out_tensor.device) global_start_offset = i * block_size target_indices = torch.tensor( start_pos + global_start_offset, dtype=torch.int32, device=out_tensor.device) + offset_tensor values_to_store = torch.tensor( index_start + global_start_offset, dtype=torch.int32, device=out_tensor.device) + offset_tensor mask = (target_indices >= start_pos) & \ (target_indices < end_pos) & \ (offset_tensor < num_tokens) out_tensor[target_indices[mask]] = values_to_store[mask]