# SPDX-License-Identifier: Apache-2.0 import torch import torch.nn as nn from vllm.attention.layer import Attention from vllm.config import (CompilationLevel, VllmConfig, get_layers_from_vllm_config) from vllm.distributed.parallel_state import get_pp_group from vllm.forward_context import set_forward_context from vllm.logger import init_logger from vllm.model_executor.model_loader import get_model from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM from vllm.triton_utils import tl, triton from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata from vllm.v1.sample.metadata import SamplingMetadata logger = init_logger(__name__) PADDING_SLOT_ID = -1 class EagleProposer: def __init__( self, vllm_config: VllmConfig, device: torch.device, ): self.vllm_config = vllm_config self.speculative_config = vllm_config.speculative_config self.draft_model_config = self.speculative_config.draft_model_config self.method = self.speculative_config.method self.dtype = vllm_config.model_config.dtype self.max_model_len = vllm_config.model_config.max_model_len self.block_size = vllm_config.cache_config.block_size self.num_speculative_tokens = ( self.speculative_config.num_speculative_tokens) self.max_num_tokens = ( vllm_config.scheduler_config.max_num_batched_tokens) # 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 = self.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.max_num_tokens, dtype=torch.int32, device=device) self.positions = torch.zeros(self.max_num_tokens, dtype=torch.int64, device=device) self.hidden_states = torch.zeros( (self.max_num_tokens, self.hidden_size), dtype=self.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) 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: num_tokens = target_token_ids.shape[0] batch_size = next_token_ids.shape[0] last_token_indices = cu_num_tokens[1:] - 1 if self.method == "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 # FA requires seq_len to have dtype int32. seq_lens = (target_positions[last_token_indices] + 1).int() # FIXME(woosuk): The below two ops cause synchronization. Optimize. max_seq_len = seq_lens.max().item() max_num_tokens = (cu_num_tokens[1:] - cu_num_tokens[:-1]).max().item() attn_metadata = FlashAttentionMetadata( num_actual_tokens=num_tokens, max_query_len=max_num_tokens, query_start_loc=cu_num_tokens, max_seq_len=max_seq_len, seq_lens=seq_lens, block_table=block_table, slot_mapping=target_slot_mapping, # TODO(woosuk): Support cascade attention. use_cascade=False, common_prefix_len=0, cu_prefix_query_lens=None, prefix_kv_lens=None, suffix_kv_lens=None, ) 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 # copy inputs to buffer for cudagraph self.positions[:num_tokens] = target_positions self.hidden_states[:num_tokens] = target_hidden_states with set_forward_context(attn_metadata, self.vllm_config, 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, None) draft_token_ids = logits.argmax(dim=-1) # Early exit if there is only one draft token to be generated. if self.num_speculative_tokens == 1: # [batch_size, 1] return draft_token_ids.view(-1, 1) # Generate the remaining draft tokens. draft_token_ids_list = [draft_token_ids] positions = target_positions[last_token_indices] 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 attn_metadata.num_actual_tokens = batch_size attn_metadata.max_query_len = 1 attn_metadata.query_start_loc = self.arange[:batch_size + 1] for _ in range(self.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_list[-1].int() positions += 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 >= self.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 = torch.where(exceeds_max_model_len, 0, positions) # Increment the sequence lengths. attn_metadata.max_seq_len += 1 attn_metadata.seq_lens += 1 # 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 # sequence length to 1 to minimize their overheads in attention. attn_metadata.seq_lens.masked_fill_(exceeds_max_model_len, 1) # Compute the slot mapping. block_numbers = clamped_positions // self.block_size block_ids = block_table.gather(dim=1, index=block_numbers.view(-1, 1)) block_ids = block_ids.view(-1) attn_metadata.slot_mapping = (block_ids * self.block_size + clamped_positions % 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. attn_metadata.slot_mapping.masked_fill_(exceeds_max_model_len, PADDING_SLOT_ID) # 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 # Run the model. with set_forward_context(attn_metadata, self.vllm_config, 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], None) # TODO(wenlong): get more than one token for tree attention draft_token_ids = logits.argmax(dim=-1) draft_token_ids_list.append(draft_token_ids) # [batch_size, num_speculative_tokens] draft_token_ids = torch.stack(draft_token_ids_list, dim=1) return draft_token_ids @staticmethod def prepare_inputs( # [batch_size + 1] cu_target_query_lens: torch.Tensor, # [batch_size] num_rejected_tokens: torch.Tensor, ) -> 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.empty_like(cu_target_query_lens) torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:]) cu_num_tokens[0] = 0 # FIXME(woosuk): Avoid synchronization. num_tokens = cu_num_tokens[-1].item() token_indices = torch.empty( num_tokens, dtype=torch.int32, device=cu_num_tokens.device, ) batch_size = num_rejected_tokens.shape[0] BLOCK_SIZE = 1024 prepare_input_kernel[(batch_size, )]( token_indices, cu_target_query_lens, cu_num_tokens, BLOCK_SIZE=BLOCK_SIZE, ) return cu_num_tokens, token_indices def load_model(self, target_model: nn.Module) -> None: draft_model_config = \ self.vllm_config.speculative_config.draft_model_config 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=draft_model_config) draft_attn_layer_names = ( get_layers_from_vllm_config(self.vllm_config, Attention).keys() - target_attn_layer_names) assert len(draft_attn_layer_names) == 1 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 = target_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.vllm_config.speculative_config.method != "eagle3" and \ hasattr(target_model, "lm_head"): logger.info("Loading EAGLE LM head weights from the target model.") self.model.lm_head = target_model.lm_head @torch.inference_mode() def dummy_run( self, num_tokens: int, ) -> None: with set_forward_context(None, self.vllm_config, 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], ) # NOTE(woosuk): Currently, the below code is not used and we always use argmax # to sample the draft tokens. We will use this after we find a way to manage # the draft prob tensor. # Refer to https://github.com/vllm-project/vllm/pull/16899 for the details. # FIXME(woosuk): The logic here is duplicated with the main sampling code. # We should refactor this to reuse the same sampling implementation. def compute_probs_and_sample_next_token( logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> tuple[torch.Tensor, torch.Tensor]: if sampling_metadata.all_greedy: # For greedy requests, draft_probs is not used in rejection sampling. # Therefore, we can just return the logits. probs = logits next_token_ids = logits.argmax(dim=-1) return next_token_ids, probs is_greedy = sampling_metadata.temperature == -1 temperature = torch.where(is_greedy, 1.0, sampling_metadata.temperature) logits.div_(temperature.view(-1, 1)) probs = logits.softmax(dim=-1, dtype=torch.float32) # NOTE(woosuk): Currently, we ignore most of the sampling parameters in # generating the draft tokens. We only use the temperature. While this # could degrade the acceptance rate, it does not affect the distribution # of the generated tokens after rejection sampling. # TODO(woosuk): Consider seeds. q = torch.empty_like(probs) q.exponential_() # NOTE(woosuk): We shouldn't use `probs.div_(q)` because the draft_probs # will be used later for rejection sampling. next_token_ids = probs.div(q).argmax(dim=-1).view(-1) if not sampling_metadata.all_random: greedy_token_ids = probs.argmax(dim=-1) next_token_ids = torch.where( is_greedy, greedy_token_ids, next_token_ids, ) return next_token_ids, probs @triton.jit def prepare_input_kernel( out_ptr, cu_query_lens_ptr, cu_num_tokens_ptr, BLOCK_SIZE: tl.constexpr, ): pid = tl.program_id(0) # [start_pos, end_pos) start_pos = tl.load(cu_num_tokens_ptr + pid) end_pos = tl.load(cu_num_tokens_ptr + pid + 1) num_tokens = end_pos - start_pos index_start = tl.load(cu_query_lens_ptr + pid) num_blocks = tl.cdiv(num_tokens, BLOCK_SIZE) for i in tl.range(num_blocks): offset = i * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) tl.store( out_ptr + start_pos + offset, index_start + offset, mask=offset < num_tokens, )