# SPDX-License-Identifier: Apache-2.0 import torch import torch.nn as nn import triton import triton.language as tl from vllm.config import VllmConfig, set_current_vllm_config from vllm.forward_context import set_forward_context from vllm.model_executor.model_loader.loader import get_model_loader from vllm.model_executor.model_loader.utils import set_default_torch_dtype from vllm.model_executor.models.llama_eagle import EagleLlamaForCausalLM from vllm.v1.attention.backends.flash_attn import FlashAttentionMetadata from vllm.v1.sample.metadata import SamplingMetadata PADDING_SLOT_ID = -1 class EagleProposer: def __init__( self, vllm_config: VllmConfig, device: torch.device, ): self.vllm_config = vllm_config self.num_speculative_tokens = ( vllm_config.speculative_config.num_speculative_tokens) self.max_model_len = vllm_config.model_config.max_model_len self.block_size = vllm_config.cache_config.block_size # 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, ) -> tuple[torch.Tensor, torch.Tensor]: num_tokens = target_token_ids.shape[0] batch_size = next_token_ids.shape[0] last_token_indices = cu_num_tokens[1:] - 1 input_ids = torch.empty_like(target_token_ids) # Shift the input ids by one token. # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3] input_ids[:-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] 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, ) with set_forward_context(attn_metadata, self.vllm_config): hidden_states = self.model( input_ids=input_ids, hidden_states=target_hidden_states, positions=target_positions, ) sample_hidden_states = hidden_states[last_token_indices] logits = self.model.compute_logits(sample_hidden_states, None) draft_token_ids, draft_probs = compute_probs_and_sample_next_token( logits, sampling_metadata) # Early exit if there is only one draft token to be generated. if self.num_speculative_tokens == 1: # [batch_size, 1] and [batch_size, 1, vocab_size] return draft_token_ids.view(-1, 1), draft_probs.unsqueeze(dim=1) # Generate the remaining draft tokens. draft_token_ids_list = [draft_token_ids] draft_probs_list = [draft_probs] positions = target_positions[last_token_indices] hidden_states = sample_hidden_states 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. input_ids = draft_token_ids_list[-1] 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) # Run the model. with set_forward_context(attn_metadata, self.vllm_config): hidden_states = self.model( input_ids=input_ids, hidden_states=hidden_states, positions=clamped_positions, ) logits = self.model.compute_logits(hidden_states, None) draft_token_ids, probs = compute_probs_and_sample_next_token( logits, sampling_metadata) draft_token_ids_list.append(draft_token_ids) draft_probs_list.append(probs) # [batch_size, num_speculative_tokens] draft_token_ids = torch.stack(draft_token_ids_list, dim=1) # [batch_size, num_speculative_tokens, vocab_size] draft_probs = torch.stack(draft_probs_list, dim=1) return draft_token_ids, draft_probs @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 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: loader = get_model_loader(self.vllm_config.load_config) target_layer_num = self.vllm_config.model_config.get_num_layers( self.vllm_config.parallel_config) draft_model_config = \ self.vllm_config.speculative_config.draft_model_config # FIXME(lily): This does not handle with distributed inference. target_device = self.vllm_config.device_config.device # We need to set the vllm_config here to register attention # layers in the forward context. with set_default_torch_dtype( draft_model_config.dtype), set_current_vllm_config( self.vllm_config): self.model = EagleLlamaForCausalLM( model_config=draft_model_config, start_layer_id=target_layer_num).to(target_device) self.model.load_weights( loader.get_all_weights( self.vllm_config.speculative_config.draft_model_config, self.model)) self.model.lm_head = target_model.lm_head # 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, )