import logging import os import time from contextlib import contextmanager from typing import List, Optional, Tuple import torch from huggingface_hub import snapshot_download from sglang.srt.distributed import GroupCoordinator, patch_tensor_parallel_group from sglang.srt.layers.dp_attention import disable_dp_size from sglang.srt.layers.logits_processor import LogitsProcessorOutput from sglang.srt.layers.sampler import get_token_ids_logprobs, get_top_logprobs from sglang.srt.managers.schedule_batch import ScheduleBatch from sglang.srt.managers.tp_worker import TpModelWorker from sglang.srt.model_executor.forward_batch_info import ( CaptureHiddenMode, ForwardBatch, ForwardMode, ) from sglang.srt.server_args import ServerArgs from sglang.srt.speculative.eagle_draft_cuda_graph_runner import ( EAGLEDraftCudaGraphRunner, ) from sglang.srt.speculative.eagle_utils import ( EagleDraftInput, EagleVerifyInput, EagleVerifyOutput, assign_draft_cache_locs, fast_topk, select_top_k_tokens, ) from sglang.srt.speculative.spec_info import SpeculativeAlgorithm from sglang.srt.utils import empty_context, get_available_gpu_memory, is_cuda_available if is_cuda_available(): from sgl_kernel import segment_packbits logger = logging.getLogger(__name__) @contextmanager def draft_tp_context(tp_group: GroupCoordinator): # Draft model doesn't use dp and has its own tp group. # We disable mscclpp now because it doesn't support 2 comm groups. with disable_dp_size(), patch_tensor_parallel_group(tp_group): yield class EAGLEWorker(TpModelWorker): def __init__( self, server_args: ServerArgs, gpu_id: int, tp_rank: int, dp_rank: Optional[int], nccl_port: int, target_worker: TpModelWorker, ): # Parse arguments self.server_args = server_args self.topk = server_args.speculative_eagle_topk self.speculative_num_steps = server_args.speculative_num_steps self.padded_static_len = self.speculative_num_steps + 1 self.enable_nan_detection = server_args.enable_nan_detection self.gpu_id = gpu_id self.device = server_args.device self.target_worker = target_worker self.speculative_algorithm = SpeculativeAlgorithm.from_string( server_args.speculative_algorithm ) # Override context length with target model's context length server_args.context_length = target_worker.model_runner.model_config.context_len # Do not capture cuda graph in `super().__init__()` # It will be captured later. backup_disable_cuda_graph = server_args.disable_cuda_graph server_args.disable_cuda_graph = True # Share the allocator with a target worker. # Draft and target worker own their own KV cache pools. self.req_to_token_pool, self.token_to_kv_pool_allocator = ( target_worker.get_memory_pool() ) # Load hot token ids if self.speculative_algorithm.is_eagle3(): if server_args.speculative_token_map is not None: logger.warning( "Speculative token map specified, but EAGLE3 models already have this. Ignoring the specified token map." ) self.hot_token_id = None elif server_args.speculative_token_map is not None: self.hot_token_id = load_token_map(server_args.speculative_token_map) server_args.json_model_override_args = ( f'{{"hot_vocab_size": {len(self.hot_token_id)}}}' ) else: self.hot_token_id = None # Init draft worker with empty_context(): super().__init__( gpu_id=gpu_id, tp_rank=tp_rank, server_args=server_args, nccl_port=nccl_port, dp_rank=dp_rank, is_draft_worker=True, req_to_token_pool=self.req_to_token_pool, token_to_kv_pool_allocator=self.token_to_kv_pool_allocator, ) embed, head = self.target_worker.model_runner.model.get_embed_and_head() if self.speculative_algorithm.is_eagle3(): # EAGLE3 models don't share lm_head self.draft_model_runner.model.set_embed(embed) # grab hot token ids self.hot_token_id = self.draft_model_runner.model.get_hot_token_id().to( embed.device ) else: if self.hot_token_id is not None: head = head.clone() self.hot_token_id = self.hot_token_id.to(head.device) head.data = head.data[self.hot_token_id] # Share the embedding and lm_head self.draft_model_runner.model.set_embed_and_head(embed, head) # Init attention backend and cuda graphs self.draft_model_runner.server_args.disable_cuda_graph = ( backup_disable_cuda_graph ) self.draft_tp_context = ( draft_tp_context if server_args.enable_dp_attention else empty_context ) with self.draft_tp_context(self.draft_model_runner.tp_group): self.init_attention_backend() self.init_cuda_graphs() def init_attention_backend(self): # Create multi-step attn backends and cuda graph runners if self.server_args.attention_backend == "flashinfer": from sglang.srt.layers.attention.flashinfer_backend import ( FlashInferMultiStepDraftBackend, ) self.draft_attn_backend = FlashInferMultiStepDraftBackend( self.draft_model_runner, self.topk, self.speculative_num_steps, ) self.draft_extend_attn_backend = None self.padded_static_len = self.speculative_num_steps + 1 self.has_prefill_wrapper_verify = True elif self.server_args.attention_backend == "triton": from sglang.srt.layers.attention.triton_backend import ( TritonMultiStepDraftBackend, ) self.draft_attn_backend = TritonMultiStepDraftBackend( self.draft_model_runner, self.topk, self.speculative_num_steps, ) self.draft_extend_attn_backend = None self.padded_static_len = self.speculative_num_steps + 1 self.has_prefill_wrapper_verify = False elif self.server_args.attention_backend == "flashinfer_mla": from sglang.srt.layers.attention.flashinfer_mla_backend import ( FlashInferMLAMultiStepDraftBackend, ) self.draft_attn_backend = FlashInferMLAMultiStepDraftBackend( self.draft_model_runner, self.topk, self.speculative_num_steps, ) self.draft_extend_attn_backend = None self.padded_static_len = self.speculative_num_steps + 1 self.has_prefill_wrapper_verify = True else: raise ValueError( f"EAGLE is not supportted in attention backend {self.server_args.attention_backend}" ) self.draft_model_runner.draft_attn_backend = self.draft_attn_backend def init_cuda_graphs(self): """Capture cuda graphs.""" self.cuda_graph_runner = None self.cuda_graph_runner_for_draft_extend = None if self.server_args.disable_cuda_graph: return # Capture draft tic = time.time() before_mem = get_available_gpu_memory(self.device, self.gpu_id) logger.info( f"Capture draft cuda graph begin. This can take up to several minutes. avail mem={before_mem:.2f} GB" ) self.cuda_graph_runner = EAGLEDraftCudaGraphRunner(self) after_mem = get_available_gpu_memory(self.device, self.gpu_id) logger.info( f"Capture draft cuda graph end. Time elapsed: {time.time() - tic:.2f} s. avail mem={after_mem:.2f} GB. mem usage={(before_mem - after_mem):.2f} GB." ) # Capture extend if self.draft_extend_attn_backend: raise NotImplementedError() @property def draft_model_runner(self): return self.model_runner def forward_batch_speculative_generation( self, batch: ScheduleBatch ) -> Tuple[LogitsProcessorOutput, List[int], int, int]: """Run speculative decoding forward. NOTE: Many states of batch is modified as you go through. It is not guaranteed that the final output batch have the same state as the input. Args: batch: The batch to run forward. The state of the batch is modified as it runs. Returns: A tuple of the final logit output of the target model, next tokens accepeted, the batch id (used for overlap schedule), and number of accepeted tokens. """ if batch.forward_mode.is_decode(): with self.draft_tp_context(self.draft_model_runner.tp_group): spec_info, to_free_cache_loc = self.draft(batch) logits_output, verify_output, model_worker_batch = self.verify( batch, spec_info ) # Free cache loc (we put it here to avoid synchronization and hide kernel launch overhead.) self.token_to_kv_pool_allocator.free(to_free_cache_loc) # If it is None, it means all requests are finished if batch.spec_info.verified_id is not None: with self.draft_tp_context(self.draft_model_runner.tp_group): self.forward_draft_extend_after_decode(batch) return ( logits_output, verify_output.verified_id, model_worker_batch.bid, sum(verify_output.accept_length_per_req_cpu), ) elif batch.forward_mode.is_idle(): model_worker_batch = batch.get_model_worker_batch() logits_output, next_token_ids, _ = ( self.target_worker.forward_batch_generation( ForwardBatch.init_new( model_worker_batch, self.target_worker.model_runner ) ) ) return logits_output, next_token_ids, model_worker_batch.bid, 0, False else: logits_output, next_token_ids, bid = self.forward_target_extend(batch) with self.draft_tp_context(self.draft_model_runner.tp_group): self.forward_draft_extend( batch, logits_output.hidden_states, next_token_ids ) return logits_output, next_token_ids, bid, 0 def forward_target_extend( self, batch: ScheduleBatch ) -> Tuple[LogitsProcessorOutput, List[int], int]: """Run the target extend. Args: batch: The batch to run. States could be modified. Returns: logits_output: The output of logits. It will contain the full hidden states. next_token_ids: Next token ids generated. bid: The model batch ID. Used for overlap schedule. """ # Forward with the target model and get hidden states. # We need the full hidden states to prefill the KV cache of the draft model. model_worker_batch = batch.get_model_worker_batch() model_worker_batch.capture_hidden_mode = CaptureHiddenMode.FULL logits_output, next_token_ids = self.target_worker.forward_batch_generation( model_worker_batch ) return logits_output, next_token_ids, model_worker_batch.bid def draft(self, batch: ScheduleBatch): # Parse args num_seqs = batch.batch_size() spec_info = batch.spec_info # Accumulate penalty if batch.sampling_info.penalizer_orchestrator.is_required: # This is a relaxed version of penalties for speculative decoding. batch.sampling_info.penalizer_orchestrator.cumulate_output_tokens( spec_info.verified_id.to(torch.int64) ) # Allocate cache locations out_cache_loc = batch.alloc_token_slots( num_seqs * self.topk * self.speculative_num_steps ) assign_draft_cache_locs[(num_seqs,)]( batch.req_pool_indices, batch.req_to_token_pool.req_to_token, batch.seq_lens, out_cache_loc, batch.req_to_token_pool.req_to_token.shape[1], self.topk, self.speculative_num_steps, ) batch.out_cache_loc = out_cache_loc batch.seq_lens_sum = torch.sum(batch.seq_lens).item() spec_info.positions = batch.seq_lens.repeat_interleave(self.topk, dim=0) # Get forward batch spec_info.capture_hidden_mode = CaptureHiddenMode.LAST model_worker_batch = batch.get_model_worker_batch() forward_batch = ForwardBatch.init_new( model_worker_batch, self.draft_model_runner ) can_cuda_graph = self.cuda_graph_runner and self.cuda_graph_runner.can_run( forward_batch ) if can_cuda_graph: score_list, token_list, parents_list = self.cuda_graph_runner.replay( forward_batch ) else: # Initialize attention backend self.draft_attn_backend.init_forward_metadata(forward_batch) forward_batch = ForwardBatch.init_new( model_worker_batch, self.draft_model_runner ) # Run forward steps score_list, token_list, parents_list = self.draft_forward(forward_batch) ret = EagleVerifyInput.create( spec_info.verified_id, score_list, token_list, parents_list, batch.seq_lens, batch.seq_lens_sum, self.topk, self.speculative_num_steps, self.server_args.speculative_num_draft_tokens, ) return ret, out_cache_loc def draft_forward(self, forward_batch: ForwardBatch): # Parse args spec_info = forward_batch.spec_info out_cache_loc = forward_batch.out_cache_loc topk_p, topk_index, hidden_states = ( spec_info.topk_p, spec_info.topk_index, spec_info.hidden_states, ) if self.hot_token_id is not None: topk_index = self.hot_token_id[topk_index] # Return values score_list: List[torch.Tensor] = [] token_list: List[torch.Tensor] = [] parents_list: List[torch.Tensor] = [] # Forward multiple steps scores = None for i in range(self.speculative_num_steps): input_ids, hidden_states, scores, tree_info = select_top_k_tokens( i, topk_p, topk_index, hidden_states, scores, self.topk ) score_list.append(tree_info[0]) token_list.append(tree_info[1]) parents_list.append(tree_info[2]) # We don't need to run the last forward. we get 1 token from draft prefill and (#spec steps - 1) tokens here if i == self.speculative_num_steps - 1: break # Set inputs forward_batch.input_ids = input_ids out_cache_loc = out_cache_loc.view(forward_batch.batch_size, -1) forward_batch.out_cache_loc = out_cache_loc[ :, self.topk * i : self.topk * (i + 1) ].flatten() forward_batch.positions.add_(1) forward_batch.attn_backend = self.draft_attn_backend.attn_backends[i] spec_info.hidden_states = hidden_states # Run forward logits_output = self.draft_model_runner.model.forward( forward_batch.input_ids, forward_batch.positions, forward_batch ) self._detect_nan_if_needed(logits_output) probs = torch.softmax(logits_output.next_token_logits, dim=-1) topk_p, topk_index = fast_topk(probs, self.topk, dim=-1) if self.hot_token_id is not None: topk_index = self.hot_token_id[topk_index] hidden_states = logits_output.hidden_states return score_list, token_list, parents_list def verify(self, batch: ScheduleBatch, spec_info: EagleVerifyInput): spec_info.prepare_for_verify(batch) batch.forward_mode = ForwardMode.TARGET_VERIFY batch.spec_info = spec_info model_worker_batch = batch.get_model_worker_batch() logits_output, _ = self.target_worker.forward_batch_generation( model_worker_batch, skip_sample=True ) self._detect_nan_if_needed(logits_output) spec_info.hidden_states = logits_output.hidden_states res: EagleVerifyOutput = spec_info.verify( batch, logits_output, self.token_to_kv_pool_allocator ) # Post process based on verified outputs. # Pick indices that we care (accepeted) logits_output.next_token_logits = logits_output.next_token_logits[ res.accepeted_indices ] logits_output.hidden_states = logits_output.hidden_states[res.accepeted_indices] # Prepare the batch for the next draft forwards. batch.forward_mode = ForwardMode.DECODE batch.spec_info = res.draft_input if batch.return_logprob: self.add_logprob_values(batch, res, logits_output) return logits_output, res, model_worker_batch def add_logprob_values( self, batch: ScheduleBatch, res: EagleVerifyOutput, logits_output: LogitsProcessorOutput, ): # Extract args logits_output = res.logits_output top_logprobs_nums = batch.top_logprobs_nums token_ids_logprobs = batch.token_ids_logprobs logprobs = torch.nn.functional.log_softmax( logits_output.next_token_logits, dim=-1 ) batch_next_token_ids = res.verified_id num_tokens_per_req = [accept + 1 for accept in res.accept_length_per_req_cpu] # We should repeat top_logprobs_nums to match num_tokens_per_req. top_logprobs_nums_repeat_interleaved = [] token_ids_logprobs_repeat_interleaved = [] for num, num_tokens in zip(top_logprobs_nums, num_tokens_per_req): top_logprobs_nums_repeat_interleaved.extend([num] * num_tokens) for token_ids, num_tokens in zip(token_ids_logprobs, num_tokens_per_req): token_ids_logprobs_repeat_interleaved.extend([token_ids] * num_tokens) # Extract logprobs if any(x > 0 for x in top_logprobs_nums): ( logits_output.next_token_top_logprobs_val, logits_output.next_token_top_logprobs_idx, ) = get_top_logprobs(logprobs, top_logprobs_nums_repeat_interleaved) if any(x is not None for x in token_ids_logprobs): ( logits_output.next_token_token_ids_logprobs_val, logits_output.next_token_token_ids_logprobs_idx, ) = get_token_ids_logprobs(logprobs, token_ids_logprobs_repeat_interleaved) logits_output.next_token_logprobs = logprobs[ torch.arange(len(batch_next_token_ids), device=batch.sampling_info.device), batch_next_token_ids, ] # Add output logprobs to the request pt = 0 next_token_logprobs = logits_output.next_token_logprobs.tolist() verified_ids = batch_next_token_ids.tolist() for req, num_tokens in zip(batch.reqs, num_tokens_per_req): for _ in range(num_tokens): if req.return_logprob: req.output_token_logprobs_val.append(next_token_logprobs[pt]) req.output_token_logprobs_idx.append(verified_ids[pt]) if req.top_logprobs_num > 0: req.output_top_logprobs_val.append( res.logits_output.next_token_top_logprobs_val[pt] ) req.output_top_logprobs_idx.append( res.logits_output.next_token_top_logprobs_idx[pt] ) pt += 1 def forward_draft_extend( self, batch: ScheduleBatch, hidden_states: torch.Tensor, next_token_ids: List[int], ): """Run draft model extend. This API modifies the states of the batch. Args: batch: The batch to run. hidden_states: Hidden states from the target model forward next_token_ids: Next token ids generated from the target forward. """ batch.spec_info = EagleDraftInput( hidden_states=hidden_states, verified_id=next_token_ids, ) batch.spec_info.prepare_for_extend(batch) batch.spec_info.capture_hidden_mode = CaptureHiddenMode.LAST model_worker_batch = batch.get_model_worker_batch() forward_batch = ForwardBatch.init_new( model_worker_batch, self.draft_model_runner ) forward_batch.return_logprob = False logits_output = self.draft_model_runner.forward(forward_batch) self._detect_nan_if_needed(logits_output) assert isinstance(forward_batch.spec_info, EagleDraftInput) assert forward_batch.spec_info is batch.spec_info self.capture_for_decode(logits_output, forward_batch.spec_info) def forward_draft_extend_after_decode(self, batch: ScheduleBatch): # Backup fileds that will be modified in-place seq_lens_backup = batch.seq_lens.clone() req_pool_indices_backup = batch.req_pool_indices accept_length_backup = batch.spec_info.accept_length return_logprob_backup = batch.return_logprob # Prepare metadata batch.forward_mode = ForwardMode.DRAFT_EXTEND batch.spec_info.prepare_extend_after_decode( batch, self.speculative_num_steps, ) batch.spec_info.capture_hidden_mode = CaptureHiddenMode.LAST batch.return_logprob = False model_worker_batch = batch.get_model_worker_batch() forward_batch = ForwardBatch.init_new( model_worker_batch, self.draft_model_runner ) # Run logits_output = self.draft_model_runner.forward(forward_batch) self._detect_nan_if_needed(logits_output) self.capture_for_decode(logits_output, forward_batch.spec_info) # Restore backup. # This is because `seq_lens` can be modified in `prepare_extend_after_decode` batch.forward_mode = ForwardMode.DECODE batch.seq_lens = seq_lens_backup batch.req_pool_indices = req_pool_indices_backup batch.spec_info.accept_length = accept_length_backup batch.return_logprob = return_logprob_backup def capture_for_decode( self, logits_output: LogitsProcessorOutput, draft_input: EagleDraftInput ): probs = torch.softmax(logits_output.next_token_logits, dim=-1) draft_input.topk_p, draft_input.topk_index = fast_topk(probs, self.topk, dim=-1) draft_input.hidden_states = logits_output.hidden_states def _detect_nan_if_needed(self, logits_output: LogitsProcessorOutput): if self.enable_nan_detection: logits = logits_output.next_token_logits if torch.any(torch.isnan(logits)): logger.error("Detected errors during sampling! NaN in the logits.") raise ValueError("Detected errors during sampling! NaN in the logits.") def load_token_map(token_map_path: str) -> List[int]: if not os.path.exists(token_map_path): cache_dir = snapshot_download( os.path.dirname(token_map_path), ignore_patterns=["*.bin", "*.safetensors"], ) token_map_path = os.path.join(cache_dir, os.path.basename(token_map_path)) hot_token_id = torch.load(token_map_path, weights_only=True) return torch.tensor(hot_token_id, dtype=torch.int32)