"""A tensor parallel worker.""" import logging import multiprocessing import pickle import time import warnings from typing import List, Optional import torch import torch.distributed as dist from sglang.global_config import global_config from sglang.srt.constrained.fsm_cache import FSMCache from sglang.srt.constrained.jump_forward import JumpForwardCache from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer from sglang.srt.managers.controller.infer_batch import ( FINISH_ABORT, BaseFinishReason, Batch, ForwardMode, Req, ) from sglang.srt.managers.controller.model_runner import ModelRunner from sglang.srt.managers.controller.radix_cache import RadixCache from sglang.srt.managers.controller.schedule_heuristic import ScheduleHeuristic from sglang.srt.managers.io_struct import ( AbortReq, BatchTokenIDOut, FlushCacheReq, TokenizedGenerateReqInput, ) from sglang.srt.model_config import ModelConfig from sglang.srt.server_args import ServerArgs from sglang.srt.utils import ( get_int_token_logit_bias, is_multimodal_model, set_random_seed, suppress_other_loggers, ) from sglang.utils import get_exception_traceback logger = logging.getLogger("srt.tp_worker") class ModelTpServer: def __init__( self, gpu_id: int, tp_rank: int, server_args: ServerArgs, nccl_port: int, model_overide_args: dict, ): suppress_other_loggers() # Copy arguments self.gpu_id = gpu_id self.tp_rank = tp_rank self.tp_size = server_args.tp_size self.dp_size = server_args.dp_size self.schedule_heuristic = server_args.schedule_heuristic self.disable_regex_jump_forward = server_args.disable_regex_jump_forward # Init model and tokenizer self.model_config = ModelConfig( server_args.model_path, server_args.trust_remote_code, context_length=server_args.context_length, model_overide_args=model_overide_args, ) self.model_runner = ModelRunner( model_config=self.model_config, mem_fraction_static=server_args.mem_fraction_static, gpu_id=gpu_id, tp_rank=tp_rank, tp_size=server_args.tp_size, nccl_port=nccl_port, server_args=server_args, ) if is_multimodal_model(server_args.model_path): self.processor = get_processor( server_args.tokenizer_path, tokenizer_mode=server_args.tokenizer_mode, trust_remote_code=server_args.trust_remote_code, ) self.tokenizer = self.processor.tokenizer else: self.tokenizer = get_tokenizer( server_args.tokenizer_path, tokenizer_mode=server_args.tokenizer_mode, trust_remote_code=server_args.trust_remote_code, ) self.max_total_num_tokens = self.model_runner.max_total_num_tokens self.max_prefill_tokens = ( 16384 if server_args.max_prefill_tokens is None else server_args.max_prefill_tokens ) self.max_running_requests = min( ( self.max_total_num_tokens // 2 if server_args.max_running_requests is None else server_args.max_running_requests ), self.model_runner.req_to_token_pool.size - 1, ) self.int_token_logit_bias = torch.tensor( get_int_token_logit_bias(self.tokenizer, self.model_config.vocab_size) ) self.max_req_input_len = min( self.model_config.context_len - 1, self.max_total_num_tokens - 1, ) set_random_seed(server_args.random_seed) # Print info logger.info( f"[gpu_id={self.gpu_id}] " f"max_total_num_tokens={self.max_total_num_tokens}, " f"max_prefill_tokens={self.max_prefill_tokens}, " f"max_running_requests={self.max_running_requests}, " f"context_len={self.model_config.context_len}" ) # Init cache self.tree_cache = RadixCache( req_to_token_pool=self.model_runner.req_to_token_pool, token_to_kv_pool=self.model_runner.token_to_kv_pool, disable=server_args.disable_radix_cache, ) self.tree_cache_metrics = {"total": 0, "hit": 0} self.scheduler = ScheduleHeuristic( self.schedule_heuristic, self.max_running_requests, self.max_prefill_tokens, self.max_total_num_tokens, self.tree_cache, ) self.req_to_token_pool = self.model_runner.req_to_token_pool self.token_to_kv_pool = self.model_runner.token_to_kv_pool # Init running status self.forward_queue: List[Req] = [] self.running_batch: Batch = None self.out_pyobjs = [] self.decode_forward_ct = 0 self.stream_interval = server_args.stream_interval self.num_generated_tokens = 0 self.last_stats_tic = time.time() # Init the FSM cache for constrained generation self.regex_fsm_cache = FSMCache( server_args.tokenizer_path, { "tokenizer_mode": server_args.tokenizer_mode, "trust_remote_code": server_args.trust_remote_code, }, ) self.jump_forward_cache = JumpForwardCache() # Init new token estimation assert ( server_args.schedule_conservativeness >= 0 ), "Invalid schedule_conservativeness" self.min_new_token_ratio = min( global_config.base_min_new_token_ratio * server_args.schedule_conservativeness, 1.0, ) self.new_token_ratio = self.min_new_token_ratio self.new_token_ratio_decay = global_config.new_token_ratio_decay self.new_token_ratio_recovery = global_config.new_token_ratio_recovery def exposed_step(self, recv_reqs): try: # Recv requests for recv_req in recv_reqs: if isinstance(recv_req, TokenizedGenerateReqInput): self.handle_generate_request(recv_req) elif isinstance(recv_req, FlushCacheReq): self.flush_cache() elif isinstance(recv_req, AbortReq): self.abort_request(recv_req) else: raise ValueError(f"Invalid request: {recv_req}") # Forward self.forward_step() except Exception: logger.error("Exception in ModelTpServer:\n" + get_exception_traceback()) raise # Return results ret = self.out_pyobjs self.out_pyobjs = [] return ret @torch.inference_mode() def forward_step(self): new_batch = self.get_new_prefill_batch() if new_batch is not None: # Run a new prefill batch self.forward_prefill_batch(new_batch) self.cache_filled_batch(new_batch) if not new_batch.is_empty(): if self.running_batch is None: self.running_batch = new_batch else: self.running_batch.merge(new_batch) else: # Run a decode batch if self.running_batch is not None: # Run a few decode batches continuously for reducing overhead for _ in range(global_config.num_continue_decode_steps): self.num_generated_tokens += len(self.running_batch.reqs) self.forward_decode_batch(self.running_batch) # Print stats if self.tp_rank == 0 and self.decode_forward_ct % 40 == 0: self.print_stats() if self.running_batch.is_empty(): self.running_batch = None break if self.out_pyobjs and self.running_batch.has_stream(): break else: self.check_memory() self.new_token_ratio = global_config.init_new_token_ratio def print_stats(self): num_used = self.max_total_num_tokens - ( self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size() ) throughput = self.num_generated_tokens / (time.time() - self.last_stats_tic) self.num_generated_tokens = 0 self.last_stats_tic = time.time() logger.info( f"[gpu_id={self.gpu_id}] Decode batch. " f"#running-req: {len(self.running_batch.reqs)}, " f"#token: {num_used}, " f"token usage: {num_used / self.max_total_num_tokens:.2f}, " f"gen throughput (token/s): {throughput:.2f}, " f"#queue-req: {len(self.forward_queue)}" ) def check_memory(self): available_size = ( self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size() ) if available_size != self.max_total_num_tokens: warnings.warn( "Warning: " f"available_size={available_size}, max_total_num_tokens={self.max_total_num_tokens}\n" "KV cache pool leak detected!" ) def handle_generate_request( self, recv_req: TokenizedGenerateReqInput, ): req = Req(recv_req.rid, recv_req.input_text, recv_req.input_ids) req.pixel_values = recv_req.pixel_values if req.pixel_values is not None: req.pad_value = [ (recv_req.image_hash) % self.model_config.vocab_size, (recv_req.image_hash >> 16) % self.model_config.vocab_size, (recv_req.image_hash >> 32) % self.model_config.vocab_size, (recv_req.image_hash >> 64) % self.model_config.vocab_size, ] req.image_size = recv_req.image_size ( req.origin_input_ids, req.image_offset, ) = self.model_runner.model.pad_input_ids( req.origin_input_ids_unpadded, req.pad_value, req.pixel_values.shape, req.image_size, ) req.sampling_params = recv_req.sampling_params req.return_logprob = recv_req.return_logprob req.logprob_start_len = recv_req.logprob_start_len req.top_logprobs_num = recv_req.top_logprobs_num req.stream = recv_req.stream req.tokenizer = self.tokenizer # Init regex fsm if req.sampling_params.regex is not None: req.regex_fsm = self.regex_fsm_cache.query(req.sampling_params.regex) if not self.disable_regex_jump_forward: req.jump_forward_map = self.jump_forward_cache.query( req.sampling_params.regex ) # Truncate prompts that are too long if len(req.origin_input_ids) >= self.max_req_input_len: logger.warn( "Request length is longer than the KV cache pool size or " "the max context length. Truncated!!!" ) req.origin_input_ids = req.origin_input_ids[: self.max_req_input_len] req.sampling_params.max_new_tokens = min( ( req.sampling_params.max_new_tokens if req.sampling_params.max_new_tokens is not None else 1 << 30 ), self.max_req_input_len - 1 - len(req.origin_input_ids), ) self.forward_queue.append(req) def get_new_prefill_batch(self) -> Optional[Batch]: running_bs = ( len(self.running_batch.reqs) if self.running_batch is not None else 0 ) if running_bs >= self.max_running_requests: return # Compute matched prefix length for req in self.forward_queue: req.input_ids = req.origin_input_ids + req.output_ids prefix_indices, last_node = self.tree_cache.match_prefix(req.input_ids) if req.return_logprob: prefix_indices = prefix_indices[: req.logprob_start_len] req.extend_input_len = len(req.input_ids) - len(prefix_indices) req.prefix_indices = prefix_indices req.last_node = last_node # Get priority queue self.forward_queue = self.scheduler.get_priority_queue(self.forward_queue) # Add requests if there is available space can_run_list = [] new_batch_total_tokens = 0 new_batch_input_tokens = 0 available_size = ( self.token_to_kv_pool.available_size() + self.tree_cache.evictable_size() ) if self.running_batch: available_size -= sum( [ (r.sampling_params.max_new_tokens - len(r.output_ids)) * self.new_token_ratio for r in self.running_batch.reqs ] ) for req in self.forward_queue: if req.return_logprob and req.normalized_prompt_logprob is None: # Need at least two tokens to compute normalized logprob if req.extend_input_len < 2: delta = 2 - req.extend_input_len req.extend_input_len += delta req.prefix_indices = req.prefix_indices[:-delta] if req.image_offset is not None: req.image_offset += delta if req.extend_input_len == 0 and req.sampling_params.max_new_tokens > 0: # Need at least one token to compute logits req.extend_input_len = 1 req.prefix_indices = req.prefix_indices[:-1] if req.image_offset is not None: req.image_offset += 1 if ( req.extend_input_len + req.sampling_params.max_new_tokens + new_batch_total_tokens < available_size and ( req.extend_input_len + new_batch_input_tokens <= self.max_prefill_tokens or len(can_run_list) == 0 ) ): delta = self.tree_cache.inc_lock_ref(req.last_node) available_size += delta if not ( req.extend_input_len + req.sampling_params.max_new_tokens + new_batch_total_tokens < available_size ): # Undo locking delta = self.tree_cache.dec_lock_ref(req.last_node) available_size += delta break else: # Add this request to the running batch can_run_list.append(req) new_batch_total_tokens += ( req.extend_input_len + req.sampling_params.max_new_tokens ) new_batch_input_tokens += req.extend_input_len else: break if running_bs + len(can_run_list) >= self.max_running_requests: break if len(can_run_list) == 0: return None # Print stats if self.tp_rank == 0: hit_tokens = sum(len(x.prefix_indices) for x in can_run_list) self.tree_cache_metrics["total"] += ( hit_tokens + new_batch_input_tokens ) / 10**9 self.tree_cache_metrics["hit"] += hit_tokens / 10**9 tree_cache_hit_rate = ( self.tree_cache_metrics["hit"] / self.tree_cache_metrics["total"] ) logger.info( f"[gpu_id={self.gpu_id}] Prefill batch. " f"#new-seq: {len(can_run_list)}, " f"#new-token: {new_batch_input_tokens}, " f"#cached-token: {hit_tokens}, " f"cache hit rate: {100.0 * tree_cache_hit_rate:.2f}%, " f"#running-req: {running_bs}, " f"#queue-req: {len(self.forward_queue) - len(can_run_list)}" ) # Return the new batch new_batch = Batch.init_new( can_run_list, self.req_to_token_pool, self.token_to_kv_pool, self.tree_cache, ) self.forward_queue = [x for x in self.forward_queue if x not in can_run_list] return new_batch def forward_prefill_batch(self, batch: Batch): # Build batch tensors batch.prepare_for_extend( self.model_config.vocab_size, self.int_token_logit_bias ) # Forward and sample the next tokens if batch.extend_num_tokens != 0: output = self.model_runner.forward(batch, ForwardMode.EXTEND) next_token_ids = batch.sample(output.next_token_logits) # Move logprobs to cpu if output.next_token_logprobs is not None: output.next_token_logprobs = output.next_token_logprobs[ torch.arange(len(next_token_ids), device=next_token_ids.device), next_token_ids, ].tolist() output.prefill_token_logprobs = output.prefill_token_logprobs.tolist() output.normalized_prompt_logprobs = ( output.normalized_prompt_logprobs.tolist() ) next_token_ids = next_token_ids.tolist() else: next_token_ids = [self.tokenizer.eos_token_id] * len(batch.reqs) # Check finish conditions pt = 0 for i, req in enumerate(batch.reqs): req.completion_tokens_wo_jump_forward += 1 req.output_ids.append(next_token_ids[i]) req.check_finished() if req.return_logprob: self.add_logprob_return_values(i, req, pt, next_token_ids, output) pt += req.extend_input_len self.handle_finished_requests(batch) def add_logprob_return_values(self, i, req, pt, next_token_ids, output): if req.normalized_prompt_logprob is None: req.normalized_prompt_logprob = output.normalized_prompt_logprobs[i] if req.prefill_token_logprobs is None: # If logprob_start_len > 0, then first logprob_start_len prompt tokens will be ignored. req.prefill_token_logprobs = list( zip( output.prefill_token_logprobs[pt : pt + req.extend_input_len - 1], req.input_ids[-req.extend_input_len + 1 :], ) ) if req.logprob_start_len == 0: req.prefill_token_logprobs = [ (None, req.input_ids[0]) ] + req.prefill_token_logprobs if req.last_update_decode_tokens != 0: req.decode_token_logprobs.extend( list( zip( output.prefill_token_logprobs[ pt + req.extend_input_len - req.last_update_decode_tokens : pt + req.extend_input_len - 1 ], req.input_ids[-req.last_update_decode_tokens + 1 :], ) ) ) req.decode_token_logprobs.append( (output.next_token_logprobs[i], next_token_ids[i]) ) if req.top_logprobs_num > 0: if req.prefill_top_logprobs is None: req.prefill_top_logprobs = output.prefill_top_logprobs[i] if req.logprob_start_len == 0: req.prefill_top_logprobs = [None] + req.prefill_top_logprobs if req.last_update_decode_tokens != 0: req.decode_top_logprobs.extend( output.prefill_top_logprobs[i][-req.last_update_decode_tokens + 1 :] ) req.decode_top_logprobs.append(output.decode_top_logprobs[i]) def cache_filled_batch(self, batch: Batch): req_pool_indices_cpu = batch.req_pool_indices.cpu().numpy() for i, req in enumerate(batch.reqs): new_prefix_indices, new_last_node = self.tree_cache.cache_req( token_ids=tuple(req.origin_input_ids + req.output_ids)[:-1], last_uncached_pos=len(req.prefix_indices), req_pool_idx=req_pool_indices_cpu[i], del_in_memory_pool=False, old_last_node=req.last_node, ) req.prefix_indices, req.last_node = new_prefix_indices, new_last_node def forward_decode_batch(self, batch: Batch): # Check if decode out of memory if not batch.check_decode_mem(): old_ratio = self.new_token_ratio retracted_reqs, new_token_ratio = batch.retract_decode() self.new_token_ratio = new_token_ratio logger.info( "decode out of memory happened, " f"#retracted_reqs: {len(retracted_reqs)}, " f"#new_token_ratio: {old_ratio:.4f} -> {self.new_token_ratio:.4f}" ) self.forward_queue.extend(retracted_reqs) else: self.new_token_ratio = max( self.new_token_ratio - self.new_token_ratio_decay, self.min_new_token_ratio, ) if not self.disable_regex_jump_forward: # Check for jump-forward jump_forward_reqs = batch.check_for_jump_forward(self.model_runner) self.forward_queue.extend(jump_forward_reqs) if batch.is_empty(): return # Update batch tensors self.decode_forward_ct = (self.decode_forward_ct + 1) % (1 << 30) batch.prepare_for_decode() # Forward and sample the next tokens output = self.model_runner.forward(batch, ForwardMode.DECODE) next_token_ids = batch.sample(output.next_token_logits) # Move logprobs to cpu if output.next_token_logprobs is not None: next_token_logprobs = output.next_token_logprobs[ torch.arange(len(next_token_ids), device=next_token_ids.device), next_token_ids, ].tolist() next_token_ids = next_token_ids.tolist() # Check finish condition for i, (req, next_token_id) in enumerate(zip(batch.reqs, next_token_ids)): req.completion_tokens_wo_jump_forward += 1 req.output_ids.append(next_token_id) req.check_finished() if req.return_logprob: req.decode_token_logprobs.append( (next_token_logprobs[i], next_token_id) ) if req.top_logprobs_num > 0: req.decode_top_logprobs.append(output.decode_top_logprobs[i]) self.handle_finished_requests(batch) def handle_finished_requests(self, batch: Batch): output_rids = [] output_vids = [] decoded_texts = [] output_read_ids = [] output_read_offsets = [] output_skip_special_tokens = [] output_spaces_between_special_tokens = [] output_meta_info = [] output_finished_reason: List[BaseFinishReason] = [] finished_indices = [] unfinished_indices = [] for i, req in enumerate(batch.reqs): if req.finished(): finished_indices.append(i) else: unfinished_indices.append(i) if req.finished() or ( ( req.stream and ( self.decode_forward_ct % self.stream_interval == 0 or len(req.output_ids) == 1 ) ) ): output_rids.append(req.rid) output_vids.append(req.vid) decoded_texts.append(req.decoded_text) read_ids, read_offset = req.init_incremental_detokenize() output_read_ids.append(read_ids) output_read_offsets.append(read_offset) output_skip_special_tokens.append( req.sampling_params.skip_special_tokens ) output_spaces_between_special_tokens.append( req.sampling_params.spaces_between_special_tokens ) meta_info = { "prompt_tokens": len(req.origin_input_ids), "completion_tokens": len(req.output_ids), "completion_tokens_wo_jump_forward": req.completion_tokens_wo_jump_forward, "finish_reason": str(req.finished_reason), } if req.return_logprob: ( meta_info["prefill_token_logprobs"], meta_info["decode_token_logprobs"], meta_info["prefill_top_logprobs"], meta_info["decode_top_logprobs"], meta_info["normalized_prompt_logprob"], ) = ( req.prefill_token_logprobs, req.decode_token_logprobs, req.prefill_top_logprobs, req.decode_top_logprobs, req.normalized_prompt_logprob, ) output_meta_info.append(meta_info) output_finished_reason.append(req.finished_reason) # Send to detokenizer if output_rids: self.out_pyobjs.append( BatchTokenIDOut( output_rids, output_vids, decoded_texts, output_read_ids, output_read_offsets, output_skip_special_tokens, output_spaces_between_special_tokens, output_meta_info, output_finished_reason, ) ) # Remove finished reqs if finished_indices: # Update radix cache req_pool_indices_cpu = batch.req_pool_indices.tolist() for i in finished_indices: req = batch.reqs[i] self.tree_cache.cache_req( token_ids=tuple(req.origin_input_ids + req.output_ids)[:-1], last_uncached_pos=len(req.prefix_indices), req_pool_idx=req_pool_indices_cpu[i], ) self.tree_cache.dec_lock_ref(req.last_node) # Update batch tensors if unfinished_indices: batch.filter_batch(unfinished_indices) else: batch.reqs = [] def flush_cache(self): if len(self.forward_queue) == 0 and ( self.running_batch is None or len(self.running_batch.reqs) == 0 ): self.tree_cache.reset() self.tree_cache_metrics = {"total": 0, "hit": 0} self.regex_fsm_cache.reset() self.req_to_token_pool.clear() self.token_to_kv_pool.clear() torch.cuda.empty_cache() logger.info("Cache flushed successfully!") else: warnings.warn( f"Cache not flushed because there are pending requests. " f"#queue-req: {len(self.forward_queue)}, " f"#running-req: {0 if self.running_batch is None else len(self.running_batch.reqs)}" ) def abort_request(self, recv_req): # Delete requests in the waiting queue to_del = None for i, req in enumerate(self.forward_queue): if req.rid == recv_req.rid: to_del = i break if to_del is not None: del self.forward_queue[to_del] # Delete requests in the running batch if self.running_batch: for req in self.running_batch.reqs: if req.rid == recv_req.rid: req.finished_reason = FINISH_ABORT() break def run_tp_server( gpu_id: int, tp_rank: int, server_args: ServerArgs, nccl_port: int, model_overide_args: dict, ): """Run a tensor parallel server.""" try: model_server = ModelTpServer( gpu_id, tp_rank, server_args, nccl_port, model_overide_args, ) tp_cpu_group = model_server.model_runner.tp_group.cpu_group while True: recv_reqs = broadcast_recv_input(None, tp_rank, tp_cpu_group) model_server.exposed_step(recv_reqs) except Exception: logger.error("Exception in run_tp_server:\n" + get_exception_traceback()) raise def launch_tp_servers( gpu_ids, tp_rank_range, server_args, nccl_port, model_overide_args ): """Launch multiple tensor parallel servers.""" procs = [] for i in tp_rank_range: proc = multiprocessing.Process( target=run_tp_server, args=(gpu_ids[i], i, server_args, nccl_port, model_overide_args), ) proc.start() procs.append(proc) return procs def broadcast_recv_input(data, rank, dist_group): """Broadcast inputs from rank=0 to all other ranks with torch.dist backend.""" if rank == 0: if len(data) == 0: tensor_size = torch.tensor([0], dtype=torch.long) dist.broadcast(tensor_size, src=0, group=dist_group) else: serialized_data = pickle.dumps(data) size = len(serialized_data) tensor_data = torch.ByteTensor(list(serialized_data)) tensor_size = torch.tensor([size], dtype=torch.long) dist.broadcast(tensor_size, src=0, group=dist_group) dist.broadcast(tensor_data, src=0, group=dist_group) else: tensor_size = torch.tensor([0], dtype=torch.long) dist.broadcast(tensor_size, src=0, group=dist_group) size = tensor_size.item() if size == 0: return [] tensor_data = torch.empty(size, dtype=torch.uint8) dist.broadcast(tensor_data, src=0, group=dist_group) serialized_data = bytes(tensor_data.tolist()) data = pickle.loads(serialized_data) return data