""" Copyright 2023-2024 SGLang Team Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ """A tensor parallel worker.""" import logging import threading import time from queue import Queue from typing import Optional import torch from sglang.srt.managers.io_struct import UpdateWeightReqInput from sglang.srt.managers.schedule_batch import ModelWorkerBatch from sglang.srt.managers.tp_worker import TpModelWorker from sglang.srt.model_executor.forward_batch_info import ForwardBatch from sglang.srt.server_args import ServerArgs logger = logging.getLogger(__name__) @torch.compile(dynamic=True) def resolve_future_token_ids(input_ids, future_token_ids_map): input_ids[:] = torch.where( input_ids < 0, future_token_ids_map[torch.clamp(-input_ids, min=0)], input_ids, ) class TpModelWorkerClient: """A tensor parallel model worker.""" def __init__( self, server_args: ServerArgs, gpu_id: int, tp_rank: int, dp_rank: Optional[int], nccl_port: int, ): # Load the model self.worker = TpModelWorker(server_args, gpu_id, tp_rank, dp_rank, nccl_port) self.max_running_requests = self.worker.max_running_requests self.device = self.worker.device # Init future mappings self.future_token_ids_ct = 0 self.future_token_ids_limit = self.max_running_requests * 3 self.future_token_ids_map = torch.empty( (self.max_running_requests * 5,), dtype=torch.int32, device=self.device ) # Launch threads self.input_queue = Queue() self.output_queue = Queue() self.forward_stream = torch.cuda.Stream() self.forward_thread = threading.Thread( target=self.forward_thread_func, ) self.forward_thread.start() self.copy_queue = Queue() self.copy_thread = threading.Thread( target=self.copy_thread_func, ) self.copy_thread.start() def get_worker_info(self): return self.worker.get_worker_info() def get_pad_input_ids_func(self): return self.worker.get_pad_input_ids_func() def get_tp_cpu_group(self): return self.worker.get_tp_cpu_group() def get_memory_pool(self): return ( self.worker.model_runner.req_to_token_pool, self.worker.model_runner.token_to_kv_pool, ) def forward_thread_func(self): with torch.cuda.stream(self.forward_stream): self.forward_thread_func_() @torch.inference_mode() def forward_thread_func_(self): while True: self.has_inflight_batch = False model_worker_batch, future_token_ids_ct = self.input_queue.get() if not model_worker_batch: break self.has_inflight_batch = True self.launch_event = threading.Event() # Resolve future tokens in the input input_ids = model_worker_batch.input_ids resolve_future_token_ids(input_ids, self.future_token_ids_map) # Run forward logits_output, next_token_ids = self.worker.forward_batch_generation( model_worker_batch ) # Update the future token ids map bs = len(model_worker_batch.seq_lens) self.future_token_ids_map[ future_token_ids_ct + 1 : future_token_ids_ct + bs + 1 ] = next_token_ids # Copy results to the CPU if model_worker_batch.return_logprob: logits_output.next_token_logprobs = logits_output.next_token_logprobs[ torch.arange(len(next_token_ids), device=self.device), next_token_ids, ].to("cpu", non_blocking=True) if logits_output.input_token_logprobs is not None: logits_output.input_token_logprobs = ( logits_output.input_token_logprobs.to("cpu", non_blocking=True) ) logits_output.normalized_prompt_logprobs = ( logits_output.normalized_prompt_logprobs.to( "cpu", non_blocking=True ) ) next_token_ids = next_token_ids.to("cpu", non_blocking=True) copy_event = torch.cuda.Event(blocking=True) copy_event.record() self.launch_event.set() self.copy_queue.put((copy_event, logits_output, next_token_ids)) def copy_thread_func(self): while True: copy_event, logits_output, next_token_ids = self.copy_queue.get() if not copy_event: break while not copy_event.query(): time.sleep(1e-5) if logits_output.next_token_logprobs is not None: logits_output.next_token_logprobs = ( logits_output.next_token_logprobs.tolist() ) if logits_output.input_token_logprobs is not None: logits_output.input_token_logprobs = ( logits_output.input_token_logprobs.tolist() ) logits_output.normalized_prompt_logprobs = ( logits_output.normalized_prompt_logprobs.tolist() ) self.output_queue.put((logits_output, next_token_ids.tolist())) def resulve_batch_result(self, bid: int): logits_output, next_token_ids = self.output_queue.get() if self.has_inflight_batch: # Wait until the batch is launched self.launch_event.wait() return logits_output, next_token_ids def forward_batch_generation(self, model_worker_batch: ModelWorkerBatch): # Push a new batch to the queue self.input_queue.put((model_worker_batch.copy(), self.future_token_ids_ct)) # Allocate output future objects bs = len(model_worker_batch.seq_lens) future_next_token_ids = torch.arange( -(self.future_token_ids_ct + 1), -(self.future_token_ids_ct + 1 + bs), -1, dtype=torch.int32, device=self.device, ) self.future_token_ids_ct = ( self.future_token_ids_ct + bs ) % self.future_token_ids_limit return None, future_next_token_ids def forward_batch_embedding(self, model_worker_batch: ModelWorkerBatch): forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner) logits_output = self.model_runner.forward(forward_batch) embeddings = logits_output.embeddings return embeddings def update_weights(self, recv_req: UpdateWeightReqInput): success, message = self.model_runner.update_weights( recv_req.model_path, recv_req.load_format ) return success, message def __delete__(self): self.input_queue.put((None, None)) self.copy_queue.put((None, None, None))