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Split the overlapped version of TpModelWorkerClient into a separate f…
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,174 @@ | ||
| """ | ||
| 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__) | ||
|
|
||
|
|
||
| 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 | ||
|
|
||
| # Create future mappings | ||
| self.future_logits_output_dict = dict() | ||
| self.future_logits_output_ct = 0 | ||
| self.future_token_ids_ct = 0 | ||
| self.future_token_ids_map = torch.empty( | ||
| (self.max_running_requests * 5,), dtype=torch.int32, device=self.device | ||
| ) | ||
| self.future_token_ids_limit = self.max_running_requests * 3 | ||
| self.future_token_ids_output = dict() | ||
|
|
||
| # Launch a thread | ||
| self.future_event_map = dict() | ||
| self.forward_queue = Queue() | ||
| self.forward_stream = torch.cuda.Stream() | ||
| self.forward_thread = threading.Thread( | ||
| target=self.forward_thread_func, | ||
| ) | ||
| self.forward_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: | ||
| tic1 = time.time() | ||
| model_worker_batch, future_logits_output, future_next_token_ids = ( | ||
| self.forward_queue.get() | ||
| ) | ||
|
|
||
| # Resolve future tokens in the input | ||
| tic2 = time.time() | ||
| resolved_input_ids = model_worker_batch.input_ids | ||
| future_mask = resolved_input_ids < 0 | ||
| resolved_input_ids[future_mask] = self.future_token_ids_map[ | ||
| -resolved_input_ids[future_mask] | ||
| ] | ||
|
|
||
| # Run forward | ||
| logits_output, next_token_ids = self.worker.forward_batch_generation( | ||
| model_worker_batch | ||
| ) | ||
|
|
||
| # Set future values | ||
| if model_worker_batch.return_logprob: | ||
| self.future_logits_output_dict[future_logits_output] = logits_output | ||
|
|
||
| self.future_token_ids_map[-future_next_token_ids] = next_token_ids.to( | ||
| torch.int32 | ||
| ) | ||
| self.future_token_ids_output[model_worker_batch.bid] = ( | ||
| next_token_ids.tolist() | ||
| ) | ||
| self.future_event_map[model_worker_batch.bid].set() | ||
|
|
||
| if False: | ||
| tic3 = time.time() | ||
| self.acc_time_with_waiting += tic3 - tic1 | ||
| self.acc_time_without_waiting += tic3 - tic2 | ||
| if self.forward_queue.qsize() == 0: | ||
| logger.info( | ||
| f"{self.acc_time_with_waiting=:.3f}, {self.acc_time_without_waiting=:.3f}, {self.forward_queue.qsize()=}" | ||
| ) | ||
|
|
||
| def resolve_future_token_ids(self, bid: int): | ||
| self.future_event_map[bid].wait() | ||
| ret = self.future_token_ids_output[bid] | ||
| del self.future_event_map[bid] | ||
| return ret | ||
|
|
||
| def resolve_future_logits_output(self, future_obj): | ||
| return self.future_logits_output_dict.pop(future_obj) | ||
|
|
||
| def forward_batch_generation(self, model_worker_batch: ModelWorkerBatch): | ||
| # Allocate output future objects | ||
| future_logits_output = self.future_logits_output_ct | ||
| self.future_logits_output_ct += 1 | ||
|
|
||
| bs = len(model_worker_batch.seq_lens) | ||
| with torch.cuda.stream(self.forward_stream): | ||
| future_next_token_ids = -torch.arange( | ||
| self.future_token_ids_ct + 1, | ||
| self.future_token_ids_ct + 1 + bs, | ||
| dtype=torch.int32, | ||
| device=self.device, | ||
| ) | ||
| self.future_token_ids_ct = ( | ||
| self.future_token_ids_ct + bs | ||
| ) % self.future_token_ids_limit | ||
| ret = future_logits_output, future_next_token_ids | ||
|
|
||
| self.future_event_map[model_worker_batch.bid] = threading.Event() | ||
| self.forward_queue.put( | ||
| (model_worker_batch.copy(), future_logits_output, future_next_token_ids) | ||
| ) | ||
| return ret | ||
|
|
||
| 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 |
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