172 lines
6.3 KiB
Python
172 lines
6.3 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""A tensor parallel worker."""
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import logging
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import threading
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from typing import Optional
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from sglang.srt.configs.model_config import ModelConfig
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from sglang.srt.hf_transformers_utils import get_processor, get_tokenizer
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from sglang.srt.managers.io_struct import (
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GetWeightsByNameReqInput,
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UpdateWeightFromDiskReqInput,
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)
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from sglang.srt.managers.schedule_batch import ModelWorkerBatch, global_server_args_dict
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_executor.model_runner import ModelRunner
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.utils import broadcast_pyobj, set_random_seed
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logger = logging.getLogger(__name__)
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class TpModelWorker:
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"""A tensor parallel model worker."""
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def __init__(
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self,
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server_args: ServerArgs,
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gpu_id: int,
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tp_rank: int,
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dp_rank: Optional[int],
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nccl_port: int,
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):
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# Parse args
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self.tp_rank = tp_rank
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# Init model and tokenizer
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self.model_config = ModelConfig(
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server_args.model_path,
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trust_remote_code=server_args.trust_remote_code,
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context_length=server_args.context_length,
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model_override_args=server_args.json_model_override_args,
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is_embedding=server_args.is_embedding,
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)
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self.model_runner = ModelRunner(
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model_config=self.model_config,
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mem_fraction_static=server_args.mem_fraction_static,
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gpu_id=gpu_id,
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tp_rank=tp_rank,
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tp_size=server_args.tp_size,
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nccl_port=nccl_port,
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server_args=server_args,
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)
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if server_args.skip_tokenizer_init:
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self.tokenizer = self.processor = None
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else:
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if self.model_config.is_multimodal:
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self.processor = get_processor(
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server_args.tokenizer_path,
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tokenizer_mode=server_args.tokenizer_mode,
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trust_remote_code=server_args.trust_remote_code,
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)
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self.tokenizer = self.processor.tokenizer
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else:
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self.tokenizer = get_tokenizer(
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server_args.tokenizer_path,
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tokenizer_mode=server_args.tokenizer_mode,
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trust_remote_code=server_args.trust_remote_code,
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)
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self.device = self.model_runner.device
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# Profile number of tokens
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self.max_total_num_tokens = self.model_runner.max_total_num_tokens
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self.max_prefill_tokens = server_args.max_prefill_tokens
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self.max_running_requests = min(
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(
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self.max_total_num_tokens // 2
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if server_args.max_running_requests is None
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else server_args.max_running_requests
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),
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self.model_runner.req_to_token_pool.size,
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)
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self.max_req_len = min(
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self.model_config.context_len - 1,
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self.max_total_num_tokens - 1,
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)
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self.max_req_input_len = self.max_req_len - 5
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assert (
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self.max_req_len > 0 and self.max_req_input_len > 0
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), "Memory pool size is too small"
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# Sync random seed across TP workers
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self.random_seed = broadcast_pyobj(
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[server_args.random_seed],
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self.tp_rank,
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self.model_runner.tp_group.cpu_group,
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)[0]
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set_random_seed(self.random_seed)
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def get_worker_info(self):
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return (
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self.max_total_num_tokens,
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self.max_prefill_tokens,
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self.max_running_requests,
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self.max_req_len,
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self.max_req_input_len,
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self.random_seed,
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self.device,
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global_server_args_dict,
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self.model_runner.req_to_token_pool.size,
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self.model_runner.req_to_token_pool.max_context_len,
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self.model_runner.token_to_kv_pool.size,
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)
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def get_pad_input_ids_func(self):
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return getattr(self.model_runner.model, "pad_input_ids", None)
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def get_tp_cpu_group(self):
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return self.model_runner.tp_group.cpu_group
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def get_memory_pool(self):
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return (
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self.model_runner.req_to_token_pool,
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self.model_runner.token_to_kv_pool,
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)
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def forward_batch_idle(self, model_worker_batch: ModelWorkerBatch):
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forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)
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self.model_runner.forward(forward_batch)
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def forward_batch_generation(
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self,
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model_worker_batch: ModelWorkerBatch,
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launch_done: Optional[threading.Event] = None,
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):
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forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)
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logits_output = self.model_runner.forward(forward_batch)
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if launch_done:
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launch_done.set()
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next_token_ids = self.model_runner.sample(logits_output, model_worker_batch)
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return logits_output, next_token_ids
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def forward_batch_embedding(self, model_worker_batch: ModelWorkerBatch):
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forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)
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logits_output = self.model_runner.forward(forward_batch)
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embeddings = logits_output.embeddings
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return embeddings
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def update_weights_from_disk(self, recv_req: UpdateWeightFromDiskReqInput):
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success, message = self.model_runner.update_weights_from_disk(
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recv_req.model_path, recv_req.load_format
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)
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return success, message
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def get_weights_by_name(self, recv_req: GetWeightsByNameReqInput):
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parameter = self.model_runner.get_weights_by_name(
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recv_req.name, recv_req.truncate_size
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)
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return parameter
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