2c4b4b786b
Co-authored-by: ronnie_zheng <zl19940307@163.com> Co-authored-by: yezhifeng (D) <y00897525@china.huawei.com> Co-authored-by: anon189Ty <Stari_Falcon@outlook.com> Co-authored-by: Maksim <makcum888e@mail.ru> Co-authored-by: ssshinigami <44640852+ssshinigami@users.noreply.github.com>
95 lines
3.3 KiB
Python
95 lines
3.3 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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"""Run the model with npu graph and torch.compile."""
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from __future__ import annotations
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import logging
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import threading
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from typing import TYPE_CHECKING, Optional, Union
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import torch
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from sglang.srt.model_executor.cuda_graph_runner import CudaGraphRunner
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logger = logging.getLogger(__name__)
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if TYPE_CHECKING:
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from sglang.srt.model_executor.model_runner import ModelRunner
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from sglang.srt.layers.logits_processor import LogitsProcessorOutput
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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class NPUGraphRunner(CudaGraphRunner):
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"""A NPUGraphRunner runs the forward pass of a model with npu graph and torch.compile."""
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def __init__(self, model_runner: ModelRunner):
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super().__init__(model_runner)
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def _create_device_graph(self):
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return torch.npu.NPUGraph()
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def _capture_graph(self, graph, pool, stream, run_once_fn):
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with torch.npu.graph(
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graph,
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pool=pool,
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stream=stream,
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auto_dispatch_capture=True,
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):
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out = run_once_fn()
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return out
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def _update_inputs(self, seq_lens):
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self.graphs[self.bs].update(
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cpu_update_input=[{"actual_seq_lengths_kv": seq_lens}]
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)
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def _cache_loc_dtype(self):
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return torch.int32
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def replay(
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self,
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forward_batch: ForwardBatch,
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skip_attn_backend_init: bool = False,
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pp_proxy_tensors: Optional[PPProxyTensors] = None,
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) -> Union[LogitsProcessorOutput, PPProxyTensors]:
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if not skip_attn_backend_init:
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self.replay_prepare(forward_batch, pp_proxy_tensors)
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else:
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# In speculative decoding, these two fields are still needed.
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self.input_ids[: self.raw_num_token].copy_(forward_batch.input_ids)
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self.positions[: self.raw_num_token].copy_(forward_batch.positions)
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# Replay
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seq_lens = forward_batch.seq_lens.cpu().tolist() + [0] * (self.bs - self.raw_bs)
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thread = threading.Thread(target=self._update_inputs, args=(seq_lens,))
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thread.start()
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self.graphs[self.bs].replay()
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thread.join()
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output = self.output_buffers[self.bs]
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if isinstance(output, LogitsProcessorOutput):
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return LogitsProcessorOutput(
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next_token_logits=output.next_token_logits[: self.raw_num_token],
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hidden_states=(
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output.hidden_states[: self.raw_num_token]
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if output.hidden_states is not None
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else None
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),
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)
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else:
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assert isinstance(output, PPProxyTensors)
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return PPProxyTensors({k: v[: self.bs] for k, v in output.tensors.items()})
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