173 lines
6.5 KiB
Python
173 lines
6.5 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 os
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import threading
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from pathlib import Path
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from typing import TYPE_CHECKING, Dict, Optional, Union
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import numpy as np
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import torch
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from sglang.srt.configs.model_config import AttentionArch, is_deepseek_nsa
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from sglang.srt.layers.dp_attention import get_attention_tp_size
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from sglang.srt.model_executor.cuda_graph_runner import CudaGraphRunner
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from sglang.srt.utils import is_npu
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is_npu = is_npu()
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if is_npu:
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import torch_npu
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from torch_npu.profiler import ProfilerActivity, profile
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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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self.update_attr_name = None
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self.update_attr_type = None
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self.model_runner = model_runner
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self._init_arch_map()
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def _init_arch_map(self):
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self.attr_name: Dict[str, str] = {
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AttentionArch.MLA: "actual_seq_lengths_kv",
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AttentionArch.MHA: "context_lens",
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}
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self.attr_type: Dict[str, Union[list, torch.Tensor]] = {
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AttentionArch.MLA: [],
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AttentionArch.MHA: torch.Tensor(),
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}
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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 _get_update_attr_name(self, model_runner):
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if self.bs < get_attention_tp_size():
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return self.attr_name[AttentionArch.MLA]
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return self.attr_name[model_runner.model_config.attention_arch]
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def _get_update_attr_type(self, model_runner):
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if self.bs < get_attention_tp_size():
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return self.attr_type[AttentionArch.MLA]
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return self.attr_type[model_runner.model_config.attention_arch]
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def _update_inputs(self, seq_lens):
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if isinstance(self.update_attr_type, torch.Tensor):
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seq_lens = torch.from_numpy(np.array(seq_lens).astype(np.int32))
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self.graphs[self.bs].update(
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cpu_update_input=[{self.update_attr_name: 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 _init_profile_context_and_memory_record(self):
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output_dir = os.path.join(
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os.getenv("SGLANG_TORCH_PROFILER_DIR", "/tmp"), "graph_capture_profile"
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)
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if not Path(output_dir).exists():
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Path(output_dir).mkdir(parents=True, exist_ok=True)
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logger.info(
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f"Profiling starts for graph capture for NPU. Traces will be saved to: {output_dir}"
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)
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experimental_config = torch_npu.profiler._ExperimentalConfig(
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export_type=[torch_npu.profiler.ExportType.Text],
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profiler_level=torch_npu.profiler.ProfilerLevel.Level1,
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)
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profile_context = profile(
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activities=[ProfilerActivity.CPU, ProfilerActivity.NPU],
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record_shapes=True,
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profile_memory=True,
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on_trace_ready=torch_npu.profiler.tensorboard_trace_handler(
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output_dir, async_mode=True
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),
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experimental_config=experimental_config,
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)
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return profile_context
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def _post_process_after_profile(self, prof_context):
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# for NPU, profile data will be saved to disk for further analysis.
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pass
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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.buffers.input_ids[: self.raw_num_token].copy_(forward_batch.input_ids)
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self.buffers.positions[: self.raw_num_token].copy_(forward_batch.positions)
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self.update_attr_name = self._get_update_attr_name(self.model_runner)
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self.update_attr_type = self._get_update_attr_type(self.model_runner)
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# Replay
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if not is_deepseek_nsa(self.model_runner.model_config.hf_config):
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if forward_batch.forward_mode.is_target_verify():
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seq_lens_cpu = forward_batch.seq_lens.cpu() + self.num_tokens_per_bs
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seq_lens = seq_lens_cpu.tolist() + [0] * (self.bs - self.raw_bs)
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else:
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seq_lens = forward_batch.seq_lens.cpu().tolist() + [0] * (
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self.bs - self.raw_bs
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)
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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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else:
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self.graphs[self.bs].replay()
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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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