mirror of
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129 lines
4.3 KiB
Python
129 lines
4.3 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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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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from dataclasses import dataclass
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from typing import Callable, Dict, Optional
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import paddle.device.cuda.graphs as graphs
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import paddle.nn.layer
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from fastdeploy.config import LLMConfig
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from fastdeploy.utils import get_logger
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logger = get_logger("cudagrpah_piecewise_backend",
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"cudagraph_piecewise_backend.log")
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@dataclass
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class ConcreteSizeEntry:
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""" Record the concrete information corresponding to the current batch size """
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# Concrete batch size
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runtime_bs: int
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# The size is in cudagraph_capture_sizes
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use_cuda_graph: bool = True
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# Has runtime-bs been captured before
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captured: bool = False
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# Need to be captured callable object（dynamic graph or static grpah backend）
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runnable: Callable = None # type: ignore
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# Number of completed warmups
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num_finished_warmup: int = 0
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# Captured cuda graph object corresponding to the current batch size
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cuda_graph: Optional[graphs.CUDAGraph] = None
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# Output buffer of cudagraph
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output_buffer: Optional[paddle.Tensor] = None
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# for cudagraph debugging, track the input addresses
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# during capture, and check if they are the same during replay
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input_addresses: Optional[list[int]] = None
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class CudaGraphPiecewiseBackend:
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""" """
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def __init__(
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self,
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llm_config: LLMConfig,
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runnable: Callable,
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):
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self.llm_config = llm_config
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self.runnable = runnable
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self.cuda_graph_capture_size = llm_config.graph_opt_config.cudagraph_capture_sizes
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# runtime_bs -> ConcreteSizeEntry
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self.concrete_size_entries: Dict[int, ConcreteSizeEntry] = {}
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for shape in self.cuda_graph_capture_size:
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self.concrete_size_entries[shape] = ConcreteSizeEntry(
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runtime_bs=shape)
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print("create all batch size entry")
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def __call__(self, **kwargs):
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# Get batch size
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input_ids: paddle.Tensor = kwargs['input_ids']
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batch_size = input_ids.shape[0]
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entry = self.concrete_size_entries.get(batch_size)
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if entry.runnable is None:
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entry.runnable = self.runnable
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print(
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f"[CUDA GRAPH] new entry lazy initialize with batch size {batch_size}"
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)
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if not entry.use_cuda_graph:
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return entry.runnable(**kwargs)
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# Capture a new cuda graph
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if entry.cuda_graph is None:
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# Warmup the model
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for n in range(entry.num_finished_warmup):
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entry.num_finished_warmup += 1
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entry.runnable(**kwargs)
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print(
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f"[CUDA GRAPH] warm up for batch size "
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f"{batch_size}, finished ({n+1}/{entry.num_finished_warmup}) times"
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)
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# Store input addresses for debug
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input_addresses = [
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x.data_ptr() for (_, x) in kwargs.items()
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if isinstance(x, paddle.Tensor)
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]
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entry.input_addresses = input_addresses
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new_grpah = graphs.CUDAGraph()
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paddle.device.synchronize()
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# Capture
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new_grpah.capture_begin()
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output = entry.runnable(**kwargs)
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new_grpah.capture_end()
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# Store output buffer
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entry.cuda_graph = new_grpah
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entry.output_buffer = paddle.zeros_like(output)
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output._share_buffer_to(entry.output_buffer)
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output._clear
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paddle.device.synchronize()
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print(
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f"[CUDA GRAPH] cuda graph captured for batch size {batch_size}"
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)
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# Replay
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entry.cuda_graph.replay()
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print(f"[CUDA GRAPH] cuda graph replayed for batch size {batch_size}")
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return entry.output_buffer
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