引擎缓存

随着模型尺寸增大，编译成本也会增加。对于 AOT (Ahead-of-Time) 方法，如 torch.dynamo.compile，此成本 upfront 支付。然而，如果权重发生变化、会话结束或您正在使用 JIT (Just-in-Time) 方法，如 torch.compile，当图变得无效时，它们会被重新编译，此成本会重复支付。引擎缓存是一种通过将已构建的引擎保存到磁盘并在可能时重复使用它们来减轻此成本的方法。本教程演示了如何在 PyTorch 中使用 TensorRT 的引擎缓存。引擎缓存可以显著加速后续的模型编译，因为它重用了先前构建的 TensorRT 引擎。

我们将探讨两种方法

1. 使用 torch_tensorrt.dynamo.compile

2. 将 TensorRT 作为后端与 torch.compile 一起使用

该示例使用预训练的 ResNet18 模型，并展示了无缓存、启用缓存和重用缓存引擎之间的编译差异。

import os
from typing import Dict, Optional

import numpy as np
import torch
import torch_tensorrt as torch_trt
import torchvision.models as models
from torch_tensorrt.dynamo._defaults import TIMING_CACHE_PATH
from torch_tensorrt.dynamo._engine_cache import BaseEngineCache

np.random.seed(0)
torch.manual_seed(0)

model = models.resnet18(pretrained=True).eval().to("cuda")
enabled_precisions = {torch.float}
debug = False
min_block_size = 1
use_python_runtime = False


def remove_timing_cache(path=TIMING_CACHE_PATH):
    if os.path.exists(path):
        os.remove(path)

JIT 编译的引擎缓存

引擎缓存的主要目标是帮助加速 JIT 工作流。torch.compile 在模型构建方面提供了极大的灵活性，这使其成为寻找加速工作流时的良好首选工具。然而，历史上，编译成本，特别是重新编译成本，一直是许多用户的障碍。如果在某种原因下子图变得无效，在添加引擎缓存之前，该图将从头开始重新构建。现在，当引擎被构建时，通过设置 cache_built_engines=True，引擎会保存到磁盘，并与相应的 PyTorch 子图的哈希值关联。如果在随后的编译中，无论是在当前会话还是新会话中，缓存都将提取已构建的引擎并重新适配 (refit) 权重，这可以将编译时间减少几个数量级。因此，为了将新引擎插入缓存（即 cache_built_engines=True），该引擎必须是可重新适配的 (immutable_weights=False)。有关更多详细信息，请参阅使用新权重 Refit（重新适配）Torch-TensorRT 程序。

def torch_compile(iterations=3):
    times = []
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)

    # The 1st iteration is to measure the compilation time without engine caching
    # The 2nd and 3rd iterations are to measure the compilation time with engine caching.
    # Since the 2nd iteration needs to compile and save the engine, it will be slower than the 1st iteration.
    # The 3rd iteration should be faster than the 1st iteration because it loads the cached engine.
    for i in range(iterations):
        inputs = [torch.rand((100, 3, 224, 224)).to("cuda")]
        # remove timing cache and reset dynamo just for engine caching messurement
        remove_timing_cache()
        torch._dynamo.reset()

        if i == 0:
            cache_built_engines = False
            reuse_cached_engines = False
        else:
            cache_built_engines = True
            reuse_cached_engines = True

        start.record()
        compiled_model = torch.compile(
            model,
            backend="tensorrt",
            options={
                "use_python_runtime": True,
                "enabled_precisions": enabled_precisions,
                "debug": debug,
                "min_block_size": min_block_size,
                "immutable_weights": False,
                "cache_built_engines": cache_built_engines,
                "reuse_cached_engines": reuse_cached_engines,
            },
        )
        compiled_model(*inputs)  # trigger the compilation
        end.record()
        torch.cuda.synchronize()
        times.append(start.elapsed_time(end))

    print("----------------torch_compile----------------")
    print("disable engine caching, used:", times[0], "ms")
    print("enable engine caching to cache engines, used:", times[1], "ms")
    print("enable engine caching to reuse engines, used:", times[2], "ms")


torch_compile()

AOT 编译的引擎缓存

与 JIT 工作流类似，AOT 工作流也可以从引擎缓存中受益。当相同的架构或通用子图被重新编译时，缓存将提取先前构建的引擎并重新适配权重。

def dynamo_compile(iterations=3):
    times = []
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)

    example_inputs = (torch.randn((100, 3, 224, 224)).to("cuda"),)
    # Mark the dim0 of inputs as dynamic
    batch = torch.export.Dim("batch", min=1, max=200)
    exp_program = torch.export.export(
        model, args=example_inputs, dynamic_shapes={"x": {0: batch}}
    )

    # The 1st iteration is to measure the compilation time without engine caching
    # The 2nd and 3rd iterations are to measure the compilation time with engine caching.
    # Since the 2nd iteration needs to compile and save the engine, it will be slower than the 1st iteration.
    # The 3rd iteration should be faster than the 1st iteration because it loads the cached engine.
    for i in range(iterations):
        inputs = [torch.rand((100 + i, 3, 224, 224)).to("cuda")]
        remove_timing_cache()  # remove timing cache just for engine caching messurement
        if i == 0:
            cache_built_engines = False
            reuse_cached_engines = False
        else:
            cache_built_engines = True
            reuse_cached_engines = True

        start.record()
        trt_gm = torch_trt.dynamo.compile(
            exp_program,
            tuple(inputs),
            use_python_runtime=use_python_runtime,
            enabled_precisions=enabled_precisions,
            debug=debug,
            min_block_size=min_block_size,
            immutable_weights=False,
            cache_built_engines=cache_built_engines,
            reuse_cached_engines=reuse_cached_engines,
            engine_cache_size=1 << 30,  # 1GB
        )
        # output = trt_gm(*inputs)
        end.record()
        torch.cuda.synchronize()
        times.append(start.elapsed_time(end))

    print("----------------dynamo_compile----------------")
    print("disable engine caching, used:", times[0], "ms")
    print("enable engine caching to cache engines, used:", times[1], "ms")
    print("enable engine caching to reuse engines, used:", times[2], "ms")


dynamo_compile()

自定义引擎缓存

默认情况下，引擎缓存存储在系统的临时目录中。可以通过传递 engine_cache_dir 和 engine_cache_size 来自定义缓存目录和大小限制。用户还可以通过扩展 BaseEngineCache 类来定义自己的引擎缓存实现。这允许实现远程或共享缓存（如果需要）。

自定义引擎缓存应实现以下方法

• save: 将引擎 blob 保存到缓存。

• load: 从缓存加载引擎 blob。

缓存系统提供的哈希值是原始 PyTorch 子图（降低后）的、与权重无关的哈希值。blob 以 pickle 格式包含序列化的引擎、调用规范数据和权重映射信息

下面是实现 RAMEngineCache 的自定义引擎缓存实现示例。

class RAMEngineCache(BaseEngineCache):
    def __init__(
        self,
    ) -> None:
        """
        Constructs a user held engine cache in memory.
        """
        self.engine_cache: Dict[str, bytes] = {}

    def save(
        self,
        hash: str,
        blob: bytes,
    ):
        """
        Insert the engine blob to the cache.

        Args:
            hash (str): The hash key to associate with the engine blob.
            blob (bytes): The engine blob to be saved.

        Returns:
            None
        """
        self.engine_cache[hash] = blob

    def load(self, hash: str) -> Optional[bytes]:
        """
        Load the engine blob from the cache.

        Args:
            hash (str): The hash key of the engine to load.

        Returns:
            Optional[bytes]: The engine blob if found, None otherwise.
        """
        if hash in self.engine_cache:
            return self.engine_cache[hash]
        else:
            return None


def torch_compile_my_cache(iterations=3):
    times = []
    engine_cache = RAMEngineCache()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)

    # The 1st iteration is to measure the compilation time without engine caching
    # The 2nd and 3rd iterations are to measure the compilation time with engine caching.
    # Since the 2nd iteration needs to compile and save the engine, it will be slower than the 1st iteration.
    # The 3rd iteration should be faster than the 1st iteration because it loads the cached engine.
    for i in range(iterations):
        inputs = [torch.rand((100, 3, 224, 224)).to("cuda")]
        # remove timing cache and reset dynamo just for engine caching messurement
        remove_timing_cache()
        torch._dynamo.reset()

        if i == 0:
            cache_built_engines = False
            reuse_cached_engines = False
        else:
            cache_built_engines = True
            reuse_cached_engines = True

        start.record()
        compiled_model = torch.compile(
            model,
            backend="tensorrt",
            options={
                "use_python_runtime": True,
                "enabled_precisions": enabled_precisions,
                "debug": debug,
                "min_block_size": min_block_size,
                "immutable_weights": False,
                "cache_built_engines": cache_built_engines,
                "reuse_cached_engines": reuse_cached_engines,
                "custom_engine_cache": engine_cache,
            },
        )
        compiled_model(*inputs)  # trigger the compilation
        end.record()
        torch.cuda.synchronize()
        times.append(start.elapsed_time(end))

    print("----------------torch_compile----------------")
    print("disable engine caching, used:", times[0], "ms")
    print("enable engine caching to cache engines, used:", times[1], "ms")
    print("enable engine caching to reuse engines, used:", times[2], "ms")


torch_compile_my_cache()