TVM 加速模型,優化推斷

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TVM 是一個開源深度學習編譯器,可適用於各類 CPUs, GPUs 及其他專用加速器。它的目標是使得我們能夠在任何硬體上優化和執行自己的模型。不同於深度學習框架關注模型生產力,TVM 更關注模型在硬體上的效能和效率。

本文只簡單介紹 TVM 的編譯流程,及如何自動調優自己的模型。更深入瞭解,可見 TVM 官方內容:

編譯流程

TVM 文件 Design and Architecture 講述了例項編譯流程、邏輯結構元件、裝置目標實現等。其中流程見下圖:

從高層次上看,包含了如下步驟:

  • 匯入(Import):前端元件將模型提取進 IRModule,其是模型內部表示(IR)的函式集合。
  • 轉換(Transformation):編譯器將 IRModule 轉換為另一個功能等效或近似等效(如量化情況下)的 IRModule。大多轉換都是獨立於目標(後端)的。TVM 也允許目標影響轉換通道的配置。
  • 目標翻譯(Target Translation):編譯器翻譯(程式碼生成) IRModule 到目標上的可執行格式。目標翻譯結果被封裝為 runtime.Module,可以在目標執行時環境中匯出、載入和執行。
  • 執行時執行(Runtime Execution):使用者載入一個 runtime.Module 並在支援的執行時環境中執行編譯好的函式。

調優模型

TVM 文件User Tutorial 從怎麼編譯優化模型開始,逐步深入到 TE, TensorIR, Relay 等更底層的邏輯結構元件。

這裡只講下如何用 AutoTVM 自動調優模型,實際瞭解 TVM 編譯、調優、執行模型的過程。原文見 Compiling and Optimizing a Model with the Python Interface (AutoTVM)

準備 TVM

首先,安裝 TVM。可見文件Installing TVM,或筆記「TVM 安裝」。

之後,即可通過 TVM Python API 來調優模型。我們先匯入如下依賴:

import onnx
from tvm.contrib.download import download_testdata
from PIL import Image
import numpy as np
import tvm.relay as relay
import tvm
from tvm.contrib import graph_executor

準備模型,並載入

獲取預訓練的 ResNet-50 v2 ONNX 模型,並載入:

model_url = "".join(
    [
        "https://github.com/onnx/models/raw/",
        "main/vision/classification/resnet/model/",
        "resnet50-v2-7.onnx",
    ]
)

model_path = download_testdata(model_url, "resnet50-v2-7.onnx", module="onnx")
onnx_model = onnx.load(model_path)

準備圖片,並前處理

獲取一張測試圖片,並前處理成 224x224 NCHW 格式:

img_url = "https://s3.amazonaws.com/model-server/inputs/kitten.jpg"
img_path = download_testdata(img_url, "imagenet_cat.png", module="data")

# Resize it to 224x224
resized_image = Image.open(img_path).resize((224, 224))
img_data = np.asarray(resized_image).astype("float32")

# Our input image is in HWC layout while ONNX expects CHW input, so convert the array
img_data = np.transpose(img_data, (2, 0, 1))

# Normalize according to the ImageNet input specification
imagenet_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
imagenet_stddev = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
norm_img_data = (img_data / 255 - imagenet_mean) / imagenet_stddev

# Add the batch dimension, as we are expecting 4-dimensional input: NCHW.
img_data = np.expand_dims(norm_img_data, axis=0)

編譯模型,用 TVM Relay

TVM 匯入 ONNX 模型成 Relay,並建立 TVM 圖模型:

target = input("target [llvm]: ")
if not target:
    target = "llvm"
    # target = "llvm -mcpu=core-avx2"
    # target = "llvm -mcpu=skylake-avx512"

# The input name may vary across model types. You can use a tool
# like Netron to check input names
input_name = "data"
shape_dict = {input_name: img_data.shape}

mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)

with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target=target, params=params)

dev = tvm.device(str(target), 0)
module = graph_executor.GraphModule(lib["default"](dev))

其中 target 是目標硬體平臺。 llvm 指用 CPU,建議指明架構指令集,可更優化效能。如下命令可檢視 CPU:

$ llc --version | grep CPU
  Host CPU: skylake
$ lscpu

或直接上廠商網站(如Intel® Products)檢視產品引數。

執行模型,用 TVM Runtime

用 TVM Runtime 執行模型,進行預測:

dtype = "float32"
module.set_input(input_name, img_data)
module.run()
output_shape = (1, 1000)
tvm_output = module.get_output(0, tvm.nd.empty(output_shape)).numpy()

收集優化前的效能資料

收集優化前的效能資料:

import timeit

timing_number = 10
timing_repeat = 10
unoptimized = (
    np.array(timeit.Timer(lambda: module.run()).repeat(repeat=timing_repeat, number=timing_number))
    * 1000
    / timing_number
)
unoptimized = {
    "mean": np.mean(unoptimized),
    "median": np.median(unoptimized),
    "std": np.std(unoptimized),
}

print(unoptimized)

之後,用以對比優化後的效能。

後處理輸出,得知預測結果

輸出的預測結果,後處理成可讀的分類結果:

from scipy.special import softmax

# Download a list of labels
labels_url = "https://s3.amazonaws.com/onnx-model-zoo/synset.txt"
labels_path = download_testdata(labels_url, "synset.txt", module="data")

with open(labels_path, "r") as f:
    labels = [l.rstrip() for l in f]

# Open the output and read the output tensor
scores = softmax(tvm_output)
scores = np.squeeze(scores)
ranks = np.argsort(scores)[::-1]
for rank in ranks[0:5]:
    print("class='%s' with probability=%f" % (labels[rank], scores[rank]))

調優模型,獲取調優資料

於目標硬體平臺,用 AutoTVM 自動調優,獲取調優資料:

import tvm.auto_scheduler as auto_scheduler
from tvm.autotvm.tuner import XGBTuner
from tvm import autotvm

number = 10
repeat = 1
min_repeat_ms = 0  # since we're tuning on a CPU, can be set to 0
timeout = 10  # in seconds

# create a TVM runner
runner = autotvm.LocalRunner(
    number=number,
    repeat=repeat,
    timeout=timeout,
    min_repeat_ms=min_repeat_ms,
    enable_cpu_cache_flush=True,
)

tuning_option = {
    "tuner": "xgb",
    "trials": 10,
    "early_stopping": 100,
    "measure_option": autotvm.measure_option(
        builder=autotvm.LocalBuilder(build_func="default"), runner=runner
    ),
    "tuning_records": "resnet-50-v2-autotuning.json",
}

# begin by extracting the tasks from the onnx model
tasks = autotvm.task.extract_from_program(mod["main"], target=target, params=params)

# Tune the extracted tasks sequentially.
for i, task in enumerate(tasks):
    prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
    tuner_obj = XGBTuner(task, loss_type="rank")
    tuner_obj.tune(
        n_trial=min(tuning_option["trials"], len(task.config_space)),
        early_stopping=tuning_option["early_stopping"],
        measure_option=tuning_option["measure_option"],
        callbacks=[
            autotvm.callback.progress_bar(tuning_option["trials"], prefix=prefix),
            autotvm.callback.log_to_file(tuning_option["tuning_records"]),
        ],
    )

上述 tuning_option 選用的 XGBoost Grid 演算法進行優化搜尋,資料記錄進 tuning_records

重編譯模型,用調優資料

重新編譯出一個優化模型,依據調優資料:

with autotvm.apply_history_best(tuning_option["tuning_records"]):
    with tvm.transform.PassContext(opt_level=3, config={}):
        lib = relay.build(mod, target=target, params=params)

dev = tvm.device(str(target), 0)
module = graph_executor.GraphModule(lib["default"](dev))


# Verify that the optimized model runs and produces the same results

dtype = "float32"
module.set_input(input_name, img_data)
module.run()
output_shape = (1, 1000)
tvm_output = module.get_output(0, tvm.nd.empty(output_shape)).numpy()

scores = softmax(tvm_output)
scores = np.squeeze(scores)
ranks = np.argsort(scores)[::-1]
for rank in ranks[0:5]:
    print("class='%s' with probability=%f" % (labels[rank], scores[rank]))

對比調優與非調優模型

收集優化後的效能資料,與優化前的對比:

import timeit

timing_number = 10
timing_repeat = 10
optimized = (
    np.array(timeit.Timer(lambda: module.run()).repeat(repeat=timing_repeat, number=timing_number))
    * 1000
    / timing_number
)
optimized = {"mean": np.mean(optimized), "median": np.median(optimized), "std": np.std(optimized)}

print("optimized: %s" % (optimized))
print("unoptimized: %s" % (unoptimized))

調優模型,整個過程的執行結果,如下:

$ time python autotvm_tune.py
# TVM 編譯執行模型
## Downloading and Loading the ONNX Model
## Downloading, Preprocessing, and Loading the Test Image
## Compile the Model With Relay
target [llvm]: llvm -mcpu=core-avx2
One or more operators have not been tuned. Please tune your model for better performance. Use DEBUG logging level to see more details.
## Execute on the TVM Runtime
## Collect Basic Performance Data
{'mean': 44.97057118016528, 'median': 42.52320024970686, 'std': 6.870915251002107}
## Postprocess the output
class='n02123045 tabby, tabby cat' with probability=0.621104
class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
# AutoTVM 調優模型 [Y/n]
## Tune the model
[Task  1/25]  Current/Best:  156.96/ 353.76 GFLOPS | Progress: (10/10) | 4.78 s Done.
[Task  2/25]  Current/Best:   54.66/ 241.25 GFLOPS | Progress: (10/10) | 2.88 s Done.
[Task  3/25]  Current/Best:  116.71/ 241.30 GFLOPS | Progress: (10/10) | 3.48 s Done.
[Task  4/25]  Current/Best:  119.92/ 184.18 GFLOPS | Progress: (10/10) | 3.48 s Done.
[Task  5/25]  Current/Best:   48.92/ 158.38 GFLOPS | Progress: (10/10) | 3.13 s Done.
[Task  6/25]  Current/Best:  156.89/ 230.95 GFLOPS | Progress: (10/10) | 2.82 s Done.
[Task  7/25]  Current/Best:   92.33/ 241.99 GFLOPS | Progress: (10/10) | 2.40 s Done.
[Task  8/25]  Current/Best:   50.04/ 331.82 GFLOPS | Progress: (10/10) | 2.64 s Done.
[Task  9/25]  Current/Best:  188.47/ 409.93 GFLOPS | Progress: (10/10) | 4.44 s Done.
[Task 10/25]  Current/Best:   44.81/ 181.67 GFLOPS | Progress: (10/10) | 2.32 s Done.
[Task 11/25]  Current/Best:   83.74/ 312.66 GFLOPS | Progress: (10/10) | 2.74 s Done.
[Task 12/25]  Current/Best:   96.48/ 294.40 GFLOPS | Progress: (10/10) | 2.82 s Done.
[Task 13/25]  Current/Best:  123.74/ 354.34 GFLOPS | Progress: (10/10) | 2.62 s Done.
[Task 14/25]  Current/Best:   23.76/ 178.71 GFLOPS | Progress: (10/10) | 2.90 s Done.
[Task 15/25]  Current/Best:  119.18/ 534.63 GFLOPS | Progress: (10/10) | 2.49 s Done.
[Task 16/25]  Current/Best:  101.24/ 172.92 GFLOPS | Progress: (10/10) | 2.49 s Done.
[Task 17/25]  Current/Best:  309.85/ 309.85 GFLOPS | Progress: (10/10) | 2.69 s Done.
[Task 18/25]  Current/Best:   54.45/ 368.31 GFLOPS | Progress: (10/10) | 2.46 s Done.
[Task 19/25]  Current/Best:   78.69/ 162.43 GFLOPS | Progress: (10/10) | 3.29 s Done.
[Task 20/25]  Current/Best:   40.78/ 317.50 GFLOPS | Progress: (10/10) | 4.52 s Done.
[Task 21/25]  Current/Best:  169.03/ 296.36 GFLOPS | Progress: (10/10) | 3.95 s Done.
[Task 22/25]  Current/Best:   90.96/ 210.43 GFLOPS | Progress: (10/10) | 2.28 s Done.
[Task 23/25]  Current/Best:   48.93/ 217.36 GFLOPS | Progress: (10/10) | 2.87 s Done.
[Task 25/25]  Current/Best:    0.00/   0.00 GFLOPS | Progress: (0/10) | 0.00 s Done.
[Task 25/25]  Current/Best:   25.50/  33.86 GFLOPS | Progress: (10/10) | 9.28 s Done.
## Compiling an Optimized Model with Tuning Data
class='n02123045 tabby, tabby cat' with probability=0.621104
class='n02123159 tiger cat' with probability=0.356378
class='n02124075 Egyptian cat' with probability=0.019712
class='n02129604 tiger, Panthera tigris' with probability=0.001215
class='n04040759 radiator' with probability=0.000262
## Comparing the Tuned and Untuned Models
optimized: {'mean': 34.736288779822644, 'median': 34.547542000655085, 'std': 0.5144378649382363}
unoptimized: {'mean': 44.97057118016528, 'median': 42.52320024970686, 'std': 6.870915251002107}

real    3m23.904s
user    5m2.900s
sys     5m37.099s

對比效能資料,可以發現:調優模型的執行速度更快、更平穩。

參考

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