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[quant] Quantized Average Pool Refactoring (pytorch#42009)
Summary: **cc** z-a-f. Refactor `qavg_pool(2,3)d_nhwc_kernel` as mentioned in pytorch#40316. # Benchmarks ## Python Before | After  ## C++  ## Notes - It does seem that for `qint8` and `quint8` there is a noticeable 2x increase in speed at least when the `channels > 64` in the benchmarks. ## Reproduce ### Python ``` import time import numpy as np import torch from termcolor import colored def time_avg_pool2d(X, kernel, stride, padding, ceil_mode, count_include_pad, divisor_override, iterations): X, (scale, zero_point, torch_type) = X qX_nchw = torch.quantize_per_tensor(torch.from_numpy(X), scale=scale, zero_point=zero_point, dtype=torch_type) qX_nhwc = qX_nchw.contiguous(memory_format=torch.channels_last) assert(qX_nhwc.stride() != sorted(qX_nhwc.stride())) assert(qX_nchw.is_contiguous(memory_format=torch.contiguous_format)) assert(qX_nhwc.is_contiguous(memory_format=torch.channels_last)) start = time.time() for _ in range(iterations): X_hat = torch.nn.quantized.functional.avg_pool2d(qX_nchw, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) qnchw_end = time.time() - start start = time.time() for _ in range(iterations): X_hat = torch.nn.quantized.functional.avg_pool2d(qX_nhwc, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) qnhwc_end = time.time() - start return qnchw_end*1000/iterations, qnhwc_end*1000/iterations def time_avg_pool3d(X, kernel, stride, padding, ceil_mode, count_include_pad, divisor_override, iterations): X, (scale, zero_point, torch_type) = X qX_ncdhw = torch.quantize_per_tensor(torch.from_numpy(X), scale=scale, zero_point=zero_point, dtype=torch_type) qX_ndhwc = qX_ncdhw.contiguous(memory_format=torch.channels_last_3d) assert(qX_ndhwc.stride() != sorted(qX_ndhwc.stride())) assert(qX_ncdhw.is_contiguous(memory_format=torch.contiguous_format)) assert(qX_ndhwc.is_contiguous(memory_format=torch.channels_last_3d)) start = time.time() for _ in range(iterations): X_hat = torch.nn.quantized.functional.avg_pool3d(qX_ncdhw, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) qncdhw_end = time.time() - start start = time.time() for _ in range(iterations): X_hat = torch.nn.quantized.functional.avg_pool3d(qX_ndhwc, kernel_size=kernel, stride=stride, padding=padding, ceil_mode=ceil_mode, count_include_pad=count_include_pad, divisor_override=divisor_override) qndhwc_end = time.time() - start return qncdhw_end*1000/iterations, qndhwc_end*1000/iterations iterations = 10000 print("iterations = {}".format(iterations)) print("Benchmark", "Time(ms)", sep="\t\t\t\t\t") for torch_type in (torch.qint8, torch.quint8, torch.qint32): for channel in (4,8,64,256): X = np.random.rand(1, channel, 56, 56).astype(np.float32), (0.5, 1, torch_type) ts = time_avg_pool2d(X, 4, None, 0, True, True, None, iterations) print(colored("avg_pool2d({}, {}, {})".format(str(torch_type), channel, "nchw"), 'green'), colored(ts[0], 'yellow'), sep="\t") print(colored("avg_pool2d({}, {}, {})".format(str(torch_type), channel, "nhwc"), 'green'), colored(ts[1], 'yellow'), sep="\t") for torch_type in (torch.qint8, torch.quint8, torch.qint32): for channel in (4,8,64,256): X = np.random.rand(1, channel, 56, 56, 4).astype(np.float32), (0.5, 1, torch_type) ts = time_avg_pool3d(X, 4, None, 0, True, True, None, iterations) print(colored("avg_pool3d({}, {}, {})".format(str(torch_type), channel, "ncdhw"), 'green'), colored(ts[0], 'yellow'), sep="\t") print(colored("avg_pool3d({}, {}, {})".format(str(torch_type), channel, "ndhwc"), 'green'), colored(ts[1], 'yellow'), sep="\t") ``` ### C++ 1. `git clone https://github.com/google/benchmark.git` 2. `git clone https://github.com/google/googletest.git benchmark/googletest` ``` # CMakeLists.txt cmake_minimum_required(VERSION 3.10 FATAL_ERROR) project(time_avg_pool VERSION 0.1.0) find_package(Torch REQUIRED) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}") add_subdirectory(benchmark) add_executable(time_average_pool time_average_pool.cpp) target_link_libraries(time_average_pool ${TORCH_LIBRARIES}) set_property(TARGET time_average_pool PROPERTY CXX_STANDARD 14) target_link_libraries(time_average_pool benchmark::benchmark) ``` ``` // time_average_pool.cpp #include <benchmark/benchmark.h> #include <torch/torch.h> torch::Device device(torch::kCPU); static void BM_TORCH_QAVG_POOL2D_NCHW_SINGLE_THREADED(benchmark::State& state) { torch::init_num_threads(); torch::set_num_threads(1); auto x_nchw = torch::rand({1, state.range(0), 56, 56}, device); auto qx_nchw = torch::quantize_per_tensor(x_nchw, 0.5, 1, torch::kQUInt8); torch::Tensor X_hat; for (auto _ : state) X_hat = torch::nn::functional::avg_pool2d( qx_nchw, torch::nn::AvgPool2dOptions({4, 4}).ceil_mode(true).count_include_pad( true)); } static void BM_TORCH_QAVG_POOL2D_NHWC_SINGLE_THREADED(benchmark::State& state) { torch::init_num_threads(); torch::set_num_threads(1); auto x_nchw = torch::rand({1, state.range(0), 56, 56}, device); auto qx_nchw = torch::quantize_per_tensor(x_nchw, 0.5, 1, torch::kQUInt8); auto qx_nhwc = qx_nchw.contiguous(torch::MemoryFormat::ChannelsLast); torch::Tensor X_hat; for (auto _ : state) X_hat = torch::nn::functional::avg_pool2d( qx_nhwc, torch::nn::AvgPool2dOptions({4, 4}).ceil_mode(true).count_include_pad( true)); } static void BM_TORCH_QAVG_POOL2D_NCHW(benchmark::State& state) { auto x_nchw = torch::rand({1, state.range(0), 56, 56}, device); auto qx_nchw = torch::quantize_per_tensor(x_nchw, 0.5, 1, torch::kQUInt8); torch::Tensor X_hat; for (auto _ : state) X_hat = torch::nn::functional::avg_pool2d( qx_nchw, torch::nn::AvgPool2dOptions({4, 4}).ceil_mode(true).count_include_pad( true)); } static void BM_TORCH_QAVG_POOL2D_NHWC(benchmark::State& state) { auto x_nchw = torch::rand({1, state.range(0), 56, 56}, device); auto qx_nchw = torch::quantize_per_tensor(x_nchw, 0.5, 1, torch::kQUInt8); auto qx_nhwc = qx_nchw.contiguous(torch::MemoryFormat::ChannelsLast); torch::Tensor X_hat; for (auto _ : state) X_hat = torch::nn::functional::avg_pool2d( qx_nhwc, torch::nn::AvgPool2dOptions({4, 4}).ceil_mode(true).count_include_pad( true)); } static void BM_TORCH_QAVG_POOL3D_NCDHW_SINGLE_THREADED( benchmark::State& state) { torch::init_num_threads(); torch::set_num_threads(1); auto x_ncdhw = torch::rand({1, state.range(0), 56, 56, 4}, device); auto qx_ncdhw = torch::quantize_per_tensor(x_ncdhw, 0.5, 1, torch::kQUInt8); torch::Tensor X_hat; for (auto _ : state) X_hat = torch::nn::functional::avg_pool3d( qx_ncdhw, torch::nn::AvgPool3dOptions({5, 5, 5}) .ceil_mode(true) .count_include_pad(true)); } static void BM_TORCH_QAVG_POOL3D_NDHWC_SINGLE_THREADED( benchmark::State& state) { torch::init_num_threads(); torch::set_num_threads(1); auto x_ncdhw = torch::rand({1, state.range(0), 56, 56, 4}, device); auto qx_ncdhw = torch::quantize_per_tensor(x_ncdhw, 0.5, 1, torch::kQUInt8); auto qx_ndhwc = qx_ncdhw.contiguous(torch::MemoryFormat::ChannelsLast3d); torch::Tensor X_hat; for (auto _ : state) X_hat = torch::nn::functional::avg_pool3d( qx_ndhwc, torch::nn::AvgPool3dOptions({5, 5, 5}) .ceil_mode(true) .count_include_pad(true)); } static void BM_TORCH_QAVG_POOL3D_NCDHW(benchmark::State& state) { auto x_ncdhw = torch::rand({1, state.range(0), 56, 56, 4}, device); auto qx_ncdhw = torch::quantize_per_tensor(x_ncdhw, 0.5, 1, torch::kQUInt8); torch::Tensor X_hat; for (auto _ : state) X_hat = torch::nn::functional::avg_pool3d( qx_ncdhw, torch::nn::AvgPool3dOptions({5, 5, 5}) .ceil_mode(true) .count_include_pad(true)); } static void BM_TORCH_QAVG_POOL3D_NDHWC(benchmark::State& state) { auto x_ncdhw = torch::rand({1, state.range(0), 56, 56, 4}, device); auto qx_ncdhw = torch::quantize_per_tensor(x_ncdhw, 0.5, 1, torch::kQUInt8); auto qx_ndhwc = qx_ncdhw.contiguous(torch::MemoryFormat::ChannelsLast3d); torch::Tensor X_hat; for (auto _ : state) X_hat = torch::nn::functional::avg_pool3d( qx_ndhwc, torch::nn::AvgPool3dOptions({5, 5, 5}) .ceil_mode(true) .count_include_pad(true)); } BENCHMARK(BM_TORCH_QAVG_POOL2D_NCHW)->RangeMultiplier(8)->Range(4, 256); BENCHMARK(BM_TORCH_QAVG_POOL2D_NHWC)->RangeMultiplier(8)->Range(4, 256); BENCHMARK(BM_TORCH_QAVG_POOL3D_NCDHW)->RangeMultiplier(8)->Range(4, 256); BENCHMARK(BM_TORCH_QAVG_POOL3D_NDHWC)->RangeMultiplier(8)->Range(4, 256); BENCHMARK(BM_TORCH_QAVG_POOL2D_NCHW_SINGLE_THREADED) ->RangeMultiplier(8) ->Range(4, 256); BENCHMARK(BM_TORCH_QAVG_POOL2D_NHWC_SINGLE_THREADED) ->RangeMultiplier(8) ->Range(4, 256); BENCHMARK(BM_TORCH_QAVG_POOL3D_NCDHW_SINGLE_THREADED) ->RangeMultiplier(8) ->Range(4, 256); BENCHMARK(BM_TORCH_QAVG_POOL3D_NDHWC_SINGLE_THREADED) ->RangeMultiplier(8) ->Range(4, 256); BENCHMARK_MAIN(); ``` 3. `mkdir build && cd build` 4. ```cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH=`python -c 'import torch;print(torch.utils.cmake_prefix_path)'` .. ``` 5. `cmake --build . --config Release` 6. `./time_average_pool` # Further notes - I've used `istrideB, istrideD, istrideH, strideW, strideC` to match `_qadaptive_avg_pool_kernel` since there's some code duplication there as mentioned in pytorch#40316. Pull Request resolved: pytorch#42009 Reviewed By: pbelevich Differential Revision: D22794441 Pulled By: z-a-f fbshipit-source-id: 16710202811a1fbe1c99ea4d9b45876d6d28a8da
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