CUTLASS 2.2 (#96)
Adds support for NVIDIA Ampere Architecture features. CUDA 11 Toolkit recommended.
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Andrew Kerr
GitHub
209
test/unit/core/bfloat16.cu
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209
test/unit/core/bfloat16.cu
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/***************************************************************************************************
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* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without modification, are permitted
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* provided that the following conditions are met:
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* * Redistributions of source code must retain the above copyright notice, this list of
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* conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright notice, this list of
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* conditions and the following disclaimer in the documentation and/or other materials
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* provided with the distribution.
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* * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
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* to endorse or promote products derived from this software without specific prior written
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* permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
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* FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
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* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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* STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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/*! \file
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\brief Statically sized array of elements that accommodates all CUTLASS-supported numeric types
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and is safe to use in a union.
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*/
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#include "../common/cutlass_unit_test.h"
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#include "cutlass/array.h"
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#include "cutlass/core_io.h"
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#include "cutlass/numeric_types.h"
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#include "cutlass/numeric_conversion.h"
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#include "cutlass/layout/matrix.h"
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#include "cutlass/util/device_memory.h"
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#include "cutlass/util/host_tensor.h"
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/////////////////////////////////////////////////////////////////////////////////////////////////
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__global__ void convert_bf16_f32(cutlass::bfloat16_t *output, float const *input, int N) {
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int tid = threadIdx.x + blockIdx.x * blockDim.x;
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if (tid < N) {
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output[tid] = static_cast<cutlass::bfloat16_t>(input[tid]);
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}
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}
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__global__ void convert_and_pack_bf16(cutlass::bfloat16_t *output, float const *input, int N) {
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int tid = threadIdx.x + blockIdx.x * blockDim.x;
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if (tid * 2 < N) {
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cutlass::NumericArrayConverter<cutlass::bfloat16_t, float, 2> convert;
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cutlass::Array<cutlass::bfloat16_t, 2> *dst_ptr =
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reinterpret_cast<cutlass::Array<cutlass::bfloat16_t, 2> *>(output + tid * 2);
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cutlass::Array<float, 2> const *src_ptr =
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reinterpret_cast<cutlass::Array<float, 2> const *>(input + tid * 2);
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*dst_ptr = convert(*src_ptr);
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}
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}
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TEST(bfloat16_t, device_conversion) {
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using T = cutlass::bfloat16_t;
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using S = float;
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int const N = 256;
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cutlass::HostTensor<T, cutlass::layout::RowMajor> destination({N, 1});
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cutlass::HostTensor<S, cutlass::layout::RowMajor> source({N, 1});
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for (int i = 0; i < N; ++i) {
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source.at({i, 0}) = float(i - 128);
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destination.at({i, 0}) = T(0);
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}
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source.sync_device();
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destination.sync_device();
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convert_bf16_f32<<< dim3(1,1), dim3(N, 1) >>>(destination.device_data(), source.device_data(), N);
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ASSERT_EQ(cudaGetLastError(), cudaSuccess) << "Kernel launch error.";
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destination.sync_host();
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int errors = 0;
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for (int i = 0; i < N; ++i) {
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T got = destination.at({i, 0});
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S expected = source.at({i, 0});
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if (S(got) != expected) {
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++errors;
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if (errors < 10) {
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std::cerr << "Basic conversion error - [" << i << "] - got " << got << ", expected " << expected << "\n";
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}
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}
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destination.at({i, 0}) = T(0);
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}
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destination.sync_device();
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convert_and_pack_bf16<<< dim3(1,1), dim3(N, 1) >>>(destination.device_data(), source.device_data(), N);
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ASSERT_EQ(cudaGetLastError(), cudaSuccess) << "Kernel launch error.";
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destination.sync_host();
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for (int i = 0; i < N; ++i) {
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T got = destination.at({i, 0});
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S expected = source.at({i, 0});
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if (S(got) != expected) {
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++errors;
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if (errors < 10) {
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std::cerr << "Convert and pack error - [" << i << "] - got " << got << ", expected " << expected << "\n";
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}
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}
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}
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EXPECT_EQ(errors, 0);
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Host
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//
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/////////////////////////////////////////////////////////////////////////////////////////////////
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TEST(bfloat16_t, host_conversion) {
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for (int i = -128; i < 128; ++i) {
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float f = static_cast<float>(i);
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cutlass::bfloat16_t x = static_cast<cutlass::bfloat16_t>(i);
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cutlass::bfloat16_t y = static_cast<cutlass::bfloat16_t>(f);
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EXPECT_TRUE(static_cast<int>(x) == i);
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EXPECT_TRUE(static_cast<float>(y) == f);
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}
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// Try out user-defined literals
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EXPECT_TRUE(cutlass::bfloat16_t(7) == 7_bf16);
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EXPECT_TRUE(7 == static_cast<int>(7_bf16));
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}
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TEST(bfloat16_t, host_arithmetic) {
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for (int i = -100; i < 100; ++i) {
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for (int j = -100; j < 100; ++j) {
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cutlass::bfloat16_t x = static_cast<cutlass::bfloat16_t>(i);
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cutlass::bfloat16_t y = static_cast<cutlass::bfloat16_t>(j);
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EXPECT_TRUE(static_cast<int>(x + y) == (i + j));
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}
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}
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}
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TEST(bfloat16_t, host_round) {
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struct {
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uint32_t f32_bits;
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uint16_t expected;
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} tests[] = {
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{0x40040000, 0x4004}, // M=0, R=0, S=0 => rtz
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{0x40048000, 0x4004}, // M=0, R=1, S=0 => rtz
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{0x40040001, 0x4004}, // M=0, R=1, S=1 => +inf
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{0x4004c000, 0x4005}, // M=0, R=1, S=1 => +inf
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{0x4004a000, 0x4005}, // M=0, R=1, S=1 => +inf
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{0x40050000, 0x4005}, // M=1, R=0, S=0 => rtz
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{0x40054000, 0x4005}, // M=1, R=0, S=1 => rtz
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{0x40058000, 0x4006}, // M=1, R=1, S=0 => +inf
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{0x40058001, 0x4006}, // M=1, R=1, S=1 => +inf
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{0x7f800000, 0x7f80}, // +inf
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{0xff800000, 0xff80}, // -inf
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{0x7fffffff, 0x7fff}, // canonical NaN
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{0x7ff00001, 0x7fff}, // NaN -> canonical NaN
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{0xfff00010, 0x7fff}, // Nan -> canonical NaN
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{0, 0}
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};
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bool running = true;
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for (int i = 0; running; ++i) {
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float f32 = reinterpret_cast<float const &>(tests[i].f32_bits);
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cutlass::bfloat16_t bf16 = cutlass::bfloat16_t(f32);
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bool passed = (tests[i].expected == bf16.raw());
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EXPECT_TRUE(passed)
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<< "Error - convert(f32: 0x" << std::hex << tests[i].f32_bits
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<< ") -> 0x" << std::hex << tests[i].expected << "\ngot: 0x" << std::hex << bf16.raw();
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if (!tests[i].f32_bits) {
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running = false;
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}
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}
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}
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/////////////////////////////////////////////////////////////////////////////////////////////////
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//
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// Device
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//
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/////////////////////////////////////////////////////////////////////////////////////////////////
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