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Fix/float16 style (#12446)

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* "rewrite the test case"

* "follow comment"
bugfix/anakin-compile
dzhwinter 7 years ago committed by GitHub
parent 91fb0156ca
commit 6d3da458a7
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 119 additions and 84 deletions
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183
paddle/fluid/platform/cuda_helper_test.cu
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@ -13,7 +13,6 @@
// limitations under the License.
#include <gtest/gtest.h>
#include <bitset>
#include <iostream>
#include <random>
@ -25,13 +24,13 @@
using paddle::platform::PADDLE_CUDA_NUM_THREADS;
using paddle::platform::float16;
#define CUDA_ATOMIC_KERNEL(op, T) \
__global__ void op##Kernel(const T* data_a, T* data_b, size_t num) { \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num; \
i += blockDim.x * gridDim.x) { \
paddle::platform::CudaAtomic##op(&data_b[i], data_a[i]); \
} \
template <typename T>
__global__ void AddKernel(const T* data_a, T* data_b, size_t num) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
i += blockDim.x * gridDim.x) {
paddle::platform::CudaAtomicAdd(&data_b[i], data_a[i]);
}
}
template <typename T>
struct AddFunctor {
@ -39,80 +38,116 @@ struct AddFunctor {
};
template <typename T>
struct SubFunctor {
T operator()(const T& a, const T& b) { return a - b; }
};
// NOTE(dzhwinter): the float16 add has small underflow/overflow
// so we use EXPECT_NEAR to check the result.
#define ARITHMETIC_KERNEL_LAUNCH(op, T) \
void Test##T##op(size_t num) { \
T *in1, *in2, *out; \
T *d_in1, *d_in2; \
size_t size = sizeof(T) * num; \
cudaMalloc(reinterpret_cast<void**>(&d_in1), size); \
cudaMalloc(reinterpret_cast<void**>(&d_in2), size); \
in1 = reinterpret_cast<T*>(malloc(size)); \
in2 = reinterpret_cast<T*>(malloc(size)); \
out = reinterpret_cast<T*>(malloc(size)); \
std::minstd_rand engine; \
std::uniform_real_distribution<double> dist(0.0, 1.0); \
for (size_t i = 0; i < num; ++i) { \
in1[i] = static_cast<T>(dist(engine)); \
in2[i] = static_cast<T>(dist(engine)); \
} \
cudaMemcpy(d_in1, in1, size, cudaMemcpyHostToDevice); \
cudaMemcpy(d_in2, in2, size, cudaMemcpyHostToDevice); \
op##Kernel<<<1, PADDLE_CUDA_NUM_THREADS>>>(d_in1, d_in2, num); \
cudaDeviceSynchronize(); \
cudaMemcpy(out, d_in2, size, cudaMemcpyDeviceToHost); \
cudaDeviceSynchronize(); \
for (size_t i = 0; i < num; ++i) { \
EXPECT_NEAR(static_cast<float>(out[i]), \
static_cast<float>(op##Functor<T>()(in1[i], in2[i])), \
0.001); \
} \
free(in1); \
free(in2); \
free(out); \
cudaFree(d_in1); \
cudaFree(d_in2); \
void TestCase(size_t num) {
T *in1, *in2, *out;
T *d_in1, *d_in2;
size_t size = sizeof(T) * num;
cudaMalloc(reinterpret_cast<void**>(&d_in1), size);
cudaMalloc(reinterpret_cast<void**>(&d_in2), size);
in1 = reinterpret_cast<T*>(malloc(size));
in2 = reinterpret_cast<T*>(malloc(size));
out = reinterpret_cast<T*>(malloc(size));
std::minstd_rand engine;
std::uniform_real_distribution<double> dist(0.0, 1.0);
for (size_t i = 0; i < num; ++i) {
in1[i] = static_cast<T>(dist(engine));
in2[i] = static_cast<T>(dist(engine));
}
CUDA_ATOMIC_KERNEL(Add, float);
CUDA_ATOMIC_KERNEL(Add, double);
CUDA_ATOMIC_KERNEL(Add, float16);
ARITHMETIC_KERNEL_LAUNCH(Add, float);
ARITHMETIC_KERNEL_LAUNCH(Add, double);
ARITHMETIC_KERNEL_LAUNCH(Add, float16);
namespace paddle {
namespace platform {
USE_CUDA_ATOMIC(Sub, int);
};
};
CUDA_ATOMIC_KERNEL(Sub, int);
ARITHMETIC_KERNEL_LAUNCH(Sub, int);
cudaMemcpy(d_in1, in1, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, in2, size, cudaMemcpyHostToDevice);
AddKernel<T><<<1, PADDLE_CUDA_NUM_THREADS>>>(d_in1, d_in2, num);
cudaDeviceSynchronize();
cudaMemcpy(out, d_in2, size, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
for (size_t i = 0; i < num; ++i) {
// NOTE(dzhwinter): the float16 add has small underflow/overflow
// so we use EXPECT_NEAR to check the result.
EXPECT_NEAR(static_cast<float>(out[i]),
static_cast<float>(AddFunctor<T>()(in1[i], in2[i])), 0.001);
}
free(in1);
free(in2);
free(out);
cudaFree(d_in1);
cudaFree(d_in2);
}
// cuda primitives
TEST(CudaAtomic, Add) {
TestfloatAdd(static_cast<size_t>(10));
TestfloatAdd(static_cast<size_t>(1024 * 1024));
TestdoubleAdd(static_cast<size_t>(10));
TestdoubleAdd(static_cast<size_t>(1024 * 1024));
}
TestCase<float>(static_cast<size_t>(10));
TestCase<float>(static_cast<size_t>(1024 * 1024));
TEST(CudaAtomic, Sub) {
TestintSub(static_cast<size_t>(10));
TestintSub(static_cast<size_t>(1024 * 1024));
TestCase<double>(static_cast<size_t>(10));
TestCase<double>(static_cast<size_t>(1024 * 1024));
}
TEST(CudaAtomic, float16) {
using paddle::platform::float16;
Testfloat16Add(static_cast<size_t>(1));
Testfloat16Add(static_cast<size_t>(2));
Testfloat16Add(static_cast<size_t>(3));
TestCase<float16>(static_cast<size_t>(1));
TestCase<float16>(static_cast<size_t>(2));
TestCase<float16>(static_cast<size_t>(3));
TestCase<float16>(static_cast<size_t>(10));
TestCase<float16>(static_cast<size_t>(1024 * 1024));
}
// unalignment of uint8
void TestUnalign(size_t num, const int shift_bit) {
PADDLE_ENFORCE(num % 2 == 0, "must be a multiple of 2");
float16 *in1, *in2, *out;
float16 *d_in1, *d_in2;
size_t size = sizeof(uint8_t) * (num + shift_bit);
size_t array_size = sizeof(float16) * (num / 2);
cudaMalloc(reinterpret_cast<void**>(&d_in1), size);
cudaMalloc(reinterpret_cast<void**>(&d_in2), size);
in1 = reinterpret_cast<float16*>(malloc(size));
in2 = reinterpret_cast<float16*>(malloc(size));
out = reinterpret_cast<float16*>(malloc(size));
// right shift 1, mimic the unalignment of address
float16* r_in1 =
reinterpret_cast<float16*>(reinterpret_cast<uint8_t*>(in1) + shift_bit);
float16* r_in2 =
reinterpret_cast<float16*>(reinterpret_cast<uint8_t*>(in2) + shift_bit);
std::minstd_rand engine;
std::uniform_real_distribution<double> dist(0.0, 1.0);
for (size_t i = 0; i < num / 2; ++i) {
r_in1[i] = static_cast<float16>(dist(engine));
r_in2[i] = static_cast<float16>(dist(engine));
}
cudaMemcpy(d_in1, r_in1, array_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, r_in2, array_size, cudaMemcpyHostToDevice);
AddKernel<float16><<<1, PADDLE_CUDA_NUM_THREADS>>>(d_in1, d_in2, num / 2);
cudaDeviceSynchronize();
cudaMemcpy(out, d_in2, array_size, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
for (size_t i = 0; i < num / 2; ++i) {
// NOTE(dzhwinter): the float16 add has small underflow/overflow
// so we use EXPECT_NEAR to check the result.
EXPECT_NEAR(static_cast<float>(out[i]),
static_cast<float>(AddFunctor<float16>()(r_in1[i], r_in2[i])),
0.001);
}
free(in1);
free(in2);
free(out);
cudaFree(d_in1);
cudaFree(d_in2);
}
TEST(CudaAtomic, float16Unalign) {
// same with float16 testcase
TestUnalign(static_cast<size_t>(2), /*shift_bit*/ 2);
TestUnalign(static_cast<size_t>(1024), /*shift_bit*/ 2);
TestUnalign(static_cast<size_t>(1024 * 1024), /*shift_bit*/ 2);
// shift the address.
TestUnalign(static_cast<size_t>(2), /*shift_bit*/ 1);
TestUnalign(static_cast<size_t>(1024), /*shift_bit*/ 1);
TestUnalign(static_cast<size_t>(1024 * 1024), /*shift_bit*/ 1);
Testfloat16Add(static_cast<size_t>(10));
Testfloat16Add(static_cast<size_t>(1024 * 1024));
TestUnalign(static_cast<size_t>(2), /*shift_bit*/ 3);
TestUnalign(static_cast<size_t>(1024), /*shift_bit*/ 3);
TestUnalign(static_cast<size_t>(1024 * 1024), /*shift_bit*/ 3);
}

20
paddle/fluid/platform/cuda_primitives.h
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@ -79,41 +79,41 @@ CUDA_ATOMIC_WRAPPER(Add, double) {
// convert the value into float and do the add arithmetic.
// then store the result into a uint32.
inline __device__ uint32_t add_to_low_half(uint32_t val, float x) {
inline static __device__ uint32_t add_to_low_half(uint32_t val, float x) {
float16 low_half;
// the float16 in lower 16bits
low_half.x = static_cast<uint16_t>(val & 0xffffu);
low_half.x = static_cast<uint16_t>(val & 0xFFFFu);
low_half = static_cast<float16>(static_cast<float>(low_half) + x);
return (val & 0xffff0000u) | low_half.x;
return (val & 0xFFFF0000u) | low_half.x;
}
inline __device__ uint32_t add_to_high_half(uint32_t val, float x) {
inline static __device__ uint32_t add_to_high_half(uint32_t val, float x) {
float16 high_half;
// the float16 in higher 16bits
high_half.x = static_cast<uint16_t>(val >> 16);
high_half = static_cast<float16>(static_cast<float>(high_half) + x);
return (val & 0xffffu) | (static_cast<uint32_t>(high_half.x) << 16);
return (val & 0xFFFFu) | (static_cast<uint32_t>(high_half.x) << 16);
}
CUDA_ATOMIC_WRAPPER(Add, float16) {
// concrete packed float16 value may exsits in lower or higher 16bits
// of the 32bits address.
uint32_t *address_as_ui =
reinterpret_cast<uint32_t *>(reinterpret_cast<char *>(address) -
(reinterpret_cast<size_t>(address) & 2));
uint32_t *address_as_ui = reinterpret_cast<uint32_t *>(
reinterpret_cast<char *>(address) -
(reinterpret_cast<uintptr_t>(address) & 0x02));
float val_f = static_cast<float>(val);
uint32_t old = *address_as_ui;
uint32_t sum;
uint32_t newval;
uint32_t assumed;
if (((size_t)address & 2) == 0) {
if (((uintptr_t)address & 0x02) == 0) {
// the float16 value stay at lower 16 bits of the address.
do {
assumed = old;
old = atomicCAS(address_as_ui, assumed, add_to_low_half(assumed, val_f));
} while (old != assumed);
float16 ret;
ret.x = old & 0xffffu;
ret.x = old & 0xFFFFu;
return ret;
} else {
// the float16 value stay at higher 16 bits of the address.

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