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test_cuda.cpp
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#ifdef USE_CUDA
#include <cmath>
#include <sstream>
#include <stdexcept>
#include <gtest/gtest.h>
#include "test/cpp/tensorexpr/test_base.h"
#include <torch/csrc/jit/testing/file_check.h>
#include "test/cpp/tensorexpr/padded_buffer.h"
#include "torch/csrc/jit/tensorexpr/cuda_codegen.h"
#include "torch/csrc/jit/tensorexpr/ir_simplifier.h"
#include "torch/csrc/jit/tensorexpr/loopnest.h"
#include "torch/csrc/jit/tensorexpr/tensor.h"
#include <torch/csrc/jit/testing/file_check.h>
#include <c10/cuda/CUDACachingAllocator.h>
#include <c10/util/Half.h>
namespace torch {
namespace jit {
using namespace torch::jit::tensorexpr;
using namespace torch::jit::tensorexpr;
template <typename ctype>
static void testCudaTestVectorAdd01_impl() {
KernelScope kernel_scope;
const int num_iter = 3;
const int block_count = 16;
const int block_size = 128;
Dtype dtype = ToDtype<ctype>();
Placeholder a_buf("a", dtype, {num_iter, block_count, block_size});
Placeholder b_buf("b", dtype, {num_iter, block_count, block_size});
Tensor* c = Compute(
"c",
{
{num_iter, "n"},
{block_count, "b_id"},
{block_size, "t_id"},
},
[&](const VarHandle& n, const VarHandle& b_id, const VarHandle& t_id) {
return a_buf.load(n, b_id, t_id) + b_buf.load(n, b_id, t_id);
});
LoopNest l({c});
std::vector<For*> loops = l.getLoopStmtsFor(c);
l.setGPUBlockIndex(loops[1], 0);
l.setGPUThreadIndex(loops[2], 0);
l.prepareForCodegen();
Stmt* stmt = l.root_stmt();
CudaCodeGen cuda_cg(stmt, c, a_buf, b_buf);
const int N = block_count * block_size * num_iter;
PaddedBuffer<ctype> a_v(N);
PaddedBuffer<ctype> b_v(N);
PaddedBuffer<ctype> c_v(N);
PaddedBuffer<ctype> c_ref(N);
for (int i = 0; i < N; i++) {
a_v(i) = ctype(i);
b_v(i) = ctype(i * 3 + 7);
c_ref(i) = a_v(i) + b_v(i);
}
// TODO: move gpu support into PaddedBuffer
ctype* a_dev = nullptr;
cudaMalloc(&a_dev, N * sizeof(ctype));
ctype* b_dev = nullptr;
cudaMalloc(&b_dev, N * sizeof(ctype));
ctype* c_dev = nullptr;
cudaMalloc(&c_dev, N * sizeof(ctype));
cudaMemcpy(a_dev, a_v.data(), N * sizeof(ctype), cudaMemcpyHostToDevice);
cudaMemcpy(b_dev, b_v.data(), N * sizeof(ctype), cudaMemcpyHostToDevice);
cudaMemcpy(c_dev, c_v.data(), N * sizeof(ctype), cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
cuda_cg(c_dev, a_dev, b_dev);
cudaDeviceSynchronize();
cudaMemcpy(c_v.data(), c_dev, N * sizeof(ctype), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(c_v, c_ref, 1e-5);
cudaFree(a_dev);
cudaFree(b_dev);
cudaFree(c_dev);
}
float sigmoid(float x) {
return 1.0f / (1.0f + expf(-0.0f - x));
}
TEST(Cuda, Sigmoid_CUDA) {
KernelScope kernel_scope;
const int num_iter = 3;
const int block_count = 16;
const int block_size = 128;
Dtype dtype = ToDtype<float>();
Placeholder a_buf("a", dtype, {num_iter, block_count, block_size});
Tensor* c = Compute(
"c",
{
{num_iter, "n"},
{block_count, "b_id"},
{block_size, "t_id"},
},
[&](const VarHandle& n, const VarHandle& b_id, const VarHandle& t_id) {
return sigmoid(sigmoid(a_buf.load(n, b_id, t_id)));
});
LoopNest l({c});
std::vector<For*> loops = l.getLoopStmtsFor(c);
l.setGPUBlockIndex(loops[1], 0);
l.setGPUThreadIndex(loops[2], 0);
l.prepareForCodegen();
Stmt* stmt = l.root_stmt();
CudaCodeGen cuda_cg(stmt, c, a_buf);
const int N = block_count * block_size * num_iter;
PaddedBuffer<float> a_v(N);
PaddedBuffer<float> c_v(N);
PaddedBuffer<float> c_ref(N);
for (int i = 0; i < N; i++) {
a_v(i) = float(i);
c_ref(i) = sigmoid(sigmoid(a_v(i)));
}
// TODO: move gpu support into PaddedBuffer
float* a_dev = nullptr;
cudaMalloc(&a_dev, N * sizeof(float));
float* c_dev = nullptr;
cudaMalloc(&c_dev, N * sizeof(float));
cudaMemcpy(a_dev, a_v.data(), N * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(c_dev, c_v.data(), N * sizeof(float), cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
cuda_cg(c_dev, a_dev);
cudaDeviceSynchronize();
cudaMemcpy(c_v.data(), c_dev, N * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(c_v, c_ref, 1e-5);
cudaFree(a_dev);
cudaFree(c_dev);
}
TEST(Cuda, TestVectorAdd01_CUDA) {
// floating types.
testCudaTestVectorAdd01_impl<float>();
testCudaTestVectorAdd01_impl<at::Half>();
testCudaTestVectorAdd01_impl<double>();
// integer types.
testCudaTestVectorAdd01_impl<int8_t>();
testCudaTestVectorAdd01_impl<uint8_t>();
testCudaTestVectorAdd01_impl<int16_t>();
testCudaTestVectorAdd01_impl<int32_t>();
testCudaTestVectorAdd01_impl<int64_t>();
}
static void testCudaTestVectorAdd02_impl(int N, int block_size) {
KernelScope kernel_scope;
Placeholder a_buf("a", kFloat, {N});
Placeholder b_buf("b", kFloat, {N});
Tensor* c = Compute(
"c",
{
{N, "N"},
},
[&](const VarHandle& n) { return a_buf.load(n) + b_buf.load(n); });
LoopNest l({c});
For* n_outer;
For* n_inner;
std::vector<For*> loops = l.getLoopStmtsFor(c);
l.splitWithMask(loops[0], block_size, &n_outer, &n_inner);
l.setGPUBlockIndex(n_outer, 0);
l.setGPUThreadIndex(n_inner, 0);
l.prepareForCodegen();
Stmt* stmt = l.root_stmt();
CudaCodeGen cuda_cg(stmt, c, a_buf, b_buf);
PaddedBuffer<float> a_v(N);
PaddedBuffer<float> b_v(N);
PaddedBuffer<float> c_v(N);
PaddedBuffer<float> c_ref(N);
for (int i = 0; i < N; i++) {
a_v(i) = i;
b_v(i) = i * 3 + 7;
c_ref(i) = a_v(i) + b_v(i);
}
// TODO: move gpu support into PaddedBuffer
float* a_dev = nullptr;
cudaMalloc(&a_dev, N * sizeof(float));
float* b_dev = nullptr;
cudaMalloc(&b_dev, N * sizeof(float));
float* c_dev = nullptr;
cudaMalloc(&c_dev, N * sizeof(float));
cudaMemcpy(a_dev, a_v.data(), N * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(b_dev, b_v.data(), N * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(c_dev, c_v.data(), N * sizeof(float), cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
cuda_cg(c_dev, a_dev, b_dev);
cudaDeviceSynchronize();
cudaMemcpy(c_v.data(), c_dev, N * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(c_v, c_ref, 1e-5);
cudaFree(a_dev);
cudaFree(b_dev);
cudaFree(c_dev);
}
TEST(Cuda, TestVectorAdd02_CUDA) {
testCudaTestVectorAdd02_impl(1024, 128);
testCudaTestVectorAdd02_impl(1030, 128);
}
TEST(Cuda, HalfCast_CUDA) {
KernelScope ks;
auto half = ToDtype<at::Half>();
Placeholder a("a", half, {4});
Tensor* b = Compute("b", {{4, "n"}}, [&](const VarHandle& i) {
return Cast::make(kFloat, a.load(i));
});
LoopNest l({b});
l.prepareForCodegen();
Stmt* s = l.root_stmt();
CudaCodeGen cg(s, {a, b});
std::vector<at::Half> aData(4, 2.0f);
std::vector<float> bData(4, 0.0f);
at::Half* aDev = nullptr;
float* bDev = nullptr;
auto aSize = aData.size() * sizeof(aData[0]);
auto bSize = bData.size() * sizeof(bData[0]);
cudaMalloc(&aDev, aSize);
cudaMalloc(&bDev, bSize);
cudaMemcpy(aDev, aData.data(), aSize, cudaMemcpyHostToDevice);
cudaMemcpy(bDev, bData.data(), bSize, cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
cg.call({aDev, bDev});
cudaDeviceSynchronize();
cudaMemcpy(aData.data(), aDev, aSize, cudaMemcpyDeviceToHost);
cudaMemcpy(bData.data(), bDev, bSize, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
assertAllEqual(bData, 2.0f);
cudaFree(aDev);
cudaFree(bDev);
}
TEST(Cuda, DynamicShape2D_CUDA) {
KernelScope kernel_scope;
auto testWithSize = [](int32_t M, int32_t N) {
VarHandle m("m", kInt);
VarHandle n("n", kInt);
Placeholder a(BufHandle("a", {m, n}, kFloat));
Placeholder b(BufHandle("b", {m, n}, kFloat));
Tensor* c = Compute(
"c", {{m, "m"}, {n, "n"}}, [&](const VarHandle& i, const VarHandle& j) {
return a.load(i, j) + b.load(i, j);
});
LoopNest l({c});
l.prepareForCodegen();
Stmt* s = l.root_stmt();
CudaCodeGen cg(s, {a, b, c, m, n});
std::vector<float> aData(M * N, 1.0f);
std::vector<float> bData(M * N, 2.0f);
std::vector<float> cData(M * N, 0.0f);
float* aDev = nullptr;
float* bDev = nullptr;
float* cDev = nullptr;
cudaMalloc(&aDev, aData.size() * sizeof(aData[0]));
cudaMalloc(&bDev, bData.size() * sizeof(bData[0]));
cudaMalloc(&cDev, cData.size() * sizeof(cData[0]));
cudaMemcpy(
aDev,
aData.data(),
aData.size() * sizeof(aData[0]),
cudaMemcpyHostToDevice);
cudaMemcpy(
bDev,
bData.data(),
bData.size() * sizeof(bData[0]),
cudaMemcpyHostToDevice);
cudaMemcpy(
cDev,
cData.data(),
cData.size() * sizeof(cData[0]),
cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
cg.call({aDev, bDev, cDev, M, N});
cudaDeviceSynchronize();
cudaMemcpy(
cData.data(),
cDev,
cData.size() * sizeof(cData[0]),
cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(cData, std::vector<float>(M * N, 3.0f), 1e-7);
cudaFree(aDev);
cudaFree(bDev);
cudaFree(cDev);
};
testWithSize(32, 32);
testWithSize(1, 16);
testWithSize(27, 13);
}
TEST(Cuda, TestRand01_CUDA) {
KernelScope kernel_scope;
const int num_iter = 3;
const int block_count = 16;
const int block_size = 128;
Tensor* c = Compute(
"c",
{
{num_iter, "n"},
{block_count, "b_id"},
{block_size, "t_id"},
},
[&](const VarHandle& n, const VarHandle& b_id, const VarHandle& t_id) {
return Intrinsics::make(IntrinsicsOp::kRand, kFloat);
});
LoopNest l({c});
std::vector<For*> loops = l.getLoopStmtsFor(c);
l.setGPUBlockIndex(loops[1], 0);
l.setGPUThreadIndex(loops[2], 0);
l.prepareForCodegen();
Stmt* stmt = l.root_stmt();
CudaCodeGen cuda_cg(stmt, c);
const int N = block_count * block_size * num_iter;
PaddedBuffer<float> c_v(N);
// TODO: move gpu support into PaddedBuffer
float* c_dev = nullptr;
cudaMalloc(&c_dev, N * sizeof(float));
cudaDeviceSynchronize();
cuda_cg(c_dev);
cudaDeviceSynchronize();
cudaMemcpy(c_v.data(), c_dev, N * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
float sum1 = 0;
float sum2 = 0;
float sum3 = 0;
for (int i = 0; i < N; i++) {
float v = c_v.data()[i];
sum1 += v;
sum2 += v * v;
sum3 += v * v * v;
ASSERT_TRUE(v >= 0 && v < 1);
}
sum1 /= N;
sum2 /= N;
sum3 /= N;
float sum1_mean = 1.f / 2;
float sum2_mean = 1.f / 3;
float sum3_mean = 1.f / 4;
ASSERT_NEAR(sum1, sum1_mean, 2e-2);
ASSERT_NEAR(sum2, sum2_mean, 2e-2);
ASSERT_NEAR(sum3, sum3_mean, 2e-2);
cudaFree(c_dev);
}
TEST(Cuda, DynamicShapeSplit_CUDA) {
KernelScope ks;
constexpr int N = 4096;
VarHandle n("n", kInt);
Placeholder a(BufHandle("a", {n}, kFloat));
Tensor* b = Compute(
"b", {{n, "n"}}, [&](const VarHandle& i) { return a.load(i) * 2.0f; });
LoopNest l({b});
For* outer;
For* inner;
std::vector<For*> loops = l.getLoopStmtsFor(b);
l.splitWithMask(loops[0], 1024, &outer, &inner);
l.setGPUBlockIndex(outer, 0);
l.setGPUThreadIndex(inner, 0);
Stmt* s = l.root_stmt();
CudaCodeGen cg(s, {a, b, n});
std::vector<float> aData(N, 1.0f);
std::vector<float> bData(N, 1.0f);
float* aDev = nullptr;
float* bDev = nullptr;
cudaMalloc(&aDev, aData.size() * sizeof(aData[0]));
cudaMalloc(&bDev, bData.size() * sizeof(bData[0]));
cudaMemcpy(
aDev,
aData.data(),
aData.size() * sizeof(aData[0]),
cudaMemcpyHostToDevice);
cudaMemcpy(
bDev,
bData.data(),
bData.size() * sizeof(aData[0]),
cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
cg.call({aDev, bDev, N});
cudaDeviceSynchronize();
cudaMemcpy(
bData.data(),
bDev,
bData.size() * sizeof(aData[0]),
cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(bData, std::vector<float>(N, 2.0f), 1e-7);
cudaFree(aDev);
cudaFree(bDev);
}
TEST(Cuda, OneBlockOneThreadGlobalReduce1_CUDA) {
const static int N = 1024;
KernelScope kernel_scope;
Placeholder data_buf("data", kFloat, {N});
Placeholder output_buf("output", kFloat, {1});
// The test adds the following code for trivial reduction:
// for (int bidx = 0; bidx < 1; bidx++) { // blockIdx.x
// for (int tidx = 0; tidx < 1; tidx++) { // threadIdx.x
// output[0] = 0.f;
// for (int i1 = 0; i1 < 1024; i1++) {
// output[0] = output[0] + data[i1];
// }
// }
// }
Store* init_store = output_buf.store({0}, 0.f);
VarHandle i1("i1", kInt);
ExprHandle load_data = Load::make(BufHandle(data_buf.data()), {i1}, 1);
ExprHandle load_output = Load::make(BufHandle(output_buf.data()), {0}, 1);
ExprHandle add_value = load_output + load_data;
Store* store_output = output_buf.store({0}, add_value);
For* for_output = For::make(i1, 0, N, store_output);
Stmt* reduce_block = Block::make({init_store, for_output});
VarHandle thread_idx("tidx", kInt);
LoopOptions thread_idx_options;
thread_idx_options.set_gpu_thread_index(0);
For* thread_idx_loop =
For::make(thread_idx, 0, 1, reduce_block, thread_idx_options);
VarHandle block_idx("bidx", kInt);
LoopOptions block_idx_options;
block_idx_options.set_gpu_block_index(0);
For* block_idx_loop =
For::make(block_idx, 0, 1, thread_idx_loop, block_idx_options);
CudaCodeGen cuda_cg(block_idx_loop, data_buf, output_buf);
PaddedBuffer<float> data_v(N);
PaddedBuffer<float> output_v(1, "output_v");
PaddedBuffer<float> output_ref(1, "output_ref");
output_ref(0) = 0;
for (int i = 0; i < N; i++) {
data_v(i) = i;
output_ref(0) += data_v(i);
}
float* data_dev = nullptr;
cudaMalloc(&data_dev, N * sizeof(float));
cudaMemcpy(
data_dev, data_v.data(), N * sizeof(float), cudaMemcpyHostToDevice);
float* output_dev = nullptr;
cudaMalloc(&output_dev, 1 * sizeof(float));
cudaDeviceSynchronize();
cuda_cg(data_dev, output_dev);
cudaDeviceSynchronize();
cudaMemcpy(
output_v.data(), output_dev, 1 * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(output_v, output_ref, 1e-5);
cudaFree(data_dev);
cudaFree(output_dev);
}
TEST(Cuda, OneBlockMultiThreadGlobalReduce1_CUDA) {
const static int N = 1024;
KernelScope kernel_scope;
// This test does the following reduction:
// clang-format off
// for b in 0..1 // block-idx
// for t in 0..1024: // thread-idx
// if t < 1:
// b[0] = 0
// // implied sync_threads
// for t in 0..1024: // thread-idx
// b[0] = b[0] + a[t] // implied atomic
// clang-format on
Placeholder a_buf("a", kFloat, {N});
Placeholder b_buf("b", kFloat, {1});
Store* init_store = b_buf.store({0}, 0.f);
VarHandle t("t", kInt);
VarHandle b("b", kInt);
// for t in 0..1024: // thread-idx
// if t < 1:
// b[0] = 0
ExprHandle cond_t_lt_1 =
CompareSelect::make(t, 1, CompareSelectOperation::kLT);
Cond* masked_init_b = Cond::make(cond_t_lt_1, init_store, nullptr);
LoopOptions thread_idx_options;
thread_idx_options.set_gpu_thread_index(0);
For* for_init = For::make(t, 0, N, masked_init_b, thread_idx_options);
// for t in 0..1024: // thread-idx
// b[0] = b[0] + a[t] // implied atomic
ExprHandle load_a = Load::make(BufHandle(a_buf.data()), {t}, 1);
ExprHandle load_b = Load::make(BufHandle(b_buf.data()), {0}, 1);
ExprHandle add_value = load_b + load_a;
Store* store_b = b_buf.store({0}, add_value);
For* for_b = For::make(t, 0, N, store_b, thread_idx_options);
Stmt* reduce_block = Block::make({for_init, for_b});
VarHandle block_idx("bidx", kInt);
LoopOptions block_idx_options;
block_idx_options.set_gpu_block_index(0);
For* block_idx_loop =
For::make(block_idx, 0, 1, reduce_block, block_idx_options);
CudaCodeGen cuda_cg(block_idx_loop, a_buf, b_buf);
PaddedBuffer<float> a_v(N);
PaddedBuffer<float> b_v(1, "b_v");
PaddedBuffer<float> b_ref(1, "b_ref");
b_ref(0) = 0;
for (int i = 0; i < N; i++) {
a_v(i) = i;
b_ref(0) += a_v(i);
}
float* a_dev = nullptr;
cudaMalloc(&a_dev, N * sizeof(float));
cudaMemcpy(a_dev, a_v.data(), N * sizeof(float), cudaMemcpyHostToDevice);
float* b_dev = nullptr;
cudaMalloc(&b_dev, 1 * sizeof(float));
cudaDeviceSynchronize();
cuda_cg(a_dev, b_dev);
cudaDeviceSynchronize();
cudaMemcpy(b_v.data(), b_dev, 1 * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(b_v, b_ref, 1e-5);
cudaFree(a_dev);
cudaFree(b_dev);
}
TEST(Cuda, NoThreadIdxWrite_1_CUDA) {
KernelScope kernel_scope;
// This test does the following reduction:
//
// for k in 0..1: // block-idx
// a[0] = 0
// for n in 0..2:
// a[0] = a[0] + n
// for m in 0..1024: // thread-idx
// b[m] = m
// a[1] = 1
// for l in 0..2:
// a[1] = a[1] + n
//
// note that the statements not covered by thread-idx are supposed to be
// covered by its own thread-idx
const static int N = 1024;
Placeholder a_buf("a", kFloat, {2});
Placeholder b_buf("b", kFloat, {N});
VarHandle k("k", kInt);
VarHandle l("l", kInt);
VarHandle m("m", kInt);
VarHandle n("n", kInt);
// a[0] = 0
// for n in 0..2:
// a[0] = a[0] + n
Store* store_a0_0 = a_buf.store({0}, 0.f);
ExprHandle load_a0 = Load::make(BufHandle(a_buf.data()), {0}, 1);
ExprHandle v1 = load_a0 + n;
Store* store_a0_v1 = a_buf.store({0}, v1);
For* loop_a_0 = For::make(n, 0, 2, store_a0_v1);
// for m in 0..1024: // thread-idx
// b[m] = m
Store* store_bm_m = b_buf.store({m}, m + 0.f);
LoopOptions thread_idx_options;
thread_idx_options.set_gpu_thread_index(0);
For* loop_b_1 = For::make(m, 0, N, store_bm_m, thread_idx_options);
// a[1] = 1
// for l in 0..2:
// a[1] = a[1] + l
Store* store_a1_1 = a_buf.store({1}, 1.f);
ExprHandle load_a1 = a_buf.load(1);
ExprHandle v2 = load_a1 + l;
Store* store_a1_v2 = a_buf.store({1}, v2);
For* loop_a_1 = For::make(l, 0, 2, store_a1_v2);
Stmt* reduce_block =
Block::make({store_a0_0, loop_a_0, loop_b_1, store_a1_1, loop_a_1});
VarHandle block_idx("bidx", kInt);
LoopOptions block_idx_options;
block_idx_options.set_gpu_block_index(0);
For* block_idx_loop =
For::make(block_idx, 0, 1, reduce_block, block_idx_options);
CudaCodeGen cuda_cg(block_idx_loop, a_buf, b_buf);
PaddedBuffer<float> a_v(2);
PaddedBuffer<float> b_v(N, "b_v");
PaddedBuffer<float> a_ref(2, "a_ref");
PaddedBuffer<float> b_ref(N, "b_ref");
a_ref(0) = 0;
for (int i = 0; i < 2; i++) {
a_ref(0) += i;
}
a_ref(1) = a_ref(0) + 1;
for (int i = 0; i < N; i++) {
b_ref(i) = i;
}
// TODO: add check of the generated code.
float* a_dev = nullptr;
cudaMalloc(&a_dev, 2 * sizeof(float));
float* b_dev = nullptr;
cudaMalloc(&b_dev, N * sizeof(float));
cudaDeviceSynchronize();
cuda_cg(a_dev, b_dev);
cudaDeviceSynchronize();
cudaMemcpy(a_v.data(), a_dev, 2 * sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(b_v.data(), b_dev, N * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(a_v, a_ref, 1e-5);
ExpectAllNear(b_v, b_ref, 1e-5);
cudaFree(a_dev);
cudaFree(b_dev);
}
TEST(Cuda, SharedMemReduce_1_CUDA) {
// FIXME: this test is flaky in CI.
KernelScope kernel_scope;
// This test does the following:
// for k in 0..1: // block-idx
// alloc(c, 64)
// for n in 0..64: // thread-idx
// c(n) = 0
// for m in 0..128:
// for n in 0..64: // thread_idx
// c(n) = c(n) + a(k, m, n)
// b(k) = 0
// for n in 0..64: // thread_idx
// b(k) = b(k) + c(n)
// free(c)
const int M = 128;
const int N = 64;
const int kTotalSize = M * N;
LoopOptions thread_idx_opt;
thread_idx_opt.set_gpu_thread_index(0);
LoopOptions block_idx_opt;
block_idx_opt.set_gpu_block_index(0);
Placeholder a("a", kFloat, {1, M, N});
Placeholder b("b", kFloat, {1});
VarHandle k("k", kInt);
VarHandle m("m", kInt);
VarHandle n("n", kInt);
std::vector<Stmt*> block;
VarHandle c_var("c", kHandle);
std::vector<const Expr*> dims;
dims.push_back(ExprHandle(N).node());
BufHandle c{new Buf(c_var.node(), dims, kFloat)};
{
// alloc(c, 64);
Allocate* alloc = Allocate::make(c_var, kFloat, {N});
block.push_back(alloc);
}
{
// for n in 0..64: // thread-idx
// c(n) = 0
Store* store_cn_0 = Store::make(c, {n}, 0.f, 1);
For* loop_n1 = For::make(n, 0, N, store_cn_0, thread_idx_opt);
block.push_back(loop_n1);
}
{
// for m in 0..128:
// for n in 0..64: // thread_idx
// c(n) = c(n) + a(k, m, n)
ExprHandle load_cn = Load::make(kFloat, c, {n}, 1);
ExprHandle a_kmn =
Load::make(BufHandle(a.data()), {k * (M * N) + m * N + n}, 1);
ExprHandle v_add = load_cn + a_kmn;
Store* store_cn_v = Store::make(c, {n}, v_add);
For* loop_n2 = For::make(n, 0, N, store_cn_v, thread_idx_opt);
For* loop_m1 = For::make(m, 0, M, loop_n2);
block.push_back(loop_m1);
}
{
// b(k) = 0
// for n in 0..64: // thread_idx
// b(k) = b(k) + c(n)
Store* store_bk_0 = b.store({k}, 0.f);
block.push_back(store_bk_0);
ExprHandle load_bk = b.load(k);
ExprHandle load_cn = Load::make(kFloat, c, {n}, 1);
ExprHandle v_add = load_bk + load_cn;
Store* store_bk = b.store({k}, v_add);
For* loop_n3 = For::make(n, 0, N, store_bk, thread_idx_opt);
block.push_back(loop_n3);
}
{
// free(c)
Free* free_stmt = Free::make(c_var);
block.push_back(free_stmt);
}
Block* reduce_body = Block::make(block);
For* loop_k1 = For::make(k, 0, 1, reduce_body, block_idx_opt);
// TODO: check the generated code for correctness.
CudaCodeGen cuda_cg(loop_k1, a, b);
std::ostringstream oss;
oss << *cuda_cg.stmt();
// Check the c write is not masked, but the d write is.
const std::string& verification_pattern =
R"IR(
# CHECK: c_1 = 0
# CHECK: for (int m = 0; m < 128
# CHECK: c_1 = c_1 +
# CHECK: __syncthreads();
# CHECK: if (threadIdx.x<1
# CHECK: b[blockIdx.x] =
# CHECK: __syncthreads();
# CHECK: atomicAdd(&b[blockIdx.x], c_1)
)IR";
torch::jit::testing::FileCheck().run(verification_pattern, oss.str());
PaddedBuffer<float> a_v(1, M, N, "a_v");
PaddedBuffer<float> b_v(1, "b_v");
PaddedBuffer<float> b_ref(1, "b_ref");
b_ref(0) = 0;
for (int i = 0; i < M; i++) {
for (int j = 0; j < N; j++) {
int v = i + j;
a_v(0, i, j) = v;
b_ref(0) += v;
}
}
float* a_dev = nullptr;
cudaMalloc(&a_dev, kTotalSize * sizeof(float));
cudaMemcpy(
a_dev, a_v.data(), kTotalSize * sizeof(float), cudaMemcpyHostToDevice);
float* b_dev = nullptr;
cudaMalloc(&b_dev, 1 * sizeof(float));
cudaDeviceSynchronize();
cuda_cg(a_dev, b_dev);
cudaDeviceSynchronize();
cudaMemcpy(b_v.data(), b_dev, 1 * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(b_v, b_ref, 1e-5);
cudaFree(a_dev);
cudaFree(b_dev);
}
TEST(Cuda, LocalMemReduce_1_CUDA) {
KernelScope kernel_scope;
// This test does the following:
// for k in 0..1: // block-idx
// b(k) = 0
// for n in 0..64: // thread-idx
// alloc(c, 1)
// c(0) = 0
// for m in 0..128:
// c(0) = c(0) + a(k, m, n)
// b(k) = b(k) + c(0)
// free(c)
const int M = 128;
const int N = 64;
const int kTotalSize = M * N;
LoopOptions thread_idx_opt;
thread_idx_opt.set_gpu_thread_index(0);
LoopOptions block_idx_opt;
block_idx_opt.set_gpu_block_index(0);
Placeholder a("a", kFloat, {1, M, N});
Placeholder b("b", kFloat, {1});
VarHandle k("k", kInt);
VarHandle m("m", kInt);
VarHandle n("n", kInt);
VarHandle c_var("c", kHandle);
std::vector<const Expr*> dims;
dims.push_back(ExprHandle(N).node());
BufHandle c{new Buf(c_var.node(), dims, kFloat)};
std::vector<Stmt*> block_k;
{
// b(k) = 0
Store* store_bk_0 = b.store({k}, 0.f);
block_k.push_back(store_bk_0);
}
std::vector<Stmt*> block_n;
{
// alloc(c, 1);
Allocate* alloc = Allocate::make(c_var, kFloat, {1});
block_n.push_back(alloc);
}
{
// c(0) = 0
Store* store_c0_0 = Store::make(c, {0}, 0.f, 1);
block_n.push_back(store_c0_0);
}
{
// for m in 0..128:
// c(0) = c(0) + a(k, m, n)
ExprHandle load_c0 = Load::make(kFloat, c, {0}, 1);
ExprHandle a_kmn = a.load(k * (M * N) + m * N + n);
ExprHandle v_add = load_c0 + a_kmn;
Store* store_c0_v = Store::make(c, {0}, v_add);
For* loop_m = For::make(m, 0, M, store_c0_v);
block_n.push_back(loop_m);
}
{
// b(k) = b(k) + c(0)
ExprHandle load_bk = b.load(k);
ExprHandle load_c0 = Load::make(kFloat, c, {0}, 1);
ExprHandle v_add = load_bk + load_c0;
Store* store_bk = b.store({k}, v_add);
block_n.push_back(store_bk);
}
{
// free(c)
Free* free_stmt = Free::make(c_var);
block_n.push_back(free_stmt);
}
{
Block* block_n_stmt = Block::make(block_n);
For* for_n = For::make(n, 0, N, block_n_stmt, thread_idx_opt);
block_k.push_back(for_n);
}
Block* block_k_stmt = Block::make(block_k);
For* loop_k = For::make(k, 0, 1, block_k_stmt, block_idx_opt);
CudaCodeGen cuda_cg(loop_k, a, b);
PaddedBuffer<float> a_v(1, M, N, "a_v");
PaddedBuffer<float> b_v(1, "b_v");
PaddedBuffer<float> b_ref(1, "b_ref");
b_ref(0) = 0;
for (int i = 0; i < M; i++) {
for (int j = 0; j < N; j++) {
int v = i + j;
a_v(0, i, j) = v;
b_ref(0) += v;
}
}
float* a_dev = nullptr;
cudaMalloc(&a_dev, kTotalSize * sizeof(float));
cudaMemcpy(
a_dev, a_v.data(), kTotalSize * sizeof(float), cudaMemcpyHostToDevice);
float* b_dev = nullptr;
cudaMalloc(&b_dev, 1 * sizeof(float));
cudaDeviceSynchronize();
cuda_cg(a_dev, b_dev);
cudaDeviceSynchronize();
cudaMemcpy(b_v.data(), b_dev, 1 * sizeof(float), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
ExpectAllNear(b_v, b_ref, 1e-5);
cudaFree(a_dev);
cudaFree(b_dev);
}
TEST(Cuda, HalfSupport_CUDA) {
KernelScope ks;
auto half = ToDtype<at::Half>();
Placeholder a("a", half, {4});
Tensor* b = Compute("b", {{4, "n"}}, [&](const VarHandle& i) {
return Cast::make(half, ExprHandle(2.0f) * a.load(i));
});
Tensor* c = Compute("c", {{4, "n"}}, [&](const VarHandle& i) {
return Cast::make(kFloat, Cast::make(half, ExprHandle(42)) + b->call(i));
});
Tensor* d = Compute("d", {{4, "n"}}, [&](const VarHandle& i) {
return Cast::make(half, c->call(i));
});
LoopNest l({b, c, d});
l.prepareForCodegen();
Stmt* s = l.root_stmt();
CudaCodeGen cg(s, {a, b, c, d});
std::vector<at::Half> aData(4, 2.0f);
std::vector<float> cData(4, 0.0f);
std::vector<at::Half> dData(4, 0.0f);
at::Half* aDev = nullptr;
at::Half* bDev = nullptr;
at::Half* cDev = nullptr;
at::Half* dDev = nullptr;
auto aSize = aData.size() * sizeof(aData[0]);
auto bSize = aData.size() * sizeof(aData[0]);
auto cSize = cData.size() * sizeof(float);
auto dSize = dData.size() * sizeof(dData[0]);
cudaMalloc(&aDev, aSize);
cudaMalloc(&bDev, bSize);
cudaMalloc(&cDev, cSize);
cudaMalloc(&dDev, dSize);
cudaMemcpy(aDev, aData.data(), aSize, cudaMemcpyHostToDevice);
cudaMemcpy(cDev, cData.data(), cSize, cudaMemcpyHostToDevice);
cudaMemcpy(dDev, dData.data(), dSize, cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
cg.call({aDev, bDev, cDev, dDev});
cudaDeviceSynchronize();
cudaMemcpy(aData.data(), aDev, aSize, cudaMemcpyDeviceToHost);
cudaMemcpy(cData.data(), cDev, cSize, cudaMemcpyDeviceToHost);
cudaMemcpy(dData.data(), dDev, dSize, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
assertAllEqual(cData, 46.0f);
cudaFree(aDev);
cudaFree(bDev);
cudaFree(cDev);
cudaFree(dDev);
}
TEST(Cuda, HalfPropagation_CUDA) {
KernelScope kernel_scope;
auto half = ToDtype<at::Half>();
Placeholder a("a", half, {4});
Tensor* relu = Compute("relu", {{4, "n"}}, [&](const VarHandle& i) {
return Max::make(a.load(i), ExprHandle(new HalfImm(0)), true);
});
LoopNest l({relu});
l.prepareForCodegen();
Stmt* s = l.root_stmt();