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-rw-r--r--unsupported/test/cxx11_tensor_device.cpp279
1 files changed, 244 insertions, 35 deletions
diff --git a/unsupported/test/cxx11_tensor_device.cpp b/unsupported/test/cxx11_tensor_device.cpp
index caf2e9735..f331cb481 100644
--- a/unsupported/test/cxx11_tensor_device.cpp
+++ b/unsupported/test/cxx11_tensor_device.cpp
@@ -22,17 +22,43 @@ using Eigen::RowMajor;
// Context for evaluation on cpu
struct CPUContext {
- CPUContext(const Eigen::Tensor<float, 3>& in1, Eigen::Tensor<float, 3>& in2, Eigen::Tensor<float, 3>& out) : in1_(in1), in2_(in2), out_(out) { }
+ CPUContext(const Eigen::Tensor<float, 3>& in1, Eigen::Tensor<float, 3>& in2, Eigen::Tensor<float, 3>& out) : in1_(in1), in2_(in2), out_(out), kernel_1d_(2), kernel_2d_(Eigen::array<int, 2>(2,2)), kernel_3d_(Eigen::array<int, 3>(2,2,2)) {
+ kernel_1d_(0) = 3.14f;
+ kernel_1d_(1) = 2.7f;
+
+ kernel_2d_(Eigen::array<int, 2>(0,0)) = 3.14f;
+ kernel_2d_(Eigen::array<int, 2>(1,0)) = 2.7f;
+ kernel_2d_(Eigen::array<int, 2>(0,1)) = 0.2f;
+ kernel_2d_(Eigen::array<int, 2>(1,1)) = 7.0f;
+
+ kernel_3d_(Eigen::array<int, 3>(0,0,0)) = 3.14f;
+ kernel_3d_(Eigen::array<int, 3>(0,1,0)) = 2.7f;
+ kernel_3d_(Eigen::array<int, 3>(0,0,1)) = 0.2f;
+ kernel_3d_(Eigen::array<int, 3>(0,1,1)) = 7.0f;
+ kernel_3d_(Eigen::array<int, 3>(1,0,0)) = -1.0f;
+ kernel_3d_(Eigen::array<int, 3>(1,1,0)) = -0.3f;
+ kernel_3d_(Eigen::array<int, 3>(1,0,1)) = -0.7f;
+ kernel_3d_(Eigen::array<int, 3>(1,1,1)) = -0.5f;
+ }
+
+ const Eigen::DefaultDevice& device() const { return cpu_device_; }
const Eigen::Tensor<float, 3>& in1() const { return in1_; }
const Eigen::Tensor<float, 3>& in2() const { return in2_; }
- Eigen::TensorDevice<Eigen::Tensor<float, 3>, Eigen::DefaultDevice> out() { return TensorDevice<Eigen::Tensor<float, 3>, Eigen::DefaultDevice>(cpu_device_, out_); }
+ Eigen::Tensor<float, 3>& out() { return out_; }
+ const Eigen::Tensor<float, 1>& kernel1d() const { return kernel_1d_; }
+ const Eigen::Tensor<float, 2>& kernel2d() const { return kernel_2d_; }
+ const Eigen::Tensor<float, 3>& kernel3d() const { return kernel_3d_; }
private:
const Eigen::Tensor<float, 3>& in1_;
const Eigen::Tensor<float, 3>& in2_;
Eigen::Tensor<float, 3>& out_;
+ Eigen::Tensor<float, 1> kernel_1d_;
+ Eigen::Tensor<float, 2> kernel_2d_;
+ Eigen::Tensor<float, 3> kernel_3d_;
+
Eigen::DefaultDevice cpu_device_;
};
@@ -40,19 +66,45 @@ struct CPUContext {
// Context for evaluation on GPU
struct GPUContext {
GPUContext(const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1, Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2, Eigen::TensorMap<Eigen::Tensor<float, 3> >& out) : in1_(in1), in2_(in2), out_(out), gpu_device_(&stream_) {
- cudaStreamCreate(&stream_);
+ assert(cudaMalloc((void**)(&kernel_1d_), 2*sizeof(float)) == cudaSuccess);
+ float kernel_1d_val[] = {3.14f, 2.7f};
+ assert(cudaMemcpy(kernel_1d_, kernel_1d_val, 2*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess);
+
+ assert(cudaMalloc((void**)(&kernel_2d_), 4*sizeof(float)) == cudaSuccess);
+ float kernel_2d_val[] = {3.14f, 2.7f, 0.2f, 7.0f};
+ assert(cudaMemcpy(kernel_2d_, kernel_2d_val, 4*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess);
+
+ assert(cudaMalloc((void**)(&kernel_3d_), 8*sizeof(float)) == cudaSuccess);
+ float kernel_3d_val[] = {3.14f, -1.0f, 2.7f, -0.3f, 0.2f, -0.7f, 7.0f, -0.5f};
+ assert(cudaMemcpy(kernel_3d_, kernel_3d_val, 8*sizeof(float), cudaMemcpyHostToDevice) == cudaSuccess);
+
+ assert(cudaStreamCreate(&stream_) == cudaSuccess);
}
~GPUContext() {
- cudaStreamDestroy(stream_);
+ assert(cudaFree(kernel_1d_) == cudaSuccess);
+ assert(cudaFree(kernel_2d_) == cudaSuccess);
+ assert(cudaFree(kernel_3d_) == cudaSuccess);
+ assert(cudaStreamDestroy(stream_) == cudaSuccess);
}
+
+ const Eigen::GpuDevice& device() const { return gpu_device_; }
+
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1() const { return in1_; }
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2() const { return in2_; }
- Eigen::TensorDevice<Eigen::TensorMap<Eigen::Tensor<float, 3> >, Eigen::GpuDevice> out() { return TensorDevice<Eigen::TensorMap<Eigen::Tensor<float, 3> >, Eigen::GpuDevice>(gpu_device_, out_); }
+ Eigen::TensorMap<Eigen::Tensor<float, 3> >& out() { return out_; }
+ Eigen::TensorMap<Eigen::Tensor<float, 1> > kernel1d() const { return Eigen::TensorMap<Eigen::Tensor<float, 1> >(kernel_1d_, 2); }
+ Eigen::TensorMap<Eigen::Tensor<float, 2> > kernel2d() const { return Eigen::TensorMap<Eigen::Tensor<float, 2> >(kernel_2d_, Eigen::array<int, 2>(2, 2)); }
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > kernel3d() const { return Eigen::TensorMap<Eigen::Tensor<float, 3> >(kernel_3d_, Eigen::array<int, 3>(2, 2, 2)); }
private:
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in1_;
const Eigen::TensorMap<Eigen::Tensor<float, 3> >& in2_;
Eigen::TensorMap<Eigen::Tensor<float, 3> >& out_;
+
+ float* kernel_1d_;
+ float* kernel_2d_;
+ float* kernel_3d_;
+
cudaStream_t stream_;
Eigen::GpuDevice gpu_device_;
};
@@ -62,49 +114,151 @@ struct GPUContext {
template <typename Context>
static void test_contextual_eval(Context* context)
{
- context->out() = context->in1() + context->in2() * 3.14f + context->in1().constant(2.718f);
+ context->out().device(context->device()) = context->in1() + context->in2() * 3.14f + context->in1().constant(2.718f);
}
template <typename Context>
static void test_forced_contextual_eval(Context* context)
{
- context->out() = (context->in1() + context->in2()).eval() * 3.14f + context->in1().constant(2.718f);
+ context->out().device(context->device()) = (context->in1() + context->in2()).eval() * 3.14f + context->in1().constant(2.718f);
+}
+
+template <typename Context>
+static void test_contraction(Context* context)
+{
+ Eigen::array<std::pair<int, int>, 2> dims;
+ dims[0] = std::make_pair(1, 1);
+ dims[1] = std::make_pair(2, 2);
+
+ Eigen::array<int, 2> shape(40, 50*70);
+
+ Eigen::DSizes<int, 2> indices(0,0);
+ Eigen::DSizes<int, 2> sizes(40,40);
+
+ context->out().reshape(shape).slice(indices, sizes).device(context->device()) = context->in1().contract(context->in2(), dims);
+}
+
+
+template <typename Context>
+static void test_1d_convolution(Context* context)
+{
+ Eigen::DSizes<int, 3> indices(Eigen::array<int, 3>(0,0,0));
+ Eigen::DSizes<int, 3> sizes(Eigen::array<int, 3>(40,49,70));
+
+ Eigen::array<int, 1> dims(1);
+ context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel1d(), dims);
+}
+
+template <typename Context>
+static void test_2d_convolution(Context* context)
+{
+ Eigen::DSizes<int, 3> indices(Eigen::array<int, 3>(0,0,0));
+ Eigen::DSizes<int, 3> sizes(Eigen::array<int, 3>(40,49,69));
+
+ Eigen::array<int, 2> dims(1,2);
+ context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel2d(), dims);
+}
+
+template <typename Context>
+static void test_3d_convolution(Context* context)
+{
+ Eigen::DSizes<int, 3> indices(Eigen::array<int, 3>(0,0,0));
+ Eigen::DSizes<int, 3> sizes(Eigen::array<int, 3>(39,49,69));
+
+ Eigen::array<int, 3> dims(0,1,2);
+ context->out().slice(indices, sizes).device(context->device()) = context->in1().convolve(context->kernel3d(), dims);
}
+
static void test_cpu() {
- Eigen::Tensor<float, 3> in1(Eigen::array<int, 3>(2,3,7));
- Eigen::Tensor<float, 3> in2(Eigen::array<int, 3>(2,3,7));
- Eigen::Tensor<float, 3> out(Eigen::array<int, 3>(2,3,7));
+ Eigen::Tensor<float, 3> in1(Eigen::array<int, 3>(40,50,70));
+ Eigen::Tensor<float, 3> in2(Eigen::array<int, 3>(40,50,70));
+ Eigen::Tensor<float, 3> out(Eigen::array<int, 3>(40,50,70));
- in1.setRandom();
- in2.setRandom();
+ in1 = in1.random() + in1.constant(10.0f);
+ in2 = in2.random() + in2.constant(10.0f);
CPUContext context(in1, in2, out);
test_contextual_eval(&context);
- for (int i = 0; i < 2; ++i) {
- for (int j = 0; j < 3; ++j) {
- for (int k = 0; k < 7; ++k) {
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 50; ++j) {
+ for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), in1(Eigen::array<int, 3>(i,j,k)) + in2(Eigen::array<int, 3>(i,j,k)) * 3.14f + 2.718f);
}
}
}
test_forced_contextual_eval(&context);
- for (int i = 0; i < 2; ++i) {
- for (int j = 0; j < 3; ++j) {
- for (int k = 0; k < 7; ++k) {
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 50; ++j) {
+ for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), (in1(Eigen::array<int, 3>(i,j,k)) + in2(Eigen::array<int, 3>(i,j,k))) * 3.14f + 2.718f);
}
}
}
+
+ test_contraction(&context);
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 40; ++j) {
+ const float result = out(Eigen::array<int, 3>(i,j,0));
+ float expected = 0;
+ for (int k = 0; k < 50; ++k) {
+ for (int l = 0; l < 70; ++l) {
+ expected += in1(Eigen::array<int, 3>(i, k, l)) * in2(Eigen::array<int, 3>(j, k, l));
+ }
+ }
+ VERIFY_IS_APPROX(expected, result);
+ }
+ }
+
+ test_1d_convolution(&context);
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 49; ++j) {
+ for (int k = 0; k < 70; ++k) {
+ VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), (in1(Eigen::array<int, 3>(i,j,k)) * 3.14f + in1(Eigen::array<int, 3>(i,j+1,k)) * 2.7f));
+ }
+ }
+ }
+
+ test_2d_convolution(&context);
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 49; ++j) {
+ for (int k = 0; k < 69; ++k) {
+ const float result = out(Eigen::array<int, 3>(i,j,k));
+ const float expected = (in1(Eigen::array<int, 3>(i,j,k)) * 3.14f + in1(Eigen::array<int, 3>(i,j+1,k)) * 2.7f) +
+ (in1(Eigen::array<int, 3>(i,j,k+1)) * 0.2f + in1(Eigen::array<int, 3>(i,j+1,k+1)) * 7.0f);
+ if (fabs(expected) < 1e-4 && fabs(result) < 1e-4) {
+ continue;
+ }
+ VERIFY_IS_APPROX(expected, result);
+ }
+ }
+ }
+
+ test_3d_convolution(&context);
+ for (int i = 0; i < 39; ++i) {
+ for (int j = 0; j < 49; ++j) {
+ for (int k = 0; k < 69; ++k) {
+ const float result = out(Eigen::array<int, 3>(i,j,k));
+ const float expected = (in1(Eigen::array<int, 3>(i,j,k)) * 3.14f + in1(Eigen::array<int, 3>(i,j+1,k)) * 2.7f +
+ in1(Eigen::array<int, 3>(i,j,k+1)) * 0.2f + in1(Eigen::array<int, 3>(i,j+1,k+1)) * 7.0f) +
+ (in1(Eigen::array<int, 3>(i+1,j,k)) * -1.0f + in1(Eigen::array<int, 3>(i+1,j+1,k)) * -0.3f +
+ in1(Eigen::array<int, 3>(i+1,j,k+1)) * -0.7f + in1(Eigen::array<int, 3>(i+1,j+1,k+1)) * -0.5f);
+ if (fabs(expected) < 1e-4 && fabs(result) < 1e-4) {
+ continue;
+ }
+ VERIFY_IS_APPROX(expected, result);
+ }
+ }
+ }
}
static void test_gpu() {
- Eigen::Tensor<float, 3> in1(Eigen::array<int, 3>(2,3,7));
- Eigen::Tensor<float, 3> in2(Eigen::array<int, 3>(2,3,7));
- Eigen::Tensor<float, 3> out(Eigen::array<int, 3>(2,3,7));
- in1.setRandom();
- in2.setRandom();
+ Eigen::Tensor<float, 3> in1(Eigen::array<int, 3>(40,50,70));
+ Eigen::Tensor<float, 3> in2(Eigen::array<int, 3>(40,50,70));
+ Eigen::Tensor<float, 3> out(Eigen::array<int, 3>(40,50,70));
+ in1 = in1.random() + in1.constant(10.0f);
+ in2 = in2.random() + in2.constant(10.0f);
std::size_t in1_bytes = in1.size() * sizeof(float);
std::size_t in2_bytes = in2.size() * sizeof(float);
@@ -120,32 +274,87 @@ static void test_gpu() {
cudaMemcpy(d_in1, in1.data(), in1_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_in2, in2.data(), in2_bytes, cudaMemcpyHostToDevice);
- Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, Eigen::array<int, 3>(2,3,7));
- Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<int, 3>(2,3,7));
- Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<int, 3>(2,3,7));
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in1(d_in1, Eigen::array<int, 3>(40,50,70));
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_in2(d_in2, Eigen::array<int, 3>(40,50,70));
+ Eigen::TensorMap<Eigen::Tensor<float, 3> > gpu_out(d_out, Eigen::array<int, 3>(40,50,70));
GPUContext context(gpu_in1, gpu_in2, gpu_out);
test_contextual_eval(&context);
- cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost);
- for (int i = 0; i < 2; ++i) {
- for (int j = 0; j < 3; ++j) {
- for (int k = 0; k < 7; ++k) {
+ assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 50; ++j) {
+ for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), in1(Eigen::array<int, 3>(i,j,k)) + in2(Eigen::array<int, 3>(i,j,k)) * 3.14f + 2.718f);
}
}
}
test_forced_contextual_eval(&context);
- cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost);
- for (int i = 0; i < 2; ++i) {
- for (int j = 0; j < 3; ++j) {
- for (int k = 0; k < 7; ++k) {
+ assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 50; ++j) {
+ for (int k = 0; k < 70; ++k) {
VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), (in1(Eigen::array<int, 3>(i,j,k)) + in2(Eigen::array<int, 3>(i,j,k))) * 3.14f + 2.718f);
}
}
}
-}
+ test_contraction(&context);
+ assert(cudaMemcpy(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost) == cudaSuccess);
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 40; ++j) {
+ const float result = out(Eigen::array<int, 3>(i,j,0));
+ float expected = 0;
+ for (int k = 0; k < 50; ++k) {
+ for (int l = 0; l < 70; ++l) {
+ expected += in1(Eigen::array<int, 3>(i, k, l)) * in2(Eigen::array<int, 3>(j, k, l));
+ }
+ }
+ VERIFY_IS_APPROX(expected, result);
+ }
+ }
+
+ test_1d_convolution(&context);
+ assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
+ assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 49; ++j) {
+ for (int k = 0; k < 70; ++k) {
+ VERIFY_IS_APPROX(out(Eigen::array<int, 3>(i,j,k)), (in1(Eigen::array<int, 3>(i,j,k)) * 3.14f + in1(Eigen::array<int, 3>(i,j+1,k)) * 2.7f));
+ }
+ }
+ }
+
+ test_2d_convolution(&context);
+ assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
+ assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
+ for (int i = 0; i < 40; ++i) {
+ for (int j = 0; j < 49; ++j) {
+ for (int k = 0; k < 69; ++k) {
+ const float result = out(Eigen::array<int, 3>(i,j,k));
+ const float expected = (in1(Eigen::array<int, 3>(i,j,k)) * 3.14f + in1(Eigen::array<int, 3>(i,j+1,k)) * 2.7f +
+ in1(Eigen::array<int, 3>(i,j,k+1)) * 0.2f + in1(Eigen::array<int, 3>(i,j+1,k+1)) * 7.0f);
+ VERIFY_IS_APPROX(expected, result);
+ }
+ }
+ }
+
+ test_3d_convolution(&context);
+ assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, context.device().stream()) == cudaSuccess);
+ assert(cudaStreamSynchronize(context.device().stream()) == cudaSuccess);
+ for (int i = 0; i < 39; ++i) {
+ for (int j = 0; j < 49; ++j) {
+ for (int k = 0; k < 69; ++k) {
+ const float result = out(Eigen::array<int, 3>(i,j,k));
+ const float expected = (in1(Eigen::array<int, 3>(i,j,k)) * 3.14f + in1(Eigen::array<int, 3>(i,j+1,k)) * 2.7f +
+ in1(Eigen::array<int, 3>(i,j,k+1)) * 0.2f + in1(Eigen::array<int, 3>(i,j+1,k+1)) * 7.0f +
+ in1(Eigen::array<int, 3>(i+1,j,k)) * -1.0f + in1(Eigen::array<int, 3>(i+1,j+1,k)) * -0.3f +
+ in1(Eigen::array<int, 3>(i+1,j,k+1)) * -0.7f + in1(Eigen::array<int, 3>(i+1,j+1,k+1)) * -0.5f);
+ VERIFY_IS_APPROX(expected, result);
+ }
+ }
+ }
+}
void test_cxx11_tensor_device()
generated by cgit v1.2.3 (git 2.39.1) at 2024-05-25 06:02:13 +0000