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This commit is contained in:
Tobias Wood
2023-11-29 11:12:48 +00:00
parent 9ea520fc45
commit f38e16c193
534 changed files with 103368 additions and 116934 deletions
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43
unsupported/Eigen/CXX11/src/Tensor/TensorConvolutionSycl.h
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@@ -61,7 +61,7 @@ struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, Inp
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim2 boolean_check) const {
return (boolean_check[0] && boolean_check[1]);
}
void operator()(cl::sycl::nd_item<2> itemID) const {
void operator()(cl::sycl::nd_item<2> itemID) const {
auto buffer_ptr = buffer_acc;
auto kernel_ptr = kernel_filter;
// the required row to be calculated for the for each plane in shered memory
@@ -140,20 +140,20 @@ struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, Inp
const auto input_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0],
itemID.get_group(1) * itemID.get_local_range()[1]};
// fill the local memory
bool in_range_dim2 = itemID.get_global_id(2) < input_range[2];
for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) {
const size_t local_input_offset = num_input[0] * (j + plane_kernel_offset);
bool in_range_dim1 = ((j + input_offset[1]) < (input_range[1] + kernel_size[1] - 1));
bool in_range_dim1 = ((j + input_offset[1]) < (input_range[1] + kernel_size[1] - 1));
for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) {
const size_t local_index = i + local_input_offset;
const size_t tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(
i + input_offset[0], j + input_offset[1]);
local_acc[local_index] = (((i + input_offset[0]) < (input_range[0] + kernel_size[0] - 1)) &&
in_range_dim1 && in_range_dim2)
? device_evaluator.coeff(tensor_index)
: CoeffReturnType(0);
local_acc[local_index] =
(((i + input_offset[0]) < (input_range[0] + kernel_size[0] - 1)) && in_range_dim1 && in_range_dim2)
? device_evaluator.coeff(tensor_index)
: CoeffReturnType(0);
}
}
@@ -224,7 +224,7 @@ struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, Inp
const auto input_offset = cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range()};
const auto output_offset =
cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range() + itemID.get_local_id()};
cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range() + itemID.get_local_id()};
for (size_t p = 0; p < numP; p++) {
/// fill the shared memory
@@ -234,7 +234,7 @@ struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, Inp
bool cond_k_dim = (k + input_offset[2] < (input_range[2] + kernel_size[2] - 1));
for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) {
bool cond_j_dim = cond_k_dim && (j + input_offset[1] < (input_range[1] + kernel_size[1] - 1));
size_t local_index_dim1 = (num_input[0] * j) + local_index_dim2;
size_t local_index_dim1 = (num_input[0] * j) + local_index_dim2;
for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) {
bool conds = cond_j_dim && (i + input_offset[0] < (input_range[0] + kernel_size[0] - 1));
const size_t local_index = local_index_dim1 + i;
@@ -410,9 +410,11 @@ struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelAr
typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV1D>
ConvKernel;
m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
m_inputImpl, m_kernel, data, cl::sycl::nd_range<2>(global_range, local_range), local_memory_size,
indexMapper, kernel_size, cl::sycl::range<2>(input_dim[0], input_dim[1])).wait();
m_device
.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
m_inputImpl, m_kernel, data, cl::sycl::nd_range<2>(global_range, local_range), local_memory_size,
indexMapper, kernel_size, cl::sycl::range<2>(input_dim[0], input_dim[1]))
.wait();
break;
}
@@ -441,9 +443,11 @@ struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelAr
typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV2D>
ConvKernel;
m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
indexMapper, kernel_size, cl::sycl::range<3>{input_dim[0], input_dim[1], input_dim[2]}).wait();
m_device
.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
indexMapper, kernel_size, cl::sycl::range<3>{input_dim[0], input_dim[1], input_dim[2]})
.wait();
break;
}
@@ -481,9 +485,11 @@ struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelAr
typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims,
typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV3D>
ConvKernel;
m_device.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
indexMapper, kernel_size, cl::sycl::range<3>(input_dim[0], input_dim[1], input_dim[2]), numP).wait();
m_device
.template binary_kernel_launcher<CoeffReturnType, ConvKernel>(
m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size,
indexMapper, kernel_size, cl::sycl::range<3>(input_dim[0], input_dim[1], input_dim[2]), numP)
.wait();
break;
}
@@ -521,7 +527,6 @@ struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelAr
TensorOpCost(0, 0, convolve_compute_cost, vectorized, PacketSize));
}
private:
// No assignment (copies are needed by the kernels)
TensorEvaluator &operator=(const TensorEvaluator &);