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#include <ATen/mps/MPSProfiler.h>
#include <ATen/native/mps/OperationUtils.h>
#include "mps_helpers.h"
#include "mps_kernels.h"
namespace vision {
namespace ops {
namespace {
at::Tensor roi_align_forward_kernel(const at::Tensor& input,
const at::Tensor& rois,
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width,
int64_t sampling_ratio,
bool aligned) {
using namespace at::native::mps;
TORCH_CHECK(input.is_mps(), "input must be a MPS tensor");
TORCH_CHECK(rois.is_mps(), "rois must be a MPS tensor");
TORCH_CHECK(rois.size(1) == 5, "rois must have shape as Tensor[K, 5]");
at::TensorArg input_t{input, "input", 1}, rois_t{rois, "rois", 2};
at::CheckedFrom c = "roi_align_forward_kernel";
at::checkAllSameGPU(c, {input_t, rois_t});
at::checkAllSameType(c, {input_t, rois_t});
int64_t num_rois = rois.size(0);
int64_t channels = input.size(1);
int64_t height = input.size(2);
int64_t width = input.size(3);
float spatial_scale_f = static_cast<float>(spatial_scale);
at::Tensor output = at::zeros({num_rois, channels, pooled_height, pooled_width}, input.options());
int64_t output_size = num_rois * pooled_height * pooled_width * channels;
if (output.numel() == 0) {
return output;
}
auto input_ = input.contiguous();
auto rois_ = rois.contiguous();
id<MTLBuffer> inputBuffer = getMTLBufferStorage(input_);
id<MTLBuffer> roisBuffer = getMTLBufferStorage(rois_);
id<MTLBuffer> outputBuffer = getMTLBufferStorage(output);
id<MTLDevice> device = MPSDevice::getInstance()->device();
MPSStream* mpsStream = getCurrentMPSStream();
dispatch_sync(mpsStream->queue(), ^() {
@autoreleasepool {
id<MTLComputeCommandEncoder> computeEncoder = mpsStream->commandEncoder();
MTLSize threadgroupsPerGrid = MTLSizeMake(
std::min(ceil_div(static_cast<int64_t>(output_size), static_cast<int64_t>(512)), static_cast<int64_t>(4096)),
1,
1);
const std::string kernel = "roi_align_" + scalarToMetalTypeString(input.scalar_type());
id<MTLComputePipelineState> visionPSO = mps::visionPipelineState(device, kernel);
// this function call is a no-op if MPS Profiler is not enabled
getMPSProfiler().beginProfileKernel(visionPSO, kernel, {input_, rois_});
[computeEncoder setComputePipelineState:visionPSO];
// [N, C, H, W]
[computeEncoder setBuffer:inputBuffer offset:input_.storage_offset() * input_.element_size() atIndex:0];
[computeEncoder setBuffer:roisBuffer offset:rois_.storage_offset() * rois_.element_size() atIndex:1];
[computeEncoder setBuffer:outputBuffer offset:output.storage_offset() * output.element_size() atIndex:2];
[computeEncoder setBytes:&output_size length:sizeof(int64_t) atIndex:3];
[computeEncoder setBytes:&channels length:sizeof(int64_t) atIndex:4];
[computeEncoder setBytes:&height length:sizeof(int64_t) atIndex:5];
[computeEncoder setBytes:&width length:sizeof(int64_t) atIndex:6];
[computeEncoder setBytes:&pooled_height length:sizeof(int64_t) atIndex:7];
[computeEncoder setBytes:&pooled_width length:sizeof(int64_t) atIndex:8];
[computeEncoder setBytes:&sampling_ratio length:sizeof(int64_t) atIndex:9];
[computeEncoder setBytes:&aligned length:sizeof(bool) atIndex:10];
[computeEncoder setBytes:&spatial_scale_f length:sizeof(float) atIndex:11];
// A threadGroup is equivalent to a cuda's block.
NSUInteger tgSize = visionPSO.maxTotalThreadsPerThreadgroup;
if (tgSize > threadsPerBlock) {
tgSize = threadsPerBlock;
}
MTLSize threadGroupSize = MTLSizeMake(tgSize, 1, 1);
[computeEncoder dispatchThreadgroups:threadgroupsPerGrid threadsPerThreadgroup:threadGroupSize];
getMPSProfiler().endProfileKernel(visionPSO);
}
});
return output;
}
at::Tensor roi_align_backward_kernel(const at::Tensor& grad,
const at::Tensor& rois,
double spatial_scale,
int64_t pooled_height,
int64_t pooled_width,
int64_t batch_size,
int64_t channels,
int64_t height,
int64_t width,
int64_t sampling_ratio,
bool aligned) {
using namespace at::native::mps;
TORCH_CHECK(grad.is_mps(), "grad must be a MPS tensor");
TORCH_CHECK(rois.is_mps(), "rois must be a MPS tensor");
TORCH_CHECK(grad.scalar_type() != at::kHalf, "MPS does not support roi_align backward with float16 inputs.");
at::TensorArg grad_t{grad, "input", 1}, rois_t{rois, "rois", 2};
at::CheckedFrom c = "roi_align_backward_kernel";
at::checkAllSameGPU(c, {grad_t, rois_t});
at::checkAllSameType(c, {grad_t, rois_t});
float spatial_scale_f = static_cast<float>(spatial_scale);
at::Tensor grad_input = at::zeros({batch_size, channels, height, width}, grad.options());
if (grad.numel() == 0) {
return grad_input;
}
int64_t n_stride = grad.stride(0);
int64_t c_stride = grad.stride(1);
int64_t h_stride = grad.stride(2);
int64_t w_stride = grad.stride(3);
int64_t output_size = grad.numel();
at::globalContext().alertNotDeterministic("roi_align_backward_kernel");
auto rois_ = rois.contiguous();
id<MTLBuffer> inputBuffer = getMTLBufferStorage(grad);
id<MTLBuffer> roisBuffer = getMTLBufferStorage(rois_);
id<MTLBuffer> outputBuffer = getMTLBufferStorage(grad_input);
id<MTLDevice> device = MPSDevice::getInstance()->device();
MPSStream* mpsStream = getCurrentMPSStream();
dispatch_sync(mpsStream->queue(), ^() {
@autoreleasepool {
id<MTLComputeCommandEncoder> computeEncoder = mpsStream->commandEncoder();
MTLSize threadgroupsPerGrid = MTLSizeMake(
std::min(ceil_div(static_cast<int64_t>(grad.numel()), static_cast<int64_t>(512)), static_cast<int64_t>(4096)),
1,
1);
const std::string kernel = "roi_align_backward_" + scalarToMetalTypeString(grad.scalar_type());
id<MTLComputePipelineState> visionPSO = mps::visionPipelineState(device, kernel);
// this function call is a no-op if MPS Profiler is not enabled
getMPSProfiler().beginProfileKernel(visionPSO, kernel, {grad, rois_});
[computeEncoder setComputePipelineState:visionPSO];
// [N, C, H, W]
[computeEncoder setBuffer:inputBuffer offset:grad.storage_offset() * grad.element_size() atIndex:0];
[computeEncoder setBuffer:roisBuffer offset:rois_.storage_offset() * rois_.element_size() atIndex:1];
[computeEncoder setBuffer:outputBuffer offset:grad_input.storage_offset() * grad_input.element_size() atIndex:2];
[computeEncoder setBytes:&output_size length:sizeof(int64_t) atIndex:3];
[computeEncoder setBytes:&channels length:sizeof(int64_t) atIndex:4];
[computeEncoder setBytes:&height length:sizeof(int64_t) atIndex:5];
[computeEncoder setBytes:&width length:sizeof(int64_t) atIndex:6];
[computeEncoder setBytes:&pooled_height length:sizeof(int64_t) atIndex:7];
[computeEncoder setBytes:&pooled_width length:sizeof(int64_t) atIndex:8];
[computeEncoder setBytes:&sampling_ratio length:sizeof(int64_t) atIndex:9];
[computeEncoder setBytes:&aligned length:sizeof(bool) atIndex:10];
[computeEncoder setBytes:&spatial_scale_f length:sizeof(float) atIndex:11];
[computeEncoder setBytes:&n_stride length:sizeof(int64_t) atIndex:12];
[computeEncoder setBytes:&c_stride length:sizeof(int64_t) atIndex:13];
[computeEncoder setBytes:&h_stride length:sizeof(int64_t) atIndex:14];
[computeEncoder setBytes:&w_stride length:sizeof(int64_t) atIndex:15];
// A threadGroup is equivalent to a cuda's block.
NSUInteger tgSize = visionPSO.maxTotalThreadsPerThreadgroup;
if (tgSize > threadsPerBlock) {
tgSize = threadsPerBlock;
}
MTLSize threadGroupSize = MTLSizeMake(tgSize, 1, 1);
[computeEncoder dispatchThreadgroups:threadgroupsPerGrid threadsPerThreadgroup:threadGroupSize];
getMPSProfiler().endProfileKernel(visionPSO);
}
});
return grad_input;
}
} // namespace
TORCH_LIBRARY_IMPL(torchvision, MPS, m) {
m.impl(TORCH_SELECTIVE_NAME("torchvision::roi_align"), TORCH_FN(roi_align_forward_kernel));
m.impl(TORCH_SELECTIVE_NAME("torchvision::_roi_align_backward"), TORCH_FN(roi_align_backward_kernel));
}
} // namespace ops
} // namespace vision
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