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Port deform_conv2d MPS kernel to the stable-ABI extension #9604

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1 change: 1 addition & 0 deletions setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -163,6 +163,7 @@ def get_macros_and_flags():
CSRS_DIR / "ops/nms.cpp",
CSRS_DIR / "ops/cpu/nms_kernel.cpp",
CSRS_DIR / "ops/mps/nms_kernel.mm",
CSRS_DIR / "ops/mps/deform_conv2d_kernel.mm",
CSRS_DIR / "ops/quantized/cpu/qnms_kernel.cpp",
CSRS_DIR / "io/image/common_stable.cpp",
CSRS_DIR / "io/image/cpu/encode_png.cpp",
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313 changes: 187 additions & 126 deletions torchvision/csrc/ops/mps/deform_conv2d_kernel.mm
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Original file line number Diff line number Diff line change
@@ -1,19 +1,100 @@
#include <ATen/ATen.h>
#include <ATen/mps/MPSProfiler.h>
#include <ATen/native/mps/OperationUtils.h>
#include "mps_kernels.h"
#include <torch/csrc/inductor/aoti_torch/c/shim_mps.h>
#include <torch/csrc/stable/library.h>
#include <torch/csrc/stable/ops.h>
#include <torch/csrc/stable/tensor.h>
#include <torch/headeronly/core/DeviceType.h>
#include <torch/headeronly/core/ScalarType.h>
#include <torch/headeronly/util/Exception.h>

#include <cstdint>
#include <string>

#include "../StableABICompat.h"
#include "deform_conv2d_metal_shader.h"

namespace vision {
namespace ops {

namespace {

at::Tensor deform_conv2d_forward_kernel(
const at::Tensor& input,
const at::Tensor& weight,
const at::Tensor& offset,
const at::Tensor& mask,
const at::Tensor& bias,
using torch::stable::Tensor;

AOTIMetalShaderLibraryHandle deform_conv2d_shader_library() {
static AOTIMetalShaderLibraryHandle library = []() {
AOTIMetalShaderLibraryHandle handle = nullptr;
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_create_shader_library(
deform_conv2d_metal_shader, &handle));
return handle;
}();
return library;
}

const char* metal_type_string(torch::headeronly::ScalarType scalar_type) {
if (scalar_type == torch::headeronly::ScalarType::Float) {
return "float";
}
if (scalar_type == torch::headeronly::ScalarType::Half) {
return "half";
}
return "";
}

// The im2col kernel takes its (h, w)-style pairs as Metal `int2&` params (8
// bytes). The shim only sets int64 args, so pack each pair into one int64
// (little-endian: x in the low 32 bits, y in the high 32 bits). Single `int&`
// params read the low 32 bits of the int64, and `bool&` reads the low byte.
int64_t pack_int2(int64_t x, int64_t y) {
return static_cast<int64_t>(
static_cast<uint32_t>(x) | (static_cast<uint64_t>(static_cast<uint32_t>(y)) << 32));
}

struct DeformIm2colArgs {
AtenTensorHandle input;
AtenTensorHandle offset;
AtenTensorHandle mask;
AtenTensorHandle columns;
int64_t input_size;
int64_t weight_size;
int64_t pad;
int64_t stride;
int64_t dilation;
int64_t batch;
int64_t in_channels;
int64_t n_offset_grps;
int64_t out_size;
int64_t use_mask;
uint64_t num_kernels;
};

void deform_im2col_encode(
AOTIMetalKernelFunctionHandle func,
void* user_data) {
const auto* a = static_cast<const DeformIm2colArgs*>(user_data);
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_start_encoding(func));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_tensor(func, 0, a->input));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_tensor(func, 1, a->offset));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_tensor(func, 2, a->mask));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 3, a->input_size));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 4, a->weight_size));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 5, a->pad));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 6, a->stride));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 7, a->dilation));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 8, a->batch));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 9, a->in_channels));
TORCH_ERROR_CODE_CHECK(
aoti_torch_mps_set_arg_int(func, 10, a->n_offset_grps));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 11, a->out_size));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_int(func, 12, a->use_mask));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_set_arg_tensor(func, 13, a->columns));
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_dispatch_single(func, a->num_kernels));
}

Tensor deform_conv2d_forward_kernel(
const Tensor& input,
const Tensor& weight,
const Tensor& offset,
const Tensor& mask,
const Tensor& bias,
int64_t stride_h,
int64_t stride_w,
int64_t pad_h,
Expand All @@ -23,126 +104,106 @@
int64_t n_weight_grps,
int64_t n_offset_grps,
bool use_mask) {
using namespace at::native::mps;
at::Tensor input_c = input.contiguous();
at::Tensor weight_c = weight.contiguous();
at::Tensor offset_c = offset.contiguous();
at::Tensor mask_c = mask.contiguous();
at::Tensor bias_c = bias.contiguous();

TORCH_CHECK(input_c.ndimension() == 4, "Input tensor must be 4D");
TORCH_CHECK(weight_c.ndimension() == 4, "Weight tensor must be 4D");
TORCH_CHECK(offset_c.ndimension() == 4, "Offset tensor must be 4D");
TORCH_CHECK(!use_mask || mask_c.ndimension() == 4, "Mask tensor must be 4D if use_mask is true");
TORCH_CHECK(input_c.is_mps(), "input must be a MPS tensor");
TORCH_CHECK(weight.is_mps(), "weight must be a MPS tensor");
TORCH_CHECK(offset.is_mps(), "offset must be a MPS tensor");
TORCH_CHECK(mask.is_mps(), "mask must be a MPS tensor");
TORCH_CHECK(bias.is_mps(), "bias must be a MPS tensor");

at::DeviceGuard guard(input_c.device());

uint32_t batch = input_c.size(0);
uint32_t in_channels = input_c.size(1);
uint32_t in_h = input_c.size(2);
uint32_t in_w = input_c.size(3);
uint32_t weight_h = weight_c.size(2);
uint32_t weight_w = weight_c.size(3);
uint32_t out_channels = weight_c.size(0);
uint32_t ker_h = dilation_h * (weight_h - 1) + 1;
uint32_t ker_w = dilation_w * (weight_w - 1) + 1;
uint32_t out_h = ((in_h + 2 * pad_h - ker_h) / stride_h) + 1;
uint32_t out_w = ((in_w + 2 * pad_w - ker_w) / stride_w) + 1;
uint32_t pad_h_u = static_cast<uint32_t>(pad_h);
uint32_t pad_w_u = static_cast<uint32_t>(pad_w);
uint32_t stride_h_u = static_cast<uint32_t>(stride_h);
uint32_t stride_w_u = static_cast<uint32_t>(stride_w);
uint32_t dilation_h_u = static_cast<uint32_t>(dilation_h);
uint32_t dilation_w_u = static_cast<uint32_t>(dilation_w);

TORCH_CHECK(weight_c.size(1) * n_weight_grps == in_channels,
"Input channels (", in_channels,
") must equal weight.size(1) * n_weight_grps (", weight_c.size(1), " * ", n_weight_grps, ")");
TORCH_CHECK(weight_c.size(0) % n_weight_grps == 0,
"Weight tensor's out channels (", weight_c.size(0),
") must be divisible by n_weight_grps (", n_weight_grps, ")");
TORCH_CHECK(offset_c.size(1) == n_offset_grps * 2 * weight_h * weight_w,
"Offset tensor shape[1] is invalid: got ", offset_c.size(1),
", expected ", n_offset_grps * 2 * weight_h * weight_w);
TORCH_CHECK(!use_mask || mask_c.size(1) == n_offset_grps * weight_h * weight_w,
"Mask tensor shape[1] is invalid: got ", mask_c.size(1),
", expected ", n_offset_grps * weight_h * weight_w);
TORCH_CHECK(in_channels % n_offset_grps == 0,
"Input tensor channels (", in_channels,
") must be divisible by n_offset_grps (", n_offset_grps, ")");
TORCH_CHECK(offset_c.size(0) == batch,
"Offset tensor batch size (", offset_c.size(0),
") must match input tensor batch size (", batch, ")");
TORCH_CHECK(offset_c.size(2) == out_h && offset_c.size(3) == out_w,
"Offset tensor spatial dimensions (", offset_c.size(2), ", ", offset_c.size(3),
") must match calculated output dimensions (", out_h, ", ", out_w, ")");
TORCH_CHECK(!use_mask || mask_c.size(0) == batch,
"Mask tensor batch size (", mask_c.size(0),
") must match input tensor batch size (", batch, ")");
TORCH_CHECK(!use_mask || (mask_c.size(2) == out_h && mask_c.size(3) == out_w),
"Mask tensor spatial dimensions (", mask_c.size(2), ", ", mask_c.size(3),
") must match calculated output dimensions (", out_h, ", ", out_w, ")");
TORCH_CHECK(out_h > 0 && out_w > 0,
"Calculated output size too small - out_h: ", out_h, " out_w: ", out_w);

auto columns = at::empty({in_channels * weight_h * weight_w, batch * out_h * out_w}, input_c.options());

id<MTLBuffer> inputBuffer = getMTLBufferStorage(input_c);
id<MTLBuffer> offsetBuffer = getMTLBufferStorage(offset_c);
id<MTLBuffer> maskBuffer = use_mask ? getMTLBufferStorage(mask_c) : nil;
id<MTLBuffer> outputBuffer = getMTLBufferStorage(columns);

id<MTLDevice> device = MPSDevice::getInstance()->device();
std::string kernelName = "deformable_im2col_" + scalarToMetalTypeString(input.scalar_type());
id<MTLComputePipelineState> pipelineState = mps::visionPipelineState(device, kernelName);

int num_kernels = in_channels * out_h * out_w * batch;
NSUInteger threadsPerThreadgroup = pipelineState.maxTotalThreadsPerThreadgroup;
NSUInteger threadgroups = (num_kernels + threadsPerThreadgroup - 1) / threadsPerThreadgroup;
MTLSize threadGroupSize = MTLSizeMake(threadsPerThreadgroup, 1, 1);
MTLSize threadgroupsPerGrid = MTLSizeMake(threadgroups, 1, 1);

MPSStream* mpsStream = getCurrentMPSStream();
dispatch_sync(mpsStream->queue(), ^{
@autoreleasepool {
id<MTLComputeCommandEncoder> computeEncoder = mpsStream->commandEncoder();
[computeEncoder setComputePipelineState:pipelineState];
at::native::mps::mtl_setArgs(computeEncoder, inputBuffer, offsetBuffer, maskBuffer,
std::array<uint32_t, 2>{in_h, in_w},
std::array<uint32_t, 2>{weight_h, weight_w},
std::array<uint32_t, 2>{pad_h_u, pad_w_u},
std::array<uint32_t, 2>{stride_h_u, stride_w_u},
std::array<uint32_t, 2>{dilation_h_u, dilation_w_u},
batch, in_channels, n_offset_grps,
std::array<uint32_t, 2>{out_h, out_w},
use_mask, outputBuffer);
[computeEncoder dispatchThreadgroups:threadgroupsPerGrid threadsPerThreadgroup:threadGroupSize];
}
});
int in_channels_per_grp = in_channels / n_weight_grps;
int out_channels_per_grp = out_channels / n_weight_grps;
auto weight_grouped = weight_c.view({n_weight_grps, out_channels_per_grp, in_channels_per_grp, weight_h, weight_w});
auto columns_grouped = columns.view({n_weight_grps,
(in_channels * weight_h * weight_w) / n_weight_grps,
batch * out_h * out_w});
auto weight_reshaped = weight_grouped.reshape({n_weight_grps, out_channels_per_grp, -1});
auto out_grouped = at::bmm(weight_reshaped, columns_grouped);
auto out = out_grouped.reshape({n_weight_grps * out_channels_per_grp, batch, out_h, out_w})
.transpose(0, 1);
return out + bias_c.view({1, out_channels, 1, 1});
Tensor input_c = torch::stable::contiguous(input);
Tensor weight_c = torch::stable::contiguous(weight);
Tensor offset_c = torch::stable::contiguous(offset);
Tensor mask_c = torch::stable::contiguous(mask);
Tensor bias_c = torch::stable::contiguous(bias);

STD_TORCH_CHECK(input_c.dim() == 4, "Input tensor must be 4D");
STD_TORCH_CHECK(weight_c.dim() == 4, "Weight tensor must be 4D");
STD_TORCH_CHECK(offset_c.dim() == 4, "Offset tensor must be 4D");
STD_TORCH_CHECK(
!use_mask || mask_c.dim() == 4,
"Mask tensor must be 4D if use_mask is true");
STD_TORCH_CHECK(
input_c.device().type() == torch::headeronly::DeviceType::MPS,
"input must be a MPS tensor");

int64_t batch = input_c.size(0);
int64_t in_channels = input_c.size(1);
int64_t in_h = input_c.size(2);
int64_t in_w = input_c.size(3);
int64_t weight_h = weight_c.size(2);
int64_t weight_w = weight_c.size(3);
int64_t out_channels = weight_c.size(0);
int64_t ker_h = dilation_h * (weight_h - 1) + 1;
int64_t ker_w = dilation_w * (weight_w - 1) + 1;
int64_t out_h = ((in_h + 2 * pad_h - ker_h) / stride_h) + 1;
int64_t out_w = ((in_w + 2 * pad_w - ker_w) / stride_w) + 1;

STD_TORCH_CHECK(
weight_c.size(1) * n_weight_grps == in_channels,
"Input channels must equal weight.size(1) * n_weight_grps");
STD_TORCH_CHECK(
out_channels % n_weight_grps == 0,
"Weight tensor's out channels must be divisible by n_weight_grps");
STD_TORCH_CHECK(out_h > 0 && out_w > 0, "Calculated output size too small");

Tensor columns = torch::stable::new_empty(
input_c, {in_channels * weight_h * weight_w, batch * out_h * out_w});

const std::string kernel = "deformable_im2col_" +
std::string(metal_type_string(input_c.scalar_type()));
AOTIMetalKernelFunctionHandle func = nullptr;
TORCH_ERROR_CODE_CHECK(aoti_torch_mps_get_kernel_function(
deform_conv2d_shader_library(), kernel.c_str(), &func));

DeformIm2colArgs args{
input_c.get(),
offset_c.get(),
// When use_mask is false the kernel never reads the mask buffer; bind the
// input handle as a harmless placeholder so a valid buffer is always set.
use_mask ? mask_c.get() : input_c.get(),
columns.get(),
pack_int2(in_h, in_w),
pack_int2(weight_h, weight_w),
pack_int2(pad_h, pad_w),
pack_int2(stride_h, stride_w),
pack_int2(dilation_h, dilation_w),
batch,
in_channels,
n_offset_grps,
pack_int2(out_h, out_w),
use_mask ? 1 : 0,
static_cast<uint64_t>(in_channels * out_h * out_w * batch)};
TORCH_ERROR_CODE_CHECK(
aoti_torch_mps_run_command_block(func, &deform_im2col_encode, &args));

int64_t in_channels_per_grp = in_channels / n_weight_grps;
int64_t out_channels_per_grp = out_channels / n_weight_grps;
Tensor weight_grouped = torch::stable::view(
weight_c,
{n_weight_grps,
out_channels_per_grp,
in_channels_per_grp,
weight_h,
weight_w});
Tensor columns_grouped = torch::stable::view(
columns,
{n_weight_grps,
(in_channels * weight_h * weight_w) / n_weight_grps,
batch * out_h * out_w});
Tensor weight_reshaped = torch::stable::reshape(
weight_grouped, {n_weight_grps, out_channels_per_grp, -1});
Tensor out_grouped = torch::stable::matmul(weight_reshaped, columns_grouped);
Tensor out = torch::stable::transpose(
torch::stable::reshape(
out_grouped,
{n_weight_grps * out_channels_per_grp, batch, out_h, out_w}),
0,
1);
Tensor bias_view =
torch::stable::view(bias_c, {1, out_channels, 1, 1});
// subtract(out, bias_view, alpha=-1) computes out - (-1) * bias_view, i.e.
// out + bias_view; stable ops.h ships subtract but not add.
return torch::stable::subtract(out, bias_view, /*alpha=*/-1.0);
}

} // namespace

TORCH_LIBRARY_IMPL(torchvision, MPS, m) {
m.impl(
TORCH_SELECTIVE_NAME("torchvision::deform_conv2d"),
TORCH_FN(deform_conv2d_forward_kernel));
STABLE_TORCH_LIBRARY_IMPL(torchvision, MPS, m) {
m.impl("deform_conv2d", TORCH_BOX(&deform_conv2d_forward_kernel));
}

} // namespace ops
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