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Conv.cpp
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Conv.cpp
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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/Config.h>
#include <torch/library.h>
#include <ATen/core/Tensor.h>
#include <ATen/native/ConvUtils.h>
#include <ATen/native/utils/ParamUtils.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/NativeFunctions.h>
#include <ATen/Functions.h>
#else
#include <ATen/ops/_add_relu_native.h>
#include <ATen/ops/_to_dense_native.h>
#include <ATen/ops/convolution.h>
#include <ATen/ops/empty.h>
#include <ATen/ops/empty_like.h>
#include <ATen/ops/mkldnn_convolution_native.h>
#endif
#if !AT_MKLDNN_ENABLED()
namespace at { namespace native {
Tensor mkldnn_convolution(
const Tensor& input, const Tensor& weight, const std::optional<Tensor>& bias_opt,
IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups) {
TORCH_CHECK(false, "mkldnn_convolution_forward: ATen not compiled with MKLDNN support");
}
REGISTER_NO_CPU_DISPATCH(mkldnn_convolution_backward_stub);
REGISTER_NO_CPU_DISPATCH(mkldnn_convolution_transpose_stub);
REGISTER_NO_CPU_DISPATCH(mkldnn_convolution_transpose_backward_stub);
}}
#else // AT_MKLDNN_ENABLED
#include <ATen/native/mkldnn/MKLDNNCommon.h>
#include <ATen/native/mkldnn/Utils.h>
#include <ATen/native/ConvUtils.h>
#include <c10/util/irange.h>
namespace at { namespace native {
// follow check rules from native/Convolution.cpp without transpose supported
static void check_shape_forward(const Tensor& input,
const Tensor& weight,
const Tensor& bias,
const IntArrayRef& padding,
const IntArrayRef& stride,
const IntArrayRef& dilation,
const int64_t groups) {
#define MKLDNN_CONV_ARG_CHECK(IT, OP) std::any_of(IT.begin(), IT.end(), [](auto x) { return x OP 0; })
auto is_padding_neg = MKLDNN_CONV_ARG_CHECK(padding, <);
auto is_stride_nonpos = MKLDNN_CONV_ARG_CHECK(stride, <=);
auto is_dilation_nonpos = MKLDNN_CONV_ARG_CHECK(dilation, <=);
#undef MKLDNN_CONV_ARG_CHECK
TORCH_CHECK(!is_padding_neg, "negative padding is not supported");
TORCH_CHECK(!is_stride_nonpos, "non-positive stride is not supported");
TORCH_CHECK(!is_dilation_nonpos, "non-positive dilation is not supported");
TORCH_CHECK(groups > 0, "non-positive groups is not supported");
int64_t k = input.ndimension();
const IntArrayRef& weight_sizes = weight.sizes();
int64_t weight_dim = weight_sizes.size();
TORCH_CHECK(weight_dim == k,
"Expected ", weight_dim, "-dimensional input for ", weight_dim,
"-dimensional weight ", weight_sizes, ", but got ", k, "-dimensional input of size ",
input.sizes(), " instead");
TORCH_CHECK(weight_sizes[0] >= groups,
"Given groups=", groups, ", expected weight to be at least ", groups,
" at dimension 0, but got weight of size ", weight_sizes, " instead");
TORCH_CHECK(weight_sizes[0] % groups == 0,
"Given groups=", groups, ", expected weight to be divisible by ",
groups, " at dimension 0, but got weight of size [", weight_sizes,
"] instead");
TORCH_CHECK(input.size(1) == (weight_sizes[1] * groups),
"Given groups=", groups, ", weight of size ", weight_sizes,
", expected input", input.sizes(), " to have ",
(weight_sizes[1] * groups), " channels, but got ", input.size(1),
" channels instead");
TORCH_CHECK(!bias.defined() || (bias.ndimension() == 1 && bias.size(0) == weight_sizes[0]),
"Given weight of size ", weight_sizes,
", expected bias to be 1-dimensional with ", weight_sizes[0], " elements",
", but got bias of size ", bias.sizes(), " instead");
std::vector<int64_t> input_shape;
std::vector<int64_t> kernel_shape;
bool kernel_size_correct = true;
for (const auto i : c10::irange(2, k)) {
input_shape.push_back(input.size(i) + 2 * padding[i-2]);
// log new kernel size considering dilation
kernel_shape.push_back(dilation[i-2] * (weight_sizes[i]-1) + 1);
if (input_shape.back() < kernel_shape.back()) {
kernel_size_correct = false;
}
}
TORCH_CHECK(input_shape.size() == kernel_shape.size(), "Inconsistent shape between Input and Kernel");
if (!kernel_size_correct) {
// If kernel size is incorrect
std::ostringstream input_ss;
std::ostringstream kernel_ss;
std::string separator = "";
for (int i = 0, len = input_shape.size(); i < len; ++i) {
input_ss << separator << input_shape[i];
kernel_ss << separator << kernel_shape[i];
separator = " x ";
}
TORCH_CHECK(false, "Calculated padded input size per channel: (", input_ss.str(), "). "
"Kernel size: (", kernel_ss.str(), "). Kernel size can't be greater than actual input size");
}
}
#define MKLDNNTensor(itensor, options) \
new_with_itensor_mkldnn( \
std::move(itensor), \
optTypeMetaToScalarType(options.dtype_opt()), \
options.device_opt())
// Note [MKLDNN Convolution Memory Formats]
// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
// MKLDNN has 3 types of memory formats in convolution:
//
// In case memory format passed from PyTorch (aka. user layout)
// differs from the internal layout which MKLDNN used, a `reorder` is needed;
// otherwise when user layout is identical to internal layout,
// MKLDNN uses a memory `view` upon an existing CPU tensor.
//
// 1. NCHW (CPU tensor, contiguous)
// input reorder: NCHW(user) -> Blocked(internal)
// weight reorder: OIHW(user) -> Blocked(internal)
// output reorder: Blocked(internal) -> NCHW(user)
//
// 2. NHWC: (CPU tensor, channels last)
// input view: NHWC(user) -> NHWC(internal)
// weight reorder: OHWI(user) -> Blocked(internal)
// output view: NHWC(internal) -> NHWC(user)
//
// 3. Blocked (MKLDNN tensor):
// By explicitly converting a tensor to mkldnn, e.g. `x.to_mkldnn()`,
// blocked format will propagate between layers. Input, output will be in blocked format.
//
// For inference case, weight can be prepacked into blocked format by
// (so as to save weight reoder overhead):
// model = torch.utils.mkldnn.to_mkldnn(model)
//
// For training case, grad_output can be CPU tensor or MKLDNN tensor,
// but weight/bias and grad_weight/grad_bias are always CPU tensor.
//
static inline at::MemoryFormat mkldnn_convolution_memory_format(int64_t dims, bool is_channels_last) {
auto memory_format = at::MemoryFormat::Contiguous;
if (is_channels_last) {
memory_format = dims == 4 ? at::MemoryFormat::ChannelsLast : at::MemoryFormat::ChannelsLast3d;
}
return memory_format;
}
static void _mkldnn_convolution_out (
const Tensor& input_t,
const Tensor& weight_t,
const Tensor& bias,
std::vector<int64_t>& output_sizes,
ideep::tensor& y,
IntArrayRef stride,
IntArrayRef dilation,
IntArrayRef padding,
int64_t groups,
bool is_channels_last,
const ideep::attr_t& op_attr) {
auto memory_format = mkldnn_convolution_memory_format(input_t.ndimension(), is_channels_last);
auto input = input_t.is_mkldnn() ? input_t : input_t.contiguous(memory_format);
auto weight = weight_t.is_mkldnn() ? weight_t : weight_t.contiguous(memory_format);
const ideep::tensor x = itensor_from_tensor(input, /*from_const_data_ptr*/true);
const ideep::tensor w = itensor_from_tensor(weight, /*from_const_data_ptr*/true);
if (bias.defined()) {
const ideep::tensor b = itensor_from_tensor(bias, /*from_const_data_ptr*/true);
ideep::convolution_forward::compute_v3(
x,
w,
b,
{output_sizes.cbegin(), output_sizes.cend()},
y,
{stride.begin(), stride.end()},
{dilation.begin(), dilation.end()},
{padding.begin(), padding.end()},
{padding.begin(), padding.end()},
groups,
is_channels_last,
op_attr);
} else {
ideep::convolution_forward::compute_v3(
x,
w,
{output_sizes.cbegin(), output_sizes.cend()},
y,
{stride.begin(), stride.end()},
{dilation.begin(), dilation.end()},
{padding.begin(), padding.end()},
{padding.begin(), padding.end()},
groups,
is_channels_last,
op_attr);
}
}
static Tensor _mkldnn_convolution(
const Tensor& input_t,
const Tensor& weight_t,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
bool use_channels_last,
c10::string_view attr = "none",
torch::List<std::optional<at::Scalar>> scalars =
torch::List<std::optional<at::Scalar>>(),
std::optional<c10::string_view> algorithm = std::nullopt) {
ideep::attr_t op_attr = ideep::attr_t();
if (attr != "none") {
auto it = fusion_unary_attr_map().find(attr);
TORCH_CHECK(
it != fusion_unary_attr_map().end(), "Fusion behavior undefined.");
op_attr = it->second(scalars, algorithm);
}
// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> bias_maybe_owned = at::borrow_from_optional_tensor(bias_opt);
const Tensor& bias = *bias_maybe_owned;
mkldnn_check_low_precision(input_t.scalar_type(), "mkldnn_convolution");
int64_t dim = input_t.ndimension() - 2;
const auto padding_expanded = expand_param_if_needed(padding, "padding", dim);
const auto stride_expanded = expand_param_if_needed(stride, "stride", dim);
const auto dilation_expanded = expand_param_if_needed(dilation, "dilation", dim);
check_shape_forward(input_t, weight_t, bias, padding_expanded, stride_expanded, dilation_expanded, groups);
auto memory_format =
mkldnn_convolution_memory_format(input_t.ndimension(), use_channels_last);
auto output_sizes = conv_output_size(input_t.sizes(), weight_t.sizes(), padding_expanded, stride_expanded, dilation_expanded);
auto output = at::empty({0}, input_t.options());
ideep::tensor y;
if (use_channels_last) {
output.resize_(output_sizes, memory_format);
y = itensor_from_tensor(output);
}
_mkldnn_convolution_out(
input_t,
weight_t,
bias,
output_sizes,
y,
stride_expanded,
dilation_expanded,
padding_expanded,
groups,
use_channels_last,
op_attr);
if (input_t.is_mkldnn()) {
return MKLDNNTensor(y, input_t.options());
} else if (!use_channels_last) {
return mkldnn_to_dense(MKLDNNTensor(y, input_t.options()));
} else {
return output;
}
}
Tensor mkldnn_convolution(
const Tensor& input_t,
const Tensor& weight_t,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups) {
bool use_channels_last = mkldnn_conv_use_channels_last(input_t, weight_t);
return _mkldnn_convolution(
input_t,
weight_t,
bias_opt,
padding,
stride,
dilation,
groups,
use_channels_last);
}
namespace{
Tensor mkldnn_convolution_pointwise(
const Tensor& input_t,
const Tensor& weight_t,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
c10::string_view attr,
torch::List<std::optional<at::Scalar>> scalars,
std::optional<c10::string_view> algorithm) {
c10::impl::ExcludeDispatchKeyGuard edkg(c10::autograd_dispatch_keyset);
bool use_channels_last =
weight_t.is_mkldnn() || mkldnn_conv_use_channels_last(input_t, weight_t);
return _mkldnn_convolution(
input_t,
weight_t,
bias_opt,
padding,
stride,
dilation,
groups,
use_channels_last,
attr,
scalars,
algorithm);
}
// Fuse convolution+binary_op+unary_op for good performance, which doing such
// operation: output=unary_op(binary_op(conv(input_t, ...), other_t, alpha)).
// The binary_attr means which binary_op is, it can be "add", or
// other binary operation. the unary_attr means which unary_op is,
// it can be "relu" or other unary operation, if it is none, meaning that
// there doesn't have a unary post op. unary_scalars and unary_algorithm
// are the parameters of the unary op, such as "hardtanh" has scalar parameters,
// "gelu" has algorithm parameters.
Tensor mkldnn_convolution_pointwise_binary(
const Tensor& input_t,
const Tensor& other_t,
const Tensor& weight_t,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
c10::string_view binary_attr,
std::optional<at::Scalar> alpha,
std::optional<c10::string_view> unary_attr,
torch::List<std::optional<at::Scalar>> unary_scalars,
std::optional<c10::string_view> unary_algorithm) {
TORCH_CHECK(
input_t.ndimension() == 4 || input_t.ndimension() == 5,
"mkldnn_convolution_pointwise_binary: currently only support 2d and 3d")
TORCH_CHECK(
!alpha.has_value() || alpha.value().to<float>() == 1.0,
"mkldnn_convolution_pointwise_binary: the alpha value should be none or 1.0");
c10::MaybeOwned<Tensor> bias_maybe_owned =
at::borrow_from_optional_tensor(bias_opt);
const Tensor& bias = *bias_maybe_owned;
// Make sure inputs have same type(device, layout, dtype), device is cpu and
// dtype is float, bfloat16 or half.
check_mkldnn_binary_fusion_inputs(input_t, other_t, weight_t, bias);
int64_t dim = input_t.ndimension() - 2;
const auto padding_expanded = expand_param_if_needed(padding, "padding", dim);
const auto stride_expanded = expand_param_if_needed(stride, "stride", dim);
const auto dilation_expanded = expand_param_if_needed(dilation, "dilation", dim);
check_shape_forward(
input_t, weight_t, bias, padding_expanded, stride_expanded, dilation_expanded, groups);
auto output_sizes = conv_output_size(
input_t.sizes(), weight_t.sizes(), padding_expanded, stride_expanded, dilation_expanded);
// TODO: support broadcast binary fusion.
TORCH_CHECK(
output_sizes == other_t.sizes(),
"Binary Fusion's inputs should have same shape");
// Only calling fusion path for channels_last path.
// TODO: OneDNN doesn't optimize well for groups > 1 case, it will be enabled
// at next OneDNN release.
bool use_channels_last =
weight_t.is_mkldnn() || mkldnn_conv_use_channels_last(input_t, weight_t);
bool can_be_fused = groups == 1 && use_channels_last;
c10::string_view unary_attr_value = "none";
ideep::algorithm unary_alg;
if (unary_attr.has_value()) {
auto it_unary = fusion_unary_alg_map().find(unary_attr.value());
// Now, we only support conv+binary+relu.
TORCH_CHECK(
it_unary != fusion_unary_alg_map().end(),
"Unary Fusion behavior undefined.");
unary_attr_value = unary_attr.value();
unary_alg = it_unary->second;
}
auto it_binary = fusion_binary_alg_map().find(binary_attr);
TORCH_CHECK(
it_binary != fusion_binary_alg_map().end(),
"Binary Fusion behavior undefined.");
c10::impl::ExcludeDispatchKeyGuard edkg(c10::autograd_dispatch_keyset);
if (can_be_fused) {
auto memory_format =
mkldnn_convolution_memory_format(input_t.ndimension(), true);
auto input = input_t.contiguous(memory_format);
auto weight =
weight_t.is_mkldnn() ? weight_t : weight_t.contiguous(memory_format);
auto other = other_t.contiguous(memory_format);
auto output = at::empty_like(other);
const ideep::tensor x = itensor_from_tensor(input);
const ideep::tensor w = itensor_from_tensor(weight);
const ideep::tensor z = itensor_from_tensor(other);
ideep::tensor y = itensor_from_tensor(output);
auto output_size = other.sizes().vec();
ideep::tag format_tag = ideep::tag::nhwc;
if (input_t.ndimension() == 5) {
format_tag = ideep::tag::ndhwc;
}
auto other_desc = ideep::tensor::desc(
output_size, get_mkldnn_dtype(weight.scalar_type()), format_tag);
ideep::attr_t op_attr;
ideep::post_ops po;
po.append_binary(it_binary->second, other_desc);
if (unary_attr_value != "none") {
po.append_eltwise(unary_alg, 0.f, 0.f);
}
op_attr.set_post_ops(po);
if (bias.defined()) {
const ideep::tensor b = itensor_from_tensor(bias);
ideep::convolution_forward::compute_binary(
x,
z,
w,
b,
output_size,
y,
stride_expanded,
dilation_expanded,
padding_expanded,
padding_expanded,
groups,
/* is_channels_last */ true,
op_attr);
} else {
ideep::convolution_forward::compute_binary(
x,
z,
w,
output_size,
y,
stride_expanded,
dilation_expanded,
padding_expanded,
padding_expanded,
groups,
/* is_channels_last */ true,
op_attr);
}
return output;
} else {
// Fallback case, if inputs are not channels last or have different dtype,
// OneDNN fusion may have performance regression.
Tensor output;
if (weight_t.is_mkldnn()) {
output = _mkldnn_convolution(
input_t, weight_t, bias, padding_expanded, stride_expanded, dilation, groups, true);
} else {
output = at::convolution(
input_t, weight_t, bias, stride_expanded, padding_expanded, dilation_expanded, false, 0, groups);
}
if (binary_attr == "add" && unary_attr_value != "none") {
output = at::native::add_relu_(output, other_t);
return output;
}
if (binary_attr == "add") {
output.add_(other_t);
} else if (binary_attr == "sub") {
output.sub_(other_t);
} else if (binary_attr == "mul") {
output.mul_(other_t);
} else {
output.div_(other_t);
}
if (unary_attr_value != "none") {
output.relu_();
}
return output;
}
}
// Fuse convolution+binary_op+unary_op for good performance, which doing
// such operation: other_t=unary_op(binary_op(conv(input_t, ...), other_t,
// alpha)). The binary_attr means which binary_op is, it can be "add", or other
// binary operation. the unary_attr means which unary_op is, it can be "relu" or
// other unary operation, if it is none, meaning that there doesn't have a unary
// post op. unary_scalars and unary_algorithm are the parameters of the unary
// op, such as "hardtanh" has scalar parameters "gelu" has algorithm parameters.
Tensor& mkldnn_convolution_pointwise_binary_(
Tensor& other_t,
const Tensor& input_t,
const Tensor& weight_t,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
c10::string_view binary_attr,
std::optional<at::Scalar> alpha,
std::optional<c10::string_view> unary_attr,
torch::List<std::optional<at::Scalar>> unary_scalars,
std::optional<c10::string_view> unary_algorithm) {
// other_t += convolution(...), other_t = unary(other_t)
TORCH_CHECK(
input_t.ndimension() == 4 || input_t.ndimension() == 5,
"mkldnn_convolution_add_: currently only support 2d and 3d")
TORCH_CHECK(
binary_attr == "add",
"mkldnn_convolution_pointwise_binary_: only support binary op fusion")
TORCH_CHECK(
!alpha.has_value() || alpha.value().to<float>() == 1.0,
"mkldnn_convolution_pointwise_binary: the alpha value for the binary op should be none(meaning 1.0) or 1.0");
TORCH_CHECK(
!unary_attr.has_value() || unary_attr.value() == "relu",
"mkldnn_convolution_pointwise_binary: only support none or relu unary op fusion after binary op");
c10::MaybeOwned<Tensor> bias_maybe_owned =
at::borrow_from_optional_tensor(bias_opt);
const Tensor& bias = *bias_maybe_owned;
// Make sure inputs have same type(device, layout, dtype), device is cpu and
// dtype is float, bfloat16 or half.
check_mkldnn_binary_fusion_inputs(input_t, other_t, weight_t, bias);
int64_t dim = input_t.ndimension() - 2;
const auto padding_expanded = expand_param_if_needed(padding, "padding", dim);
const auto stride_expanded = expand_param_if_needed(stride, "stride", dim);
const auto dilation_expanded = expand_param_if_needed(dilation, "dilation", dim);
check_shape_forward(
input_t, weight_t, bias, padding, stride, dilation, groups);
auto output_sizes = conv_output_size(
input_t.sizes(), weight_t.sizes(), padding_expanded, stride_expanded, dilation_expanded);
TORCH_CHECK(
output_sizes == other_t.sizes(),
"Add Fusion's inputs should have same shape");
// Only calling fusion path for channels_last path and the output is contiguous tensor(channels_last).
bool can_be_fused = (weight_t.is_mkldnn() ||
mkldnn_conv_use_channels_last(input_t, weight_t)) &&
(other_t.is_contiguous(at::MemoryFormat::ChannelsLast) ||
other_t.is_contiguous(at::MemoryFormat::ChannelsLast3d));
c10::impl::ExcludeDispatchKeyGuard edkg(c10::autograd_dispatch_keyset);
if (can_be_fused) {
ideep::tensor y = itensor_from_tensor(other_t);
ideep::attr_t op_attr;
if (unary_attr.has_value()) {
op_attr = ideep::attr_t::residual();
} else {
op_attr = ideep::attr_t::fuse_sum();
}
_mkldnn_convolution_out(
input_t,
weight_t,
bias,
output_sizes,
y,
stride_expanded,
dilation_expanded,
padding_expanded,
groups,
true,
op_attr);
} else {
// Fallback case, if inputs are not channels last or have different dtype,
// OneDNN fusion may have performance regression.
Tensor output;
if (weight_t.is_mkldnn()) {
output = _mkldnn_convolution(
input_t, weight_t, bias, padding_expanded, stride_expanded, dilation_expanded, groups, true);
} else {
output = at::convolution(
input_t, weight_t, bias, stride_expanded, padding_expanded, dilation_expanded, false, 0, groups);
}
if (unary_attr.has_value()) {
other_t = at::native::add_relu_(other_t, output);
} else {
other_t.add_(output);
}
}
return other_t;
}
std::vector<int64_t> _original_deconv_weight_size(
const Tensor& weight_t,
int64_t groups) {
TORCH_CHECK(weight_t.is_mkldnn() || weight_t.is_meta(), "expects weight_t to be mkldnn or meta tensor");
// The size of weight_t is the prepacked size.
// Groups > 1: [g*o, i/g, ...]
// Groups == 1: [o, i, ...]
// Returns original weight size in [i, o, ...]
auto dim = weight_t.sizes().size();
TORCH_CHECK(dim > 2);
std::vector<int64_t> weight_IOHW_sizes(dim);
if (groups > 1) {
weight_IOHW_sizes[0] = weight_t.sizes()[1] * groups;
weight_IOHW_sizes[1] = weight_t.sizes()[0] / groups;
} else {
weight_IOHW_sizes[0] = weight_t.sizes()[1];
weight_IOHW_sizes[1] = weight_t.sizes()[0];
}
for (const auto d : c10::irange(2, dim)) {
weight_IOHW_sizes[d] = weight_t.sizes()[d];
}
return weight_IOHW_sizes;
}
Tensor _mkldnn_convolution_transpose(
const Tensor& input_t,
const Tensor& weight_t,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef output_padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
bool use_channels_last,
c10::string_view attr = "none",
torch::List<std::optional<at::Scalar>> scalars =
torch::List<std::optional<at::Scalar>>(),
std::optional<c10::string_view> algorithm = std::nullopt) {
ideep::attr_t op_attr = ideep::attr_t();
if (attr != "none") {
auto it = fusion_unary_attr_map().find(attr);
TORCH_CHECK(it != fusion_unary_attr_map().end(), "Fusion behavior undefined.");
op_attr = it->second(scalars, algorithm);
}
// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> bias_maybe_owned = at::borrow_from_optional_tensor(bias_opt);
const Tensor& bias = *bias_maybe_owned;
mkldnn_check_low_precision(input_t.scalar_type(), "mkldnn_convolution_transpose");
std::vector<int64_t> weight_IOHW_sizes = weight_t.is_mkldnn() ? _original_deconv_weight_size(weight_t, groups) : weight_t.sizes().vec();
auto memory_format =
mkldnn_convolution_memory_format(input_t.ndimension(), use_channels_last);
auto input = input_t.is_mkldnn() ? input_t : input_t.contiguous(memory_format);
auto weight = weight_t.is_mkldnn() ? weight_t : weight_t.contiguous(memory_format);
int64_t dim = input.ndimension() - 2;
const auto padding_expanded = expand_param_if_needed(padding, "padding", dim);
const auto stride_expanded = expand_param_if_needed(stride, "stride", dim);
const auto dilation_expanded = expand_param_if_needed(dilation, "dilation", dim);
const auto output_padding_expanded = expand_param_if_needed(output_padding, "output_padding", dim);
auto output_sizes = conv_input_size(input.sizes(), weight_IOHW_sizes, padding_expanded, output_padding_expanded, stride_expanded, dilation_expanded, groups);
auto output = at::empty({0}, input.options());
const ideep::tensor x = itensor_from_tensor(input, /*from_const_data_ptr*/true);
ideep::tensor w = itensor_from_tensor(weight, /*from_const_data_ptr*/true);
if (!weight.is_mkldnn()) {
// mkldnn transposed convolution has weight in logical order of OIHW or OIDHW,
// while PyTorch has IOHW or IODHW, `._tranpose()` switches strides (no memory copy).
w.transpose_(0, 1);
}
ideep::tensor y;
if (use_channels_last) {
output.resize_(output_sizes, memory_format);
y = itensor_from_tensor(output);
}
if (bias.defined()) {
const ideep::tensor b = itensor_from_tensor(bias, /*from_const_data_ptr*/true);
ideep::convolution_transpose_forward::compute_v3(
x,
w,
b,
output_sizes,
y,
stride_expanded,
padding_expanded,
padding_r(padding_expanded, output_padding_expanded),
dilation.vec(),
groups,
use_channels_last,
op_attr);
} else {
ideep::convolution_transpose_forward::compute_v3(
x,
w,
output_sizes,
y,
stride_expanded,
padding_expanded,
padding_r(padding_expanded, output_padding_expanded),
dilation.vec(),
groups,
use_channels_last,
op_attr);
}
if (input.is_mkldnn()) {
return MKLDNNTensor(y, input.options());
} else if (!use_channels_last) {
return mkldnn_to_dense(MKLDNNTensor(y, input.options()));
} else {
return output;
}
}
Tensor mkldnn_convolution_transpose_pointwise(
const Tensor& input_t,
const Tensor& weight_t,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef output_padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
c10::string_view attr,
torch::List<std::optional<at::Scalar>> scalars,
std::optional<c10::string_view> algorithm) {
c10::impl::ExcludeDispatchKeyGuard edkg(c10::autograd_dispatch_keyset);
bool use_channels_last =
weight_t.is_mkldnn() || mkldnn_conv_use_channels_last(input_t, weight_t);
return _mkldnn_convolution_transpose(
input_t,
weight_t,
bias_opt,
padding,
output_padding,
stride,
dilation,
groups,
use_channels_last,
attr,
scalars,
algorithm
);
}
Tensor mkldnn_convolution_transpose_pointwise_meta(
const Tensor& input_t,
const Tensor& weight_t,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef output_padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
c10::string_view attr,
torch::List<std::optional<at::Scalar>> scalars,
std::optional<c10::string_view> algorithm) {
std::vector<int64_t> weight_IOHW_sizes = _original_deconv_weight_size(weight_t, groups);
int64_t dim = input_t.ndimension() - 2;
const auto padding_expanded = expand_param_if_needed(padding, "padding", dim);
const auto stride_expanded = expand_param_if_needed(stride, "stride", dim);
const auto dilation_expanded = expand_param_if_needed(dilation, "dilation", dim);
const auto output_padding_expanded = expand_param_if_needed(output_padding, "output_padding", dim);
auto output_sizes = conv_input_size(input_t.sizes(), weight_IOHW_sizes, padding_expanded , output_padding_expanded , stride_expanded , dilation_expanded , groups);
auto output = at::empty(output_sizes, input_t.options());
return output;
}
Tensor mkldnn_convolution_backward_input(
IntArrayRef input_size,
const Tensor& grad_output,
const Tensor& weight,
IntArrayRef padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
bool bias_defined,
bool is_channels_last) {
auto grad_input = at::empty({0}, grad_output.options());
auto grad_y = itensor_from_tensor(grad_output, /*from_const_data_ptr*/true);
auto w = itensor_view_from_dense(weight, /*from_const_data_ptr*/true);
ideep::tensor grad_x;
if (is_channels_last) {
auto memory_format = mkldnn_convolution_memory_format(grad_output.ndimension(), is_channels_last);
grad_input.resize_(input_size, memory_format);
grad_x = itensor_from_tensor(grad_input);
}
ideep::convolution_backward_data::compute_v2(
grad_y,
w,
input_size.vec(),
grad_x,
stride.vec(),
dilation.vec(),
padding.vec(),
padding.vec(),
groups,
is_channels_last);
if (grad_output.is_mkldnn()) {
return MKLDNNTensor(grad_x, grad_output.options());
} else if (!is_channels_last){
return mkldnn_to_dense(MKLDNNTensor(grad_x, grad_output.options()));
} else {
return grad_input;
}
}
std::tuple<Tensor, Tensor> mkldnn_convolution_backward_weights(
IntArrayRef weight_size,
const Tensor& grad_output,
const Tensor& input,
IntArrayRef padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
bool bias_defined,
bool is_channels_last) {
const ideep::tensor grad_y = itensor_from_tensor(grad_output, /*from_const_data_ptr*/true);
const ideep::tensor x = itensor_from_tensor(input, /*from_const_data_ptr*/true);
ideep::tensor grad_w, grad_b;
if (bias_defined) {
ideep::convolution_backward_weights::compute_v2(
x,
grad_y,
weight_size.vec(),
grad_w,
grad_b,
stride.vec(),
dilation.vec(),
padding.vec(),
padding.vec(),
groups,
is_channels_last);
} else {
ideep::convolution_backward_weights::compute_v2(
x,
grad_y,
weight_size.vec(),
grad_w,
stride.vec(),
dilation.vec(),
padding.vec(),
padding.vec(),
groups,
is_channels_last);
}
if (!is_channels_last) {
return std::make_tuple(
mkldnn_to_dense(MKLDNNTensor(grad_w, grad_output.options())),
bias_defined ? mkldnn_to_dense(MKLDNNTensor(grad_b, grad_output.options())) : Tensor());
} else {
auto memory_format = mkldnn_convolution_memory_format(grad_output.ndimension(), is_channels_last);
return std::make_tuple(
mkldnn_to_dense(MKLDNNTensor(grad_w, grad_output.options())).to(memory_format),
bias_defined ? mkldnn_to_dense(MKLDNNTensor(grad_b, grad_output.options())) : Tensor());
}
}
std::tuple<Tensor, Tensor, Tensor> mkldnn_convolution_backward(
const Tensor& input_t, const Tensor& grad_output_t, const Tensor& weight_t,
IntArrayRef padding, IntArrayRef stride, IntArrayRef dilation, int64_t groups, std::array<bool,3> output_mask)
{
bool is_channels_last = mkldnn_conv_use_channels_last(input_t, weight_t);
auto memory_format = mkldnn_convolution_memory_format(input_t.ndimension(), is_channels_last);
Tensor grad_output = grad_output_t.is_mkldnn() ? grad_output_t : grad_output_t.contiguous(memory_format);
Tensor input = input_t.is_mkldnn() ? input_t : input_t.contiguous(memory_format);
Tensor weight = weight_t.is_mkldnn() ? weight_t : weight_t.contiguous(memory_format);
int64_t dim = input.ndimension() - 2;
const auto padding_expanded = expand_param_if_needed(padding, "padding", dim);
const auto stride_expanded = expand_param_if_needed(stride, "stride", dim);
const auto dilation_expanded = expand_param_if_needed(dilation, "dilation", dim);
Tensor grad_input, grad_weight, grad_bias;
if (output_mask[0]) {
grad_input = mkldnn_convolution_backward_input(
input.sizes(), grad_output, weight, padding_expanded, stride_expanded, dilation_expanded, groups, output_mask[2], is_channels_last);
}
if (output_mask[1] || output_mask[2]) {
std::tie(grad_weight, grad_bias) = mkldnn_convolution_backward_weights(
weight.sizes(), grad_output, input, padding_expanded, stride_expanded, dilation_expanded, groups, output_mask[2], is_channels_last);
}
return std::make_tuple(grad_input, grad_weight, grad_bias);
}
}
REGISTER_ALL_CPU_DISPATCH(mkldnn_convolution_backward_stub, &mkldnn_convolution_backward);
namespace{
Tensor mkldnn_convolution_transpose(
const Tensor& input,
const Tensor& weight,
const std::optional<Tensor>& bias_opt,
IntArrayRef padding,
IntArrayRef output_padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups)
{
bool use_channels_last = mkldnn_conv_use_channels_last(input, weight);
return _mkldnn_convolution_transpose(
input,
weight,
bias_opt,
padding,
output_padding,
stride,
dilation,
groups,
use_channels_last
);
}
Tensor mkldnn_convolution_transpose_backward_input(
IntArrayRef input_size,
const Tensor& grad_output,
const Tensor& weight,
IntArrayRef padding,
IntArrayRef output_padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
bool bias_defined,
bool is_channels_last) {
auto grad_input = at::empty({0}, grad_output.options());
auto grad_y = itensor_from_tensor(grad_output, /*from_const_data_ptr*/true);
auto w = itensor_view_from_dense(weight, /*from_const_data_ptr*/true).transpose_(0, 1);
ideep::tensor grad_x;
if (is_channels_last) {
auto memory_format = mkldnn_convolution_memory_format(grad_output.ndimension(), is_channels_last);
grad_input.resize_(input_size, memory_format);
grad_x = itensor_from_tensor(grad_input);
}
ideep::convolution_transpose_backward_data::compute_v3(
grad_y,
w,
input_size.vec(),
grad_x,
stride.vec(),
padding.vec(),
padding_r(padding, output_padding),
dilation.vec(),
groups,
is_channels_last);
if (grad_output.is_mkldnn()) {
return MKLDNNTensor(grad_x, grad_output.options());
} else if (!is_channels_last){
return mkldnn_to_dense(MKLDNNTensor(grad_x, grad_output.options()));
} else {
return grad_input;
}
}
std::tuple<Tensor,Tensor> mkldnn_convolution_transpose_backward_weights(
IntArrayRef weight_size,
const Tensor& grad_output,
const Tensor& input,
IntArrayRef padding,
IntArrayRef output_padding,
IntArrayRef stride,
IntArrayRef dilation,
int64_t groups,
bool bias_defined,
bool is_channels_last) {
auto grad_y = itensor_from_tensor(grad_output, /*from_const_data_ptr*/true);
auto x = itensor_from_tensor(input, /*from_const_data_ptr*/true);
ideep::tensor grad_w, grad_b;
if (bias_defined) {
ideep::convolution_transpose_backward_weights::compute_v3(
x,
grad_y,
weight_size.vec(),
grad_w,
grad_b,
stride.vec(),
padding.vec(),
padding_r(padding, output_padding),
dilation.vec(),
groups,
is_channels_last);
} else {
ideep::convolution_transpose_backward_weights::compute_v3(
x,
grad_y,
weight_size.vec(),
grad_w,
stride.vec(),
padding.vec(),
padding_r(padding, output_padding),
dilation.vec(),
groups,