std::tuple<at::Tensor,at::Tensor,at::Tensor> mkldnn_convolution_backward(
const at::Tensor& input, const at::Tensor& grad_output_t, const at::Tensor& weight,
IntList padding, IntList stride, IntList dilation, std::array<bool,3> output_mask)
{
Tensor grad_output = grad_output_t.contiguous();
Tensor grad_input, grad_weight, grad_bias;
if (output_mask[0]) {
grad_input = at::mkldnn_convolution_backward_input(
input.sizes(), grad_output, weight, padding, stride, dilation, output_mask[2]);
}
if (output_mask[1] || output_mask[2]) {
std::tie(grad_weight, grad_bias) = at::mkldnn_convolution_backward_weights(
weight.sizes(), grad_output, input, padding, stride, dilation, output_mask[2]);
}
return std::tuple<Tensor, Tensor, Tensor>{grad_input, grad_weight, grad_bias};
}
at::Tensor mkldnn_convolution(
const at::Tensor& input, const at::Tensor& weight, const at::Tensor& bias,
IntList padding, IntList stride, IntList dilation)
{
auto output = input.type().tensor(conv_output_size(
input.sizes(), weight.sizes(), padding, stride, dilation));
auto cpu_engine = CpuEngine::Instance().get_engine();
int32_t n = input.size(0);
int32_t ic = input.size(1);
int32_t ih = input.size(2);
int32_t iw = input.size(3);
int32_t oc = output.size(1);
int32_t oh = output.size(2);
int32_t ow = output.size(3);
int32_t kh = weight.size(2);
int32_t kw = weight.size(3);
int32_t sh = stride[0];
int32_t sw = stride[1];
int32_t ph = padding[0];
int32_t pw = padding[1];
auto data_t = memory::data_type::f32;
auto format_any = memory::format::any;
auto format_nchw = memory::format::nchw;
auto format_oihw = memory::format::oihw;
auto format_x = memory::format::x;
memory::dims input_tz = {n, ic, ih, iw};
memory::dims weight_tz = {oc, ic, kh, kw};
memory::dims bias_tz = {oc};
memory::dims output_tz = {n, oc, oh, ow};
memory::dims _stride = {sh, sw};
memory::dims _padding = {ph, pw};
auto input_md = memory::desc({input_tz}, data_t, format_any);
auto weight_md = memory::desc({weight_tz}, data_t, format_any);
auto bias_md = memory::desc({bias_tz}, data_t, format_any);
auto output_md = memory::desc({output_tz}, data_t, format_any);
std::shared_ptr<convolution_forward::desc> conv_forward_desc;
if (bias.defined()) {
conv_forward_desc.reset(new convolution_forward::desc(prop_kind::forward,
convolution_direct, input_md, weight_md, bias_md, output_md,
_stride, _padding, _padding, padding_kind::zero));
} else {
conv_forward_desc.reset(new convolution_forward::desc(prop_kind::forward,
convolution_direct, input_md, weight_md, output_md,
_stride, _padding, _padding, padding_kind::zero));
}
std::shared_ptr<convolution_forward::primitive_desc> conv_forward_pd;
conv_forward_pd.reset(new convolution_forward::primitive_desc(
*conv_forward_desc, cpu_engine));
auto input_usr_memory = memory({{{input_tz}, data_t, format_nchw}, cpu_engine},
input.data_ptr());
auto weight_usr_memory = memory({{{weight_tz}, data_t, format_oihw}, cpu_engine},
weight.data_ptr());
auto output_usr_memory = memory({{{output_tz}, data_t, format_nchw}, cpu_engine},
output.data_ptr());
std::vector<primitive> net;
auto input_pd = conv_forward_pd->src_primitive_desc();
auto input_memory = input_usr_memory;
if (input_usr_memory.get_primitive_desc() != memory::primitive_desc(input_pd)) {
input_memory = memory(input_pd);
net.push_back(reorder(input_usr_memory, input_memory));
}
auto weight_pd = conv_forward_pd->weights_primitive_desc();
auto weight_memory = weight_usr_memory;
if (weight_usr_memory.get_primitive_desc() != memory::primitive_desc(weight_pd)) {
weight_memory = memory(weight_pd);
net.push_back(reorder(weight_usr_memory, weight_memory));
}
auto output_pd = conv_forward_pd->dst_primitive_desc();
auto output_memory = output_usr_memory;
if (output_usr_memory.get_primitive_desc() != memory::primitive_desc(output_pd)) {
output_memory = memory(output_pd);
}
std::shared_ptr<convolution_forward> conv_forward;
std::shared_ptr<memory> bias_usr_memory;
if (bias.defined()) {
bias_usr_memory.reset(new memory({{{bias_tz}, data_t, format_x}, cpu_engine},
bias.data_ptr()));
conv_forward.reset(new convolution_forward(*conv_forward_pd, input_memory,
weight_memory, *bias_usr_memory, output_memory));
} else {
conv_forward.reset(new convolution_forward(*conv_forward_pd, input_memory,
weight_memory, output_memory));
}
net.push_back(*conv_forward);
if (output_memory != output_usr_memory) {
net.push_back(reorder(output_memory, output_usr_memory));
}
Stream::Instance().get_stream().submit(net);
return output;
}
