diff options
Diffstat (limited to 'reference_model/src/ops/tensor_ops.cc')
| -rw-r--r-- | reference_model/src/ops/tensor_ops.cc | 171 |
1 files changed, 88 insertions, 83 deletions
diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc index 732480c..6144dbc 100644 --- a/reference_model/src/ops/tensor_ops.cc +++ b/reference_model/src/ops/tensor_ops.cc @@ -26,7 +26,7 @@ int check_pool2d_attribute(tosa::TosaPoolAttribute* attribute, std::vector<int32_t> output_shape, std::string& msg) { - if (attribute->padding().size() != 4) + if (attribute->pad().size() != 4) { msg = "illegal size for attribute padding"; return 1; @@ -44,7 +44,7 @@ int check_pool2d_attribute(tosa::TosaPoolAttribute* attribute, return 1; } - for (int32_t i : attribute->padding()) + for (int32_t i : attribute->pad()) { if (i < 0) { @@ -76,10 +76,10 @@ int check_pool2d_attribute(tosa::TosaPoolAttribute* attribute, int32_t OH = output_shape[1]; int32_t OW = output_shape[2]; - int32_t pad_top = attribute->padding()[0]; - int32_t pad_bottom = attribute->padding()[1]; - int32_t pad_left = attribute->padding()[2]; - int32_t pad_right = attribute->padding()[3]; + int32_t pad_top = attribute->pad()[0]; + int32_t pad_bottom = attribute->pad()[1]; + int32_t pad_left = attribute->pad()[2]; + int32_t pad_right = attribute->pad()[3]; int32_t stride_y = attribute->stride()[0]; int32_t stride_x = attribute->stride()[1]; @@ -125,9 +125,9 @@ int check_conv_attribute_qinfo(tosa::TosaConvAttribute* attribute, DType WeightDtype, std::string& msg) { - if (attribute->padding().size() != (2 * conv_dimension)) + if (attribute->pad().size() != (2 * conv_dimension)) { - msg = "Illegal size for attribute padding"; + msg = "Illegal size for attribute pad"; return 1; } @@ -143,7 +143,7 @@ int check_conv_attribute_qinfo(tosa::TosaConvAttribute* attribute, return 1; } - for (int32_t i : attribute->padding()) + for (int32_t i : attribute->pad()) { if (i < 0) { @@ -191,12 +191,12 @@ int check_conv_attribute_qinfo(tosa::TosaConvAttribute* attribute, int32_t dilation_x = attribute->dilation()[1 + offset_d]; offset_d *= 2; - int32_t pad_d0 = conv_dimension == 3 ? attribute->padding()[0] : 0; - int32_t pad_d1 = conv_dimension == 3 ? attribute->padding()[1] : 0; - int32_t pad_top = attribute->padding()[0 + offset_d]; - int32_t pad_bottom = attribute->padding()[1 + offset_d]; - int32_t pad_left = attribute->padding()[2 + offset_d]; - int32_t pad_right = attribute->padding()[3 + offset_d]; + int32_t pad_d0 = conv_dimension == 3 ? attribute->pad()[0] : 0; + int32_t pad_d1 = conv_dimension == 3 ? attribute->pad()[1] : 0; + int32_t pad_top = attribute->pad()[0 + offset_d]; + int32_t pad_bottom = attribute->pad()[1 + offset_d]; + int32_t pad_left = attribute->pad()[2 + offset_d]; + int32_t pad_right = attribute->pad()[3 + offset_d]; int32_t full_D = ID - 1 + pad_d0 + pad_d1 - (kernel_d - 1) * dilation_d; int32_t full_H = IH - 1 + pad_top + pad_bottom - (kernel_h - 1) * dilation_y; @@ -442,10 +442,10 @@ int OpAvgPool2d<Dtype>::eval() ERROR_IF(in_batch != out_batch, "OpAvgPool2d: tensor batch mismatch %d != %d", in_batch, out_batch); ERROR_IF(in_channels != out_channels, "OpAvgPool2d: tensor channel mismatch %d != %d", in_channels, out_channels); - int padding_top = this->attribute->padding()[0]; - int padding_bottom = this->attribute->padding()[1]; - int padding_left = this->attribute->padding()[2]; - int padding_right = this->attribute->padding()[3]; + int pad_top = this->attribute->pad()[0]; + int pad_bottom = this->attribute->pad()[1]; + int pad_left = this->attribute->pad()[2]; + int pad_right = this->attribute->pad()[3]; int kernel_h = this->attribute->kernel()[0]; int kernel_w = this->attribute->kernel()[1]; int stride_h = this->attribute->stride()[0]; @@ -453,9 +453,9 @@ int OpAvgPool2d<Dtype>::eval() DEBUG_INFO(OP, "perform AvgPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%d,%d], " - "stride=[%d,%d], padding=[%d,%d,%d,%d]", + "stride=[%d,%d], pad=[%d,%d,%d,%d]", in_batch, in_height, in_width, in_channels, out_batch, out_height, out_width, out_channels, kernel_h, - kernel_w, stride_h, stride_w, padding_top, padding_bottom, padding_left, padding_right); + kernel_w, stride_h, stride_w, pad_top, pad_bottom, pad_left, pad_right); Eigen::array<Eigen::Index, 2> im2col_input_dims; im2col_input_dims[0] = kernel_h * kernel_w; @@ -467,11 +467,11 @@ int OpAvgPool2d<Dtype>::eval() col2im_output_dims[2] = out_width; col2im_output_dims[3] = out_channels; - Eigen::array<std::pair<int32_t, int32_t>, 4> padding; - padding[0] = std::make_pair(0, 0); - padding[1] = std::make_pair(padding_top, padding_bottom); - padding[2] = std::make_pair(padding_left, padding_right); - padding[3] = std::make_pair(0, 0); + Eigen::array<std::pair<int32_t, int32_t>, 4> pad; + pad[0] = std::make_pair(0, 0); + pad[1] = std::make_pair(pad_top, pad_bottom); + pad[2] = std::make_pair(pad_left, pad_right); + pad[3] = std::make_pair(0, 0); ETensor4<InEigenType> input_val = this->in->getTensor(); if (this->qinfo) @@ -479,7 +479,7 @@ int OpAvgPool2d<Dtype>::eval() input_val = input_val - (InEigenType)this->qinfo->input_zp(); } - ETensor4<InEigenType> input_padded = input_val.pad(padding); + ETensor4<InEigenType> input_padded = input_val.pad(pad); // assuming input and output have same scales // so input and output scaling is not required @@ -511,8 +511,8 @@ int OpAvgPool2d<Dtype>::eval() // calculate 1d height/width div_map (number of elements this pooling window covers) // and outer product to get 2d div_map, then reshape/broadcast to [N, H, W, C] - ETensor1<int32_t> div_map_h = calculate_div_map_1d(in_height, out_height, kernel_h, stride_h, padding_top, padding_bottom); - ETensor1<int32_t> div_map_w = calculate_div_map_1d(in_width, out_width, kernel_w, stride_w, padding_left, padding_right); + ETensor1<int32_t> div_map_h = calculate_div_map_1d(in_height, out_height, kernel_h, stride_h, pad_top, pad_bottom); + ETensor1<int32_t> div_map_w = calculate_div_map_1d(in_width, out_width, kernel_w, stride_w, pad_left, pad_right); Eigen::array<Eigen::IndexPair<Eigen::Index>, 1> contract_dims = { Eigen::IndexPair<Eigen::Index>(1, 0) }; Eigen::array<Eigen::Index, 4> bcast{ out_batch, 1, 1, out_channels }; @@ -636,10 +636,11 @@ int OpConv2d<InDtype, WeightDtype>::eval() out_channels); ERROR_IF(b_out_channels != out_channels, "OpConv2d: bias channel mismatch %d != %d", b_out_channels, out_channels); - int padding_top = this->attribute->padding()[0]; - int padding_bottom = this->attribute->padding()[1]; - int padding_left = this->attribute->padding()[2]; - int padding_right = this->attribute->padding()[3]; + int pad_top = this->attribute->pad()[0]; + int pad_bottom = this->attribute->pad()[1]; + int pad_left = this->attribute->pad()[2]; + int pad_right = this->attribute->pad()[3]; + int stride_h = this->attribute->stride()[0]; int stride_w = this->attribute->stride()[1]; int dilation_h = this->attribute->dilation()[0]; @@ -647,10 +648,10 @@ int OpConv2d<InDtype, WeightDtype>::eval() DEBUG_INFO(OP, "perform OpConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], " - "stride=[%d,%d], dilation=[%d,%d], padding=[%d,%d,%d,%d]", + "stride=[%d,%d], dilation=[%d,%d], pad=[%d,%d,%d,%d]", in_batch, in_height, in_width, in_channels, f_height, f_width, f_in_channels, f_out_channels, out_batch, - out_height, out_width, out_channels, stride_h, stride_w, dilation_h, dilation_w, padding_top, - padding_bottom, padding_left, padding_right); + out_height, out_width, out_channels, stride_h, stride_w, dilation_h, dilation_w, pad_top, + pad_bottom, pad_left, pad_right); // GEMM-conv2d, left matrix is input, right matrix is weight Eigen::array<Eigen::Index, 2> im2col_input_dims; @@ -682,11 +683,11 @@ int OpConv2d<InDtype, WeightDtype>::eval() Eigen::array<Eigen::IndexPair<Eigen::Index>, 1> contract_dims = { Eigen::IndexPair<Eigen::Index>(1, 0) }; - Eigen::array<std::pair<int32_t, int32_t>, 4> padding; - padding[0] = std::make_pair(0, 0); - padding[1] = std::make_pair(padding_top, padding_bottom); - padding[2] = std::make_pair(padding_left, padding_right); - padding[3] = std::make_pair(0, 0); + Eigen::array<std::pair<int32_t, int32_t>, 4> pad; + pad[0] = std::make_pair(0, 0); + pad[1] = std::make_pair(pad_top, pad_bottom); + pad[2] = std::make_pair(pad_left, pad_right); + pad[3] = std::make_pair(0, 0); TIn input_val = this->input->getTensor(); TWeight weight_val = this->weight->getTensor(); @@ -696,7 +697,7 @@ int OpConv2d<InDtype, WeightDtype>::eval() weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp(); } - ETensor4<InEigenType> input_padded = input_val.pad(padding); + ETensor4<InEigenType> input_padded = input_val.pad(pad); // extract_image_patches() output [N, KH, KW, H * W, C] // need to transpose to [N, H * W, KH, KW, C] @@ -825,15 +826,17 @@ int OpConv3d<InDtype, WeightDtype>::eval() out_channels); ERROR_IF(b_out_channels != out_channels, "OpConv3d: bias channel mismatch %d != %d", b_out_channels, out_channels); - int padding_d0 = this->attribute->padding()[0]; - int padding_d1 = this->attribute->padding()[1]; - int padding_top = this->attribute->padding()[2]; - int padding_bottom = this->attribute->padding()[3]; - int padding_left = this->attribute->padding()[4]; - int padding_right = this->attribute->padding()[5]; + int pad_d0 = this->attribute->pad()[0]; + int pad_d1 = this->attribute->pad()[1]; + int pad_top = this->attribute->pad()[2]; + int pad_bottom = this->attribute->pad()[3]; + int pad_left = this->attribute->pad()[4]; + int pad_right = this->attribute->pad()[5]; + int stride_d = this->attribute->stride()[0]; int stride_h = this->attribute->stride()[1]; int stride_w = this->attribute->stride()[2]; + int dilation_d = this->attribute->dilation()[0]; int dilation_h = this->attribute->dilation()[1]; int dilation_w = this->attribute->dilation()[2]; @@ -841,17 +844,17 @@ int OpConv3d<InDtype, WeightDtype>::eval() DEBUG_INFO( OP, "perform OpConv3d, input.shape=[%d,%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d,%d], output.shape=[%d,%d,%d,%d,%d], " - "stride=[%d,%d,%d], dilation=[%d,%d,%d], padding=[%d,%d,%d,%d,%d,%d]", + "stride=[%d,%d,%d], dilation=[%d,%d,%d], pad=[%d,%d,%d,%d,%d,%d]", in_batch, in_depth, in_height, in_width, in_channels, f_out_channels, f_depth, f_height, f_width, f_in_channels, out_batch, out_depth, out_height, out_width, out_channels, stride_d, stride_h, stride_w, dilation_d, dilation_h, - dilation_w, padding_d0, padding_d1, padding_top, padding_bottom, padding_left, padding_right); + dilation_w, pad_d0, pad_d1, pad_top, pad_bottom, pad_left, pad_right); - Eigen::array<std::pair<int32_t, int32_t>, 5> padding; - padding[0] = std::make_pair(0, 0); - padding[1] = std::make_pair(padding_d0, padding_d1); - padding[2] = std::make_pair(padding_top, padding_bottom); - padding[3] = std::make_pair(padding_left, padding_right); - padding[4] = std::make_pair(0, 0); + Eigen::array<std::pair<int32_t, int32_t>, 5> pad; + pad[0] = std::make_pair(0, 0); + pad[1] = std::make_pair(pad_d0, pad_d1); + pad[2] = std::make_pair(pad_top, pad_bottom); + pad[3] = std::make_pair(pad_left, pad_right); + pad[4] = std::make_pair(0, 0); TIn input_val = this->input->getTensor(); TWeight weight_val = this->weight->getTensor(); @@ -861,7 +864,7 @@ int OpConv3d<InDtype, WeightDtype>::eval() weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp(); } - ETensor5<InEigenType> input_padded = input_val.pad(padding); + ETensor5<InEigenType> input_padded = input_val.pad(pad); // 1. initialize with bias Eigen::array<Eigen::Index, 5> reshape_dim; @@ -1013,10 +1016,11 @@ int OpDepthwiseConv2d<InDtype, WeightDtype>::eval() ERROR_IF(b_out_channels != out_channels, "OpDepthwiseConv2d: bias channels mismatch %d != %d", b_out_channels, out_channels); - int padding_top = this->attribute->padding()[0]; - int padding_bottom = this->attribute->padding()[1]; - int padding_left = this->attribute->padding()[2]; - int padding_right = this->attribute->padding()[3]; + int pad_top = this->attribute->pad()[0]; + int pad_bottom = this->attribute->pad()[1]; + int pad_left = this->attribute->pad()[2]; + int pad_right = this->attribute->pad()[3]; + int stride_h = this->attribute->stride()[0]; int stride_w = this->attribute->stride()[1]; int dilation_h = this->attribute->dilation()[0]; @@ -1024,16 +1028,16 @@ int OpDepthwiseConv2d<InDtype, WeightDtype>::eval() DEBUG_INFO(OP, "perform OpDepthwiseConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], " - "output.shape=[%d,%d,%d,%d], stride=[%d,%d], dilation=[%d,%d], padding=[%d,%d,%d,%d]", + "output.shape=[%d,%d,%d,%d], stride=[%d,%d], dilation=[%d,%d], pad=[%d,%d,%d,%d]", in_batch, in_height, in_width, in_channels, f_height, f_width, f_in_channels, f_multiplier, out_batch, - out_height, out_width, out_channels, stride_h, stride_w, dilation_h, dilation_w, padding_top, - padding_bottom, padding_left, padding_right); + out_height, out_width, out_channels, stride_h, stride_w, dilation_h, dilation_w, pad_top, + pad_bottom, pad_left, pad_right); - Eigen::array<std::pair<int32_t, int32_t>, 4> padding; - padding[0] = std::make_pair(0, 0); - padding[1] = std::make_pair(padding_top, padding_bottom); - padding[2] = std::make_pair(padding_left, padding_right); - padding[3] = std::make_pair(0, 0); + Eigen::array<std::pair<int32_t, int32_t>, 4> pad; + pad[0] = std::make_pair(0, 0); + pad[1] = std::make_pair(pad_top, pad_bottom); + pad[2] = std::make_pair(pad_left, pad_right); + pad[3] = std::make_pair(0, 0); TIn input_val = this->input->getTensor(); TWeight weight_val = this->weight->getTensor(); @@ -1043,10 +1047,10 @@ int OpDepthwiseConv2d<InDtype, WeightDtype>::eval() weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp(); } - ETensor4<InEigenType> input_padded = input_val.pad(padding); + ETensor4<InEigenType> input_padded = input_val.pad(pad); // GEMM doesn't fit well with DepthwiseConv2d - // 1. use extract_image_patches() to handle stride/dilation/padding + // 1. use extract_image_patches() to handle stride/dilation/pad // 2. perform direct convolution // 1. extract_image_patches() output [N, KH, KW, OH * OW, IC] @@ -1411,10 +1415,11 @@ int OpMaxPool2d<Dtype>::eval() ERROR_IF(in_batch != out_batch, "OpMaxPool2d: tensor batch mismatch %d != %d", in_batch, out_batch); ERROR_IF(in_channels != out_channels, "OpMaxPool2d: tensor channel mismatch %d != %d", in_channels, out_channels); - int padding_top = this->attribute->padding()[0]; - int padding_bottom = this->attribute->padding()[1]; - int padding_left = this->attribute->padding()[2]; - int padding_right = this->attribute->padding()[3]; + int pad_top = this->attribute->pad()[0]; + int pad_bottom = this->attribute->pad()[1]; + int pad_left = this->attribute->pad()[2]; + int pad_right = this->attribute->pad()[3]; + int kernel_h = this->attribute->kernel()[0]; int kernel_w = this->attribute->kernel()[1]; int stride_h = this->attribute->stride()[0]; @@ -1422,9 +1427,9 @@ int OpMaxPool2d<Dtype>::eval() DEBUG_INFO(OP, "perform MaxPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%d,%d], " - "stride=[%d,%d], padding=[%d,%d,%d,%d]", + "stride=[%d,%d], pad=[%d,%d,%d,%d]", in_batch, in_height, in_width, in_channels, out_batch, out_height, out_width, out_channels, kernel_h, - kernel_w, stride_h, stride_w, padding_top, padding_bottom, padding_left, padding_right); + kernel_w, stride_h, stride_w, pad_top, pad_bottom, pad_left, pad_right); Eigen::array<Eigen::Index, 2> im2col_input_dims; im2col_input_dims[0] = kernel_h * kernel_w; @@ -1436,13 +1441,13 @@ int OpMaxPool2d<Dtype>::eval() col2im_output_dims[2] = out_width; col2im_output_dims[3] = out_channels; - Eigen::array<std::pair<int32_t, int32_t>, 4> padding; - padding[0] = std::make_pair(0, 0); - padding[1] = std::make_pair(padding_top, padding_bottom); - padding[2] = std::make_pair(padding_left, padding_right); - padding[3] = std::make_pair(0, 0); + Eigen::array<std::pair<int32_t, int32_t>, 4> pad; + pad[0] = std::make_pair(0, 0); + pad[1] = std::make_pair(pad_top, pad_bottom); + pad[2] = std::make_pair(pad_left, pad_right); + pad[3] = std::make_pair(0, 0); - ETensor4<InEigenType> input_padded = this->in->getTensor().pad(padding, std::numeric_limits<InEigenType>::lowest()); + ETensor4<InEigenType> input_padded = this->in->getTensor().pad(pad, std::numeric_limits<InEigenType>::lowest()); // extract_image_patches() output [N, KH, KW, H * W, C] // transpose to [KH, KW, N, H * W, C] |