| |
| // Copyright (c) 2020-2023, ARM Limited. |
| // |
| // Licensed under the Apache License, Version 2.0 (the "License"); |
| // you may not use this file except in compliance with the License. |
| // You may obtain a copy of the License at |
| // |
| // http://www.apache.org/licenses/LICENSE-2.0 |
| // |
| // Unless required by applicable law or agreed to in writing, software |
| // distributed under the License is distributed on an "AS IS" BASIS, |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| // See the License for the specific language governing permissions and |
| // limitations under the License. |
| |
| #include "tensor_ops.h" |
| #include "quant_util.h" |
| #include "template_types.h" |
| #include "half.hpp" |
| |
| using namespace TosaReference; |
| using namespace Eigen; |
| using namespace tosa; |
| |
| int check_pool2d_attribute(tosa::TosaPoolAttribute* attribute, |
| std::vector<int32_t> input_shape, |
| std::vector<int32_t> output_shape, |
| std::string& msg) |
| { |
| if (attribute->pad().size() != 4) |
| { |
| msg = "illegal size for attribute padding"; |
| return 1; |
| } |
| |
| if (attribute->kernel().size() != 2) |
| { |
| msg = "illegal size for attribute kernel"; |
| return 1; |
| } |
| |
| if (attribute->stride().size() != 2) |
| { |
| msg = "illegal size for attribute stride"; |
| return 1; |
| } |
| |
| for (int32_t i : attribute->pad()) |
| { |
| if (i < 0) |
| { |
| msg = "At least one pad is smaller than zero"; |
| return 1; |
| } |
| } |
| |
| for (int32_t i : attribute->kernel()) |
| { |
| if (i < 1) |
| { |
| msg = "At least one kernel dimension is smaller than one"; |
| return 1; |
| } |
| } |
| |
| for (int32_t i : attribute->stride()) |
| { |
| if (i < 1) |
| { |
| msg = "At least one stride dimension is smaller than one"; |
| return 1; |
| } |
| } |
| |
| int32_t IH = input_shape[1]; |
| int32_t IW = input_shape[2]; |
| int32_t OH = output_shape[1]; |
| int32_t OW = output_shape[2]; |
| |
| 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]; |
| int32_t kernel_y = attribute->kernel()[0]; |
| int32_t kernel_x = attribute->kernel()[1]; |
| |
| if (pad_top >= kernel_y || pad_bottom >= kernel_y || pad_left >= kernel_x || pad_right >= kernel_x) |
| { |
| msg = "At least one pad is >= kernel dimension"; |
| return 1; |
| } |
| |
| int32_t full_H = IH + pad_top + pad_bottom - kernel_y; |
| int32_t full_W = IW + pad_left + pad_right - kernel_x; |
| |
| if ((full_H % stride_y != 0) || |
| (full_W % stride_x != 0)) |
| { |
| msg = "Parameters must yield exact integer output dimensions"; |
| return 1; |
| } |
| |
| if ((OH != (full_H / stride_y) + 1) || |
| (OW != (full_W / stride_x) + 1)) |
| { |
| msg = "Mismatch between output shape provided and expected output shape (" + |
| std::to_string((full_H / stride_y) + 1) + "," + |
| std::to_string((full_W / stride_x) + 1) + ")"; |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| int check_conv_attribute(tosa::TosaConvAttribute* attribute, |
| uint32_t conv_dimension, |
| std::vector<int32_t> input_shape, |
| std::vector<int32_t> output_shape, |
| std::vector<int32_t> weights, |
| uint32_t offset_kernel, |
| DType InDtype, |
| DType WeightDtype, |
| std::string& msg) |
| { |
| if (attribute->pad().size() != (2 * conv_dimension)) |
| { |
| msg = "Illegal size for attribute pad"; |
| return 1; |
| } |
| |
| if (attribute->stride().size() != conv_dimension) |
| { |
| msg = "Illegal size for attribute stride"; |
| return 1; |
| } |
| |
| if (attribute->dilation().size() != conv_dimension) |
| { |
| msg = "Illegal size for attribute dilation"; |
| return 1; |
| } |
| |
| for (int32_t i : attribute->pad()) |
| { |
| if (i < 0) |
| { |
| msg = "At least one pad is smaller than zero"; |
| return 1; |
| } |
| } |
| |
| for (int32_t i : attribute->stride()) |
| { |
| if (i < 1) |
| { |
| msg = "At least one stride dimension is smaller than one"; |
| return 1; |
| } |
| } |
| |
| for (int32_t i : attribute->dilation()) |
| { |
| if (i < 1) |
| { |
| msg = "At least one dilation dimension is smaller than one"; |
| return 1; |
| } |
| } |
| |
| ASSERT_MSG(conv_dimension == 2 || conv_dimension == 3, "Unsupported convolution dimension") |
| |
| int32_t offset_d = conv_dimension == 3 ? 1 : 0; |
| int32_t ID = conv_dimension == 3 ? input_shape[1] : 1; |
| int32_t IH = input_shape[1 + offset_d]; |
| int32_t IW = input_shape[2 + offset_d]; |
| int32_t OD = conv_dimension == 3 ? output_shape[1] : 1; |
| int32_t OH = output_shape[1 + offset_d]; |
| int32_t OW = output_shape[2 + offset_d]; |
| |
| int32_t stride_d = conv_dimension == 3 ? attribute->stride()[0] : 1; |
| int32_t stride_y = attribute->stride()[0 + offset_d]; |
| int32_t stride_x = attribute->stride()[1 + offset_d]; |
| int32_t kernel_d = conv_dimension == 3 ? weights[offset_kernel] : 1; |
| int32_t kernel_h = weights[offset_kernel + offset_d]; |
| int32_t kernel_w = weights[offset_kernel + 1 + offset_d]; |
| int32_t dilation_d = conv_dimension == 3 ? attribute->dilation()[0] : 1; |
| int32_t dilation_y = attribute->dilation()[0 + offset_d]; |
| int32_t dilation_x = attribute->dilation()[1 + offset_d]; |
| |
| offset_d *= 2; |
| 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; |
| int32_t full_W = IW - 1 + pad_left + pad_right - (kernel_w - 1) * dilation_x; |
| |
| if ((full_H % stride_y != 0) || |
| (full_W % stride_x != 0) || |
| (full_D % stride_d != 0)) |
| { |
| msg = "Parameters must yield exact integer output dimensions"; |
| return 1; |
| } |
| |
| if ((OH != (full_H / stride_y) + 1) || |
| (OW != (full_W / stride_x) + 1) || |
| (OD != (full_D / stride_d) + 1)) |
| { |
| std::string msg_d = ""; |
| if (conv_dimension == 3) |
| { |
| msg_d += std::to_string((full_D / stride_d) + 1) + ","; |
| } |
| msg = "Mismatch between output shape provided and expected output shape (" + |
| msg_d + |
| std::to_string((full_H / stride_y) + 1) + "," + |
| std::to_string((full_W / stride_x) + 1) + ")"; |
| return 1; |
| } |
| |
| if (InDtype != DType_INT8 && attribute->input_zp() != 0) { |
| msg = "Input zero point must be zero for non-int8 data"; |
| return 1; |
| } |
| if (WeightDtype != DType_INT8 && attribute->weight_zp() != 0) { |
| msg = "Weight zero point must be zero for non-int8 data"; |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| int check_fft_shape(const std::vector<int32_t>& in_real, |
| const std::vector<int32_t>& in_imag, |
| const std::vector<int32_t>& out_real, |
| const std::vector<int32_t>& out_imag, |
| std::string& msg) { |
| const bool is_rfft = in_imag.empty(); |
| auto is_power_of_two = [](int32_t n) -> bool |
| { |
| return (n & (n-1)) == 0 && n > 0; |
| }; |
| |
| if (!is_power_of_two(in_real[1]) || !is_power_of_two(in_real[2])) |
| { |
| msg = "Input height and width must be a power of two"; |
| return 1; |
| } |
| |
| // RFFT does not have a second input |
| if (!is_rfft) |
| { |
| bool input_check = true; |
| for (size_t i = 0; i < in_real.size(); i++) |
| { |
| if (in_real[i] != in_imag[i]) |
| { |
| input_check = false; |
| break; |
| } |
| } |
| if (!input_check) |
| { |
| msg = "Mismatch between real input shape and imaginary input shape"; |
| return 1; |
| } |
| } |
| |
| bool output_check = true; |
| for (size_t i = 0; i < out_real.size(); i++) |
| { |
| if (out_real[i] != out_imag[i]) |
| { |
| output_check = false; |
| break; |
| } |
| } |
| if (!output_check) |
| { |
| msg = "Mismatch between real output shape and imaginary output shape"; |
| return 1; |
| } |
| |
| if (in_real[0] != out_real[0]) |
| { |
| msg = "Input and output batch size don't match"; |
| return 1; |
| } |
| if (in_real[1] != out_real[1]) |
| { |
| msg = "Input and output height don't match"; |
| return 1; |
| } |
| |
| if (is_rfft) |
| { |
| if (in_real[2] / 2 + 1 != out_real[2]) |
| { |
| msg = "Output width is expected to match input width / 2 + 1"; |
| return 1; |
| } |
| } else { |
| if (in_real[2] != out_real[2]) |
| { |
| msg = "Input and output width don't match"; |
| return 1; |
| } |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| OpArgMax<Rank, Dtype>::OpArgMax(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_ARGMAX, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(1, 4); |
| |
| INIT_ATTRIBUTE(Axis); |
| } |
| |
| template <int Rank, DType Dtype> |
| OpArgMax<Rank, Dtype>::~OpArgMax() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpArgMax<Rank, Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0])) |
| { |
| return 1; |
| } |
| |
| int32_t output_rank = inputs[0]->getRank() - 1; |
| if (output_rank != outputs[0]->getRank()) |
| { |
| printNodeValidationError("OpArgMax: Output rank needs to be rank(input) - 1"); |
| return 1; |
| } |
| |
| if (outputs[0]->getDtype() != DType_INT32) |
| { |
| printNodeValidationError("OpArgMax: Output data type not supported for this configuration of operator"); |
| return 1; |
| } |
| |
| input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| if (attribute->axis() < 0 || attribute->axis() >= input->getRank()) |
| { |
| printNodeValidationError("OpArgMax: Axis needs to be within [0, rank(input)]"); |
| return 1; |
| } |
| |
| bool shape_check = true; |
| for (int32_t i = 0; i < input->getRank(); i++) |
| { |
| if (i < attribute->axis()) |
| { |
| if (input->getShape()[i] != output->getShape()[i]) |
| { |
| shape_check = false; |
| break; |
| } |
| } |
| else if (i > attribute->axis()) |
| { |
| if (input->getShape()[i] != output->getShape()[i - 1]) |
| { |
| shape_check = false; |
| break; |
| } |
| } |
| // No need to check i == axis |
| } |
| if (!shape_check) |
| { |
| printNodeValidationError("OpArgMax: Mismatch between output shape provided and expected output shape"); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <int Rank, DType Dtype> |
| int OpArgMax<Rank, Dtype>::eval() |
| { |
| Eigen::Tensor<DenseIndex, Rank - 1> index = this->input->getTensor().argmax(attribute->axis()); |
| |
| this->output->getTensor() = index.unaryExpr([](DenseIndex in) -> OutEigenType { return (OutEigenType)in; }); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype, DType AccDtype> |
| OpAvgPool2d<Dtype, AccDtype>::OpAvgPool2d(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_AVG_POOL2D, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(Pool); |
| } |
| |
| template <DType Dtype, DType AccDtype> |
| OpAvgPool2d<Dtype, AccDtype>::~OpAvgPool2d() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType Dtype, DType AccDtype> |
| int OpAvgPool2d<Dtype, AccDtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| if (inputs[0]->matchType(*outputs[0])) |
| { |
| printNodeValidationError("OpAvgPool2d: input and output tensor type mismatch"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| ERROR_IF(Dtype != DType_INT8 && attribute->input_zp() != 0, "OpAvgPool2d: Input zeropoint must be zero for non int8_t data"); |
| ERROR_IF(Dtype != DType_INT8 && attribute->output_zp() != 0, "OpAvgPool2d: Output zeropoint must be zero for non int8_t data"); |
| |
| std::string msg; |
| if (check_pool2d_attribute(attribute, in->getShape(), out->getShape(), msg)) |
| { |
| msg = "OpAvgPool2d: " + msg; |
| printNodeValidationError(msg.c_str()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| // This calculates the number of padding elements used for each location along an axis |
| // Average pooling only divides by the number of elements used, not including padding. |
| // This function uses left/right, but is also used for vertical padding with top/bottom |
| template <DType Dtype, DType AccDtype> |
| ETensor1<int32_t> OpAvgPool2d<Dtype, AccDtype>::calculate_div_map_1d(int in_size, int out_size, int kernel_size, int stride, int32_t pad_left, int32_t pad_right) |
| { |
| ETensor1<int32_t> result(out_size); |
| |
| result.setConstant(kernel_size); |
| |
| // adjust divisors on the left side for padding |
| // We start at the leftmost output element, and remove pad_left - (index * stride) elements |
| // until we have no more padding being used |
| for(int index = 0; (index <= pad_left / stride) && (index < out_size); index++) { |
| int32_t adjust = pad_left - (index * stride); |
| result(index) -= adjust; |
| } |
| |
| // The process repeats on the right side. Padding starts taking effect as we |
| // near the rightmost input element. The first output element which touches |
| // padding is defined in the initialization of index below. Then we keep moving |
| // to the right, increasing padding until we get to the last output element. |
| int index = std::max(0, ((pad_left + in_size - kernel_size) / stride) + 1); |
| for (; index < out_size; index++) { |
| int32_t adjust = ((index * stride) + kernel_size) - (pad_left + in_size); |
| result(index) -= adjust; |
| } |
| return result; |
| } |
| |
| // assuming input and output tensor have same scales like tflite reference |
| // so no need to scale input and output |
| template <DType Dtype, DType AccDtype> |
| int OpAvgPool2d<Dtype, AccDtype>::eval() |
| { |
| int in_batch = this->in->getShape()[0]; |
| int in_height = this->in->getShape()[1]; |
| int in_width = this->in->getShape()[2]; |
| int in_channels = this->in->getShape()[3]; |
| |
| int out_batch = this->out->getShape()[0]; |
| int out_height = this->out->getShape()[1]; |
| int out_width = this->out->getShape()[2]; |
| int out_channels = this->out->getShape()[3]; |
| |
| 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 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]; |
| int stride_w = this->attribute->stride()[1]; |
| |
| tosa::DType accum_dtype = (tosa::DType)this->attribute->accum_dtype(); |
| |
| DEBUG_INFO(OP, |
| "perform AvgPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%d,%d], " |
| "stride=[%d,%d], pad=[%d,%d,%d,%d], accum_dtype=%s", |
| in_batch, in_height, in_width, in_channels, out_batch, out_height, out_width, out_channels, kernel_h, |
| kernel_w, stride_h, stride_w, pad_top, pad_bottom, pad_left, pad_right, EnumNamesDType()[accum_dtype]); |
| |
| Eigen::array<Eigen::Index, 2> im2col_input_dims; |
| im2col_input_dims[0] = kernel_h * kernel_w; |
| im2col_input_dims[1] = out_batch * out_height * out_width * out_channels; |
| |
| Eigen::array<Eigen::Index, 4> col2im_output_dims; |
| col2im_output_dims[0] = out_batch; |
| col2im_output_dims[1] = out_height; |
| col2im_output_dims[2] = out_width; |
| col2im_output_dims[3] = out_channels; |
| |
| 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 (Dtype == DType_INT8) |
| { |
| input_val = input_val - (InEigenType)attribute->input_zp(); |
| } |
| |
| ETensor4<InEigenType> input_padded = input_val.pad(pad); |
| |
| // assuming input and output have same scales |
| // so input and output scaling is not required |
| // TODO: check if this assumption TOSA made |
| |
| // extract_image_patches() output [N, KH, KW, H * W, C] |
| // transpose to [KH, KW, N, H * W, C] |
| // reshape to [KH * KW, N * H * W * C] |
| ETensor2<InEigenType> input_extract_patches = |
| input_padded.extract_image_patches(kernel_h, kernel_w, stride_h, stride_w, 1, 1, Eigen::PADDING_VALID) |
| .shuffle(Eigen::array<Eigen::Index, 5>{ 1, 2, 0, 3, 4 }) |
| .reshape(im2col_input_dims); |
| |
| // 1D result with [N * H * W * C] |
| ETensor1<AccEigenType> out_1d(this->out->getElementCount()); |
| out_1d.setZero(); |
| |
| // sum pool |
| for (size_t i = 0; i < this->out->getElementCount(); i++) |
| { |
| for (int32_t j = 0; j < kernel_h * kernel_w; j++) |
| { |
| out_1d(i) += (AccEigenType)input_extract_patches(j, i); |
| } |
| } |
| |
| // reshape result to [N, H, W, C] and divide with div_map |
| ETensor4<AccEigenType> sum = out_1d.reshape(col2im_output_dims); |
| |
| // 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, 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 }; |
| |
| ETensor2<int32_t> dm2_w = div_map_w.reshape(Eigen::array<Eigen::Index, 2>{ 1, out_width }); |
| ETensor2<int32_t> dm2_h = div_map_h.reshape(Eigen::array<Eigen::Index, 2>{ out_height, 1 }); |
| ETensor4<int32_t> div_map = |
| dm2_h.contract(dm2_w, contract_dims) |
| .reshape(Eigen::array<Eigen::Index, 4>{ 1, out_height, out_width, 1 }) |
| .broadcast(bcast); |
| if (Dtype != DType_FP32 && Dtype != DType_FP16 && Dtype != DType_BF16) |
| { |
| try |
| { |
| this->out->getTensor() = sum.binaryExpr(div_map, [](AccEigenType value, int32_t div) -> OutEigenType { |
| int32_t multiplier, shift; |
| TosaReference::QuantUtil::reciprocal_scale(div, multiplier, shift); |
| |
| return (OutEigenType)TosaReference::QuantUtil::apply_scale_32(value, multiplier, shift, false); |
| }); |
| } |
| catch (std::string desc) |
| { |
| REQUIRE(false, "OpAvgPool2d apply_scale_32() fails: %s.", desc.c_str()); |
| } |
| this->out->getTensor() = this->out->getTensor() + (OutEigenType)(attribute->output_zp()); |
| this->out->getTensor() = this->out->getTensor().cwiseMax((OutEigenType)QMin); |
| this->out->getTensor() = this->out->getTensor().cwiseMin((OutEigenType)QMax); |
| } |
| else |
| { |
| // Case for float-types |
| this->out->getTensor() = (sum / div_map.template cast<AccEigenType>()).template cast<OutEigenType>(); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpConv2d<InDtype, WeightDtype, OutDtype>::OpConv2d(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_CONV2D, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(Conv); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpConv2d<InDtype, WeightDtype, OutDtype>::~OpConv2d() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // 'bias' checked separatedly since it doens't make sense to make required rank ranging from 1 to 4 |
| if (inputs[2]->getRank() != 1) |
| { |
| printNodeValidationError("OpConv2d: bias tensor must be rank 1"); |
| } |
| |
| ERROR_IF(outputs[0]->getDtype() != OutDtype, |
| "OpConv2d: Output data type not supported for this configuration of operator"); |
| |
| input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); |
| bias = dynamic_cast<TosaReference::TensorTemplate<TBias>*>(inputs[2]); |
| output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| std::string msg; |
| if (check_conv_attribute(attribute, 2 /* conv_dimension */, input->getShape(), output->getShape(), |
| weight->getShape(), 1 /* offset_kernel */, InDtype, WeightDtype, msg)) |
| { |
| msg = "OpConv2d: " + msg; |
| printNodeValidationError(msg.c_str()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpConv2d<InDtype, WeightDtype, OutDtype>::eval() |
| { |
| int in_batch = this->input->getShape()[0]; |
| int in_height = this->input->getShape()[1]; |
| int in_width = this->input->getShape()[2]; |
| int in_channels = this->input->getShape()[3]; |
| |
| int f_out_channels = this->weight->getShape()[0]; |
| int f_height = this->weight->getShape()[1]; |
| int f_width = this->weight->getShape()[2]; |
| int f_in_channels = this->weight->getShape()[3]; |
| |
| int b_out_channels = this->bias->getShape()[0]; |
| |
| int out_batch = this->output->getShape()[0]; |
| int out_height = this->output->getShape()[1]; |
| int out_width = this->output->getShape()[2]; |
| int out_channels = this->output->getShape()[3]; |
| |
| ERROR_IF(in_batch != out_batch, "OpConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| ERROR_IF(f_in_channels != in_channels, "OpConv2d: tensor input channel mismatch %d != %d", f_in_channels, |
| in_channels); |
| ERROR_IF(f_out_channels != out_channels, "OpConv2d: tensor output channel mismatch %d != %d", f_out_channels, |
| out_channels); |
| ERROR_IF(b_out_channels != out_channels, "OpConv2d: bias channel mismatch %d != %d", b_out_channels, out_channels); |
| |
| 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]; |
| int dilation_w = this->attribute->dilation()[1]; |
| |
| 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], 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, 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; |
| im2col_input_dims[0] = out_batch * out_height * out_width; |
| im2col_input_dims[1] = f_height * f_width * f_in_channels; |
| |
| Eigen::array<Eigen::Index, 2> im2col_weight_dims; |
| im2col_weight_dims[0] = f_height * f_width * f_in_channels; |
| im2col_weight_dims[1] = f_out_channels; |
| |
| Eigen::array<Eigen::Index, 2> bias_reshaped_dims; |
| bias_reshaped_dims[0] = 1; |
| bias_reshaped_dims[1] = b_out_channels; |
| |
| Eigen::array<Eigen::Index, 4> weight_zp_bcast_dims; |
| weight_zp_bcast_dims[0] = f_height; |
| weight_zp_bcast_dims[1] = f_width; |
| weight_zp_bcast_dims[2] = f_in_channels; |
| |
| Eigen::array<Eigen::Index, 2> bias_bcast_dims; |
| bias_bcast_dims[0] = out_batch * out_height * out_width; |
| bias_bcast_dims[1] = 1; |
| |
| Eigen::array<Eigen::Index, 4> col2im_output_dims; |
| col2im_output_dims[0] = out_batch; |
| col2im_output_dims[1] = out_height; |
| col2im_output_dims[2] = out_width; |
| col2im_output_dims[3] = out_channels; |
| |
| Eigen::array<Eigen::IndexPair<Eigen::Index>, 1> contract_dims = { Eigen::IndexPair<Eigen::Index>(1, 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(); |
| if (InDtype == DType_INT8 || WeightDtype == DType_INT8) |
| { |
| input_val = input_val - (InEigenType)attribute->input_zp(); |
| weight_val = weight_val - (WeightEigenType)attribute->weight_zp(); |
| } |
| |
| 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] |
| ETensor5<InEigenType> input_extract_patches = |
| input_padded |
| .extract_image_patches(f_height, f_width, stride_h, stride_w, dilation_h, dilation_w, Eigen::PADDING_VALID) |
| .shuffle(Eigen::array<Eigen::Index, 5>{ 0, 3, 1, 2, 4 }); |
| |
| // reshape input to [N * H * W, KH * KW * C] |
| ETensor2<InEigenType> im2col_input = input_extract_patches.reshape(im2col_input_dims); |
| |
| // transpose and reshape weight from [OC, H, W, IC] to [H * W * IC, OC] |
| ETensor2<WeightEigenType> im2col_weight = |
| weight_val.shuffle(Eigen::array<Eigen::Index, 4>({ 1, 2, 3, 0 })).reshape(im2col_weight_dims); |
| |
| // don't need to apply bias_multiplier ( * bias_scale and >> bias_shift) since tflite already scale it |
| // and reshaped from [C] to [1, C], and broadcast to [N * H * W, C] |
| ETensor2<OutEigenType> bias_2d = (this->bias->getTensor().reshape(bias_reshaped_dims).broadcast(bias_bcast_dims)).template cast<OutEigenType>(); |
| |
| // output matrix is [N * H * W, C] |
| ETensor2<OutEigenType> contracted_result = |
| (im2col_input.template cast<AccEigenType>().contract(im2col_weight.template cast<AccEigenType>(), contract_dims)).template cast<OutEigenType>(); |
| |
| // adding bias |
| ETensor2<OutEigenType> biased_output = contracted_result + bias_2d; |
| |
| // reshape back to [N, H, W, C] |
| this->output->getTensor() = biased_output.reshape(col2im_output_dims); |
| |
| if (OutDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((OutEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((OutEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpConv3d<InDtype, WeightDtype, OutDtype>::OpConv3d(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_CONV3D, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(5); |
| |
| INIT_ATTRIBUTE(Conv); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpConv3d<InDtype, WeightDtype, OutDtype>::~OpConv3d() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpConv3d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // 'bias' checked separatedly since it doens't make sense to make required rank ranging from 1 to 4 |
| if (inputs[2]->getRank() != 1) |
| { |
| printNodeValidationError("OpConv3d: bias tensor must be rank 1"); |
| } |
| |
| ERROR_IF(outputs[0]->getDtype() != OutDtype, |
| "OpConv3d: Output data type not supported for this configuration of operator"); |
| |
| input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); |
| bias = dynamic_cast<TosaReference::TensorTemplate<TBias>*>(inputs[2]); |
| output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| std::string msg; |
| if (check_conv_attribute(attribute, 3 /* conv_dimension */, input->getShape(), output->getShape(), |
| weight->getShape(), 1 /* offset_kernel */, InDtype, WeightDtype, msg)) |
| { |
| msg = "OpConv3d: " + msg; |
| printNodeValidationError(msg.c_str()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpConv3d<InDtype, WeightDtype, OutDtype>::eval() |
| { |
| int in_batch = this->input->getShape()[0]; |
| int in_depth = this->input->getShape()[1]; |
| int in_height = this->input->getShape()[2]; |
| int in_width = this->input->getShape()[3]; |
| int in_channels = this->input->getShape()[4]; |
| |
| int f_out_channels = this->weight->getShape()[0]; |
| int f_depth = this->weight->getShape()[1]; |
| int f_height = this->weight->getShape()[2]; |
| int f_width = this->weight->getShape()[3]; |
| int f_in_channels = this->weight->getShape()[4]; |
| |
| int b_out_channels = this->bias->getShape()[0]; |
| |
| int out_batch = this->output->getShape()[0]; |
| int out_depth = this->output->getShape()[1]; |
| int out_height = this->output->getShape()[2]; |
| int out_width = this->output->getShape()[3]; |
| int out_channels = this->output->getShape()[4]; |
| |
| ERROR_IF(in_batch != out_batch, "OpConv3d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| ERROR_IF(f_in_channels != in_channels, "OpConv3d: tensor input channel mismatch %d != %d", f_in_channels, |
| in_channels); |
| ERROR_IF(f_out_channels != out_channels, "OpConv3d: tensor output channel mismatch %d != %d", f_out_channels, |
| out_channels); |
| ERROR_IF(b_out_channels != out_channels, "OpConv3d: bias channel mismatch %d != %d", b_out_channels, out_channels); |
| |
| 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]; |
| |
| 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], 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, pad_d0, pad_d1, pad_top, pad_bottom, pad_left, pad_right); |
| |
| 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(); |
| if (InDtype == DType_INT8 || WeightDtype == DType_INT8) |
| { |
| input_val = input_val - (InEigenType)attribute->input_zp(); |
| weight_val = weight_val - (WeightEigenType)attribute->weight_zp(); |
| } |
| |
| ETensor5<InEigenType> input_padded = input_val.pad(pad); |
| |
| // 1. initialize with bias |
| Eigen::array<Eigen::Index, 5> reshape_dim; |
| reshape_dim.fill(1); |
| reshape_dim[4] = b_out_channels; |
| |
| Eigen::array<Eigen::Index, 5> bcast; |
| bcast[0] = out_batch; |
| bcast[1] = out_depth; |
| bcast[2] = out_height; |
| bcast[3] = out_width; |
| bcast[4] = 1; |
| this->output->getTensor() = this->bias->getTensor().reshape(reshape_dim).broadcast(bcast); |
| |
| // 2. direct convolution |
| AccEigenType acc(0.0); |
| int d_idx, h_idx, w_idx; |
| |
| for (int ob = 0; ob < out_batch; ob++) |
| { |
| for (int od = 0; od < out_depth; od++) |
| { |
| for (int oh = 0; oh < out_height; oh++) |
| { |
| for (int ow = 0; ow < out_width; ow++) |
| { |
| for (int oc = 0; oc < out_channels; oc++) |
| { |
| // Initialize accumulator with bias value |
| acc = (AccEigenType)this->output->getTensor()(ob, od, oh, ow, oc); |
| for (int fd = 0; fd < f_depth; fd++) |
| { |
| d_idx = od * stride_d + fd * dilation_d; |
| for (int fh = 0; fh < f_height; fh++) |
| { |
| h_idx = oh * stride_h + fh * dilation_h; |
| for (int fw = 0; fw < f_width; fw++) |
| { |
| w_idx = ow * stride_w + fw * dilation_w; |
| for (int ic = 0; ic < in_channels; ic++) |
| { |
| acc += ((AccEigenType)input_padded(ob, d_idx, h_idx, w_idx, ic) * |
| (AccEigenType)weight_val(oc, fd, fh, fw, ic)); |
| } |
| } |
| } |
| } |
| this->output->getTensor()(ob, od, oh, ow, oc) = (OutEigenType)acc; |
| } |
| } |
| } |
| } |
| } |
| |
| if (OutDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((OutEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((OutEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::OpDepthwiseConv2d(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_DEPTHWISE_CONV2D, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(Conv); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::~OpDepthwiseConv2d() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| // 'bias' checked separatedly since it doens't make sense to make required rank ranging from 1 to 4 |
| if (inputs[2]->getRank() != 1) |
| { |
| printNodeValidationError("OpDepthwiseConv2d: bias tensor must be rank 1"); |
| } |
| |
| ERROR_IF(outputs[0]->getDtype() != OutDtype, |
| "OpDepthwiseConv2d: Output data type not supported for this configuration of operator"); |
| |
| input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); |
| bias = dynamic_cast<TosaReference::TensorTemplate<TBias>*>(inputs[2]); |
| output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| std::string msg; |
| if (check_conv_attribute(attribute, 2 /* conv_dimension */, input->getShape(), output->getShape(), |
| weight->getShape(), 0 /* offset_kernel */, InDtype, WeightDtype, msg)) |
| { |
| msg = "OpDepthwiseConv2d: " + msg; |
| printNodeValidationError(msg.c_str()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpDepthwiseConv2d<InDtype, WeightDtype, OutDtype>::eval() |
| { |
| int in_batch = this->input->getShape()[0]; |
| int in_height = this->input->getShape()[1]; |
| int in_width = this->input->getShape()[2]; |
| int in_channels = this->input->getShape()[3]; |
| |
| int f_height = this->weight->getShape()[0]; |
| int f_width = this->weight->getShape()[1]; |
| int f_in_channels = this->weight->getShape()[2]; |
| int f_multiplier = this->weight->getShape()[3]; |
| |
| int b_out_channels = this->bias->getShape()[0]; |
| |
| int out_batch = this->output->getShape()[0]; |
| int out_height = this->output->getShape()[1]; |
| int out_width = this->output->getShape()[2]; |
| int out_channels = this->output->getShape()[3]; |
| |
| ERROR_IF(in_batch != out_batch, "OpDepthwiseConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| ERROR_IF(f_in_channels != in_channels, "OpDepthwiseConv2d: tensor input channel mismatch %d != %d", f_in_channels, |
| in_channels); |
| ERROR_IF(in_channels * f_multiplier != out_channels, "OpDepthwiseConv2d: tensor output channel mismatch %d != %d", |
| in_channels * f_multiplier, out_channels); |
| ERROR_IF(b_out_channels != out_channels, "OpDepthwiseConv2d: bias channels mismatch %d != %d", b_out_channels, |
| out_channels); |
| |
| 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]; |
| int dilation_w = this->attribute->dilation()[1]; |
| |
| 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], 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, pad_top, |
| pad_bottom, pad_left, pad_right); |
| |
| 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(); |
| if (InDtype == DType_INT8 || WeightDtype == DType_INT8) |
| { |
| input_val = input_val - (InEigenType)attribute->input_zp(); |
| weight_val = weight_val - (WeightEigenType)attribute->weight_zp(); |
| } |
| |
| ETensor4<InEigenType> input_padded = input_val.pad(pad); |
| |
| // GEMM doesn't fit well with DepthwiseConv2d |
| // 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] |
| ETensor5<InEigenType> input_extract_patches = input_padded.extract_image_patches( |
| f_height, f_width, stride_h, stride_w, dilation_h, dilation_w, Eigen::PADDING_VALID); |
| |
| Eigen::array<Eigen::Index, 4> reshape_dim; |
| reshape_dim.fill(1); |
| reshape_dim[3] = b_out_channels; |
| |
| Eigen::array<Eigen::Index, 4> bcast; |
| bcast[0] = out_batch; |
| bcast[1] = out_height; |
| bcast[2] = out_width; |
| bcast[3] = 1; |
| |
| // initialize with bias |
| this->output->getTensor() = this->bias->getTensor().reshape(reshape_dim).broadcast(bcast); |
| |
| // 2. direct depthwise convolution |
| for (int ob = 0; ob < out_batch; ob++) |
| { |
| for (int oh = 0; oh < out_height; oh++) |
| { |
| for (int ow = 0; ow < out_width; ow++) |
| { |
| for (int ic = 0; ic < in_channels; ic++) |
| { |
| for (int cm = 0; cm < f_multiplier; cm++) |
| { |
| for (int fh = 0; fh < f_height; fh++) |
| { |
| for (int fw = 0; fw < f_width; fw++) |
| { |
| // Perform multiplication in AccEigenType then cast to OutEigenType |
| this->output->getTensor()(ob, oh, ow, ic * f_multiplier + cm) += |
| (OutEigenType)((AccEigenType)input_extract_patches(ob, fh, fw, ow * out_height + oh, ic) * |
| (AccEigenType)weight_val(fh, fw, ic, cm)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| if (OutDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((OutEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((OutEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpFullyConnected<InDtype, WeightDtype, OutDtype>::OpFullyConnected(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_FULLY_CONNECTED, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(2); |
| |
| INIT_ATTRIBUTE(FullyConnected); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpFullyConnected<InDtype, WeightDtype, OutDtype>::~OpFullyConnected() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpFullyConnected<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); |
| bias = dynamic_cast<TosaReference::TensorTemplate<TBias>*>(inputs[2]); |
| |
| if (input->getShape()[1] != weight->getShape()[1]) |
| { |
| printNodeValidationError("OpFullyConnected operator input.shape[1] should match weight.shape[1]"); |
| return 1; |
| } |
| |
| if (weight->getShape()[0] != bias->getShape()[0]) |
| { |
| printNodeValidationError("OpFullyConnected operator bias.shape[0] should match weight.shape[0]"); |
| return 1; |
| } |
| |
| ERROR_IF(outputs[0]->getDtype() != OutDtype, |
| "OpFullyConnected: Output data type not supported for this configuration of operator"); |
| |
| output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| ERROR_IF(InDtype != DType_INT8 && attribute->input_zp() != 0, "OpFullyConnected: Input zeropoint must be zero for non int8_t data"); |
| ERROR_IF(WeightDtype != DType_INT8 && attribute->weight_zp() != 0, "OpFullyConnected: Weight zeropoint must be zero for non int8_t data"); |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpFullyConnected<InDtype, WeightDtype, OutDtype>::eval() |
| { |
| typedef Eigen::Tensor<int, 1>::DimensionPair DimPair; |
| Eigen::array<DimPair, 1> dims{ { DimPair(1, 0) } }; |
| |
| Eigen::array<Eigen::Index, 2> weight_shuffle{ 1, 0 }; |
| |
| Eigen::array<Eigen::Index, 2> bias_reshape; |
| bias_reshape[0] = 1; |
| bias_reshape[1] = this->bias->getShape()[0]; |
| |
| Eigen::array<Eigen::Index, 2> bias_bcast; |
| bias_bcast[0] = this->input->getShape()[0]; |
| bias_bcast[1] = 1; |
| |
| TIn input_val = this->input->getTensor(); |
| TWeight weight_val = this->weight->getTensor().shuffle(weight_shuffle); |
| if (InDtype == DType_INT8 || WeightDtype == DType_INT8) |
| { |
| input_val = input_val - (InEigenType)attribute->input_zp(); |
| weight_val = weight_val - (WeightEigenType)attribute->weight_zp(); |
| } |
| |
| this->output->getTensor() = |
| input_val.template cast<AccEigenType>().contract(weight_val.template cast<AccEigenType>(), dims).template cast<OutEigenType>() + |
| this->bias->getTensor().reshape(bias_reshape).broadcast(bias_bcast); |
| |
| if (OutDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((OutEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((OutEigenType)AccQMax); |
| } |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype, DType OutDtype> |
| OpMatMul<Dtype, OutDtype>::OpMatMul(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_MATMUL, id_) |
| { |
| setRequiredOperands(2, 1); |
| setRequiredRank(3); |
| |
| INIT_ATTRIBUTE(MatMul); |
| } |
| |
| template <DType Dtype, DType OutDtype> |
| OpMatMul<Dtype, OutDtype>::~OpMatMul() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType Dtype, DType OutDtype> |
| int OpMatMul<Dtype, OutDtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| ERROR_IF(outputs[0]->getDtype() != OutDtype, |
| "OpMatMul: Output data type not supported for this configuration of operator"); |
| |
| a = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| b = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[1]); |
| output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| ASSERT_MEM(a && b && output); |
| |
| // a: [N, H, C] |
| // b: [N, C, W] |
| // c: [N, H, W] |
| |
| // Check N |
| if (a->getShape()[0] != b->getShape()[0] || a->getShape()[0] != output->getShape()[0]) |
| { |
| printNodeValidationError("OpMatMul operator a.shape[0], b.shape[0] and output.shape[0] should match"); |
| return 1; |
| } |
| N = a->getShape()[0]; |
| |
| // Check C |
| if (a->getShape()[2] != b->getShape()[1]) |
| { |
| printNodeValidationError("OpMatMul operator a.shape[2] should match b.shape[1]"); |
| return 1; |
| } |
| C = a->getShape()[2]; |
| |
| // Check H |
| if (a->getShape()[1] != output->getShape()[1]) |
| { |
| printNodeValidationError("OpMatMul operator a.shape[1] should match output.shape[1]"); |
| return 1; |
| } |
| H = a->getShape()[1]; |
| |
| // Check W |
| if (b->getShape()[2] != output->getShape()[2]) |
| { |
| printNodeValidationError("OpMatMul operator output.shape[2] should match output.shape[2]"); |
| return 1; |
| } |
| W = b->getShape()[2]; |
| |
| ERROR_IF(Dtype != DType_INT8 && attribute->a_zp() != 0, "OpMatMul: A zeropoint must be zero for non int8_t data"); |
| ERROR_IF(Dtype != DType_INT8 && attribute->b_zp() != 0, "OpMatMul: B zeropoint must be zero for non int8_t data"); |
| |
| return 0; |
| } |
| |
| template <DType Dtype, DType OutDtype> |
| int OpMatMul<Dtype, OutDtype>::eval() |
| { |
| typedef Eigen::Tensor<int, 1>::DimensionPair DimPair; |
| Eigen::array<DimPair, 1> dims{ { DimPair(1, 0) } }; |
| |
| TIn a_val = this->a->getTensor(); |
| TIn b_val = this->b->getTensor(); |
| if (Dtype == DType_INT8) |
| { |
| a_val = a_val - (InEigenType)attribute->a_zp(); |
| b_val = b_val - (InEigenType)attribute->b_zp(); |
| } |
| |
| Eigen::array<Eigen::Index, 2> a_rank2_shape({ H, C }); |
| Eigen::array<Eigen::Index, 2> b_rank2_shape({ C, W }); |
| Eigen::array<Eigen::Index, 3> output_rank3_shape({ 1, H, W }); |
| |
| Eigen::array<Eigen::Index, 3> a_size_array({ 1, H, C }); |
| Eigen::array<Eigen::Index, 3> b_size_array({ 1, C, W }); |
| |
| Eigen::array<Eigen::Index, 3> a_begin_array({ 0, 0, 0 }); |
| Eigen::array<Eigen::Index, 3> b_begin_array({ 0, 0, 0 }); |
| |
| // Iterate N dimension. |
| for (int i = 0; i < N; i++) |
| { |
| a_begin_array[0] = i; |
| b_begin_array[0] = i; |
| |
| TInRank2 a_rank2_val = a_val.slice(a_begin_array, a_size_array).reshape(a_rank2_shape); |
| TInRank2 b_rank2_val = b_val.slice(b_begin_array, b_size_array).reshape(b_rank2_shape); |
| TAccRank2 output_rank2_val = |
| a_rank2_val.template cast<AccEigenType>().contract(b_rank2_val.template cast<AccEigenType>(), dims); |
| TOut output_rank3_val = output_rank2_val.reshape(output_rank3_shape).template cast<OutEigenType>(); |
| if (i == 0) |
| { |
| this->output->getTensor() = output_rank3_val; |
| } |
| else |
| { |
| TOut temp = this->output->getTensor().concatenate(output_rank3_val, 0); |
| this->output->getTensor() = temp; |
| } |
| } |
| |
| if (OutDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((OutEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((OutEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| OpMaxPool2d<Dtype>::OpMaxPool2d(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_MAX_POOL2D, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(Pool); |
| } |
| |
| template <DType Dtype> |
| OpMaxPool2d<Dtype>::~OpMaxPool2d() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType Dtype> |
| int OpMaxPool2d<Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| if (inputs[0]->matchType(*outputs[0])) |
| { |
| printNodeValidationError("OpMaxPool2d: input and output tensor type mismatch"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| std::string msg; |
| if (check_pool2d_attribute(attribute, in->getShape(), out->getShape(), msg)) |
| { |
| msg = "OpMaxPool2d: " + msg; |
| printNodeValidationError(msg.c_str()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType Dtype> |
| int OpMaxPool2d<Dtype>::eval() |
| { |
| int in_batch = this->in->getShape()[0]; |
| int in_height = this->in->getShape()[1]; |
| int in_width = this->in->getShape()[2]; |
| int in_channels = this->in->getShape()[3]; |
| |
| int out_batch = this->out->getShape()[0]; |
| int out_height = this->out->getShape()[1]; |
| int out_width = this->out->getShape()[2]; |
| int out_channels = this->out->getShape()[3]; |
| |
| 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 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]; |
| int stride_w = this->attribute->stride()[1]; |
| |
| DEBUG_INFO(OP, |
| "perform MaxPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%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, pad_top, pad_bottom, pad_left, pad_right); |
| |
| Eigen::array<Eigen::Index, 2> im2col_input_dims; |
| im2col_input_dims[0] = kernel_h * kernel_w; |
| im2col_input_dims[1] = out_batch * out_height * out_width * out_channels; |
| |
| Eigen::array<Eigen::Index, 4> col2im_output_dims; |
| col2im_output_dims[0] = out_batch; |
| col2im_output_dims[1] = out_height; |
| col2im_output_dims[2] = out_width; |
| col2im_output_dims[3] = out_channels; |
| |
| 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(pad, std::numeric_limits<InEigenType>::lowest()); |
| |
| // extract_image_patches() output [N, KH, KW, H * W, C] |
| // transpose to [KH, KW, N, H * W, C] |
| // reshape to [KH * KW, N * H * W * C] |
| // |
| // Set the padding value to be the most negative value that can be |
| // represented by the datatype to ensure that any padding values will be equal |
| // to or smaller than the actual maximum in the KH x KW patch. |
| ETensor2<InEigenType> input_extract_patches = |
| input_padded |
| .extract_image_patches(kernel_h, kernel_w, stride_h, stride_w, 1, 1, Eigen::PADDING_VALID, |
| std::numeric_limits<InEigenType>::lowest()) |
| .shuffle(Eigen::array<Eigen::Index, 5>{ 1, 2, 0, 3, 4 }) |
| .reshape(im2col_input_dims); |
| |
| // Get the maximum of the KHxHW patches along axis 0 |
| Eigen::Tensor<DenseIndex, 1> tensor_argmax = input_extract_patches.argmax(0); |
| |
| // 1D result with [N * H * W * C] |
| ETensor1<OutEigenType> out_1d(this->out->getElementCount()); |
| |
| // index input_patches with argmax array should give the result |
| for (size_t i = 0; i < this->out->getElementCount(); i++) |
| { |
| out_1d(i) = (OutEigenType)input_extract_patches(tensor_argmax(i), i); |
| } |
| |
| // reshape result to [N, H, W, C] |
| this->out->getTensor() = out_1d.reshape(col2im_output_dims); |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| OpFFT2d<Dtype>::OpFFT2d(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_FFT2D, id_) |
| { |
| setRequiredOperands(2, 2); |
| setRequiredRank(3); |
| |
| INIT_ATTRIBUTE(FFT); |
| } |
| |
| template <DType Dtype> |
| OpFFT2d<Dtype>::~OpFFT2d() { |
| if (attribute) |
| delete attribute; |
| } |
| |
| |
| template <DType Dtype> |
| int OpFFT2d<Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || |
| validateRequiredRank(outputs[0]) || validateRequiredRank(outputs[1])) |
| { |
| return 1; |
| } |
| |
| if (inputs[0]->matchType(*outputs[0]) || inputs[1]->matchType(*outputs[1]) || |
| inputs[0]->matchType(*inputs[1])) |
| { |
| printNodeValidationError("OpFFT2d: input and output tensor type mismatch"); |
| return 1; |
| } |
| |
| in_real = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| in_imag = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[1]); |
| out_real = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| out_imag = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[1]); |
| |
| ASSERT_MEM(in_real && in_imag && out_real && out_imag); |
| |
| std::string msg; |
| if (check_fft_shape(in_real->getShape(), in_imag->getShape(), |
| out_real->getShape(), out_imag->getShape(), msg)) |
| { |
| msg = "OpFFT2d: " + msg; |
| printNodeValidationError(msg.c_str()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType Dtype> |
| int OpFFT2d<Dtype>::eval() |
| { |
| int in_real_batch = this->in_real->getShape()[0]; |
| int in_real_height = this->in_real->getShape()[1]; |
| int in_real_width = this->in_real->getShape()[2]; |
| |
| int in_imag_batch = this->in_imag->getShape()[0]; |
| int in_imag_height = this->in_imag->getShape()[1]; |
| int in_imag_width = this->in_imag->getShape()[2]; |
| |
| int out_real_batch = this->out_real->getShape()[0]; |
| int out_real_height = this->out_real->getShape()[1]; |
| int out_real_width = this->out_real->getShape()[2]; |
| |
| int out_imag_batch = this->out_imag->getShape()[0]; |
| int out_imag_height = this->out_imag->getShape()[1]; |
| int out_imag_width = this->out_imag->getShape()[2]; |
| |
| DEBUG_INFO(OP, |
| "perform OpFFT2d, input.shapes=[[%d,%d,%d],[%d,%d,%d]], output.shapes=[[%d,%d,%d],[%d,%d,%d]]", |
| in_real_batch, in_real_height, in_real_width, |
| in_imag_batch, in_imag_height, in_imag_width, |
| out_real_batch, out_real_height, out_real_width, |
| out_imag_batch, out_imag_height, out_imag_width); |
| |
| OutEigenType sum_real, sum_imag, a, sign_val = 1.0; |
| |
| if (attribute->inverse()) { |
| sign_val = -1.0; |
| } |
| |
| for (int n = 0; n < in_real_batch; n++) |
| { |
| for (int oy = 0; oy < out_real_height; oy++) |
| { |
| for (int ox = 0; ox < out_real_width; ox++) |
| { |
| sum_real = 0.0; |
| sum_imag = 0.0; |
| for (int iy = 0; iy < in_real_height; iy++) |
| { |
| for (int ix = 0; ix < in_real_width; ix++) |
| { |
| OutEigenType val_real = this->in_real->getTensor()(n, iy, ix); |
| OutEigenType val_imag = this->in_imag->getTensor()(n, iy, ix); |
| // Use explicit cast to ensure intermmediate calculations are completed using OutEigenType |
| a = sign_val * 2 * M_PI * ((iy * (OutEigenType)oy) / in_real_height + (ix * (OutEigenType)ox) / in_real_width); |
| sum_real += val_real * cos(a) + val_imag * sin(a); |
| sum_imag += -val_real * sin(a) + val_imag * cos(a); |
| } |
| } |
| this->out_real->getTensor()(n, oy, ox) = sum_real; |
| this->out_imag->getTensor()(n, oy, ox) = sum_imag; |
| } |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| OpRFFT2d<Dtype>::OpRFFT2d(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_RFFT2D, id_) |
| { |
| setRequiredOperands(1, 2); |
| setRequiredRank(3); |
| } |
| |
| template <DType Dtype> |
| OpRFFT2d<Dtype>::~OpRFFT2d() {} |
| |
| |
| template <DType Dtype> |
| int OpRFFT2d<Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0]) || |
| validateRequiredRank(outputs[1])) |
| { |
| return 1; |
| } |
| |
| if (inputs[0]->matchType(*outputs[0]) || inputs[0]->matchType(*outputs[1])) |
| { |
| printNodeValidationError("OpRFFT2d: input and output tensor type mismatch"); |
| return 1; |
| } |
| |
| in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| out_real = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| out_imag = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[1]); |
| |
| ASSERT_MEM(in && out_real && out_imag); |
| |
| std::string msg; |
| if (check_fft_shape(in->getShape(), {}, |
| out_real->getShape(), out_imag->getShape(), msg)) |
| { |
| msg = "OpRFFT2d: " + msg; |
| printNodeValidationError(msg.c_str()); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType Dtype> |
| int OpRFFT2d<Dtype>::eval() |
| { |
| int32_t in_batch = in->getShape()[0]; |
| int32_t in_height = in->getShape()[1]; |
| int32_t in_width = in->getShape()[2]; |
| |
| int32_t out_real_batch = out_real->getShape()[0]; |
| int32_t out_real_height = out_real->getShape()[1]; |
| int32_t out_real_width = out_real->getShape()[2]; |
| |
| int32_t out_imag_batch = out_imag->getShape()[0]; |
| int32_t out_imag_height = out_imag->getShape()[1]; |
| int32_t out_imag_width = out_imag->getShape()[2]; |
| |
| DEBUG_INFO(OP, |
| "perform OpRFFT2d, input.shape=[%d,%d,%d], output_real.shape=[%d,%d,%d], " |
| "output_imag.shape=[%d,%d,%d]", |
| in_batch, in_height, in_width, |
| out_real_batch, out_real_height, out_real_width, |
| out_imag_batch, out_imag_height, out_imag_width); |
| |
| OutEigenType sum_real, sum_imag, a; |
| |
| for (int n = 0; n < in_batch; n++) |
| { |
| for (int oy = 0; oy < out_real_height; oy++) |
| { |
| for (int ox = 0; ox < out_real_width; ox++) |
| { |
| sum_real = 0.0; |
| sum_imag = 0.0; |
| for (int iy = 0; iy < in_height; iy++) |
| { |
| for (int ix = 0; ix < in_width; ix++) |
| { |
| // Use explicit cast to ensure intermmediate calculations are completed using OutEigenType |
| a = 2 * M_PI * ((iy * (OutEigenType)oy) / in_height + (ix * (OutEigenType)ox) / in_width); |
| sum_real += this->in->getTensor()(n, iy, ix) * cos(a); |
| sum_imag += -this->in->getTensor()(n, iy, ix) * sin(a); |
| } |
| } |
| this->out_real->getTensor()(n, oy, ox) = sum_real; |
| this->out_imag->getTensor()(n, oy, ox) = sum_imag; |
| } |
| } |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::OpTransposeConv2d(SubgraphTraverser* sgt_, |
| TosaAttributeBase* attribute_, |
| uint64_t id_) |
| : GraphNode(sgt_, Op_TRANSPOSE_CONV2D, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(TransposeConv); |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::~OpTransposeConv2d() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| ERROR_IF(outputs[0]->getDtype() != OutDtype, |
| "OpTransposeConv2d: Output data type not supported for this configuration of operator"); |
| |
| input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| weight = dynamic_cast<TosaReference::TensorTemplate<TWeight>*>(inputs[1]); |
| bias = dynamic_cast<TosaReference::TensorTemplate<TBias>*>(inputs[2]); |
| output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| if (attribute->out_pad().size() != 4) |
| { |
| printNodeValidationError("OpTransposeConv2d: illegal size for attribute out_pad"); |
| return 1; |
| } |
| |
| if (attribute->stride().size() != 2) |
| { |
| printNodeValidationError("OpTransposeConv2d: illegal size for attribute stride"); |
| return 1; |
| } |
| |
| if (attribute->output_shape().size() != 4) |
| { |
| printNodeValidationError("OpTransposeConv2d: illegal size for attribute output_shape"); |
| return 1; |
| } |
| |
| |
| |
| for (int32_t i : attribute->stride()) |
| { |
| if (i < 1) |
| { |
| printNodeValidationError("OpTransposeConv2d: At least one stride is smaller than one"); |
| return 1; |
| } |
| } |
| |
| for (int d = 0; d < 4; d++) |
| { |
| if (attribute->output_shape()[d] != this->output->getShape()[d]) |
| { |
| printNodeValidationError("OpTransposeConv2d: illegal size for attribute output_shape"); |
| return 1; |
| } |
| } |
| |
| int32_t IH = input->getShape()[1]; |
| int32_t IW = input->getShape()[2]; |
| int32_t OH = output->getShape()[1]; |
| int32_t OW = output->getShape()[2]; |
| |
| int32_t stride_y = attribute->stride()[0]; |
| int32_t stride_x = attribute->stride()[1]; |
| int32_t kernel_h = weight->getShape()[1]; |
| int32_t kernel_w = weight->getShape()[2]; |
| |
| int32_t out_pad_top = attribute->out_pad()[0]; |
| int32_t out_pad_bottom = attribute->out_pad()[1]; |
| int32_t out_pad_left = attribute->out_pad()[2]; |
| int32_t out_pad_right = attribute->out_pad()[3]; |
| |
| for (size_t i = 0; i < attribute->out_pad().size(); i++) |
| { |
| ERROR_IF(attribute->out_pad()[i] <= -(weight->getShape()[(i / 2) + 1]), "OpTransposeConv2d: At least one out_pad value is larger than kernel size"); |
| } |
| |
| int32_t H = (IH - 1) * stride_y + out_pad_top + out_pad_bottom + kernel_h; |
| int32_t W = (IW - 1) * stride_x + out_pad_left + out_pad_right + kernel_w; |
| |
| if ((OH != H) || (OW != W)) |
| { |
| std::string msg = "OpTransposeConv2d: Mismatch between output shape provided and expected output shape (" + |
| std::to_string(H) + "," + |
| std::to_string(W) + ")"; |
| printNodeValidationError(msg.c_str()); |
| return 1; |
| } |
| |
| ERROR_IF(InDtype != DType_INT8 && attribute->input_zp() != 0, "OpTransposeConv2d: Input zeropoint must be zero for non int8_t data"); |
| ERROR_IF(WeightDtype != DType_INT8 && attribute->weight_zp() != 0, "OpTransposeConv2d: Weight zeropoint must be zero for non int8_t data"); |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType WeightDtype, DType OutDtype> |
| int OpTransposeConv2d<InDtype, WeightDtype, OutDtype>::eval() |
| { |
| int in_batch = this->input->getShape()[0]; |
| int in_height = this->input->getShape()[1]; |
| int in_width = this->input->getShape()[2]; |
| int in_channels = this->input->getShape()[3]; |
| |
| int f_out_channels = this->weight->getShape()[0]; |
| int f_height = this->weight->getShape()[1]; |
| int f_width = this->weight->getShape()[2]; |
| int f_in_channels = this->weight->getShape()[3]; |
| |
| int b_out_channels = this->bias->getShape()[0]; |
| |
| int out_batch = this->output->getShape()[0]; |
| int out_height = this->output->getShape()[1]; |
| int out_width = this->output->getShape()[2]; |
| int out_channels = this->output->getShape()[3]; |
| |
| int out_pad_top = this->attribute->out_pad()[0]; |
| int out_pad_bottom = this->attribute->out_pad()[1]; |
| int out_pad_left = this->attribute->out_pad()[2]; |
| int out_pad_right = this->attribute->out_pad()[3]; |
| |
| int stride_h = this->attribute->stride()[0]; |
| int stride_w = this->attribute->stride()[1]; |
| |
| ERROR_IF(in_batch != out_batch, "OpTransposeConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| ERROR_IF(f_in_channels != in_channels, "OpTransposeConv2d: tensor input channel mismatch %d != %d", f_in_channels, |
| in_channels); |
| ERROR_IF(f_out_channels != out_channels, "OpTransposeConv2d: tensor output channel mismatch %d != %d", |
| f_out_channels, out_channels); |
| ERROR_IF(b_out_channels != out_channels, "OpDepthwiseConv2d: bias channels mismatch %d != %d", b_out_channels, |
| out_channels); |
| |
| DEBUG_INFO(OP, |
| "perform OpTransposeConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], " |
| "output.shape=[%d,%d,%d,%d], stride=[%d,%d], out_pad=[%d,%d,%d,%d]", |
| in_batch, in_height, in_width, in_channels, f_height, f_width, f_out_channels, f_in_channels, |
| out_batch, out_height, out_width, out_channels, stride_h, stride_w, out_pad_top, |
| out_pad_bottom, out_pad_left, out_pad_right); |
| |
| TIn input_val = this->input->getTensor(); |
| TWeight weight_val = this->weight->getTensor(); |
| if (InDtype == DType_INT8 || WeightDtype == DType_INT8) |
| { |
| input_val = input_val - (InEigenType)attribute->input_zp(); |
| weight_val = weight_val - (WeightEigenType)attribute->weight_zp(); |
| } |
| |
| Eigen::array<Eigen::Index, 4> reshape_dim; |
| reshape_dim.fill(1); |
| reshape_dim[3] = b_out_channels; |
| |
| Eigen::array<Eigen::Index, 4> bcast; |
| bcast[0] = out_batch; |
| bcast[1] = out_height; |
| bcast[2] = out_width; |
| bcast[3] = 1; |
| |
| // initialize with bias |
| this->output->getTensor() = this->bias->getTensor().reshape(reshape_dim).broadcast(bcast); |
| |
| int out_x_origin, out_y_origin; |
| int out_x, out_y; |
| |
| // reference implementation from: tensorflow/tensorflow/lite/kernels/internal/reference/reference_ops.h |
| for (int ob = 0; ob < out_batch; ob++) |
| { |
| for (int ih = 0; ih < in_height; ih++) |
| { |
| for (int iw = 0; iw < in_width; iw++) |
| { |
| out_x_origin = iw * stride_w + out_pad_left; |
| out_y_origin = ih * stride_h + out_pad_top; |
| for (int ic = 0; ic < in_channels; ic++) |
| { |
| for (int fh = 0; fh < f_height; fh++) |
| { |
| for (int fw = 0; fw < f_width; fw++) |
| { |
| out_x = out_x_origin + fw; |
| out_y = out_y_origin + fh; |
| for (int oc = 0; oc < out_channels; oc++) |
| { |
| if ((out_x >= 0 && out_x < out_width) && (out_y >= 0 && out_y < out_height)) |
| { |
| this->output->getTensor()(ob, out_y, out_x, oc) += |
| (OutEigenType) ((AccEigenType)input_val(ob, ih, iw, ic) * |
| (AccEigenType)weight_val(oc, fh, fw, ic)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| if (OutDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((OutEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((OutEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| // template explicit instantiation |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpArgMax, FP16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpArgMax, BF16); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpArgMax, FP32); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpArgMax, INT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpArgMax, INT16); |
| |
| DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, FP16, FP16); |
| DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, FP16, FP32); |
| DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, BF16, FP32); |
| DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, FP32, FP32); |
| DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, INT8, INT32); |
| DEF_INSTANTIATE_TWO_TYPE(OpAvgPool2d, INT16, INT32); |
| |
| // [in_t, weight_t, out_t] |
| DEF_INSTANTIATE_THREE_TYPE(OpConv2d, FP16, FP16, FP16); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv2d, FP16, FP16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv2d, BF16, BF16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv2d, FP32, FP32, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv2d, INT8, INT4, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv2d, INT8, INT8, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv2d, INT16, INT8, INT48); |
| |
| DEF_INSTANTIATE_THREE_TYPE(OpConv3d, FP16, FP16, FP16); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv3d, FP16, FP16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv3d, BF16, BF16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv3d, FP32, FP32, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv3d, INT8, INT4, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv3d, INT8, INT8, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpConv3d, INT16, INT8, INT48); |
| |
| DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, FP16, FP16, FP16); |
| DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, FP16, FP16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, BF16, BF16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, FP32, FP32, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, INT8, INT4, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, INT8, INT8, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, INT16, INT8, INT48); |
| |
| DEF_INSTANTIATE_ONE_TYPE(OpFFT2d, FP32); |
| |
| DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, FP16, FP16, FP16); |
| DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, FP16, FP16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, BF16, BF16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, FP32, FP32, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, INT8, INT4, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, INT8, INT8, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, INT16, INT8, INT48); |
| |
| DEF_INSTANTIATE_TWO_TYPE(OpMatMul, INT8, INT32); |
| DEF_INSTANTIATE_TWO_TYPE(OpMatMul, INT16, INT48); |
| DEF_INSTANTIATE_TWO_TYPE(OpMatMul, FP16, FP16); |
| DEF_INSTANTIATE_TWO_TYPE(OpMatMul, FP16, FP32); |
| DEF_INSTANTIATE_TWO_TYPE(OpMatMul, BF16, FP32); |
| DEF_INSTANTIATE_TWO_TYPE(OpMatMul, FP32, FP32); |
| |
| DEF_INSTANTIATE_ONE_TYPE(OpMaxPool2d, FP16); |
| DEF_INSTANTIATE_ONE_TYPE(OpMaxPool2d, BF16); |
| DEF_INSTANTIATE_ONE_TYPE(OpMaxPool2d, FP32); |
| DEF_INSTANTIATE_ONE_TYPE(OpMaxPool2d, INT8); |
| DEF_INSTANTIATE_ONE_TYPE(OpMaxPool2d, INT16); |
| |
| DEF_INSTANTIATE_ONE_TYPE(OpRFFT2d, FP32); |
| |
| DEF_INSTANTIATE_THREE_TYPE(OpTransposeConv2d, FP16, FP16, FP16); |
| DEF_INSTANTIATE_THREE_TYPE(OpTransposeConv2d, FP16, FP16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpTransposeConv2d, BF16, BF16, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpTransposeConv2d, FP32, FP32, FP32); |
| DEF_INSTANTIATE_THREE_TYPE(OpTransposeConv2d, INT8, INT4, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpTransposeConv2d, INT8, INT8, INT32); |
| DEF_INSTANTIATE_THREE_TYPE(OpTransposeConv2d, INT16, INT8, INT48); |