| |
| // Copyright (c) 2020, 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" |
| |
| using namespace TosaReference; |
| using namespace Eigen; |
| using namespace tosa; |
| |
| template <int Rank, DType Dtype> |
| OpArgMax<Rank, Dtype>::OpArgMax(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_ARGMAX, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(0, 6); |
| |
| 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]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| input = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| output = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]); |
| |
| 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> |
| OpAvgPool2d<Dtype>::OpAvgPool2d(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_AVG_POOL2D, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(Pool2d); |
| INIT_QINFO(Unary); |
| } |
| |
| template <DType Dtype> |
| OpAvgPool2d<Dtype>::~OpAvgPool2d() |
| { |
| if (attribute) |
| delete attribute; |
| } |
| |
| template <DType Dtype> |
| int OpAvgPool2d<Dtype>::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]); |
| |
| if (!in->hasFormat(Format_NHWC)) |
| { |
| printNodeValidationError("OpAvgPool2d: unsupported tensor format"); |
| return 1; |
| } |
| |
| if (attribute->padding().size() != 4) |
| { |
| printNodeValidationError("OpAvgPool2d: illegal size for attribute padding"); |
| return 1; |
| } |
| |
| if (attribute->kernel().size() != 2) |
| { |
| printNodeValidationError("OpAvgPool2d: illegal size for attribute kernel"); |
| return 1; |
| } |
| |
| if (attribute->stride().size() != 2) |
| { |
| printNodeValidationError("OpAvgPool2d: illegal size for attribute stride"); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType Dtype> |
| ETensor1<int32_t> OpAvgPool2d<Dtype>::calculate_div_map_1d(int in_size, int out_size, int kernel_size, int stride) |
| { |
| ETensor1<int32_t> result(out_size); |
| |
| int32_t total_pad = (out_size - 1) * stride + kernel_size - in_size; |
| total_pad = total_pad < 0 ? 0 : total_pad; |
| |
| int32_t pad_left = total_pad >> 1; |
| int32_t pad_right = total_pad - pad_left; |
| |
| result.setConstant(kernel_size); |
| |
| // the index left to 'left_index' and index right to 'right_index' indicates |
| // the input window of this output covers a pad bit |
| int32_t left_index = pad_left / stride; |
| int32_t right_index = pad_right / stride; |
| |
| // not handle ultra small activation yet |
| ASSERT_MSG_NODE((out_size - 1 - right_index) >= left_index, "AvgPool2d: Small activations not supported yet"); |
| |
| // minus the number of pad bit this index cover |
| while (left_index >= 0) |
| { |
| result(left_index) -= (pad_left - left_index * stride); |
| left_index--; |
| } |
| |
| while (right_index >= 0) |
| { |
| result(out_size - 1 - right_index) -= (pad_right - right_index * stride); |
| right_index--; |
| } |
| |
| return result; |
| } |
| |
| // assuming input and output tensor have same scales like tflite reference |
| // so no need to scale input and output |
| template <DType Dtype> |
| int OpAvgPool2d<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]; |
| |
| ASSERT_MSG_NODE(in_batch == out_batch, "OpAvgPool2d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| |
| 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 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 AvgPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%d,%d], " |
| "stride=[%d,%d], padding=[%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); |
| |
| 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> 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); |
| |
| ETensor4<InEigenType> input_val = this->in->getTensor(); |
| if (this->qinfo) |
| { |
| input_val = input_val - (InEigenType)this->qinfo->input_zp(); |
| } |
| |
| ETensor4<InEigenType> input_padded = input_val.pad(padding); |
| |
| // 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); |
| ETensor1<int32_t> div_map_w = calculate_div_map_1d(in_width, out_width, kernel_w, stride_w); |
| 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 }; |
| |
| ETensor4<int32_t> div_map = |
| div_map_h.reshape(Eigen::array<Eigen::Index, 2>{ out_height, 1 }) |
| .contract(div_map_w.reshape(Eigen::array<Eigen::Index, 2>{ 1, out_width }), contract_dims) |
| .reshape(Eigen::array<Eigen::Index, 4>{ 1, out_height, out_width, 1 }) |
| .broadcast(bcast); |
| |
| if (Dtype != DType_FLOAT) |
| { |
| this->out->getTensor() = sum.binaryExpr(div_map, [](AccEigenType value, int32_t div) -> OutEigenType { |
| int32_t multiplier, shift; |
| TosaReference::QuantUtil<AccDtype>::reciprocal_scale(div, multiplier, shift); |
| |
| return (OutEigenType)TosaReference::QuantUtil<AccDtype>::apply_scale(value, multiplier, shift, false); |
| }); |
| this->out->getTensor() = this->out->getTensor() + (OutEigenType)(this->qinfo->output_zp()); |
| this->out->getTensor() = this->out->getTensor().cwiseMax((OutEigenType)QMin); |
| this->out->getTensor() = this->out->getTensor().cwiseMin((OutEigenType)QMax); |
| } |
| else |
| { |
| this->out->getTensor() = (sum / div_map.template cast<AccEigenType>()).template cast<OutEigenType>(); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| OpConv2d<InDtype, WeightDtype>::OpConv2d(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_CONV2D, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(Conv2d); |
| INIT_QINFO(Conv); |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| OpConv2d<InDtype, WeightDtype>::~OpConv2d() |
| { |
| if (attribute) |
| delete attribute; |
| if (qinfo) |
| delete qinfo; |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| int OpConv2d<InDtype, WeightDtype>::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"); |
| } |
| |
| if (inputs[1]->getIsConst() == 0) |
| { |
| printNodeValidationError("OpConv2d: weight tensor is not const typed"); |
| } |
| |
| 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<TAcc>*>(outputs[0]); |
| |
| if (!input->hasFormat(Format_NHWC)) |
| { |
| printNodeValidationError("OpConv2d: unsupported input tensor format"); |
| return 1; |
| } |
| |
| if (!weight->hasFormat(Format_OHWI)) |
| { |
| printNodeValidationError("OpConv2d: unsupported weight tensor format"); |
| return 1; |
| } |
| |
| if (attribute->padding().size() != 4) |
| { |
| printNodeValidationError("OpConv2d: illegal size for attribute padding"); |
| return 1; |
| } |
| |
| if (attribute->stride().size() != 2) |
| { |
| printNodeValidationError("OpConv2d: illegal size for attribute stride"); |
| return 1; |
| } |
| |
| if (attribute->dilation().size() != 2) |
| { |
| printNodeValidationError("OpConv2d: illegal size for attribute dilation"); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| int OpConv2d<InDtype, WeightDtype>::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]; |
| |
| ASSERT_MSG_NODE(in_batch == out_batch, "OpConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| ASSERT_MSG_NODE(f_in_channels == in_channels, "OpConv2d: tensor input channel mismatch %d != %d", f_in_channels, |
| in_channels); |
| ASSERT_MSG_NODE(f_out_channels == out_channels, "OpConv2d: tensor output channel mismatch %d != %d", f_out_channels, |
| out_channels); |
| ASSERT_MSG_NODE(b_out_channels == out_channels, "OpConv2d: tensor output 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 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], padding=[%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); |
| |
| // 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> 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); |
| |
| TIn input_val = this->input->getTensor(); |
| TWeight weight_val = this->weight->getTensor(); |
| if (this->qinfo) |
| { |
| input_val = input_val - (InEigenType)this->qinfo->input_zp(); |
| weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp(); |
| } |
| |
| ETensor4<InEigenType> input_padded = input_val.pad(padding); |
| |
| // 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<AccEigenType> bias_2d = this->bias->getTensor().reshape(bias_reshaped_dims).broadcast(bias_bcast_dims); |
| |
| // output matrix is [N * H * W, C] |
| ETensor2<AccEigenType> contracted_result = |
| im2col_input.template cast<AccEigenType>().contract(im2col_weight.template cast<AccEigenType>(), contract_dims); |
| |
| // adding bias |
| ETensor2<AccEigenType> biased_output = contracted_result + bias_2d.template cast<AccEigenType>(); |
| |
| // reshape back to [N, H, W, C] |
| this->output->getTensor() = biased_output.reshape(col2im_output_dims); |
| |
| if (AccDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((AccEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((AccEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| OpDepthwiseConv2d<InDtype, WeightDtype>::OpDepthwiseConv2d(TosaAttributeBase* attribute_, |
| TosaQuantInfoBase* qinfo_, |
| uint64_t id_) |
| : GraphNode(Op_DEPTHWISE_CONV2D, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(Conv2d); |
| INIT_QINFO(Conv); |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| OpDepthwiseConv2d<InDtype, WeightDtype>::~OpDepthwiseConv2d() |
| { |
| if (attribute) |
| delete attribute; |
| if (qinfo) |
| delete qinfo; |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| int OpDepthwiseConv2d<InDtype, WeightDtype>::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"); |
| } |
| |
| if (inputs[1]->getIsConst() == 0) |
| { |
| printNodeValidationError("OpDepthwiseConv2d: weight tensor is not const typed"); |
| } |
| |
| 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<TAcc>*>(outputs[0]); |
| |
| if (!input->hasFormat(Format_NHWC)) |
| { |
| printNodeValidationError("OpDepthwiseConv2d: unsupported input tensor format"); |
| return 1; |
| } |
| |
| if (!weight->hasFormat(Format_HWIM)) |
| { |
| printNodeValidationError("OpDepthwiseConv2d: unsupported weight tensor format"); |
| return 1; |
| } |
| |
| if (attribute->padding().size() != 4) |
| { |
| printNodeValidationError("OpDepthwiseConv2d: illegal size for attribute padding"); |
| return 1; |
| } |
| |
| if (attribute->stride().size() != 2) |
| { |
| printNodeValidationError("OpDepthwiseConv2d: illegal size for attribute stride"); |
| return 1; |
| } |
| |
| if (attribute->dilation().size() != 2) |
| { |
| printNodeValidationError("OpDepthwiseConv2d: illegal size for attribute dilation"); |
| return 1; |
| } |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| int OpDepthwiseConv2d<InDtype, WeightDtype>::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]; |
| |
| ASSERT_MSG_NODE(in_batch == out_batch, "OpDepthwiseConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| ASSERT_MSG_NODE(f_in_channels == in_channels, "OpDepthwiseConv2d: tensor input channel mismatch %d != %d", |
| f_in_channels, in_channels); |
| ASSERT_MSG_NODE(in_channels * f_multiplier == out_channels, |
| "OpDepthwiseConv2d: tensor output channel mismatch %d != %d", in_channels * f_multiplier, |
| out_channels); |
| ASSERT_MSG_NODE(b_out_channels == out_channels, "OpDepthwiseConv2d: tensor b_out_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 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], padding=[%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); |
| |
| 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); |
| |
| TIn input_val = this->input->getTensor(); |
| TWeight weight_val = this->weight->getTensor(); |
| if (this->qinfo) |
| { |
| input_val = input_val - (InEigenType)this->qinfo->input_zp(); |
| weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp(); |
| } |
| |
| ETensor4<InEigenType> input_padded = input_val.pad(padding); |
| |
| // GEMM doesn't fit well with DepthwiseConv2d |
| // 1. use extract_image_patches() to handle stride/dilation/padding |
| // 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++) |
| { |
| this->output->getTensor()(ob, oh, ow, ic * f_multiplier + cm) += |
| ((AccEigenType)input_extract_patches(ob, fh, fw, ow * out_height + oh, ic) * |
| (AccEigenType)weight_val(fh, fw, ic, cm)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| if (AccDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((AccEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((AccEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| OpFullyConnected<InDtype, WeightDtype>::OpFullyConnected(TosaAttributeBase* attribute_, |
| TosaQuantInfoBase* qinfo_, |
| uint64_t id_) |
| : GraphNode(Op_FULLY_CONNECTED, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(2); |
| |
| INIT_QINFO(Conv); |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| OpFullyConnected<InDtype, WeightDtype>::~OpFullyConnected() |
| { |
| if (qinfo) |
| delete qinfo; |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| int OpFullyConnected<InDtype, WeightDtype>::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; |
| } |
| |
| output = dynamic_cast<TosaReference::TensorTemplate<TAcc>*>(outputs[0]); |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType WeightDtype> |
| int OpFullyConnected<InDtype, WeightDtype>::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 (this->qinfo) |
| { |
| input_val = input_val - (InEigenType)this->qinfo->input_zp(); |
| weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp(); |
| } |
| |
| this->output->getTensor() = |
| input_val.template cast<AccEigenType>().contract(weight_val.template cast<AccEigenType>(), dims) + |
| this->bias->getTensor().reshape(bias_reshape).broadcast(bias_bcast); |
| |
| if (AccDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((AccEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((AccEigenType)AccQMax); |
| } |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| OpMatMul<Dtype>::OpMatMul(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_MATMUL, id_) |
| { |
| setRequiredOperands(2, 1); |
| setRequiredRank(2); |
| |
| INIT_QINFO(MatMul); |
| } |
| |
| template <DType Dtype> |
| OpMatMul<Dtype>::~OpMatMul() |
| { |
| if (qinfo) |
| delete qinfo; |
| } |
| |
| template <DType Dtype> |
| int OpMatMul<Dtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| a = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]); |
| b = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[1]); |
| |
| if (a->getShape()[1] != b->getShape()[0]) |
| { |
| printNodeValidationError("OpMatMul operator a.shape[1] should match b.shape[0]"); |
| return 1; |
| } |
| |
| c = dynamic_cast<TosaReference::TensorTemplate<TAcc>*>(outputs[0]); |
| |
| return 0; |
| } |
| |
| template <DType Dtype> |
| int OpMatMul<Dtype>::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 (this->qinfo) |
| { |
| a_val = a_val - (InEigenType)this->qinfo->a_zp(); |
| b_val = b_val - (InEigenType)this->qinfo->b_zp(); |
| } |
| |
| this->c->getTensor() = a_val.template cast<AccEigenType>().contract(b_val.template cast<AccEigenType>(), dims); |
| |
| if (AccDtype == DType_INT48) |
| { |
| this->c->getTensor() = this->c->getTensor().cwiseMax((AccEigenType)AccQMin); |
| this->c->getTensor() = this->c->getTensor().cwiseMin((AccEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| template <DType Dtype> |
| OpMaxPool2d<Dtype>::OpMaxPool2d(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) |
| : GraphNode(Op_MAX_POOL2D, id_) |
| { |
| setRequiredOperands(1, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(Pool2d); |
| } |
| |
| 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]); |
| |
| if (!in->hasFormat(Format_NHWC)) |
| { |
| printNodeValidationError("OpMaxPool2d: unsupported tensor format"); |
| return 1; |
| } |
| |
| if (attribute->padding().size() != 4) |
| { |
| printNodeValidationError("OpMaxPool2d: illegal size for attribute padding"); |
| return 1; |
| } |
| |
| if (attribute->kernel().size() != 2) |
| { |
| printNodeValidationError("OpMaxPool2d: illegal size for attribute kernel"); |
| return 1; |
| } |
| |
| if (attribute->stride().size() != 2) |
| { |
| printNodeValidationError("OpMaxPool2d: illegal size for attribute stride"); |
| 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]; |
| |
| ASSERT_MSG_NODE(in_batch == out_batch, "OpMaxPool2d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| |
| 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 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], padding=[%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); |
| |
| 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> 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); |
| |
| ETensor4<InEigenType> input_padded = this->in->getTensor().pad(padding, 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 InDtype, DType OutDtype> |
| OpTransposeConv2d<InDtype, OutDtype>::OpTransposeConv2d(TosaAttributeBase* attribute_, |
| TosaQuantInfoBase* qinfo_, |
| uint64_t id_) |
| : GraphNode(Op_TRANSPOSE_CONV2D, id_) |
| { |
| setRequiredOperands(3, 1); |
| setRequiredRank(4); |
| |
| INIT_ATTRIBUTE(TransposeConv2d); |
| INIT_QINFO(Conv); |
| } |
| |
| template <DType InDtype, DType OutDtype> |
| OpTransposeConv2d<InDtype, OutDtype>::~OpTransposeConv2d() |
| { |
| if (attribute) |
| delete attribute; |
| if (qinfo) |
| delete qinfo; |
| } |
| |
| template <DType InDtype, DType OutDtype> |
| int OpTransposeConv2d<InDtype, OutDtype>::checkTensorAttributes() |
| { |
| if (validateRequiredOperands()) |
| return 1; |
| |
| if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0])) |
| { |
| return 1; |
| } |
| |
| if (inputs[1]->getIsConst() == 0) |
| { |
| printNodeValidationError("OpTransposeConv2d: weight tensor is not const typed"); |
| } |
| |
| 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<TAcc>*>(outputs[0]); |
| |
| if (!input->hasFormat(Format_NHWC)) |
| { |
| printNodeValidationError("OpTransposeConv2d: unsupported input tensor format"); |
| return 1; |
| } |
| |
| if (!weight->hasFormat(Format_OHWI)) |
| { |
| printNodeValidationError("OpTransposeConv2d: unsupported weight tensor format"); |
| return 1; |
| } |
| |
| if (attribute->outpad().size() != 2) |
| { |
| printNodeValidationError("OpTransposeConv2d: illegal size for attribute outpad"); |
| return 1; |
| } |
| |
| if (attribute->stride().size() != 2) |
| { |
| printNodeValidationError("OpTransposeConv2d: illegal size for attribute stride"); |
| return 1; |
| } |
| |
| if (attribute->dilation().size() != 2) |
| { |
| printNodeValidationError("OpTransposeConv2d: illegal size for attribute dilation"); |
| return 1; |
| } |
| |
| if (attribute->output_shape().size() != 4) |
| { |
| printNodeValidationError("OpTransposeConv2d: illegal size for attribute output_shape"); |
| 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; |
| } |
| } |
| |
| return 0; |
| } |
| |
| template <DType InDtype, DType OutDtype> |
| int OpTransposeConv2d<InDtype, 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 padding_top = this->attribute->outpad()[0]; |
| int padding_left = this->attribute->outpad()[1]; |
| 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]; |
| |
| ASSERT_MSG_NODE(in_batch == out_batch, "OpTransposeConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); |
| ASSERT_MSG_NODE(f_in_channels == in_channels, "OpTransposeConv2d: tensor input channel mismatch %d != %d", |
| f_in_channels, in_channels); |
| ASSERT_MSG_NODE(f_out_channels == out_channels, "OpTransposeConv2d: tensor output channel mismatch %d != %d", |
| f_out_channels, out_channels); |
| ASSERT_MSG_NODE(b_out_channels == out_channels, "OpDepthwiseConv2d: tensor b_out_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], dilation=[%d,%d], padding=[%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, dilation_h, dilation_w, padding_top, |
| padding_left); |
| |
| TIn input_val = this->input->getTensor(); |
| TWeight weight_val = this->weight->getTensor(); |
| if (this->qinfo) |
| { |
| input_val = input_val - (InEigenType)this->qinfo->input_zp(); |
| weight_val = weight_val - (WeightEigenType)this->qinfo->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 - padding_left; |
| out_y_origin = ih * stride_h - padding_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 * dilation_w; |
| out_y = out_y_origin + fh * dilation_h; |
| 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) += |
| ((AccEigenType)input_val(ob, ih, iw, ic) * |
| (AccEigenType)weight_val(oc, fh, fw, ic)); |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| } |
| |
| if (AccDtype == DType_INT48) |
| { |
| this->output->getTensor() = this->output->getTensor().cwiseMax((AccEigenType)AccQMin); |
| this->output->getTensor() = this->output->getTensor().cwiseMin((AccEigenType)AccQMax); |
| } |
| |
| return GraphNode::eval(); |
| } |
| |
| // template explicit instantiation |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpArgMax, FLOAT); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpArgMax, AINT8); |
| DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpArgMax, INT16); |
| |
| DEF_INSTANTIATE_ONE_TYPE(OpAvgPool2d, FLOAT) |
| DEF_INSTANTIATE_ONE_TYPE(OpAvgPool2d, AINT8) |
| DEF_INSTANTIATE_ONE_TYPE(OpAvgPool2d, INT16) |
| |
| DEF_INSTANTIATE_TWO_TYPE(OpConv2d, FLOAT, FLOAT); |
| DEF_INSTANTIATE_TWO_TYPE(OpConv2d, AINT8, INT4); |
| DEF_INSTANTIATE_TWO_TYPE(OpConv2d, AINT8, INT8); |
| DEF_INSTANTIATE_TWO_TYPE(OpConv2d, AINT8, AINT8); |
| DEF_INSTANTIATE_TWO_TYPE(OpConv2d, INT16, INT8); |
| |
| DEF_INSTANTIATE_TWO_TYPE(OpDepthwiseConv2d, FLOAT, FLOAT); |
| DEF_INSTANTIATE_TWO_TYPE(OpDepthwiseConv2d, AINT8, INT4); |
| DEF_INSTANTIATE_TWO_TYPE(OpDepthwiseConv2d, AINT8, INT8); |
| DEF_INSTANTIATE_TWO_TYPE(OpDepthwiseConv2d, AINT8, AINT8); |
| DEF_INSTANTIATE_TWO_TYPE(OpDepthwiseConv2d, INT16, INT8); |
| |
| DEF_INSTANTIATE_TWO_TYPE(OpFullyConnected, FLOAT, FLOAT); |
| DEF_INSTANTIATE_TWO_TYPE(OpFullyConnected, AINT8, INT4); |
| DEF_INSTANTIATE_TWO_TYPE(OpFullyConnected, AINT8, INT8); |
| DEF_INSTANTIATE_TWO_TYPE(OpFullyConnected, AINT8, AINT8); |
| DEF_INSTANTIATE_TWO_TYPE(OpFullyConnected, INT16, INT8); |
| |
| DEF_INSTANTIATE_ONE_TYPE(OpMatMul, AINT8); |
| DEF_INSTANTIATE_ONE_TYPE(OpMatMul, INT16); |
| DEF_INSTANTIATE_ONE_TYPE(OpMatMul, FLOAT); |
| |
| DEF_INSTANTIATE_ONE_TYPE(OpMaxPool2d, FLOAT); |
| DEF_INSTANTIATE_ONE_TYPE(OpMaxPool2d, AINT8); |
| DEF_INSTANTIATE_ONE_TYPE(OpMaxPool2d, INT16); |
| |
| DEF_INSTANTIATE_TWO_TYPE(OpTransposeConv2d, FLOAT, FLOAT); |
| DEF_INSTANTIATE_TWO_TYPE(OpTransposeConv2d, AINT8, INT4); |
| DEF_INSTANTIATE_TWO_TYPE(OpTransposeConv2d, AINT8, INT8); |
| DEF_INSTANTIATE_TWO_TYPE(OpTransposeConv2d, AINT8, AINT8); |
| DEF_INSTANTIATE_TWO_TYPE(OpTransposeConv2d, INT16, INT8); |