diff options
Diffstat (limited to 'reference_model')
-rw-r--r-- | reference_model/src/ops/op_factory.cc | 6 | ||||
-rw-r--r-- | reference_model/src/ops/tensor_ops.cc | 200 | ||||
-rw-r--r-- | reference_model/src/ops/tensor_ops.h | 32 | ||||
-rw-r--r-- | reference_model/src/subgraph_traverser.cc | 1 |
4 files changed, 239 insertions, 0 deletions
diff --git a/reference_model/src/ops/op_factory.cc b/reference_model/src/ops/op_factory.cc index 193b2af..3bc55a8 100644 --- a/reference_model/src/ops/op_factory.cc +++ b/reference_model/src/ops/op_factory.cc @@ -64,6 +64,12 @@ GraphNode* OpFactory::newOp(SubgraphTraverser* sgt, DEF_FACTORY_TWO_TYPE(OpConv2d, INT8, INT8); DEF_FACTORY_TWO_TYPE(OpConv2d, INT16, INT8); break; + case Op_CONV3D: + DEF_FACTORY_TWO_TYPE(OpConv3d, FLOAT, FLOAT); + DEF_FACTORY_TWO_TYPE(OpConv3d, INT8, INT4); + DEF_FACTORY_TWO_TYPE(OpConv3d, INT8, INT8); + DEF_FACTORY_TWO_TYPE(OpConv3d, INT16, INT8); + break; case Op_DEPTHWISE_CONV2D: DEF_FACTORY_TWO_TYPE(OpDepthwiseConv2d, FLOAT, FLOAT); DEF_FACTORY_TWO_TYPE(OpDepthwiseConv2d, INT8, INT4); diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc index a150656..a0a1f04 100644 --- a/reference_model/src/ops/tensor_ops.cc +++ b/reference_model/src/ops/tensor_ops.cc @@ -482,6 +482,201 @@ int OpConv2d<InDtype, WeightDtype>::eval() } template <DType InDtype, DType WeightDtype> +OpConv3d<InDtype, WeightDtype>::OpConv3d(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, + TosaQuantInfoBase* qinfo_, + uint64_t id_) + : GraphNode(sgt_, Op_CONV3D, id_) +{ + setRequiredOperands(3, 1); + setRequiredRank(5); + + INIT_ATTRIBUTE(Conv); + INIT_QINFO(Conv); +} + +template <DType InDtype, DType WeightDtype> +OpConv3d<InDtype, WeightDtype>::~OpConv3d() +{ + if (attribute) + delete attribute; + if (qinfo) + delete qinfo; +} + +template <DType InDtype, DType WeightDtype> +int OpConv3d<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("OpConv3d: bias tensor must be rank 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]); + output = dynamic_cast<TosaReference::TensorTemplate<TAcc>*>(outputs[0]); + + if (attribute->padding().size() != 6) + { + printNodeValidationError("OpConv3d: illegal size for attribute padding"); + return 1; + } + + if (attribute->stride().size() != 3) + { + printNodeValidationError("OpConv3d: illegal size for attribute stride"); + return 1; + } + + if (attribute->dilation().size() != 3) + { + printNodeValidationError("OpConv3d: illegal size for attribute dilation"); + return 1; + } + + return 0; +} + +template <DType InDtype, DType WeightDtype> +int OpConv3d<InDtype, WeightDtype>::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 padding_d0 = this->attribute->padding()[0]; + int padding_d1 = this->attribute->padding()[1]; + int padding_top = this->attribute->padding()[2]; + int padding_bottom = this->attribute->padding()[3]; + int padding_left = this->attribute->padding()[4]; + int padding_right = this->attribute->padding()[5]; + int 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], padding=[%d,%d,%d,%d,%d,%d]", + in_batch, in_depth, in_height, in_width, in_channels, f_out_channels, f_depth, f_height, f_width, f_in_channels, + out_batch, out_depth, out_height, out_width, out_channels, stride_d, stride_h, stride_w, dilation_d, dilation_h, + dilation_w, padding_d0, padding_d1, padding_top, padding_bottom, padding_left, padding_right); + + Eigen::array<std::pair<int32_t, int32_t>, 5> padding; + padding[0] = std::make_pair(0, 0); + padding[1] = std::make_pair(padding_d0, padding_d1); + padding[2] = std::make_pair(padding_top, padding_bottom); + padding[3] = std::make_pair(padding_left, padding_right); + padding[4] = std::make_pair(0, 0); + + 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(); + } + + ETensor5<InEigenType> input_padded = input_val.pad(padding); + + // 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; + 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++) + { + acc = 0; + 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) = acc; + } + } + } + } + } + + 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(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, @@ -1221,6 +1416,11 @@ DEF_INSTANTIATE_TWO_TYPE(OpConv2d, INT8, INT4); DEF_INSTANTIATE_TWO_TYPE(OpConv2d, INT8, INT8); DEF_INSTANTIATE_TWO_TYPE(OpConv2d, INT16, INT8); +DEF_INSTANTIATE_TWO_TYPE(OpConv3d, FLOAT, FLOAT); +DEF_INSTANTIATE_TWO_TYPE(OpConv3d, INT8, INT4); +DEF_INSTANTIATE_TWO_TYPE(OpConv3d, INT8, INT8); +DEF_INSTANTIATE_TWO_TYPE(OpConv3d, INT16, INT8); + DEF_INSTANTIATE_TWO_TYPE(OpDepthwiseConv2d, FLOAT, FLOAT); DEF_INSTANTIATE_TWO_TYPE(OpDepthwiseConv2d, INT8, INT4); DEF_INSTANTIATE_TWO_TYPE(OpDepthwiseConv2d, INT8, INT8); diff --git a/reference_model/src/ops/tensor_ops.h b/reference_model/src/ops/tensor_ops.h index eea351d..2174d62 100644 --- a/reference_model/src/ops/tensor_ops.h +++ b/reference_model/src/ops/tensor_ops.h @@ -109,6 +109,38 @@ protected: }; template <DType InDtype, DType WeightDtype> +class OpConv3d : public GraphNode +{ +public: + OpConv3d(SubgraphTraverser* sgt_, TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_); + virtual ~OpConv3d(); + + virtual int checkTensorAttributes() final; + virtual int eval() final; + + static constexpr DType AccDtype = GetAccDType<InDtype, WeightDtype>::value; + + using InEigenType = typename GetEigenType<InDtype>::type; + using WeightEigenType = typename GetEigenType<WeightDtype>::type; + using AccEigenType = typename GetEigenType<AccDtype>::type; + using TIn = Eigen::Tensor<InEigenType, 5>; + using TWeight = Eigen::Tensor<WeightEigenType, 5>; + using TBias = Eigen::Tensor<AccEigenType, 1>; + using TAcc = Eigen::Tensor<AccEigenType, 5>; + + static constexpr int64_t AccQMin = GetQMin<AccDtype>::value; + static constexpr int64_t AccQMax = GetQMax<AccDtype>::value; + +protected: + TosaReference::TensorTemplate<TIn>* input; + TosaReference::TensorTemplate<TWeight>* weight; + TosaReference::TensorTemplate<TBias>* bias; + TosaReference::TensorTemplate<TAcc>* output; + tosa::TosaConvAttribute* attribute; + tosa::TosaConvQuantInfo* qinfo; +}; + +template <DType InDtype, DType WeightDtype> class OpDepthwiseConv2d : public GraphNode { public: diff --git a/reference_model/src/subgraph_traverser.cc b/reference_model/src/subgraph_traverser.cc index ef7bae6..4dba669 100644 --- a/reference_model/src/subgraph_traverser.cc +++ b/reference_model/src/subgraph_traverser.cc @@ -116,6 +116,7 @@ int SubgraphTraverser::initializeGraph() switch (op->GetOp()) { case Op_CONV2D: + case Op_CONV3D: case Op_DEPTHWISE_CONV2D: case Op_TRANSPOSE_CONV2D: case Op_FULLY_CONNECTED: |