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[Relay][QNN] Moving Conv, Dense, Concatenate InferTypes to header for…
… sharing.
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one | ||
| * or more contributor license agreements. See the NOTICE file | ||
| * distributed with this work for additional information | ||
| * regarding copyright ownership. The ASF licenses this file | ||
| * to you 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. | ||
| */ | ||
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| /*! | ||
| * Copyright (c) 2019 by Contributors | ||
| * \file src/relay/op/nn/convolution.h | ||
| * \brief Properties def of convlution operator for sharing. | ||
| */ | ||
| #ifndef TVM_RELAY_OP_NN_CONVOLUTION_H_ | ||
| #define TVM_RELAY_OP_NN_CONVOLUTION_H_ | ||
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| #include <string> | ||
| #include <utility> | ||
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| namespace tvm { | ||
| namespace relay { | ||
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| template <typename AttrType> | ||
| bool Conv2DRel(const Array<Type>& types, int num_inputs, const Attrs& attrs, | ||
| const TypeReporter& reporter) { | ||
| CHECK_EQ(types.size(), 3); | ||
| const auto* data = types[0].as<TensorTypeNode>(); | ||
| const auto* weight = types[1].as<TensorTypeNode>(); | ||
| if (data == nullptr) return false; | ||
| static const Layout kNCHW("NCHW"); | ||
| static const Layout kOIHW("OIHW"); | ||
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| const AttrType* param = attrs.as<AttrType>(); | ||
| CHECK(param != nullptr); | ||
| const Layout in_layout(param->data_layout); | ||
| const Layout kernel_layout(param->kernel_layout); | ||
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| const auto trans_in_layout = BijectiveLayoutNode::make(in_layout, kNCHW); | ||
| CHECK(trans_in_layout.defined()) | ||
| << "Conv only support input layouts that are convertible from NCHW." | ||
| << " But got " << in_layout; | ||
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| const auto trans_kernel_layout = BijectiveLayoutNode::make(kernel_layout, kOIHW); | ||
| CHECK(trans_kernel_layout.defined()) | ||
| << "Conv only support kernel layouts that are convertible from OIHW." | ||
| << " But got " << kernel_layout; | ||
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| Layout out_layout(param->out_layout == "" ? param->data_layout : param->out_layout); | ||
| const auto trans_out_layout = BijectiveLayoutNode::make(out_layout, kNCHW); | ||
| CHECK(trans_out_layout.defined()) | ||
| << "Conv only support output layouts that are convertible from NCHW." | ||
| << " But got " << out_layout; | ||
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| Array<IndexExpr> dshape_nchw = trans_in_layout.ForwardShape(data->shape); | ||
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| IndexExpr channels, dilated_ksize_y, dilated_ksize_x; | ||
| // infer weight if the kernel_size and channels are defined | ||
| if (param->kernel_size.defined() && param->channels.defined()) { | ||
| CHECK_EQ(param->kernel_size.size(), 2); | ||
| CHECK_EQ(param->dilation.size(), 2); | ||
| Array<IndexExpr> wshape; | ||
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| if (tvm::ir::Equal(param->channels, param->groups)) { | ||
| // infer weight's shape for depthwise convolution | ||
| wshape = {{dshape_nchw[1], param->groups / dshape_nchw[1], param->kernel_size[0], | ||
| param->kernel_size[1]}}; | ||
| } else { | ||
| wshape = {{param->channels, dshape_nchw[1] / param->groups, param->kernel_size[0], | ||
| param->kernel_size[1]}}; | ||
| } | ||
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| wshape = trans_kernel_layout.BackwardShape(wshape); | ||
| channels = param->channels; | ||
| dilated_ksize_y = 1 + (param->kernel_size[0] - 1) * param->dilation[0]; | ||
| dilated_ksize_x = 1 + (param->kernel_size[1] - 1) * param->dilation[1]; | ||
| DataType weight_dtype = data->dtype; | ||
| if (weight != nullptr) { | ||
| weight_dtype = weight->dtype; | ||
| } | ||
| // assign result to reporter | ||
| reporter->Assign(types[1], TensorTypeNode::make(wshape, weight_dtype)); | ||
| } else { | ||
| // use weight to infer the conv shape. | ||
| if (weight == nullptr) return false; | ||
| auto wshape = trans_kernel_layout.ForwardShape(weight->shape); | ||
| if (param->kernel_size.defined()) { | ||
| CHECK_EQ(param->kernel_size.size(), 2); | ||
| // check the size | ||
| CHECK(reporter->AssertEQ(param->kernel_size[0], wshape[2]) && | ||
| reporter->AssertEQ(param->kernel_size[1], wshape[3])) | ||
| << "Conv2D: shape of weight is inconsistent with kernel_size, " | ||
| << " kernel_size=" << param->kernel_size << " wshape=" << wshape; | ||
| } | ||
| if (param->channels.defined()) { | ||
| CHECK(reporter->AssertEQ(param->channels, wshape[0])) | ||
| << "Conv2D: shape of weight is inconsistent with channels, " | ||
| << " channels=" << param->channels << " wshape=" << wshape; | ||
| } | ||
| CHECK(reporter->AssertEQ(dshape_nchw[1] / param->groups, wshape[1])); | ||
| channels = wshape[0]; | ||
| dilated_ksize_y = 1 + (wshape[2] - 1) * param->dilation[0]; | ||
| dilated_ksize_x = 1 + (wshape[3] - 1) * param->dilation[1]; | ||
| } | ||
| // dilation | ||
| Array<IndexExpr> oshape({dshape_nchw[0], channels, 0, 0}); | ||
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| oshape.Set(2, (dshape_nchw[2] + param->padding[0] * 2 - dilated_ksize_y) / param->strides[0] + 1); | ||
| oshape.Set(3, (dshape_nchw[3] + param->padding[1] * 2 - dilated_ksize_x) / param->strides[1] + 1); | ||
| DataType out_dtype = param->out_dtype; | ||
| if (out_dtype.bits() == 0) { | ||
| out_dtype = data->dtype; | ||
| } | ||
| oshape = trans_out_layout.BackwardShape(oshape); | ||
| // assign output type | ||
| reporter->Assign(types[2], TensorTypeNode::make(oshape, out_dtype)); | ||
| return true; | ||
| } | ||
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| } // namespace relay | ||
| } // namespace tvm | ||
| #endif // TVM_RELAY_OP_NN_CONVOLUTION_H_ |
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