-
Notifications
You must be signed in to change notification settings - Fork 3.5k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[Relay][QNN] Moving Conv, Dense, Concatenate InferTypes to header for…
… sharing.
- Loading branch information
1 parent
187600d
commit 3350a25
Showing
6 changed files
with
322 additions
and
228 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,123 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
|
|
||
| /*! | ||
| * 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_ | ||
|
|
||
| #include <string> | ||
| #include <utility> | ||
|
|
||
| namespace tvm { | ||
| namespace relay { | ||
|
|
||
| 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"); | ||
|
|
||
| const AttrType* param = attrs.as<AttrType>(); | ||
| CHECK(param != nullptr); | ||
| const Layout in_layout(param->data_layout); | ||
| const Layout kernel_layout(param->kernel_layout); | ||
|
|
||
| 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; | ||
|
|
||
| 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; | ||
|
|
||
| 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; | ||
|
|
||
| Array<IndexExpr> dshape_nchw = trans_in_layout.ForwardShape(data->shape); | ||
|
|
||
| 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({param->channels, dshape_nchw[1] / param->groups, param->kernel_size[0], | ||
| param->kernel_size[1]}); | ||
| 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}); | ||
|
|
||
| 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; | ||
| } | ||
|
|
||
| } // namespace relay | ||
| } // namespace tvm | ||
| #endif // TVM_RELAY_OP_NN_CONVOLUTION_H_ |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,71 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
|
|
||
| /*! | ||
| * Copyright (c) 2019 by Contributors | ||
| * \file src/relay/op/nn/nn.h | ||
| * \brief Properties def of nn operators for sharing. | ||
| */ | ||
| #ifndef TVM_RELAY_OP_NN_NN_H_ | ||
| #define TVM_RELAY_OP_NN_NN_H_ | ||
|
|
||
| #include <utility> | ||
|
|
||
| namespace tvm { | ||
| namespace relay { | ||
|
|
||
| template <typename AttrType> | ||
| bool DenseRel(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; | ||
|
|
||
| const AttrType* param = attrs.as<AttrType>(); | ||
| CHECK(param != nullptr); | ||
|
|
||
| CHECK(static_cast<int>(data->shape.size()) != 0); | ||
|
|
||
| Array<tvm::Expr> oshape = data->shape; | ||
| if (param->units.defined()) { | ||
| Array<tvm::Expr> dshape = data->shape; | ||
| // validate the weight shape is proper if defined | ||
| // Assign weight type | ||
| Array<IndexExpr> wshape({param->units, dshape[dshape.size() - 1]}); | ||
| reporter->Assign(types[1], TensorTypeNode::make(wshape, data->dtype)); | ||
| oshape.Set((oshape.size() - 1), param->units); | ||
| } else { | ||
| if (weight == nullptr) return false; | ||
| Array<tvm::Expr> wshape = weight->shape; | ||
| oshape.Set((oshape.size() - 1), wshape[0]); | ||
| } | ||
|
|
||
| DataType out_dtype = param->out_dtype; | ||
| if (out_dtype.bits() == 0) { | ||
| out_dtype = data->dtype; | ||
| } | ||
| // assign output type | ||
| reporter->Assign(types[2], TensorTypeNode::make(oshape, out_dtype)); | ||
| return true; | ||
| } | ||
|
|
||
| } // namespace relay | ||
| } // namespace tvm | ||
| #endif // TVM_RELAY_OP_NN_NN_H_ |
Oops, something went wrong.