wehub-resource-sync
82 lines
2.5 KiB
C++
82 lines
2.5 KiB
C++
/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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/*!
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* \brief Dense op constructions
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* \file nn/dense.h
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*/
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#ifndef TVM_TOPI_NN_DENSE_H_
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#define TVM_TOPI_NN_DENSE_H_
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#include <tvm/te/operation.h>
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#include <tvm/topi/tags.h>
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#include <string>
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namespace tvm {
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namespace topi {
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namespace nn {
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using namespace tvm::te;
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/*!
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* \brief Creates an operation that calculates data * weight^T + bias
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*
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* \param data Tensor with shape [batch, in_dim]
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* \param weight Tensor with shape [out_dim, in_dim]
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* \param bias Tensor with shape [out_dim]. Optional; to omit bias, pass Tensor()
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* \param out_dtype Output data type. Used for mixed precision.
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*
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* \return Tensor with shape [batch, out_dim]
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*/
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inline tvm::te::Tensor dense(const tvm::te::Tensor& data, const tvm::te::Tensor& weight,
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const tvm::te::Tensor& bias, const PrimType& out_dtype) {
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TVM_FFI_ICHECK_EQ(data->shape.size(), 2) << "dense requires 2-D data";
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TVM_FFI_ICHECK_EQ(weight->shape.size(), 2) << "dense requires 2-D weight";
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if (bias.defined()) {
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TVM_FFI_ICHECK_EQ(bias->shape.size(), 1) << "dense requires 1-D bias";
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}
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auto batch = data->shape[0];
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auto in_dim = data->shape[1];
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auto out_dim = weight->shape[0];
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auto k = tvm::te::reduce_axis(Range(0, in_dim), "k");
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auto matmul = tvm::te::compute(
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{batch, out_dim},
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[&](PrimVar i, PrimVar j) {
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return tvm::sum(tvm::cast(out_dtype, data(i, k)) * tvm::cast(out_dtype, weight(j, k)), {k});
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},
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"tensor", "dense");
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if (bias.defined()) {
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matmul = tvm::te::compute(
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{batch, out_dim},
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[&](PrimVar i, PrimVar j) { return matmul(i, j) + tvm::cast(out_dtype, bias(j)); },
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"tensor", kBroadcast);
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}
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return matmul;
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}
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} // namespace nn
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} // namespace topi
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} // namespace tvm
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#endif // TVM_TOPI_NN_DENSE_H_
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