146 lines
4.6 KiB
C++
146 lines
4.6 KiB
C++
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/lite/delegates/gpu/gl/kernels/mul.h"
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#include <algorithm>
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#include <cstdint>
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#include <cstring>
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#include <string>
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#include <vector>
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#include "absl/memory/memory.h"
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#include "absl/strings/str_cat.h"
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#include "tensorflow/lite/delegates/gpu/common/status.h"
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#include "tensorflow/lite/delegates/gpu/common/types.h"
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namespace tflite {
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namespace gpu {
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namespace gl {
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namespace {
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class ApplyMask : public NodeShader {
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public:
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static bool IsSupported(const GenerationContext& ctx) {
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const auto inputs = ctx.graph->FindInputs(ctx.node->id);
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if (inputs.size() != 2) return false;
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const auto& shape0 = inputs[0]->tensor.shape;
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const auto& shape1 = inputs[1]->tensor.shape;
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// [H, W, C] x [H, W, 0][0]
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if (shape1.c == 1) return true;
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if (shape0.c != shape1.c) return false;
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// [H, W, C] x [H, W, C]
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if (shape0.h == shape1.h && shape0.w == shape1.w) return true;
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// [H, W, C] x [0, 0, C]
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return shape1.h == 1 && shape1.w == 1;
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}
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Status GenerateCode(const GenerationContext& ctx,
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GeneratedCode* generated_code) const final {
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if (!IsSupported(ctx)) {
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return InvalidArgumentError(
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"This case is not supported by apply mask operation");
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}
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const auto inputs = ctx.graph->FindInputs(ctx.node->id);
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const auto& shape0 = inputs[0]->tensor.shape;
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const auto& shape1 = inputs[1]->tensor.shape;
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std::string source = "value_0 = $input_data_0[gid.x, gid.y, gid.z]$ * ";
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if (shape1.c == 1) {
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// [H, W, C] x [H, W, 0][0]
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absl::StrAppend(&source, "$input_data_1[gid.x, gid.y, 0]$.x;");
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} else if (shape0.h == shape1.h && shape0.w == shape1.w) {
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// [H, W, C] x [H, W, C]
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absl::StrAppend(&source, "$input_data_1[gid.x, gid.y, gid.z]$;");
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} else {
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// [H, W, C] x [0, 0, C]
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absl::StrAppend(&source, "$input_data_1[0, 0, gid.z]$;");
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}
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*generated_code = {
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/*parameters=*/{},
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/*objects=*/{},
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/*shared_variables=*/{},
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/*workload=*/uint3(),
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/*workgroup=*/uint3(),
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/*source_code=*/std::move(source),
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/*input=*/IOStructure::ONLY_DEFINITIONS,
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/*output=*/IOStructure::AUTO,
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};
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return OkStatus();
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}
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};
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class MultiplyScalar : public NodeShader {
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public:
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Status GenerateCode(const GenerationContext& ctx,
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GeneratedCode* generated_code) const final {
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auto attr = absl::any_cast<MultiplyScalarAttributes>(
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ctx.node->operation.attributes);
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auto muls = absl::get_if<Tensor<Linear, DataType::FLOAT32>>(&attr.param);
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auto scalar = absl::get_if<float>(&attr.param);
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if (scalar) {
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*generated_code = {
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/*parameters=*/{{"scalar", *scalar}},
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/*objects=*/{},
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/*shared_variables=*/{},
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/*workload=*/uint3(),
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/*workgroup=*/uint3(),
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/*source_code=*/"value_0 *= $scalar$;",
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/*input=*/IOStructure::AUTO,
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/*output=*/IOStructure::AUTO,
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};
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} else {
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if (!muls) {
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return InvalidArgumentError("Empty parameters for Multiplication.");
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}
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auto shape = ctx.graph->FindInputs(ctx.node->id)[0]->tensor.shape;
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*generated_code = {
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/*parameters=*/{},
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/*objects=*/{{"mul_buffer", MakeReadonlyObject(muls->data)}},
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/*shared_variables=*/{},
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// Declare workload explicitly because shader depends on gid.z.
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/*workload=*/
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uint3(shape.w, shape.h, IntegralDivideRoundUp(shape.c, 4)),
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/*workgroup=*/uint3(),
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/*source_code=*/"value_0 *= $mul_buffer[gid.z]$;",
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/*input=*/IOStructure::AUTO,
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/*output=*/IOStructure::AUTO,
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};
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}
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return OkStatus();
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}
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};
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} // namespace
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std::unique_ptr<NodeShader> NewApplyMaskNodeShader() {
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return absl::make_unique<ApplyMask>();
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}
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std::unique_ptr<NodeShader> NewMultiplyScalarNodeShader() {
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return absl::make_unique<MultiplyScalar>();
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}
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} // namespace gl
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} // namespace gpu
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} // namespace tflite
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