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[NNAdapter] Add fill_range fuse pass、eliminate range op、move scale and expand_v2 op #7200
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// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
// | ||
// Licensed 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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#include "core/operation/expand.h" | ||
#include <vector> | ||
#include "core/hal/types.h" | ||
#include "utility/debug.h" | ||
#include "utility/logging.h" | ||
#include "utility/modeling.h" | ||
#include "utility/utility.h" | ||
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namespace nnadapter { | ||
namespace operation { | ||
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int PrepareExpand(hal::Operation* operation) { | ||
EXPAND_OPERATION_EXTRACT_INPUTS_OUTPUTS | ||
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// Infer the shape and type of output operands | ||
auto input_type = input_operand->type; | ||
auto& output_type = output_operand->type; | ||
CopyOperandTypeWithQuantParams(&output_type, input_type); | ||
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output_type.dimensions.count = shape_count; | ||
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auto infer_output_shape = [&](int32_t* input_dimensions_data, | ||
uint32_t input_dimensions_count, | ||
int32_t* output_dimensions_data) { | ||
std::vector<int> input_dims_vec; | ||
for (uint32_t i = 0; i < input_dimensions_count; i++) { | ||
input_dims_vec.push_back(input_dimensions_data[i]); | ||
} | ||
auto diff = shape_count - input_dimensions_count; | ||
input_dims_vec.insert(input_dims_vec.begin(), diff, 1); | ||
std::vector<int> final_expand_shape(input_dimensions_count); | ||
for (uint32_t i = 0; i < input_dims_vec.size(); ++i) { | ||
NNADAPTER_CHECK_NE(shape_data[i], 0) | ||
<< "The expanded size cannot be zero."; | ||
if (i < diff) { | ||
// shape_data = [3,4,-1,-1], X = [10,2] --> // final_expand_shape = | ||
// [3,4,10,2] | ||
NNADAPTER_CHECK_GT(shape_data[i], 0) | ||
<< "The expanded size " << shape_data[i] | ||
<< "for non-existing dimensions must be positive for expand_v2 op."; | ||
final_expand_shape[i] = shape_data[i]; | ||
} else if (shape_data[i] > 0) { | ||
// shape_data = [3,4,10,4], X = [10,1] --> final_expand_shape = | ||
// [3,4,10,4] | ||
if (input_dims_vec[i] != 1) { | ||
NNADAPTER_CHECK_EQ(input_dims_vec[i], shape_data[i]) | ||
<< "The value " << input_dims_vec[i] | ||
<< " of the non-singleton dimension does not match the " | ||
"corresponding value " | ||
<< shape_data[i] << " in shape for expand_v2 op."; | ||
final_expand_shape[i] = shape_data[i]; | ||
} else { | ||
final_expand_shape[i] = shape_data[i]; | ||
} | ||
} else { | ||
// shape_data = [3,4,-1,-1], X = [10,2] --> final_expand_shape = | ||
// [3,4,10,2] | ||
NNADAPTER_CHECK_EQ(shape_data[i], -1) | ||
<< "When the value in shape is negative for expand_v2 op, " | ||
"only -1 is supported, but the value received is " | ||
<< shape_data[i]; | ||
final_expand_shape[i] = input_dims_vec[i]; | ||
} | ||
} | ||
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for (uint32_t i = 0; i < shape_count; ++i) { | ||
shape_data[i] = final_expand_shape[i]; | ||
output_dimensions_data[i] = final_expand_shape[i]; | ||
} | ||
}; | ||
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infer_output_shape(input_type.dimensions.data, | ||
input_type.dimensions.count, | ||
output_type.dimensions.data); | ||
for (uint32_t i = 0; i < input_type.dimensions.dynamic_count; i++) { | ||
infer_output_shape(input_type.dimensions.dynamic_data[i], | ||
input_type.dimensions.count, | ||
output_type.dimensions.dynamic_data[i]); | ||
} | ||
NNADAPTER_VLOG(5) << "output: " << OperandToString(output_operand); | ||
return NNADAPTER_NO_ERROR; | ||
} | ||
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} // namespace operation | ||
} // namespace nnadapter |
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@@ -29,8 +29,26 @@ namespace operation { | |||
NNADAPTER_VLOG(5) << "input_operand: " << OperandToString(input_operand); \ | ||||
/* Shape */ \ | ||||
auto shape_operand = input_operands[1]; \ | ||||
NNADAPTER_VLOG(5) << "shape operand: " \ | ||||
<< OperandValueToString(shape_operand); \ | ||||
NNADAPTER_VLOG(5) << "shape operand: " << OperandToString(shape_operand); \ | ||||
uint32_t shape_count; \ | ||||
int32_t* shape_data; \ | ||||
auto& shape_type = shape_operand->type; \ | ||||
if (IsConstantOperand(shape_operand)) { \ | ||||
shape_count = shape_operand->length / sizeof(int32_t); \ | ||||
shape_data = reinterpret_cast<int32_t*>(shape_operand->buffer); \ | ||||
} else if (shape_type.lifetime == NNADAPTER_TEMPORARY_SHAPE) { \ | ||||
auto shape_operand_dimension = \ | ||||
*reinterpret_cast<NNAdapterOperandDimensionType*>( \ | ||||
shape_operand->buffer); \ | ||||
shape_count = shape_operand_dimension.count; \ | ||||
shape_data = shape_operand_dimension.data; \ | ||||
} else { \ | ||||
shape_count = shape_operand->type.dimensions.count; \ | ||||
shape_data = shape_operand->type.dimensions.data; \ | ||||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 为什么要等于shape_operand的维度data?
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. done |
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} \ | ||||
for (uint32_t i = 0; i < shape_count; i++) { \ | ||||
NNADAPTER_VLOG(5) << "shape[" << i << "] = " << shape_data[i]; \ | ||||
} \ | ||||
/* Output */ \ | ||||
auto output_operand = output_operands[0]; \ | ||||
NNADAPTER_VLOG(5) << "output_operand: " << OperandToString(output_operand); | ||||
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// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
// | ||
// Licensed 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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#include "core/operation/range.h" | ||
#include "core/hal/types.h" | ||
#include "utility/debug.h" | ||
#include "utility/logging.h" | ||
#include "utility/modeling.h" | ||
#include "utility/utility.h" | ||
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namespace nnadapter { | ||
namespace operation { | ||
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int PrepareRange(hal::Operation* operation) { | ||
RANGE_OPERATION_EXTRACT_INPUTS_OUTPUTS | ||
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// Infer the shape and type of output operands | ||
auto& output_type = output_operand->type; | ||
NNADAPTER_CHECK_EQ(start_operand->type.dimensions.count, 1); | ||
NNADAPTER_CHECK_EQ(limit_operand->type.dimensions.count, 1); | ||
NNADAPTER_CHECK_EQ(delta_operand->type.dimensions.count, 1); | ||
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if (IsConstantOperand(start_operand) && IsConstantOperand(limit_operand) && | ||
IsConstantOperand(delta_operand)) { | ||
auto start_data = reinterpret_cast<float*>(start_operand->buffer)[0]; | ||
auto limit_data = reinterpret_cast<float*>(limit_operand->buffer)[0]; | ||
auto delta_data = reinterpret_cast<float*>(delta_operand->buffer)[0]; | ||
output_type.dimensions.data[0] = | ||
GetSpanCount(start_data, limit_data, delta_data); | ||
} else { | ||
output_type.dimensions.data[0] = NNADAPTER_UNKNOWN; | ||
} | ||
output_type.precision = start_operand->type.precision; | ||
output_type.lifetime = NNADAPTER_TEMPORARY_VARIABLE; | ||
output_type.dimensions.count = 1; | ||
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NNADAPTER_VLOG(5) << "output: " << OperandToString(output_operand); | ||
return NNADAPTER_NO_ERROR; | ||
} | ||
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} // namespace operation | ||
} // namespace nnadapter |
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@@ -34,10 +34,13 @@ int ConvertExpand(Converter* converter, hal::Operation* operation) { | |
if (!input_operator) { | ||
input_operator = converter->ConvertOperand(input_operand); | ||
} | ||
auto shape_operator = converter->ConvertOperand(shape_operand); | ||
auto expand_op = converter->AddOperator<ge::op::Expand>(output_operand); | ||
auto expand_op = converter->AddOperator<ge::op::ExpandD>(output_operand); | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 为什么不用之前的ge::op::Expand支持shape作为输入,而是用只支持shape属性的ExpandD? There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 因为在跑ppyolo模型时,expand算子出现了bug。后来参照训练那边的实现,同时改了两处地方修复了。 |
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std::vector<int64_t> expand_shape(shape_count); | ||
for (uint32_t i = 0; i < shape_count; i++) { | ||
expand_shape[i] = shape_data[i]; | ||
} | ||
expand_op->set_attr_shape(ge::Operator::OpListInt(expand_shape)); | ||
SET_INPUT(expand_op, x, input_operator); | ||
SET_INPUT(expand_op, shape, shape_operator); | ||
MAP_OUTPUT(expand_op, y, output_operand); | ||
return NNADAPTER_NO_ERROR; | ||
} | ||
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. | ||
// | ||
// Licensed 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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#include "lite/core/optimizer/mir/elimination/range_calc_offline_pass.h" | ||
#include <algorithm> | ||
#include <cmath> | ||
#include <list> | ||
#include <memory> | ||
#include <set> | ||
#include <vector> | ||
#include "lite/core/optimizer/mir/pass.h" | ||
#include "lite/core/optimizer/mir/pass_registry.h" | ||
#include "lite/core/optimizer/mir/pattern_matcher.h" | ||
#include "lite/model_parser/cpp_desc.h" | ||
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namespace paddle { | ||
namespace lite { | ||
namespace mir { | ||
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template <typename T> | ||
int64_t GetSpanCount(T start, T end, T step) { | ||
return std::is_integral<T>::value | ||
? ((std::abs(end - start) + std::abs(step) - 1) / std::abs(step)) | ||
: std::ceil(std::abs((end - start) / step)); | ||
} | ||
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void RangeCalcOfflinePass::Apply(const std::unique_ptr<SSAGraph>& graph) { | ||
RemoveRangePattern(graph); | ||
} | ||
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void RangeCalcOfflinePass::RemoveRangePattern( | ||
const std::unique_ptr<SSAGraph>& graph) { | ||
for (auto& node : graph->StmtTopologicalOrder()) { | ||
if (node->AsStmt().picked_kernel().op_type() != "range") continue; | ||
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std::set<const Node*> nodes2rm_; | ||
auto& range_instruct = node->AsStmt(); | ||
auto* scope = range_instruct.op()->scope(); | ||
auto op_desc = range_instruct.mutable_op_info(); | ||
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// Get range's input tensor | ||
auto start_var = scope->FindVar(op_desc->Input("Start").front()); | ||
auto end_var = scope->FindVar(op_desc->Input("End").front()); | ||
auto step_var = scope->FindVar(op_desc->Input("Step").front()); | ||
auto start_t = start_var->GetMutable<lite::Tensor>(); | ||
auto end_t = end_var->GetMutable<lite::Tensor>(); | ||
auto step_t = step_var->GetMutable<lite::Tensor>(); | ||
if (!start_t->persistable() || !end_t->persistable() || | ||
!step_t->persistable()) { | ||
LOG(WARNING) << "RangeCalcOfflinePass does not support input that is not " | ||
"persistent"; | ||
return; | ||
} | ||
auto start = start_t->mutable_data<float>()[0]; | ||
auto end = end_t->mutable_data<float>()[0]; | ||
auto step = step_t->mutable_data<float>()[0]; | ||
// Get range's output tensor | ||
auto out_var = scope->FindVar(op_desc->Output("Out").front()); | ||
auto out_t = out_var->GetMutable<lite::Tensor>(); | ||
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// Calc range | ||
int64_t size = GetSpanCount(start, end, step); | ||
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out_t->Resize(DDim({size})); | ||
auto out_data = out_t->mutable_data<float>(); | ||
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float value = start; | ||
for (int64_t i = 0; i < size; ++i) { | ||
out_data[i] = value; | ||
value += step; | ||
} | ||
// Offline calc range, only retain output tensor as persistable tensor | ||
out_t->set_persistable(true); | ||
auto range_outlinks = node->outlinks; | ||
for (auto& range_out_link : range_outlinks) { | ||
range_out_link->arg()->is_weight = true; | ||
} | ||
nodes2rm_.insert(node); | ||
GraphSafeRemoveNodes(graph.get(), nodes2rm_); | ||
} | ||
} | ||
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} // namespace mir | ||
} // namespace lite | ||
} // namespace paddle | ||
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REGISTER_MIR_PASS(range_calc_offline_pass, | ||
paddle::lite::mir::RangeCalcOfflinePass) | ||
.BindTargets({TARGET(kNNAdapter)}); |
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// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
// | ||
// Licensed 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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#pragma once | ||
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#include <limits> | ||
#include <memory> | ||
#include <string> | ||
#include <vector> | ||
#include "lite/core/optimizer/mir/pass.h" | ||
#include "lite/core/optimizer/mir/pass_registry.h" | ||
#include "lite/core/tensor.h" | ||
#include "lite/core/types.h" | ||
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namespace paddle { | ||
namespace lite { | ||
namespace mir { | ||
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class RangeCalcOfflinePass : public mir::StmtPass { | ||
public: | ||
void Apply(const std::unique_ptr<SSAGraph>& graph) override; | ||
void RemoveRangePattern(const std::unique_ptr<SSAGraph>& graph); | ||
}; | ||
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} // namespace mir | ||
} // namespace lite | ||
} // namespace paddle |
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不建议把这些判断放在.h里面,将其放在.cc的infershape里面吧,而且else部分的代码有问题,shape_operand->type.dimensions.data 指的是 shape_operand 自己的维度信息