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[NPU] add where_index op and tests #34951

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97 changes: 97 additions & 0 deletions paddle/fluid/operators/where_index_op_npu.cc
Original file line number Diff line number Diff line change
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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. */

#include "paddle/fluid/operators/where_index_op.h"
#include "paddle/fluid/operators/npu_op_runner.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

template <typename T>
class NPUWhereIndexKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto& dev_ctx =
context.template device_context<platform::NPUDeviceContext>();
auto* condition = context.Input<Tensor>("Condition");
auto* out = context.Output<Tensor>("Out");

auto dims = condition->dims();
const int rank = dims.size();

auto place = context.GetPlace();
const aclrtStream& stream = dev_ctx.stream();

// Run Cast and ReduceSum to get 0 dim of Out
Tensor booled_cond;
if (condition->type() != framework::proto::VarType::BOOL) {
auto bool_type = ConvertToNpuDtype(framework::proto::VarType::BOOL);
booled_cond.mutable_data<bool>(dims, place);
const auto& booled_runner =
NpuOpRunner("Cast", {*condition}, {booled_cond},
{{"dst_type", static_cast<int>(bool_type)}});
booled_runner.Run(stream);
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@qili93 qili93 Aug 17, 2021

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根据WhereIndexOpMaker,AddInput("Condition", "A bool tensor whose rank is at least 1"); 这里的 condition 数据类型必须为bool类型,可以不需要Cast,但新增 PADDLE_ENFORCE_EQ保证输入为bool类型。

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多谢,如沟通,where_index算子支持上层API paddle.nonzero和paddle.fluid.layers.where,需支持多种数据类型的输入,此处cast保证多种类型数据(非bool)场景下后面计算Tensor中true/非0值个数的正确性。

} else {
booled_cond.ShareDataWith(*condition);
}
Tensor casted_cond;
auto dst_dtype = ConvertToNpuDtype(framework::proto::VarType::INT64);
casted_cond.mutable_data<int64_t>(dims, place);
const auto& cast_runner =
NpuOpRunner("Cast", {booled_cond}, {casted_cond},
{{"dst_type", static_cast<int>(dst_dtype)}});
cast_runner.Run(stream);

Tensor sumed_true_num;
sumed_true_num.mutable_data<int64_t>({1}, place);
Tensor cond_axes;
cond_axes.mutable_data<int>({dims.size()}, place);
std::vector<int> axes_vec;
for (int i = 0; i < dims.size(); ++i) {
axes_vec.push_back(i);
}
framework::TensorFromVector<int>(axes_vec, dev_ctx, &cond_axes);
const auto& sum_runner =
NpuOpRunner("ReduceSum", {casted_cond, cond_axes}, {sumed_true_num},
{{"keep_dims", false}});
sum_runner.Run(stream);

Tensor local_true_num;
TensorCopySync(sumed_true_num, platform::CPUPlace(), &local_true_num);
auto true_num = *local_true_num.data<int64_t>();

out->Resize(framework::make_ddim({true_num, rank}));
out->mutable_data<int64_t>(place);

if (true_num == 0) {
return;
}
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上面这段逻辑貌似稍微有点复杂哦,这里是总共做了几步操作

  1. BOOL -> INT64 类型
  2. 然后对数值进行reducesum求和
  3. 然后把和从NPU拷贝到CPU端
  4. 判断CPU端的和是否为0,如果是0,就直接返回

问下这段逻辑可以直接省掉,直接调用Where OP吗?还是说直接调用Where会出错?如果出错的话可以试一下NonZero这个算子。

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@Aganlengzi Aganlengzi Aug 17, 2021

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多谢,已尝试直接调用Where或NonZero OP,目前均无法完成计算:
两个OP都是动态shape的Output,但需要调用实际算子前指定Output的shape,shape维度可以是准确值或较大合理值,但当前交互机制无法在调用NPU算子后获取Output的准确shape信息,所以较大合理值方式无法使用;采用设置准确值的方式需要进行前序计算——根据ReduceSum支持的类型进行Cast和结果回传,其中ReduceSum的结果为单一int64_t。


out->set_layout(DataLayout::kAnyLayout);
NpuOpRunner runner{"Where", {*condition}, {*out}};
runner.Run(stream);
}
};

} // namespace operators
} // namespace paddle

namespace ops = paddle::operators;
REGISTER_OP_NPU_KERNEL(where_index, ops::NPUWhereIndexKernel<int64_t>,
ops::NPUWhereIndexKernel<int>,
ops::NPUWhereIndexKernel<bool>,
ops::NPUWhereIndexKernel<float>,
ops::NPUWhereIndexKernel<double>);
106 changes: 106 additions & 0 deletions python/paddle/fluid/tests/unittests/npu/test_where_index_npu.py
Original file line number Diff line number Diff line change
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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.

from __future__ import print_function

import numpy as np
import unittest
import paddle
import sys
sys.path.append("..")
from op_test import OpTest
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard

paddle.enable_static()


class TestWhereIndexOp(OpTest):
def setUp(self):
self.set_npu()
self.op_type = "where_index"
self.place = paddle.NPUPlace(0)
self.init_config()

def test_check_output(self):
self.check_output_with_place(self.place)

def init_config(self):
self.inputs = {'Condition': np.array([True, False, True]), }

self.outputs = {'Out': np.array([[0], [2]], dtype='int64')}

def set_npu(self):
self.__class__.use_npu = True


class TestNotBool(TestWhereIndexOp):
def init_config(self):
self.inputs = {'Condition': np.array([1, 0, 8]), }
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这里的单测设计和Paddle的CPU/CUDA端的代码, test_where_index.py不太一样哦,这里Condition输入应该只接受BOOL的数据类型。参考 test_where_index.py 修改一下单测吧。

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多谢,评估where_index算子需支持多种数据类型的输入,此处CPU/GPU UT代码中未包含相应的检查,此处添加非bool类型的UT验证对非bool类型输入处理的正确性。


self.outputs = {'Out': np.array([[0], [2]], dtype='int64')}


class TestAllFalse(TestWhereIndexOp):
def init_config(self):
self.inputs = {'Condition': np.array([False, False, False]), }

self.outputs = {'Out': np.array([], dtype='int64')}


class TestRank2(TestWhereIndexOp):
def init_config(self):
self.inputs = {'Condition': np.array([[True, False], [False, True]]), }

self.outputs = {'Out': np.array([[0, 0], [1, 1]], dtype='int64')}


class TestRank3(TestWhereIndexOp):
def init_config(self):
self.inputs = {
'Condition': np.array([[[True, False], [False, True]],
[[False, True], [True, False]],
[[False, False], [False, True]]]),
}

self.outputs = {
'Out': np.array(
[[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0], [2, 1, 1]],
dtype='int64')
}


class TestWhereOpError(unittest.TestCase):
def test_api(self):
with program_guard(Program(), Program()):
cond = fluid.layers.data(name='cond', shape=[4], dtype='bool')
result = fluid.layers.where(cond)

exe = fluid.Executor(paddle.NPUPlace(0))
exe.run(fluid.default_startup_program())
cond_i = np.array([True, False, False, False]).astype("bool")
out = exe.run(fluid.default_main_program(), feed={'cond': cond_i})


class TestWhereRaiseError(unittest.TestCase):
def test_errors(self):
def test_type():
fluid.layers.where([10])

self.assertRaises(TypeError, test_type)


if __name__ == "__main__":
unittest.main()