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add lookup_sparse_table_op #10164
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Yancey1989
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PaddlePaddle:develop
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Yancey1989:lookup_sparse_table_op
May 2, 2018
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add lookup_sparse_table_op #10164
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e8d8021
add lookup_sparse_table_op
Yancey1989 8aea5ca
add attr auto_grown_table
Yancey1989 e393c86
Merge branch 'develop' of github.com:PaddlePaddle/Paddle into lookup_…
Yancey1989 dccd013
refine distribute transpiler
Yancey1989 fb1167c
delete unused comment
Yancey1989 1a93253
fix unittest
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. | ||
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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 | ||
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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 | ||
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 <algorithm> | ||
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#include "paddle/fluid/framework/data_type.h" | ||
#include "paddle/fluid/framework/op_registry.h" | ||
#include "paddle/fluid/operators/math/math_function.h" | ||
#include "paddle/fluid/platform/device_context.h" | ||
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namespace paddle { | ||
namespace operators { | ||
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constexpr int64_t kNoPadding = -1; | ||
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class LookupSparseTableInferShape : public framework::InferShapeBase { | ||
public: | ||
void operator()(framework::InferShapeContext *ctx) const override { | ||
PADDLE_ENFORCE(ctx->HasOutput("Out"), | ||
"Output(Out) of LookupSparseTableOp should not be null."); | ||
auto shape_w = ctx->GetInputDim("W"); | ||
auto shape_ids = ctx->GetInputDim("Ids"); | ||
shape_w[0] = shape_ids.size(); | ||
ctx->SetOutputDim("Out", shape_w); | ||
} | ||
}; | ||
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class LookupSparseTableOp : public framework::OperatorBase { | ||
public: | ||
using framework::OperatorBase::OperatorBase; | ||
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private: | ||
void RunImpl(const framework::Scope &scope, | ||
const platform::Place &dev_place) const override { | ||
auto out_var = scope.FindVar(Output("Out")); | ||
auto w_var = scope.FindVar(Input("W")); | ||
auto ids_var = scope.FindVar(Input("Ids")); | ||
unsigned int seed = static_cast<unsigned int>(Attr<int>("seed")); | ||
float min = Attr<float>("min"); | ||
float max = Attr<float>("max"); | ||
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PADDLE_ENFORCE(out_var->IsType<framework::LoDTensor>(), | ||
"The type of Out var should be LodTensor."); | ||
PADDLE_ENFORCE(w_var->IsType<framework::SelectedRows>(), | ||
"The type of W var should be SelectedRows."); | ||
PADDLE_ENFORCE(ids_var->IsType<framework::SelectedRows>(), | ||
"The type of Ids var should be SelectedRows."); | ||
auto &ids_t = ids_var->Get<framework::SelectedRows>(); | ||
auto out_t = out_var->GetMutable<framework::LoDTensor>(); | ||
auto w_t = w_var->GetMutable<framework::SelectedRows>(); | ||
auto keys = ids_t.rows(); | ||
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// TODO(Yancey1989): support CUDA Place for the sparse table | ||
platform::CPUPlace cpu; | ||
auto out_shape = w_t->value().dims(); | ||
out_shape[0] = keys.size(); | ||
out_t->Resize(out_shape); | ||
out_t->mutable_data(cpu, w_t->value().type()); | ||
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PADDLE_ENFORCE_EQ(framework::ToDataType(w_t->value().type()), | ||
framework::proto::VarType::FP32, | ||
"The sparse table only support FP32"); | ||
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auto non_keys_pair = w_t->Get(keys, out_t); | ||
auto value_shape = w_t->value().dims(); | ||
value_shape[0] = 1; | ||
for (const auto &it : non_keys_pair) { | ||
const auto key = it.first; | ||
const auto index = it.second; | ||
framework::Tensor value; | ||
value.Resize(value_shape); | ||
auto data = value.mutable_data<float>(cpu); | ||
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std::minstd_rand engine; | ||
engine.seed(seed); | ||
std::uniform_real_distribution<float> dist(min, max); | ||
int64_t size = value.numel(); | ||
for (int64_t i = 0; i < size; ++i) { | ||
data[i] = dist(engine); | ||
} | ||
w_t->Set(key, value); | ||
memory::Copy(cpu, out_t->mutable_data<float>(cpu) + index * value.numel(), | ||
cpu, value.data<float>(), value.numel() * sizeof(float)); | ||
} | ||
} | ||
}; | ||
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class LookupSparseTableOpMaker : public framework::OpProtoAndCheckerMaker { | ||
public: | ||
LookupSparseTableOpMaker(OpProto *proto, OpAttrChecker *op_checker) | ||
: framework::OpProtoAndCheckerMaker(proto, op_checker) { | ||
AddInput("W", | ||
"(SelectedRows) The input represents embedding table, " | ||
"which is a learnable parameter."); | ||
AddInput("Ids", | ||
"(SelectedRows) Ids's type should be SelectedRows " | ||
"the rows of Ids contains the Ids to be looked up in W."); | ||
AddOutput("Out", | ||
"(SelectedRows) The lookup results, which have the " | ||
"same type as W."); | ||
AddAttr<int64_t>("padding_idx", | ||
"(int64, default -1) " | ||
"If the value is -1, it makes no effect to lookup. " | ||
"Otherwise the given value indicates padding the output " | ||
"with zeros whenever lookup encounters it in Ids.") | ||
.SetDefault(kNoPadding); | ||
AddAttr<float>("min", | ||
"(float, default -1.0) " | ||
"Minimum value of uniform random") | ||
.SetDefault(-1.0f); | ||
AddAttr<float>("max", | ||
"(float, default 1.0) " | ||
"Maximun value of uniform random") | ||
.SetDefault(1.0f); | ||
AddAttr<int>("seed", | ||
"(int, default 0) " | ||
"Random seed used for generating samples. " | ||
"0 means use a seed generated by the system." | ||
"Note that if seed is not 0, this operator will always " | ||
"generate the same random numbers every time.") | ||
.SetDefault(0); | ||
AddComment(R"DOC( | ||
Lookup Sprase Tablel Operator. | ||
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This operator is used to perform lookup on parameter W, | ||
then concatenated into a sparse tensor. | ||
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The type of Ids(Input) is SelectedRows, the rows of Ids contains | ||
the ids to be looked up in W; | ||
if the Id is not in the sparse table, this operator will return a | ||
random value and set the value into the table for the next looking up. | ||
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)DOC"); | ||
} | ||
}; | ||
} // namespace operators | ||
} // namespace paddle | ||
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namespace ops = paddle::operators; | ||
REGISTER_OPERATOR(lookup_sparse_table, ops::LookupSparseTableOp, | ||
ops::LookupSparseTableInferShape, | ||
ops::LookupSparseTableOpMaker, | ||
paddle::framework::EmptyGradOpMaker); |
86 changes: 86 additions & 0 deletions
86
python/paddle/fluid/tests/unittests/test_lookup_sparse_table_op.py
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# Copyright (c) 2018 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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import unittest | ||
import numpy as np | ||
from op_test import OpTest | ||
import paddle.fluid.core as core | ||
from paddle.fluid.op import Operator | ||
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def output_hist(out): | ||
hist, _ = np.histogram(out, range=(-5, 10)) | ||
hist = hist.astype("float32") | ||
hist /= float(out.size) | ||
prob = 0.1 * np.ones((10)) | ||
return hist, prob | ||
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class TestLookupSpraseTable(OpTest): | ||
def check_with_place(self, place): | ||
scope = core.Scope() | ||
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# create and initialize Id Variable | ||
ids = scope.var("Ids").get_selected_rows() | ||
ids_array = [0, 2, 3, 5, 100] | ||
ids.set_rows(ids_array) | ||
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# create and initialize W Variable | ||
rows = [0, 1, 2, 3, 4, 5, 6] | ||
row_numel = 10000 | ||
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w_selected_rows = scope.var('W').get_selected_rows() | ||
w_selected_rows.set_height(len(rows)) | ||
w_selected_rows.set_rows(rows) | ||
w_array = np.ones((len(rows), row_numel)).astype("float32") | ||
for i in range(len(rows)): | ||
w_array[i] *= i | ||
w_tensor = w_selected_rows.get_tensor() | ||
w_tensor.set(w_array, place) | ||
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# create Out Variable | ||
out_tensor = scope.var('Out').get_tensor() | ||
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# create and run lookup_table operator | ||
lookup_table = Operator( | ||
"lookup_sparse_table", | ||
W='W', | ||
Ids='Ids', | ||
Out='Out', | ||
min=-5.0, | ||
max=10.0, | ||
seed=10) | ||
lookup_table.run(scope, place) | ||
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# get result from Out | ||
result_array = np.array(out_tensor) | ||
# all(): return True if all elements of the iterable are true (or if the iterable is empty) | ||
for idx, row in enumerate(ids_array[:-2]): | ||
assert (row == result_array[idx]).all() | ||
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# check the random value | ||
hist, prob = output_hist(result_array[-1]) | ||
self.assertTrue( | ||
np.allclose( | ||
hist, prob, rtol=0, atol=0.01), "hist: " + str(hist)) | ||
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def test_w_is_selected_rows(self): | ||
places = [core.CPUPlace()] | ||
# currently only support CPU | ||
for place in places: | ||
self.check_with_place(place) | ||
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if __name__ == "__main__": | ||
unittest.main() |
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Can add an attribute "auto_add_id" or something like this.
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Done.