未验证 提交 33b4920d 编写于 作者: Q Qiyang Min 提交者: GitHub

Merge pull request #14057 from velconia/continue_hash_op

[1.1] Add hash_op implementation
......@@ -179,6 +179,7 @@ include(external/eigen) # download eigen3
include(external/pybind11) # download pybind11
include(external/cares)
include(external/cub)
include(external/xxhash) # download xxhash
if (NOT WIN32)
# there is no official support of snappystream, warpctc, nccl, cupti in windows
......
INCLUDE(ExternalProject)
set(XXHASH_SOURCE_DIR ${THIRD_PARTY_PATH}/xxhash)
set(XXHASH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/xxhash)
set(XXHASH_INCLUDE_DIR "${XXHASH_INSTALL_DIR}/include")
IF(WITH_STATIC_LIB)
SET(BUILD_CMD make lib)
ELSE()
SET(BUILD_CMD sed -i "s/-Wstrict-prototypes -Wundef/-Wstrict-prototypes -Wundef -fPIC/g" ${XXHASH_SOURCE_DIR}/src/extern_xxhash/Makefile && make lib)
ENDIF()
ExternalProject_Add(
extern_xxhash
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/Cyan4973/xxHash"
GIT_TAG "v0.6.5"
PREFIX ${XXHASH_SOURCE_DIR}
DOWNLOAD_NAME "xxhash"
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_IN_SOURCE 1
PATCH_COMMAND
BUILD_COMMAND ${BUILD_CMD}
INSTALL_COMMAND export PREFIX=${XXHASH_INSTALL_DIR}/ && make install
TEST_COMMAND ""
)
set(XXHASH_LIBRARIES "${XXHASH_INSTALL_DIR}/lib/libxxhash.a")
INCLUDE_DIRECTORIES(${XXHASH_INCLUDE_DIR})
add_library(xxhash STATIC IMPORTED GLOBAL)
set_property(TARGET xxhash PROPERTY IMPORTED_LOCATION ${XXHASH_LIBRARIES})
include_directories(${XXHASH_INCLUDE_DIR})
add_dependencies(xxhash extern_xxhash)
LIST(APPEND external_project_dependencies xxhash)
IF(WITH_C_API)
INSTALL(DIRECTORY ${XXHASH_INCLUDE_DIR} DESTINATION third_party/xxhash)
IF(ANDROID)
INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${XXHASH_LIBRARIES} DESTINATION third_party/xxhash/lib)
ENDIF()
ENDIF()
......@@ -67,6 +67,13 @@ copy(boost_lib
DEPS boost
)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/xxhash")
copy(xxhash_lib
SRCS ${XXHASH_INCLUDE_DIR} ${XXHASH_LIBRARIES}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS xxhash
)
if(NOT PROTOBUF_FOUND)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/protobuf")
copy(protobuf_lib
......
......@@ -176,6 +176,7 @@ paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label'
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......
......@@ -52,6 +52,7 @@ include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
if (NOT WIN32)
include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
......@@ -77,6 +78,7 @@ endif(NOT WIN32)
link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
link_directories("${PADDLE_LIB}/paddle/lib")
add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
......@@ -107,7 +109,7 @@ if (NOT WIN32)
set(EXTERNAL_LIB "-lrt -ldl -lpthread")
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags protobuf snappystream snappy z
glog gflags protobuf snappystream snappy z xxhash
${EXTERNAL_LIB})
else()
set(DEPS ${DEPS}
......
......@@ -268,6 +268,7 @@ if (WITH_GPU AND TENSORRT_FOUND)
else()
set(DEPS_OPS ${DEPS_OPS} tensorrt_engine_op)
endif()
op_library(hash_op DEPS xxhash)
op_library(clip_by_norm_op DEPS selected_rows_functor selected_rows)
op_library(sum_op DEPS selected_rows_functor)
op_library(sgd_op DEPS selected_rows_functor)
......
/* Copyright (c) 2016 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/hash_op.h"
#include <string>
#include <vector>
namespace paddle {
namespace operators {
class HashOp : public framework::OperatorWithKernel {
public:
HashOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of HashOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of HashOp should not be null.");
auto dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(dims.size(), 2UL,
"The input of hash_op's dimensions must be 2");
std::vector<int64_t> out_dims;
out_dims.reserve(dims.size() + 1);
// copy all dims except the last one
for (size_t i = 0u; i != dims.size() - 1; ++i) {
out_dims.emplace_back(dims[i]);
}
int num_hash = ctx->Attrs().Get<int>("num_hash");
out_dims.emplace_back(num_hash);
// keep the last dim to 1
out_dims.emplace_back(1);
ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class HashOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) Input tensor of scale operator.");
AddOutput("Out", "(Tensor) Output tensor of scale operator.");
AddComment(R"DOC(
**Hash Operator**
$$Out = scale * X$$
)DOC");
AddAttr<int>("num_hash", "").SetDefault(1);
AddAttr<int>("mod_by", "").SetDefault(100000);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(hash, ops::HashOp, ops::HashOpMaker);
REGISTER_OP_CPU_KERNEL(hash, ops::HashKerel<int>, ops::HashKerel<int64_t>);
/* Copyright (c) 2016 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. */
#pragma once
extern "C" {
#include <xxhash.h>
}
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
// template <typename DeviceContext, typename T>
template <typename T>
class HashKerel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& context) const {
auto* out_t = context.Output<framework::LoDTensor>("Out");
auto* in_t = context.Input<framework::LoDTensor>("X");
int mod_by = context.Attr<int>("mod_by");
int num_hash = context.Attr<int>("num_hash");
auto* output = out_t->mutable_data<T>(context.GetPlace());
auto in_dims = in_t->dims();
auto in_lod = in_t->lod();
PADDLE_ENFORCE_EQ(
static_cast<uint64_t>(in_dims[0]), in_lod[0].back(),
"The actual input data's size mismatched with LoD information.");
auto seq_length = in_dims[0];
auto last_dim = in_dims[in_dims.size() - 1];
auto* input = in_t->data<T>();
for (int idx = 0; idx < seq_length; ++idx) {
for (int ihash = 0; ihash != num_hash; ++ihash) {
output[idx * num_hash + ihash] =
XXH64(input, sizeof(int) * last_dim, ihash) % mod_by;
}
input += last_dim;
}
}
};
} // namespace operators
} // namespace paddle
......@@ -15,6 +15,7 @@ include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
......@@ -27,6 +28,7 @@ link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
add_executable(demo_trainer demo_trainer.cc)
......@@ -62,5 +64,5 @@ target_link_libraries(demo_trainer
${ARCHIVE_END}
${MATH_LIB}
${MKLDNN_LIB}
glog gflags protobuf snappystream snappy z
glog gflags protobuf snappystream snappy z xxhash
${EXTERNAL_LIB})
......@@ -156,6 +156,7 @@ __all__ = [
'maxout',
'sequence_reverse',
'affine_channel',
'hash',
]
......@@ -7551,3 +7552,31 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
attrs={"data_layout": data_layout},
outputs={"Out": out})
return out
def hash(input, hash_size, num_hash=1, name=None):
"""
hash the input
Args:
input (Variable): The input variable which is a one-hot word.
hash_size (int): The space size for hash algorithm.
num_hash (int): The times of hash, default 1.
name (str, default None): The name of this layer.
Returns:
Variable: The hash result variable which is a LoDTensor.
Examples:
.. code-block:: python
word_dict = paddle.dataset.imdb.word_dict()
x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
out = fluid.layers.hash(input=x, len(word_dict))
"""
helper = LayerHelper('hash', **locals())
out = helper.create_variable_for_type_inference(
helper.input_dtype(), stop_gradient=True)
helper.append_op(
type='hash',
inputs={'X': input},
outputs={'Out': out},
attrs={'num_hash': num_hash,
'mod_by': hash_size})
return out
# 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.
import unittest
import numpy as np
from op_test import OpTest
class TestScaleOp(OpTest):
def setUp(self):
self.op_type = "hash"
self.init_test_case()
self.inputs = {'X': (self.in_seq, self.lod)}
self.attrs = {'num_hash': 4, 'mod_by': 10000}
self.outputs = {'Out': (self.out_seq, self.lod)}
def init_test_case(self):
np.random.seed = 1
self.in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
self.lod = [[9, 4, 11, 6]]
# self.out_seq = np.ones([30, 4, 1], dtype=np.int32)
self.out_seq = [
[[9662], [9217], [1129], [8487]], [[9662], [9217], [1129], [8487]],
[[8310], [1327], [1654], [4567]], [[6897], [3218], [2013], [1241]],
[[9407], [6715], [6949], [8094]], [[8473], [694], [5142], [2479]],
[[8310], [1327], [1654], [4567]], [[6897], [3218], [2013], [1241]],
[[4372], [9456], [8204], [6695]], [[6897], [3218], [2013], [1241]],
[[8473], [694], [5142], [2479]], [[4372], [9456], [8204], [6695]],
[[4372], [9456], [8204], [6695]], [[8473], [694], [5142], [2479]],
[[9407], [6715], [6949], [8094]], [[9369], [4525], [8935], [9210]],
[[4372], [9456], [8204], [6695]], [[4372], [9456], [8204], [6695]],
[[9369], [4525], [8935], [9210]], [[6897], [3218], [2013], [1241]],
[[9038], [7951], [5953], [8657]], [[9407], [6715], [6949], [8094]],
[[9662], [9217], [1129], [8487]], [[9369], [4525], [8935], [9210]],
[[9038], [7951], [5953], [8657]], [[9662], [9217], [1129], [8487]],
[[9369], [4525], [8935], [9210]], [[1719], [5986], [9919], [3421]],
[[4372], [9456], [8204], [6695]], [[9038], [7951], [5953], [8657]]
]
self.out_seq = np.array(self.out_seq)
def test_check_output(self):
self.check_output()
if __name__ == "__main__":
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册