提交 6d0d29f6 编写于 作者: Q qingqing01 提交者: GitHub

Merge pull request #4001 from qingqing01/lod_tensor_py

Correctly use host_vector in LoDTensor and expose LoDTensor to Python.
......@@ -9,6 +9,7 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
cc_test(variable_test SRCS variable_test.cc)
......
......@@ -18,8 +18,10 @@
#ifndef PADDLE_ONLY_CPU
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/system/cuda/experimental/pinned_allocator.h>
#endif
#include <glog/logging.h>
#include "paddle/framework/ddim.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/enforce.h"
......@@ -32,7 +34,8 @@ template <typename T>
using Vector = std::vector<T>;
#else
template <typename T>
using Vector = thrust::host_vector<T>;
using Vector = thrust::host_vector<
T, thrust::system::cuda::experimental::pinned_allocator<T>>;
#endif
using LoD = std::vector<Vector<size_t>>;
......
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 <cuda.h>
#include <cuda_runtime.h>
#include "paddle/framework/lod_tensor.h"
#include "paddle/platform/assert.h"
#include <gtest/gtest.h>
__global__ void test(size_t* a, int size) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size;
i += blockDim.x * gridDim.x) {
a[i] *= 2;
}
}
TEST(LoDTensor, LoDInGPU) {
paddle::framework::Tensor tensor;
paddle::framework::LoDTensor lod_tensor;
paddle::platform::GPUPlace place(0);
paddle::framework::LoD src_lod;
src_lod.push_back(std::vector<size_t>{0, 2, 4, 6, 8, 10, 12, 14});
tensor.Resize({14, 16});
tensor.mutable_data<float>(place);
lod_tensor.set_lod(src_lod);
lod_tensor.set_tensor(&tensor);
CHECK_EQ(lod_tensor.lod_element(0, 2), 4);
CHECK_EQ(lod_tensor.lod_element(0, 4), 8);
auto lod = lod_tensor.lod();
test<<<1, 8>>>(lod[0].data(), lod[0].size());
cudaDeviceSynchronize();
for (size_t i = 0; i < src_lod[0].size(); ++i) {
CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
}
}
......@@ -119,4 +119,4 @@ TEST(math, im2col) {
#ifndef PADDLE_ONLY_CPU
testIm2col<paddle::platform::GPUPlace>();
#endif
}
\ No newline at end of file
}
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <vector>
#include "paddle/framework/backward.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
......@@ -58,6 +59,8 @@ namespace paddle {
namespace framework {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using LoD = framework::LoD;
static size_t UniqueIntegerGenerator() {
static std::atomic<size_t> generator;
......@@ -117,6 +120,60 @@ PYBIND11_PLUGIN(core) {
return self.data<float>()[offset];
});
py::class_<LoDTensor>(m, "LoDTensor", R"DOC(LoD(Leval of Ddetails) Tensor.
The tensor and LoD info should be created before creating the LoDTensor, then
call the set_tensor and set_lod functions to set them.
)DOC")
.def("__init__",
[](LoDTensor &instance,
const std::vector<std::vector<size_t>> &lod,
Tensor *t) {
#ifdef PADDLE_ONLY_CPU
new (&instance) LoDTensor(lod, t);
#else
paddle::framework::LoD new_lod;
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
new (&instance) LoDTensor(new_lod, t);
#endif
})
.def("set_tensor",
[](LoDTensor &self, Tensor *tensor) { self.set_tensor(tensor); })
.def("set_lod",
[](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
#ifdef PADDLE_ONLY_CPU
self.set_lod(lod);
#else
paddle::framework::LoD new_lod;
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
self.set_lod(new_lod);
#endif
})
.def("tensor",
[](LoDTensor &self) -> Tensor & { return self.tensor(); },
py::return_value_policy::reference)
.def("lod", [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
#ifdef PADDLE_ONLY_CPU
return self.lod();
#else
auto lod = self.lod();
std::vector<std::vector<size_t>> new_lod;
new_lod.reserve(lod.size());
std::transform(lod.begin(), lod.end(), std::back_inserter(new_lod),
[](paddle::framework::Vector<size_t> item) ->
std::vector<size_t> {
std::vector<size_t> v;
v.reserve(item.size());
std::copy(item.begin(), item.end(), std::back_inserter(v));
return v;
});
return new_lod;
#endif
});
py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
......@@ -128,6 +185,11 @@ All parameter, weight, gradient are variables in Paddle.
.def("get_tensor",
[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
py::return_value_policy::reference)
.def("get_lod_tensor",
[](Variable &self) -> LoDTensor * {
return self.GetMutable<LoDTensor>();
},
py::return_value_policy::reference)
.def("get_net",
[](Variable &self) -> operators::NetOp * {
return self.GetMutable<operators::NetOp>();
......
......@@ -3,7 +3,7 @@ import unittest
import numpy
class TestScope(unittest.TestCase):
class TestTensor(unittest.TestCase):
def test_int_tensor(self):
scope = core.Scope()
var = scope.new_var("test_tensor")
......@@ -20,8 +20,8 @@ class TestScope(unittest.TestCase):
tensor.set(tensor_array, place)
tensor_array_2 = numpy.array(tensor)
self.assertEqual(1.0, tensor_array_2[3, 9])
self.assertEqual(2.0, tensor_array_2[19, 11])
self.assertEqual(1, tensor_array_2[3, 9])
self.assertEqual(2, tensor_array_2[19, 11])
def test_float_tensor(self):
scope = core.Scope()
......@@ -43,6 +43,84 @@ class TestScope(unittest.TestCase):
self.assertAlmostEqual(1.0, tensor_array_2[3, 9])
self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
def test_int_lod_tensor(self):
places = [core.CPUPlace(), core.GPUPlace(0)]
for place in places:
scope = core.Scope()
var = scope.new_var("test_tensor")
var_lod = scope.new_var("test_lod_tensor")
tensor = var.get_tensor()
lod_tensor = var_lod.get_lod_tensor()
tensor.set_dims([4, 4, 6])
tensor.alloc_int(place)
array = numpy.array(tensor)
array[0, 0, 0] = 3
array[3, 3, 5] = 10
tensor.set(array, place)
lod_tensor.set_tensor(tensor)
lod_tensor.set_lod([[0, 2, 4]])
lod_v = numpy.array(lod_tensor.tensor())
self.assertTrue(numpy.alltrue(array == lod_v))
lod = lod_tensor.lod()
self.assertEqual(0, lod[0][0])
self.assertEqual(2, lod[0][1])
self.assertEqual(4, lod[0][2])
def test_float_lod_tensor(self):
places = [core.CPUPlace(), core.GPUPlace(0)]
for place in places:
scope = core.Scope()
var = scope.new_var("test_tensor")
var_lod = scope.new_var("test_lod_tensor")
tensor = var.get_tensor()
lod_tensor = var_lod.get_lod_tensor()
tensor.set_dims([5, 2, 3, 4])
tensor.alloc_float(place)
tensor_array = numpy.array(tensor)
self.assertEqual((5, 2, 3, 4), tensor_array.shape)
tensor_array[0, 0, 0, 0] = 1.0
tensor_array[0, 0, 0, 1] = 2.0
tensor.set(tensor_array, place)
lod_tensor.set_tensor(tensor)
lod_v = numpy.array(lod_tensor.tensor())
self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
self.assertEqual(len(lod_tensor.lod()), 0)
lod_py = [[0, 2, 5], [0, 2, 4, 5]]
lod_tensor.set_lod(lod_py)
lod = lod_tensor.lod()
self.assertListEqual(lod_py, lod)
def test_lod_tensor_init(self):
scope = core.Scope()
var = scope.new_var("test_tensor")
place = core.CPUPlace()
tensor = var.get_tensor()
tensor.set_dims([5, 2, 3, 4])
tensor.alloc_float(place)
tensor_array = numpy.array(tensor)
tensor_array[0, 0, 0, 0] = 1.0
tensor_array[0, 0, 0, 1] = 2.0
tensor.set(tensor_array, place)
lod_py = [[0, 2, 5], [0, 2, 4, 5]]
lod_tensor = core.LoDTensor(lod_py, tensor)
lod_v = numpy.array(lod_tensor.tensor())
self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
self.assertListEqual(lod_py, lod_tensor.lod())
if __name__ == '__main__':
unittest.main()
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