未验证 提交 ca1e18c0 编写于 作者: T tangwei12 提交者: GitHub

Merge pull request #12469 from seiriosPlus/sum_op_dim_fix

sum_op selectedRows dim bug fix
......@@ -34,15 +34,15 @@
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::CPUDeviceContext;
using framework::DataLayout;
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
using mkldnn::stream;
using mkldnn::sum;
using mkldnn::reorder;
using paddle::framework::Tensor;
using paddle::platform::CPUDeviceContext;
using paddle::platform::MKLDNNDeviceContext;
using platform::to_void_cast;
template <typename T>
......@@ -175,18 +175,35 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto& sel_row = get_selected_row(i);
first_dim += sel_row.rows().size();
}
auto in_dim =
framework::vectorize(get_selected_row(N - 1).value().dims());
std::vector<int64_t> in_dim;
for (int i = 0; i < N; i++) {
auto& sel_row = get_selected_row(i);
if (sel_row.rows().size() > 0) {
in_dim = framework::vectorize(sel_row.value().dims());
break;
}
}
if (in_dim.empty()) {
VLOG(3) << "WARNING: all the inputs are empty";
in_dim = framework::vectorize(get_selected_row(N - 1).value().dims());
} else {
in_dim[0] = static_cast<int64_t>(first_dim);
}
in_dim[0] = static_cast<int64_t>(first_dim);
out_value->Resize(framework::make_ddim(in_dim));
out_value->mutable_data<T>(ctx.GetPlace());
// if all the input sparse vars are empty, no need to
// merge these vars.
if (first_dim == 0UL) {
return;
}
out_value->mutable_data<T>(ctx.GetPlace());
math::SelectedRowsAddTo<CPUDeviceContext, T> functor;
int64_t offset = 0;
for (int i = 0; i < N; i++) {
......
......@@ -105,18 +105,30 @@ class SumKernel : public framework::OpKernel<T> {
auto &sel_row = get_selected_row(i);
first_dim += sel_row.rows().size();
}
auto in_dim =
framework::vectorize(get_selected_row(N - 1).value().dims());
in_dim[0] = static_cast<int64_t>(first_dim);
std::vector<int64_t> in_dim;
for (int i = 0; i < N; i++) {
auto &sel_row = get_selected_row(i);
if (sel_row.rows().size() > 0) {
in_dim = framework::vectorize(sel_row.value().dims());
break;
}
}
if (in_dim.empty()) {
VLOG(3) << "WARNING: all the inputs are empty";
in_dim = framework::vectorize(get_selected_row(N - 1).value().dims());
} else {
in_dim[0] = static_cast<int64_t>(first_dim);
}
out_value->Resize(framework::make_ddim(in_dim));
out_value->mutable_data<T>(context.GetPlace());
// if all the input sparse vars are empty, no need to
// merge these vars.
if (first_dim == 0UL) {
return;
}
out_value->mutable_data<T>(context.GetPlace());
math::SelectedRowsAddTo<DeviceContext, T> functor;
......
......@@ -17,6 +17,8 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid.core as core
from paddle.fluid.op import Operator
class TestSumOp(OpTest):
......@@ -42,5 +44,66 @@ class TestSumOp(OpTest):
pass
class TestSelectedRowsSumOp(OpTest):
def check_with_place(self, place):
scope = core.Scope()
self.check_input_and_optput(scope, place, True, True, True)
self.check_input_and_optput(scope, place, False, True, True)
self.check_input_and_optput(scope, place, False, False, True)
self.check_input_and_optput(scope, place, False, False, False)
def check_input_and_optput(self,
scope,
place,
w1_has_data=False,
w2_has_data=False,
w3_has_data=False):
self.create_selected_rows(scope, place, "W1", w1_has_data)
self.create_selected_rows(scope, place, "W2", w2_has_data)
self.create_selected_rows(scope, place, "W3", w3_has_data)
# create Out Variable
out = scope.var('Out').get_selected_rows()
# create and run sum operator
sum_op = Operator("sum", X=["W1", "W2", "W3"], Out='Out')
sum_op.run(scope, place)
has_data_w_num = 0
for w in [w1_has_data, w2_has_data, w3_has_data]:
if not w:
has_data_w_num += 1
self.assertEqual(7 * has_data_w_num, len(out.rows()))
def create_selected_rows(self, scope, place, var_name, isEmpty):
# create and initialize W Variable
if not isEmpty:
rows = [0, 1, 2, 3, 4, 5, 6]
row_numel = 12
else:
rows = []
row_numel = 12
var = scope.var(var_name)
w_selected_rows = var.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)
return var
def test_w_is_selected_rows(self):
places = [core.CPUPlace()]
# currently only support CPU
for place in places:
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
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