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update reduce_sum op on xpu (#29367)

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* update reduce_sum op on xpu

* update reduce_sum op on xpu

* support running on xpu
revert-31562-mean
TTerror 5 years ago committed by GitHub
parent c7cada8571
commit a5fcc4b545
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2 changed files with 185 additions and 200 deletions
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162
paddle/fluid/operators/reduce_ops/reduce_sum_op_xpu.cc
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@ -16,6 +16,8 @@
#include "paddle/fluid/operators/reduce_ops/reduce_sum_op.h"
#include <memory>
#include <string>
#include "paddle/fluid/platform/xpu_header.h"
namespace paddle {
namespace operators {
@ -27,86 +29,120 @@ class ReduceSumXPUKernel : public framework::OpKernel<T> {
platform::is_xpu_place(context.GetPlace()), true,
platform::errors::Unavailable("This kernel only runs on XPU."));
bool reduce_all = context.Attr<bool>("reduce_all");
auto* input = context.Input<Tensor>("X");
auto* output = context.Output<Tensor>("Out");
output->mutable_data<T>(context.GetPlace());
auto dims = context.Attr<std::vector<int>>("dim");
auto* x = context.Input<Tensor>("X");
auto* y = context.Output<Tensor>("Out");
y->mutable_data<T>(context.GetPlace());
auto& dev_ctx = context.template device_context<DeviceContext>();
int out_dtype = context.Attr<int>("out_dtype");
PADDLE_ENFORCE_EQ(
out_dtype == -1, true,
platform::errors::InvalidArgument(
"XPU only support out_dtype == -1 in reduce_sum op."));
const auto* x_data = x->data<T>();
auto* y_data = y->data<T>();
const auto& input_dim_size = x->dims().size();
std::vector<int> true_dims;
for (size_t i = 0; i < dims.size(); ++i) {
if (dims[i] < 0) {
true_dims.push_back(dims[i] + input_dim_size);
} else {
true_dims.push_back(dims[i]);
}
}
std::vector<int> reduce_dims;
std::vector<int> xdims((input_dim_size));
for (int i = 0; i < input_dim_size; ++i) {
xdims[i] = x->dims()[i];
}
if (reduce_all) {
int input_len = input->numel();
int r = xpu::sum(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
input_len);
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::External("XPU kernel error!"));
for (int i = 0; i < input_dim_size; ++i) {
reduce_dims.push_back(i);
}
} else {
int ndim = input->dims().size();
std::vector<int> idims;
for (int i = 0; i < input->dims().size(); i++) {
idims.push_back(input->dims()[i]);
std::set<int> dims_set(true_dims.begin(), true_dims.end());
for (auto i = 0; i < input_dim_size; i++) {
if (dims_set.find(i) != dims_set.end()) {
if (x->dims()[i] != 1) {
reduce_dims.push_back(i);
}
}
}
auto dims = context.Attr<std::vector<int>>("dim");
int rdim = dims.size();
int r =
xpu::reduce(dev_ctx.x_context(), input->data<T>(), output->data<T>(),
idims.data(), ndim, dims.data(), rdim, xpu::REDUCE_SUM);
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::External("XPU kernel error!"));
}
if (reduce_dims.size() == 0) {
int r = xpu::copy<T>(dev_ctx.x_context(), x_data, y_data,
x->numel() * sizeof(T));
PADDLE_ENFORCE_EQ(
r == xpu::Error_t::SUCCESS, true,
platform::errors::External("XPU copy in reduce_sum op return "
"wrong value[%d %s].",
r, XPUAPIErrorMsg[r]));
} else {
int r = xpu::reduce_sum<T>(dev_ctx.x_context(), x_data, y_data, xdims,
reduce_dims);
PADDLE_ENFORCE_EQ(
r == xpu::Error_t::SUCCESS, true,
platform::errors::External("XPU reduce_sum in reduce_sum op return"
" wrong value[%d %s].",
r, XPUAPIErrorMsg[r]));
}
}
};
template <typename DeviceContext, typename T>
class ReduceSumGradXPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto dims = context.Attr<std::vector<int>>("dim");
bool reduce_all = context.Attr<bool>("reduce_all");
auto* input0 = context.Input<Tensor>("X");
auto* input2 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* output = context.Output<Tensor>(framework::GradVarName("X"));
output->mutable_data<T>(context.GetPlace());
const auto* input2_d = input2->data<T>();
auto* output_d = output->data<T>();
auto* x = context.Input<Tensor>("X");
auto* out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* x_grad = context.Output<Tensor>(framework::GradVarName("X"));
int in_dtype = context.Attr<int>("in_dtype");
PADDLE_ENFORCE_EQ(
in_dtype == -1, true,
platform::errors::InvalidArgument(
"XPU only support in_dtype == -1 in reduce_sum_grad op."));
auto& dev_ctx = context.template device_context<DeviceContext>();
int r = 0;
std::vector<int> idims;
int reduce_dim = 0;
if (reduce_all) {
idims.push_back(input0->numel());
idims.push_back(1);
idims.push_back(1);
r = xpu::reduce_grad(dev_ctx.x_context(), input2_d, output_d,
idims.data(), idims.size(), &reduce_dim, 1,
xpu::REDUCE_SUM);
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::External("XPU kernel error!"));
} else if (dims.size() == 1) {
// handle reduce by one dimension
int reduce_dim_index = dims[0];
if (reduce_dim_index < 0) {
reduce_dim_index += input0->dims().size();
}
auto& input_dim = input0->dims();
int before_dim = 1;
for (int i = 0; i < reduce_dim_index; ++i) {
before_dim *= input_dim[i];
x_grad->mutable_data<T>(context.GetPlace());
const auto* out_data = out->data<T>();
auto* x_grad_data = x_grad->data<T>();
const auto& input_dim_size = x->dims().size();
std::vector<int> true_dims;
for (size_t i = 0; i < dims.size(); ++i) {
if (dims[i] < 0) {
true_dims.push_back(dims[i] + input_dim_size);
} else {
true_dims.push_back(dims[i]);
}
int reduce_dim = input_dim[reduce_dim_index];
int after_dim = 1;
for (int i = reduce_dim_index + 1; i < input_dim.size(); ++i) {
after_dim *= input_dim[i];
}
std::vector<int> ydims(input_dim_size);
std::vector<int> xdims((input_dim_size));
std::set<int> dims_set(true_dims.begin(), true_dims.end());
for (auto i = 0; i < input_dim_size; i++) {
xdims[i] = x->dims()[i];
if (dims_set.find(i) != dims_set.end() || reduce_all) {
ydims[i] = 1;
} else {
ydims[i] = x->dims()[i];
}
idims.push_back(before_dim);
idims.push_back(input_dim[reduce_dim_index]);
idims.push_back(after_dim);
reduce_dim = 1;
r = xpu::reduce_grad(dev_ctx.x_context(), input2_d, output_d,
idims.data(), idims.size(), &reduce_dim, 1,
xpu::REDUCE_SUM);
PADDLE_ENFORCE_EQ(r == xpu::Error_t::SUCCESS, true,
platform::errors::External("XPU kernel error!"));
} else {
PADDLE_THROW(
platform::errors::Unimplemented("unsupport reduce sum grad"));
}
int r = xpu::broadcast<T>(dev_ctx.x_context(), out_data, x_grad_data, ydims,
xdims);
PADDLE_ENFORCE_EQ(
r == xpu::Error_t::SUCCESS, true,
platform::errors::External("XPU broadcast in reduce_sum_grad op return"
" wrong value[%d %s].",
r, XPUAPIErrorMsg[r]));
}
};

223
python/paddle/fluid/tests/unittests/xpu/test_reduce_sum_op_xpu.py
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