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未验证 提交 32f42e94 编写于 作者: W WangZhen 提交者： GitHub
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[OpAttr]Adapt tensor axis for reduce_min/max/mean/sum/prod (#45078) 

* [OpAttr]Adapt tensor axis for reduce_min/max/mean/sum/prod
上级 e221a600
隐藏空白更改
内联 并排
Showing with 513 addition and 189 deletion
paddle/fluid/operators/reduce_ops/reduce_max_op.cc
浏览文件 @ 32f42e94
......@@ -25,9 +25,10 @@ class ReduceMaxOpMaker : public ops::ReduceOpMaker {
virtual std::string GetOpType() const { return "Reduce reduce_max"; }
};
DECLARE_INFER_SHAPE_FUNCTOR(reduce_max,
ReduceMaxInferShapeFunctor,
PD_INFER_META(phi::ReduceInferMetaBase));
DECLARE_INFER_SHAPE_FUNCTOR(
reduce_max,
ReduceMaxInferShapeFunctor,
PD_INFER_META(phi::ReduceIntArrayAxisInferMetaBase));
REGISTER_OPERATOR(
reduce_max,
......
paddle/fluid/operators/reduce_ops/reduce_mean_op.cc
浏览文件 @ 32f42e94
......@@ -97,9 +97,10 @@ class __reduce_meanMaker__ : public ops::ReduceOpMaker {
virtual std::string GetOpType() const { return "Reduce reduce_mean"; }
};
DECLARE_INFER_SHAPE_FUNCTOR(reduce_mean,
ReduceMeanInferShapeFunctor,
PD_INFER_META(phi::ReduceInferMetaBase));
DECLARE_INFER_SHAPE_FUNCTOR(
reduce_mean,
ReduceMeanInferShapeFunctor,
PD_INFER_META(phi::ReduceIntArrayAxisInferMetaBase));
REGISTER_OPERATOR(reduce_mean,
ops::ReduceOp,
......
paddle/fluid/operators/reduce_ops/reduce_min_op.cc
浏览文件 @ 32f42e94
......@@ -25,9 +25,10 @@ class ReduceMinOpMaker : public ops::ReduceOpMaker {
virtual std::string GetOpType() const { return "Reduce reduce_min"; }
};
DECLARE_INFER_SHAPE_FUNCTOR(reduce_min,
ReduceMinInferShapeFunctor,
PD_INFER_META(phi::ReduceInferMetaBase));
DECLARE_INFER_SHAPE_FUNCTOR(
reduce_min,
ReduceMinInferShapeFunctor,
PD_INFER_META(phi::ReduceIntArrayAxisInferMetaBase));
REGISTER_OPERATOR(
reduce_min,
......
paddle/fluid/operators/reduce_ops/reduce_op.h
浏览文件 @ 32f42e94
......@@ -698,7 +698,8 @@ class ReduceOpMaker : public framework::OpProtoAndCheckerMaker {
"Must be in the range [-rank(input), rank(input)). "
"If `dim[i] < 0`, the dims[i] to reduce is `rank + dims[i]`. "
"Note that reducing on the first dim will make the LoD info lost.")
.SetDefault({0});
.SetDefault({0})
.SupportTensor();
AddAttr<bool>("keep_dim",
"(bool, default false) "
"If true, retain the reduced dimension with length 1.")
......
paddle/fluid/operators/reduce_ops/reduce_prod_op.cc
浏览文件 @ 32f42e94
......@@ -35,9 +35,10 @@ class ReduceProdOpMaker : public ops::ReduceOpMaker {
virtual std::string GetOpType() const { return "Reduce reduce_prod"; }
};
DECLARE_INFER_SHAPE_FUNCTOR(reduce_prod,
ReduceProdInferShapeFunctor,
PD_INFER_META(phi::ReduceInferMetaBase));
DECLARE_INFER_SHAPE_FUNCTOR(
reduce_prod,
ReduceProdInferShapeFunctor,
PD_INFER_META(phi::ReduceIntArrayAxisInferMetaBase));
REGISTER_OPERATOR(
reduce_prod,
......
paddle/phi/api/yaml/legacy_api.yaml
浏览文件 @ 32f42e94
......@@ -1668,10 +1668,10 @@
func : matrix_rank_tol
- api : max
args : (Tensor x, int64_t[] dims={}, bool keep_dim=false)
args : (Tensor x, IntArray dims={}, bool keep_dim=false)
output : Tensor(out)
infer_meta :
func : ReduceInferMeta
func : ReduceIntArrayAxisInferMeta
kernel :
func : max
backward : max_grad
......@@ -1713,10 +1713,10 @@
backward : maxout_grad
- api : mean
args : (Tensor x, int64_t[] dims={}, bool keep_dim=false)
args : (Tensor x, IntArray dims={}, bool keep_dim=false)
output : Tensor(out)
infer_meta :
func : ReduceInferMeta
func : ReduceIntArrayAxisInferMeta
kernel :
func : mean
backward : mean_grad
......@@ -1762,10 +1762,10 @@
backward : meshgrid_grad
- api : min
args : (Tensor x, int64_t[] dims={}, bool keep_dim=false)
args : (Tensor x, IntArray dims={}, bool keep_dim=false)
output : Tensor(out)
infer_meta :
func : ReduceInferMeta
func : ReduceIntArrayAxisInferMeta
kernel :
func : min
backward : min_grad
......@@ -2091,10 +2091,10 @@
backward : reciprocal_grad
- api : reduce_prod
args : (Tensor x, int64_t[] dims, bool keep_dim, bool reduce_all)
args : (Tensor x, IntArray dims, bool keep_dim, bool reduce_all)
output : Tensor
infer_meta :
func : ReduceInferMetaBase
func : ReduceIntArrayAxisInferMetaBase
kernel :
func : prod_raw
backward : reduce_prod_grad
......@@ -2555,7 +2555,7 @@
backward : subtract_grad
- api : sum
args : (Tensor x, int64_t[] dims={}, DataType out_dtype=DataType::UNDEFINED, bool keep_dim=false)
args : (Tensor x, IntArray dims={}, DataType out_dtype=DataType::UNDEFINED, bool keep_dim=false)
output : Tensor(out)
infer_meta :
func : SumInferMeta
......
paddle/phi/api/yaml/legacy_backward.yaml
浏览文件 @ 32f42e94
......@@ -1451,8 +1451,8 @@
func : matrix_power_grad
- backward_api : max_grad
forward: max (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false)
forward: max (Tensor x, IntArray dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, IntArray dims={}, bool keep_dim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
......@@ -1509,14 +1509,14 @@
func : mean_all_grad
- backward_api : mean_double_grad
forward: mean_grad (Tensor x, Tensor grad_out, int64_t[] dims={}, bool keep_dim=false, bool reduce_all = false) -> Tensor(grad_x)
args : (Tensor grad_x_grad, int64_t[] dims={}, bool keep_dim=false)
forward: mean_grad (Tensor x, Tensor grad_out, IntArray dims={}, bool keep_dim=false, bool reduce_all = false) -> Tensor(grad_x)
args : (Tensor grad_x_grad, IntArray dims={}, bool keep_dim=false)
output : Tensor(grad_out_grad)
invoke : mean(grad_x_grad, dims, keep_dim)
- backward_api : mean_grad
forward: mean (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false)
forward: mean (Tensor x, IntArray dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out_grad, IntArray dims={}, bool keep_dim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
......@@ -1536,8 +1536,8 @@
func : meshgrid_grad
- backward_api : min_grad
forward: min (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false)
forward: min (Tensor x, IntArray dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, IntArray dims={}, bool keep_dim=false, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
......@@ -1849,8 +1849,8 @@
inplace : (out_grad -> x_grad)
- backward_api : reduce_prod_grad
forward : reduce_prod (Tensor x, int64_t[] dims, bool keep_dim, bool reduce_all) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims, bool keep_dim, bool reduce_all)
forward : reduce_prod (Tensor x, IntArray dims, bool keep_dim, bool reduce_all) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, IntArray dims, bool keep_dim, bool reduce_all)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
......@@ -2386,14 +2386,14 @@
inplace : (out_grad -> x_grad)
- backward_api : sum_double_grad
forward : sum_grad (Tensor x, Tensor grad_out, int64_t[] dims, bool keep_dim, bool reduce_all=false) -> Tensor(grad_x)
args : (Tensor grad_x_grad, int64_t[] dims={}, bool keep_dim=false)
forward : sum_grad (Tensor x, Tensor grad_out, IntArray dims, bool keep_dim, bool reduce_all=false) -> Tensor(grad_x)
args : (Tensor grad_x_grad, IntArray dims={}, bool keep_dim=false)
output : Tensor(grad_out_grad)
invoke : sum(grad_x_grad, dims, grad_x_grad.dtype(), keep_dim)
- backward_api : sum_grad
forward : sum (Tensor x, int64_t[] dims={}, DataType out_dtype=DataType::UNDEFINED, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out_grad, int64_t[] dims, bool keep_dim, bool reduce_all=false)
forward : sum (Tensor x, IntArray dims={}, DataType out_dtype=DataType::UNDEFINED, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out_grad, IntArray dims, bool keep_dim, bool reduce_all=false)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
......
paddle/phi/infermeta/unary.cc
浏览文件 @ 32f42e94
......@@ -2656,6 +2656,33 @@ DDim ReduceInferDim(const MetaTensor& x,
return out_dim;
}
DDim ReduceInferDimForIntArrayAxis(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
bool reduce_all) {
std::vector<int64_t> vec_axis = axis.GetData();
std::vector<int64_t> vec_dim;
if (reduce_all) {
if (keep_dim) {
vec_dim = std::vector<int64_t>(x.dims().size(), 1);
} else {
vec_dim = {1};
}
} else {
if (keep_dim) {
vec_dim = std::vector<int64_t>(x.dims().size(), -1);
} else {
auto x_rank = static_cast<size_t>(x.dims().size());
if (vec_axis.size() >= x_rank) {
vec_dim = {-1};
} else {
vec_dim = std::vector<int64_t>(x.dims().size() - vec_axis.size(), -1);
}
}
}
return phi::make_ddim(vec_dim);
}
void ReduceInferMeta(const MetaTensor& x,
const std::vector<int64_t>& axis,
bool keep_dim,
......@@ -2678,6 +2705,34 @@ void ReduceInferMetaBase(const MetaTensor& x,
out->set_layout(x.layout());
}
void ReduceIntArrayAxisInferMetaBase(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
bool reduce_all,
MetaTensor* out,
MetaConfig config) {
if (config.is_runtime || !axis.FromTensor()) {
ReduceInferMetaBase(x, axis.GetData(), keep_dim, reduce_all, out);
} else {
DDim out_dim = ReduceInferDimForIntArrayAxis(x, axis, keep_dim, reduce_all);
out->set_dims(out_dim);
out->set_dtype(x.dtype());
out->set_layout(x.layout());
}
}
void ReduceIntArrayAxisInferMeta(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
MetaTensor* out,
MetaConfig config) {
bool reduce_all = false;
if (axis.size() == 0) {
reduce_all = true;
}
ReduceIntArrayAxisInferMetaBase(x, axis, keep_dim, reduce_all, out, config);
}
void RepeatInterleaveInferMeta(const MetaTensor& x,
int repeats,
int dim,
......@@ -3354,24 +3409,31 @@ void StridedSliceInferMeta(const MetaTensor& x,
api.yaml
*/
void SumInferMeta(const MetaTensor& x,
const std::vector<int64_t>& axis,
const IntArray& axis,
DataType dtype,
bool keep_dim,
MetaTensor* out) {
MetaTensor* out,
MetaConfig config) {
bool reduce_all = false;
if (axis.size() == 0) {
reduce_all = true;
}
SumRawInferMeta(x, axis, keep_dim, reduce_all, dtype, out);
SumRawInferMeta(x, axis, keep_dim, reduce_all, dtype, out, config);
}
void SumRawInferMeta(const MetaTensor& x,
const std::vector<int64_t>& axis,
const IntArray& axis,
bool keep_dim,
bool reduce_all,
DataType dtype,
MetaTensor* out) {
DDim out_dim = ReduceInferDim(x, axis, keep_dim, reduce_all);
MetaTensor* out,
MetaConfig config) {
DDim out_dim;
if (config.is_runtime || !axis.FromTensor()) {
out_dim = ReduceInferDim(x, axis.GetData(), keep_dim, reduce_all);
} else {
out_dim = ReduceInferDimForIntArrayAxis(x, axis, keep_dim, reduce_all);
}
DataType out_dtype;
if (dtype != DataType::UNDEFINED) {
......
paddle/phi/infermeta/unary.h
浏览文件 @ 32f42e94
......@@ -372,6 +372,19 @@ void ReduceInferMetaBase(const MetaTensor& x,
bool reduce_all,
MetaTensor* out);
void ReduceIntArrayAxisInferMetaBase(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
bool reduce_all,
MetaTensor* out,
MetaConfig config = MetaConfig());
void ReduceIntArrayAxisInferMeta(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
MetaTensor* out,
MetaConfig config = MetaConfig());
void RepeatInterleaveInferMeta(const MetaTensor& x,
int repeats,
int dim,
......@@ -477,17 +490,19 @@ void StridedSliceInferMeta(const MetaTensor& x,
MetaConfig config = MetaConfig());
void SumInferMeta(const MetaTensor& x,
const std::vector<int64_t>& axis,
const IntArray& axis,
DataType dtype,
bool keep_dim,
MetaTensor* out);
MetaTensor* out,
MetaConfig config = MetaConfig());
void SumRawInferMeta(const MetaTensor& x,
const std::vector<int64_t>& axis,
const IntArray& axis,
bool keep_dim,
bool reduce_all,
DataType dtype,
MetaTensor* out);
MetaTensor* out,
MetaConfig config = MetaConfig());
void SvdInferMeta(const MetaTensor& x,
bool full_matrices,
......
paddle/phi/kernels/cpu/graph_send_ue_recv_grad_kernel.cc
浏览文件 @ 32f42e94
......@@ -73,7 +73,7 @@ void CalculateXGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
memcpy(x_grad, x_grad_out.data<T>(), x_grad_out.numel() * sizeof(T));
......@@ -131,7 +131,7 @@ void CalculateXGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
memcpy(x_grad, x_grad_out.data<T>(), x_grad_out.numel() * sizeof(T));
......@@ -166,7 +166,7 @@ void CalculateXGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
memcpy(x_grad, x_grad_out.data<T>(), x_grad_out.numel() * sizeof(T));
......@@ -220,7 +220,7 @@ void CalculateXGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
memcpy(x_grad, x_grad_out.data<T>(), x_grad_out.numel() * sizeof(T));
......
paddle/phi/kernels/cpu/graph_send_uv_grad_kernel.cc
浏览文件 @ 32f42e94
......@@ -86,7 +86,7 @@ void CalculateGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
memcpy(x_grad, x_grad_out.data<T>(), x_grad_out.numel() * sizeof(T));
......@@ -148,7 +148,7 @@ void CalculateGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
memcpy(x_grad, x_grad_out.data<T>(), x_grad_out.numel() * sizeof(T));
......
paddle/phi/kernels/cpu/identity_loss_kernel.cc
浏览文件 @ 32f42e94
......@@ -31,7 +31,7 @@ void IdentityLossKernel(const Context& dev_ctx,
case 0:
// sum
phi::SumRawKernel<T>(
dev_ctx, x, std::vector<int64_t>{0}, false, true, out->dtype(), out);
dev_ctx, x, phi::IntArray({0}), false, true, out->dtype(), out);
break;
case 1:
// mean
......
paddle/phi/kernels/cpu/matrix_rank_tol_kernel.cc
浏览文件 @ 32f42e94
......@@ -118,7 +118,7 @@ void MatrixRankTolKernel(const Context& dev_ctx,
dev_ctx.template Alloc<T>(&max_eigenvalue_tensor);
phi::MaxKernel<T, Context>(dev_ctx,
eigenvalue_tensor,
std::vector<int64_t>{-1},
phi::IntArray({-1}),
false,
&max_eigenvalue_tensor);
......
paddle/phi/kernels/cpu/reduce_max_kernel.cc
浏览文件 @ 32f42e94
......@@ -24,13 +24,13 @@ namespace phi {
template <typename T, typename Context>
void MaxRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out) {
auto out_dtype = x.dtype();
phi::Reduce<CPUContext, T, phi::funcs::MaxFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/cpu/reduce_mean_grad_kernel.cc
浏览文件 @ 32f42e94
......@@ -24,12 +24,18 @@ template <typename T, typename Context>
void ReduceMeanGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad) {
ReduceGradKernel<Context, T, funcs::MeanGradFunctor, true>(
dev_ctx, x, paddle::none, out_grad, dims, keep_dim, reduce_all, x_grad);
ReduceGradKernel<Context, T, funcs::MeanGradFunctor, true>(dev_ctx,
x,
paddle::none,
out_grad,
dims.GetData(),
keep_dim,
reduce_all,
x_grad);
}
} // namespace phi
......
paddle/phi/kernels/cpu/reduce_mean_kernel.cc
浏览文件 @ 32f42e94
......@@ -24,13 +24,13 @@ namespace phi {
template <typename T, typename Context>
void MeanRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out) {
auto out_dtype = x.dtype();
phi::Reduce<CPUContext, T, phi::funcs::MeanFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/cpu/reduce_min_kernel.cc
浏览文件 @ 32f42e94
......@@ -24,13 +24,13 @@ namespace phi {
template <typename T, typename Context>
void MinRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out) {
auto out_dtype = x.dtype();
phi::Reduce<CPUContext, T, phi::funcs::MinFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/cpu/reduce_prod_kernel.cc
浏览文件 @ 32f42e94
......@@ -24,13 +24,13 @@ namespace phi {
template <typename T, typename Context>
void ProdRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out) {
auto out_dtype = x.dtype();
phi::Reduce<CPUContext, T, phi::funcs::ProdFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/cpu/reduce_sum_grad_kernel.cc
浏览文件 @ 32f42e94
......@@ -73,7 +73,7 @@ template <typename T, typename Context>
void ReduceSumGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad) {
......@@ -84,17 +84,25 @@ void ReduceSumGradKernel(const Context& dev_ctx,
DenseTensor x_grad_tmp =
phi::Empty<Context>(dev_ctx, std::move(x_grad_meta));
ComputeFromInput<T, Context>(dev_ctx, x, out_grad, dims, &x_grad_tmp);
ComputeFromInput<T, Context>(
dev_ctx, x, out_grad, dims.GetData(), &x_grad_tmp);
phi::CastKernel<T>(dev_ctx, x_grad_tmp, x.dtype(), x_grad);
} else {
ComputeFromInput<T, Context>(dev_ctx, x, out_grad, dims, x_grad);
ComputeFromInput<T, Context>(
dev_ctx, x, out_grad, dims.GetData(), x_grad);
}
}
ReduceGradKernel<Context, T, funcs::SumGradFunctor, true>(
dev_ctx, x, paddle::none, out_grad, dims, keep_dim, reduce_all, x_grad);
ReduceGradKernel<Context, T, funcs::SumGradFunctor, true>(dev_ctx,
x,
paddle::none,
out_grad,
dims.GetData(),
keep_dim,
reduce_all,
x_grad);
}
} // namespace phi
......
paddle/phi/kernels/cpu/reduce_sum_kernel.cc
浏览文件 @ 32f42e94
......@@ -24,7 +24,7 @@ namespace phi {
template <typename T, typename Context>
void SumRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DataType out_dtype,
......@@ -33,7 +33,7 @@ void SumRawKernel(const Context& dev_ctx,
out_dtype = out->dtype();
}
phi::Reduce<CPUContext, T, phi::funcs::SumFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/gpu/graph_send_ue_recv_grad_kernel.cu
浏览文件 @ 32f42e94
......@@ -158,7 +158,7 @@ void CalculateXGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
#ifdef PADDLE_WITH_HIP
......@@ -235,7 +235,7 @@ void CalculateXGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
#ifdef PADDLE_WITH_HIP
......@@ -281,7 +281,7 @@ void CalculateXGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
#ifdef PADDLE_WITH_HIP
......@@ -349,7 +349,7 @@ void CalculateXGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
// TODO(daisiming): Whether use x_grad instead.
......
paddle/phi/kernels/gpu/graph_send_uv_grad_kernel.cu
浏览文件 @ 32f42e94
......@@ -108,7 +108,7 @@ void CalculateGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
#ifdef PADDLE_WITH_HIP
......@@ -190,7 +190,7 @@ void CalculateGrad(const Context& ctx,
DenseTensor x_grad_out = phi::Sum<T, Context>(
ctx,
x_grad_v2,
reduce_idx,
phi::IntArray(reduce_idx),
paddle::experimental::CppTypeToDataType<T>::Type(),
true);
#ifdef PADDLE_WITH_HIP
......
paddle/phi/kernels/gpu/matrix_rank_tol_kernel.cu
浏览文件 @ 32f42e94
......@@ -378,7 +378,7 @@ void MatrixRankTolKernel(const Context& dev_ctx,
phi::MaxKernel<T, Context>(dev_ctx,
eigenvalue_tensor,
std::vector<int64_t>{-1},
phi::IntArray({-1}),
false,
&max_eigenvalue_tensor);
......
paddle/phi/kernels/gpu/reduce_mean_grad_kernel.cu
浏览文件 @ 32f42e94
......@@ -25,13 +25,13 @@ template <typename T, typename Context>
void ReduceMeanGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad) {
int dim_size = x.dims().size();
std::vector<int> reduce_dims =
funcs::details::GetReduceDim(dims, dim_size, reduce_all);
funcs::details::GetReduceDim(dims.GetData(), dim_size, reduce_all);
int reduce_num = 1;
for (auto i : reduce_dims) {
reduce_num *= (x.dims())[i];
......@@ -41,7 +41,7 @@ void ReduceMeanGradKernel(const Context& dev_ctx,
dev_ctx,
x,
out_grad,
dims,
dims.GetData(),
keep_dim,
reduce_all,
x_grad,
......
paddle/phi/kernels/gpu/reduce_sum_grad_kernel.cu
浏览文件 @ 32f42e94
......@@ -25,7 +25,7 @@ template <typename T, typename Context>
void ReduceSumGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad) {
......@@ -41,7 +41,7 @@ void ReduceSumGradKernel(const Context& dev_ctx,
reduce_all = true;
}
std::vector<int> reduce_dims =
funcs::details::GetReduceDim(dims, dim_size, reduce_all);
funcs::details::GetReduceDim(dims.GetData(), dim_size, reduce_all);
auto update_dims = vectorize(d_x->dims());
int reduce_num = 1;
......
paddle/phi/kernels/impl/einsum_grad_impl.h
浏览文件 @ 32f42e94
......@@ -80,7 +80,7 @@ DenseTensor PerformTileAndReduction(const Context& dev_ctx,
if (to_reduce.size() != 0) {
ret = Sum<T, Context>(dev_ctx,
after_tile,
to_reduce,
phi::IntArray(to_reduce),
after_tile.dtype(),
false); // not keep dim.
} else {
......
paddle/phi/kernels/impl/einsum_impl.h
浏览文件 @ 32f42e94
......@@ -383,7 +383,8 @@ DenseTensor PerformReduction(const Context& dev_ctx,
VLOG(5) << "call PerformReduction: with axis: "
<< paddle::string::join_strings(indices, ",");
if (indices.size() == 0) return tensor;
return Sum<T, Context>(dev_ctx, tensor, indices, tensor.dtype(), true);
return Sum<T, Context>(
dev_ctx, tensor, phi::IntArray(indices), tensor.dtype(), true);
}
inline bool is_no_need_transpose(const std::vector<int>& axis) {
......
paddle/phi/kernels/impl/lstsq_kernel_impl.h
浏览文件 @ 32f42e94
......@@ -69,8 +69,8 @@ inline void GetResidualsTensor(const DeviceContext& dev_ctx,
dev_ctx.template Alloc<T>(pow_tensor);
phi::PowKernel<T>(dev_ctx, sub_tensor, Scalar(2), pow_tensor);
auto sum_tensor =
phi::Sum<T>(dev_ctx, *pow_tensor, {-2}, pow_tensor->dtype(), false);
auto sum_tensor = phi::Sum<T>(
dev_ctx, *pow_tensor, phi::IntArray({-2}), pow_tensor->dtype(), false);
phi::Copy<DeviceContext>(
dev_ctx, sum_tensor, dev_ctx.GetPlace(), true, residuals);
} else {
......
paddle/phi/kernels/impl/reduce_max_grad_kernel_impl.h
浏览文件 @ 32f42e94
......@@ -25,12 +25,12 @@ void ReduceMaxGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad) {
ReduceGradKernel<Context, T, funcs::MaxOrMinGradFunctor>(
dev_ctx, x, out, out_grad, dims, keep_dim, reduce_all, x_grad);
dev_ctx, x, out, out_grad, dims.GetData(), keep_dim, reduce_all, x_grad);
}
} // namespace phi
paddle/phi/kernels/impl/reduce_min_grad_kernel_impl.h
浏览文件 @ 32f42e94
......@@ -25,12 +25,12 @@ void ReduceMinGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad) {
ReduceGradKernel<Context, T, funcs::MaxOrMinGradFunctor>(
dev_ctx, x, out, out_grad, dims, keep_dim, reduce_all, x_grad);
dev_ctx, x, out, out_grad, dims.GetData(), keep_dim, reduce_all, x_grad);
}
} // namespace phi
paddle/phi/kernels/impl/reduce_prod_grad_kernel_impl.h
浏览文件 @ 32f42e94
......@@ -14,6 +14,7 @@
#pragma once
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/kernels/funcs/reduce_functor.h"
#include "paddle/phi/kernels/impl/reduce_grad.h"
#include "paddle/phi/kernels/reduce_prod_grad_kernel.h"
......@@ -25,12 +26,12 @@ void ReduceProdGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad) {
ReduceGradKernel<Context, T, funcs::ProdGradFunctor>(
dev_ctx, x, out, out_grad, dims, keep_dim, reduce_all, x_grad);
dev_ctx, x, out, out_grad, dims.GetData(), keep_dim, reduce_all, x_grad);
}
} // namespace phi
paddle/phi/kernels/kps/reduce_max_kernel.cu
浏览文件 @ 32f42e94
......@@ -12,22 +12,22 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/reduce_max_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/reduce.h"
#include "paddle/phi/kernels/reduce_max_kernel.h"
namespace phi {
template <typename T, typename Context>
void MaxRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out) {
auto out_dtype = x.dtype();
phi::Reduce<T, kps::MaxFunctor, kps::IdentityFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/kps/reduce_mean_kernel.cu
浏览文件 @ 32f42e94
......@@ -12,22 +12,22 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/reduce_mean_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/reduce.h"
#include "paddle/phi/kernels/reduce_mean_kernel.h"
namespace phi {
template <typename T, typename Context>
void MeanRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out) {
auto out_dtype = x.dtype();
phi::Reduce<T, kps::AddFunctor, kps::IdentityFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out, true);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out, true);
}
} // namespace phi
......
paddle/phi/kernels/kps/reduce_min_kernel.cu
浏览文件 @ 32f42e94
......@@ -12,22 +12,22 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/reduce_min_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/reduce.h"
#include "paddle/phi/kernels/reduce_min_kernel.h"
namespace phi {
template <typename T, typename Context>
void MinRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out) {
auto out_dtype = x.dtype();
phi::Reduce<T, kps::MinFunctor, kps::IdentityFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/kps/reduce_prod_kernel.cu
浏览文件 @ 32f42e94
......@@ -12,22 +12,22 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/reduce_prod_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/reduce.h"
#include "paddle/phi/kernels/reduce_prod_kernel.h"
namespace phi {
template <typename T, typename Context>
void ProdRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out) {
auto out_dtype = x.dtype();
phi::Reduce<T, kps::MulFunctor, kps::IdentityFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/kps/reduce_sum_kernel.cu
浏览文件 @ 32f42e94
......@@ -12,16 +12,16 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/kernels/reduce_sum_kernel.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/gpu/reduce.h"
#include "paddle/phi/kernels/reduce_sum_kernel.h"
namespace phi {
template <typename T, typename Context>
void SumRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DataType out_dtype,
......@@ -30,7 +30,7 @@ void SumRawKernel(const Context& dev_ctx,
out_dtype = out->dtype();
}
phi::Reduce<T, kps::AddFunctor, kps::IdentityFunctor>(
dev_ctx, x, reduce_all, dims, keep_dim, out_dtype, out);
dev_ctx, x, reduce_all, dims.GetData(), keep_dim, out_dtype, out);
}
} // namespace phi
......
paddle/phi/kernels/reduce_max_grad_kernel.h
浏览文件 @ 32f42e94
......@@ -15,6 +15,7 @@
#pragma once
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
......@@ -23,7 +24,7 @@ void ReduceMaxGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad);
......
paddle/phi/kernels/reduce_max_kernel.cc
浏览文件 @ 32f42e94
......@@ -22,7 +22,7 @@ namespace phi {
template <typename T, typename Context>
void MaxKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
DenseTensor* out) {
bool reduce_all = false;
......
paddle/phi/kernels/reduce_max_kernel.h
浏览文件 @ 32f42e94
......@@ -14,13 +14,14 @@
#pragma once
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void MaxRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out);
......@@ -28,7 +29,7 @@ void MaxRawKernel(const Context& dev_ctx,
template <typename T, typename Context>
void MaxKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
DenseTensor* out);
......
paddle/phi/kernels/reduce_mean_grad_kernel.h
浏览文件 @ 32f42e94
......@@ -15,6 +15,7 @@
#pragma once
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
......@@ -22,7 +23,7 @@ template <typename T, typename Context>
void ReduceMeanGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad);
......
paddle/phi/kernels/reduce_mean_kernel.cc
浏览文件 @ 32f42e94
......@@ -22,7 +22,7 @@ namespace phi {
template <typename T, typename Context>
void MeanKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
DenseTensor* out) {
bool reduce_all = false;
......
paddle/phi/kernels/reduce_mean_kernel.h
浏览文件 @ 32f42e94
......@@ -22,7 +22,7 @@ namespace phi {
template <typename T, typename Context>
void MeanRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out);
......@@ -30,14 +30,14 @@ void MeanRawKernel(const Context& dev_ctx,
template <typename T, typename Context>
void MeanKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
DenseTensor* out);
template <typename T, typename Context>
DenseTensor Mean(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& axis,
const IntArray& axis,
bool keep_dim) {
DenseTensor dense_out;
MetaTensor meta_out(&dense_out);
......
paddle/phi/kernels/reduce_min_grad_kernel.h
浏览文件 @ 32f42e94
......@@ -15,6 +15,7 @@
#pragma once
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
......@@ -23,7 +24,7 @@ void ReduceMinGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad);
......
paddle/phi/kernels/reduce_min_kernel.cc
浏览文件 @ 32f42e94
......@@ -22,7 +22,7 @@ namespace phi {
template <typename T, typename Context>
void MinKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
DenseTensor* out) {
bool reduce_all = false;
......
paddle/phi/kernels/reduce_min_kernel.h
浏览文件 @ 32f42e94
......@@ -14,6 +14,7 @@
#pragma once
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
......@@ -21,7 +22,7 @@ namespace phi {
template <typename T, typename Context>
void MinRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out);
......@@ -29,7 +30,7 @@ void MinRawKernel(const Context& dev_ctx,
template <typename T, typename Context>
void MinKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
DenseTensor* out);
......
paddle/phi/kernels/reduce_prod_grad_kernel.h
浏览文件 @ 32f42e94
......@@ -15,6 +15,7 @@
#pragma once
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
......@@ -23,7 +24,7 @@ void ReduceProdGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad);
......
paddle/phi/kernels/reduce_prod_kernel.cc
浏览文件 @ 32f42e94
......@@ -22,7 +22,7 @@ namespace phi {
template <typename T, typename Context>
void ProdKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
DenseTensor* out) {
bool reduce_all = false;
......
paddle/phi/kernels/reduce_prod_kernel.h
浏览文件 @ 32f42e94
......@@ -14,13 +14,14 @@
#pragma once
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void ProdRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* out);
......@@ -28,7 +29,7 @@ void ProdRawKernel(const Context& dev_ctx,
template <typename T, typename Context>
void ProdKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
DenseTensor* out);
......
paddle/phi/kernels/reduce_sum_grad_kernel.h
浏览文件 @ 32f42e94
......@@ -15,6 +15,7 @@
#pragma once
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
......@@ -22,7 +23,7 @@ template <typename T, typename Context>
void ReduceSumGradKernel(const Context& dev_ctx,
const DenseTensor& x,
const DenseTensor& out_grad,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DenseTensor* x_grad);
......
paddle/phi/kernels/reduce_sum_kernel.cc
浏览文件 @ 32f42e94
......@@ -22,7 +22,7 @@ namespace phi {
template <typename T, typename Context>
void SumKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
DataType out_dtype,
bool keep_dim,
DenseTensor* out) {
......
paddle/phi/kernels/reduce_sum_kernel.h
浏览文件 @ 32f42e94
......@@ -14,6 +14,7 @@
#pragma once
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/infermeta/unary.h"
......@@ -21,7 +22,7 @@ namespace phi {
template <typename T, typename Context>
void SumRawKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
bool keep_dim,
bool reduce_all,
DataType out_dtype,
......@@ -30,7 +31,7 @@ void SumRawKernel(const Context& dev_ctx,
template <typename T, typename Context>
void SumKernel(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& dims,
const IntArray& dims,
DataType out_dtype,
bool keep_dim,
DenseTensor* out);
......@@ -38,7 +39,7 @@ void SumKernel(const Context& dev_ctx,
template <typename T, typename Context>
DenseTensor Sum(const Context& dev_ctx,
const DenseTensor& x,
const std::vector<int64_t>& axis,
const IntArray& axis,
DataType dtype,
bool keep_dim) {
DenseTensor dense_out;
......
paddle/phi/tests/kernels/test_sum_dev_api.cc
浏览文件 @ 32f42e94
......@@ -51,8 +51,8 @@ TEST(DEV_API, sum) {
.get());
// 2. test API
auto out =
phi::Sum<float>(dev_ctx, dense_x, axis, phi::DataType::FLOAT32, false);
auto out = phi::Sum<float>(
dev_ctx, dense_x, phi::IntArray(axis), phi::DataType::FLOAT32, false);
// 3. check result
ASSERT_EQ(out.dims().size(), 1);
......
python/paddle/fluid/tests/unittests/test_max_op.py
浏览文件 @ 32f42e94
......@@ -14,12 +14,16 @@
from __future__ import print_function
import os
import unittest
import tempfile
import numpy as np
from op_test import OpTest, skip_check_grad_ci, check_out_dtype
import paddle
from paddle.fluid.framework import _test_eager_guard
import paddle.fluid.core as core
import paddle.inference as paddle_infer
from test_sum_op import TestReduceOPTensorAxisBase
class ApiMaxTest(unittest.TestCase):
......@@ -70,15 +74,6 @@ class ApiMaxTest(unittest.TestCase):
self.assertRaises(TypeError, test_input_type)
def test_axis_type():
with paddle.static.program_guard(paddle.static.Program(),
paddle.static.Program()):
data = paddle.static.data("data", shape=[10, 10], dtype="int64")
axis = paddle.static.data("axis", shape=[10, 10], dtype="int64")
result_min = paddle.min(data, axis)
self.assertRaises(TypeError, test_axis_type)
def test_imperative_api(self):
paddle.disable_static()
np_x = np.array([10, 10]).astype('float64')
......@@ -124,5 +119,29 @@ class TestOutDtype(unittest.TestCase):
expect_dtypes=['float32', 'float64', 'int32', 'int64'])
class TestMaxWithTensorAxis1(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.max
self.np_api = np.max
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
self.np_axis = np.array([1, 2], dtype='int64')
self.tensor_axis = paddle.to_tensor([1, 2], dtype='int64')
class TestMaxWithTensorAxis2(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.max
self.np_api = np.max
self.x = paddle.randn([10, 10, 9, 9], dtype='float64')
self.np_axis = np.array([0, 1, 2], dtype='int64')
self.tensor_axis = [
0,
paddle.to_tensor([1], 'int64'),
paddle.to_tensor([2], 'int64')
]
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_mean_op.py
浏览文件 @ 32f42e94
......@@ -22,6 +22,7 @@ import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
from paddle.fluid.framework import _test_eager_guard
from test_sum_op import TestReduceOPTensorAxisBase
np.random.seed(10)
......@@ -408,6 +409,30 @@ class TestMeanAPI(unittest.TestCase):
self.assertRaises(TypeError, paddle.mean, x)
class TestMeanWithTensorAxis1(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.mean
self.np_api = np.mean
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
self.np_axis = np.array([1, 2], dtype='int64')
self.tensor_axis = paddle.to_tensor([1, 2], dtype='int64')
class TestMeanWithTensorAxis2(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.mean
self.np_api = np.mean
self.x = paddle.randn([10, 10, 9, 9], dtype='float64')
self.np_axis = np.array([0, 1, 2], dtype='int64')
self.tensor_axis = [
0,
paddle.to_tensor([1], 'int64'),
paddle.to_tensor([2], 'int64')
]
if __name__ == "__main__":
paddle.enable_static()
unittest.main()
python/paddle/fluid/tests/unittests/test_min_op.py
浏览文件 @ 32f42e94
......@@ -14,12 +14,16 @@
from __future__ import print_function
import os
import unittest
import tempfile
import numpy as np
from op_test import OpTest, skip_check_grad_ci, check_out_dtype
import paddle
import paddle.fluid.core as core
from paddle.fluid.framework import _test_eager_guard
import paddle.inference as paddle_infer
from test_sum_op import TestReduceOPTensorAxisBase
class ApiMinTest(unittest.TestCase):
......@@ -70,15 +74,6 @@ class ApiMinTest(unittest.TestCase):
self.assertRaises(TypeError, test_input_type)
def test_axis_type():
with paddle.static.program_guard(paddle.static.Program(),
paddle.static.Program()):
data = paddle.static.data("data", shape=[10, 10], dtype="int64")
axis = paddle.static.data("axis", shape=[10, 10], dtype="int64")
result_min = paddle.min(data, axis)
self.assertRaises(TypeError, test_axis_type)
def test_imperative_api(self):
paddle.disable_static()
np_x = np.array([10, 10]).astype('float64')
......@@ -103,5 +98,30 @@ class TestOutDtype(unittest.TestCase):
expect_dtypes=['float32', 'float64', 'int32', 'int64'])
class TestMinWithTensorAxis1(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.min
self.np_api = np.min
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
self.np_axis = np.array([1, 2], dtype='int64')
self.tensor_axis = paddle.to_tensor([1, 2], dtype='int64')
class TestMinWithTensorAxis2(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.min
self.np_api = np.min
self.x = paddle.randn([10, 10, 9, 9], dtype='float64')
self.np_axis = np.array([0, 1, 2], dtype='int64')
self.tensor_axis = [
0,
paddle.to_tensor([1], 'int64'),
paddle.to_tensor([2], 'int64')
]
self.keepdim = True
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_prod_op.py
浏览文件 @ 32f42e94
......@@ -17,6 +17,7 @@ from __future__ import print_function
import paddle
import unittest
import numpy as np
from test_sum_op import TestReduceOPTensorAxisBase
class TestProdOp(unittest.TestCase):
......@@ -168,5 +169,29 @@ class TestProdOpError(unittest.TestCase):
self.assertRaises(TypeError, paddle.prod, x, 'bool')
class TestProdWithTensorAxis1(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.prod
self.np_api = np.prod
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
self.np_axis = np.array([1, 2], dtype='int64')
self.tensor_axis = paddle.to_tensor([1, 2], dtype='int64')
class TestProdWithTensorAxis2(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.prod
self.np_api = np.prod
self.x = paddle.randn([10, 10, 9, 9], dtype='float64')
self.np_axis = np.array([0, 1, 2], dtype='int64')
self.tensor_axis = [
0,
paddle.to_tensor([1], 'int64'),
paddle.to_tensor([2], 'int64')
]
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_sum_op.py
浏览文件 @ 32f42e94
......@@ -14,7 +14,9 @@
from __future__ import print_function
import os
import unittest
import tempfile
import numpy as np
from op_test import OpTest
import paddle
......@@ -27,6 +29,7 @@ from paddle.fluid.tests.unittests.op_test import (OpTest,
convert_uint16_to_float)
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import _test_eager_guard
import paddle.inference as paddle_infer
class TestSumOp(OpTest):
......@@ -483,6 +486,100 @@ class TestSumOpError(unittest.TestCase):
create_test_sum_fp16_class(TestSelectedRowsSumOp)
create_test_sum_fp16_class(TestLoDTensorAndSelectedRowsOp)
class TestReduceOPTensorAxisBase(unittest.TestCase):
def setUp(self):
paddle.disable_static()
paddle.seed(2022)
self.temp_dir = tempfile.TemporaryDirectory()
self.save_path = os.path.join(self.temp_dir.name, 'reduce_tensor_axis')
self.place = paddle.CUDAPlace(
0) if paddle.is_compiled_with_cuda() else paddle.CPUPlace()
self.keepdim = False
self.init_data()
def tearDwon(self):
self.temp_dir.cleanup()
def init_data(self):
self.pd_api = paddle.sum
self.np_api = np.sum
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
self.np_axis = np.array((1, 2), dtype='int64')
self.tensor_axis = paddle.to_tensor(self.np_axis, dtype='int64')
def test_dygraph(self):
self.x.stop_gradient = False
pd_out = self.pd_api(self.x, self.tensor_axis)
np_out = self.np_api(self.x.numpy(), tuple(self.np_axis))
np.testing.assert_allclose(
pd_out.numpy() if pd_out.size > 1 else pd_out.item(), np_out)
pd_out.backward()
self.assertEqual(self.x.gradient().shape, tuple(self.x.shape))
def test_static_and_infer(self):
paddle.enable_static()
main_prog = paddle.static.Program()
starup_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, starup_prog):
# run static
x = paddle.static.data(shape=self.x.shape,
name='x',
dtype='float32')
if isinstance(self.tensor_axis, paddle.Tensor):
axis = paddle.assign(self.np_axis)
else:
axis = []
for i, item in enumerate(self.tensor_axis):
if isinstance(item, int):
axis.append(item)
else:
axis.append(paddle.full([1], self.np_axis[i], 'int64'))
linear = paddle.nn.Linear(x.shape[-1], 5)
linear_out = linear(x)
out = self.pd_api(linear_out, axis, keepdim=self.keepdim)
exe = paddle.static.Executor(self.place)
exe.run(starup_prog)
static_out = exe.run(feed={'x': self.x.numpy().astype('float32')},
fetch_list=[out])
# run infer
paddle.static.save_inference_model(self.save_path, [x], [out], exe)
config = paddle_infer.Config(self.save_path + '.pdmodel',
self.save_path + '.pdiparams')
if paddle.is_compiled_with_cuda():
config.enable_use_gpu(100, 0)
else:
config.disable_gpu()
predictor = paddle_infer.create_predictor(config)
input_names = predictor.get_input_names()
input_handle = predictor.get_input_handle(input_names[0])
fake_input = self.x.numpy().astype('float32')
input_handle.reshape(self.x.shape)
input_handle.copy_from_cpu(fake_input)
predictor.run()
output_names = predictor.get_output_names()
output_handle = predictor.get_output_handle(output_names[0])
infer_out = output_handle.copy_to_cpu()
np.testing.assert_allclose(static_out[0], infer_out)
class TestSumWithTensorAxis1(TestReduceOPTensorAxisBase):
def init_data(self):
self.pd_api = paddle.sum
self.np_api = np.sum
self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
self.np_axis = np.array([0, 1, 2], dtype='int64')
self.tensor_axis = [
0,
paddle.to_tensor([1], 'int64'),
paddle.to_tensor([2], 'int64')
]
if __name__ == "__main__":
enable_static()
unittest.main()
python/paddle/tensor/math.py
浏览文件 @ 32f42e94
......@@ -34,6 +34,7 @@ from ..fluid.framework import _in_legacy_dygraph
from ..framework import _varbase_creator, convert_np_dtype_to_dtype_
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
from ..fluid.dygraph.inplace_utils import inplace_apis_in_dygraph_only
from ..fluid.layers import utils
# TODO: define math functions
# yapf: disable
......@@ -1144,11 +1145,22 @@ def sum(x, axis=None, dtype=None, keepdim=False, name=None):
out8 = paddle.sum(x, axis=0) # [1, 1, 1, 1]
out9 = paddle.sum(x, axis=1) # [4, 0]
"""
if axis is not None and not isinstance(axis, (list, tuple)):
axis = [axis]
if isinstance(axis, Variable):
reduce_all_flag = True if axis.shape[0] == len(x.shape) else False
else:
if axis is not None and not isinstance(axis, (list, tuple)):
axis = [axis]
if not axis:
axis = []
if not axis:
axis = []
if len(axis) == 0:
reduce_all_flag = True
else:
if len(axis) == len(x.shape):
reduce_all_flag = True
else:
reduce_all_flag = False
dtype_flag = False
if dtype is not None:
......@@ -1158,16 +1170,12 @@ def sum(x, axis=None, dtype=None, keepdim=False, name=None):
if in_dygraph_mode():
return _C_ops.sum(x, axis, dtype, keepdim)
if len(axis) == 0:
reduce_all_flag = True
else:
if len(axis) == len(x.shape):
reduce_all_flag = True
else:
reduce_all_flag = False
if not isinstance(axis, Variable):
axis = axis if axis != None and axis != [] and axis != () else [0]
if utils._contain_var(axis):
axis = utils._convert_to_tensor_list(axis)
if _in_legacy_dygraph():
axis = axis if axis != None and axis != [] else [0]
if dtype_flag:
return _legacy_C_ops.reduce_sum(x, 'dim', axis, 'keep_dim', keepdim,
'reduce_all', reduce_all_flag, 'in_dtype',
......@@ -1177,7 +1185,7 @@ def sum(x, axis=None, dtype=None, keepdim=False, name=None):
'reduce_all', reduce_all_flag)
attrs = {
'dim': axis if axis != None and axis != [] and axis != () else [0],
'dim': axis,
'keep_dim': keepdim,
'reduce_all': reduce_all_flag
}
......@@ -1194,7 +1202,7 @@ def sum(x, axis=None, dtype=None, keepdim=False, name=None):
u'bool', u'float16', u'float32', u'float64',
u'int32', u'int64', u'complex64', u'complex128'], 'sum')
check_type(axis, 'axis', (int, list, tuple, type(None)), 'sum')
check_type(axis, 'axis', (int, list, tuple, type(None), Variable), 'sum')
helper = LayerHelper('sum', **locals())
if dtype_flag:
......@@ -2058,6 +2066,11 @@ def _get_reduce_axis(axis):
axis = []
return reduce_all, axis
def _get_reduce_axis_with_tensor(axis):
if isinstance(axis, Variable):
return False, axis
return _get_reduce_axis(axis)
def _get_reduce_all_value(axis):
"""
Internal function for max, min, amax and amin.
......@@ -2154,7 +2167,7 @@ def max(x, axis=None, keepdim=False, name=None):
#[7., 8.], [[[0., 0.], [0., 0.]], [[0., 0.], [1., 1.]]]
"""
reduce_all, axis = _get_reduce_axis(axis)
reduce_all, axis = _get_reduce_axis_with_tensor(axis)
if in_dygraph_mode():
return _C_ops.max(x, axis, keepdim)
if _in_legacy_dygraph():
......@@ -2164,6 +2177,8 @@ def max(x, axis=None, keepdim=False, name=None):
helper = LayerHelper('max', **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'max')
if not isinstance(axis, Variable) and utils._contain_var(axis):
axis = utils._convert_to_tensor_list(axis)
out = helper.create_variable_for_type_inference(
dtype=x.dtype)
......@@ -2255,7 +2270,7 @@ def min(x, axis=None, keepdim=False, name=None):
#[1., 2.], [[[1., 1.], [0., 0.]], [[0., 0.], [0., 0.]]]
"""
reduce_all, axis = _get_reduce_axis(axis)
reduce_all, axis = _get_reduce_axis_with_tensor(axis)
if in_dygraph_mode():
return _C_ops.min(x, axis, keepdim)
......@@ -2266,6 +2281,8 @@ def min(x, axis=None, keepdim=False, name=None):
helper = LayerHelper('min', **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int32', 'int64'], 'min')
if not isinstance(axis, Variable) and utils._contain_var(axis):
axis = utils._convert_to_tensor_list(axis)
out = helper.create_variable_for_type_inference(
dtype=x.dtype)
......@@ -3369,19 +3386,22 @@ def prod(x, axis=None, keepdim=False, dtype=None, name=None):
x = cast(x, dtype)
dim = axis
if dim is not None and not isinstance(dim, list):
if isinstance(dim, tuple):
dim = list(dim)
elif isinstance(dim, int):
dim = [dim]
else:
raise TypeError(
"The type of axis must be int, list or tuple, but received {}".
format(type(dim)))
if isinstance(dim, Variable):
reduce_all = True if axis.shape[0] == len(x.shape) else False
else:
if dim is not None and not isinstance(dim, list):
if isinstance(dim, tuple):
dim = list(dim)
elif isinstance(dim, int):
dim = [dim]
else:
raise TypeError(
"The type of axis must be int, list or tuple, but received {}".
format(type(dim)))
reduce_all = True if dim is None or len(dim) == 0 or len(dim) == len(x.shape) else False
if dim is None or len(dim) == 0:
dim = [0]
reduce_all = True if dim is None or len(dim) == 0 or len(dim) == len(x.shape) else False
if dim is None or len(dim) == 0:
dim = [0]
if in_dygraph_mode():
return _C_ops.reduce_prod(x, dim, keepdim, reduce_all)
......@@ -3393,6 +3413,8 @@ def prod(x, axis=None, keepdim=False, dtype=None, name=None):
check_variable_and_dtype(
x, 'x/input', ['float32', 'float64', 'int32', 'int64'], 'reduce_prod')
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if not isinstance(dim, Variable) and utils._contain_var(dim):
dim = utils._convert_to_tensor_list(dim)
helper.append_op(
type='reduce_prod',
inputs={'X': x},
......
python/paddle/tensor/stat.py
浏览文件 @ 32f42e94
......@@ -21,6 +21,7 @@ from ..framework import core
from paddle.fluid.framework import _in_legacy_dygraph, in_dygraph_mode
from .search import where
from ..fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
from ..fluid.layers import utils
import paddle
from paddle import _C_ops, _legacy_C_ops
......@@ -80,17 +81,20 @@ def mean(x, axis=None, keepdim=False, name=None):
# [ 8.5 12.5 16.5]
"""
if isinstance(axis, int):
axis = [axis]
reduce_all = True if axis is None \
or len(axis)==0 \
or len(axis) == len(x.shape) else False
if axis is None or len(axis) == 0:
axis = [0]
if isinstance(axis, Variable):
reduce_all = True if axis.shape[0] == len(x.shape) else False
else:
if isinstance(axis, int):
axis = [axis]
reduce_all = True if axis is None \
or len(axis)==0 \
or len(axis) == len(x.shape) else False
if axis is None or len(axis) == 0:
axis = [0]
if in_dygraph_mode():
if reduce_all:
axis = range(len(x.shape))
axis = list(range(len(x.shape)))
return _C_ops.mean(x, axis, keepdim)
if _in_legacy_dygraph():
return _legacy_C_ops.reduce_mean(x, 'dim', axis, 'keep_dim', keepdim,
......@@ -99,12 +103,17 @@ def mean(x, axis=None, keepdim=False, name=None):
check_variable_and_dtype(x, 'x/input',
['uint16', 'float16', 'float32', 'float64'],
'mean/reduce_mean')
check_type(axis, 'axis/dim', (int, list, tuple), 'mean/reduce_mean')
check_type(axis, 'axis/dim', (int, list, tuple, Variable),
'mean/reduce_mean')
if isinstance(axis, (list, tuple)):
for item in axis:
check_type(item, 'elements of axis/dim', (int), 'mean/reduce_mean')
check_type(item, 'elements of axis/dim', (int, Variable),
'mean/reduce_mean')
helper = LayerHelper('mean', **locals())
if not isinstance(axis, Variable) and utils._contain_var(axis):
axis = utils._convert_to_tensor_list(axis)
attrs = {'dim': axis, 'keep_dim': keepdim, 'reduce_all': reduce_all}
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(type='reduce_mean',
......
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