【Hackathon 6th Fundable Projects 3 No.199】fluid operator l1_norm (#63631)

co63oc · web-flow · commit 5f6e9d424d0b · 2024-04-24T14:25:04.000+08:00
* Fix

* Fix

* Fix

* Fix

* Fix
diff --git a/paddle/fluid/operators/l1_norm_op.cc b/paddle/fluid/operators/l1_norm_op.cc
diff --git a/paddle/fluid/operators/l1_norm_op.h b/paddle/fluid/operators/l1_norm_op.h
diff --git a/paddle/phi/api/yaml/backward.yaml b/paddle/phi/api/yaml/backward.yaml
@@ -1331,6 +1331,17 @@
     func : kthvalue_grad
     data_type : out_grad
 
+- backward_op : l1_norm_grad
+  forward : l1_norm (Tensor x) -> Tensor(out)
+  args : (Tensor x, Tensor out_grad)
+  output : Tensor(x_grad)
+  infer_meta :
+    func : UnchangedInferMeta
+    param : [x]
+  kernel :
+    func : l1_norm_grad
+    data_type : x
+
 - backward_op : label_smooth_grad
   forward : label_smooth (Tensor label, Tensor prior_dist, float epsilon) -> Tensor(out)
   args : (Tensor out_grad, float epsilon)
diff --git a/paddle/phi/api/yaml/op_compat.yaml b/paddle/phi/api/yaml/op_compat.yaml
@@ -1895,6 +1895,15 @@
   outputs :
     {out : Out, indices : Indices}
 
+- op : l1_norm
+  inputs :
+    x : X
+  outputs :
+    out : Out
+  backward : l1_norm_grad
+  extra :
+    attrs : [bool use_mkldnn = false, bool use_cudnn = false]
+
 - op : label_smooth
   inputs :
     {label : X, prior_dist : PriorDist}
diff --git a/paddle/phi/api/yaml/ops.yaml b/paddle/phi/api/yaml/ops.yaml
@@ -1595,6 +1595,17 @@
   backward : kthvalue_grad
   interfaces : paddle::dialect::InferSymbolicShapeInterface
 
+- op : l1_norm
+  args : (Tensor x)
+  output : Tensor(out)
+  infer_meta :
+    func : L1NormInferMeta
+  kernel :
+    func : l1_norm
+    data_type : x
+  inplace: (x -> out)
+  backward : l1_norm_grad
+
 - op : label_smooth
   args : (Tensor label, Tensor prior_dist, float epsilon = 0.0f)
   output : Tensor (out)
diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc
@@ -4263,6 +4263,11 @@ void SquaredL2NormInferMeta(const MetaTensor& x, MetaTensor* out) {
   out->set_dtype(x.dtype());
 }
 
+void L1NormInferMeta(const MetaTensor& x, MetaTensor* out) {
+  out->set_dims(common::make_ddim({}));
+  out->set_dtype(x.dtype());
+}
+
 void SqueezeInferMeta(const MetaTensor& x,
                       const IntArray& axes,
                       MetaTensor* out,
diff --git a/paddle/phi/infermeta/unary.h b/paddle/phi/infermeta/unary.h
@@ -673,6 +673,8 @@ void SplitWithNumInferMeta(const MetaTensor& x_meta,
 
 void SquaredL2NormInferMeta(const MetaTensor& x, MetaTensor* out);
 
+void L1NormInferMeta(const MetaTensor& x, MetaTensor* out);
+
 void SqueezeInferMeta(const MetaTensor& x,
                       const IntArray& axes,
                       MetaTensor* out,
diff --git a/paddle/phi/kernels/cpu/l1_norm_grad_kernel.cc b/paddle/phi/kernels/cpu/l1_norm_grad_kernel.cc
@@ -0,0 +1,18 @@
+// Copyright (c) 2024 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
+//
+//     https://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/phi/kernels/l1_norm_kernel.h"
+
+PD_REGISTER_KERNEL(
+    l1_norm_grad, CPU, ALL_LAYOUT, phi::L1NormGradKernel, float) {}
diff --git a/paddle/phi/kernels/cpu/l1_norm_kernel.cc b/paddle/phi/kernels/cpu/l1_norm_kernel.cc
@@ -0,0 +1,17 @@
+// Copyright (c) 2024 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
+//
+//     https://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/phi/kernels/l1_norm_kernel.h"
+
+PD_REGISTER_KERNEL(l1_norm, CPU, ALL_LAYOUT, phi::L1NormKernel, float) {}
diff --git a/paddle/phi/kernels/gpu/l1_norm_grad_kernel.cu b/paddle/phi/kernels/gpu/l1_norm_grad_kernel.cu
@@ -0,0 +1,18 @@
+// Copyright (c) 2024 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
+//
+//     https://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/phi/kernels/l1_norm_kernel.h"
+
+PD_REGISTER_KERNEL(
+    l1_norm_grad, GPU, ALL_LAYOUT, phi::L1NormGradKernel, float) {}
diff --git a/paddle/phi/kernels/gpu/l1_norm_kernel.cu b/paddle/phi/kernels/gpu/l1_norm_kernel.cu
@@ -0,0 +1,17 @@
+// Copyright (c) 2024 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
+//
+//     https://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/phi/kernels/l1_norm_kernel.h"
+
+PD_REGISTER_KERNEL(l1_norm, GPU, ALL_LAYOUT, phi::L1NormKernel, float) {}
diff --git a/paddle/phi/kernels/l1_norm_kernel.h b/paddle/phi/kernels/l1_norm_kernel.h
@@ -0,0 +1,59 @@
+// Copyright (c) 2024 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
+//
+//     https://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
+#include <memory>
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
+
+namespace phi {
+// Out = sum(abs(X))
+template <typename T, typename Context>
+void L1NormKernel(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  DenseTensor* out) {
+  dev_ctx.template Alloc<T>(out);
+
+  auto x_tmp = phi::EigenVector<T>::Flatten(x);
+  auto out_tmp = phi::EigenScalar<T>::From(*out);
+  auto& dev = *dev_ctx.eigen_device();
+
+  phi::funcs::EigenL1Norm<std::decay_t<decltype(dev)>, T>::Eval(
+      dev, out_tmp, x_tmp);
+}
+
+// dX = dout * sign(X)
+template <typename T, typename Context>
+void L1NormGradKernel(const Context& dev_ctx,
+                      const DenseTensor& x,
+                      const DenseTensor& out_grad,
+                      DenseTensor* x_grad) {
+  PADDLE_ENFORCE_EQ(out_grad.numel(),
+                    1,
+                    phi::errors::InvalidArgument(
+                        "Input(GRAD@Out) of L1NormGradOp should be a scalar."));
+  dev_ctx.template Alloc<T>(x_grad);
+
+  auto x_eigen = phi::EigenVector<T>::Flatten(x);
+  auto d_out_eigen = phi::EigenVector<T>::Flatten(out_grad);
+  auto dx_eigen = phi::EigenVector<T>::Flatten(*x_grad);
+  auto& dev = *dev_ctx.eigen_device();
+
+  Eigen::DSizes<Eigen::DenseIndex, 1> x_dsize(x.numel());
+  phi::funcs::EigenL1NormGrad<std::decay_t<decltype(dev)>, T>::Eval(
+      dev, dx_eigen, d_out_eigen, x_eigen, x_dsize);
+}
+}  // namespace phi
diff --git a/test/deprecated/legacy_test/test_l1_norm_op.py b/test/deprecated/legacy_test/test_l1_norm_op.py
