From f9e55dee9cc1c7cac70bd87200d228aec931deea Mon Sep 17 00:00:00 2001
From: Aganlengzi <aganlengzi@gmail.com>
Date: Tue, 31 May 2022 14:52:36 +0800
Subject: [PATCH] [NPU] fix arg_max and reduce_max (#42887)

* fix arg_max and reduce_max

* add arg_max ut
---
 paddle/fluid/operators/arg_max_op_npu.cc      |  9 ++++++-
 .../operators/reduce_ops/reduce_max_op_npu.cc | 24 +++++++++++++++--
 .../unittests/npu/test_arg_max_op_npu.py      | 27 +++++++++++++++++++
 3 files changed, 57 insertions(+), 3 deletions(-)

diff --git a/paddle/fluid/operators/arg_max_op_npu.cc b/paddle/fluid/operators/arg_max_op_npu.cc
index 680183b6ad..5c6b276c01 100644
--- a/paddle/fluid/operators/arg_max_op_npu.cc
+++ b/paddle/fluid/operators/arg_max_op_npu.cc
@@ -34,11 +34,18 @@ struct VisitDataArgNPUMaxFunctor {
     out.template mutable_data<Tout>(ctx.GetPlace());
     auto axis = ctx.Attr<int64_t>("axis");
     auto dtype = ctx.Attr<int>("dtype");
+    const bool& flatten = ctx.Attr<bool>("flatten");
+
+    Tensor transformed_x(x.type());
+    transformed_x.ShareDataWith(x);
+    if (flatten) {
+      transformed_x.Resize(phi::make_ddim({x.numel()}));
+    }
 
     auto stream = ctx.template device_context<NPUDeviceContext>().stream();
     NpuOpRunner runner;
     runner.SetType("ArgMaxV2")
-        .AddInput(x)
+        .AddInput(transformed_x)
         .AddInput(std::vector<int64_t>{axis})
         .AddOutput(out)
         .AddAttrDataType("dtype", dtype)
diff --git a/paddle/fluid/operators/reduce_ops/reduce_max_op_npu.cc b/paddle/fluid/operators/reduce_ops/reduce_max_op_npu.cc
index 04660fb501..e3d8d15a30 100644
--- a/paddle/fluid/operators/reduce_ops/reduce_max_op_npu.cc
+++ b/paddle/fluid/operators/reduce_ops/reduce_max_op_npu.cc
@@ -112,6 +112,8 @@ class ReduceMaxGradNPUKernel : public framework::OpKernel<T> {
     auto* x = context.Input<Tensor>("X");
     auto* out = context.Input<Tensor>("Out");
     auto* out_grad = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto reduce_dims = context.Attr<std::vector<int>>("dim");
+    bool reduce_all = context.Attr<bool>("reduce_all");
     int in_dtype = context.Attr<int>("in_dtype");
 
     PADDLE_ENFORCE_EQ(
@@ -129,12 +131,30 @@ class ReduceMaxGradNPUKernel : public framework::OpKernel<T> {
 
     // broadcast
     auto x_dims_vec = phi::vectorize(x->dims());
+    if (reduce_all) {
+      reduce_dims.clear();
+      for (size_t d = 0; d < x_dims_vec.size(); ++d) {
+        reduce_dims.push_back(static_cast<int>(d));
+      }
+    }
+
+    Tensor tmp_out, tmp_out_grad;
+    auto tmp_out_dims_vec = x_dims_vec;
+    for (auto d : reduce_dims) {
+      tmp_out_dims_vec[d] = 1;
+    }
+
+    tmp_out.ShareDataWith(*out);
+    tmp_out.Resize(phi::make_ddim(tmp_out_dims_vec));
+    tmp_out_grad.ShareDataWith(*out_grad);
+    tmp_out_grad.Resize(phi::make_ddim(tmp_out_dims_vec));
+
     Tensor transformed_out(x->type());
     transformed_out.Resize(phi::make_ddim(x_dims_vec));
     transformed_out.mutable_data<T>(place);
     NpuOpRunner r_brd_out;
     r_brd_out.SetType("BroadcastTo")
-        .AddInput(*out)
+        .AddInput(tmp_out)
         .AddInput(std::move(x_dims_vec))
         .AddOutput(transformed_out)
         .Run(stream);
@@ -143,7 +163,7 @@ class ReduceMaxGradNPUKernel : public framework::OpKernel<T> {
     transformed_out_grad.mutable_data<T>(place);
     NpuOpRunner r_brd_out_grad;
     r_brd_out_grad.SetType("BroadcastTo")
-        .AddInput(*out_grad)
+        .AddInput(tmp_out_grad)
         .AddInput(std::move(x_dims_vec))
         .AddOutput(transformed_out_grad)
         .Run(stream);
diff --git a/python/paddle/fluid/tests/unittests/npu/test_arg_max_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_arg_max_op_npu.py
index 85ade1179b..c613538372 100644
--- a/python/paddle/fluid/tests/unittests/npu/test_arg_max_op_npu.py
+++ b/python/paddle/fluid/tests/unittests/npu/test_arg_max_op_npu.py
@@ -328,5 +328,32 @@ class TestArgMaxAPI_2(unittest.TestCase):
             run(place)
 
 
+class TestArgMaxAPI_3(unittest.TestCase):
+    def initTestCase(self):
+        self.dims = (1, 9)
+        self.dtype = 'float32'
+
+    def setUp(self):
+        self.initTestCase()
+        self.__class__.use_npu = True
+        self.place = [paddle.NPUPlace(0)]
+
+    def test_dygraph_api(self):
+        def run(place):
+            paddle.disable_static(place)
+            np.random.seed(2021)
+            numpy_input = (np.random.random(self.dims)).astype(self.dtype)
+            tensor_input = paddle.to_tensor(numpy_input)
+            numpy_output = np.argmax(numpy_input).reshape([1])
+            paddle_output = paddle.argmax(tensor_input)
+            self.assertEqual(
+                np.allclose(numpy_output, paddle_output.numpy()), True)
+            self.assertEqual(numpy_output.shape, paddle_output.numpy().shape)
+            paddle.enable_static()
+
+        for place in self.place:
+            run(place)
+
+
 if __name__ == '__main__':
     unittest.main()
-- 
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