From 70dc5f49b705337798e272a5dd22aed665165c3a Mon Sep 17 00:00:00 2001
From: pangyoki <pangyoki@126.com>
Date: Fri, 21 May 2021 10:35:11 +0800
Subject: [PATCH] [NPU] cast indices and label if their type is not consistent
 in accuracy npu op (#33016)

* cast indices and label if their type is not consistent

* fix bug

* add unittest
---
 .../operators/metrics/accuracy_op_npu.cc      | 32 ++++++++++++-
 .../unittests/npu/test_accuracy_op_npu.py     | 48 +++++++++++++++++++
 2 files changed, 79 insertions(+), 1 deletion(-)

diff --git a/paddle/fluid/operators/metrics/accuracy_op_npu.cc b/paddle/fluid/operators/metrics/accuracy_op_npu.cc
index 9c5e157a97..c18b8590db 100644
--- a/paddle/fluid/operators/metrics/accuracy_op_npu.cc
+++ b/paddle/fluid/operators/metrics/accuracy_op_npu.cc
@@ -39,12 +39,42 @@ class AccuracyNPUKernel : public framework::OpKernel<T> {
       return;
     }
 
+    // cast `indices` or `label` if their type is not consistent
+    Tensor cast_indices(framework::proto::VarType::INT32);
+    Tensor cast_label(framework::proto::VarType::INT32);
+    if (indices->type() != label->type()) {
+      auto dst_dtype = ConvertToNpuDtype(framework::proto::VarType::INT32);
+      if (indices->type() != framework::proto::VarType::INT32) {
+        cast_indices.Resize(indices->dims());
+        cast_indices.mutable_data<int>(ctx.GetPlace());
+        auto runner_cast_indices =
+            NpuOpRunner("Cast", {*indices}, {cast_indices},
+                        {{"dst_type", static_cast<int>(dst_dtype)}});
+        runner_cast_indices.Run(stream);
+      } else {
+        cast_indices.ShareDataWith(*indices);
+      }
+      if (label->type() != framework::proto::VarType::INT32) {
+        cast_label.Resize(label->dims());
+        cast_label.mutable_data<int>(ctx.GetPlace());
+        auto runner_cast_label =
+            NpuOpRunner("Cast", {*label}, {cast_label},
+                        {{"dst_type", static_cast<int>(dst_dtype)}});
+        runner_cast_label.Run(stream);
+      } else {
+        cast_label.ShareDataWith(*label);
+      }
+    } else {
+      cast_indices.ShareDataWith(*indices);
+      cast_label.ShareDataWith(*label);
+    }
+
     // equal
     Tensor tmp_equal(framework::proto::VarType::BOOL);
     tmp_equal.Resize(inference->dims());
     tmp_equal.mutable_data<bool>(ctx.GetPlace());
     auto runner_equal =
-        NpuOpRunner("Equal", {*indices, *label}, {tmp_equal}, {});
+        NpuOpRunner("Equal", {cast_indices, cast_label}, {tmp_equal}, {});
     runner_equal.Run(stream);
 
     // cast equal
diff --git a/python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py
index aa22863983..5aeca5abd9 100644
--- a/python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py
+++ b/python/paddle/fluid/tests/unittests/npu/test_accuracy_op_npu.py
@@ -87,5 +87,53 @@ class TestAccuracy2(TestAccuracy):
         }
 
 
+class TestAccuracyType(TestAccuracy):
+    def setUp(self):
+        self.op_type = "accuracy"
+        self.set_npu()
+        self.init_dtype()
+        np.random.seed(SEED)
+        n = 8192
+        infer = np.random.random((n, 100)).astype(self.dtype)
+        indices = np.random.randint(0, 1000, (n, 100)).astype('int64')
+        label = np.random.randint(0, 1000, (n, 1)).astype('int32')
+        self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
+        num_correct = 0
+        for rowid in range(n):
+            for ele in indices[rowid]:
+                if ele == label[rowid]:
+                    num_correct += 1
+                    break
+        self.outputs = {
+            'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
+            'Correct': np.array([num_correct]).astype("int32"),
+            'Total': np.array([n]).astype("int32")
+        }
+
+
+class TestAccuracyType2(TestAccuracy):
+    def setUp(self):
+        self.op_type = "accuracy"
+        self.set_npu()
+        self.init_dtype()
+        np.random.seed(SEED)
+        n = 8192
+        infer = np.random.random((n, 100)).astype(self.dtype)
+        indices = np.random.randint(0, 1000, (n, 100)).astype('int32')
+        label = np.random.randint(0, 1000, (n, 1)).astype('int64')
+        self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
+        num_correct = 0
+        for rowid in range(n):
+            for ele in indices[rowid]:
+                if ele == label[rowid]:
+                    num_correct += 1
+                    break
+        self.outputs = {
+            'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
+            'Correct': np.array([num_correct]).astype("int32"),
+            'Total': np.array([n]).astype("int32")
+        }
+
+
 if __name__ == '__main__':
     unittest.main()
-- 
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