Add test_dropout_nd (#1673)

* add test_dropout_nd

* PR comment

Author: zhangboSJTU

api/tests/dropout_nd.py

@@ -0,0 +1,65 @@
+#   Copyright (c) 2023 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
+#
+#     http://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.
+
+from common_import import *
+import paddle
+from paddle import _legacy_C_ops
+
+
+# no dropout_nd in pytorch
+def dropout_nd(x,
+               p=0.5,
+               axis=None,
+               training=True,
+               mode="upscale_in_train",
+               name=None):
+    drop_axes = [axis] if isinstance(axis, int) else list(axis)
+    seed = None
+    mode = ('downgrade_in_infer'
+            if mode == 'downscale_in_infer' else mode)  # semantic transfer
+    out = _legacy_C_ops.dropout_nd(
+        x,
+        'dropout_prob',
+        p,
+        'is_test',
+        not training,
+        'fix_seed',
+        seed is not None,
+        'seed',
+        seed if seed is not None else 0,
+        'dropout_implementation',
+        mode,
+        'axis',
+        drop_axes, )
+    return out
+
+
+@benchmark_registry.register("dropout_nd")
+class PaddleDropoutNdConfig(APIConfig):
+    def __init__(self):
+        super(PaddleDropoutNdConfig, self).__init__('dropout_nd')
+        self.run_torch = False
+
+
+@benchmark_registry.register("dropout_nd")
+class PaddleDropoutNd(PaddleOpBenchmarkBase):
+    def build_graph(self, config):
+        x = self.variable(name='x', shape=config.x_shape, dtype=config.x_dtype)
+        result = dropout_nd(
+            x=x, p=config.p, axis=config.axis, mode=config.mode)
+
+        self.feed_list = [x]
+        self.fetch_list = [result]
+        if config.backward:
+            self.append_gradients(result[0], self.feed_list)

api/tests_v2/configs/dropout_nd.json

@@ -0,0 +1,66 @@
+[{
+    "op": "dropout_nd",
+    "param_info": {
+        "mode": {
+            "type": "string",
+            "value": "upscale_in_train"
+        },
+        "p": {
+            "type": "float",
+            "value": "0.5"
+        },
+        "x": {
+            "dtype": "float32",
+            "shape": "[-1L, 16L, -1L]",
+            "type": "Variable"
+        },
+        "axis": {
+            "type": "list",
+            "value": "[1]"
+        }
+    },
+    "repeat": 2000
+}, {
+    "op": "dropout_nd",
+    "param_info": {
+        "mode": {
+            "type": "string",
+            "value": "downscale_in_infer"
+        },
+        "p": {
+            "type": "float",
+            "value": "0.5"
+        },
+        "x": {
+            "dtype": "float32",
+            "shape": "[-1L, 16L, -1L, -1L]",
+            "type": "Variable"
+        },
+        "axis": {
+            "type": "list",
+            "value": "[0,1]"
+        }
+    },
+    "repeat": 2000
+}, {
+    "op": "dropout_nd",
+    "param_info": {
+        "mode": {
+            "type": "string",
+            "value": "upscale_in_train"
+        },
+        "p": {
+            "type": "float",
+            "value": "0.1"
+        },
+        "x": {
+            "dtype": "float32",
+            "shape": "[32L, 128L, 768L]",
+            "type": "Variable"
+        },
+        "axis": {
+            "type": "list",
+            "value": "[0]"
+        }
+    }
+}]