Zeros_like_test

op_acc_stable_run.py

@@ -227,4 +227,4 @@ def check_tensor_diff(x, y, *, atol, rtol, err_msg=""):
             y = y.astype(paddle.float32)
         x = x.numpy()
         y = y.numpy()
-        np.testing.assert_allclose(x, y, atol=atol, rtol=rtol, err_msg=err_msg)
+        np.testing.assert_allclose(x, y, atol=atol, rtol=rtol, err_msg=err_msg)

tests/softmax_test.py

@@ -45,4 +45,4 @@ def check_diff(self, paddle, pd_ret, th_ret):
 
 
 if __name__ == "__main__":
-    op_acc_stable_run(SoftmaxTest)
+    op_acc_stable_run(SoftmaxTest)

tests/zeros_like_test.py

@@ -0,0 +1,59 @@
+# 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.
+
+import numpy as np
+from op_acc_stable_run import check_tensor_diff, op_acc_stable_run
+
+class Zero_like_Test:
+    def __init__(self, x_shape, dtype):
+        self.x_shape = x_shape
+        self.dtype = dtype 
+
+    def set_configs(self, paddle):
+        self.tmp_cache_path = "."
+        self.inputs = {
+            "x": paddle.to_tensor(np.random.random(size=self.x_shape).astype(self.dtype) - 0.5) ,
+        }
+
+    def run_paddle(self, paddle):
+        x = self.inputs["x"]
+        y = paddle.zeros_like(x, dtype=self.dtype)
+        return y
+
+    def run_torch(self, torch):
+        def convert_dtype(dtype):
+            ret = None
+            if dtype == "float32":
+                ret = torch.float32
+            elif dtype == "float16":
+                ret = torch.float16
+            elif dtype == "bfloat16":
+                ret = torch.bfloat16
+            elif dtype == "int64":
+                ret = torch.int64
+            elif dtype == "uint16":
+                ret = torch.bfloat16
+            return ret
+        x = self.inputs["x"]
+        type = convert_dtype(self.dtype)
+        y = torch.zeros_like(x, dtype=type)
+        return y
+
+    def check_diff(self, paddle, pd_ret, th_ret):
+        assert len(pd_ret) == len(th_ret)
+        for pd, th in zip(pd_ret, th_ret):
+            check_tensor_diff(pd, th, atol=1e-6, rtol=1e-6)
+
+if __name__ == "__main__":
+    op_acc_stable_run(Zero_like_Test(x_shape = [1, 8192], dtype ='int64'))