chore: import upstream snapshot with attribution

test/ir/inference/test_trt_slice_plugin.py

@@ -0,0 +1,190 @@
+# Copyright (c) 2020 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 unittest
+
+import numpy as np
+from inference_pass_test import InferencePassTest
+
+import paddle
+from paddle import base
+from paddle.base import core
+from paddle.base.core import AnalysisConfig
+from paddle.static import nn
+
+
+# normal starts && ends
+class SlicePluginTRTTest(InferencePassTest):
+    def setUpSliceParams(self):
+        self.params_axes = [1, 3]
+        self.params_starts = [0, 1]
+        self.params_ends = [2, 3]
+
+    def setUpTensorRTParams(self):
+        self.trt_parameters = SlicePluginTRTTest.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False
+        )
+        self.enable_trt = True
+
+    def setUp(self):
+        self.setUpSliceParams()
+        self.setUpTensorRTParams()
+        with paddle.pir_utils.OldIrGuard():
+            with base.program_guard(self.main_program, self.startup_program):
+                data = paddle.static.data(
+                    name="data", shape=[3, 3, 3, 3], dtype="float32"
+                )
+                axes = self.params_axes
+                starts = self.params_starts
+                ends = self.params_ends
+                slice_out = paddle.slice(
+                    data, axes=axes, starts=starts, ends=ends
+                )
+                out = nn.batch_norm(slice_out, is_test=True)
+
+            self.feeds = {
+                "data": np.random.random((3, 3, 3, 3)).astype("float32"),
+            }
+            self.dynamic_shape_params = SlicePluginTRTTest.DynamicShapeParam(
+                {'data': [3, 3, 3, 3]},
+                {'data': [3, 3, 3, 3]},
+                {'data': [3, 3, 3, 3]},
+                False,
+            )
+            self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+        for i in range(len(use_gpu)):
+            atol = 1e-5
+            if self.trt_parameters.precision == AnalysisConfig.Precision.Half:
+                atol = 1e-3
+            self.check_output_with_option(use_gpu[i], atol)
+
+
+# negative starts && ends
+class SlicePluginTRTTestNegativeStartsAndEnds(SlicePluginTRTTest):
+    def setUpSliceParams(self):
+        self.params_axes = [2, 3]
+        self.params_starts = [-3, -2]
+        self.params_ends = [-1, 3]
+
+
+# exceeded bound starts && ends
+class SlicePluginTRTTestStartsAndEndsBoundCheck(SlicePluginTRTTest):
+    def setUpSliceParams(self):
+        self.params_axes = [2, 3]
+        self.params_starts = [-5, -2]
+        self.params_ends = [-1, 8]
+
+
+# fp16
+class SlicePluginTRTTestFp16(SlicePluginTRTTest):
+    def setUpTensorRTParams(self):
+        self.trt_parameters = SlicePluginTRTTest.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Half, False, False
+        )
+        self.enable_trt = True
+
+
+class StaticSlicePluginTRTTestFp16(SlicePluginTRTTest):
+    def setUpTensorRTParams(self):
+        self.trt_parameters = SlicePluginTRTTest.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Half, True, False
+        )
+        self.enable_trt = True
+
+
+class StaticSlicePluginTRTTestFp32(SlicePluginTRTTest):
+    def setUpTensorRTParams(self):
+        self.trt_parameters = SlicePluginTRTTest.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, True, False
+        )
+        self.enable_trt = True
+
+
+class SlicePluginTRTTestInt32(SlicePluginTRTTest):
+    def setUp(self):
+        self.setUpSliceParams()
+        self.setUpTensorRTParams()
+        with paddle.pir_utils.OldIrGuard():
+            with base.program_guard(self.main_program, self.startup_program):
+                data = paddle.static.data(
+                    name="data", shape=[3, 3, 3, 3], dtype="int32"
+                )
+                axes = self.params_axes
+                starts = self.params_starts
+                ends = self.params_ends
+                slice_out = paddle.slice(
+                    data, axes=axes, starts=starts, ends=ends
+                )
+                cast_out = paddle.cast(slice_out, 'float32')
+                out = nn.batch_norm(cast_out, is_test=True)
+
+            self.feeds = {
+                "data": np.random.random((3, 3, 3, 3)).astype("int32"),
+            }
+            self.dynamic_shape_params = (
+                SlicePluginTRTTestInt32.DynamicShapeParam(
+                    {'data': [3, 3, 3, 3]},
+                    {'data': [3, 3, 3, 3]},
+                    {'data': [3, 3, 3, 3]},
+                    False,
+                )
+            )
+            self.fetch_list = [out]
+
+
+class StaticSlicePluginTRTTestInt32(SlicePluginTRTTest):
+    def setUpTensorRTParams(self):
+        self.trt_parameters = SlicePluginTRTTest.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, True, False
+        )
+        self.enable_trt = True
+
+    def setUp(self):
+        self.setUpSliceParams()
+        self.setUpTensorRTParams()
+        with paddle.pir_utils.OldIrGuard():
+            with base.program_guard(self.main_program, self.startup_program):
+                data = paddle.static.data(
+                    name="data", shape=[3, 3, 3, 3], dtype="int32"
+                )
+                axes = self.params_axes
+                starts = self.params_starts
+                ends = self.params_ends
+                slice_out = paddle.slice(
+                    data, axes=axes, starts=starts, ends=ends
+                )
+                cast_out = paddle.cast(slice_out, 'float32')
+                out = nn.batch_norm(cast_out, is_test=True)
+
+            self.feeds = {
+                "data": np.random.random((3, 3, 3, 3)).astype("int32"),
+            }
+            self.dynamic_shape_params = (
+                StaticSlicePluginTRTTestInt32.DynamicShapeParam(
+                    {'data': [3, 3, 3, 3]},
+                    {'data': [3, 3, 3, 3]},
+                    {'data': [3, 3, 3, 3]},
+                    False,
+                )
+            )
+            self.fetch_list = [out]
+
+
+if __name__ == "__main__":
+    unittest.main()