add ut for lookup_table op trt converter (#53563)

test/ir/inference/test_trt_convert_lookup_table_v2.py

@@ -0,0 +1,142 @@
+# 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 unittest
+from functools import partial
+from typing import Any, Dict, List
+
+import numpy as np
+from program_config import ProgramConfig, TensorConfig
+from trt_layer_auto_scan_test import TrtLayerAutoScanTest
+
+import paddle.inference as paddle_infer
+
+
+class TrtConvertLookupTableV2Test(TrtLayerAutoScanTest):
+    def sample_program_configs(self):
+        def generate_input1(dims, attrs: List[Dict[str, Any]]):
+            if dims == 1:
+                return np.array([32]).astype(np.int64)
+            elif dims == 2:
+                return np.array([[3, 16, 24], [6, 4, 47]]).astype(np.int64)
+            else:
+                return np.array(
+                    [
+                        [[3, 16, 24], [30, 16, 14], [2, 6, 24]],
+                        [[3, 26, 34], [3, 16, 24], [3, 6, 4]],
+                        [[3, 16, 24], [53, 16, 54], [30, 1, 24]],
+                    ]
+                ).astype(np.int64)
+
+        def generate_input2(dims, attrs: List[Dict[str, Any]]):
+            return np.random.uniform(-1, 1, [64, 4]).astype('float32')
+
+        for dims in [1, 2, 3]:
+            self.dims = dims
+
+            ops_config = [
+                {
+                    "op_type": "lookup_table_v2",
+                    "op_inputs": {"Ids": ["indices"], "W": ["data"]},
+                    "op_outputs": {"Out": ["out_data"]},
+                    "op_attrs": {},
+                }
+            ]
+            ops = self.generate_op_config(ops_config)
+
+            program_config = ProgramConfig(
+                ops=ops,
+                weights={
+                    "data": TensorConfig(
+                        data_gen=partial(generate_input2, {}, {})
+                    )
+                },
+                inputs={
+                    "indices": TensorConfig(
+                        data_gen=partial(generate_input1, dims, {})
+                    )
+                },
+                outputs=["out_data"],
+            )
+
+            yield program_config
+
+    def sample_predictor_configs(
+        self, program_config
+    ) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.dims == 1:
+                self.dynamic_shape.min_input_shape = {
+                    "indices": [1],
+                    "data": [64, 4],
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "indices": [1],
+                    "data": [64, 4],
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "indices": [1],
+                    "data": [64, 4],
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {
+                    "indices": [2, 3],
+                    "data": [64, 4],
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "indices": [2, 3],
+                    "data": [64, 4],
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "indices": [2, 3],
+                    "data": [64, 4],
+                }
+            else:
+                self.dynamic_shape.min_input_shape = {
+                    "indices": [3, 3, 3],
+                    "data": [64, 4],
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "indices": [3, 3, 3],
+                    "data": [64, 4],
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "indices": [3, 3, 3],
+                    "data": [64, 4],
+                }
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            return 1, 2
+
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+
+        # for dynamic_shape mode
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True
+        ), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True
+        ), (1e-3, 1e-3)
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()

test/ir/inference/test_trt_convert_p_norm.py

@@ -23,7 +23,7 @@
 import paddle.inference as paddle_infer
 
 
-class TrtConvertCeluTest(TrtLayerAutoScanTest):
+class TrtConvertPNormTest(TrtLayerAutoScanTest):
     def sample_program_configs(self):
         def generate_input1(dims, attrs: List[Dict[str, Any]]):
             if dims == 1: