[Paddle Inference]Add stack op TRT converter unittest (#35531)

* add_stack_teller

* add_stack_teller

* add_stack_teller

* add_stack_teller

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_stack.py

+# Copyright (c) 2021 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 trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+
+
+class TrtConvertStackTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        outputs = program_config.outputs
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        #The input dimension should be less than the set axis.
+        if len(inputs['stack_input1'].shape) < attrs[0]['axis']:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.ones([batch, 3, 24, 24]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([batch, 3, 24]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([batch, 24]).astype(np.float32)
+            elif self.dims == 1:
+                return np.ones([24]).astype(np.float32)
+
+        def generate_input2(attrs: List[Dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.ones([batch, 3, 24, 24]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([batch, 3, 24]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([batch, 24]).astype(np.float32)
+            elif self.dims == 1:
+                return np.ones([24]).astype(np.float32)
+
+        def generate_input3(attrs: List[Dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.ones([batch, 3, 24, 24]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([batch, 3, 24]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([batch, 24]).astype(np.float32)
+            elif self.dims == 1:
+                return np.ones([24]).astype(np.float32)
+
+        for dims in [1, 2, 3, 4]:
+            for batch in [1, 2, 4]:
+                for axis in [-2, -1, 0, 1, 2, 3]:
+                    self.dims = dims
+                    dics = [{"axis": axis}, {}]
+                    ops_config = [{
+                        "op_type": "stack",
+                        "op_inputs": {
+                            "X":
+                            ["stack_input1", "stack_input2", "stack_input3"]
+                        },
+                        "op_outputs": {
+                            "Y": ["stack_output"]
+                        },
+                        "op_attrs": dics[0]
+                    }]
+                    ops = self.generate_op_config(ops_config)
+                    program_config = ProgramConfig(
+                        ops=ops,
+                        weights={},
+                        inputs={
+                            "stack_input1": TensorConfig(data_gen=partial(
+                                generate_input1, dics, batch)),
+                            "stack_input2": TensorConfig(data_gen=partial(
+                                generate_input2, dics, batch)),
+                            "stack_input3": TensorConfig(data_gen=partial(
+                                generate_input3, dics, batch))
+                        },
+                        outputs=["stack_output"])
+
+                    yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "stack_input1": [1, 3, 24, 24],
+                    "stack_input2": [1, 3, 24, 24],
+                    "stack_input3": [1, 3, 24, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "stack_input1": [4, 3, 48, 48],
+                    "stack_input2": [4, 3, 48, 48],
+                    "stack_input3": [4, 3, 48, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "stack_input1": [1, 3, 24, 24],
+                    "stack_input2": [1, 3, 24, 24],
+                    "stack_input3": [1, 3, 24, 24]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "stack_input1": [1, 3, 24],
+                    "stack_input2": [1, 3, 24],
+                    "stack_input3": [1, 3, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "stack_input1": [4, 3, 48],
+                    "stack_input2": [4, 3, 48],
+                    "stack_input3": [4, 3, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "stack_input1": [1, 3, 24],
+                    "stack_input2": [1, 3, 24],
+                    "stack_input3": [1, 3, 24]
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {
+                    "stack_input1": [1, 24],
+                    "stack_input2": [1, 24],
+                    "stack_input3": [1, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "stack_input1": [4, 48],
+                    "stack_input2": [4, 48],
+                    "stack_input3": [4, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "stack_input1": [1, 24],
+                    "stack_input2": [1, 24],
+                    "stack_input3": [1, 24]
+                }
+            elif self.dims == 1:
+                self.dynamic_shape.min_input_shape = {
+                    "stack_input1": [24],
+                    "stack_input2": [24],
+                    "stack_input3": [24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "stack_input1": [48],
+                    "stack_input2": [48],
+                    "stack_input3": [48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "stack_input1": [24],
+                    "stack_input2": [24],
+                    "stack_input3": [24]
+                }
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        def generate_trt_nodes_num(attrs, dynamic_shape):
+            if dynamic_shape == True:
+                return 1, 4
+            else:
+                return 0, 5
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        # for static_shape
+        clear_dynamic_shape()
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False), 1e-5
+
+        # for dynamic_shape
+        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-5
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()