chore: import upstream snapshot with attribution

test/ir/inference/test_trt_convert_stack.py

@@ -0,0 +1,199 @@
+# 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 __future__ import annotations
+
+import unittest
+from functools import partial
+from typing import Any
+
+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 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))
+        ]
+        # axis must be inside [-(rank+1), rank+1)
+        if len(inputs['stack_input1'].shape) < attrs[0]['axis']:
+            return False
+        if -(len(inputs['stack_input1'].shape) + 1) > 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.random.random([batch, 3, 24, 24]).astype(np.float32)
+            else:
+                return np.random.random([]).astype(np.float32)
+
+        def generate_input2(attrs: list[dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.random.random([batch, 3, 24, 24]).astype(np.float32)
+            else:
+                return np.random.random([]).astype(np.float32)
+
+        def generate_input3(attrs: list[dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.random.random([batch, 3, 24, 24]).astype(np.float32)
+            else:
+                return np.random.random([]).astype(np.float32)
+
+        for dims in [0, 4]:
+            for batch in [1]:
+                for axis in [-1, 0, 1]:
+                    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 generate_dynamic_shape(self, 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],
+            }
+        else:
+            self.dynamic_shape.min_input_shape = {
+                "stack_input1": [],
+                "stack_input2": [],
+                "stack_input3": [],
+            }
+            self.dynamic_shape.max_input_shape = {
+                "stack_input1": [],
+                "stack_input2": [],
+                "stack_input3": [],
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "stack_input1": [],
+                "stack_input2": [],
+                "stack_input3": [],
+            }
+        return self.dynamic_shape
+
+    def sample_predictor_configs(
+        self, program_config, run_pir=False
+    ) -> tuple[paddle_infer.Config, list[int], float]:
+        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:
+                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()
+        if not run_pir:
+            self.trt_param.precision = paddle_infer.PrecisionType.Float32
+            program_config.set_input_type(np.float32)
+            yield (
+                self.create_inference_config(),
+                generate_trt_nodes_num(attrs, False),
+                1e-5,
+            )
+            self.trt_param.precision = paddle_infer.PrecisionType.Half
+            program_config.set_input_type(np.float16)
+            yield (
+                self.create_inference_config(),
+                generate_trt_nodes_num(attrs, False),
+                1e-3,
+            )
+
+        # for dynamic_shape
+        self.generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        program_config.set_input_type(np.float32)
+        yield (
+            self.create_inference_config(),
+            generate_trt_nodes_num(attrs, True),
+            1e-5,
+        )
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        program_config.set_input_type(np.float16)
+        yield (
+            self.create_inference_config(),
+            generate_trt_nodes_num(attrs, True),
+            1e-3,
+        )
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test(run_pir=True)
+
+
+if __name__ == "__main__":
+    unittest.main()