add elementwise trt converter test cases (#35552)

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

@@ -95,6 +95,9 @@ class AutoScanTest(unittest.TestCase):
         first = tensors[0]
         for group in tensors[1:]:
             for key, arr in group.items():
+                self.assertTrue(
+                    first[key].shape == arr.shape,
+                    "The output shape of GPU and TensorRT are not equal.")
                 self.assertTrue(
                     np.allclose(
                         first[key], arr, atol=threshold),

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.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 TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(shape):
+            return np.random.random(shape).astype(np.float32)
+
+        def generate_weight():
+            return np.random.randn(32).astype(np.float32)
+
+        for batch in [1, 2, 4]:
+            for shape in [[32], [batch, 32], [batch, 64, 32],
+                          [batch, 8, 16, 32]]:
+                for op_type in ["elementwise_add", "elementwise_mul"]:
+                    for axis in [len(shape) - 1, -1]:
+                        self.dims = len(shape)
+                        dics = [{"axis": axis}]
+                        ops_config = [{
+                            "op_type": op_type,
+                            "op_inputs": {
+                                "X": ["input_data"],
+                                "Y": ["weight"]
+                            },
+                            "op_outputs": {
+                                "Out": ["output_data"]
+                            },
+                            "op_attrs": dics[0]
+                        }]
+                        ops = self.generate_op_config(ops_config)
+
+                        program_config = ProgramConfig(
+                            ops=ops,
+                            weights={
+                                "weight":
+                                TensorConfig(data_gen=partial(generate_weight))
+                            },
+                            inputs={
+                                "input_data": TensorConfig(
+                                    data_gen=partial(generate_input, shape)),
+                            },
+                            outputs=["output_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 = {"input_data": [4]}
+                self.dynamic_shape.max_input_shape = {"input_data": [256]}
+                self.dynamic_shape.opt_input_shape = {"input_data": [16]}
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"input_data": [1, 4]}
+                self.dynamic_shape.max_input_shape = {"input_data": [4, 256]}
+                self.dynamic_shape.opt_input_shape = {"input_data": [2, 16]}
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {"input_data": [1, 4, 4]}
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 256, 256]
+                }
+                self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 16]}
+            elif self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 4, 4, 4]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 256, 128, 256]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [2, 32, 32, 16]
+                }
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        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(), (0, 3), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 3), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (1, 2), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 2), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+class TrtConvertElementwiseTest_two_input_without_broadcast(
+        TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        if len(inputs['input_data1'].shape) == 1:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(shape):
+            return np.random.random(shape).astype(np.float32)
+
+        for shape in [[4], [4, 32], [2, 64, 32], [1, 8, 16, 32]]:
+            for op_type in ["elementwise_add", "elementwise_mul"]:
+                for axis in [0, -1]:
+                    self.dims = len(shape)
+                    dics = [{"axis": axis}]
+                    ops_config = [{
+                        "op_type": op_type,
+                        "op_inputs": {
+                            "X": ["input_data1"],
+                            "Y": ["input_data2"]
+                        },
+                        "op_outputs": {
+                            "Out": ["output_data"]
+                        },
+                        "op_attrs": dics[0]
+                    }]
+                    ops = self.generate_op_config(ops_config)
+
+                    program_config = ProgramConfig(
+                        ops=ops,
+                        weights={},
+                        inputs={
+                            "input_data1": TensorConfig(
+                                data_gen=partial(generate_input, shape)),
+                            "input_data2": TensorConfig(
+                                data_gen=partial(generate_input, shape))
+                        },
+                        outputs=["output_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 = {
+                    "input_data1": [1],
+                    "input_data2": [1]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data1": [256],
+                    "input_data2": [128]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data1": [16],
+                    "input_data2": [32]
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data1": [1, 4],
+                    "input_data2": [1, 4]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data1": [128, 256],
+                    "input_data2": [128, 256]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data1": [2, 16],
+                    "input_data2": [32, 64]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data1": [1, 4, 4],
+                    "input_data2": [1, 4, 4]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data1": [128, 256, 128],
+                    "input_data2": [128, 128, 256]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data1": [2, 32, 16],
+                    "input_data2": [2, 64, 64]
+                }
+            elif self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data1": [1, 4, 4, 4],
+                    "input_data2": [1, 4, 4, 4]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data1": [8, 32, 64, 64],
+                    "input_data2": [8, 128, 64, 128]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data1": [2, 32, 32, 16],
+                    "input_data2": [2, 64, 32, 32]
+                }
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        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(), (1, 3), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 3), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (1, 3), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 3), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        if len(inputs['input_data1'].shape) == 1 or len(inputs['input_data2']
+                                                        .shape) == 1:
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(shape):
+            return np.random.random(shape).astype(np.float32)
+
+        input1_shape_list = [[4, 32], [2, 4, 32], [4, 2, 4, 32]]
+        input2_shape1_list = [[32], [4, 32], [2, 4, 32]]
+        input2_shape2_list = [[1, 32], [1, 1, 32], [1, 1, 1, 32]]
+        input2_shape3_list = [[1, 32], [1, 4, 32], [4, 32]]
+        input2_shape_list = [
+            input2_shape1_list, input2_shape2_list, input2_shape3_list
+        ]
+        axis1_list = [[-1], [1, -1], [1, -1]]
+        axis2_list = [[-1], [-1], [-1]]
+        axis3_list = [[-1], [-1], [2, -1]]
+        axis_list = [axis1_list, axis2_list, axis3_list]
+
+        for i in range(3):
+            input1_shape = input1_shape_list[i]
+            for j in range(3):
+                input2_shape = input2_shape_list[j][i]
+                for op_type in ["elementwise_add", "elementwise_mul"]:
+                    for axis in axis_list[j][i]:
+                        self.dims1 = len(input1_shape)
+                        self.dims2 = len(input2_shape)
+                        dics = [{"axis": axis}]
+                        ops_config = [{
+                            "op_type": op_type,
+                            "op_inputs": {
+                                "X": ["input_data1"],
+                                "Y": ["input_data2"]
+                            },
+                            "op_outputs": {
+                                "Out": ["output_data"]
+                            },
+                            "op_attrs": dics[0]
+                        }]
+                        ops = self.generate_op_config(ops_config)
+
+                        program_config = ProgramConfig(
+                            ops=ops,
+                            weights={},
+                            inputs={
+                                "input_data1": TensorConfig(data_gen=partial(
+                                    generate_input, input1_shape)),
+                                "input_data2": TensorConfig(data_gen=partial(
+                                    generate_input, input2_shape))
+                            },
+                            outputs=["output_data"])
+
+                        yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            max_shape = [[128], [128, 128], [128, 128, 128],
+                         [128, 128, 128, 128]]
+            min_shape = [[1], [1, 1], [1, 1, 1], [1, 1, 1, 1]]
+            opt_shape = [[32], [32, 32], [32, 32, 32], [32, 32, 32, 32]]
+
+            self.dynamic_shape.min_input_shape = {
+                "input_data1": min_shape[self.dims1 - 1],
+                "input_data2": min_shape[self.dims2 - 1]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data1": max_shape[self.dims1 - 1],
+                "input_data2": max_shape[self.dims2 - 1]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data1": opt_shape[self.dims1 - 1],
+                "input_data2": opt_shape[self.dims2 - 1]
+            }
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        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(), (1, 3), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 3), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (1, 3), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 3), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -99,6 +99,9 @@ class TrtLayerAutoScanTest(AutoScanTest):
                             tensor: Dict[str, np.array],
                             baseline: Dict[str, np.array]):
         for key, arr in tensor.items():
+            self.assertTrue(
+                baseline[key].shape == arr.shape,
+                "The output shape of GPU and TensorRT are not equal.")
             self.assertTrue(
                 np.allclose(
                     baseline[key], arr, atol=threshold),
@@ -219,6 +222,8 @@ class TrtLayerAutoScanTest(AutoScanTest):
                         self.run_test_config(model, params, prog_config,
                                              pred_config, feed_data))
                     self.assert_tensors_near(threshold, results[-1], results[0])
+                    if not skip_flag:
+                        self.assert_op_size(nodes_num[0], nodes_num[1])
                 except Exception as e:
                     self.fail_log(
                         str(prog_config) + ' vs ' + self.inference_config_str(
@@ -227,9 +232,6 @@ class TrtLayerAutoScanTest(AutoScanTest):
                     status = False
                     continue
 
-                if not skip_flag:
-                    self.assert_op_size(nodes_num[0], nodes_num[1])
-
                 self.success_log('RUN ' + str(prog_config) + ' vs ' +
                                  self.inference_config_str(pred_config))