add test (#35316)

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_layer_norm.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
+import unittest
+
+
+class TrtConvertLayerNormTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        if attrs[0]['epsilon'] < 0 or attrs[0]['epsilon'] > 0.001:
+            return False
+        if attrs[0]['begin_norm_axis'] <= 0 or attrs[0]['begin_norm_axis'] >= (
+                len(inputs['input_data'].shape) - 1):
+            return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]], shape_input):
+            return np.ones(shape_input).astype(np.float32)
+
+        def generate_input2(attrs: List[Dict[str, Any]], shape_input):
+            begin = attrs[0]["begin_norm_axis"]
+            sum = 1
+            for x in range(begin, len(shape_input)):
+                sum *= shape_input[x]
+            return np.ones([sum]).astype(np.float32)
+
+        for epsilon in [0.0005, -1, 1]:
+            for begin_norm_axis in [1, 0, -1, 2, 3]:
+                dics = [{
+                    "epsilon": epsilon,
+                    "begin_norm_axis": begin_norm_axis
+                }, {}]
+
+                ops_config = [{
+                    "op_type": "layer_norm",
+                    "op_inputs": {
+                        "X": ["input_data"],
+                        "Scale": ["scale_data"],
+                        "Bias": ["bias_data"]
+                    },
+                    "op_outputs": {
+                        "Y": ["y_data"],
+                        "Mean": ["saved_mean_data"],
+                        "Variance": ["saved_variance_data"]
+                    },
+                    "op_attrs": dics[0]
+                }]
+                ops = self.generate_op_config(ops_config)
+                shape_input = [1, 3, 64, 64]
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={
+                        "bias_data": TensorConfig(data_gen=partial(
+                            generate_input2, dics, shape_input)),
+                        "scale_data": TensorConfig(data_gen=partial(
+                            generate_input2, dics, shape_input))
+                    },
+                    inputs={
+                        "input_data": TensorConfig(data_gen=partial(
+                            generate_input1, dics, shape_input))
+                    },
+                    outputs=["y_data"])
+
+                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]}
+            self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]}
+            self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]}
+
+        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):
+            inputs = program_config.inputs
+            #if not dynamic_shape:
+            #    if attrs[0]["begin_norm_axis"] >= len(inputs["input_data"].shape) - 1:
+            #        print ("iiiiiii")
+            #        return 0, 3
+            return 1, 2
+
+        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-2
+
+        # 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-2
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()