add emb_eltwise_layernorm trt converter test case (#36027)

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_emb_eltwise_layernorm.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 TrtConvertEmbEltwiseLayernormTest1(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(batch, input_size):
+            return np.random.randint(
+                0, 7, size=(batch, input_size, 1)).astype(np.int64)
+
+        def generate_weight1(size11, size2):
+            return np.random.randn(size11, size2).astype(np.float32)
+
+        def generate_weight2(size12, size2):
+            return np.random.randn(size12, size2).astype(np.float32)
+
+        def generate_weight3(size13, size2):
+            return np.random.randn(size13, size2).astype(np.float32)
+
+        def generate_weight4(size2):
+            return np.random.randn(size2).astype(np.float32)
+
+        for input_size in [16, 128]:
+            for batch in [1, 2, 4]:
+                for size1 in [[8, 513, 768], [513, 768, 8], [768, 8, 513]]:
+                    size11 = size1[0]
+                    size12 = size1[1]
+                    size13 = size1[2]
+                    for size2 in [32, 768]:
+                        for norm_axis in [2]:
+                            for epsilon in [0.0001, 0.0005]:
+                                for axis1 in [0, -1]:
+                                    for axis2 in [0, -1]:
+                                        for type in [
+                                                "lookup_table",
+                                                "lookup_table_v2"
+                                        ]:
+                                            dics = [{
+                                                "is_sparse": False,
+                                                "is_distributed": False,
+                                                "padding_idx": -1,
+                                                "is_test": True
+                                            }, {
+                                                "is_sparse": False,
+                                                "is_distributed": False,
+                                                "padding_idx": -1,
+                                            }, {
+                                                "axis": axis1
+                                            }, {
+                                                "axis": axis2
+                                            }, {
+                                                "begin_norm_axis": norm_axis,
+                                                "epsilon": epsilon
+                                            }]
+                                            ops_config = [{
+                                                "op_type": type,
+                                                "op_inputs": {
+                                                    "Ids": ["input_data1"],
+                                                    "W": ["embedding1_weight"]
+                                                },
+                                                "op_outputs": {
+                                                    "Out":
+                                                    ["embedding1_output"]
+                                                },
+                                                "op_attrs": dics[0]
+                                                if type == "lookup_table" else
+                                                dics[1]
+                                            }, {
+                                                "op_type": type,
+                                                "op_inputs": {
+                                                    "Ids": ["input_data2"],
+                                                    "W": ["embedding2_weight"]
+                                                },
+                                                "op_outputs": {
+                                                    "Out":
+                                                    ["embedding2_output"]
+                                                },
+                                                "op_attrs": dics[0]
+                                                if type == "lookup_table" else
+                                                dics[1]
+                                            }, {
+                                                "op_type": type,
+                                                "op_inputs": {
+                                                    "Ids": ["input_data3"],
+                                                    "W": ["embedding3_weight"]
+                                                },
+                                                "op_outputs": {
+                                                    "Out":
+                                                    ["embedding3_output"]
+                                                },
+                                                "op_attrs": dics[0]
+                                                if type == "lookup_table" else
+                                                dics[1]
+                                            }, {
+                                                "op_type": "elementwise_add",
+                                                "op_inputs": {
+                                                    "X": ["embedding2_output"],
+                                                    "Y": ["embedding3_output"]
+                                                },
+                                                "op_outputs": {
+                                                    "Out": [
+                                                        "elementwise_add1_output"
+                                                    ]
+                                                },
+                                                "op_attrs": dics[2]
+                                            }, {
+                                                "op_type": "elementwise_add",
+                                                "op_inputs": {
+                                                    "X": [
+                                                        "elementwise_add1_output"
+                                                    ],
+                                                    "Y": ["embedding1_output"]
+                                                },
+                                                "op_outputs": {
+                                                    "Out": [
+                                                        "elementwise_add2_output"
+                                                    ]
+                                                },
+                                                "op_attrs": dics[3]
+                                            }, {
+                                                "op_type": "layer_norm",
+                                                "op_inputs": {
+                                                    "X": [
+                                                        "elementwise_add2_output"
+                                                    ],
+                                                    "Bias":
+                                                    ["layer_norm_bias"],
+                                                    "Scale":
+                                                    ["layer_norm_scale"]
+                                                },
+                                                "op_outputs": {
+                                                    "Y":
+                                                    ["layer_norm_output1"],
+                                                    "Mean":
+                                                    ["layer_norm_output2"],
+                                                    "Variance":
+                                                    ["layer_norm_output3"]
+                                                },
+                                                "op_attrs": dics[4]
+                                            }]
+                                            ops = self.generate_op_config(
+                                                ops_config)
+
+                                            program_config = ProgramConfig(
+                                                ops=ops,
+                                                weights={
+                                                    "embedding1_weight":
+                                                    TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_weight1,
+                                                            size11, size2)),
+                                                    "embedding2_weight":
+                                                    TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_weight2,
+                                                            size12, size2)),
+                                                    "embedding3_weight":
+                                                    TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_weight3,
+                                                            size13, size2)),
+                                                    "layer_norm_bias":
+                                                    TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_weight4,
+                                                            size2)),
+                                                    "layer_norm_scale":
+                                                    TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_weight4,
+                                                            size2))
+                                                },
+                                                inputs={
+                                                    "input_data1": TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_input,
+                                                            batch, input_size)),
+                                                    "input_data2": TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_input,
+                                                            batch, input_size)),
+                                                    "input_data3": TensorConfig(
+                                                        data_gen=partial(
+                                                            generate_input,
+                                                            batch, input_size))
+                                                },
+                                                outputs=["layer_norm_output1"])
+
+                                            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_data1": [1, 4, 1],
+                "input_data2": [1, 4, 1],
+                "input_data3": [1, 4, 1]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data1": [4, 512, 1],
+                "input_data2": [4, 512, 1],
+                "input_data3": [4, 512, 1]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data1": [2, 128, 1],
+                "input_data2": [2, 128, 1],
+                "input_data3": [2, 128, 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(), (0, 5), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 5), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (1, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (1, 4), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()