add gather_nd trt converter test cases (#35464)

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -328,6 +328,8 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
     }
 
     if (op_type == "gather_nd") {
+      if (!with_dynamic_shape) return false;
+
       auto* block = desc.Block();
       auto x_var_name = desc.Input("X")[0];
       auto index_var_name = desc.Input("Index")[0];
@@ -343,12 +345,17 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
 
       const auto index_shape = index_var_desc->GetShape();
       const auto x_shape = x_var_desc->GetShape();
+      if (x_shape.size() <= 2) {
+        VLOG(3) << "gather_nd op requires the input's dimension to be greater "
+                   "than 2";
+        return false;
+      }
+
       if (x_shape.size() != index_shape.size()) {
         VLOG(3) << "gather_nd op Index input dims size [" << index_shape.size()
                 << " ] not equal to x dims size [" << x_shape.size() << "]";
         return false;
       }
-      if (!with_dynamic_shape) return false;
     }
 
     if (op_type == "yolo_box") {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.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 TrtConvertGatherNdTest_dim_4_1(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1():
+            return np.random.random([2, 32, 64, 64]).astype(np.float32)
+
+        def generate_input2():
+            return np.ones([1]).astype(np.int32)
+
+        ops_config = [{
+            "op_type": "gather_nd",
+            "op_inputs": {
+                "X": ["input_data"],
+                "Index": ["index_data"]
+            },
+            "op_outputs": {
+                "Out": ["output_data"]
+            },
+            "op_attrs": {}
+        }]
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input1)),
+                "index_data": TensorConfig(data_gen=partial(generate_input2)),
+            },
+            outputs=["output_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, 8, 8, 8],
+                "index_data": [1]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [4, 32, 64, 64],
+                "index_data": [1]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [2, 4, 64, 64],
+                "index_data": [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, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (0, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+class TrtConvertGatherNdTest_dim_4_1_2(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1():
+            return np.random.random([2, 32, 64, 64]).astype(np.float32)
+
+        def generate_input2():
+            return np.array([1, 2]).astype(np.int32)
+
+        ops_config = [{
+            "op_type": "gather_nd",
+            "op_inputs": {
+                "X": ["input_data"],
+                "Index": ["index_data"]
+            },
+            "op_outputs": {
+                "Out": ["output_data"]
+            },
+            "op_attrs": {}
+        }]
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input1)),
+                "index_data": TensorConfig(data_gen=partial(generate_input2)),
+            },
+            outputs=["output_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, 8, 8, 8],
+                "index_data": [1]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [4, 32, 64, 64],
+                "index_data": [4]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [2, 4, 64, 64],
+                "index_data": [2]
+            }
+
+        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, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (0, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+class TrtConvertGatherNdTest_dim_4_2(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1():
+            return np.random.random([2, 32, 64, 64]).astype(np.float32)
+
+        def generate_input2():
+            return np.ones([2, 2]).astype(np.int32)
+
+        ops_config = [{
+            "op_type": "gather_nd",
+            "op_inputs": {
+                "X": ["input_data"],
+                "Index": ["index_data"]
+            },
+            "op_outputs": {
+                "Out": ["output_data"]
+            },
+            "op_attrs": {}
+        }]
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input1)),
+                "index_data": TensorConfig(data_gen=partial(generate_input2)),
+            },
+            outputs=["output_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, 8, 8, 8],
+                "index_data": [1, 2]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [4, 32, 64, 64],
+                "index_data": [4, 4]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [2, 4, 64, 64],
+                "index_data": [2, 2]
+            }
+
+        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, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (0, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+class TrtConvertGatherNdTest_dim_4_3(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1():
+            return np.random.random([2, 32, 64, 64]).astype(np.float32)
+
+        def generate_input2():
+            return np.ones([2, 2, 4]).astype(np.int32)
+
+        ops_config = [{
+            "op_type": "gather_nd",
+            "op_inputs": {
+                "X": ["input_data"],
+                "Index": ["index_data"]
+            },
+            "op_outputs": {
+                "Out": ["output_data"]
+            },
+            "op_attrs": {}
+        }]
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input1)),
+                "index_data": TensorConfig(data_gen=partial(generate_input2)),
+            },
+            outputs=["output_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, 8, 8, 8],
+                "index_data": [1, 2, 2]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [4, 32, 64, 64],
+                "index_data": [4, 4, 4]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [2, 4, 64, 64],
+                "index_data": [2, 2, 2]
+            }
+
+        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, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 1e-5
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (0, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 1e-5
+
+    def test(self):
+        self.run_test()
+
+
+class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1():
+            return np.random.random([2, 32]).astype(np.float32)
+
+        def generate_input2():
+            return np.ones([2, 2]).astype(np.int32)
+
+        ops_config = [{
+            "op_type": "gather_nd",
+            "op_inputs": {
+                "X": ["input_data"],
+                "Index": ["index_data"]
+            },
+            "op_outputs": {
+                "Out": ["output_data"]
+            },
+            "op_attrs": {}
+        }]
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input1)),
+                "index_data": TensorConfig(data_gen=partial(generate_input2)),
+            },
+            outputs=["output_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, 4],
+                "index_data": [1, 1]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [4, 64],
+                "index_data": [4, 2]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [2, 8],
+                "index_data": [2, 2]
+            }
+
+        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, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 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 add_skip_trt_case(self):
+        def teller(program_config, predictor_config):
+            if len(self.dynamic_shape.min_input_shape) != 0:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller, SkipReasons.TRT_NOT_SUPPORT,
+            "Need to repair the case: the output of trt and GPU has diff when inputs' dim is 1 and 2."
+        )
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1():
+            return np.random.random([2, 32, 256]).astype(np.float32)
+
+        def generate_input2():
+            return np.ones([2, 2, 2]).astype(np.int32)
+
+        ops_config = [{
+            "op_type": "gather_nd",
+            "op_inputs": {
+                "X": ["input_data"],
+                "Index": ["index_data"]
+            },
+            "op_outputs": {
+                "Out": ["output_data"]
+            },
+            "op_attrs": {}
+        }]
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={
+                "input_data": TensorConfig(data_gen=partial(generate_input1)),
+                "index_data": TensorConfig(data_gen=partial(generate_input2)),
+            },
+            outputs=["output_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, 4, 4],
+                "index_data": [1, 1, 1]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [4, 64, 512],
+                "index_data": [4, 2, 4]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [2, 8, 64],
+                "index_data": [2, 2, 2]
+            }
+
+        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, 4), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (0, 4), 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()