[Paddle Inference] Add_expand_v2_trt_layer (#47002)

paddle/fluid/inference/api/analysis_predictor.cc

@@ -2263,6 +2263,7 @@ USE_TRT_CONVERTER(layernorm_shift_partition)
 USE_TRT_CONVERTER(generic_plugin_creater)
 USE_TRT_CONVERTER(custom_plugin_creater)
 USE_TRT_CONVERTER(lookup_table)
+USE_TRT_CONVERTER(expand_v2)
 #if PADDLE_WITH_CUSPARSELT && IS_TRT_VERSION_GE(8000)
 USE_TRT_CONVERTER(sparse_fc)
 USE_TRT_CONVERTER(sparse_multihead_matmul)

paddle/fluid/inference/tensorrt/convert/CMakeLists.txt

@@ -78,7 +78,8 @@ list(
   fused_token_prune_op.cc
   layernorm_shift_partition_op.cc
   generic_and_custom_plugin_creater.cc
-  fused_lookup_tables_op.cc)
+  fused_lookup_tables_op.cc
+  expand_v2_op.cc)
 
 if(${TENSORRT_MAJOR_VERSION} GREATER_EQUAL 7 AND NOT WIN32)
   list(APPEND CONVERT_FILES emb_eltwise_layernorm.cc

paddle/fluid/inference/tensorrt/convert/expand_v2_op.cc

+/* Copyright (c) 2022 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. */
+
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace framework {
+class Scope;
+
+namespace proto {
+class OpDesc;
+}  // namespace proto
+}  // namespace framework
+}  // namespace paddle
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class ExpandV2OpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope,
+                  bool test_mode) override {
+    framework::OpDesc op_desc(op, nullptr);
+    auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
+    auto input_dims = input->getDimensions();
+    auto output_name = op_desc.Output("Out")[0];
+    auto rank = input_dims.nbDims;
+    std::vector<int32_t> shape =
+        PADDLE_GET_CONST(std::vector<int32_t>, op_desc.GetAttr("shape"));
+    int32_t nbDims_num = shape.size();
+
+    auto* shape_tensor =
+        Add1DConstantLayer(shape, output_name + "_shape_tensor_");
+    nvinfer1::ITensor* input_shape_tensor;
+    if (rank < nbDims_num) {
+      auto* one_rank_tensor =
+          Add1DConstantLayer(std::vector<int32_t>(nbDims_num - rank, 1),
+                             output_name + "_one_rank_tensor_");
+      auto in_shape_tensor = Shape(input);
+      std::vector<nvinfer1::ITensor*> itensors;
+      itensors.push_back(one_rank_tensor);
+      itensors.push_back(in_shape_tensor);
+      input_shape_tensor = Concat(itensors);
+    } else {
+      input_shape_tensor = Shape(input);
+    }
+
+    auto* shuffle = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
+    shuffle->setInput(1, *input_shape_tensor);
+
+    std::vector<int32_t> start_vec(nbDims_num, 0);
+    nvinfer1::Dims start;
+    start.nbDims = nbDims_num;
+    for (int32_t i = 0; i < nbDims_num; ++i) {
+      start.d[i] = start_vec[i];
+    }
+    nvinfer1::Dims size;
+    size.nbDims = nbDims_num;
+    nvinfer1::Dims stride;
+    stride.nbDims = nbDims_num;
+
+    auto starts_tensor =
+        Add1DConstantLayer(start_vec, output_name + "_start_tensor_");
+    auto one_tensor = Add1DConstantLayer(1, output_name + "_one_tensor_");
+
+    auto sizes_tensor = Max(input_shape_tensor, shape_tensor);
+    auto input_sub_tensor = Sub(input_shape_tensor, one_tensor);
+    auto strides_tensor = Min(one_tensor, input_sub_tensor);
+
+    auto layer = TRT_ENGINE_ADD_LAYER(
+        engine_, Slice, *shuffle->getOutput(0), start, size, stride);
+    layer->setInput(1, *starts_tensor);
+    layer->setInput(2, *sizes_tensor);
+    layer->setInput(3, *strides_tensor);
+
+    RreplenishLayerAndOutput(layer, "expand_v2", {output_name}, test_mode);
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(expand_v2, ExpandV2OpConverter);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -2109,6 +2109,27 @@ struct SimpleOpTypeSetTeller : public Teller {
       }
     }
 
+    if (op_type == "expand_v2") {
+      if (!with_dynamic_shape) {
+        return false;
+      }
+      if (!desc.HasAttr("shape")) {
+        return false;
+      }
+      auto expand_v2_inputs = desc.Inputs();
+      if (expand_v2_inputs.find("Shape") != expand_v2_inputs.end()) {
+        if (desc.Input("Shape").size() >= 1) {
+          return false;
+        }
+      }
+      if (expand_v2_inputs.find("expand_shapes_tensor") !=
+          expand_v2_inputs.end()) {
+        if (desc.Input("expand_shapes_tensor").size() >= 1) {
+          return false;
+        }
+      }
+    }
+
     if (use_no_calib_int8) {
       return int8_teller_set.count(op_type);
     } else {
@@ -2232,7 +2253,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       "unsqueeze2",
       "layernorm_shift_partition",
       "lookup_table",
-      "lookup_table_v2"};
+      "lookup_table_v2",
+      "expand_v2"};
   std::unordered_set<std::string> teller_set{
       "mul",
       "matmul",
@@ -2348,7 +2370,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       "fused_token_prune",
       "layernorm_shift_partition",
       "lookup_table",
-      "lookup_table_v2"};
+      "lookup_table_v2",
+      "expand_v2"};
 };
 
 struct GenericPluginTeller : public Teller {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_expand_v2.py

+# Copyright (c) 2022 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
+from program_config import TensorConfig, ProgramConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import List, Dict, Any
+import unittest
+
+
+class TrtConvertExpandV2Test(TrtLayerAutoScanTest):
+
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+        if len(attrs[0]['shape']) < self.dims:
+            return False
+        if self.dims == 1:
+            if len(attrs[0]['shape']) == 4:
+                return False
+        return True
+
+    def sample_program_configs(self):
+
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            if self.dims == 4:
+                self.input_shape = [1, 1, 4, 6]
+                return np.random.random([1, 1, 4, 6]).astype(np.float32)
+            elif self.dims == 3:
+                self.input_shape = [1, 8, 6]
+                return np.random.random([1, 8, 6]).astype(np.float32)
+            elif self.dims == 2:
+                self.input_shape = [1, 48]
+                return np.random.random([1, 48]).astype(np.float32)
+            elif self.dims == 1:
+                self.input_shape = [48]
+                return np.random.random([48]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.array([1, 48]).astype(np.int32)
+
+        def generate_shapeT1_data(attrs: List[Dict[str, Any]]):
+            return np.array([2]).astype(np.int32)
+
+        def generate_shapeT2_data(attrs: List[Dict[str, Any]]):
+            return np.array([24]).astype(np.int32)
+
+        for dims in [4, 3, 2, 1]:
+            for shape in [[10, 12, -1, -1], [8, 64, -1, -1], [6, 8, -1]]:
+                dics = [
+                    {
+                        "shape": shape,
+                    },
+                ]
+                self.dims = dims
+                dics_intput = [{"X": ["expand_v2_input"]}]
+
+                ops_config = [{
+                    "op_type": "expand_v2",
+                    "op_inputs": dics_intput[0],
+                    "op_outputs": {
+                        "Out": ["expand_v2_out"]
+                    },
+                    "op_attrs": dics[0]
+                }]
+                ops = self.generate_op_config(ops_config)
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "expand_v2_input":
+                        TensorConfig(data_gen=partial(generate_input1, dics))
+                    },
+                    outputs=["expand_v2_out"])
+
+                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+
+        def generate_dynamic_shape(attrs):
+            if self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "expand_v2_input": [1, 1, 4, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "expand_v2_input": [10, 1, 4, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "expand_v2_input": [1, 1, 4, 6]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "expand_v2_input": [1, 8, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "expand_v2_input": [4, 8, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "expand_v2_input": [1, 8, 6]
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {
+                    "expand_v2_input": [1, 48]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "expand_v2_input": [4, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "expand_v2_input": [1, 48]
+                }
+            elif self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"expand_v2_input": [48]}
+                self.dynamic_shape.max_input_shape = {"expand_v2_input": [48]}
+                self.dynamic_shape.opt_input_shape = {"expand_v2_input": [48]}
+
+        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, 2
+            else:
+                return 0, 3
+
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+
+        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-5
+
+        # 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-5
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+class TrtConvertExpandV2Test2(TrtLayerAutoScanTest):
+
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+        return True
+
+    def sample_program_configs(self):
+
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            if self.dims == 1:
+                self.input_shape = [1]
+                return np.random.random([1]).astype(np.float32)
+
+        for dims in [1]:
+            for shape in [[10, 12, -1, -1], [8, 64, -1, -1]]:
+                dics = [
+                    {
+                        "shape": shape,
+                    },
+                ]
+                self.dims = dims
+                dics_intput = [
+                    {
+                        "X": ["expand_v2_input"],
+                        "Shape": ["shapeT1_data"]
+                    },
+                ]
+                ops_config = [
+                    {
+                        "op_type": "fill_constant",
+                        "op_inputs": {},
+                        "op_outputs": {
+                            "Out": ["shapeT1_data"]
+                        },
+                        "op_attrs": {
+                            "dtype": 2,
+                            "str_value": "10",
+                            "shape": [1],
+                        },
+                    },
+                    {
+                        "op_type": "expand_v2",
+                        "op_inputs": dics_intput[0],
+                        "op_outputs": {
+                            "Out": ["expand_v2_out"]
+                        },
+                        "op_attrs": dics[0]
+                    },
+                ]
+                ops = self.generate_op_config(ops_config)
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "expand_v2_input":
+                        TensorConfig(data_gen=partial(generate_input1, dics))
+                    },
+                    outputs=["expand_v2_out"])
+
+                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+
+        def generate_dynamic_shape():
+            if self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"expand_v2_input": [1]}
+                self.dynamic_shape.max_input_shape = {"expand_v2_input": [1]}
+                self.dynamic_shape.opt_input_shape = {"expand_v2_input": [1]}
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        clear_dynamic_shape()
+        # for dynamic_shape
+        generate_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
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+class TrtConvertExpandV2Test3(TrtLayerAutoScanTest):
+
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+        return True
+
+    def sample_program_configs(self):
+
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            if self.dims == 4:
+                self.input_shape = [1, 1, 4, 6]
+                return np.random.random([1, 1, 4, 6]).astype(np.float32)
+            elif self.dims == 3:
+                self.input_shape = [1, 4, 6]
+                return np.random.random([1, 4, 6]).astype(np.float32)
+
+        for dims in [4, 3]:
+            for shape in [[10, 12, -1, -1], [8, 64, -1, -1]]:
+                dics = [
+                    {
+                        "shape": shape,
+                    },
+                ]
+                self.dims = dims
+                dics_intput = [
+                    {
+                        "X": ["expand_v2_input"],
+                        "expand_shapes_tensor": [
+                            "shapeT1_data", "shapeT2_data", "shapeT3_data",
+                            "shapeT4_data"
+                        ]
+                    },
+                ]
+                ops_config = [
+                    {
+                        "op_type": "fill_constant",
+                        "op_inputs": {},
+                        "op_outputs": {
+                            "Out": ["shapeT1_data"]
+                        },
+                        "op_attrs": {
+                            "dtype": 2,
+                            "str_value": "10",
+                            "shape": [1],
+                        },
+                    },
+                    {
+                        "op_type": "fill_constant",
+                        "op_inputs": {},
+                        "op_outputs": {
+                            "Out": ["shapeT2_data"]
+                        },
+                        "op_attrs": {
+                            "dtype": 2,
+                            "str_value": "12",
+                            "shape": [1],
+                        },
+                    },
+                    {
+                        "op_type": "fill_constant",
+                        "op_inputs": {},
+                        "op_outputs": {
+                            "Out": ["shapeT3_data"]
+                        },
+                        "op_attrs": {
+                            "dtype": 2,
+                            "str_value": "4",
+                            "shape": [1],
+                        },
+                    },
+                    {
+                        "op_type": "fill_constant",
+                        "op_inputs": {},
+                        "op_outputs": {
+                            "Out": ["shapeT4_data"]
+                        },
+                        "op_attrs": {
+                            "dtype": 2,
+                            "str_value": "6",
+                            "shape": [1],
+                        },
+                    },
+                    {
+                        "op_type": "expand_v2",
+                        "op_inputs": dics_intput[0],
+                        "op_outputs": {
+                            "Out": ["expand_v2_out"]
+                        },
+                        "op_attrs": dics[0]
+                    },
+                ]
+                ops = self.generate_op_config(ops_config)
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "expand_v2_input":
+                        TensorConfig(data_gen=partial(generate_input1, dics))
+                    },
+                    outputs=["expand_v2_out"])
+
+                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+
+        def generate_dynamic_shape():
+            if self.dims == 4:
+                self.dynamic_shape.min_input_shape = {
+                    "expand_v2_input": [1, 1, 4, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "expand_v2_input": [10, 1, 4, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "expand_v2_input": [1, 1, 4, 6]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "expand_v2_input": [1, 4, 6]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "expand_v2_input": [4, 4, 6]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "expand_v2_input": [1, 4, 6]
+                }
+
+        def clear_dynamic_shape():
+            self.dynamic_shape.min_input_shape = {}
+            self.dynamic_shape.max_input_shape = {}
+            self.dynamic_shape.opt_input_shape = {}
+
+        clear_dynamic_shape()
+        # for dynamic_shape
+        generate_dynamic_shape()
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        yield self.create_inference_config(), (4, 3), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        yield self.create_inference_config(), (4, 3), 1e-5
+
+    def add_skip_trt_case(self):
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()