strided_slice (#41573) (#41914)

paddle/fluid/inference/api/analysis_predictor.cc

@@ -1761,6 +1761,7 @@ USE_TRT_CONVERTER(deformable_conv);
 USE_TRT_CONVERTER(pool3d)
 USE_TRT_CONVERTER(fused_preln_embedding_eltwise_layernorm)
 USE_TRT_CONVERTER(preln_skip_layernorm)
+USE_TRT_CONVERTER(strided_slice)
 #endif
 
 namespace paddle_infer {

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

@@ -23,6 +23,7 @@ nv_library(tensorrt_converter
                 pool3d_op.cc
                 deformable_conv_op.cc
                 preln_emb_eltwise_layernorm.cc
+		strided_slice_op.cc
                 preln_skip_layernorm.cc
            DEPS tensorrt_engine tensorrt_plugin operator scope framework_proto op_registry)
 

paddle/fluid/inference/tensorrt/convert/strided_slice_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 {
+
+/*
+ * Stack converter from fluid to tensorRT.
+ */
+class StridedSliceOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope, bool test_mode) override {
+    VLOG(4) << "convert fluid StridedSlice op to tensorrt Slice layer";
+
+    framework::OpDesc op_desc(op, nullptr);
+    auto* input = engine_->GetITensor(op_desc.Input("Input")[0]);
+    nvinfer1::Dims input_dims = input->getDimensions();
+
+    std::vector<int> axes =
+        BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("axes"));
+    std::vector<int> starts =
+        BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("starts"));
+    std::vector<int> ends =
+        BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("ends"));
+    std::vector<int> strides =
+        BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("strides"));
+
+    nvinfer1::Dims start;
+    start.nbDims = input_dims.nbDims;
+    int axes_size = axes.size();
+    for (int i = 0; i < start.nbDims; i++) {
+      start.d[i] = 0;
+    }
+    for (int i = 0; i < axes_size; i++) {
+      start.d[axes[i]] = starts[i];
+    }
+
+    nvinfer1::Dims stride;
+    stride.nbDims = input_dims.nbDims;
+    for (int i = 0; i < stride.nbDims; i++) {
+      stride.d[i] = 1;
+    }
+    for (int i = 0; i < axes_size; i++) {
+      stride.d[axes[i]] = strides[i];
+    }
+
+    nvinfer1::Dims size;
+    size.nbDims = input_dims.nbDims;
+    for (int i = 0; i < size.nbDims; i++) {
+      size.d[i] = 1;
+    }
+
+    auto output_name = op_desc.Output("Out")[0];
+
+    auto create_weights = [&](const std::vector<int>& data,
+                              const std::string& type) -> int* {
+      std::unique_ptr<framework::Tensor> tmp_tensor(new framework::Tensor());
+      int data_size = data.size();
+      tmp_tensor->Resize({data_size});
+      auto* tmp_data = tmp_tensor->mutable_data<int>(platform::CPUPlace());
+      for (int i = 0; i < data_size; i++) {
+        tmp_data[i] = data[i];
+      }
+
+      engine_->SetWeights(output_name + "_add_slice_op_" + type,
+                          std::move(tmp_tensor));
+      return tmp_data;
+    };
+
+    std::vector<int> const_weight(input_dims.nbDims, 1);
+    for (int i = 0; i < axes_size; i++) {
+      const_weight[axes[i]] = strides[i];
+    }
+
+    int* weight_data = create_weights(const_weight, "size");
+
+    TensorRTEngine::Weight weight{nvinfer1::DataType::kINT32,
+                                  static_cast<void*>(weight_data),
+                                  static_cast<size_t>(input_dims.nbDims)};
+
+    int input_dim_size = input_dims.nbDims;
+    nvinfer1::Dims input_shape;
+    input_shape.nbDims = 1;
+    input_shape.d[0] = input_dim_size;
+
+    auto const_layer =
+        TRT_ENGINE_ADD_LAYER(engine_, Constant, input_shape, weight.get());
+
+    auto shape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shape, *input);
+
+    auto size_layer = TRT_ENGINE_ADD_LAYER(
+        engine_, ElementWise, *shape_layer->getOutput(0),
+        *const_layer->getOutput(0), nvinfer1::ElementWiseOperation::kDIV);
+
+    auto* layer =
+        TRT_ENGINE_ADD_LAYER(engine_, Slice, *input, start, size, stride);
+    layer->setInput(2, *size_layer->getOutput(0));
+
+    RreplenishLayerAndOutput(layer, "strided_slice", {output_name}, test_mode);
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(strided_slice, StridedSliceOpConverter);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -117,6 +117,7 @@ struct SimpleOpTypeSetTeller : public Teller {
       "multihead_matmul",
       "skip_layernorm",
       "slice",
+      "strided_slice",
       "fused_preln_embedding_eltwise_layernorm",
       "preln_skip_layernorm"};
   std::unordered_set<std::string> teller_set{
@@ -178,6 +179,7 @@ struct SimpleOpTypeSetTeller : public Teller {
       "multihead_matmul",
       "skip_layernorm",
       "slice",
+      "strided_slice",
       "fused_preln_embedding_eltwise_layernorm",
       "preln_skip_layernorm",
       "multiclass_nms3"};

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_strided_slice.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 TrtConvertStridedSliceTest(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))
+        ]
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            return np.ones([1, 56, 56, 192]).astype(np.float32)
+
+        for axes in [[1, 2]]:
+            for starts in [[1, 1]]:
+                for ends in [[10000000, 10000000]]:
+                    for decrease_axis in [[]]:
+                        for infer_flags in [[1, 1]]:
+                            for strides in [[2, 2]]:
+                                dics = [{
+                                    "axes": axes,
+                                    "starts": starts,
+                                    "ends": ends,
+                                    "decrease_axis": decrease_axis,
+                                    "infer_flags": infer_flags,
+                                    "strides": strides
+                                }]
+
+                                ops_config = [{
+                                    "op_type": "strided_slice",
+                                    "op_inputs": {
+                                        "Input": ["input_data"]
+                                    },
+                                    "op_outputs": {
+                                        "Out": ["slice_output_data"]
+                                    },
+                                    "op_attrs": dics[0]
+                                }]
+                                ops = self.generate_op_config(ops_config)
+
+                                program_config = ProgramConfig(
+                                    ops=ops,
+                                    weights={},
+                                    inputs={
+                                        "input_data": TensorConfig(
+                                            data_gen=partial(generate_input1,
+                                                             dics))
+                                    },
+                                    outputs=["slice_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, 56, 56, 192]
+            }
+            self.dynamic_shape.max_input_shape = {
+                "input_data": [8, 56, 56, 192]
+            }
+            self.dynamic_shape.opt_input_shape = {
+                "input_data": [4, 56, 56, 192]
+            }
+
+        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 dynamic_shape:
+                for i in range(len(attrs[0]["starts"])):
+                    if attrs[0]["starts"][i] < 0 or attrs[0]["ends"][i] < 0:
+                        return 0, 3
+            if not dynamic_shape:
+                for x in attrs[0]["axes"]:
+                    if x == 0:
+                        return 0, 3
+            return 1, 2
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        # 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
+
+    def test(self):
+        self.run_test()