add bitwise_or to TRT ElementWise Layer

paddle/fluid/inference/api/analysis_predictor.cc

@@ -2927,6 +2927,7 @@ USE_TRT_CONVERTER(concat);
 USE_TRT_CONVERTER(dropout);
 USE_TRT_CONVERTER(pad);
 USE_TRT_CONVERTER(bitwise_and);
+USE_TRT_CONVERTER(bitwise_or);
 #if IS_TRT_VERSION_GE(8200)
 USE_TRT_CONVERTER(pad3d);
 USE_TRT_CONVERTER(einsum)

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

@@ -86,6 +86,7 @@ list(
   fill_constant_batch_size_like_op.cc
   sum_op.cc
   bitwise_and_op.cc
+  bitwise_or_op.cc
   shape_op.cc
   fill_constant_op.cc
   fused_token_prune_op.cc

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

@@ -0,0 +1,60 @@
+// Copyright (c) 2023 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 <NvInferRuntimeCommon.h>
+#include <cstddef>
+#include <iostream>
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class BitwiseOrConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope,
+                  bool test_mode) override {
+    VLOG(4) << "convert bitwise_or op to tensorrt layer";
+
+    framework::OpDesc op_desc(op, nullptr);
+    nvinfer1::ILayer* layer = nullptr;
+
+    auto* input_tensor = engine_->GetITensor(op_desc.Input("X")[0]);
+    nvinfer1::DataType data_type = input_tensor->getType();
+
+    auto* y_tensor = engine_->GetITensor(op_desc.Input("Y")[0]);
+
+    // for bool type
+    if (data_type == nvinfer1::DataType::kBOOL) {
+      layer = TRT_ENGINE_ADD_LAYER(engine_,
+                                   ElementWise,
+                                   *input_tensor,
+                                   *y_tensor,
+                                   nvinfer1::ElementWiseOperation::kOR);
+    } else {
+      PADDLE_THROW(platform::errors::Fatal(
+          "bitwise_or TRT converter is only supported on bool"));
+    }
+
+    auto output_name = op_desc.Output("Out")[0];
+    RreplenishLayerAndOutput(layer, "bitwise_or", {output_name}, test_mode);
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(bitwise_or, BitwiseOrConverter);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -1783,6 +1783,35 @@ struct SimpleOpTypeSetTeller : public Teller {
       }
     }
 
+    if (op_type == "bitwise_or") {
+#if IS_TRT_VERSION_LT(8400)
+      VLOG(3) << "bitwise_or is not supported when TensorRT < 8.4";
+      return false;
+#endif
+      if (!with_dynamic_shape) {
+        VLOG(3) << "Ops(" << op_type << ") do not support static shape yet.";
+        return false;
+      }
+      auto x_var_name = desc.Input("X")[0];
+      auto y_var_name = desc.Input("Y")[0];
+      auto* block = desc.Block();
+      if (block == nullptr) {
+        VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
+                   "Developers need to check whether block_desc is passed in "
+                   "the pass.";
+        return false;
+      }
+      auto* x_var_desc = block->FindVar(x_var_name);
+      auto* y_var_desc = block->FindVar(y_var_name);
+      auto x_dtype = x_var_desc->GetDataType();
+      auto y_dtype = y_var_desc->GetDataType();
+      if (x_dtype != framework::proto::VarType::BOOL ||
+          y_dtype != framework::proto::VarType::BOOL) {
+        VLOG(3) << "the bitwise_or only support input of BOOL.";
+        return false;
+      }
+    }
+
     if (op_type == "pad3d") {
 #if !IS_TRT_VERSION_GE(8200)
       VLOG(3) << "pad3d is not supported when TensorRT < 8.2";
@@ -2944,7 +2973,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       "quantize_linear",
       "dequantize_linear",
       "share_data",
-      "bitwise_and"};
+      "bitwise_and",
+      "bitwise_or"};
 
   std::unordered_set<std::string> teller_set{
       "matrix_multiply",
@@ -3114,7 +3144,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       "quantize_linear",
       "dequantize_linear",
       "share_data",
-      "bitwise_and"};
+      "bitwise_and",
+      "bitwise_or"};
 };
 
 struct GenericPluginTeller : public Teller {

test/ir/inference/test_trt_convert_bitwise_or.py

@@ -0,0 +1,153 @@
+# Copyright (c) 2023 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.
+
+import unittest
+from functools import partial
+from typing import List
+
+import numpy as np
+from program_config import ProgramConfig, TensorConfig
+from trt_layer_auto_scan_test import TrtLayerAutoScanTest
+
+import paddle.inference as paddle_infer
+
+
+class TrtConvertBitwiseOrTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(batch):
+            if self.dims == 4:
+                return np.random.random([batch, 3, 3, 24]).astype(np.int32)
+            elif self.dims == 3:
+                return np.random.random([batch, 3, 24]).astype(np.bool8)
+            elif self.dims == 2:
+                return np.random.random([batch, 24]).astype(np.bool_)
+
+        for dims in [2, 3, 4]:
+            for batch in [3, 6, 9]:
+                self.dims = dims
+                ops_config = [
+                    {
+                        "op_type": "bitwise_or",
+                        "op_inputs": {
+                            "X": ["input_data1"],
+                            "Y": ["input_data2"],
+                        },
+                        "op_outputs": {"Out": ["output_data"]},
+                        "op_attrs": {},
+                    },
+                ]
+                ops = self.generate_op_config(ops_config)
+
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "input_data1": TensorConfig(
+                            data_gen=partial(generate_input, batch)
+                        ),
+                        "input_data2": TensorConfig(
+                            data_gen=partial(generate_input, batch)
+                        ),
+                    },
+                    outputs=["output_data"],
+                )
+
+                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 = {
+                    "input_data1": [1, 3 - 1, 3 - 1, 24 - 1],
+                    "input_data2": [1, 3 - 1, 3 - 1, 24 - 1],
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data1": [9, 3 + 1, 3 + 1, 24 + 1],
+                    "input_data2": [9, 3 + 1, 3 + 1, 24 + 1],
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data1": [1, 3, 3, 24],
+                    "input_data2": [1, 3, 3, 24],
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data1": [1, 3 - 1, 24 - 1],
+                    "input_data2": [1, 3 - 1, 24 - 1],
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data1": [9, 3 + 1, 24 + 1],
+                    "input_data2": [9, 3 + 1, 24 + 1],
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data1": [1, 3, 24],
+                    "input_data2": [1, 3, 24],
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data1": [1, 24],
+                    "input_data2": [1, 24],
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data1": [9, 24],
+                    "input_data2": [9, 24],
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data1": [1, 24],
+                    "input_data2": [1, 24],
+                }
+
+        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):
+            ver = paddle_infer.get_trt_compile_version()
+            trt_version = ver[0] * 1000 + ver[1] * 100 + ver[2] * 10
+            if trt_version < 8400:
+                return 0, 4
+            if self.dims == 4 or self.dims == 1:
+                return 0, 4
+            return 1, 3
+
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+        self.trt_param.max_batch_size = 9
+        self.trt_param.workspace_size = 1073741824
+
+        # for dynamic_shape
+        generate_dynamic_shape(attrs)
+        self.trt_param.precision = paddle_infer.PrecisionType.Float32
+        program_config.set_input_type(np.float32)
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True
+        ), 1e-5
+        self.trt_param.precision = paddle_infer.PrecisionType.Half
+        program_config.set_input_type(np.float16)
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, True
+        ), 1e-3
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()