add share_data op (PaddlePaddle#57933)

paddle/fluid/inference/api/analysis_predictor.cc

@@ -2954,6 +2954,7 @@ USE_TRT_CONVERTER(cumsum)
 USE_TRT_CONVERTER(assign)
 USE_TRT_CONVERTER(unbind)
 USE_TRT_CONVERTER(flip)
+USE_TRT_CONVERTER(share_data)
 #if IS_TRT_VERSION_GE(8522)
 USE_TRT_CONVERTER(flash_multihead_matmul)
 USE_TRT_CONVERTER(cross_multihead_matmul)

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

@@ -111,7 +111,8 @@ list(
   assign_op.cc
   flip_op.cc
   quantize_linear_op.cc
-  dequantize_linear_op.cc)
+  dequantize_linear_op.cc
+  share_data_op.cc)
 
 if(${TENSORRT_MAJOR_VERSION} GREATER_EQUAL 7)
   list(APPEND CONVERT_FILES emb_eltwise_layernorm.cc

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

@@ -0,0 +1,39 @@
+/* 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class ShareDataOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope,
+                  bool test_mode) override {
+    VLOG(3) << "convert a share_data op to tensorrt";
+    framework::OpDesc op_desc(op, nullptr);
+    auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
+    auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Identity, *input);
+    auto output_name = op_desc.Output("Out")[0];
+    RreplenishLayerAndOutput(layer, "share_data", {output_name}, test_mode);
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(share_data, ShareDataOpConverter);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -2918,7 +2918,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       "assign",
       "flip",
       "quantize_linear",
-      "dequantize_linear"};
+      "dequantize_linear",
+      "share_data"};
 
   std::unordered_set<std::string> teller_set{
       "matrix_multiply",
@@ -3086,7 +3087,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       "assign",
       "flip",
       "quantize_linear",
-      "dequantize_linear"};
+      "dequantize_linear",
+      "share_data"};
 };
 
 struct GenericPluginTeller : public Teller {

test/ir/inference/test_trt_convert_share_data.py

@@ -0,0 +1,155 @@
+# 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 TrtConvertShareDataTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        compile_version = paddle_infer.get_trt_compile_version()
+        runtime_version = paddle_infer.get_trt_runtime_version()
+        if (
+            compile_version[0] * 1000
+            + compile_version[1] * 100
+            + compile_version[2] * 10
+            < 8400
+        ):
+            return False
+        if (
+            runtime_version[0] * 1000
+            + runtime_version[1] * 100
+            + runtime_version[2] * 10
+            < 8400
+        ):
+            return False
+        return True
+
+    def sample_program_configs(self):
+        def generate_input(type):
+            if self.dims == 1:
+                return np.ones([1]).astype(type)
+            else:
+                return np.ones([1, 3, 64, 64]).astype(type)
+
+        for dims in [1, 4]:
+            self.dims = dims
+            for dtype in [
+                np.int32,
+                np.float32,
+                np.int64,
+            ]:
+                self.has_bool_dtype = dtype == np.bool_
+                ops_config = [
+                    {
+                        "op_type": "share_data",
+                        "op_inputs": {"X": ["input_data"]},
+                        "op_outputs": {"Out": ["output_data0"]},
+                        "op_attrs": {},
+                    },
+                    {
+                        "op_type": "share_data",
+                        "op_inputs": {"X": ["output_data0"]},
+                        "op_outputs": {"Out": ["output_data1"]},
+                        "op_attrs": {},
+                    },
+                ]
+
+                ops = self.generate_op_config(ops_config)
+
+                program_config = ProgramConfig(
+                    ops=ops,
+                    weights={},
+                    inputs={
+                        "input_data": TensorConfig(
+                            data_gen=partial(generate_input, dtype)
+                        )
+                    },
+                    outputs=["output_data1"],
+                )
+
+                yield program_config
+
+    def sample_predictor_configs(
+        self, program_config
+    ) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            if self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"input_data": [1]}
+                self.dynamic_shape.max_input_shape = {"input_data": [1]}
+                self.dynamic_shape.opt_input_shape = {"input_data": [1]}
+            else:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 3, 64, 64]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [1, 3, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "input_data": [1, 3, 64, 64]
+                }
+
+        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 not dynamic_shape and self.dims == 1:
+                return 0, 4
+            return 1, 2
+
+        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
+        program_config.set_input_type(np.float32)
+        yield self.create_inference_config(), generate_trt_nodes_num(
+            attrs, False
+        ), 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, False
+        ), 1e-2
+
+        # 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-2
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()