[TRT] Add silu converter (#45588)

* add silu converter

paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc

@@ -600,6 +600,7 @@ REGISTER_PASS_CAPABILITY(tensorrt_subgraph_pass)
             .EQ("fc", 0)
             .EQ("shuffle_channel", 0)
             .EQ("swish", 0)
+            .EQ("silu", 0)
             .EQ("split", 0)
             .LE("instance_norm", 1)
             .EQ("gelu", 0)

paddle/fluid/inference/api/analysis_predictor.cc

@@ -2132,6 +2132,7 @@ USE_TRT_CONVERTER(conv2d_transpose);
 USE_TRT_CONVERTER(leaky_relu);
 USE_TRT_CONVERTER(shuffle_channel);
 USE_TRT_CONVERTER(swish);
+USE_TRT_CONVERTER(silu);
 USE_TRT_CONVERTER(group_norm);
 USE_TRT_CONVERTER(instance_norm);
 USE_TRT_CONVERTER(layer_norm);

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

@@ -23,6 +23,7 @@ list(
   multihead_matmul_op.cc
   shuffle_channel_op.cc
   swish_op.cc
+  silu_op.cc
   instance_norm_op.cc
   stack_op.cc
   transpose_op.cc

paddle/fluid/inference/tensorrt/convert/silu_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 nvinfer1 {
+class ILayer;
+}  // namespace nvinfer1
+namespace paddle {
+namespace framework {
+class Scope;
+
+namespace proto {
+class OpDesc;
+}  // namespace proto
+}  // namespace framework
+}  // namespace paddle
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+class SiluOpConverter : public OpConverter {
+ public:
+  void operator()(const framework::proto::OpDesc& op,
+                  const framework::Scope& scope,
+                  bool test_mode) override {
+    VLOG(4) << "convert fluid silu op to tensorrt layer";
+
+    framework::OpDesc op_desc(op, nullptr);
+    // Declare inputs
+    int input_num = op_desc.Input("X").size();
+    PADDLE_ENFORCE_EQ(input_num,
+                      1,
+                      platform::errors::InvalidArgument(
+                          "The input X's size must equal to 1 in TRT silu op."
+                          " But received X's size %d.",
+                          input_num));
+    auto* input = engine_->GetITensor(op_desc.Input("X")[0]);
+    // Get output
+    size_t output_num = op_desc.Output("Out").size();
+    PADDLE_ENFORCE_EQ(
+        output_num,
+        1UL,
+        platform::errors::InvalidArgument(
+            "The output Out's size must equal to 1 in TRT silu op. "
+            "But received Out's size %u.",
+            output_num));
+
+    nvinfer1::ILayer* layer = nullptr;
+
+    auto* sigmoid = TRT_ENGINE_ADD_LAYER(
+        engine_, Activation, *input, nvinfer1::ActivationType::kSIGMOID);
+    layer = TRT_ENGINE_ADD_LAYER(engine_,
+                                 ElementWise,
+                                 *input,
+                                 *(sigmoid->getOutput(0)),
+                                 nvinfer1::ElementWiseOperation::kPROD);
+
+    auto output_name = op_desc.Output("Out")[0];
+    RreplenishLayerAndOutput(layer, "silu", {output_name}, test_mode);
+  }
+};
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+REGISTER_TRT_OP_CONVERTER(silu, SiluOpConverter);

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

+/* Copyright (c) 2018 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 <gtest/gtest.h>
+
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace tensorrt {
+
+TEST(silu_op, test_silu) {
+  std::unordered_set<std::string> parameters;
+  framework::Scope scope;
+  TRTConvertValidation validator(10, parameters, scope, 1000);
+  validator.DeclInputVar("silu_input", nvinfer1::Dims3(3, 2, 2));
+  validator.DeclOutputVar("silu_out", nvinfer1::Dims3(3, 2, 2));
+
+  // Prepare Op description
+  framework::OpDesc desc;
+  desc.SetType("silu");
+  desc.SetInput("X", {"silu_input"});
+  desc.SetOutput("Out", {"silu_out"});
+
+  validator.SetOp(*desc.Proto());
+
+  validator.Execute(1);
+}
+
+}  // namespace tensorrt
+}  // namespace inference
+}  // namespace paddle
+
+USE_OP(silu);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -121,6 +121,7 @@ struct SimpleOpTypeSetTeller : public Teller {
       "fc",
       "shuffle_channel",
       "swish",
+      "silu",
       "split",
       "instance_norm",
       "gelu",
@@ -228,6 +229,7 @@ struct SimpleOpTypeSetTeller : public Teller {
       "fc",
       "shuffle_channel",
       "swish",
+      "silu",
       "split",
       "instance_norm",
       "gelu",
@@ -309,7 +311,8 @@ bool OpTeller::Tell(const framework::ir::Node* node,
         "tan",      "tanh",  "sinh",
         "cosh",     "asin",  "acos",
         "atan",     "asinh", "atanh",
-        "ceil",     "floor", "erf"};
+        "ceil",     "floor", "erf",
+        "silu"};
     if (act_op_list.find(op_type) != act_op_list.end()) {
       auto* block = desc.Block();
       if (block == nullptr) {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_silu.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, 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 TrtConvertSwishTest(TrtLayerAutoScanTest):
+
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_configs(self):
+
+        def generate_input1(dims, attrs: List[Dict[str, Any]]):
+            if dims == 1:
+                return np.ones([3]).astype(np.float32)
+            elif dims == 2:
+                return np.ones([3, 64]).astype(np.float32)
+            elif dims == 3:
+                return np.ones([3, 64, 64]).astype(np.float32)
+            else:
+                return np.ones([1, 3, 64, 64]).astype(np.float32)
+
+        for dims in [1, 2, 3, 4]:
+            for beta in [1.0, 2.0, 3.0]:
+                self.dims = dims
+
+                ops_config = [{
+                    "op_type": "silu",
+                    "op_inputs": {
+                        "X": ["input_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, dims, {}))
+                    },
+                    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 == 1:
+                self.dynamic_shape.min_input_shape = {"input_data": [1]}
+                self.dynamic_shape.max_input_shape = {"input_data": [128]}
+                self.dynamic_shape.opt_input_shape = {"input_data": [64]}
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"input_data": [1, 32]}
+                self.dynamic_shape.max_input_shape = {"input_data": [4, 64]}
+                self.dynamic_shape.opt_input_shape = {"input_data": [3, 64]}
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {"input_data": [1, 32, 32]}
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [10, 64, 64]
+                }
+                self.dynamic_shape.opt_input_shape = {"input_data": [3, 64, 64]}
+            else:
+                self.dynamic_shape.min_input_shape = {
+                    "input_data": [1, 3, 32, 32]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "input_data": [4, 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 self.dims == 1:
+                return 0, 3
+            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
+        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-3, 1e-3)
+
+        # 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-3, 1e-3)
+
+    def test(self):
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()