[Paddle Inference] add generic plugin for p_norm (#53278)

paddle/fluid/inference/tensorrt/dynamic_shape_infermeta.cc

@@ -14,7 +14,7 @@
 
 #include "paddle/fluid/inference/tensorrt/dynamic_shape_infermeta_factory.h"
 #include "paddle/fluid/inference/tensorrt/helper.h"
-#include "paddle/fluid/platform/enforce.h"
+#include "paddle/phi/core/enforce.h"
 #include "paddle/phi/kernels/funcs/unfold_functor.h"
 
 namespace paddle {
@@ -322,20 +322,51 @@ nvinfer1::DimsExprs PNormInferMeta(
     int nb_inputs,
     nvinfer1::IExprBuilder& expr_builder,  // NOLINT
     const framework::OpDesc& op_desc) {
-  const nvinfer1::DimsExprs x_dim = inputs[0];
-  std::vector<const nvinfer1::IDimensionExpr*> reduce_dims;
-  std::vector<const nvinfer1::IDimensionExpr*> keep_dims;
-
   bool asvector = PADDLE_GET_CONST(bool, op_desc.GetAttr("asvector"));
   bool keepdim = PADDLE_GET_CONST(bool, op_desc.GetAttr("keepdim"));
   int axis = PADDLE_GET_CONST(int, op_desc.GetAttr("axis"));
 
+  auto x_dim = inputs[0];
+  auto x_rank = x_dim.nbDims;
+
+  PADDLE_ENFORCE_GE(axis,
+                    -x_rank,
+                    phi::errors::InvalidArgument(
+                        "Attr(axis) value should be in range [-R, R-1], R is "
+                        "the rank of Input(X). But received axis: %d, R: %d. "
+                        "Current Input(X)'s shape is=[%s].",
+                        axis,
+                        x_rank,
+                        x_dim.d));
+  PADDLE_ENFORCE_LT(axis,
+                    x_rank,
+                    phi::errors::InvalidArgument(
+                        "Attr(axis) value should be in range [-R, R-1], R is "
+                        "the rank of Input(X). But received axis: %d, R: %d. "
+                        "Current Input(X)'s shape is=[%s].",
+                        axis,
+                        x_rank,
+                        x_dim.d));
+
+  // TODO(liuyuanle): support asvector = True
+  PADDLE_ENFORCE_EQ(
+      asvector,
+      false,
+      phi::errors::InvalidArgument(
+          "p_norm only support asvector=false, but received asvector: %d.",
+          asvector));
+
+  std::vector<const nvinfer1::IDimensionExpr*> reduce_dims;
+
   if (asvector) {
     reduce_dims.emplace_back(expr_builder.constant(1));
-    keep_dims.emplace_back(expr_builder.constant(1));
     if (keepdim) {
       for (int i = 1; i < x_dim.nbDims; ++i) {
-        keep_dims.emplace_back(expr_builder.constant(1));
+        reduce_dims.emplace_back(expr_builder.constant(1));
+      }
+      x_dim.nbDims = reduce_dims.size();
+      for (size_t i = 0; i < reduce_dims.size(); i++) {
+        x_dim.d[i] = reduce_dims[i];
       }
     }
   } else {
@@ -347,12 +378,11 @@ nvinfer1::DimsExprs PNormInferMeta(
       reduce_dims.emplace_back(expr_builder.constant(1));
     }
   }
-  keep_dims[axis] = expr_builder.constant(1);
+  x_dim.d[axis] = expr_builder.constant(1);
 
   nvinfer1::DimsExprs output;
   if (keepdim) {
-    output.nbDims = keep_dims.size();
-    for (int i = 0; i < output.nbDims; i++) output.d[i] = keep_dims[i];
+    output = x_dim;
   } else {
     output.nbDims = reduce_dims.size();
     for (int i = 0; i < output.nbDims; i++) output.d[i] = reduce_dims[i];
@@ -396,6 +426,7 @@ PD_REGISTER_DYNAMIC_INFER_META_FN(inverse, UnchangedInferMeta);
 PD_REGISTER_DYNAMIC_INFER_META_FN(moe, MoeInferMeta);
 PD_REGISTER_DYNAMIC_INFER_META_FN(pad3d, Pad3dInferMeta);
 PD_REGISTER_DYNAMIC_INFER_META_FN(grid_sampler, GridSamplerInferMeta);
+PD_REGISTER_DYNAMIC_INFER_META_FN(p_norm, PNormInferMeta);
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tensorrt/dynamic_shape_infermeta_registry.h

@@ -28,6 +28,7 @@ USE_TRT_DYNAMIC_INFER_META_FN(scatter_nd_add);
 USE_TRT_DYNAMIC_INFER_META_FN(pad3d);
 USE_TRT_DYNAMIC_INFER_META_FN(inverse);
 USE_TRT_DYNAMIC_INFER_META_FN(grid_sampler);
+USE_TRT_DYNAMIC_INFER_META_FN(p_norm);
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/generic_plugin.cu

@@ -472,7 +472,7 @@ int GenericPlugin::enqueue(const nvinfer1::PluginTensorDesc* input_desc,
                            cudaStream_t stream) TRT_NOEXCEPT {
   platform::CUDAPlace place(platform::GetCurrentDeviceId());
 
-  // [TODO]now generic plugin do not support FP16 and INT8 precision
+  // [TODO]now generic plugin do not support INT8 precision
   auto protoType2PhiType =
       [&](int proto_type,
           nvinfer1::DataType nv_dtype) -> std::pair<phi::DataType, int> {

test/ir/inference/test_trt_convert_p_norm.py

+# 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 Any, Dict, 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 TrtConvertCeluTest(TrtLayerAutoScanTest):
+    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 [2, 3, 4]:
+            # TODO(liuyuanle): support asvector = True
+            for asvector in [False]:
+                for keepdim in [False, True]:
+                    for porder in [0, 1, 2, 3]:
+                        for axis in [-1]:
+                            self.dims = dims
+
+                            dics = [
+                                {
+                                    "asvector": asvector,
+                                    "keepdim": keepdim,
+                                    "axis": axis,
+                                    "porder": porder,
+                                }
+                            ]
+
+                            ops_config = [
+                                {
+                                    "op_type": "p_norm",
+                                    "op_inputs": {
+                                        "X": ["input_data"],
+                                    },
+                                    "op_outputs": {"Out": ["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, dims, dics
+                                        )
+                                    )
+                                },
+                                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 generate_trt_nodes_num(attrs, dynamic_shape):
+            return 1, 2
+
+        attrs = [
+            program_config.ops[i].attrs for i in range(len(program_config.ops))
+        ]
+
+        # for dynamic_shape mode
+        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()