[Paddle Inference]Add split op TRT converter unittest. (#35127)

* add_split_op

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

* add_split_teller

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

@@ -49,43 +49,21 @@ class SplitOpConverter : public OpConverter {
     } else {
       axis += (axis < 0) ? input_dims.nbDims : -1;
     }
-
-    PADDLE_ENFORCE_NE(input_dims.d[axis], -1,
-                      platform::errors::InvalidArgument(
-                          "The (%d) dim of input should not be -1", axis));
     if (num > 0) {
       int64_t in_axis_dim = input_dims.d[axis];
-      PADDLE_ENFORCE_EQ(
-          in_axis_dim % num, 0,
-          platform::errors::InvalidArgument(
-              "Invalid number to split. Tensor split does not result"
-              " in an equal division of dimensions. Axis dim = %d %% num = %d "
-              "!= 0",
-              in_axis_dim, num));
       size_t out_axis_dim = in_axis_dim / num;
       for (int i = 0; i < num; ++i) {
         output_lengths.push_back(out_axis_dim);
       }
     }
 
-    PADDLE_ENFORCE_EQ(
-        output_lengths.size(), output_num,
-        platform::errors::InvalidArgument(
-            "The output_length should be equal to the output size."));
-
     nvinfer1::ILayer* layer = nullptr;
     if (engine_->with_dynamic_shape()) {
-#if IS_TRT_VERSION_GE(6000)
       bool with_fp16 =
           engine_->WithFp16() && !engine_->disable_trt_plugin_fp16();
       plugin::SplitPluginDynamic* plugin =
           new plugin::SplitPluginDynamic(axis, output_lengths, with_fp16);
       layer = engine_->AddDynamicPlugin(&input, input_num, plugin);
-#else
-      PADDLE_THROW(platform::errors::Fatal(
-          "You are running the TRT Dynamic Shape mode, need to confirm that "
-          "your TRT version is no less than 6.0"));
-#endif
     } else {
       bool with_fp16 =
           engine_->WithFp16() && !engine_->disable_trt_plugin_fp16();

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -577,16 +577,78 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
                 << desc.Input("X").size() << ".";
         return false;
       }
+      auto split_inputs = desc.Inputs();
+      if (split_inputs.find("AxisTensor") != split_inputs.end()) {
+        if (desc.Input("AxisTensor").size() >= 1) {
+          return false;
+        }
+      }
+      if (split_inputs.find("SectionsTensorList") != split_inputs.end()) {
+        if (desc.Input("SectionsTensorList").size() >= 1) {
+          return false;
+        }
+      }
       if (!desc.HasAttr("axis")) {
         return false;
-      } else {
-        int axis = BOOST_GET_CONST(int, desc.GetAttr("axis"));
-        if (axis == 0) {
-          VLOG(3) << "Invalid split axis. Split on batch is not supported in "
-                     "TensorRT";
-          return false;
+      }
+      int axis = BOOST_GET_CONST(int, desc.GetAttr("axis"));
+
+      if (axis == 0) {
+        VLOG(3) << "Invalid split axis. Split on batch is not supported in "
+                   "TensorRT";
+        return false;
+      }
+      auto* block = desc.Block();
+      auto x_var_name = desc.Input("X")[0];
+      auto* x_var_desc = block->FindVar(x_var_name);
+      const auto x_shape = x_var_desc->GetShape();
+      size_t output_num = desc.Output("Out").size();
+      std::vector<int> output_lengths;
+      int num = 0;
+      if (desc.HasAttr("num")) {
+        num = BOOST_GET_CONST(int, desc.GetAttr("num"));
+      }
+      if (desc.HasAttr("sections")) {
+        output_lengths =
+            BOOST_GET_CONST(std::vector<int>, desc.GetAttr("sections"));
+      }
+      if (output_lengths.size() == 0 && num == 0) {
+        VLOG(3) << "sections and num cannot be equal to 0 at the same time";
+        return false;
+      }
+      if (with_dynamic_shape) {
+#if IS_TRT_VERSION_GE(6000)
+#else
+        VLOG(3) << "You are running the TRT Dynamic Shape mode, need to "
+                   "confirm that "
+                   "your TRT version is no less than 6.0";
+        return false;
+#endif
+      }
+      axis += (axis < 0) ? x_shape.size() : 0;
+      if (x_shape[axis] == -1) {
+        VLOG(3) << "The (" << axis << ") dim of input should not be -1";
+        return false;
+      }
+      if (output_lengths.size() == 0) {
+        if (num > 0) {
+          int64_t in_axis_dim = x_shape[axis];
+          if (in_axis_dim % num != 0) {
+            VLOG(3) << "Invalid number to split. Tensor split does not result"
+                       " in an equal division of dimensions. Axis dim = "
+                    << in_axis_dim << " num = " << num << "!= 0";
+            return false;
+          }
+          size_t out_axis_dim = in_axis_dim / num;
+          for (int i = 0; i < num; ++i) {
+            output_lengths.push_back(out_axis_dim);
+          }
         }
       }
+      if (output_lengths.size() != output_num) {
+        VLOG(3) << "The output_length should be equal to the output size.";
+        return false;
+      }
     }
     if (op_type == "scale") {
       auto scale_inputs = desc.Inputs();

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_split.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
+
+
+class TrtConvertSplitTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        outputs = program_config.outputs
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        # the dimensions of input and axis match
+        if len(inputs['split_input'].shape) <= attrs[0]['axis']:
+            return False
+
+        #Sections and num cannot both be equal to 0.
+        if len(attrs[0]['sections']) == 0:
+            if attrs[0]['num'] == 0:
+                return False
+
+        #When sections and num are not both equal to 0, sections has higher priority.
+        #The sum of sections should be equal to the input size.
+        if len(attrs[0]['sections']) != 0:
+            if attrs[0]['num'] != 0:
+                return False
+            if len(outputs) != len(attrs[0]['sections']):
+                return False
+            sum = 0
+            for num in attrs[0]['sections']:
+                sum += num
+            if sum != inputs['split_input'].shape[attrs[0]['axis']]:
+                return False
+
+        #The size of num should be equal to the input dimension.
+        if attrs[0]['num'] != 0:
+            if len(outputs) != attrs[0]['num']:
+                return False
+
+        #Test AxisTensor and SectionsTensorList
+        if self.num_input == 0:
+            if self.dims == 2 and attrs[0]['sections'] == [10, 14] and len(
+                    outputs) == 2:
+                return True
+            else:
+                return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]], batch):
+            if self.dims == 4:
+                return np.ones([batch, 3, 3, 24]).astype(np.float32)
+            elif self.dims == 3:
+                return np.ones([batch, 3, 24]).astype(np.float32)
+            elif self.dims == 2:
+                return np.ones([batch, 24]).astype(np.float32)
+            elif self.dims == 1:
+                return np.ones([24]).astype(np.float32)
+
+        def generate_AxisTensor(attrs: List[Dict[str, Any]]):
+            return np.ones([1]).astype(np.int32)
+
+        def generate_SectionsTensorList1(attrs: List[Dict[str, Any]]):
+            return np.array([10]).astype(np.int32)
+
+        def generate_SectionsTensorList2(attrs: List[Dict[str, Any]]):
+            return np.array([14]).astype(np.int32)
+
+        for num_input in [0, 1]:
+            for dims in [1, 2, 3, 4]:
+                for batch in [3, 6, 9]:
+                    for Out in [["output_var0", "output_var1"],
+                                ["output_var0", "output_var1", "output_var2"]]:
+                        for sections in [[], [1, 2], [2, 1], [10, 14],
+                                         [1, 1, 1], [2, 2, 2], [3, 3, 3],
+                                         [3, 7, 14]]:
+                            for num in [0, 3]:
+                                for axis in [0, 1, 2, 3]:
+                                    self.batch = batch
+                                    self.num_input = num_input
+                                    self.dims = dims
+                                    dics = [{
+                                        "sections": sections,
+                                        "num": num,
+                                        "axis": axis
+                                    }, {}]
+
+                                    dics_intput = [{
+                                        "X": ["split_input"],
+                                        "AxisTensor": ["AxisTensor"],
+                                        "SectionsTensorList": [
+                                            "SectionsTensorList1",
+                                            "SectionsTensorList2"
+                                        ]
+                                    }, {
+                                        "X": ["split_input"]
+                                    }]
+                                    dics_intputs = [{
+                                        "AxisTensor":
+                                        TensorConfig(data_gen=partial(
+                                            generate_AxisTensor, dics)),
+                                        "SectionsTensorList1": TensorConfig(
+                                            data_gen=partial(
+                                                generate_SectionsTensorList1,
+                                                dics)),
+                                        "SectionsTensorList2":
+                                        TensorConfig(data_gen=partial(
+                                            generate_SectionsTensorList2, dics))
+                                    }, {}]
+
+                                    ops_config = [{
+                                        "op_type": "split",
+                                        "op_inputs": dics_intput[num_input],
+                                        "op_outputs": {
+                                            "Out": Out
+                                        },
+                                        "op_attrs": dics[0]
+                                    }]
+                                    ops = self.generate_op_config(ops_config)
+                                    program_config = ProgramConfig(
+                                        ops=ops,
+                                        weights=dics_intputs[num_input],
+                                        inputs={
+                                            "split_input":
+                                            TensorConfig(data_gen=partial(
+                                                generate_input1, dics, batch))
+                                        },
+                                        outputs=Out)
+
+                                    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 = {
+                    "split_input": [1, 3, 3, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "split_input": [9, 3, 3, 24]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "split_input": [1, 3, 3, 24]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {"split_input": [1, 3, 24]}
+                self.dynamic_shape.max_input_shape = {"split_input": [9, 3, 24]}
+                self.dynamic_shape.opt_input_shape = {"split_input": [1, 3, 24]}
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"split_input": [1, 24]}
+                self.dynamic_shape.max_input_shape = {"split_input": [9, 24]}
+                self.dynamic_shape.opt_input_shape = {"split_input": [1, 24]}
+            elif self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"split_input": [24]}
+                self.dynamic_shape.max_input_shape = {"split_input": [24]}
+                self.dynamic_shape.opt_input_shape = {"split_input": [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):
+            if len(program_config.outputs) == 2:
+                if attrs[0]['axis'] != 0:
+                    return 1, 3
+                else:
+                    return 0, 4
+            else:
+                if attrs[0]['axis'] != 0:
+                    return 1, 4
+                else:
+                    return 0, 5
+
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        self.trt_param.max_batch_size = 9
+        # 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-5
+
+        # 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-5
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            if len(program_config.weights) == 3:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_SUPPORT,
+            "INPUT AxisTensor AND SectionsTensorList NOT SUPPORT.")
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()