add test (#35710)

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

@@ -44,22 +44,7 @@ class PadOpConverter : public OpConverter {
     const std::vector<int> paddings =
         BOOST_GET_CONST(std::vector<int>, op_desc.GetAttr("paddings"));
 
-    nvinfer1::Dims input_shape = input->getDimensions();
-    int nbDims = input_shape.nbDims;
     int pad_size = static_cast<int>(paddings.size());
-    PADDLE_ENFORCE_GE(
-        nbDims, 2,
-        platform::errors::InvalidArgument(
-            "Input X[0]'s dimension should greater than or equal to 2. "
-            "But received %d.",
-            nbDims));
-    PADDLE_ENFORCE_EQ(
-        (nbDims + 1) * 2, pad_size,
-        platform::errors::InvalidArgument("Input X[0]'s dimension(nbDims for "
-                                          "short) should meet the condition:"
-                                          "(nbDims + 1) * 2 == pad_size. But "
-                                          "received nbDims:%d, pad_size:%d.",
-                                          nbDims, pad_size));
 
     nvinfer1::DimsHW pre_pad(paddings[pad_size - 4], paddings[pad_size - 2]);
     nvinfer1::DimsHW post_pad(paddings[pad_size - 3], paddings[pad_size - 1]);

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -686,6 +686,29 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
         VLOG(3) << "The pad layer of TRT only support zero.";
         return false;
       }
+      std::vector<int64_t> shape;
+      auto* block = desc.Block();
+      for (auto& param_name : desc.Inputs()) {
+        for (auto& var_name : param_name.second) {
+          auto* var_desc = block->FindVar(var_name);
+          shape = var_desc->GetShape();
+        }
+      }
+      int nbDims = shape.size();
+      std::vector<int> paddings =
+          BOOST_GET_CONST(std::vector<int>, desc.GetAttr("paddings"));
+      int pad_size = paddings.size();
+      if (nbDims < 2) {
+        return false;
+      }
+      if (nbDims * 2 != pad_size) {
+        return false;
+      }
+      for (int i = 0; i < pad_size - 4; i++) {
+        if (paddings[i] != 0) {
+          return false;
+        }
+      }
     }
 
     if (op_type == "prelu") {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pad.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
+import unittest
+
+
+class TrtConvertPadTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        inputs = program_config.inputs
+        weights = program_config.weights
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+
+        if attrs[0]['pad_value'] != 0.0:
+            return False
+        for x in attrs[0]['paddings']:
+            if x < 0:
+                return False
+
+        return True
+
+    def sample_program_configs(self):
+        def generate_input1(attrs: List[Dict[str, Any]]):
+            return np.ones([1, 3, 64, 64]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.random.random([24, 3, 3, 3]).astype(np.float32)
+
+        for pad_value in [0.0, 1.0, 2.0, -100, 100.0]:
+            for paddings in [[0, 0, 0, 0, 1, 1, 1, 1],
+                             [0, 0, 0, 0, 1, 2, 3, 4],
+                             [0, 0, 1, 1, 1, 1, 1, 1],
+                             [0, 0, 0, 0, -1, -1, 1, 1]]:
+                dics = [{"pad_value": pad_value, "paddings": paddings}, {}]
+
+                ops_config = [{
+                    "op_type": "pad",
+                    "op_inputs": {
+                        "X": ["input_data"]
+                    },
+                    "op_outputs": {
+                        "Out": ["pad_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, dics))
+                    },
+                    outputs=["pad_output_data"])
+
+                yield program_config
+
+    def sample_predictor_configs(
+            self, program_config) -> (paddle_infer.Config, List[int], float):
+        def generate_dynamic_shape(attrs):
+            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):
+            for x in range(len(program_config.ops[0].attrs['paddings']) - 4):
+                if program_config.ops[0].attrs['paddings'][x] != 0:
+                    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-2
+
+        # 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-2
+
+    def add_skip_trt_case(self):
+        def teller1(program_config, predictor_config):
+            for x in range(len(program_config.ops[0].attrs['paddings']) - 4):
+                if program_config.ops[0].attrs['paddings'][x] != 0:
+                    return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "NOT Implemented: we need to add support pad not only inplement on h or w, such as paddings = [0, 0, 1, 1, 1, 1, 1, 1]"
+        )
+        pass
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()