[Paddle Inference]Add Transpose op TRT converter unittest (#35138)

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

* add_transpose_teller

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

@@ -9,7 +9,6 @@ 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 <bitset>
 #include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
 
 namespace paddle {
@@ -43,32 +42,11 @@ class TransposeOpConverter : public OpConverter {
         axis[i]--;
       }
     }
-
     nvinfer1::Permutation perm;
     for (int i = 0; i < dims; i++) {
       int j = engine_->with_dynamic_shape() ? i : i + 1;
       perm.order[i] = axis[j];
     }
-
-    // Permutation is valid if it has nbDims unique values from range [0,
-    // nbDims-1]
-    auto is_valid_permutation = [&](int dims,
-                                    const nvinfer1::Permutation& permutation) {
-      std::bitset<nvinfer1::Dims::MAX_DIMS> found;
-      for (int i = 0; i < dims; ++i) {
-        const int x = permutation.order[i];
-        if ((x < 0) || (x >= dims) || found[x])
-          return false;  // Out of bounds or duplicate
-        found.set(x);
-      }
-      return true;
-    };
-
-    PADDLE_ENFORCE_EQ(is_valid_permutation(dims, perm), true,
-                      platform::errors::InvalidArgument(
-                          "Invalid permutation dimensions for trt transpose op "
-                          "converter: duplicate or out of bound."));
-
     auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input);
     layer->setFirstTranspose(perm);
 

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -13,7 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/tensorrt/op_teller.h"
-
+#include <bitset>
 #include "paddle/fluid/framework/block_desc.h"
 #include "paddle/fluid/framework/data_layout.h"
 
@@ -316,11 +316,36 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
     if (op_type == "transpose2" || op_type == "transpose") {
       if (!desc.HasAttr("axis")) {
         return false;
-      } else {
-        std::vector<int> axis =
-            BOOST_GET_CONST(std::vector<int>, desc.GetAttr("axis"));
-        if (!with_dynamic_shape && axis[0] != 0) return false;
-        if (axis.size() >= nvinfer1::Dims::MAX_DIMS) return false;
+      }
+      std::vector<int> axis =
+          BOOST_GET_CONST(std::vector<int>, desc.GetAttr("axis"));
+      if (!with_dynamic_shape && axis[0] != 0) return false;
+      if (axis.size() >= nvinfer1::Dims::MAX_DIMS) return false;
+      if (axis[0] == 0 && axis.size() == 2) 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();
+      int dims = x_shape.size();
+      std::vector<int> perm(nvinfer1::Dims::MAX_DIMS);
+      for (int i = 0; i < dims; i++) {
+        perm[i] = axis[i];
+      }
+      auto is_valid_permutation = [&](int dims,
+                                      const std::vector<int>& permutation) {
+        std::bitset<nvinfer1::Dims::MAX_DIMS> found;
+        for (int i = 0; i < dims; ++i) {
+          const int x = permutation[i];
+          if ((x < 0) || (x >= dims) || found[x])
+            return false;  // Out of bounds or duplicate
+          found.set(x);
+        }
+        return true;
+      };
+      if (!is_valid_permutation(dims, perm)) {
+        VLOG(3) << "Invalid permutation dimensions for trt transpose op "
+                   "converter: duplicate or out of bound.";
       }
     }
     if (op_type == "flatten2" || op_type == "flatten") {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_transpose.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 TrtConvertTransposeTest(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 shape of input and axis should be equal.
+        if len(inputs['transpose_input'].shape) != len(attrs[0]['axis']):
+            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, 24, 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)
+
+        for dims in [2, 3, 4]:
+            for batch in [1, 2, 4]:
+                for axis in [[0, 1, 3, 2], [0, 3, 2, 1], [3, 2, 0, 1],
+                             [0, 1, 2, 3], [0, 1, 2], [2, 0, 1], [1, 0],
+                             [0, 1]]:
+                    self.dims = dims
+                    dics = [{"axis": axis}, {}]
+                    ops_config = [{
+                        "op_type": "transpose",
+                        "op_inputs": {
+                            "X": ["transpose_input"]
+                        },
+                        "op_outputs": {
+                            "Out": ["transpose_out"]
+                        },
+                        "op_attrs": dics[0]
+                    }]
+                    ops = self.generate_op_config(ops_config)
+                    program_config = ProgramConfig(
+                        ops=ops,
+                        weights={},
+                        inputs={
+                            "transpose_input": TensorConfig(data_gen=partial(
+                                generate_input1, dics, batch))
+                        },
+                        outputs=["transpose_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 = {
+                    "transpose_input": [1, 3, 24, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "transpose_input": [9, 6, 48, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "transpose_input": [1, 3, 48, 24]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {
+                    "transpose_input": [1, 3, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "transpose_input": [9, 6, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "transpose_input": [1, 3, 24]
+                }
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {
+                    "transpose_input": [1, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "transpose_input": [9, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "transpose_input": [1, 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 dynamic_shape == True:
+                return 1, 2
+            else:
+                if attrs[0]['axis'][0] == 0:
+                    return 1, 2
+                else:
+                    return 0, 3
+
+        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-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 program_config.ops[0].attrs['axis'] == [0, 1]:
+                return True
+            return False
+
+        self.add_skip_case(
+            teller1, SkipReasons.TRT_NOT_IMPLEMENTED,
+            "INPUT AXIS [0, 1] NOT SUPPORT: we need to add support in the future"
+        )
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()