[Paddle Inference]Add scale TRT converter unittest. (#35225)

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

* add_scale_teller

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -563,7 +563,19 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
         }
       }
     }
-
+    if (op_type == "scale") {
+      auto scale_inputs = desc.Inputs();
+      if (scale_inputs.find("ScaleTensor") != scale_inputs.end()) {
+        if (desc.Input("ScaleTensor").size() >= 1) {
+          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();
+      if (!with_dynamic_shape && x_shape.size() == 1) return false;
+    }
     if (op_type == "slice") {
       if (!desc.HasAttr("axes") || !desc.HasAttr("starts") ||
           !desc.HasAttr("ends") || !desc.HasAttr("decrease_axis")) {

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scale.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 TrtConvertScaleTest(TrtLayerAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        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)
+            elif self.dims == 1:
+                return np.ones([24]).astype(np.float32)
+
+        def generate_weight1(attrs: List[Dict[str, Any]]):
+            return np.ones([1]).astype(np.float32)
+
+        for num_input in [0, 1]:
+            for dims in [1, 2, 3, 4]:
+                for batch in [1, 2]:
+                    for scale in [0.1, 1.0]:
+                        for bias in [0.0, 1.2]:
+                            for bias_after_scale in [False, True]:
+                                self.num_input = num_input
+                                self.dims = dims
+                                dics = [{
+                                    "scale": scale,
+                                    "bias": bias,
+                                    "bias_after_scale": bias_after_scale
+                                }, {}]
+
+                                dics_intput = [{
+                                    "X": ["scale_input"],
+                                    "ScaleTensor": ["ScaleTensor"],
+                                }, {
+                                    "X": ["scale_input"]
+                                }]
+                                dics_intputs = [{
+                                    "ScaleTensor": TensorConfig(
+                                        data_gen=partial(generate_weight1,
+                                                         dics))
+                                }, {}]
+
+                                ops_config = [{
+                                    "op_type": "scale",
+                                    "op_inputs": dics_intput[num_input],
+                                    "op_outputs": {
+                                        "Out": ["scale_out"]
+                                    },
+                                    "op_attrs": dics[0]
+                                }]
+                                ops = self.generate_op_config(ops_config)
+                                program_config = ProgramConfig(
+                                    ops=ops,
+                                    weights=dics_intputs[num_input],
+                                    inputs={
+                                        "scale_input": TensorConfig(
+                                            data_gen=partial(generate_input1,
+                                                             dics, batch))
+                                    },
+                                    outputs=["scale_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 = {
+                    "scale_input": [1, 3, 24, 24]
+                }
+                self.dynamic_shape.max_input_shape = {
+                    "scale_input": [9, 3, 48, 48]
+                }
+                self.dynamic_shape.opt_input_shape = {
+                    "scale_input": [1, 3, 48, 24]
+                }
+            elif self.dims == 3:
+                self.dynamic_shape.min_input_shape = {"scale_input": [1, 3, 24]}
+                self.dynamic_shape.max_input_shape = {"scale_input": [9, 6, 48]}
+                self.dynamic_shape.opt_input_shape = {"scale_input": [1, 3, 24]}
+            elif self.dims == 2:
+                self.dynamic_shape.min_input_shape = {"scale_input": [1, 24]}
+                self.dynamic_shape.max_input_shape = {"scale_input": [9, 48]}
+                self.dynamic_shape.opt_input_shape = {"scale_input": [1, 24]}
+            elif self.dims == 1:
+                self.dynamic_shape.min_input_shape = {"scale_input": [24]}
+                self.dynamic_shape.max_input_shape = {"scale_input": [48]}
+                self.dynamic_shape.opt_input_shape = {"scale_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):
+            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-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) == 1:
+                return True
+            return False
+
+        self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT,
+                           "INPUT ScaleTensor and Shape NOT SUPPORT")
+
+        def teller2(program_config, predictor_config):
+            if self.dims == 1 and self.dynamic_shape.min_input_shape == 0:
+                return True
+            return False
+
+        self.add_skip_case(teller2, SkipReasons.TRT_NOT_SUPPORT,
+                           "INPUT DIM EQUAL TO 1 OF STATIC SHAPE NOT SUPPORT")
+
+    def test(self):
+        self.add_skip_trt_case()
+        self.run_test()
+
+
+if __name__ == "__main__":
+    unittest.main()