[inference][trt] fix reshape2 opteller and elementwise min/max trt registration (#46861)

paddle/fluid/inference/api/analysis_predictor.cc

@@ -2160,6 +2160,8 @@ USE_TRT_CONVERTER(elementwise_add_weight);
 USE_TRT_CONVERTER(elementwise_sub_weight);
 USE_TRT_CONVERTER(elementwise_mul_weight);
 USE_TRT_CONVERTER(elementwise_div_weight);
+USE_TRT_CONVERTER(elementwise_min_weight);
+USE_TRT_CONVERTER(elementwise_max_weight);
 USE_TRT_CONVERTER(elementwise_pow_weight);
 USE_TRT_CONVERTER(elementwise_add_tensor);
 USE_TRT_CONVERTER(elementwise_sub_tensor);

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

@@ -216,6 +216,10 @@ REGISTER_TRT_OP_CONVERTER(elementwise_sub_weight,
                           ElementwiseTensorSubOpConverter);
 REGISTER_TRT_OP_CONVERTER(elementwise_div_weight,
                           ElementwiseTensorDivOpConverter);
+REGISTER_TRT_OP_CONVERTER(elementwise_max_weight,
+                          ElementwiseTensorMaxOpConverter);
+REGISTER_TRT_OP_CONVERTER(elementwise_min_weight,
+                          ElementwiseTensorMinOpConverter);
 REGISTER_TRT_OP_CONVERTER(elementwise_pow_weight,
                           ElementwiseTensorPowOpConverter);
 

paddle/fluid/inference/tensorrt/convert/op_converter.h

@@ -76,7 +76,7 @@ class OpConverter {
           static std::unordered_set<std::string> add_tensor_op_set{
               "add", "mul", "sub", "div", "max", "min", "pow"};
           static std::unordered_set<std::string> add_weight_op_set{
-              "add", "mul", "sub", "div", "pow"};
+              "add", "mul", "sub", "div", "max", "min", "pow"};
           PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(),
                             1UL,
                             platform::errors::InvalidArgument(

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -1224,7 +1224,8 @@ struct SimpleOpTypeSetTeller : public Teller {
 
     if (op_type == "elementwise_add" || op_type == "elementwise_mul" ||
         op_type == "elementwise_sub" || op_type == "elementwise_div" ||
-        op_type == "elementwise_pow") {
+        op_type == "elementwise_pow" || op_type == "elementwise_min" ||
+        op_type == "elementwise_max") {
       if (desc.Input("X").size() != 1) {
         VLOG(3) << "The input op's Input(\"X\").size() "
                    "should equal to 1, but received Input(\"X\").size() = "
@@ -1761,13 +1762,13 @@ struct SimpleOpTypeSetTeller : public Teller {
     }
 
     if (op_type == "reshape" || op_type == "reshape2") {
-      if (with_dynamic_shape) {
-        return true;
-      }
       if (!desc.HasAttr("shape")) {
         return false;
       }
-      // Paddle-TRT does not support the input tensors: Shape and ShapeTensor
+      if (with_dynamic_shape) {
+        return true;
+      }
+      // Static shape does not support the input tensors: Shape and ShapeTensor
       auto reshape_inputs = desc.Inputs();
       if (reshape_inputs.find("Shape") != reshape_inputs.end()) {
         if (desc.Input("Shape").size() >= 1) {
@@ -2162,6 +2163,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       "elementwise_mul",
       "elementwise_div",
       "elementwise_pow",
+      "elementwise_min",
+      "elementwise_max",
       "equal",
       "dropout",
       "prelu",
@@ -2275,6 +2278,8 @@ struct SimpleOpTypeSetTeller : public Teller {
       "elementwise_mul",
       "elementwise_div",
       "elementwise_pow",
+      "elementwise_min",
+      "elementwise_max",
       "equal",
       "dropout",
       "prelu",

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py

@@ -38,7 +38,11 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest):
 
         for batch in [1, 4]:
             for shape in [[batch, 32, 16, 32]]:
-                for op_type in ["elementwise_add", "elementwise_mul"]:
+                for op_type in [
+                        "elementwise_add", "elementwise_mul", "elementwise_sub",
+                        "elementwise_div", "elementwise_pow", "elementwise_min",
+                        "elementwise_max"
+                ]:
                     for axis in [-1]:
                         self.dims = len(shape)
                         dics = [{"axis": axis}]
@@ -102,7 +106,7 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest):
         clear_dynamic_shape()
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
         yield self.create_inference_config(), generate_trt_nodes_num(
-            attrs, False), 1e-5
+            attrs, False), (1e-5, 1e-5)
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), generate_trt_nodes_num(
             attrs, False), (1e-3, 1e-3)
@@ -111,7 +115,7 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest):
         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
+            attrs, True), (1e-5, 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)
@@ -139,7 +143,11 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest):
             return np.random.randn(1).astype(np.float32)
 
         for shape in [[32]]:
-            for op_type in ["elementwise_add", "elementwise_mul"]:
+            for op_type in [
+                    "elementwise_add", "elementwise_mul", "elementwise_sub",
+                    "elementwise_div", "elementwise_pow", "elementwise_min",
+                    "elementwise_max"
+            ]:
                 for axis in [-1]:
                     self.dims = len(shape)
                     dics = [{"axis": axis}]
@@ -197,7 +205,7 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest):
         clear_dynamic_shape()
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
         yield self.create_inference_config(), generate_trt_nodes_num(
-            attrs, False), 1e-5
+            attrs, False), (1e-5, 1e-5)
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), generate_trt_nodes_num(
             attrs, False), (1e-3, 1e-3)
@@ -206,7 +214,7 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest):
         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
+            attrs, True), (1e-5, 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)
@@ -235,7 +243,11 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
         for batch in [1, 4]:
             for shape in [[32], [batch, 32], [batch, 32, 32],
                           [batch, 32, 16, 32]]:
-                for op_type in ["elementwise_add", "elementwise_mul"]:
+                for op_type in [
+                        "elementwise_add", "elementwise_mul", "elementwise_sub",
+                        "elementwise_div", "elementwise_pow", "elementwise_min",
+                        "elementwise_max"
+                ]:
                     for axis in [-1 if len(shape) == 1 else 1]:
                         self.dims = len(shape)
                         dics = [{"axis": axis}]
@@ -313,7 +325,7 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
         clear_dynamic_shape()
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
         yield self.create_inference_config(), generate_trt_nodes_num(
-            attrs, False), 1e-5
+            attrs, False), (1e-5, 1e-5)
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), generate_trt_nodes_num(
             attrs, False), (1e-3, 1e-3)
@@ -322,7 +334,7 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest):
         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
+            attrs, True), (1e-5, 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)
@@ -349,7 +361,8 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
         for shape in [[4], [4, 32], [2, 64, 32], [1, 8, 16, 32]]:
             for op_type in [
                     "elementwise_add", "elementwise_mul", "elementwise_sub",
-                    "elementwise_div", "elementwise_pow"
+                    "elementwise_div", "elementwise_pow", "elementwise_min",
+                    "elementwise_max"
             ]:
                 for axis in [0, -1]:
                     self.dims = len(shape)
@@ -457,7 +470,7 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
         clear_dynamic_shape()
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
         yield self.create_inference_config(), generate_trt_nodes_num(
-            attrs, False), 1e-5
+            attrs, False), (1e-5, 1e-5)
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), generate_trt_nodes_num(
             attrs, False), (1e-3, 1e-3)
@@ -465,7 +478,7 @@ class TrtConvertElementwiseTest_two_input_without_broadcast(
         # for dynamic_shape
         generate_dynamic_shape(attrs)
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
-        yield self.create_inference_config(), (1, 3), 1e-5
+        yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
 
@@ -518,8 +531,13 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest):
             for j in range(6):
                 input2_shape = input2_shape_list[j][i]
                 for op_type in [
-                        "elementwise_add", "elementwise_mul", "elementwise_sub",
-                        "elementwise_div", "elementwise_pow"
+                        "elementwise_add",
+                        "elementwise_mul",
+                        "elementwise_sub",
+                        "elementwise_div",
+                        "elementwise_pow",
+                        "elementwise_min",
+                        "elementwise_max",
                 ]:
                     for axis in axis_list[j][i]:
                         self.shape1 = input1_shape
@@ -588,14 +606,14 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest):
         clear_dynamic_shape()
         if self.shape1[0] == self.shape2[0]:
             self.trt_param.precision = paddle_infer.PrecisionType.Float32
-            yield self.create_inference_config(), (1, 3), 1e-5
+            yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
             self.trt_param.precision = paddle_infer.PrecisionType.Half
             yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
 
         # for dynamic_shape
         generate_dynamic_shape(attrs)
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
-        yield self.create_inference_config(), (1, 3), 1e-5
+        yield self.create_inference_config(), (1, 3), (1e-5, 1e-5)
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), (1, 3), (1e-3, 1e-3)
 
@@ -630,7 +648,10 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
                         "elementwise_sub",
                         "elementwise_div",
                         "elementwise_pow",
+                        "elementwise_min",
+                        "elementwise_max",
                 ]:
+                    self.op_type = op_type
                     for axis in [-1 if len(shape) == 1 else 1]:
                         self.dims = len(shape)
                         dics = [{"axis": axis}]
@@ -704,14 +725,14 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest):
         # for static_shape
         clear_dynamic_shape()
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
-        yield self.create_inference_config(), (0, 3), 1e-5
+        yield self.create_inference_config(), (0, 3), (1e-5, 1e-5)
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), (0, 3), (1e-3, 1e-3)
 
         # for dynamic_shape
         generate_dynamic_shape(attrs)
         self.trt_param.precision = paddle_infer.PrecisionType.Float32
-        yield self.create_inference_config(), (1, 2), 1e-5
+        yield self.create_inference_config(), (1, 2), (1e-5, 1e-5)
         self.trt_param.precision = paddle_infer.PrecisionType.Half
         yield self.create_inference_config(), (1, 2), (1e-3, 1e-3)