[Inference Zero-Dim] Support trt 0dim of gelu, hard_swish, hard_sigmoid and leaky_relu (#53714)

* support_act
* delete_silu

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -105,15 +105,15 @@ struct SimpleOpTypeSetTeller : public Teller {
         "erf",        "floor",       "round",
         "sign",       "silu",        "logical_not",
         "reciprocal", "tanh_shrink", "logsigmoid",
-        "rsqrt",      "swish"};
+        "rsqrt",      "swish",       "hard_sigmoid",
+        "hard_swish", "leaky_relu"};
     std::unordered_set<std::string> unary_list = {
         "exp",   "log",         "sqrt",       "abs",         "sin",
         "cos",   "tan",         "tanh",       "sinh",        "cosh",
         "asin",  "acos",        "atan",       "asinh",       "acosh",
         "atanh", "ceil",        "celu",       "floor",       "round",
-        "sign",       "silu", "logical_not", "reciprocal", "tanh_shrink",
-        "logsigmoid", "erf",  "bitwise_not", "equal",      "not_equal",
-        "rsqrt"};
+        "sign",  "logical_not", "reciprocal", "tanh_shrink", "logsigmoid",
+        "erf",   "bitwise_not", "equal",      "not_equal",   "rsqrt"};
 
     // Static shape does not support 0 or 1 dim's input.
     if (!with_dynamic_shape) {
@@ -962,20 +962,6 @@ struct SimpleOpTypeSetTeller : public Teller {
       }
     }
 
-    if (op_type == "hard_swish") {
-      if (desc.Input("X").size() != 1) {
-        VLOG(3) << "HardSwish op has only 1 input, but got "
-                << desc.Input("X").size();
-        return false;
-      }
-
-      if (desc.Output("Out").size() != 1) {
-        VLOG(3) << "HardSwish op has only 1 output, but got "
-                << desc.Output("Out").size();
-        return false;
-      }
-    }
-
     if (op_type == "squeeze2") {
       // If Attribute is Variable(s), HasAttr() will return False
       if (!desc.HasAttr("axes", /*with_attr_var=*/false)) {
@@ -1642,8 +1628,10 @@ struct SimpleOpTypeSetTeller : public Teller {
       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 (x_shape.size() == 1) {
-        VLOG(3) << "gelu op does not support input's dim is 1 in tensorrt.";
+      if (!with_dynamic_shape && (x_shape.size() == 1 || x_shape.size() == 0)) {
+        VLOG(3) << op_type
+                << "gelu op does not support input's dim is 1 or 0 in tensorrt "
+                   "static shape mode.";
         return false;
       }
     }
@@ -1733,20 +1721,6 @@ struct SimpleOpTypeSetTeller : public Teller {
       }
     }
 
-    if (op_type == "leaky_relu") {
-      if (desc.Input("X").size() != 1) {
-        VLOG(3) << "Invalid number of TRT leaky_relu op converter "
-                   "inputs. Expected 1, but received "
-                << desc.Input("X").size();
-        return false;
-      }
-      if (desc.Output("Out").size() != 1) {
-        VLOG(3) << "output of leaky_relu op converter should be 1, got "
-                << desc.Output("Out").size();
-        return false;
-      }
-    }
-
     if (op_type == "pad") {
       if (!desc.HasAttr("pad_value") || !desc.HasAttr("paddings")) return false;
       const float pad_value =
@@ -2388,26 +2362,6 @@ struct SimpleOpTypeSetTeller : public Teller {
       }
     }
 
-    if (op_type == "hard_sigmoid") {
-      if (!with_dynamic_shape) {
-        auto* block = desc.Block();
-        if (block == nullptr) {
-          VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
-                     "Developers need to check whether block_desc is passed in "
-                     "the pass.";
-          return false;
-        }
-        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 (x_shape.size() == 1) {
-          VLOG(3) << "Hard sigmoid does not support 1-dimensional input in "
-                     "tensorrt";
-          return false;
-        }
-      }
-    }
-
     if (op_type == "cast") {
 // trt 6015 result in Windows ppyolo_mbv3 TRT fp32 diff
 #if !IS_TRT_VERSION_GE(7000)

test/ir/inference/test_trt_convert_activation.py

@@ -60,6 +60,9 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
                     "logsigmoid",
                     "tanh_shrink",
                     "softplus",
+                    "hard_swish",
+                    "hard_sigmoid",
+                    "leaky_relu",
                 ]:
                     # few samples to reduce time
                     # for beta in [-0.2, 0.5, 0.67, 3]:
@@ -80,6 +83,18 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
                                 dics = [{"threshold": alpha}]
                             if op_type == "softplus":
                                 dics = [{"beta": beta}]
+                            if op_type == "hard_swish":
+                                dics = [
+                                    {
+                                        "threshold": 6.0,
+                                        "scale": 6.0,
+                                        "offset": 3.0,
+                                    }
+                                ]
+                            if op_type == "hard_sigmoid":
+                                dics = [{"slope": beta, "offset": alpha}]
+                            if op_type == "leaky_relu":
+                                dics = [{"alpha": alpha}]
 
                             ops_config = [
                                 {

test/ir/inference/test_trt_convert_gelu.py

@@ -29,7 +29,9 @@ class TrtConvertGeluTest(TrtLayerAutoScanTest):
 
     def sample_program_configs(self):
         def generate_input1(dims, attrs: List[Dict[str, Any]]):
-            if dims == 1:
+            if dims == 0:
+                return np.ones([]).astype(np.float32)
+            elif dims == 1:
                 return np.ones([32]).astype(np.float32)
             elif dims == 2:
                 return np.ones([3, 32]).astype(np.float32)
@@ -38,7 +40,7 @@ class TrtConvertGeluTest(TrtLayerAutoScanTest):
             else:
                 return np.ones([1, 3, 32, 32]).astype(np.float32)
 
-        for dims in [1, 2, 3, 4]:
+        for dims in [0, 1, 2, 3, 4]:
             for approximate in [True, False]:
                 self.dims = dims
                 dics = [{"approximate": approximate}]
@@ -70,7 +72,11 @@ class TrtConvertGeluTest(TrtLayerAutoScanTest):
         self, program_config
     ) -> (paddle_infer.Config, List[int], float):
         def generate_dynamic_shape(attrs):
-            if self.dims == 1:
+            if self.dims == 0:
+                self.dynamic_shape.min_input_shape = {"input_data": []}
+                self.dynamic_shape.max_input_shape = {"input_data": []}
+                self.dynamic_shape.opt_input_shape = {"input_data": []}
+            elif self.dims == 1:
                 self.dynamic_shape.min_input_shape = {"input_data": [1]}
                 self.dynamic_shape.max_input_shape = {"input_data": [64]}
                 self.dynamic_shape.opt_input_shape = {"input_data": [32]}
@@ -104,7 +110,7 @@ class TrtConvertGeluTest(TrtLayerAutoScanTest):
             runtime_version = paddle_infer.get_trt_runtime_version()
             self.assertTrue(compile_version == runtime_version)
             # Dimension one only runs on Paddle OP
-            if self.dims == 1:
+            if not dynamic_shape and (self.dims == 1 or self.dims == 0):
                 return 0, 3
             if compile_version >= valid_version:
                 return 1, 2