disable conv plugin in TRT old versions (#33198)

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

@@ -52,11 +52,6 @@ class ActivationOpConverter : public OpConverter {
         engine_->GetITensor(op_desc.Input("X")[0]);
 
     auto op_pair = ops.find(op_type_);
-    if (op_pair == ops.end()) {
-      PADDLE_THROW(platform::errors::Fatal(
-          "Wrong activation op type, the trt do not support the %s act type.",
-          op_type_));
-    }
 
     nvinfer1::IActivationLayer* layer = TRT_ENGINE_ADD_LAYER(
         engine_, Activation, *const_cast<nvinfer1::ITensor*>(input_tensor),

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

@@ -55,16 +55,6 @@ class AffineChannelOpConverter : public OpConverter {
     auto* bias_t = bias_v->GetMutable<framework::LoDTensor>();
     float* bias_ptr = engine_->GetWeightCPUData(bias_name, bias_t, false);
 
-    auto data_layout = framework::StringToDataLayout(
-        BOOST_GET_CONST(std::string, op_desc.GetAttr("data_layout")));
-
-    PADDLE_ENFORCE_EQ(
-        data_layout, framework::DataLayout::kNCHW,
-        platform::errors::InvalidArgument(
-            "TensorRT affine channel converter can only convert NCHW format. "
-            "Other format should be run in fluid mode. Report a bug on github "
-            "issue if you see this line."));
-
     // tensorrt scalend layer only support spatial dims >= 2,
     // so nhwc is not availabe (spatial dims == 0)
     const int channel_axis = engine_->with_dynamic_shape();

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

@@ -25,10 +25,6 @@ static bool CheckDims(const nvinfer1::Dims& dims_x,
     return false;
   }
   for (int i = 0; i < dims_x.nbDims; i++) {
-    // conservative judgment
-    if (dims_x.d[i] == -1 || dims_y.d[i] == -1) {
-      return false;
-    }
     if (dims_x.d[i] != dims_y.d[i]) {
       return false;
     }

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -225,6 +225,27 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
                 << desc.Output("Output").size() << " output.";
         return false;
       }
+
+// strides > 1 and 'SAME' is only supported by trt7.0 above
+#if !IS_TRT_VERSION_GE(7000)
+      if (op_type == "conv2d" || op_type == "conv2d_fusion" ||
+          op_type == "depthwise_conv2d") {
+        if (desc.HasAttr("padding_algorithm") && with_dynamic_shape) {
+          auto padding_algorithm =
+              BOOST_GET_CONST(std::string, desc.GetAttr("padding_algorithm"));
+          if (padding_algorithm == "SAME" && desc.HasAttr("strides")) {
+            const std::vector<int> strides =
+                BOOST_GET_CONST(std::vector<int>, desc.GetAttr("strides"));
+            // there is no issue if strides.size() less than 2
+            if (strides.size() > 1) {
+              for (size_t i = 0; i < strides.size(); i++) {
+                if (strides[i] > 1) return false;
+              }
+            }
+          }
+        }
+      }
+#endif
     }
 
     if (op_type == "matmul") {

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

@@ -161,5 +161,70 @@ class TensorRTSubgraphPassDepthwiseConvTransposeTest(
         self.use_cudnn = False
 
 
+class DynamicShapeTensorRTSubgraphPassConvTest(InferencePassTest):
+    def setUp(self):
+        self.set_params()
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 6, -1, -1], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=self.conv_num_filters,
+                filter_size=self.conv_filter_size,
+                groups=self.conv_groups,
+                padding=self.conv_padding,
+                bias_attr=False,
+                use_cudnn=self.use_cudnn,
+                stride=self.stride,
+                act=None)
+        self.feeds = {
+            "data": np.random.random([32, 6, 64, 64]).astype("float32"),
+        }
+        self.enable_trt = True
+        self.trt_parameters = DynamicShapeTensorRTSubgraphPassConvTest.TensorRTParam(
+            1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False)
+        self.dynamic_shape_params = DynamicShapeTensorRTSubgraphPassConvTest.DynamicShapeParam(
+            {
+                "conv2d_0.tmp_0": [1, 6, 8, 8],
+                "data": [1, 6, 8, 8],
+                "depthwise_conv2d_0.tmp_0": [1, 6, 8, 8]
+            }, {
+                "conv2d_0.tmp_0": [32, 6, 64, 64],
+                "data": [32, 6, 64, 64],
+                "depthwise_conv2d_0.tmp_0": [32, 6, 64, 64]
+            }, {
+                "conv2d_0.tmp_0": [16, 6, 16, 16],
+                "data": [16, 6, 16, 16],
+                "depthwise_conv2d_0.tmp_0": [32, 6, 64, 64]
+            }, False)
+        self.fetch_list = [conv_out]
+
+    def set_params(self):
+        self.conv_num_filters = 6
+        self.conv_filter_size = 6
+        self.conv_groups = 6
+        self.conv_padding = 'SAME'
+        self.use_cudnn = True
+        self.stride = [2, 2]
+
+    def test_check_output(self):
+        if core.is_compiled_with_cuda():
+            use_gpu = True
+            self.check_output_with_option(use_gpu)
+            self.assertTrue(
+                PassVersionChecker.IsCompatible('tensorrt_subgraph_pass'))
+
+
+class DynamicShapeTensorRTSubgraphPassDepthwiseConvTransposeTest(
+        DynamicShapeTensorRTSubgraphPassConvTest):
+    def set_params(self):
+        self.conv_num_filters = 6
+        self.conv_filter_size = 6
+        self.conv_groups = 6
+        self.conv_padding = 'SAME'
+        self.use_cudnn = False
+        self.stride = [2, 2]
+
+
 if __name__ == "__main__":
     unittest.main()