[Paddle-TRT] Convert 0D tensor to 1D tensor, increase the shape tensor's number count when collecting shape (#55503)

ckl117 · web-flow · commit a3cf25e302ac · 2023-07-24T19:39:56.000+08:00
* make 0-D tensor to 1-D tensor to support Grounding-SAM and add shape check

* recover identity_op_clean_pass.cc
diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc
@@ -2236,10 +2236,10 @@ void AnalysisPredictor::HookCollectShapeRangeInfo() {
 
     // We need collect value range for shape tensor for Paddle-TRT's use.
     // To be noticed, this method to identify all shape tensors is based on
-    // assumption that all shape tensors in the model have numbers <= 7.
+    // assumption that all shape tensors in the model have numbers <= 8.
     // This is a simple method to identify all shape tensors with some
     // mistakes, but it doesn't matter.
-    auto is_shape_tensor = tensor.numel() <= 7 && tensor.numel() >= 1;
+    auto is_shape_tensor = tensor.numel() <= 8 && tensor.numel() >= 1;
     if ((tensor.dtype() == phi::DataType::INT32 ||
          tensor.dtype() == phi::DataType::INT64) &&
         is_shape_tensor) {
diff --git a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc
@@ -30,7 +30,7 @@ class ElementwiseTensorOpConverter : public OpConverter {
     auto* X = engine_->GetITensor(op_desc.Input("X").front());
     nvinfer1::ITensor* Y = nullptr;
     auto* Y_v = scope.FindVar(op_desc.Input("Y").front());
-    if (Y_v) {
+    if (Y_v && !engine_->with_dynamic_shape()) {
       // Y is weight
       auto* Y_t = Y_v->GetMutable<phi::DenseTensor>();
       std::vector<int> dims_y = phi::vectorize<int>(Y_t->dims());
diff --git a/paddle/fluid/inference/tensorrt/convert/op_converter.h b/paddle/fluid/inference/tensorrt/convert/op_converter.h
@@ -308,9 +308,15 @@ class OpConverter {
       auto var_shape = var->GetShape();
       if (engine->with_dynamic_shape()) {
 #if IS_TRT_VERSION_GE(6000)
-        auto min_input_shape = engine->min_input_shape()[input];
-        auto max_input_shape = engine->max_input_shape()[input];
-        auto optim_input_shape = engine->optim_input_shape()[input];
+        if (!(engine->min_input_shape().count(input) &&
+              engine->max_input_shape().count(input) &&
+              engine->optim_input_shape().count(input))) {
+          PADDLE_THROW(platform::errors::InvalidArgument(
+              "Cannot get %s min/max/opt shape", input));
+        }
+        auto min_input_shape = engine->min_input_shape().at(input);
+        auto max_input_shape = engine->max_input_shape().at(input);
+        auto optim_input_shape = engine->optim_input_shape().at(input);
         size_t ranks = min_input_shape.size();
 
         std::vector<int64_t> input_shape;
@@ -732,6 +738,23 @@ class OpConverter {
       layer_name += output_tensor_names[i];
       if (i != num_out - 1) layer_name += ", ";
     }
+    for (size_t i = 0; i < num_out; i++) {
+      nvinfer1::Dims tmp_dims = layer->getOutput(i)->getDimensions();
+      std::vector<int> tmp_vec;
+      for (int i = 0; i < tmp_dims.nbDims; i++)
+        tmp_vec.push_back(tmp_dims.d[i]);
+
+      VLOG(3) << output_tensor_names[i] << "'s dimension :["
+              << string::join_strings(tmp_vec, ',') << "]";
+      // The following check may cause errors in CI, but is necessary in the
+      // latest version.
+      // PADDLE_ENFORCE_GE(
+      //     layer->getOutput(i)->getDimensions().nbDims,
+      //     0,
+      //     platform::errors::InvalidArgument(
+      //         "Error occures in Paddle-TRT layer with output name: %s",
+      //         output_tensor_names[i].c_str()));
+    }
     layer->setName((layer_name + ")").c_str());
   }
   void SetEngine(TensorRTEngine* engine) { engine_ = engine; }
diff --git a/paddle/fluid/inference/tensorrt/engine.cc b/paddle/fluid/inference/tensorrt/engine.cc
@@ -532,6 +532,11 @@ nvinfer1::ITensor *TensorRTEngine::ConvertWeight2ITensor(
   for (int64_t i = 0; i < trt_in_shape.nbDims; i++) {
     trt_in_shape.d[i] = var_dims[i];
   }
+  // Make 0-D tensor to 1-D tensor.
+  if (trt_in_shape.nbDims == 0) {
+    trt_in_shape.nbDims = 1;
+    trt_in_shape.d[0] = 1;
+  }
   // In fact , this is not always right, because we can't determine if the 0th
   // dimension is batch. Just for run chenqu's model
   if (!this->with_dynamic_shape()) {
diff --git a/paddle/fluid/inference/tensorrt/engine.h b/paddle/fluid/inference/tensorrt/engine.h
@@ -524,6 +524,10 @@ class TensorRTEngine {
     for (const auto& it : runtime_input_shape) {
       auto name = it.first;
       auto input_shape = it.second;
+      // Make 0-D tensor to 1-D tensor.
+      if (input_shape.size() == 0) {
+        input_shape.push_back(1);
+      }
       bool min_change = false;
       bool max_change = false;
       std::vector<int> bak_min_shape;
diff --git a/paddle/fluid/operators/tensorrt/tensorrt_engine_op.h b/paddle/fluid/operators/tensorrt/tensorrt_engine_op.h
@@ -594,6 +594,18 @@ class TensorRTEngineOp : public framework::OperatorBase {
         t.ShareDataWith(out);
       }
       auto t_shape = phi::vectorize<int64_t>(t.dims());
+
+      // This must be a zero dimension tensor.
+      // At present, we convert it to a 1D tensor to feed them into Trt.
+      if (t_shape.size() == 0) {
+        PADDLE_ENFORCE_EQ(
+            t.numel(),
+            1UL,
+            platform::errors::PreconditionNotMet(
+                "This tensor must have one element, but got %ld.", t.numel()));
+        t_shape.push_back(1);
+      }
+
       // Get index of profile 0 first, then plus binding offset
       const int bind_index =
           engine->engine()->getBindingIndex(x.c_str()) + binding_offset;
