[Paddle-TRT]fix trt-converter-fc_op (#32671)

* [Paddle-TRT]fix fc_op

* [Paddle-TRT]fix fc_op

* [Paddle-TRT]fix fc_op

* test_trt_subgraph_pass.py

* fix elementwise_op

* fix elementwise_op

* fix elementwise_op

* fix elementwise_op.cc

* op_teller.cc

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

@@ -66,6 +66,25 @@ class ElementwiseWeightOpConverter : public OpConverter {
                                            0};
       TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr,
                                            0};
+
+      nvinfer1::IShuffleLayer* expand_layer = nullptr;
+      nvinfer1::IShuffleLayer* squeeze_layer = nullptr;
+      int dynamic_shape_offset = engine_->with_dynamic_shape() ? 1 : 0;
+      auto input_dim = X->getDimensions();
+      if (input_dim.nbDims < 3 + dynamic_shape_offset) {
+        nvinfer1::Dims expand_shape;
+        expand_shape.nbDims = 3 + dynamic_shape_offset;
+        for (int i = 0; i < expand_shape.nbDims; i++) {
+          if (i < input_dim.nbDims) {
+            expand_shape.d[i] = input_dim.d[i] < 0 ? 0 : input_dim.d[i];
+          } else {
+            expand_shape.d[i] = 1;
+          }
+        }
+        expand_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
+        expand_layer->setReshapeDimensions(expand_shape);
+        X = expand_layer->getOutput(0);
+      }
       if (op_type_ == "add") {
         nvinfer1::IScaleLayer* scale_layer = TRT_ENGINE_ADD_LAYER(
             engine_, Scale, *X, scale_mode, shift_weights.get(),
@@ -77,7 +96,17 @@ class ElementwiseWeightOpConverter : public OpConverter {
             shift_weights.get(), power_weights.get());
         layer = scale_layer;
       }
-
+      if (input_dim.nbDims < 3 + dynamic_shape_offset) {
+        nvinfer1::Dims squeeze_shape;
+        squeeze_shape.nbDims = input_dim.nbDims;
+        for (int i = 0; i < squeeze_shape.nbDims; i++) {
+          squeeze_shape.d[i] = input_dim.d[i] < 0 ? 0 : input_dim.d[i];
+        }
+        squeeze_layer =
+            TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *(layer->getOutput(0)));
+        squeeze_layer->setReshapeDimensions(squeeze_shape);
+        layer = static_cast<nvinfer1::ILayer*>(squeeze_layer);
+      }
       auto output_name = op_desc.Output("Out")[0];
       RreplenishLayerAndOutput(layer, "elementwise_" + op_type_, {output_name},
                                test_mode);

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

@@ -37,7 +37,7 @@ class FcOpConverter : public OpConverter {
                   const framework::Scope& scope, bool test_mode) override {
     VLOG(3) << "convert a fluid fc op to tensorrt fc layer without bias";
     framework::OpDesc op_desc(op, nullptr);
-
+    auto output_name = op_desc.Output("Out").front();
     auto input_names = op_desc.InputNames();
     bool with_bias = input_names.size() >= 3;
     std::string w_name = "Y";
@@ -54,7 +54,7 @@ class FcOpConverter : public OpConverter {
         Y_v, platform::errors::NotFound(
                  "Can not find %s presistale var of fc in scope.", w_name));
     auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
-    const int x_num_col_dims =
+    int x_num_col_dims =
         op_desc.HasAttr("x_num_col_dims")
             ? BOOST_GET_CONST(int, op_desc.GetAttr("x_num_col_dims"))
             : (op_desc.HasAttr("in_num_col_dims")
@@ -106,8 +106,8 @@ class FcOpConverter : public OpConverter {
     auto regist_fc = [&](nvinfer1::ITensor* inputs, int n_output,
                          TensorRTEngine::Weight& weight,
                          TensorRTEngine::Weight& bias) {
-      nvinfer1::ILayer* fc_layer = nullptr;
       if (enable_int8) {
+        // add conv layer
         PADDLE_ENFORCE_EQ(
             op_desc.HasAttr("out_threshold"), true,
             platform::errors::InvalidArgument(
@@ -115,22 +115,46 @@ class FcOpConverter : public OpConverter {
         float out_scale =
             BOOST_GET_CONST(float, op_desc.GetAttr("out_threshold"));
         nvinfer1::DimsHW nv_ksize(1, 1);
-        fc_layer = TRT_ENGINE_ADD_LAYER(engine_, Convolution, *inputs, n_output,
+        auto* fc_layer_int8 =
+            TRT_ENGINE_ADD_LAYER(engine_, Convolution, *inputs, n_output,
                                  nv_ksize, weight.get(), bias.get());
-        engine_->SetTensorDynamicRange(fc_layer->getOutput(0), out_scale);
+        engine_->SetTensorDynamicRange(fc_layer_int8->getOutput(0), out_scale);
+        if (activation_type == "relu") {
+          nvinfer1::IActivationLayer* relu_layer_int8 = TRT_ENGINE_ADD_LAYER(
+              engine_, Activation, *(fc_layer_int8->getOutput(0)),
+              nvinfer1::ActivationType::kRELU);
+          RreplenishLayerAndOutput(relu_layer_int8, "relu_after_fc_shuffle",
+                                   {output_name}, test_mode);
         } else {
-        fc_layer = TRT_ENGINE_ADD_LAYER(engine_, FullyConnected, *inputs,
-                                        n_output, weight.get(), bias.get());
+          RreplenishLayerAndOutput(fc_layer_int8, "shuffle_after_fc",
+                                   {output_name}, test_mode);
         }
-
-      auto output_name = op_desc.Output("Out").front();
+      } else {
+        // add fc layer
+        auto* fc_layer_before =
+            TRT_ENGINE_ADD_LAYER(engine_, FullyConnected, *inputs, n_output,
+                                 weight.get(), bias.get());
+        fc_layer_before->setName(
+            ("fc_layer_before(Output: " + output_name + ")").c_str());
+        // add shuffle after fc
+        nvinfer1::Dims reshape_after_fc_dim;
+        reshape_after_fc_dim.nbDims = x_num_col_dims + 1;
+        for (int i = 0; i < reshape_after_fc_dim.nbDims; i++) {
+          reshape_after_fc_dim.d[i] = 0;
+        }
+        auto* fc_layer_float = TRT_ENGINE_ADD_LAYER(
+            engine_, Shuffle, *fc_layer_before->getOutput(0));
+        fc_layer_float->setReshapeDimensions(reshape_after_fc_dim);
         if (activation_type == "relu") {
-        nvinfer1::IActivationLayer* relu_layer =
-            TRT_ENGINE_ADD_LAYER(engine_, Activation, *(fc_layer->getOutput(0)),
+          nvinfer1::IActivationLayer* relu_layer_float = TRT_ENGINE_ADD_LAYER(
+              engine_, Activation, *(fc_layer_float->getOutput(0)),
               nvinfer1::ActivationType::kRELU);
-        RreplenishLayerAndOutput(relu_layer, "fc", {output_name}, test_mode);
+          RreplenishLayerAndOutput(relu_layer_float, "relu_after_fc_shuffle",
+                                   {output_name}, test_mode);
         } else {
-        RreplenishLayerAndOutput(fc_layer, "fc", {output_name}, test_mode);
+          RreplenishLayerAndOutput(fc_layer_float, "shuffle_after_fc",
+                                   {output_name}, test_mode);
+        }
       }
     };
 
@@ -157,154 +181,44 @@ class FcOpConverter : public OpConverter {
                                 static_cast<void*>(bias_data),
                                 static_cast<size_t>(bias_num)};
 
-    if (engine_->with_dynamic_shape()) {
-      // not NCHW layout, but NLP layout with added 'x 1 x 1'
     auto x_dim = X->getDimensions();
-      if (engine_->use_oss() && engine_->with_ernie() && x_dim.nbDims == 4 &&
-          x_dim.d[2] == 1 && x_dim.d[3] == 1 && x_num_col_dims == 2) {
-        // fc which is just after self attention
-        regist_fc(X, n_output, weight, bias);
-        return;
+    // Running the TRT Static Shape mode: x_num_col_dims-1
+    if (!engine_->with_dynamic_shape()) {
+      x_num_col_dims--;
     }
-      PADDLE_ENFORCE_LE(
-          x_dim.nbDims - x_num_col_dims, 3,
+    PADDLE_ENFORCE_GT(
+        x_dim.nbDims, x_num_col_dims,
         platform::errors::InvalidArgument(
             "Params and input dims mismatch. Paddle-TRT FC "
-              "converter expects x_dim.nbDims - x_num_col_dims <= 3, but "
-              "x_dim.nbDims = %d, x_num_col_dims = %d.",
+            "converter expects x_dim.nbDims > x_num_col_dims, but "
+            "x_dim.nbDims : %d, x_num_col_dims : %d.",
             x_dim.nbDims, x_num_col_dims));
-      auto output_name = op_desc.Output("Out").front();
     // add shuffle before fc
     nvinfer1::Dims reshape_before_fc_dim;
-      // padding shape "x 1 x 1"
-      int padding_length = 3 - (x_dim.nbDims - x_num_col_dims);
-      reshape_before_fc_dim.nbDims = x_dim.nbDims + padding_length;
-      int cur_dim_index = reshape_before_fc_dim.nbDims - 1;
-      while (padding_length-- > 0) {
-        reshape_before_fc_dim.d[cur_dim_index--] = 1;
+    reshape_before_fc_dim.nbDims = x_num_col_dims + 3;
+    // padding shape "* x q x 1 x 1"
+    for (int i = 0; i < reshape_before_fc_dim.nbDims; i++) {
+      reshape_before_fc_dim.d[i] = 1;
+    }
+    for (int i = 0; i < x_dim.nbDims; i++) {
+      if (i < x_num_col_dims) {
+        reshape_before_fc_dim.d[i] = 0;
+      } else {
+        if (x_dim.d[i] < 0) {
+          reshape_before_fc_dim.d[x_num_col_dims] = -1;
+          break;
         }
-      while (cur_dim_index >= 0) {
-        reshape_before_fc_dim.d[cur_dim_index--] = 0;
+        reshape_before_fc_dim.d[x_num_col_dims] *= x_dim.d[i];
       }
-
-      auto* reshape_before_fc_layer =
-          TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
+    }
+    auto* reshape_before_fc_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
     reshape_before_fc_layer->setReshapeDimensions(reshape_before_fc_dim);
     reshape_before_fc_layer->setName(
         ("shuffle_before_fc(Output: " + output_name + ")").c_str());
-
-      // add fc layer
-      auto* fc_layer = TRT_ENGINE_ADD_LAYER(
-          engine_, FullyConnected, *reshape_before_fc_layer->getOutput(0),
-          n_output, weight.get(), bias.get());
-      fc_layer->setName(("fc_layer(Output: " + output_name + ")").c_str());
-
-      // add shuffle after fc
-      nvinfer1::Dims reshape_after_fc_dim;
-      reshape_after_fc_dim.nbDims = x_num_col_dims + 1;
-      for (int i = 0; i < reshape_after_fc_dim.nbDims; i++) {
-        reshape_after_fc_dim.d[i] = 0;
-      }
-
-      auto* reshape_after_fc_layer =
-          TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *fc_layer->getOutput(0));
-      reshape_after_fc_layer->setReshapeDimensions(reshape_after_fc_dim);
-
-      if (activation_type == "relu") {
-        reshape_after_fc_layer->setName(
-            ("shuffle_after_fc(Output: " + output_name + ")").c_str());
-        nvinfer1::IActivationLayer* relu_layer = TRT_ENGINE_ADD_LAYER(
-            engine_, Activation, *(reshape_after_fc_layer->getOutput(0)),
-            nvinfer1::ActivationType::kRELU);
-        RreplenishLayerAndOutput(relu_layer, "relu_after_fc_shuffle",
-                                 {output_name}, test_mode);
-      } else {
-        RreplenishLayerAndOutput(reshape_after_fc_layer, "shuffle_after_fc",
-                                 {output_name}, test_mode);
-      }
-      return;
-    }
-    // in order to handle situations in NLP models(input dims < 3,
-    // x_num_col_dims != 1, etc.), reshape input to perform FC correctly.
-    auto* reshape_itensor = X;
-    int input_dims = X->getDimensions().nbDims;
-    auto input_d = X->getDimensions().d;
-    int reshape_dim3[3] = {0};
-    int reshape_dim4[4] = {0};
-    PADDLE_ENFORCE_LE(x_num_col_dims, input_dims,
-                      platform::errors::InvalidArgument(
-                          "Params and input dims mismatch. Paddle-TRT FC "
-                          "converter expects x_num_col_dims <= input dims"));
-    if (x_num_col_dims == 1) {
-      if (input_dims == 4) {
-        PADDLE_ENFORCE_EQ(
-            input_d[3], 1,
-            platform::errors::InvalidArgument(
-                "Invalid dimensions. When x_num_col_dims equals to 1 and input "
-                "dims equals to 4, the last dim of input must be 1, but got %d",
-                input_d[3]));
-      }
-      if (enable_int8) {
-        reshape_dim3[0] = 1;
-        for (int i = 0; i < 3; i++) {
-          reshape_dim3[0] *= input_d[i];
-          if (i > 0) {
-            reshape_dim3[i] = 1;
-          }
-        }
-      } else {
-        for (int i = 0; i < 3; i++) {
-          if (i < input_dims) {
-            reshape_dim3[i] = input_d[i];
-          } else {
-            reshape_dim3[i] = 1;
-          }
-        }
-      }
-
-      nvinfer1::Dims3 reshape_dim(reshape_dim3[0], reshape_dim3[1],
-                                  reshape_dim3[2]);
-      auto* reshape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
-      reshape_layer->setReshapeDimensions(reshape_dim);
-      reshape_itensor = reshape_layer->getOutput(0);
-      if (enable_int8) {
-        engine_->SetTensorDynamicRange(reshape_itensor, in_scale);
-      }
-    } else {
-      PADDLE_ENFORCE_NE(input_dims, 1,
-                        platform::errors::InvalidArgument(
-                            "Invalid dimensions. When x_num_col_dims equals to "
-                            "2, input_dims should not be 1"));
-
-      if (enable_int8) {
-        for (int i = 0; i < 4; i++) {
-          if (i == 0) {
-            reshape_dim4[i] = input_d[i];
-          } else {
-            reshape_dim4[i] = 1;
-            if (i < input_dims) {
-              reshape_dim4[1] *= input_d[i];
-            }
-          }
-        }
-      } else {
-        for (int i = 0; i < 4; i++) {
-          if (i < input_dims) {
-            reshape_dim4[i] = input_d[i];
-          } else {
-            reshape_dim4[i] = 1;
-          }
-        }
-      }
-      nvinfer1::Dims4 reshape_dim(reshape_dim4[0], reshape_dim4[1],
-                                  reshape_dim4[2], reshape_dim4[3]);
-      auto* reshape_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
-      reshape_layer->setReshapeDimensions(reshape_dim);
-      reshape_itensor = reshape_layer->getOutput(0);
+    auto* reshape_itensor = reshape_before_fc_layer->getOutput(0);
     if (enable_int8) {
       engine_->SetTensorDynamicRange(reshape_itensor, in_scale);
     }
-    }
     regist_fc(reshape_itensor, n_output, weight, bias);
   }
 };

paddle/fluid/inference/tensorrt/op_teller.cc

@@ -633,6 +633,20 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
       }
     }
 
+    if (op_type == "fc") {
+      int x_num_col_dims =
+          desc.HasAttr("x_num_col_dims")
+              ? BOOST_GET_CONST(int, desc.GetAttr("x_num_col_dims"))
+              : (desc.HasAttr("in_num_col_dims")
+                     ? BOOST_GET_CONST(int, desc.GetAttr("in_num_col_dims"))
+                     : 1);
+      if (x_num_col_dims < 1) {
+        VLOG(3) << "converter expects x_num_col_dims >= 1, "
+                   "but x_num_col_dims = %d.";
+        return false;
+      }
+    }
+
     if ((*teller)(op_type, desc, use_no_calib_int8)) return true;
   }
   return false;

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

@@ -31,10 +31,7 @@ class FCFusePassTRTTest(InferencePassTest):
                                       size=128,
                                       num_flatten_dims=1,
                                       act="relu")
-            fc_out2 = fluid.layers.fc(input=fc_out1,
-                                      size=32,
-                                      num_flatten_dims=1)
-            out = fluid.layers.softmax(input=fc_out2)
+            out = fluid.layers.softmax(input=fc_out1)
 
         self.feeds = {
             "data": np.random.random((32, 128, 2, 2)).astype("float32")
@@ -55,6 +52,60 @@ class FCFusePassTRTTest(InferencePassTest):
             self.check_output_with_option(use_gpu[i])
 
 
+class FCFusePassTRTStaticDims4Cols1Test(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[32, 128, 32, 8], dtype="float32")
+            fc_out1 = fluid.layers.fc(input=data,
+                                      size=64,
+                                      num_flatten_dims=1,
+                                      act="relu")
+            out = fluid.layers.softmax(input=fc_out1)
+
+        self.feeds = {
+            "data": np.random.random((32, 128, 32, 8)).astype("float32")
+        }
+        self.enable_trt = True
+        self.trt_parameters = FCFusePassTRTStaticDims4Cols1Test.TensorRTParam(
+            1 << 30, 32, 2, AnalysisConfig.Precision.Float32, False, False)
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+        for i in range(len(use_gpu)):
+            self.check_output_with_option(use_gpu[i])
+
+
+class FCFusePassTRTStaticDims4Cols2Test(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[3, 24, 16, 16], dtype="float32")
+            fc_out1 = fluid.layers.fc(input=data,
+                                      size=32,
+                                      num_flatten_dims=2,
+                                      act="relu")
+            out = fluid.layers.softmax(input=fc_out1)
+
+        self.feeds = {
+            "data": np.random.random((3, 24, 16, 16)).astype("float32")
+        }
+        self.enable_trt = True
+        self.trt_parameters = FCFusePassTRTStaticDims4Cols2Test.TensorRTParam(
+            1 << 30, 32, 2, AnalysisConfig.Precision.Float32, False, False)
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+        for i in range(len(use_gpu)):
+            self.check_output_with_option(use_gpu[i])
+
+
 class FCFusePassTRTDynamicDims2Test(InferencePassTest):
     def setUp(self):
         with fluid.program_guard(self.main_program, self.startup_program):

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

@@ -262,7 +262,6 @@ class TensorRTSubgraphPassInstanceNormTest(InferencePassTest):
         with fluid.program_guard(self.main_program, self.startup_program):
             data = fluid.data(
                 name="data", shape=[-1, 3, 64, 64], dtype="float32")
-            fc_out = fluid.layers.fc(input=data, size=200)
             param_attr = fluid.ParamAttr(
                 name='instance_norm_w',
                 initializer=fluid.initializer.Constant(value=1.0))
@@ -270,7 +269,7 @@ class TensorRTSubgraphPassInstanceNormTest(InferencePassTest):
                 name='instance_norm_b',
                 initializer=fluid.initializer.Constant(value=0.0))
             out = fluid.layers.instance_norm(
-                input=fc_out, param_attr=param_attr, bias_attr=bias_attr)
+                input=data, param_attr=param_attr, bias_attr=bias_attr)
         self.feeds = {
             "data": np.random.random([1, 3, 64, 64]).astype("float32"),
         }