Fix cotain contain, etc (#62319)

co63oc · web-flow · commit e989c159a045 · 2024-03-04T11:51:45.000+08:00
diff --git a/paddle/fluid/inference/tensorrt/convert/generic_and_custom_plugin_creater.cc b/paddle/fluid/inference/tensorrt/convert/generic_and_custom_plugin_creater.cc
@@ -60,7 +60,7 @@ class CustomPluginCreater : public OpConverter {
     CHECK(creator);
 
     // set attrs
-    std::vector<nvinfer1::PluginField> plugindatas;
+    std::vector<nvinfer1::PluginField> plugin_datas;
     auto &op_attrs_names = OpMetaInfoHelper::GetAttrs(op_info);
     auto &attrs = op_desc.GetAttrMap();
 
@@ -74,73 +74,73 @@ class CustomPluginCreater : public OpConverter {
     for (auto &attr_name_and_type : op_attrs_names) {
       auto attr_name =
           attr_name_and_type.substr(0, attr_name_and_type.find_first_of(":"));
-      nvinfer1::PluginField plugindata;
+      nvinfer1::PluginField plugin_data;
 
       // NOTE: to avoid string rewrite by iterator, deep copy here
       std::vector<char> plugin_attr_name(attr_name.length() + 1, 0);
       snprintf(plugin_attr_name.data(),
                attr_name.length() + 1,
                "%s",
                attr_name.c_str());
-      plugindata.name = plugin_attr_name.data();
+      plugin_data.name = plugin_attr_name.data();
 
       if (op_desc.GetAttrType(attr_name) == framework::proto::AttrType::INT) {
         int_attrs.push_back(PADDLE_GET_CONST(int, attrs.at(attr_name)));
-        plugindata.data = &int_attrs.back();
-        plugindata.type = nvinfer1::PluginFieldType::kINT32;
-        plugindata.length = 1;
+        plugin_data.data = &int_attrs.back();
+        plugin_data.type = nvinfer1::PluginFieldType::kINT32;
+        plugin_data.length = 1;
       } else if (op_desc.GetAttrType(attr_name) ==
                  framework::proto::AttrType::FLOAT) {
         float_attrs.push_back(PADDLE_GET_CONST(float, attrs.at(attr_name)));
-        plugindata.data = &float_attrs.back();
-        plugindata.type = nvinfer1::PluginFieldType::kFLOAT32;
-        plugindata.length = 1;
+        plugin_data.data = &float_attrs.back();
+        plugin_data.type = nvinfer1::PluginFieldType::kFLOAT32;
+        plugin_data.length = 1;
       } else if (op_desc.GetAttrType(attr_name) ==
                  framework::proto::AttrType::BOOLEAN) {
         int_attrs.push_back(PADDLE_GET_CONST(bool, attrs.at(attr_name)));
-        plugindata.data = &int_attrs.back();
-        plugindata.type = nvinfer1::PluginFieldType::kINT32;
-        plugindata.length = 1;
+        plugin_data.data = &int_attrs.back();
+        plugin_data.type = nvinfer1::PluginFieldType::kINT32;
+        plugin_data.length = 1;
       } else if (op_desc.GetAttrType(attr_name) ==
                  framework::proto::AttrType::STRING) {
         string_attrs.push_back(
             PADDLE_GET_CONST(std::string, attrs.at(attr_name)));
-        plugindata.data = string_attrs.back().data();
-        plugindata.type = nvinfer1::PluginFieldType::kCHAR;
-        plugindata.length =
+        plugin_data.data = string_attrs.back().data();
+        plugin_data.type = nvinfer1::PluginFieldType::kCHAR;
+        plugin_data.length =
             string_attrs.back().size() + 1;  // string ends with ‘\0’
       } else if (op_desc.GetAttrType(attr_name) ==
                  framework::proto::AttrType::INTS) {
         ints_attrs.push_back(
             PADDLE_GET_CONST(std::vector<int>, attrs.at(attr_name)));
-        plugindata.data = ints_attrs.back().data();
-        plugindata.type = nvinfer1::PluginFieldType::kINT32;
-        plugindata.length = ints_attrs.back().size();
+        plugin_data.data = ints_attrs.back().data();
+        plugin_data.type = nvinfer1::PluginFieldType::kINT32;
+        plugin_data.length = ints_attrs.back().size();
       } else if (op_desc.GetAttrType(attr_name) ==
                  framework::proto::AttrType::FLOATS) {
         floats_attrs.push_back(
             PADDLE_GET_CONST(std::vector<float>, attrs.at(attr_name)));
-        plugindata.data = floats_attrs.back().data();
-        plugindata.type = nvinfer1::PluginFieldType::kFLOAT32;
-        plugindata.length = floats_attrs.back().size();
+        plugin_data.data = floats_attrs.back().data();
+        plugin_data.type = nvinfer1::PluginFieldType::kFLOAT32;
+        plugin_data.length = floats_attrs.back().size();
       } else if (op_desc.GetAttrType(attr_name) ==
                  framework::proto::AttrType::BOOLEANS) {
         auto bools_attr =
             PADDLE_GET_CONST(std::vector<bool>, attrs.at(attr_name));
         std::vector<int> convert_to_ints_attr;
         for (bool i : bools_attr) convert_to_ints_attr.push_back(i);
         ints_attrs.push_back(convert_to_ints_attr);
-        plugindata.data = ints_attrs.back().data();
-        plugindata.type = nvinfer1::PluginFieldType::kINT32;
-        plugindata.length = ints_attrs.back().size();
+        plugin_data.data = ints_attrs.back().data();
+        plugin_data.type = nvinfer1::PluginFieldType::kINT32;
+        plugin_data.length = ints_attrs.back().size();
       } else {
         CHECK(false) << "UNKNOWN PluginFieldType.";
       }
-      plugindatas.push_back(plugindata);
+      plugin_datas.push_back(plugin_data);
     }
 
-    nvinfer1::PluginFieldCollection plugin_fc{(int32_t)plugindatas.size(),
-                                              plugindatas.data()};
+    nvinfer1::PluginFieldCollection plugin_fc{(int32_t)plugin_datas.size(),
+                                              plugin_datas.data()};
 
     auto *plugin = creator->createPlugin(op_desc.Type().c_str(), &plugin_fc);
     CHECK(plugin);
diff --git a/paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc b/paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc
@@ -74,7 +74,7 @@ class LayerNormOpConverter : public OpConverter {
 #endif
 #if IS_TRT_VERSION_LT(8600)
       // For dynamic shape & trt<8.6,
-      // the shape of mean and variance will be determine in configuPlugin.
+      // the shape of mean and variance will be determine in configurePlugin.
       auto* X = engine_->GetITensor(op_desc.Input("X").front());
       auto* Bias_v = scope.FindVar(op_desc.Input("Bias").front());
       auto* Scale_v = scope.FindVar(op_desc.Input("Scale").front());
diff --git a/paddle/fluid/inference/tensorrt/convert/layernorm_shift_partition_op.cc b/paddle/fluid/inference/tensorrt/convert/layernorm_shift_partition_op.cc
@@ -73,7 +73,7 @@ class LayerNormShiftPartitionOpConverter : public OpConverter {
     PADDLE_ENFORCE_EQ(bias_weight.get().count,
                       scale_weight.get().count,
                       platform::errors::InvalidArgument(
-                          "The num between bias_weight and cale_weight should "
+                          "The num between bias_weight and scale_weight should "
                           "be equal. (%d vs %d)",
                           bias_weight.get().count,
                           scale_weight.get().count));
diff --git a/paddle/fluid/inference/tensorrt/convert/op_converter.h b/paddle/fluid/inference/tensorrt/convert/op_converter.h
@@ -70,7 +70,7 @@ class OpConverter {
                             1UL,
                             platform::errors::InvalidArgument(
                                 "The input op's Input(\"Y\")."
-                                "size() should equal to 1, but reveceid "
+                                "size() should equal to 1, but received "
                                 "Input(\"Y\").size() = %u.",
                                 op_desc.Input("Y").size()));
           int op_type_len = op_desc.Type().size();
@@ -179,7 +179,7 @@ class OpConverter {
     (*it)(op, scope, test_mode);
 
     size_t output_num = op_desc.OutputNames().size();
-    // only one out settensordynamicRange
+    // only one out SetTensorDynamicRange
     if (op_desc.HasAttr("out_threshold")) {
       float out_scale =
           PADDLE_GET_CONST(float, op_desc.GetAttr("out_threshold"));
@@ -202,7 +202,7 @@ class OpConverter {
       VLOG(1) << "Set out scale = " << out_scale << " for tensor "
               << output_name << ".";
     }
-    // outs settensordynamicRange
+    // outs SetTensorDynamicRange
     for (size_t i = 0; i < output_num; ++i) {
       if (op_desc.HasAttr("out_" + std::to_string(i) + "_threshold")) {
         float out_scale = PADDLE_GET_CONST(
diff --git a/paddle/fluid/inference/tensorrt/convert/preln_emb_eltwise_layernorm.cc b/paddle/fluid/inference/tensorrt/convert/preln_emb_eltwise_layernorm.cc
@@ -103,7 +103,7 @@ class PrelnEmbEltwiseLayerNormOpConverter : public OpConverter {
                              slice_stride_dims);  // unuseful slice_start_dims
     slice_layer->setInput(1, *start_tensor);
     slice_layer->setInput(2, *size_tensor);
-    slice_layer->setName(("Embeltwise_slice_layer (Output: slice_max_seqlen " +
+    slice_layer->setName(("EmbEltwise_slice_layer (Output: slice_max_seqlen " +
                           op_desc.Output("Out")[0] + ")")
                              .c_str());
     engine_->SetTensorDynamicRange(slice_layer->getOutput(0), 1.0f);
@@ -114,7 +114,7 @@ class PrelnEmbEltwiseLayerNormOpConverter : public OpConverter {
     shape_dim.nbDims = 1;
     shape_dim.d[0] = -1;
     reshape_layer->setReshapeDimensions(shape_dim);
-    reshape_layer->setName(("Embeltwise_reshape_layer (Output: max_seqlen " +
+    reshape_layer->setName(("EmbEltwise_reshape_layer (Output: max_seqlen " +
                             op_desc.Output("Out")[0] + ")")
                                .c_str());
     engine_->SetTensorDynamicRange(reshape_layer->getOutput(0), 1.0f);
diff --git a/paddle/fluid/inference/tensorrt/convert/quantize_linear_op.cc b/paddle/fluid/inference/tensorrt/convert/quantize_linear_op.cc
@@ -33,7 +33,7 @@ class QuantizeLinearOpConverter : public OpConverter {
     // Create constant layer for scale
     PADDLE_ENFORCE_NOT_NULL(
         scale_var,
-        platform::errors::NotFound("Can not find %s presistale var in scope.",
+        platform::errors::NotFound("Can not find %s presistable var in scope.",
                                    op_desc.Input("Scale")[0]));
     auto* scale_t = scale_var->GetMutable<phi::DenseTensor>();
     int n_scale = scale_t->numel();
diff --git a/paddle/fluid/inference/tensorrt/convert/range_op.cc b/paddle/fluid/inference/tensorrt/convert/range_op.cc
@@ -35,15 +35,15 @@ class RangeOpConverter : public OpConverter {
     auto output_name = op_desc.Output("Out")[0];
 
     auto zero_tensor = Add1DConstantLayer(0, output_name + "_zero_tensor_");
-    auto fquotient_tensor = FloorDiv(Sub(start, end), step);
+    auto f_quotient_tensor = FloorDiv(Sub(start, end), step);
     if (start->getType() == nvinfer1::DataType::kFLOAT) {
       auto* cast_int32_layer =
-          TRT_ENGINE_ADD_LAYER(engine_, Identity, *fquotient_tensor);
+          TRT_ENGINE_ADD_LAYER(engine_, Identity, *f_quotient_tensor);
       cast_int32_layer->setOutputType(0, nvinfer1::DataType::kINT32);
       cast_int32_layer->getOutput(0)->setType(nvinfer1::DataType::kINT32);
       quotient_tensor = cast_int32_layer->getOutput(0);
     } else {
-      quotient_tensor = fquotient_tensor;
+      quotient_tensor = f_quotient_tensor;
     }
     auto number_tensor = Max(Sub(zero_tensor, quotient_tensor), zero_tensor);
     auto* start1 = engine_->GetITensor(op_desc.Input("Start")[0]);
diff --git a/paddle/fluid/inference/tensorrt/convert/reshape_op.cc b/paddle/fluid/inference/tensorrt/convert/reshape_op.cc
@@ -67,7 +67,7 @@ class ReshapeOpConverter : public OpConverter {
         layer->getOutput(0)->getDimensions().nbDims,
         0,
         platform::errors::InvalidArgument(
-            "Errors occures in Paddle-TRT reshape2 op, try to use C++ Api "
+            "Errors occurs in Paddle-TRT reshape2 op, try to use C++ Api "
             "config.Exp_DisableTensorRtOPs({\"reshape2\"})\n; or Python Api "
             "config.exp_disable_tensorrt_ops([\"reshape2\"]) to forbid "
             "reshape2 op into "
diff --git a/paddle/fluid/inference/tensorrt/convert/set_value_op.cc b/paddle/fluid/inference/tensorrt/convert/set_value_op.cc
@@ -151,7 +151,7 @@ class SetValueConverter : public OpConverter {
         platform::errors::InvalidArgument(
             "ValueTensor‘s rank not equal to Input's rank, "
             "you should try use C++ API "
-            "config.exp_disable_tensorrt_ops({\"%s\"}) to forbind this op "
+            "config.exp_disable_tensorrt_ops({\"%s\"}) to forbid this op "
             "enter into TRT, "
             "please find the %s's real name from .pdmodel or shape.txt",
             output_name,
diff --git a/paddle/fluid/inference/tensorrt/convert/skip_layernorm.cc b/paddle/fluid/inference/tensorrt/convert/skip_layernorm.cc
@@ -67,35 +67,39 @@ class SkipLayerNormOpConverter : public OpConverter {
 
     if ((x_rank == 2 && y_rank == 4) || (y_rank == 2 && x_rank == 4)) {
       if (x_rank == 2 && y_rank == 4) {
-        auto* reshape_before_skiplayn =
+        auto* reshape_before_skip_layer_n =
             TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input1);
         std::vector<nvinfer1::ITensor*> reshape_before_tensor;
         reshape_before_tensor.push_back(GetEleTensorOfShape(Shape(input1), 0));
         reshape_before_tensor.push_back(GetEleTensorOfShape(Shape(input1), 1));
         reshape_before_tensor.push_back(Add1DConstantLayer(1));
         reshape_before_tensor.push_back(Add1DConstantLayer(1));
-        reshape_before_skiplayn->setInput(1, *Concat(reshape_before_tensor));
-        reshape_before_skiplayn->setName(
-            ("reshape_before_skiplayn(Output: " + output_name + ")").c_str());
-        input1 = reshape_before_skiplayn->getOutput(0);
+        reshape_before_skip_layer_n->setInput(1,
+                                              *Concat(reshape_before_tensor));
+        reshape_before_skip_layer_n->setName(
+            ("reshape_before_skip_layer_n(Output: " + output_name + ")")
+                .c_str());
+        input1 = reshape_before_skip_layer_n->getOutput(0);
 
         if (enable_int8) {
           if (op_desc.HasAttr("X")) {
             engine_->SetTensorDynamicRange(input1, x_scale);
           }
         }
       } else {
-        auto* reshape_before_skiplayn =
+        auto* reshape_before_skip_layer_n =
             TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *input2);
         std::vector<nvinfer1::ITensor*> reshape_before_tensor;
         reshape_before_tensor.push_back(GetEleTensorOfShape(Shape(input2), 0));
         reshape_before_tensor.push_back(GetEleTensorOfShape(Shape(input2), 1));
         reshape_before_tensor.push_back(Add1DConstantLayer(1));
         reshape_before_tensor.push_back(Add1DConstantLayer(1));
-        reshape_before_skiplayn->setInput(1, *Concat(reshape_before_tensor));
-        reshape_before_skiplayn->setName(
-            ("reshape_before_skiplayn(Output: " + output_name + ")").c_str());
-        input2 = reshape_before_skiplayn->getOutput(0);
+        reshape_before_skip_layer_n->setInput(1,
+                                              *Concat(reshape_before_tensor));
+        reshape_before_skip_layer_n->setName(
+            ("reshape_before_skip_layer_n(Output: " + output_name + ")")
+                .c_str());
+        input2 = reshape_before_skip_layer_n->getOutput(0);
 
         if (enable_int8) {
           if (op_desc.HasAttr("Y")) {
diff --git a/paddle/fluid/inference/tensorrt/convert/slice_op.cc b/paddle/fluid/inference/tensorrt/convert/slice_op.cc
@@ -20,7 +20,7 @@ class SliceOpConverter : public OpConverter {
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope,
                   bool test_mode) override {
-    // This OP is implemented by trt dynamic shpae plugin.
+    // This OP is implemented by trt dynamic shape plugin.
     // Dynamic shape plugin requires TRT version greater than 6.0.
     VLOG(4) << "convert slice op to tensorrt layer";
     framework::OpDesc op_desc(op, nullptr);
diff --git a/paddle/fluid/inference/tensorrt/convert/softmax_op.cc b/paddle/fluid/inference/tensorrt/convert/softmax_op.cc
@@ -58,7 +58,7 @@ class SoftMaxOpConverter : public OpConverter {
     uint32_t axes = std::max(0, input_dims - 3);
     // TODO(cryoco): Poor workaround. Fix padded dims problem when TRT layers
     // support Nd.
-    // Tips: Dynammic shape alreay fixes.
+    // Tips: Dynamic shape already fixes.
     int padded_dims = 0;
     int explicit_batch = 0;
     if (engine_->with_dynamic_shape()) explicit_batch = 1;
diff --git a/paddle/fluid/inference/tensorrt/convert/sparse_fc_op.cc b/paddle/fluid/inference/tensorrt/convert/sparse_fc_op.cc
@@ -116,7 +116,7 @@ class SparseFcOpConverter : public OpConverter {
     PADDLE_ENFORCE_NOT_NULL(
         Y_v,
         platform::errors::NotFound(
-            "Can not find %s presistale var of sparse_fc in scope.", w_name));
+            "Can not find %s presistable var of sparse_fc in scope.", w_name));
     auto* Y_t = Y_v->GetMutable<phi::DenseTensor>();
     int x_num_col_dims =
         op_desc.HasAttr("x_num_col_dims")
diff --git a/paddle/fluid/inference/tensorrt/convert/trans_layernorm_op.cc b/paddle/fluid/inference/tensorrt/convert/trans_layernorm_op.cc
@@ -53,7 +53,7 @@ class TransLayerNormOpConverter : public OpConverter {
     nvinfer1::ILayer* layernorm_layer = nullptr;
     if (engine_->with_dynamic_shape()) {
       // For dynamic shape,
-      // the shape of mean and variance will be determine in configuPlugin.
+      // the shape of mean and variance will be determine in configurePlugin.
       std::vector<int64_t> mean_shape{1};
       std::vector<int64_t> variance_shape{1};
       bool with_fp16 =
diff --git a/paddle/fluid/inference/tensorrt/convert/ut_helper.h b/paddle/fluid/inference/tensorrt/convert/ut_helper.h
@@ -247,7 +247,7 @@ class TRTConvertValidation {
   std::unique_ptr<framework::OpDesc> op_desc_;
   const std::unordered_set<std::string>& parameters_;
   framework::Scope& scope_;
-  // The ITensor of trt does not cotain the batch size,
+  // The ITensor of trt does not contain the batch size,
   // bug, in most cases, we need to set batch size for
   // fluid's tensor shape. This variable indicates
   // whether to add batch size to tensor shape of fluid.
