Fix MaxSeqenceLenOp MaxSequenceLenOp, etc (#62181)

co63oc · web-flow · commit 108684db5854 · 2024-02-29T23:26:41.000+08:00
diff --git a/paddle/fluid/operators/im2sequence_op.h b/paddle/fluid/operators/im2sequence_op.h
@@ -48,13 +48,13 @@ class Im2SequenceKernel : public framework::OpKernel<T> {
     auto strides = ctx.Attr<std::vector<int>>("strides");
     auto paddings = ctx.Attr<std::vector<int>>("paddings");
     if (ctx.HasInput("Y") && batch_size > 1) {
-      const phi::DenseTensor* imgrealsize = ctx.Input<phi::DenseTensor>("Y");
+      const phi::DenseTensor* img_real_size = ctx.Input<phi::DenseTensor>("Y");
       auto out_stride = ctx.Attr<std::vector<int>>("out_stride");
       phi::DenseTensor cpu_shape_tensor;
       paddle::framework::TensorCopySync(
-          *imgrealsize, platform::CPUPlace(), &cpu_shape_tensor);
-      std::vector<int> imgreal_h;
-      std::vector<int> imgreal_w;
+          *img_real_size, platform::CPUPlace(), &cpu_shape_tensor);
+      std::vector<int> img_real_h;
+      std::vector<int> img_real_w;
       std::vector<int> output_height;
       std::vector<int> output_width;
       int result = 0;
@@ -72,12 +72,12 @@ class Im2SequenceKernel : public framework::OpKernel<T> {
         } else {
           tmp_real_w = tmp_real_w / out_stride[1] + 1;
         }
-        imgreal_h.push_back(tmp_real_h);
-        imgreal_w.push_back(tmp_real_w);
+        img_real_h.push_back(tmp_real_h);
+        img_real_w.push_back(tmp_real_w);
         output_height.push_back(Im2SeqOutputSize(
-            imgreal_h[i], kernels[0], paddings[0], paddings[2], strides[0]));
+            img_real_h[i], kernels[0], paddings[0], paddings[2], strides[0]));
         output_width.push_back(Im2SeqOutputSize(
-            imgreal_w[i], kernels[1], paddings[1], paddings[3], strides[1]));
+            img_real_w[i], kernels[1], paddings[1], paddings[3], strides[1]));
         result += output_height[i] * output_width[i];
       }
 
diff --git a/paddle/fluid/operators/is_empty_op.h b/paddle/fluid/operators/is_empty_op.h
@@ -29,7 +29,7 @@ class IsEmptyOpKernel : public framework::OpKernel<T> {
     auto* output_tensor = context.Output<phi::DenseTensor>("Out");
 
     // Note: is_empty is always executed on CPU and the output data should
-    // always be allocated for CPUPlace. We reigister CUDA kernel for this op to
+    // always be allocated for CPUPlace. We register CUDA kernel for this op to
     // avoid the unnecessary data transform.
     output_tensor->mutable_data<bool>(platform::CPUPlace())[0] =
         common::product(input_tensor->dims()) == 0;
diff --git a/paddle/fluid/operators/isfinite_op.cc b/paddle/fluid/operators/isfinite_op.cc
@@ -86,7 +86,7 @@ If any X contains Inf or Nan, the Out will generate a indicator.
 Out = Inf if any X contains Inf,
 Out = Nan if any X contains Nan,
 Out = 0 if no Inf/Nan detected.
-If X contains both Inf/Nan, it will return the first indicator it meeted.
+If X contains both Inf/Nan, it will return the first indicator it met.
 
 %s
 )DOC",
diff --git a/paddle/fluid/operators/linear_chain_crf_op.cc b/paddle/fluid/operators/linear_chain_crf_op.cc
@@ -55,7 +55,7 @@ class LinearChainCRFOpMaker : public framework::OpProtoAndCheckerMaker {
         "probabilities of all possible unfinished sequences of tags that end "
         "at position $k$ with tag $v$. For each $k$, "
         "$\alpha[k, v]$ is a vector of length $D$ with a component for "
-        "each tag value $v$. This vector is called a forward vecotr and "
+        "each tag value $v$. This vector is called a forward vector and "
         "will also be used in backward computations.")
         .AsIntermediate();
     AddOutput(
@@ -105,7 +105,7 @@ CRF. Please refer to https://www.cs.columbia.edu/~mcollins/fb.pdf and
 weights, denoted as $a$ here.
 3. The next D values of Input(Transition) of this operator are for ending
 weights, denoted as $b$ here.
-4. The remaning values of Input(Transition) are for transition weights,
+4. The remaining values of Input(Transition) are for transition weights,
 denoted as $w$ here.
 5. Denote Input(Label) as $s$ here.
 
diff --git a/paddle/fluid/operators/linear_chain_crf_op.h b/paddle/fluid/operators/linear_chain_crf_op.h
@@ -234,7 +234,7 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
         static_cast<size_t>(*std::max_element(lbl, lbl + seq_length)),
         tag_num,
         platform::errors::InvalidArgument(
-            "An invalid tag label that execesses the largest tag number."));
+            "An invalid tag label that excesses the largest tag number."));
 
     // Calculate the nominator part, which depends on the label sequence.
     ll += w[lbl[0]] /*start transition*/ + x[lbl[0]] +
@@ -308,7 +308,7 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
     // Now, all the inputs and outputs should be on the CPU memory.
     auto emission_dims = emission_exps->dims();
     // Beta is the memo table used in dynamic programming to calculate the
-    // backwark vectors. For a backward vector i (the i-th row of beta), it
+    // backward vectors. For a backward vector i (the i-th row of beta), it
     // captures the unnormalized probabilities of partial sequences starting
     // at position i.
     phi::DenseTensor beta;
@@ -372,7 +372,7 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
     const size_t state_trans_base_idx = 2;
 
     // Calculate the backward vectors: beta.
-    // First, calculate the initialition state.
+    // First, calculate the initial state.
     for (size_t i = 0; i < tag_num; ++i) {
       beta_value[(seq_length - 1) * tag_num + i] = w_exps[tag_num + i];
     }
@@ -411,7 +411,7 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
       T* trans_grad = transition_grad->data<T>();
       for (size_t k = 0; k < tag_num; ++k) {
         // Do not multiply by the output gradient here, because x_grad_mat has
-        // alrealy done this.
+        // already done this.
         trans_grad[k] += x_grad_mat(/*from start state*/ 0, k);
         trans_grad[tag_num + k] +=
             x_grad_mat(/*to end state*/ seq_length - 1, k);
diff --git a/paddle/fluid/operators/load_combine_op.h b/paddle/fluid/operators/load_combine_op.h
@@ -101,7 +101,7 @@ class LoadCombineOpKernel : public framework::OpKernel<T> {
           framework::NFD(it->first, &tmp);
           if (tmp.empty()) {
             VLOG(0) << "The string " << it->first
-                    << " was converted to unicode failedly! "
+                    << " was converted to unicode unsuccessfully! "
                     << "Then dropped to load it.";
             continue;
           }
diff --git a/paddle/fluid/operators/load_op.cc b/paddle/fluid/operators/load_op.cc
@@ -47,7 +47,7 @@ void LoadKernel(const Context& dev_ctx,
     PADDLE_ENFORCE_GE(seek,
                       0,
                       phi::errors::InvalidArgument(
-                          "seek witn tensor must great than or equal to 0"));
+                          "seek with tensor must great than or equal to 0"));
     framework::DeserializeFromStream(fin, out, dev_ctx, seek, shape);
   } else {
     framework::DeserializeFromStream(fin, out, dev_ctx);
diff --git a/paddle/fluid/operators/max_sequence_len_op.cc b/paddle/fluid/operators/max_sequence_len_op.cc
@@ -31,12 +31,12 @@ class OpBase;
 namespace paddle {
 namespace operators {
 
-class MaxSeqenceLenOp : public framework::OperatorBase {
+class MaxSequenceLenOp : public framework::OperatorBase {
  public:
-  MaxSeqenceLenOp(const std::string &type,
-                  const framework::VariableNameMap &inputs,
-                  const framework::VariableNameMap &outputs,
-                  const framework::AttributeMap &attrs)
+  MaxSequenceLenOp(const std::string &type,
+                   const framework::VariableNameMap &inputs,
+                   const framework::VariableNameMap &outputs,
+                   const framework::AttributeMap &attrs)
       : OperatorBase(type, inputs, outputs, attrs) {}
 
  private:
@@ -50,7 +50,7 @@ class MaxSeqenceLenOp : public framework::OperatorBase {
   }
 };
 
-class MaxSeqenceLenOpProtoMaker : public framework::OpProtoAndCheckerMaker {
+class MaxSequenceLenOpProtoMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
     AddInput("RankTable", "Input variable which is a LoDRankTable object");
@@ -65,11 +65,11 @@ class MaxSeqenceLenOpProtoMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
-class MaxSeqenceLenInferShape : public framework::InferShapeBase {
+class MaxSequenceLenInferShape : public framework::InferShapeBase {
  public:
   void operator()(framework::InferShapeContext *context) const override {
     OP_INOUT_CHECK(
-        context->HasInput("RankTable"), "Input", "RankTable", "MaxSeqenceLen");
+        context->HasInput("RankTable"), "Input", "RankTable", "MaxSequenceLen");
     context->SetOutputDim("Out", {1});
   }
 };
@@ -78,8 +78,8 @@ class MaxSeqenceLenInferShape : public framework::InferShapeBase {
 
 REGISTER_OPERATOR(
     max_sequence_len,
-    paddle::operators::MaxSeqenceLenOp,
-    paddle::operators::MaxSeqenceLenOpProtoMaker,
-    paddle::operators::MaxSeqenceLenInferShape,
+    paddle::operators::MaxSequenceLenOp,
+    paddle::operators::MaxSequenceLenOpProtoMaker,
+    paddle::operators::MaxSequenceLenInferShape,
     paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
     paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
diff --git a/paddle/fluid/operators/nce_op.cc b/paddle/fluid/operators/nce_op.cc
@@ -149,19 +149,19 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddInput(
         "CustomDistProbs",
-        "(Tensor) It is used in 'CostumDist' sampler. "
+        "(Tensor) It is used in 'CustomDist' sampler. "
         "It is a tensor with shape [num_total_classes]."
         "The i-th element is the probability of the i-th class being sampled.")
         .AsDispensable();
     AddInput(
         "CustomDistAlias",
-        "(Tensor) It is used in 'CostumDist' sampler. "
+        "(Tensor) It is used in 'CustomDist' sampler. "
         "It is a tensor with shape [num_total_classes]."
         "The i-th element is the probability of the i-th class being sampled.")
         .AsDispensable();
     AddInput(
         "CustomDistAliasProbs",
-        "(Tensor) It is used in 'CostumDist' sampler. "
+        "(Tensor) It is used in 'CustomDist' sampler. "
         "It is a tensor with shape [num_total_classes]."
         "The i-th element is the probability of the i-th class being sampled.")
         .AsDispensable();
@@ -194,7 +194,7 @@ class NCEOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(10);
     AddAttr<int>("sampler",
                  "(int) Which sampler to be used to sample negative class."
-                 "0: Uniform; 1: LogUniform; 2: CostumDist.")
+                 "0: Uniform; 1: LogUniform; 2: CustomDist.")
         .SetDefault(0);
     AddAttr<int>("seed",
                  "(int) The seed used in sampler. If it is 0, "
diff --git a/paddle/fluid/operators/nce_op.h b/paddle/fluid/operators/nce_op.h
@@ -146,7 +146,7 @@ class NCEKernel : public framework::OpKernel<T> {
       default: {
         PADDLE_THROW(platform::errors::InvalidArgument(
             "Unsupported SamplerType. SamplerType should be 0: Uniform, "
-            "1: LogUniform or 2: CostumDist. Received SamplerType: %d",
+            "1: LogUniform or 2: CustomDist. Received SamplerType: %d",
             sampler_type));
       }
     }
@@ -332,7 +332,7 @@ class NCEGradKernel : public framework::OpKernel<T> {
       default: {
         PADDLE_THROW(platform::errors::InvalidArgument(
             "Unsupported SamplerType. SamplerType should be 0: Uniform, "
-            "1: LogUniform or 2: CostumDist. Received SamplerType: %d",
+            "1: LogUniform or 2: CustomDist. Received SamplerType: %d",
             sampler_type));
       }
     }
diff --git a/paddle/fluid/operators/pad_op.cc b/paddle/fluid/operators/pad_op.cc
@@ -146,7 +146,7 @@ class PadCompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
     std::vector<int> paddings =
         static_cast<std::vector<int>>(this->Attr<std::vector<int>>("paddings"));
     float pad_value = static_cast<float>(this->Attr<float>("pad_value"));
-    VLOG(6) << "Runing add_grad composite func";
+    VLOG(6) << "Running add_grad composite func";
 
     prim::pad_grad<prim::DescTensor>(x, out_grad, paddings, pad_value, dx_ptr);
     this->RecoverOutputName(x_grad, dx_name);
diff --git a/paddle/fluid/operators/pull_box_extended_sparse_op.h b/paddle/fluid/operators/pull_box_extended_sparse_op.h
@@ -86,7 +86,7 @@ static void PushBoxExtendedSparseFunctor(
                         cur_batch_size,
                         platform::errors::PreconditionNotMet(
                             "The batch size of all input slots should be same,"
-                            "please cheack"));
+                            "please check"));
     }
     const float *grad_value = d_output[i]->data<float>();
     const float *grad_value_extend = d_output_extend[i]->data<float>();

Original file line number	Diff line number	Diff line change
`@@ -101,7 +101,7 @@ class LoadCombineOpKernel : public framework::OpKernel<T> {`
`101`	`101`	`framework::NFD(it->first, &tmp);`
`102`	`102`	`if (tmp.empty()) {`
`103`	`103`	`VLOG(0) << "The string " << it->first`
`104`		`- << " was converted to unicode failedly! "`
	`104`	`+ << " was converted to unicode unsuccessfully! "`
`105`	`105`	`<< "Then dropped to load it.";`
`106`	`106`	`continue;`
`107`	`107`	`}`
Original file line number	Diff line number	Diff line change
`@@ -146,7 +146,7 @@ class NCEKernel : public framework::OpKernel<T> {`
`146`	`146`	`default: {`
`147`	`147`	`PADDLE_THROW(platform::errors::InvalidArgument(`
`148`	`148`	`"Unsupported SamplerType. SamplerType should be 0: Uniform, "`
`149`		`- "1: LogUniform or 2: CostumDist. Received SamplerType: %d",`
	`149`	`+ "1: LogUniform or 2: CustomDist. Received SamplerType: %d",`
`150`	`150`	`sampler_type));`
`151`	`151`	`}`
`152`	`152`	`}`
`@@ -332,7 +332,7 @@ class NCEGradKernel : public framework::OpKernel<T> {`
`332`	`332`	`default: {`
`333`	`333`	`PADDLE_THROW(platform::errors::InvalidArgument(`
`334`	`334`	`"Unsupported SamplerType. SamplerType should be 0: Uniform, "`
`335`		`- "1: LogUniform or 2: CostumDist. Received SamplerType: %d",`
	`335`	`+ "1: LogUniform or 2: CustomDist. Received SamplerType: %d",`
`336`	`336`	`sampler_type));`
`337`	`337`	`}`
`338`	`338`	`}`