diff --git a/docs/ContribOperators.md b/docs/ContribOperators.md index 9fba550e0a558..7a8f3bb9bb28e 100644 --- a/docs/ContribOperators.md +++ b/docs/ContribOperators.md @@ -1839,7 +1839,7 @@ This version of the operator has been available since version 1 of the 'com.micr ### **com.microsoft.LongformerAttention** Longformer Self Attention with a local context and a global context. Tokens attend locally: Each token - attends to its W previous tokens and W succeding tokens with W being the window length. A selected few tokens + attends to its W previous tokens and W succeeding tokens with W being the window length. A selected few tokens attend globally to all other tokens. The attention mask is of shape (batch_size, sequence_length), where sequence_length is a multiple of 2W after padding. @@ -2723,7 +2723,7 @@ This version of the operator has been available since version 1 of the 'com.micr
T : tensor(uint8), tensor(int8)
-
Constrain input and output types to singed/unsigned int8 tensors.
+
Constrain input and output types to signed/unsigned int8 tensors.
@@ -2965,7 +2965,7 @@ This version of the operator has been available since version 1 of the 'com.micr
T : tensor(uint8), tensor(int8)
-
Constrain input and output types to singed/unsigned int8 tensors.
+
Constrain input and output types to signed/unsigned int8 tensors.
@@ -4002,9 +4002,9 @@ This version of the operator has been available since version 1 of the 'com.micr
char_embedding_size : int
Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.
conv_window_size : int
-
This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.
+
This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernel shape.
embedding_size : int
-
Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight
+
Integer representing the embedding vector size for each word.If not provide, use the filter size of conv weight
#### Inputs diff --git a/docs/cmake_guideline.md b/docs/cmake_guideline.md index d3f0f85a4d742..e03706476d73f 100644 --- a/docs/cmake_guideline.md +++ b/docs/cmake_guideline.md @@ -157,7 +157,7 @@ CMAKE_HOST_SYSTEM_PROCESSOR is the one you should use. What are the valid values: - macOS: it can be x86_64 or arm64. (maybe it could also be arm64e but cmake forgot to document that) -- Linux: i686, x86_64, aarch64, armv7l, ... The possible values for `uname -m` command. They sightly differ from what you can get from GCC. This sometimes confuses people: `cmake` and `uname` sit in one boat, GCC is in another boat but GCC is closer to your C/C++ source code. +- Linux: i686, x86_64, aarch64, armv7l, ... The possible values for `uname -m` command. They slightly differ from what you can get from GCC. This sometimes confuses people: `cmake` and `uname` sit in one boat, GCC is in another boat but GCC is closer to your C/C++ source code. - Windows: AMD64, ... - Android/iOS/...: we don't care. We don't use them as a development environment. diff --git a/docs/python/inference/api_summary.rst b/docs/python/inference/api_summary.rst index c6d1f4868e7b5..4f82e1242d6de 100644 --- a/docs/python/inference/api_summary.rst +++ b/docs/python/inference/api_summary.rst @@ -265,7 +265,7 @@ Internal classes ---------------- These classes cannot be instantiated by users but they are returned -by methods or functions of this libary. +by methods or functions of this library. ModelMetadata ^^^^^^^^^^^^^ diff --git a/onnxruntime/core/graph/contrib_ops/bert_defs.cc b/onnxruntime/core/graph/contrib_ops/bert_defs.cc index 1235527e57243..acffd55831071 100644 --- a/onnxruntime/core/graph/contrib_ops/bert_defs.cc +++ b/onnxruntime/core/graph/contrib_ops/bert_defs.cc @@ -100,7 +100,7 @@ ONNX_MS_OPERATOR_SET_SCHEMA(Attention, 1, constexpr const char* Longformer_Attention_doc = R"DOC( Longformer Self Attention with a local context and a global context. Tokens attend locally: Each token -attends to its W previous tokens and W succeding tokens with W being the window length. A selected few tokens +attends to its W previous tokens and W succeeding tokens with W being the window length. A selected few tokens attend globally to all other tokens. The attention mask is of shape (batch_size, sequence_length), where sequence_length is a multiple of 2W after padding. diff --git a/onnxruntime/core/graph/contrib_ops/contrib_defs.cc b/onnxruntime/core/graph/contrib_ops/contrib_defs.cc index 921d44716f12b..309e1f4ddf014 100644 --- a/onnxruntime/core/graph/contrib_ops/contrib_defs.cc +++ b/onnxruntime/core/graph/contrib_ops/contrib_defs.cc @@ -1756,14 +1756,14 @@ ONNX_MS_OPERATOR_SET_SCHEMA(WordConvEmbedding, 1, .Attr( "embedding_size", "Integer representing the embedding vector size for each word." - "If not provide, use the fileter size of conv weight", + "If not provide, use the filter size of conv weight", AttributeProto::INT, OPTIONAL_VALUE) .Attr( "conv_window_size", "This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1." "Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]..." - "If not provide, use the first dimension of conv kernal shape.", + "If not provide, use the first dimension of conv kernel shape.", AttributeProto::INT, OPTIONAL_VALUE) .Attr( diff --git a/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.cc b/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.cc index 65e37394e1f83..c5d5ea5a2e413 100644 --- a/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.cc +++ b/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.cc @@ -71,7 +71,7 @@ equal to the spatial dimension of input tensor. Input is of type uint8_t or int8 "dimensions are all 1.", "T") .TypeConstraint("T", {"tensor(uint8)", "tensor(int8)"}, - "Constrain input and output types to singed/unsigned int8 tensors.") + "Constrain input and output types to signed/unsigned int8 tensors.") .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) { propagateElemTypeFromInputToOutput(ctx, 0, 0); diff --git a/onnxruntime/core/graph/contrib_ops/quantization_defs.cc b/onnxruntime/core/graph/contrib_ops/quantization_defs.cc index 62c0d6da3c88e..66096eaf252c6 100644 --- a/onnxruntime/core/graph/contrib_ops/quantization_defs.cc +++ b/onnxruntime/core/graph/contrib_ops/quantization_defs.cc @@ -617,7 +617,7 @@ The output tensor has the same shape. "tensor. The output tensor has the same rank as the input. ", "T") .TypeConstraint("T", {"tensor(uint8)", "tensor(int8)"}, - "Constrain input and output types to singed/unsigned int8 tensors.") + "Constrain input and output types to signed/unsigned int8 tensors.") .TypeAndShapeInferenceFunction([](ONNX_NAMESPACE::InferenceContext& ctx) { // Type inference propagateElemTypeFromInputToOutput(ctx, 0, 0);