diff --git a/paddle/fluid/framework/attribute_checker.h b/paddle/fluid/framework/attribute_checker.h index fbafe9c73a9cc636a72f2c66685122a0ef53e5c8..24f3f0be96b6cb2dd004c74bb0ed4a1f34d28ed3 100644 --- a/paddle/fluid/framework/attribute_checker.h +++ b/paddle/fluid/framework/attribute_checker.h @@ -342,13 +342,12 @@ class OpAttrChecker { AttributeMap default_attrs_; // in order to improve the efficiency of dynamic graph mode, - // we divede the attribute into explicit type and implicit type. + // we divide the attribute into explicit type and implicit type. // for explicit attribute, we mean the attribute added in the customized // op makers, usually it's defined in the overloaded Make method. // for implicit attribute, we mean the attribute added outside of the Make // method like "op_role", "op_role_var", and they are useless in dynamic - // graph - // mode + // graph mode size_t explicit_checker_num_; }; diff --git a/paddle/fluid/framework/custom_operator.cc b/paddle/fluid/framework/custom_operator.cc index c58d1a57ec4663f330ef7f54e1f71eb34b04d0c8..e7ed9f2108128d818ec24fbd2cd8554bc45a345d 100644 --- a/paddle/fluid/framework/custom_operator.cc +++ b/paddle/fluid/framework/custom_operator.cc @@ -801,7 +801,7 @@ void RegisterOperatorWithMetaInfo(const std::vector& op_meta_infos, // Infer Dtype if (infer_dtype_func == nullptr) { - // use defalut InferDtype + // use default InferDtype info.infer_var_type_ = [op_inputs, op_outputs](InferVarTypeContext* ctx) { PADDLE_ENFORCE_EQ( op_inputs.size(), diff --git a/paddle/fluid/framework/data_device_transform.cc b/paddle/fluid/framework/data_device_transform.cc index 36e558c1d504d8385af784153db70f0b84cf1a8a..e65ecff60edd76e77c1f881e4cbbcb79bc24b0a2 100644 --- a/paddle/fluid/framework/data_device_transform.cc +++ b/paddle/fluid/framework/data_device_transform.cc @@ -51,8 +51,7 @@ void TransDataDevice(const Tensor &in, // the elements of learning rate are one and it's CPU side. // One solution is to use a CUDA kernel to complete the copy operation when // the transforming is from CPU to GPU and the number of elements is little. - // But the embarrassment is that this solution this solution makes training - // slower. + // But the embarrassment is that this solution makes training slower. TensorCopySync(in, dst_place, out); } diff --git a/paddle/fluid/framework/operator.h b/paddle/fluid/framework/operator.h index 7e4dc337dbfa535345fc693d2b6d8f00b2fbfbb0..edb2d539f82ef732b55386cb9eb53f0683769861 100644 --- a/paddle/fluid/framework/operator.h +++ b/paddle/fluid/framework/operator.h @@ -695,7 +695,7 @@ class OperatorWithKernel : public OperatorBase { * Transfer data from scope to a transferred scope. If there is no data need * to be transferred, it returns nullptr. * - * * transfered_inplace_vars is a output vector. + * transfered_inplace_vars is a output vector. */ Scope* PrepareData(const Scope& scope, const OpKernelType& expected_kernel_key, diff --git a/paddle/fluid/imperative/infer_shape_context.h b/paddle/fluid/imperative/infer_shape_context.h index b7345cf397356f99b9ef03dcfe5bb9741bfe9fc6..5702bcfca73296106b1d0a2cb24fd052150bd647 100644 --- a/paddle/fluid/imperative/infer_shape_context.h +++ b/paddle/fluid/imperative/infer_shape_context.h @@ -169,6 +169,7 @@ class DygraphInferShapeContext : public framework::InferShapeContext { return vec_res; } + std::string GetInputNameByIdx(size_t idx) const override { auto& op_proto = paddle::framework::OpInfoMap::Instance().Get(op_type_).proto_; diff --git a/paddle/fluid/operators/fill_any_like_op.cc b/paddle/fluid/operators/fill_any_like_op.cc index 528ea076a322be63fde9eda7f871bb4a2fb7dcdb..eb66cc88b3145cecf245879b7ed9788fbec23b68 100644 --- a/paddle/fluid/operators/fill_any_like_op.cc +++ b/paddle/fluid/operators/fill_any_like_op.cc @@ -58,7 +58,7 @@ class FillAnyLikeOpMaker : public framework::OpProtoAndCheckerMaker { AddOutput("Out", "The variable will be filled up with specified value."); AddAttr("value", "The filled value").SetDefault(0.0); AddAttr("dtype", - "Output tensor data type. defalut value is -1," + "Output tensor data type. default value is -1," "according to the input dtype.") .SetDefault(-1); AddComment(R"DOC( diff --git a/paddle/fluid/pybind/cuda_streams_py.cc b/paddle/fluid/pybind/cuda_streams_py.cc index 65e759d3b20556c20fd168e36681d44f0a683774..f805696138f0dbd3291f06467ce3e6bddd9c642b 100644 --- a/paddle/fluid/pybind/cuda_streams_py.cc +++ b/paddle/fluid/pybind/cuda_streams_py.cc @@ -321,7 +321,7 @@ void BindCudaStream(py::module *m_ptr) { Parameters: enable_timing(bool, optional): Whether the event will measure time. Default: False. blocking(bool, optional): Whether the wait() func will be blocking. Default: False; - interprocess(bool, optional): Whether the event can be shared between processes. Defalut: False. + interprocess(bool, optional): Whether the event can be shared between processes. Default: False. Examples: .. code-block:: python diff --git a/paddle/phi/api/lib/data_transform.cc b/paddle/phi/api/lib/data_transform.cc index 72e65ae5286ee562097d6a075fb73125c15220ac..04ac701ae0f5979d6607fd5250303e5f0f61fa75 100644 --- a/paddle/phi/api/lib/data_transform.cc +++ b/paddle/phi/api/lib/data_transform.cc @@ -81,7 +81,7 @@ inline phi::DenseTensor TransDataLayout(const phi::DenseTensor& tensor, } template -phi::DenseTensor CastDateType(const Context& dev_ctx, +phi::DenseTensor CastDataType(const Context& dev_ctx, const phi::DenseTensor& tensor, DataType dtype) { switch (tensor.dtype()) { @@ -111,7 +111,7 @@ phi::DenseTensor CastDateType(const Context& dev_ctx, } #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) -phi::DenseTensor CastDateType(const phi::GPUContext& dev_ctx, +phi::DenseTensor CastDataType(const phi::GPUContext& dev_ctx, const phi::DenseTensor& tensor, DataType dtype) { switch (tensor.dtype()) { @@ -151,11 +151,11 @@ inline phi::DenseTensor TransDataType(const phi::DenseTensor& tensor, if (platform::is_cpu_place(tensor.place())) { auto* dev_ctx = static_cast(pool.Get(tensor.place())); - return CastDateType(*dev_ctx, tensor, dtype); + return CastDataType(*dev_ctx, tensor, dtype); #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) } else if (platform::is_gpu_place(tensor.place())) { auto* dev_ctx = static_cast(pool.Get(tensor.place())); - return CastDateType(*dev_ctx, tensor, dtype); + return CastDataType(*dev_ctx, tensor, dtype); #endif } else { PADDLE_THROW(phi::errors::Unimplemented( diff --git a/paddle/phi/kernels/cpu/put_along_axis_kernel.cc b/paddle/phi/kernels/cpu/put_along_axis_kernel.cc index a297843b0c7cddd5d07f5cba71ccc279ebb7784f..573065cbc661573c4414993f059261bb94ce27a8 100644 --- a/paddle/phi/kernels/cpu/put_along_axis_kernel.cc +++ b/paddle/phi/kernels/cpu/put_along_axis_kernel.cc @@ -67,7 +67,7 @@ void PutAlongAxisKernel(const Context& dev_ctx, PADDLE_THROW(errors::InvalidArgument( "can not support reduce: '%s' for scatter kernel, only " "support reduce op: 'add', 'assign', 'mul' and 'multiply', the " - "defalut reduce " + "default reduce " "op is 'assign' ", reduce)); return; diff --git a/paddle/phi/kernels/gpu/put_along_axis_kernel.cu b/paddle/phi/kernels/gpu/put_along_axis_kernel.cu index b4fde608b1e7883ffc37cfaaff22aac108549790..648c0fa627b25349745c9945c73e9b9b78becd9b 100644 --- a/paddle/phi/kernels/gpu/put_along_axis_kernel.cu +++ b/paddle/phi/kernels/gpu/put_along_axis_kernel.cu @@ -68,7 +68,7 @@ void PutAlongAxisKernel(const Context& dev_ctx, PADDLE_THROW(errors::InvalidArgument( "can not support reduce: '%s' for scatter kernel, only " "support reduce op: 'add', 'assign', 'mul' and 'multiply', the " - "defalut reduce op is 'assign' ", + "default reduce op is 'assign' ", reduce)); return; } diff --git a/python/paddle/distributed/fleet/dataset/dataset.py b/python/paddle/distributed/fleet/dataset/dataset.py index de8708855a61e6f34898b289380eeaef5b5cbde8..ed06a0db6843f10a0ef2d685b95a32cd0183bd53 100755 --- a/python/paddle/distributed/fleet/dataset/dataset.py +++ b/python/paddle/distributed/fleet/dataset/dataset.py @@ -54,7 +54,7 @@ class DatasetBase(object): thread_num(int): thread num, it is the num of readers. default is 1. use_var(list): list of variables. Variables which you will use. default is []. pipe_command(str): pipe command of current dataset. A pipe command is a UNIX pipeline command that can be used only. default is "cat" - input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. defalut is 0. + input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. default is 0. fs_name(str): fs name. default is "". fs_ugi(str): fs ugi. default is "". download_cmd(str): customized download command. default is "cat" @@ -441,7 +441,7 @@ class InMemoryDataset(DatasetBase): batch_size(int): batch size. It will be effective during training. default is 1. thread_num(int): thread num, it is the num of readers. default is 1. use_var(list): list of variables. Variables which you will use. default is []. - input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. defalut is 0. + input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. default is 0. fs_name(str): fs name. default is "". fs_ugi(str): fs ugi. default is "". pipe_command(str): pipe command of current dataset. A pipe command is a UNIX pipeline command that can be used only. default is "cat" @@ -522,7 +522,7 @@ class InMemoryDataset(DatasetBase): batch_size(int): batch size. It will be effective during training. default is 1. thread_num(int): thread num, it is the num of readers. default is 1. use_var(list): list of variables. Variables which you will use. default is []. - input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. defalut is 0. + input_type(int): the input type of generated input. 0 is for one sample, 1 is for one batch. default is 0. fs_name(str): fs name. default is "". fs_ugi(str): fs ugi. default is "". pipe_command(str): pipe command of current dataset. A pipe command is a UNIX pipeline command that can be used only. default is "cat" diff --git a/python/paddle/fluid/contrib/sparsity/asp.py b/python/paddle/fluid/contrib/sparsity/asp.py index bc335dfd4b9e19b79ee25f71c774817d76eb93cb..ac97bcecf323f970961464c016afcec3cdb6e1ad 100644 --- a/python/paddle/fluid/contrib/sparsity/asp.py +++ b/python/paddle/fluid/contrib/sparsity/asp.py @@ -316,7 +316,7 @@ def prune_model(model, n=2, m=4, mask_algo='mask_1d', with_mask=True): m (int, optional): m of `n:m` sparse pattern. Default is 4. mask_algo (string, optional): The function name to generate spase mask. Default is `mask_1d`. The vaild inputs should be one of 'mask_1d', 'mask_2d_greedy' and 'mask_2d_best'. - with_mask (bool, optional): To prune mask Variables related to parameters or not. Ture is purning also, False is not. Defalut is True. + with_mask (bool, optional): To prune mask Variables related to parameters or not. Ture is purning also, False is not. Default is True. Returns: dictionary: A dictionary with key: `parameter name` (string) and value: its corresponding mask Variable. Examples: diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 2898ad64fcce6e8c524844c326ab819aa379e450..49de18acbb2135104a7cecf766676aa7c198f624 100755 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -14912,7 +14912,7 @@ def unique_with_counts(x, dtype='int32'): Args: x(Variable): A 1-D input tensor with input shape of :math:`[N]` , the input data type is float32, float64, int32, int64. - dtype(np.dtype|core.VarDesc.VarType|str): The type of count and index tensor, it could be int32, int64. Defalut value is int32. + dtype(np.dtype|core.VarDesc.VarType|str): The type of count and index tensor, it could be int32, int64. Default value is int32. Returns: tuple, the variable type in tuple is Tensor, the output :attr:`out` data type is the same as input :attr:`x`, \ diff --git a/python/paddle/fluid/tests/custom_kernel/test_custom_kernel_load.py b/python/paddle/fluid/tests/custom_kernel/test_custom_kernel_load.py index 4ca05909fb17ad9d99e863126dc76c0ab3f11075..ff7ff3e04a88e569af7b97266d41381554b912d3 100644 --- a/python/paddle/fluid/tests/custom_kernel/test_custom_kernel_load.py +++ b/python/paddle/fluid/tests/custom_kernel/test_custom_kernel_load.py @@ -48,7 +48,7 @@ class TestCustomKernelLoad(unittest.TestCase): paddle_lib_path = lib_dir self.default_path = os.path.sep.join( [paddle_lib_path, '..', '..', 'paddle-plugins']) - # copy so to defalut path + # copy so to default path cmd = 'mkdir -p {} && cp ./*.so {}'.format(self.default_path, self.default_path) os.system(cmd) # wait diff --git a/python/paddle/fluid/tests/unittests/collective/README.md b/python/paddle/fluid/tests/unittests/collective/README.md index 790d207074f80ef6191b3cfbfe3249af745fd31b..6f89b19ad8657418f4967cd19ccebe563209a251 100644 --- a/python/paddle/fluid/tests/unittests/collective/README.md +++ b/python/paddle/fluid/tests/unittests/collective/README.md @@ -8,11 +8,11 @@ * `name`: the test's name * `os`: The supported operator system, ignoring case. If the test run in multiple operator systems, use ";" to split systems, for example, `apple;linux` means the test runs on both Apple and Linux. The supported values are `linux`,`win32` and `apple`. If the value is empty, this means the test runs on all opertaor systems. * `arch`: the device's architecture. similar to `os`, multiple valuse ars splited by ";" and ignoring case. The supported architectures are `gpu`, `xpu`, `ASCEND`, `ASCEND_CL` and `rocm`. -* `timeout`: timeout of a unittest, whose unit is second. Blank means defalut. +* `timeout`: timeout of a unittest, whose unit is second. Blank means default. * `run_type`: run_type of a unittest. Supported values are `NIGHTLY`, `EXCLUSIVE`, `CINN`, `DIST`, `GPUPS`, `INFER`, `EXCLUSIVE:NIGHTLY`, `DIST:NIGHTLY`，which are case-insensitive. * `launcher`: the test launcher.Supported values are test_runner.py, dist_test.sh and custom scripts' name. Blank means test_runner.py. * `num_port`: the number of port used in a distributed unit test. Blank means automatically distributed port. -* `run_serial`: whether in serial mode. the value can be 1 or 0.Default (empty) is 0. Blank means defalut. +* `run_serial`: whether in serial mode. the value can be 1 or 0.Default (empty) is 0. Blank means default. * `ENVS`: required environments. multiple envirenmonts are splited by ";". * `conditions`: extra required conditions for some tests. The value is a list of boolean expression in cmake programmer, splited with ";". For example, the value can be `WITH_DGC;NOT WITH_NCCL` or `WITH_NCCL;${NCCL_VERSION} VERSION_GREATER_EQUAL 2212`,The relationship between these expressions is a conjunction. diff --git a/python/paddle/incubate/sparse/nn/layer/norm.py b/python/paddle/incubate/sparse/nn/layer/norm.py index 253b33cc5eef36da7271b128b6b06dc90bcaae2e..f742bd2e8961d79709c7aa7563d7d6c96a276694 100644 --- a/python/paddle/incubate/sparse/nn/layer/norm.py +++ b/python/paddle/incubate/sparse/nn/layer/norm.py @@ -78,7 +78,7 @@ class BatchNorm(paddle.nn.BatchNorm1D): If it is set to None or one attribute of ParamAttr, batch_norm will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. - data_format(str, optional): Specify the input data format, may be "NC", "NCL" or "NLC". Defalut "NCL". + data_format(str, optional): Specify the input data format, may be "NC", "NCL" or "NLC". Default "NCL". use_global_stats(bool|None, optional): Whether to use global mean and variance. If set to False, use the statistics of one mini-batch, if set to True, use the global statistics, if set to None, use global statistics in the test phase and use the statistics of one mini-batch in the training phase. Default: None. name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. diff --git a/python/paddle/nn/functional/norm.py b/python/paddle/nn/functional/norm.py index 03ba72fdda344e1d768e1987a68afb32ece1a42c..1f5d743630283d3b3ed4cf39571e2d9c0c148b6e 100644 --- a/python/paddle/nn/functional/norm.py +++ b/python/paddle/nn/functional/norm.py @@ -140,8 +140,8 @@ def batch_norm(x, bias(Tensor): The bias tensor of batch_norm can not be None. epsilon(float, optional): The small value added to the variance to prevent division by zero. Default: 1e-5. momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9. - training(bool, optional): True means train mode which compute by batch data and track global mean and var during train period. False means inference mode which compute by global mean and var which calculated by train period. Defalut False. - data_format(str, optional): Specify the input data format, may be "NC", "NCL", "NCHW", "NCDHW", "NLC", "NHWC" or "NDHWC". Defalut "NCHW". + training(bool, optional): True means train mode which compute by batch data and track global mean and var during train period. False means inference mode which compute by global mean and var which calculated by train period. Default False. + data_format(str, optional): Specify the input data format, may be "NC", "NCL", "NCHW", "NCDHW", "NLC", "NHWC" or "NDHWC". Default "NCHW". use_global_stats(bool|None, optional): Whether to use global mean and variance. If set to False, use the statistics of one mini-batch, if set to True, use the global statistics, if set to None, use global statistics in the test phase and use the statistics of one mini-batch in the training phase. Default: None. name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. @@ -392,7 +392,7 @@ def instance_norm(x, eps(float, optional): A value added to the denominator for numerical stability. Default is 1e-5. momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9. use_input_stats(bool): Default True. - data_format(str, optional): Specify the input data format, may be "NC", "NCL", "NCHW" or "NCDHW". Defalut "NCHW". + data_format(str, optional): Specify the input data format, may be "NC", "NCL", "NCHW" or "NCDHW". Default "NCHW". name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. Returns: diff --git a/python/paddle/nn/layer/norm.py b/python/paddle/nn/layer/norm.py index b051a64bfc340ce090c29e320fe053fcdd4113bf..43dd7004c490b34fc1e8586391f293e2ca5eb3ac 100644 --- a/python/paddle/nn/layer/norm.py +++ b/python/paddle/nn/layer/norm.py @@ -144,7 +144,7 @@ class InstanceNorm1D(_InstanceNormBase): will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. If the Initializer of the bias_attr is not set, the bias is initialized zero. If it is set to False, will not create bias_attr. Default: None. - data_format(str, optional): Specify the input data format, may be "NC", "NCL". Defalut "NCL". + data_format(str, optional): Specify the input data format, may be "NC", "NCL". Default "NCL". name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. @@ -743,7 +743,7 @@ class BatchNorm1D(_BatchNormBase): If it is set to None or one attribute of ParamAttr, batch_norm will create ParamAttr as bias_attr. If it is set to Fasle, the weight is not learnable. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. - data_format(str, optional): Specify the input data format, may be "NC", "NCL" or "NLC". Defalut "NCL". + data_format(str, optional): Specify the input data format, may be "NC", "NCL" or "NLC". Default "NCL". use_global_stats(bool|None, optional): Whether to use global mean and variance. If set to False, use the statistics of one mini-batch, if set to True, use the global statistics, if set to None, use global statistics in the test phase and use the statistics of one mini-batch in the training phase. Default: None. name(str, optional): Name for the BatchNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index b7dd412fb08c4083b48d1c387e632bf05b380045..6765394469b3722f9f3a167dfeb3a6da31dc005c 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -276,7 +276,7 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None): or list(int)/tuple(int) with only one element, the vector norm is computed over the axis. If `axis < 0`, the dimension to norm operation is rank(input) + axis. If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis. - Defalut value is `None`. + Default value is `None`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result tensor will have fewer dimension than the :attr:`input` unless :attr:`keepdim` is true, default @@ -2589,7 +2589,7 @@ def pinv(x, rcond=1e-15, hermitian=False, name=None): True. rcond(Tensor, optional): the tolerance value to determine - when is a singular value zero. Defalut:1e-15. + when is a singular value zero. Default:1e-15. hermitian(bool, optional): indicates whether x is Hermitian if complex or symmetric if real. Default: False.