0.9.1-rocm

docs/source/index.rst

 OneFlow API Reference
 ===================================
+
+
+Distributed performance (high efficiency) is the core technical difficulty of deep learning frameworks. 
+
+OneFlow upholds the core concept and architecture of static compilation and streaming parallelism around performance improvement and heterogeneous distributed scaling, solving the challenge of memory wall at cluster level with world-leading technology.
+
+
 .. toctree::
     :maxdepth: 1
 
@@ -12,23 +19,27 @@ OneFlow API Reference
     :caption: OneFlow Python API
 
     oneflow
+    nn
+    nn.functional
     tensor
     tensor_attributes
-    nn
-    functional
+    type_info
     autograd
     cuda
     distributed
+    distributions
+    hub
     linalg
     nn.init
     optim
-    module
     graph
+    auto_parallel
     image
-    utils
-    env
-    comm
+    utils.data
+    utils.global_view
+    utils.tensor
     one_embedding
+    environment_variables
 
 
 

docs/source/linalg.rst

 oneflow.linalg
 ===================================
-OneFlow linear algebra operations.
-----------------------------------
+
+.. The documentation is referenced from: 
+   https://pytorch.org/docs/1.10/linalg.html
+
+Common linear algebra operations.
+
+Matrix Properties
+-----------------
+
 .. currentmodule:: oneflow.linalg
-.. autofunction:: oneflow.linalg.matrix_norm
-.. autofunction:: oneflow.linalg.norm       
-.. autofunction:: oneflow.linalg.vector_norm
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    norm 
+    vector_norm
+    matrix_norm
+    diagonal
+    inv
+    cross

docs/source/module.rst

-oneflow.nn.Module
-================================================
-Module class for building neural networks
----------------------------------------------------
-.. currentmodule:: oneflow.nn
-.. autoclass:: oneflow.nn.Module
-    :members:
-

docs/source/nn.functional.rst

+oneflow.nn.functional
+===========================================
+
+.. The documentation is referenced from: https://pytorch.org/docs/1.10/nn.functional.html.
+
+.. contents:: oneflow.nn.functional
+    :depth: 2
+    :local:
+    :class: this-will-duplicate-information-and-it-is-still-useful-here
+    :backlinks: top
+
+.. currentmodule:: oneflow.nn.functional
+
+Convolution functions
+-------------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    conv1d
+    conv2d
+    conv3d
+    conv_transpose1d
+    conv_transpose2d
+    conv_transpose3d
+    fold
+    unfold
+
+BatchNorm functions
+--------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    batch_norm
+
+Pooling functions
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    avg_pool1d
+    avg_pool2d
+    avg_pool3d
+    max_pool1d
+    max_pool2d
+    max_pool3d
+    max_unpool1d
+    max_unpool2d
+    max_unpool3d
+    adaptive_avg_pool1d
+    adaptive_avg_pool2d
+    adaptive_avg_pool3d
+    adaptive_max_pool1d
+    adaptive_max_pool2d
+    adaptive_max_pool3d
+
+Non-linear activation functions
+-------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    threshold
+    relu
+    hardtanh
+    hardswish
+    relu6
+    elu
+    selu
+    celu
+    leaky_relu
+    prelu
+    glu
+    gelu
+    quick_gelu
+    logsigmoid
+    hardshrink
+    softsign
+    softplus
+    softmax
+    softshrink
+    log_softmax
+    gumbel_softmax
+    tanh
+    sigmoid
+    hardsigmoid
+    silu
+    mish
+    layer_norm
+    normalize
+
+Linear functions
+----------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    linear
+
+Dropout functions
+-----------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    dropout
+    dropout1d
+    dropout2d
+    dropout3d
+
+Sparse functions
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    embedding
+    one_hot
+
+Distance functions
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    cosine_similarity
+    pairwise_distance
+
+
+Loss functions
+--------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    sparse_softmax_cross_entropy
+    cross_entropy
+    l1_loss
+    mse_loss
+    smooth_l1_loss
+    triplet_margin_loss
+    binary_cross_entropy
+    binary_cross_entropy_with_logits
+
+Vision functions
+----------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    deform_conv2d
+    pad
+    interpolate
+    upsample
+    grid_sample
+    affine_grid
+
+Greedy decoder
+----------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    ctc_greedy_decoder
+

docs/source/nn.init.rst

 oneflow.nn.init
-===================================
-Operators for initialization
-----------------------------------
-.. currentmodule:: oneflow.nn.init
+===============
+
+.. The documentation is referenced from: 
+   https://pytorch.org/docs/1.10/nn.init.html
 
-.. autofunction:: oneflow.nn.init.xavier_uniform_
-.. autofunction:: oneflow.nn.init.xavier_normal_
-.. autofunction:: oneflow.nn.init.kaiming_uniform_
-.. autofunction:: oneflow.nn.init.kaiming_normal_
-.. autofunction:: oneflow.nn.init.orthogonal_
+.. currentmodule:: oneflow.nn.init
+.. autofunction:: calculate_gain
+.. autofunction:: uniform_
+.. autofunction:: normal_
+.. autofunction:: constant_
+.. autofunction:: ones_
+.. autofunction:: zeros_
+.. autofunction:: xavier_uniform_
+.. autofunction:: xavier_normal_
+.. autofunction:: kaiming_uniform_
+.. autofunction:: kaiming_normal_
+.. autofunction:: trunc_normal_
+.. autofunction:: orthogonal_

docs/source/nn.rst

 oneflow.nn
 ===================================
-Operators for neural networks
+
+.. The documentation is referenced from: 
+   https://pytorch.org/docs/1.10/nn.html
+
+These are the basic building blocks for graphs:
+
+.. contents:: oneflow.nn
+    :depth: 2
+    :local:
+    :class: this-will-duplicate-information-and-it-is-still-useful-here
+    :backlinks: top
+
+.. currentmodule:: oneflow.nn
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: 
+
+    Parameter
+
+
+Containers
 ----------------------------------
 .. currentmodule:: oneflow.nn
-.. automodule:: oneflow.nn
-    :members: AdaptiveAvgPool1d,
-        AdaptiveAvgPool2d,
-        AdaptiveAvgPool3d,
-        AvgPool1d,
-        AvgPool2d,
-        AvgPool3d,
-        BCELoss,
-        BCEWithLogitsLoss,
-        BatchNorm1d,
-        BatchNorm2d,
-        BatchNorm3d,
-        COCOReader,
-        CTCLoss,
-        CoinFlip,
-        ConstantPad1d,
-        ConstantPad2d,
-        ConstantPad3d,
-        Conv1d,
-        Conv2d,
-        Conv3d,
-        ConvTranspose1d,
-        ConvTranspose2d,
-        ConvTranspose3d,
-        CosineSimilarity,
-        CombinedMarginLoss,
-        CropMirrorNormalize,
-        CrossEntropyLoss,
-        Dropout,
-        ELU,
-        CELU,
-        Embedding,
-        Flatten,
-        GELU,
-        GLU,
-        GroupNorm,
-        Hardsigmoid,
-        Hardshrink,
-        Hardswish,
-        Hardtanh,
-        Identity,
-        InstanceNorm1d,
-        InstanceNorm2d,
-        InstanceNorm3d,
-        KLDivLoss,
-        L1Loss,
-        LayerNorm,
-        LeakyReLU,
-        Linear,
-        LogSigmoid,
-        LogSoftmax,
-        MSELoss,
-        MarginRankingLoss,
-        TripletMarginLoss,
-        MaxPool1d,
-        MaxPool2d,
-        MaxPool3d,
-        ModuleDict,
-        ModuleList,
-        Mish,
-        NLLLoss,
-        OFRecordImageDecoder,
-        OFRecordImageDecoderRandomCrop,
-        OFRecordRawDecoder,
-        OFRecordReader,
-        OFRecordBytesDecoder,
-        PReLU,
-        Parameter,
-        ParameterDict,
-        ParameterList,
-        PixelShuffle,
-        ReLU,
-        ReLU6,
-        ReflectionPad2d,
-        ReplicationPad2d,
-        Sequential, 
-        SELU, 
-        SiLU, 
-        Sigmoid,
-        SmoothL1Loss,
-        Softmax,
-        Softplus, 
-        Softshrink,
-        Softsign, 
-        Tanh,
-        Threshold,
-        Upsample,
-        UpsamplingBilinear2d,
-        UpsamplingNearest2d,
-        ZeroPad2d,
-        MinMaxObserver,
-        MovingAverageMinMaxObserver,
-        FakeQuantization,
-        Quantization, 
-        FusedBatchNorm1d, 
-        FusedBatchNorm2d, 
-        FusedBatchNorm3d, 
-        FusedMLP, 
-
-.. autofunction:: oneflow.nn.modules.pixelshuffle.PixelShufflev2
-
-.. autofunction:: oneflow.nn.parallel.DistributedDataParallel
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    Module
+    Sequential
+    ModuleList
+    ModuleDict
+    ParameterList
+    ParameterDict
+
+nn.Module
+----------------------------------
+.. currentmodule:: oneflow.nn.Module
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    add_module
+    apply
+    buffers
+    children
+    cpu
+    cuda
+    double
+    train
+    eval
+    extra_repr
+    float
+    forward
+    load_state_dict
+    modules
+    named_buffers
+    named_children
+    named_modules
+    named_parameters
+    parameters
+    register_buffer
+    register_forward_hook
+    register_forward_pre_hook
+    register_parameter
+    requires_grad_
+    state_dict
+    to
+    zero_grad
+
+
+
+Containers
+
+Convolution Layers
+----------------------------------
+.. currentmodule:: oneflow
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.Conv1d 
+    nn.Conv2d 
+    nn.Conv3d
+    nn.ConvTranspose1d 
+    nn.ConvTranspose2d 
+    nn.ConvTranspose3d
+    nn.Unfold
+    nn.Fold
+
+Pooling Layers
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.MaxPool1d 
+    nn.MaxPool2d 
+    nn.MaxPool3d 
+    nn.MaxUnpool1d
+    nn.MaxUnpool2d
+    nn.MaxUnpool3d
+    nn.AdaptiveAvgPool1d 
+    nn.AdaptiveAvgPool2d 
+    nn.AdaptiveAvgPool3d
+    nn.AdaptiveMaxPool1d 
+    nn.AdaptiveMaxPool2d 
+    nn.AdaptiveMaxPool3d
+    nn.AvgPool1d 
+    nn.AvgPool2d 
+    nn.AvgPool3d
+
+Padding Layers
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.ConstantPad1d 
+    nn.ConstantPad2d 
+    nn.ConstantPad3d
+    nn.ReflectionPad1d
+    nn.ReflectionPad2d
+    nn.ReplicationPad1d
+    nn.ReplicationPad2d
+    nn.ZeroPad2d
+
+Non-linear Activations (weighted sum, nonlinearity)
+----------------------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.ELU 
+    nn.Hardshrink
+    nn.Hardsigmoid 
+    nn.Hardswish 
+    nn.Hardtanh 
+    nn.LeakyReLU 
+    nn.LogSigmoid 
+    nn.PReLU 
+    nn.ReLU
+    nn.ReLU6 
+    nn.SELU 
+    nn.CELU 
+    nn.GELU 
+    nn.QuickGELU 
+    nn.SiLU 
+    nn.Sigmoid 
+    nn.Mish 
+    nn.Softplus 
+    nn.Softshrink 
+    nn.Softsign 
+    nn.Tanh 
+    nn.Threshold 
+    nn.GLU
+
+Non-linear Activations (other)
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.Softmax
+    nn.LogSoftmax
+
+Normalization Layers
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.BatchNorm1d 
+    nn.BatchNorm2d 
+    nn.BatchNorm3d
+    nn.SyncBatchNorm
+    nn.FusedBatchNorm1d 
+    nn.FusedBatchNorm2d
+    nn.FusedBatchNorm3d 
+    nn.GroupNorm 
+    nn.InstanceNorm1d 
+    nn.InstanceNorm2d 
+    nn.InstanceNorm3d 
+    nn.LayerNorm
+    nn.RMSLayerNorm
+    nn.RMSNorm
+
+Recurrent Layers
+----------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.RNN
+    nn.LSTM
+    nn.GRU
+    nn.RNNCell
+    nn.LSTMCell
+    nn.GRUCell
+
+Linear Layers
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.Identity
+    nn.Linear
+
+Dropout Layers
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.Dropout
+    nn.Dropout1d
+    nn.Dropout2d
+    nn.Dropout3d
+
+Sparse Layers
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.Embedding
+
+Distance Functions
+------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.CosineSimilarity
+    nn.PairwiseDistance
+
+Loss Functions
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.BCELoss 
+    nn.BCEWithLogitsLoss 
+    nn.CTCLoss 
+    nn.CombinedMarginLoss 
+    nn.CrossEntropyLoss 
+    nn.KLDivLoss 
+    nn.L1Loss 
+    nn.MSELoss 
+    nn.MarginRankingLoss 
+    nn.NLLLoss 
+    nn.SmoothL1Loss 
+    nn.TripletMarginLoss
+
+Vision Layers
+----------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.PixelShuffle 
+    nn.Upsample 
+    nn.UpsamplingBilinear2d 
+    nn.UpsamplingNearest2d
+
+
+DataParallel Layers (multi-GPU, distributed)
+--------------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+    
+    nn.parallel.DistributedDataParallel
+
+
+Data loading and preprocessing Layers
+----------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    nn.COCOReader
+    nn.CoinFlip
+    nn.CropMirrorNormalize
+    nn.OFRecordBytesDecoder
+    nn.OFRecordImageDecoder
+    nn.OFRecordImageDecoderRandomCrop
+    nn.OFRecordRawDecoder
+    nn.OFRecordReader
+
+Quantization Aware Training
+--------------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    nn.MinMaxObserver
+    nn.MovingAverageMinMaxObserver
+    nn.FakeQuantization
+    nn.QatConv1d
+    nn.QatConv2d
+    nn.QatConv3d
+
+Utilities
+---------
+
+From the ``oneflow.nn.utils`` module
 
 .. currentmodule:: oneflow.nn.utils
-.. autofunction:: oneflow.nn.utils.clip_grad_norm_
-.. autofunction:: oneflow.nn.utils.weight_norm
-.. autofunction:: oneflow.nn.utils.remove_weight_norm
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    clip_grad_norm_
+    clip_grad_value_
+    weight_norm
+    remove_weight_norm
+
+Utility functions in other modules
+
+.. currentmodule:: oneflow
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.utils.rnn.PackedSequence
+    nn.utils.rnn.pack_padded_sequence
+    nn.utils.rnn.pad_packed_sequence
+    nn.utils.rnn.pad_sequence
+    nn.utils.rnn.pack_sequence
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template: classtemplate.rst
+
+    nn.Flatten
+
+Quantized Functions
+--------------------
+
+Quantization refers to techniques for performing computations and 
+storing tensors at lower bitwidths than floating point precision.
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    :template:
+
+    nn.FakeQuantization
+    nn.MinMaxObserver
+    nn.MovingAverageMinMaxObserver
+    nn.Quantization

docs/source/one_embedding.rst

 oneflow.one_embedding
 ===================================
-OneFlow one_embedding operations.
+
+Embedding is an important component of recommender system, and it has also spread to many fields outside recommender systems. Each framework provides basic operators for Embedding, for example, ``flow.nn.Embedding`` in OneFlow:
+
+::
+
+    import numpy as np
+    import oneflow as flow
+    indices = flow.tensor([[1, 2, 4, 5], [4, 3, 2, 9]], dtype=flow.int)
+    embedding = flow.nn.Embedding(10, 3)
+    y = embedding(indices)
+
+
+OneEmbedding is the large-scale Embedding solution that OneFlow provides to solve the problem of large-scale deep recommender systems. OneEmbedding has the following advantages compared to ordinary opeartors:
+
+    - With Flexible hierarchical storage, OneEmbedding can place the Embedding table on GPU memory, CPU memory or SSD, and allow high-speed devices to be used as caches for low-speed devices to achieve both speed and capacity.
+
+    - OneEmbedding supports dynamic expansion.
+
+.. note ::
+    Please refer to `Large-Scale Embedding Solution: OneEmbedding <https://docs.oneflow.org/en/master/cookies/one_embedding.html>`__
+    for a brief introduction to all features related to OneEmbedding.
+
+Configure Embedding Table 
 ----------------------------------
-.. currentmodule:: oneflow.one_embedding
-.. autoclass:: MultiTableEmbedding
-    :members: forward,
-              save_snapshot,
-              load_snapshot,
-.. autofunction:: oneflow.one_embedding.MultiTableEmbedding.forward
-.. autoclass:: MultiTableMultiColumnEmbedding
-    :members: forward,
-              save_snapshot,
-              load_snapshot,
-.. autofunction:: oneflow.one_embedding.MultiTableMultiColumnEmbedding.forward
-.. autofunction:: oneflow.one_embedding.make_device_mem_store_options
-.. autofunction:: oneflow.one_embedding.make_cached_ssd_store_options       
-.. autofunction:: oneflow.one_embedding.make_cached_host_mem_store_options
-.. autofunction:: oneflow.one_embedding.make_uniform_initializer
-.. autofunction:: oneflow.one_embedding.make_normal_initializer
+
+OneEmbedding supports simultaneous creation of multiple Embedding table. The following codes configured three Embedding tables.
+
+.. code-block:: 
+
+    import oneflow as flow
+    import oneflow.nn as nn
+    import numpy as np
+
+    tables = [
+        flow.one_embedding.make_table_options(
+            flow.one_embedding.make_uniform_initializer(low=-0.1, high=0.1)
+        ),
+        flow.one_embedding.make_table_options(
+            flow.one_embedding.make_uniform_initializer(low=-0.05, high=0.05)
+        ),
+        flow.one_embedding.make_table_options(
+            flow.one_embedding.make_uniform_initializer(low=-0.15, high=0.15)
+        ),
+    ]
+
+When configuring the Embedding table, you need to specify the initialization method. The above Embedding tables are initialized in the ``uniform`` method. The result of configuring the Embedding table is stored in the ``tables`` variable
+
 .. autofunction:: oneflow.one_embedding.make_table_options
 .. autofunction:: oneflow.one_embedding.make_table
+
+initialization method
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. currentmodule:: oneflow.one_embedding
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    make_uniform_initializer
+    make_normal_initializer
+
+
+Configure the Storage Attribute of the Embedding Table
+--------------------------------------------------------------------
+Then run the following codes to configure the storage attribute of the Embedding table:
+
+.. code-block:: 
+
+    store_options = flow.one_embedding.make_cached_ssd_store_options(
+    cache_budget_mb=8142,
+    persistent_path="/your_path_to_ssd", 
+    capacity=40000000,
+    size_factor=1,              
+    physical_block_size=4096
+    )
+
+Storage Method
+^^^^^^^^^^^^^^^^^^^^
+
+.. currentmodule:: oneflow.one_embedding
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    make_device_mem_store_options
+    make_cached_ssd_store_options 
+    make_cached_host_mem_store_options
+
+.. note ::
+    
+    Please refer to `Large-Scale Embedding Solution: OneEmbedding <https://docs.oneflow.org/en/master/cookies/one_embedding.html#feature-id-and-dynamic-insertion>`__
+    for a brief introduction to learn about How to Choose the Proper Storage Configuration
+
+
+Instantiate Embedding
+--------------------------------------------------------------------
+After the above configuration is completed, you can use MultiTableEmbedding to get the instantiated Embedding layer.
+
+.. code-block:: 
+
+    embedding_size = 128
+    embedding = flow.one_embedding.MultiTableEmbedding(
+        name="my_embedding",
+        embedding_dim=embedding_size,
+        dtype=flow.float,
+        key_type=flow.int64,
+        tables=tables,
+        store_options=store_options,
+    )
+
+    embedding.to("cuda")
+
+.. note ::
+    
+    Please refer to `Large-Scale Embedding Solution: OneEmbedding <https://docs.oneflow.org/en/master/cookies/one_embedding.html#feature-id-and-multi-table-query>`__
+    for a brief introduction to learn about Feature ID and Multi-Table Query.
+
+
+MultiTableEmbedding
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: oneflow.one_embedding.MultiTableEmbedding
+
+.. currentmodule:: oneflow.one_embedding.MultiTableEmbedding
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    
+    forward
+    save_snapshot
+    load_snapshot
+
+MultiTableMultiColumnEmbedding
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: oneflow.one_embedding.MultiTableMultiColumnEmbedding
+
+.. currentmodule:: oneflow.one_embedding.MultiTableMultiColumnEmbedding
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    
+    forward
+    save_snapshot
+    load_snapshot
+
+Construct Graph for Training
+--------------------------------------------------------------------
+OneEmbedding is only supported in Graph mode.
+
+.. code-block:: 
+
+    num_tables = 3
+    mlp = flow.nn.FusedMLP(
+        in_features=embedding_size * num_tables,
+        hidden_features=[512, 256, 128],
+        out_features=1,
+        skip_final_activation=True,
+    )
+    mlp.to("cuda")
+
+    class TrainGraph(flow.nn.Graph):
+        def __init__(self,):
+            super().__init__()
+            self.embedding_lookup = embedding
+            self.mlp = mlp
+            self.add_optimizer(
+                flow.optim.SGD(self.embedding_lookup.parameters(), lr=0.1, momentum=0.0)
+            )
+            self.add_optimizer(
+                flow.optim.SGD(self.mlp.parameters(), lr=0.1, momentum=0.0)
+            )
+        def build(self, ids):
+            embedding = self.embedding_lookup(ids)
+            loss = self.mlp(flow.reshape(embedding, (-1, num_tables * embedding_size)))
+            loss = loss.sum()
+            loss.backward()
+            return loss
+
+.. note ::
+    
+    Please refer to `Distributed Training: OneEmbedding <https://docs.oneflow.org/en/master/parallelism/01_introduction.html>`__
+    for a brief introduction to learn about Graph For Training
+
+
+Persistent Read & Write
+-----------------------------------------------
+.. currentmodule:: oneflow.one_embedding
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    
+    make_persistent_table_reader
+    make_persistent_table_writer
+
 .. automodule:: oneflow.one_embedding
     :members: Ftrl
-.. autofunction:: oneflow.one_embedding.make_persistent_table_reader
-.. autofunction:: oneflow.one_embedding.make_persistent_table_writer
+

docs/source/oneflow.rst

 oneflow
 ===================================
-oneflow
-----------------------------------
+
+.. The documentation is referenced from: 
+   https://pytorch.org/docs/1.10/torch.html
+
+The oneflow package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities.
+
+It has a CUDA counterpart, that enables you to run your tensor computations on an NVIDIA GPU with compute capability >= 3.0
+
 .. currentmodule:: oneflow
-.. automodule:: oneflow
-    :members: adaptive_avg_pool1d, 
-            adaptive_avg_pool2d, 
-            adaptive_avg_pool3d, 
-            abs, 
-            acos, 
-            acosh, 
-            add, 
-            addcmul, 
-            addmm, 
-            all, 
-            amin, 
-            amax,
-            any, 
-            arccos, 
-            arcsin, 
-            arcsinh, 
-            arccosh, 
-            arctan, 
-            arctanh, 
-            argmax, 
-            argmin, 
-            arange, 
-            argsort, 
-            argwhere,
-            asin,  
-            asinh, 
-            atan, 
-            atan2, 
-            atanh, 
-            bernoulli,
-            broadcast_like, 
-            batch_gather,
-            bmm,
-            cat, 
-            concat,
-            cast, 
-            ceil, 
-            chunk, 
-            clamp, 
-            clip, 
-            cos, 
-            cosh, 
-            diag, 
-            select,
-            diagonal,
-            movedim,
-            tensor_split,
-            hsplit,
-            vsplit,
-            as_strided,
-            div, 
-            dot, 
-            eq,
-            einsum,
-            equal, 
-            expand, 
-            eye,
-            exp, 
-            expm1, 
-            erf, 
-            erfc, 
-            erfinv, 
-            flatten, 
-            flip, 
-            floor, 
-            floor_,
-            fmod,
-            full, 
-            gather, 
-            gather_nd, 
-            gelu, 
-            gt, 
-            in_top_k, 
-            index_select,
-            linspace,
-            logical_and,
-            logical_or,
-            logical_not,
-            logical_xor,
-            load, 
-            log, 
-            log2,
-            log1p, 
-            lt, 
-            le, 
-            masked_fill, 
-            masked_select, 
-            matmul, 
-            mm, 
-            mv, 
-            narrow, 
-            max, 
-            mean,
-            median,
-            mish,  
-            min, 
-            meshgrid,
-            mul, 
-            neg, 
-            negative, 
-            new_ones,
-            nonzero,
-            normal,
-            numel, 
-            ne, 
-            empty,
-            ones, 
-            ones_like, 
-            pow,
-            prod,  
-            rand,
-            randn,
-            repeat, 
-            repeat_interleave,
-            reshape, 
-            randint,
-            randperm,
-            reciprocal,
-            roc_auc_score,
-            roll,
-            round, 
-            rsqrt,
-            save, 
-            scatter,
-            scatter_add,
-            scatter_nd, 
-            tensor_scatter_nd_update,
-            sin, 
-            sin_, 
-            sinh, 
-            sign, 
-            selu, 
-            silu, 
-            slice, 
-            slice_update,
-            softsign, 
-            sort, 
-            softplus, 
-            sigmoid, 
-            softmax, 
-            squeeze, 
-            split, 
-            stack, 
-            std,
-            sub, 
-            sum, 
-            sqrt, 
-            square,  
-            swapaxes, 
-            swapdims, 
-            tan, 
-            tanh, 
-            tensor, 
-            tensordot,
-            tile, 
-            transpose,
-            t,
-            tril, 
-            unsqueeze, 
-            unbind, 
-            permute,
-            var, 
-            where, 
-            zeros, 
-            zeros_like,
-            is_nonzero,
-            is_tensor,
-            no_grad,
-            set_grad_enabled,
-            enable_grad,
-            inference_mode,
-            is_grad_enabled,
-            is_floating_point,
-            set_printoptions,
-            decode_onerec,
-            from_numpy,
-            as_tensor,
-            cumsum,
-            topk,
-            nms,
-            cumprod,
-            HalfTensor,
-            FloatTensor,
-            DoubleTensor,
-            BoolTensor,
-            ByteTensor,
-            CharTensor,
-            IntTensor,
-            LongTensor,
-            seed,
-            manual_seed,
-            initial_seed,
-            get_rng_state,
-            set_rng_state,
-            isnan,
-            isinf,
+
+
+Tensor
+-------------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    BoolTensor
+    ByteTensor
+    CharTensor
+    DoubleTensor
+    FloatTensor
+    HalfTensor
+    IntTensor
+    LongTensor
+
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    is_tensor
+    is_floating_point
+    is_nonzero
+    numel
+    set_printoptions
+    get_default_dtype
+    set_default_dtype
+    set_default_tensor_type
+
+.. _tensor-creation-ops:
+
+Creation Ops
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. note::
+    Random sampling creation ops are listed under :ref:`random-sampling` and
+    include:
+    :func:`oneflow.rand`
+    :func:`oneflow.randn`
+    :func:`oneflow.randint`
+    :func:`oneflow.randperm`
+    
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    tensor
+    as_tensor
+    as_strided
+    from_numpy
+    zeros
+    zeros_like
+    ones
+    ones_like
+    randn_like
+    randint_like
+    masked_fill
+    new_ones
+    arange
+    linspace
+    eye
+    empty
+    empty_like
+    full
+    full_like
+    tensor_scatter_nd_update
+    logspace
+
+.. _indexing-slicing-joining:
+
+Indexing, Slicing, Joining, Mutating Ops
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    argwhere
+    atleast_1d
+    atleast_2d
+    atleast_3d
+    cat
+    column_stack
+    concat
+    chunk
+    dstack
+    expand
+    gather
+    gather_nd
+    batch_gather
+    hsplit
+    hstack
+    vsplit
+    vstack
+    index_select
+    index_add
+    masked_select
+    movedim
+    narrow
+    nonzero
+    permute
+    repeat
+    reshape
+    row_stack
+    select
+    scatter
+    scatter_add
+    scatter_nd
+    slice
+    slice_update
+    split
+    squeeze
+    stack
+    swapaxes
+    swapdims
+    t
+    tile
+    transpose
+    unbind
+    unsqueeze
+    where
+    tensor_split
+
+.. _random-sampling:
+
+Random sampling
+-------------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    seed
+    manual_seed
+    initial_seed
+    get_rng_state
+    set_rng_state
+    bernoulli
+    normal
+    rand
+    randint
+    randn
+    randperm
+    multinomial
+    
+In-place random sampling
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+There are a few more in-place random sampling functions defined on Tensors as well. Click through to refer to their documentation:
+- :func:`oneflow.Tensor.normal_` - in-place version of :func:`oneflow.normal`
+- :func:`oneflow.Tensor.uniform_` - numbers sampled from the continuous uniform distribution
+
+
+
+Serialization
+-------------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    save
+    load
+
+Parallelism
+-------------------------------------------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    set_num_threads
+
+
+Locally disabling gradient computation
+-------------------------------------------
+The context managers :func:`oneflow.no_grad`, :func:`oneflow.enable_grad`, and
+:func:`oneflow.set_grad_enabled` are helpful for locally disabling and enabling
+gradient computation. These context managers are thread local, so they won't
+work if you send work to another thread using the ``threading`` module, etc.
+
+Examples::
+
+  >>> import oneflow
+  >>> x = oneflow.zeros(1, requires_grad=True)
+  >>> with oneflow.no_grad():
+  ...     y = x * 2
+  >>> y.requires_grad
+  False
+
+  >>> with oneflow.set_grad_enabled(False):
+  ...     y = x * 2
+  >>> y.requires_grad
+  False
+  
+  >>> with oneflow.set_grad_enabled(True):
+  ...     y = x * 2
+  >>> y.requires_grad
+  True
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    no_grad
+    set_grad_enabled
+    enable_grad
+    is_grad_enabled
+    inference_mode
+
+Math operations
+-------------------------------------------
+
+Pointwise Ops
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    abs 
+    acos 
+    acosh 
+    arccos 
+    arccosh
+    add 
+    addcdiv
+    addcmul
+    asin 
+    asinh 
+    arcsin 
+    arcsinh 
+    atan
+    atanh 
+    arctan 
+    arctanh 
+    atan2 
+    ceil 
+    clamp 
+    clamp_min
+    clamp_max
+    clip 
+    cos 
+    cosh 
+    div 
+    erf 
+    erfc 
+    erfinv
+    exp 
+    expm1 
+    floor 
+    floor_ 
+    fmod 
+    gelu
+    quick_gelu
+    log 
+    log1p 
+    log2 
+    log10
+    logical_and 
+    logical_not 
+    logical_or 
+    logical_xor 
+    mish
+    mul 
+    neg 
+    negative 
+    pow 
+    reciprocal 
+    round 
+    rsqrt 
+    selu
+    softmax
+    softplus
+    softsign
+    silu
+    sigmoid 
+    sign 
+    sin 
+    sinh 
+    sin_ 
+    sqrt 
+    square 
+    sub 
+    tan 
+    tanh
+    trunc
+    floor_divide
+
+Reduction Ops
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+    
+    argmax  
+    argmin  
+    amax
+    amin
+    any
+    max
+    min  
+    mean  
+    median
+    prod
+    nansum
+    std  
+    sum  
+    logsumexp
+    var
+    norm
+    all
+
+
+Comparison Ops
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    argsort 
+    eq 
+    equal 
+    gt 
+    isinf 
+    isnan 
+    le 
+    lt 
+    ne 
+    sort 
+    topk
+    ge
+    greater
+    greater_equal
+    maximum
+    minimum
+    not_equal
+    isclose
+    allclose
+
+Spectral Ops
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    hann_window
+    
+Other Ops
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    adaptive_avg_pool1d
+    adaptive_avg_pool2d
+    adaptive_avg_pool3d
+    broadcast_like 
+    cast
+    cumprod 
+    cumsum 
+    decode_onerec
+    diag 
+    diagonal 
+    einsum 
+    flatten 
+    flip 
+    in_top_k
+    meshgrid 
+    nms
+    roc_auc_score
+    roll 
     searchsorted
+    tensordot
+    tril
+    repeat_interleave
+    triu
+    cross
+    bincount
+    broadcast_shapes
+    broadcast_tensors
+    broadcast_to
+    unique
+
+BLAS and LAPACK Operations
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    addmm 
+    bmm
+    baddbmm 
+    dot 
+    matmul
+    mm
+    mv
 
-.. autofunction:: oneflow.relu
-.. autofunction:: oneflow.set_num_threads

docs/source/optim.rst

 oneflow.optim
 ===================================
-Optimizers
-----------------------------------
+
+.. The documentation is referenced from: 
+   https://pytorch.org/docs/1.10/optim.html
+
+oneflow.optim is a package implementing various optimization algorithms. Most commonly used methods are already supported, and the interface is general enough, so that more sophisticated ones can be also easily integrated in the future.
+
+How to use an optimizer
+-----------------------
+
+To use :mod:`oneflow.optim` you have to construct an optimizer object, that will hold
+the current state and will update the parameters based on the computed gradients.
+
+Constructing it
+^^^^^^^^^^^^^^^
+
+To construct an :class:`Optimizer` you have to give it an iterable containing the
+parameters (all should be :class:`~oneflow.autograd.Variable` s) to optimize. Then,
+you can specify optimizer-specific options such as the learning rate, weight decay, etc.
+
+.. note::
+    If you need to move a model to GPU via ``.cuda()``, please do so before 
+    constructing optimizers for it. Parameters of a model after ``.cuda()`` 
+    will be different objects with those before the call.
+
+    In general, you should make sure that optimized parameters live in 
+    consistent locations when optimizers are constructed and used. 
+    
+Example::
+
+    import oneflow
+    import oneflow.nn as nn
+    import oneflow.optim as optim
+
+    model = nn.Linear(16, 3)
+    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
+
+Per-parameter options
+^^^^^^^^^^^^^^^^^^^^^
+
+:class:`Optimizer` also support specifying per-parameter options. To do this, instead
+of passing an iterable of :class:`~oneflow.autograd.Variable`, pass in an iterable of
+:class:`dict`. Each of them will define a separate parameter group, and should contain
+a ``params`` key, containing a list of parameters belonging to it. Other keys
+should match the keyword arguments accepted by the optimizers, and will be used
+as optimization options for this group.
+
+.. note::
+
+    You can still pass options as keyword arguments. They will be used as
+    defaults, in the groups that didn't override them. This is useful when you
+    only want to vary a single option, while keeping all others consistent
+    between parameter groups.
+
+
+For example, this is very useful when one wants to specify per-layer learning rates::
+
+    import oneflow.nn as nn
+    import oneflow.optim as optim
+
+
+    class Model(nn.Module):
+        def __init__(self):
+            super(Model, self).__init__()
+            self.base = nn.Linear(64, 32)
+            self.classifier = nn.Linear(32, 10)
+
+        def forward(self, x):
+            out = self.base(x)
+            out = self.classifier(out)
+            return out
+
+
+    model = Model()
+    optim.SGD(
+        [
+            {"params": model.base.parameters()},
+            {"params": model.classifier.parameters(), "lr": 1e-3},
+        ],
+        lr=1e-2,
+        momentum=0.9,
+    )
+
+
+This means that ``model.base``'s parameters will use the default learning rate of ``1e-2``,
+``model.classifier``'s parameters will use a learning rate of ``1e-3``, and a momentum of
+``0.9`` will be used for all parameters.
+
+Taking an optimization step
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+All optimizers implement a :func:`~Optimizer.step` method, that updates the
+parameters. It can be used in two ways:
+
+``optimizer.step()``
+~~~~~~~~~~~~~~~~~~~~
+
+This is a simplified version supported by most optimizers. The function can be
+called once the gradients are computed using e.g.
+:func:`~oneflow.autograd.Variable.backward`.
+
+Example::
+
+    import oneflow
+    import oneflow.nn as nn
+    import oneflow.nn.functional as F
+    import oneflow.optim as optim
+    from oneflow.utils.data import Dataset, DataLoader
+
+
+    class CustomDataset(Dataset):
+        def __init__(self, num):
+            self.inputs = oneflow.randn(num, 1)
+            self.targets = oneflow.sin(self.inputs)
+
+        def __len__(self):
+            return self.inputs.shape[0]
+
+        def __getitem__(self, index):
+            return self.inputs[index], self.targets[index]
+
+
+    class Model(nn.Module):
+        def __init__(self, input_size):
+            super(Model, self).__init__()
+            self.linear1 = nn.Linear(input_size, 64)
+            self.linear2 = nn.Linear(64, input_size)
+
+        def forward(self, x):
+            out = self.linear1(x)
+            return self.linear2(F.relu(out))
+
+
+    dataset = CustomDataset(10000)
+    dataloader = DataLoader(dataset, batch_size=10)
+    model = Model(1)
+    loss_fn = nn.MSELoss()
+    optimizer = optim.SGD(model.parameters(), lr=1e-3)
+
+    for epoch in range(100):
+        for input, target in dataloader:
+            optimizer.zero_grad()
+            output = model(input)
+            loss = loss_fn(output, target)
+            loss.backward()
+            optimizer.step()
+
+.. _optimizer-algorithms:
+
 .. currentmodule:: oneflow.optim
-.. automodule:: oneflow.optim
-    :members: Adam,
-        Adagrad, 
-        AdamW,
-        Optimizer,
-        RMSprop,
-        SGD,
-        LAMB,
-        lr_scheduler
-
-.. automodule:: oneflow.optim.lr_scheduler
-    :members: CosineDecayLR,
-            CosineAnnealingLR,
-            LambdaLR,
-            StepLR,
-            MultiStepLR,
-            ExponentialLR,
-            ReduceLROnPlateau,
-            PolynomialLR
+
+
+Base class
+----------
+
+.. autoclass:: Optimizer
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    Optimizer.add_param_group
+    Optimizer.load_state_dict
+    Optimizer.state_dict
+    Optimizer.step
+    Optimizer.zero_grad
+
+Algorithms
+----------
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    Adagrad
+    Adam
+    AdamW
+    LAMB
+    RMSprop
+    SGD
+
+Adjust Learning Rate
+--------------------
+
+:mod:`oneflow.optim.lr_scheduler` provides several methods to adjust the learning
+rate based on the number of epochs. :class:`oneflow.optim.lr_scheduler.ReduceLROnPlateau`
+allows dynamic learning rate reducing based on some validation measurements.
+
+Learning rate scheduling should be applied after optimizer's update; e.g., you
+should write your code this way:
+
+Example::
+
+    import oneflow
+    import oneflow.nn as nn
+    import oneflow.nn.functional as F
+    import oneflow.optim as optim
+    from oneflow.utils.data import Dataset, DataLoader
+
+
+    class CustomDataset(Dataset):
+        def __init__(self, num):
+            self.inputs = oneflow.randn(num, 1)
+            self.targets = oneflow.sin(self.inputs)
+
+        def __len__(self):
+            return self.inputs.shape[0]
+
+        def __getitem__(self, index):
+            return self.inputs[index], self.targets[index]
+
+
+    class Model(nn.Module):
+        def __init__(self, input_size):
+            super(Model, self).__init__()
+            self.linear1 = nn.Linear(input_size, 64)
+            self.linear2 = nn.Linear(64, input_size)
+
+        def forward(self, x):
+            out = self.linear1(x)
+            return self.linear2(F.relu(out))
+
+
+    dataset = CustomDataset(10000)
+    dataloader = DataLoader(dataset, batch_size=10)
+    model = Model(1)
+    loss_fn = nn.MSELoss()
+    optimizer = optim.SGD(model.parameters(), lr=1e-3)
+    scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
+
+    for epoch in range(20):
+        for input, target in dataloader:
+            optimizer.zero_grad()
+            output = model(input)
+            loss = loss_fn(output, target)
+            loss.backward()
+            optimizer.step()
+        scheduler.step()
+
+Most learning rate schedulers can be chained (also referred to as
+chaining schedulers).
+
+Example::
+
+    import oneflow
+    import oneflow.nn as nn
+    import oneflow.nn.functional as F
+    import oneflow.optim as optim
+    from oneflow.utils.data import Dataset, DataLoader
+
+
+    class CustomDataset(Dataset):
+        def __init__(self, num):
+            self.inputs = oneflow.randn(num, 1)
+            self.targets = oneflow.sin(self.inputs)
+
+        def __len__(self):
+            return self.inputs.shape[0]
+
+        def __getitem__(self, index):
+            return self.inputs[index], self.targets[index]
+
+
+    class Model(nn.Module):
+        def __init__(self, input_size):
+            super(Model, self).__init__()
+            self.linear1 = nn.Linear(input_size, 64)
+            self.linear2 = nn.Linear(64, input_size)
+
+        def forward(self, x):
+            out = self.linear1(x)
+            return self.linear2(F.relu(out))
+
+
+    dataset = CustomDataset(10000)
+    dataloader = DataLoader(dataset, batch_size=10)
+    model = Model(1)
+    loss_fn = nn.MSELoss()
+    optimizer = optim.SGD(model.parameters(), lr=1e-3)
+    scheduler1 = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
+    scheduler2 = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5, 10], gamma=0.1)
+
+    for epoch in range(20):
+        for input, target in dataloader:
+            optimizer.zero_grad()
+            output = model(input)
+            loss = loss_fn(output, target)
+            loss.backward()
+            optimizer.step()
+        scheduler1.step()
+        scheduler2.step()
+
+In many places in the documentation, we will use the following template to refer to schedulers
+algorithms.
+
+    >>> scheduler = ...
+    >>> for epoch in range(100):
+    >>>     train(...)
+    >>>     validate(...)
+    >>>     scheduler.step()
+
+.. warning::
+  If you use the learning rate scheduler (calling ``scheduler.step()``) before the optimizer's update
+  (calling ``optimizer.step()``), this will skip the first value of the learning rate schedule. Please 
+  check if you are calling ``scheduler.step()`` at the wrong time.
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    lr_scheduler.CosineAnnealingLR
+    lr_scheduler.CosineDecayLR 
+    lr_scheduler.ExponentialLR 
+    lr_scheduler.LambdaLR 
+    lr_scheduler.MultiStepLR
+    lr_scheduler.PolynomialLR 
+    lr_scheduler.ReduceLROnPlateau 
+    lr_scheduler.StepLR
+    lr_scheduler.ConstantLR
+    lr_scheduler.LinearLR
+    lr_scheduler.ChainedScheduler
+    lr_scheduler.SequentialLR
+    lr_scheduler.CosineAnnealingWarmRestarts

docs/source/tensor_attributes.rst

 .. currentmodule:: oneflow
 
+.. _tensor-attributes-doc:
+
 Tensor Attributes
 =============================================================
+
+.. The documentation is referenced from: https://pytorch.org/docs/1.10/tensor_attributes.html.
+
+
 Each local ``oneflow.Tensor`` has a :class:`oneflow.dtype`, :class:`oneflow.device`, and global ``oneflow.Tensor`` has a :class:`oneflow.dtype`, :class:`oneflow.placement`, :class:`oneflow.sbp`.
 
+.. contents:: oneflow
+    :depth: 2
+    :local:
+    :class: this-will-duplicate-information-and-it-is-still-useful-here
+    :backlinks: top
+
+
+.. _dtype-doc:
+
+oneflow.dtype
+-----------------------
+
+.. class:: dtype
+
+A :class:`oneflow.dtype` is an object that represents the data type of a
+:class:`oneflow.Tensor`. Oneflow has eight different data types:
+
+======================================= =============================================== =============================== ==================================
+Data type                               dtype                                           CPU tensor                      GPU tensor
+======================================= =============================================== =============================== ==================================
+Boolean                                 ``oneflow.bool``                                :class:`oneflow.BoolTensor`     :class:`oneflow.cuda.BoolTensor`
+8-bit integer (unsigned)                ``oneflow.uint8``                               :class:`oneflow.ByteTensor`     :class:`oneflow.cuda.ByteTensor`
+8-bit integer (signed)                  ``oneflow.int8``                                :class:`oneflow.CharTensor`     :class:`oneflow.cuda.CharTensor`
+64-bit floating point                   ``oneflow.float64`` or ``oneflow.double``       :class:`oneflow.DoubleTensor`   :class:`oneflow.cuda.DoubleTensor`
+32-bit floating point                   ``oneflow.float32`` or ``oneflow.float``        :class:`oneflow.FloatTensor`    :class:`oneflow.cuda.FloatTensor`
+16-bit floating point                   ``oneflow.float16`` or ``oneflow.half``         :class:`oneflow.HalfTensor`     :class:`oneflow.cuda.HalfTensor`
+32-bit integer (signed)                 ``oneflow.int32`` or ``oneflow.int``            :class:`oneflow.IntTensor`      :class:`oneflow.cuda.IntTensor`
+64-bit integer (signed)                 ``oneflow.int64`` or ``oneflow.long``           :class:`oneflow.LongTensor`     :class:`oneflow.cuda.LongTensor`
+======================================= =============================================== =============================== ==================================
+
+
+To find out if a :class:`oneflow.dtype` is a floating point data type, the property :attr:`is_floating_point`
+can be used, which returns ``True`` if the data type is a floating point data type.
+
+.. _type-promotion-doc:
+
+When the dtypes of inputs to an arithmetic operation (`add`, `sub`, `div`, `mul`) differ, we promote
+by finding the minimum dtype that satisfies the following rules:
+
+* If the type of a scalar operand is of a higher category than tensor operands
+  (where complex > floating > integral > boolean), we promote to a type with sufficient size to hold
+  all scalar operands of that category.
+* If a zero-dimension tensor operand has a higher category than dimensioned operands,
+  we promote to a type with sufficient size and category to hold all zero-dim tensor operands of
+  that category.
+* If there are no higher-category zero-dim operands, we promote to a type with sufficient size
+  and category to hold all dimensioned operands.
+
+A floating point scalar operand has dtype `oneflow.get_default_dtype()` and an integral
+non-boolean scalar operand has dtype `oneflow.int64`. Unlike numpy, we do not inspect
+values when determining the minimum `dtypes` of an operand.  Quantized and complex types
+are not yet supported.
+
+Promotion Examples::
+
+    >>> float_tensor = oneflow.ones(1, dtype=oneflow.float)
+    >>> double_tensor = oneflow.ones(1, dtype=oneflow.double)
+    >>> int_tensor = oneflow.ones(1, dtype=oneflow.int)
+    >>> long_tensor = oneflow.ones(1, dtype=oneflow.long)
+    >>> uint_tensor = oneflow.ones(1, dtype=oneflow.uint8)
+    >>> double_tensor = oneflow.ones(1, dtype=oneflow.double)
+    >>> bool_tensor = oneflow.ones(1, dtype=oneflow.bool)
+    # zero-dim tensors
+    >>> long_zerodim = oneflow.tensor(1, dtype=oneflow.long)
+    >>> int_zerodim = oneflow.tensor(1, dtype=oneflow.int)
+
+    >>> a,b=oneflow.tensor(5),oneflow.tensor(5)
+    >>> oneflow.add(a, b).dtype
+    oneflow.int64
+    # 5 is an int64, but does not have higher category than int_tensor so is not considered.
+    >>> (int_tensor + 5).dtype
+    oneflow.int32
+    >>> (int_tensor + long_zerodim).dtype
+    oneflow.int64
+    >>> (long_tensor + int_tensor).dtype
+    oneflow.int64
+    >>> (bool_tensor + long_tensor).dtype
+    oneflow.int64
+    >>> (bool_tensor + uint_tensor).dtype
+    oneflow.uint8
+    >>> (float_tensor + double_tensor).dtype
+    oneflow.float64
+    >>> (bool_tensor + int_tensor).dtype
+    oneflow.int32
+    # Since long is a different kind than float, result dtype only needs to be large enough
+    # to hold the float.
+    >>> oneflow.add(long_tensor, float_tensor).dtype
+    oneflow.float32
+
+When the output tensor of an arithmetic operation is specified, we allow casting to its `dtype` except that:
+  * An integral output tensor cannot accept a floating point tensor.
+  * A boolean output tensor cannot accept a non-boolean tensor.
+  * A non-complex output tensor cannot accept a complex tensor
+
+Casting Examples::
+
+    # allowed:
+    >>> float_tensor *= float_tensor
+    >>> float_tensor *= int_tensor
+    >>> float_tensor *= uint_tensor
+    >>> float_tensor *= bool_tensor
+    >>> int_tensor *= uint_tensor
+
+    # disallowed (RuntimeError: result type can't be cast to the desired output type):
+    >>> float_tensor *= double_tensor
+    >>> int_tensor *= float_tensor
+    >>> int_tensor *= long_tensor
+    >>> uint_tensor *= int_tensor
+    >>> bool_tensor *= int_tensor
+    >>> bool_tensor *= uint_tensor
+
+.. _device-doc:
+
 oneflow.device
---------------------------------------------------------------
-.. autoclass:: oneflow.device
+------------------------
+
+.. class:: device
+
+A :class:`oneflow.device` is an object representing the device on which a :class:`oneflow.Tensor` is
+or will be allocated.
+
+The :class:`oneflow.device` contains a device type (``'cpu'`` or ``'cuda'``) and optional device
+ordinal for the device type. If the device ordinal is not present, this object will always represent
+the current device for the device type, even after :func:`oneflow.cuda.set_device()` is called; e.g.,
+a :class:`oneflow.Tensor` constructed with device ``'cuda'`` is equivalent to ``'cuda:X'`` where X is
+the result of :func:`oneflow.cuda.current_device()`.
+
+A :class:`oneflow.Tensor`'s device can be accessed via the :attr:`Tensor.device` property.
+
+A :class:`oneflow.device` can be constructed via a string or via a string and device ordinal
+
+Via a string:
+::
+
+    >>> oneflow.device('cuda:0')
+    device(type='cuda', index=0)
+
+    >>> oneflow.device('cpu')
+    device(type='cpu', index=0)
+
+    >>> oneflow.device('cuda')  # current cuda device
+    device(type='cuda', index=0)
+
+Via a string and device ordinal:
+
+::
+
+    >>> oneflow.device('cuda', 0)
+    device(type='cuda', index=0)
+
+    >>> oneflow.device('cpu', 0)
+    device(type='cpu', index=0)
+
+.. note::
+   The :class:`oneflow.device` argument in functions can generally be substituted with a string.
+   This allows for fast prototyping of code.
+
+   >>> # Example of a function that takes in a oneflow.device
+   >>> cuda1 = oneflow.device('cuda:1')
+   >>> oneflow.randn((2,3), device=cuda1)
+
+   >>> # You can substitute the oneflow.device with a string
+   >>> oneflow.randn((2,3), device='cuda:1')
+
+.. note::
+   For legacy reasons, a device can be constructed via a single device ordinal, which is treated
+   as a cuda device.  This matches :meth:`Tensor.get_device`, which returns an ordinal for cuda
+   tensors and is not supported for cpu tensors.
+
+   >>> oneflow.device(1)
+   device(type='cuda', index=1)
+
+.. note::
+   Methods which take a device will generally accept a (properly formatted) string
+   or (legacy) integer device ordinal, i.e. the following are all equivalent:
+
+   >>> oneflow.randn((2,3), device=oneflow.device('cuda:1'))
+   >>> oneflow.randn((2,3), device='cuda:1')
+   >>> oneflow.randn((2,3), device=1)  # legacy
 
 oneflow.placement
 --------------------------------------------------------------
 .. autoclass:: oneflow.placement
 
+oneflow.placement.all
+--------------------------------------------------------------
+.. autofunction:: oneflow.placement.all
+
 oneflow.env.all_device_placement
 --------------------------------------------------------------
 .. autofunction:: oneflow.env.all_device_placement

docs/source/type_info.rst

+.. currentmodule:: oneflow
+
+.. _type-info-doc:
+
+Type Info
+=========
+
+.. The documentation is referenced from: https://pytorch.org/docs/1.10/type_info.html.
+
+The numerical properties of a :class:`oneflow.dtype` can be accessed through either the :class:`oneflow.finfo` or the :class:`oneflow.iinfo`.
+
+
+.. contents:: oneflow
+    :depth: 2
+    :local:
+    :class: this-will-duplicate-information-and-it-is-still-useful-here
+    :backlinks: top
+
+oneflow.finfo
+-------------
+
+.. class:: oneflow.finfo
+
+A :class:`oneflow.finfo` is an object that represents the numerical properties of a floating point :class:`oneflow.dtype`, (i.e. ``oneflow.float32``, ``oneflow.float64`` and ``oneflow.float16``). This is similar to `numpy.finfo <https://numpy.org/doc/stable/reference/generated/numpy.finfo.html>`_.
+
+A :class:`oneflow.finfo` provides the following attributes:
+
+================== ======= ========================================================================== 
+Name               Type    Description                                                               
+================== ======= ========================================================================== 
+bits               int     The number of bits occupied by the type.                                  
+eps                float   The smallest representable number such that ``1.0 + eps != 1.0``.             
+min                float   The largest representable number.                                         
+max                float   The smallest representable number (typically ``-max``).                       
+tiny               float   The smallest positive normal number. See notes.
+resolution         float   The approximate decimal resolution of this type, i.e., ``10**-precision``.    
+================== ======= ========================================================================== 
+
+For example:
+
+.. code-block::
+
+    >>> import oneflow as flow
+    >>> flow.finfo()
+    finfo(resolution=1e-06, min=-3.40282e+38, max=3.40282e+38, eps=1.19209e-07, tiny=1.17549e-38, dtype=oneflow.float32, bits=32)
+    >>> flow.finfo(flow.float)
+    finfo(resolution=1e-06, min=-3.40282e+38, max=3.40282e+38, eps=1.19209e-07, tiny=1.17549e-38, dtype=oneflow.float32, bits=32)
+    >>> flow.finfo(flow.float16).bits
+    16
+    >>> flow.finfo(flow.float16).max
+    65504.0
+
+oneflow.iinfo
+-------------
+
+.. class:: oneflow.iinfo
+
+A :class:`oneflow.iinfo` is an object that represents the numerical properties of a integer :class:`oneflow.dtype` (i.e. ``oneflow.uint8``, ``oneflow.int8``, ``oneflow.int16``, ``oneflow.int32``, and ``oneflow.int64``). This is similar to `numpy.iinfo <https://numpy.org/doc/stable/reference/generated/numpy.iinfo.html>`_.
+
+A :class:`oneflow.iinfo` provides the following attributes:
+
+================== ======= ========================================================================== 
+Name               Type    Description                                                               
+================== ======= ========================================================================== 
+bits               int     The number of bits occupied by the type.                                  
+min                float   The largest representable number.                                         
+max                float   The smallest representable number.                       
+================== ======= ========================================================================== 
+
+For example:
+
+.. code-block ::
+
+    >>> import oneflow as flow
+    >>> flow.iinfo(flow.int8)
+    iinfo(min=-128, max=127, dtype=oneflow.int8, bits=8)
+    >>> flow.iinfo(flow.int).max
+    2147483647
+    >>> flow.iinfo(flow.int).bits
+    32

docs/source/utils.global_view.rst

+oneflow.utils.global_view
+======================================
+Some global view Ops
+--------------------------------------
+.. currentmodule:: oneflow.utils.global_view
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    to_global
+    to_local
+    global_mode
+    current_global_mode
+

docs/source/utils.rst

-oneflow.utils
-===================================
-Utils
-----------------------------------
-.. currentmodule:: oneflow.utils
-.. automodule:: oneflow.utils.data
-    :members: DataLoader,
-        Dataset,
-        IterableDataset,
-        TensorDataset,
-        ConcatDataset,
-        Subset,
-        random_split,
-        Sampler,
-        SequentialSampler,
-        RandomSampler,
-        SubsetRandomSampler,
-        BatchSampler
-        
-
-.. currentmodule:: oneflow.utils
-.. automodule:: oneflow.utils.data.distributed
-    :members: DistributedSampler
-
-
-.. autofunction:: oneflow.utils.from_torch
-.. autofunction:: oneflow.utils.to_torch

docs/source/utils.tensor.rst

+oneflow.utils.tensor
+==========================================================
+Some torch-related Ops are suitable for tensor conversion.
+----------------------------------------------------------
+.. currentmodule:: oneflow.utils.tensor
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    from_torch
+    to_torch
+

external/CMakeLists.txt

@@ -15,4 +15,5 @@ add_subdirectory(kineto)
 list(APPEND EXTERNAL_TARGETS kineto)
 
 mark_targets_as_system(${EXTERNAL_TARGETS})
+
 set_property(GLOBAL PROPERTY EXTERNAL_TARGETS ${EXTERNAL_TARGETS})

external/kineto/CMakeLists.txt

@@ -34,7 +34,9 @@ list(
   $ENV{CUPTI_ROOT}/lib
   /usr/lib
   ${CUDA_SOURCE_DIR}/targets/x86_64-linux/lib64
-  ${CUDA_SOURCE_DIR}/extras/CUPTI/lib64)
+  ${CUDA_SOURCE_DIR}/targets/x86_64-linux/lib
+  ${CUDA_SOURCE_DIR}/extras/CUPTI/lib64
+  ${CUDA_SOURCE_DIR}/extras/CUPTI/lib)
 
 find_library(
   CUDA_cupti_LIBRARY

oneflow/api/common/ofblob.h

-/*
-Copyright 2020 The OneFlow Authors. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-*/
-
-#ifndef ONEFLOW_API_COMMON_OFBLOB_H_
-#define ONEFLOW_API_COMMON_OFBLOB_H_
-
-#include "oneflow/core/common/just.h"
-#include "oneflow/core/register/ofblob.h"
-
-namespace oneflow {
-
-template<typename T>
-struct BlobBufferCopyUtil {
-  static Maybe<void> From(uint64_t of_blob_ptr, const T* buf_ptr, size_t size) {
-    auto* of_blob = reinterpret_cast<OfBlob*>(of_blob_ptr);
-    of_blob->AutoMemCopyFrom<T>(buf_ptr, size);
-    return Maybe<void>::Ok();
-  }
-
-  static Maybe<void> To(uint64_t of_blob_ptr, T* buf_ptr, size_t size) {
-    auto* of_blob = reinterpret_cast<OfBlob*>(of_blob_ptr);
-    of_blob->AutoMemCopyTo<T>(buf_ptr, size);
-    return Maybe<void>::Ok();
-  }
-};
-
-template<>
-struct BlobBufferCopyUtil<void> {
-  static Maybe<void> From(uint64_t of_blob_ptr, const void* buf_ptr, size_t size) {
-    auto* of_blob = reinterpret_cast<OfBlob*>(of_blob_ptr);
-    of_blob->AutoMemCopyFrom<void>(buf_ptr, size);
-    return Maybe<void>::Ok();
-  }
-
-  static Maybe<void> To(uint64_t of_blob_ptr, void* buf_ptr, size_t size) {
-    auto* of_blob = reinterpret_cast<OfBlob*>(of_blob_ptr);
-    of_blob->AutoMemCopyTo<void>(buf_ptr, size);
-    return Maybe<void>::Ok();
-  }
-};
-
-}  // namespace oneflow
-
-#endif  // !ONEFLOW_API_COMMON_OFBLOB_H_

oneflow/api/common/sbp.h

@@ -26,7 +26,9 @@ namespace oneflow {
 
 namespace api {
 
-inline Maybe<std::string> SbpToString(Symbol<SbpParallel> sbp_sym) {
+// NOTE: The api inferface will print the whole name of sbp.
+
+inline Maybe<std::string> ApiSbpToString(Symbol<SbpParallel> sbp_sym) {
   std::string sbp_str = "oneflow.sbp.";
   if (sbp_sym->has_broadcast_parallel()) {
     sbp_str += "broadcast";
@@ -40,11 +42,11 @@ inline Maybe<std::string> SbpToString(Symbol<SbpParallel> sbp_sym) {
   return sbp_str;
 }
 
-inline Maybe<std::string> NdSbpToString(Symbol<NdSbp> nd_sbp) {
+inline Maybe<std::string> ApiNdSbpToString(Symbol<NdSbp> nd_sbp) {
   std::string str = "(";
   for (int i = 0; i < nd_sbp->sbp_parallel_size(); ++i) {
     if (i > 0) { str += ", "; }
-    str += *JUST(SbpToString(SymbolOf(nd_sbp->sbp_parallel(i))));
+    str += *JUST(ApiSbpToString(SymbolOf(nd_sbp->sbp_parallel(i))));
   }
   if (nd_sbp->sbp_parallel_size() == 1) { str += ","; }
   str += ")";