RELEASE.md · yudanzhizhi/mindspore

# MindSpore Release Notes

[查看中文](./RELEASE_CN.md)

## MindSpore 2.0.0-rc1 Release Notes

### Major Features and Improvements

#### FrontEnd

- [BETA] Statement with "return", "return None" and with no return of function are supported in `GRAPH_MODE`.
- [BETA] Object with `list` type are supported in `GRAPH_MODE`.
- [BETA] Statement with "raise" are supported in variable condition situation in `GRAPH_MODE`.
- [STABLE] Functional call supports data sinking mode.
- [BETA] The Transformer layer in nn module is added to provide easy-to-use Transformer APIs. Batch_size does not need to be defined. Dynamic seq_length is supported.

#### DataSet

- [STABLE] In the Ascend environment，the timeout waiting time in data sink mode is adjusted to 1900s by default. This solves the problem that the GetNext operator may time out due to environment resource competition and large computing workload in data sinking mode.
- [STABLE] MindRecord supports to query the schemas and number samples. MindRecord provides multi-process writing mode, allowing users to generate MindRecord data files in parallel.
- [STABLE] The Dataset pipeline can process any Python object. For details, see [Supporting Python Objects in Dataset Pipeline](https://www.mindspore.cn/tutorials/en/r2.0/advanced/dataset/python_objects.html).

#### AutoParallel

- [STABLE] The strategies of whole parameters can be saved when saving strategy.
- [STABLE] The Conv3D/MaxPool3D/AvgPool3D distributed operator is supported.
- [STABLE] Support operator-level parallelism and optimizer-level parallelism under the PyNative with shard: parallel training and the Model API are decoupled to provide basic parallel expression capabilities.
- [STABLE] Support operator-level parallelism, and optimizer-level parallelism under the Graph mode: parallel training and the Model API are decoupled to provide basic parallel expression capabilities.
- [BETA] Supports customized distributed graph segmentation, improving the flexibility of distributed training.

#### Runtime

- [STABLE] Control flow supports subgraph sink.
- [STABLE] Support CUDA 11.6.
- [STABLE] Support for operator selection and execution of List/Tuple/Scalar type kernel to match native Python expression.
- [STABLE] Kernel that is not supported by hardware can automatically select CPU kernel.
- [STABLE] Support heterogeneous execution within subgraph.

#### Ascend

- [STABLE] Support overflow detection scheme and HCCL runtime overflow check.
- [STABLE] Support dump of communication operators.

#### Profiler

- [STABLE] Rich Profiler collection item configuration, users can collect performance data in more detail.

#### Dump

- [BETA] Single card in PyNatvie mode supports operator overflow detection.
- [BETA] Graph mode supports hccl operator dump.

### API Change

- [STABLE] Add computing APIs, such as MaxUnpool, ReplicationPad, and GaussianNLLLoss.
  For details, visit <https://www.mindspore.cn/docs/en/r2.0/api_python/mindspore.html>.
- [STABLE] Extend inventory API functions, such as AvgPool, pad, norm, and interplate.

#### operator

- [BETA] Add operator primitive for `mindspore.ops.AdaptiveAvgPool3D`.
- [BETA] Add operator primitive for `mindspore.ops.AffineGrid`.
- [BETA] Add operator primitive for `mindspore.ops.Angle`.
- [BETA] Add operator primitive for `mindspore.ops.BartlettWindow`.
- [BETA] Add operator primitive for `mindspore.ops.Bernoulli`.
- [BETA] Add operator primitive for `mindspore.ops.BesselI0`.
- [BETA] Add operator primitive for `mindspore.ops.BesselI1`.
- [BETA] Add operator primitive for `mindspore.ops.BesselJ0`.
- [BETA] Add operator primitive for `mindspore.ops.BesselJ1`.
- [BETA] Add operator primitive for `mindspore.ops.BesselK0`.
- [BETA] Add operator primitive for `mindspore.ops.BesselK0e`.
- [BETA] Add operator primitive for `mindspore.ops.BesselK1`.
- [BETA] Add operator primitive for `mindspore.ops.BesselK1e`.
- [BETA] Add operator primitive for `mindspore.ops.BesselY0`.
- [BETA] Add operator primitive for `mindspore.ops.BesselY1`.
- [BETA] Add operator primitive for `mindspore.ops.Bincount`.
- [BETA] Add operator primitive for `mindspore.ops.BlackmanWindow`.
- [BETA] Add operator primitive for `mindspore.ops.ChannelShuffle`.
- [BETA] Add operator primitive for `mindspore.ops.Cholesky`.
- [BETA] Add operator primitive for `mindspore.ops.Col2Im`.
- [BETA] Add operator primitive for `mindspore.ops.Complex`.
- [BETA] Add operator primitive for `mindspore.ops.ComplexAbs`.
- [BETA] Add operator primitive for `mindspore.ops.Cross`.
- [BETA] Add operator primitive for `mindspore.ops.CTCLossV2`.
- [BETA] Add operator primitive for `mindspore.ops.Cummin`.
- [BETA] Add operator primitive for `mindspore.ops.Diag`.
- [BETA] Add operator primitive for `mindspore.ops.Digamma`.
- [BETA] Add operator primitive for `mindspore.ops.Eig`.
- [BETA] Add operator primitive for `mindspore.ops.Expand`.
- [BETA] Add operator primitive for `mindspore.ops.Fmax`.
- [BETA] Add operator primitive for `mindspore.ops.Gcd`.
- [BETA] Add operator primitive for `mindspore.ops.Geqrf`.
- [BETA] Add operator primitive for `mindspore.ops.GLU`.
- [BETA] Add operator primitive for `mindspore.ops.GridSampler2D`.
- [BETA] Add operator primitive for `mindspore.ops.GridSampler3D`.
- [BETA] Add operator primitive for `mindspore.ops.HammingWindow`.
- [BETA] Add operator primitive for `mindspore.ops.Heaviside`.
- [BETA] Add operator primitive for `mindspore.ops.Hypot`.
- [BETA] Add operator primitive for `mindspore.ops.Igamma`.
- [BETA] Add operator primitive for `mindspore.ops.IndexFill`.
- [BETA] Add operator primitive for `mindspore.ops.InplaceIndexAdd`.
- [BETA] Add operator primitive for `mindspore.ops.InplaceUpdateV2`.
- [BETA] Add operator primitive for `mindspore.ops.Lcm`.
- [BETA] Add operator primitive for `mindspore.ops.LeftShift`.
- [BETA] Add operator primitive for `mindspore.ops.LogicalXor`.
- [BETA] Add operator primitive for `mindspore.ops.Logit`.
- [BETA] Add operator primitive for `mindspore.ops.LogSpace`.
- [BETA] Add operator primitive for `mindspore.ops.LuUnpack`.
- [BETA] Add operator primitive for `mindspore.ops.MatrixDiagPartV3`.
- [BETA] Add operator primitive for `mindspore.ops.MatrixDiagV3`.
- [BETA] Add operator primitive for `mindspore.ops.MatrixSetDiagV3`.
- [BETA] Add operator primitive for `mindspore.ops.MaxPool3DWithArgmax`.
- [BETA] Add operator primitive for `mindspore.ops.MaxUnpool2D`.
- [BETA] Add operator primitive for `mindspore.ops.MaxUnpool3D`.
- [BETA] Add operator primitive for `mindspore.ops.MultiMarginLoss`.
- [BETA] Add operator primitive for `mindspore.ops.MultinomialWithReplacement`.
- [BETA] Add operator primitive for `mindspore.ops.Mvlgamma`.
- [BETA] Add operator primitive for `mindspore.ops.NanToNum`.
- [BETA] Add operator primitive for `mindspore.ops.NextAfter`.
- [BETA] Add operator primitive for `mindspore.ops.Orgqr`.
- [BETA] Add operator primitive for `mindspore.ops.Polygamma`.
- [BETA] Add operator primitive for `mindspore.ops.Qr`.
- [BETA] Add operator primitive for `mindspore.ops.ResizeBilinearV2`.
- [BETA] Add operator primitive for `mindspore.ops.RightShift`.
- [BETA] Add operator primitive for `mindspore.ops.ScatterNdDiv`.
- [BETA] Add operator primitive for `mindspore.ops.ScatterNdMul`.
- [BETA] Add operator primitive for `mindspore.ops.SearchSorted`.
- [BETA] Add operator primitive for `mindspore.ops.Sinc`.
- [BETA] Add operator primitive for `mindspore.ops.Trace`.
- [BETA] Add operator primitive for `mindspore.ops.Tril`.
- [BETA] Add operator primitive for `mindspore.ops.TrilIndices`.
- [BETA] Add operator primitive for `mindspore.ops.TriuIndices`.
- [BETA] Add operator primitive for `mindspore.ops.UniqueConsecutive`.
- [STABLE] Add operator primitive for `mindspore.ops.Cummax`.
- [STABLE] Add operator primitive for `mindspore.ops.FillV2`.
- [STABLE] Add operator primitive for `mindspore.ops.IsClose`.
- [STABLE] Add operator primitive for `mindspore.ops.MatrixSolve`.
- [STABLE] Add operator primitive for `mindspore.ops.Median`.
- [STABLE] Add operator primitive for `mindspore.ops.MultilabelMarginLoss`.
- [STABLE] Add operator primitive for `mindspore.ops.NonZero`.
- [STABLE] Add operator primitive for `mindspore.ops.Pdist`.
- [STABLE] Add operator primitive for `mindspore.ops.Polar`.
- [STABLE] Add operator primitive for `mindspore.ops.RandomGamma`.
- [STABLE] Add operator primitive for `mindspore.ops.RandomPoisson`.
- [STABLE] Add operator primitive for `mindspore.ops.RandomShuffle`.
- [STABLE] Add operator primitive for `mindspore.ops.Renorm`.
- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdMax`.
- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdMin`.
- [STABLE] Add operator primitive for `mindspore.ops.Svd`.
- [STABLE] Add operator primitive for `mindspore.ops.TripletMarginLoss`.

#### Deleted APIs

- The `mindspore.compression` feature was deprecated at MindSpore 1.8 and is removed in this version.
  The following `mindspore.nn.quant` interfaces are also removed simultaneously: `mindspore.nn.FakeQuantWithMinMaxObserver`, `mindspore.nn.Conv2dBnFoldQuantOneConv`, `mindspore.nn.Conv2dBnFoldQuant`, `mindspore.nn.Conv2dBnWithoutFoldQuant`, `mindspore.nn.Conv2dQuant`, `mindspore.nn.DenseQuant`, `mindspore.nn.ActQuant`, `mindspore.nn.TensorAddQuant`, `mindspore.nn.ActQuant`, `mindspore.nn.MulQuant`. Please use [MindSpore Golden Stick](https://gitee.com/mindspore/golden-stick) instead to implement QuantAwareTraining in MindSpore.
- The `mindspore.dataset.close_pool`, `mindspore.dataset.to_device`, and `mindspore.dataset.set_dynamic_columns` interfaces are discarded in earlier version and being removed in this version.

#### Backwards Incompatible Change

- Interface: mindspore.set_context(mode=PYNATIVE_MODE)

Change: The default value is changed from GRAPH_MODE to PYNATIVE_MODE.

Description: If the running mode is not set and the diagram mode needs to be set, use the following method:
  mindspore.set_context(mode=GRAPH_MODE).

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  mindspore.set_context(mode=GRAPH_MODE)
  </pre>
  </td>
  <td><pre>
  mindspore.set_context(mode=PYNATIVE_MODE)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.train.Model.train

Change: The default value of dataset_sink_mode is changed from True to False.

Description: If dataset_sink_mode is not set and the data sinking mode needs to be set, use the following method:
  Model.train(dataset_sink_mode=True).

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  Model.train(dataset_sink_mode=True)
  </pre>
  </td>
  <td><pre>
  Model.train(dataset_sink_mode=False)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.export

Change: The file_format parameter is changed from AIR to no default value.

Description: If file_format is not set in the original mode, you need to set file_format additionally. In this case, use the following method:
  mindspore.export(net, *inputs, file_name, file_format="AIR", **kwargs).

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  mindspore.export(net, *inputs, file_name,
                   file_format="AIR", **kwargs)
  </pre>
  </td>
  <td><pre>
  mindspore.export(net, *inputs, file_name,
                   file_format, **kwargs)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.norm

Change: The ord parameter function is extended to support multiple forms.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.norm(input_x, axis, p=2, keep_dims=False, epsilon=1e-12)
  >>> # Example:
  >>> input = Tensor(np.array([[[1.0, 2.0], [3.0, 4.0]],
  ...                          [[5.0, 6.0], [7.0, 8.0]]]).astype(np.float32))
  >>> output = ops.norm(input, [0, 1], p=2)
  </pre></td>
  <td><pre>
  ops.norm(A, ord=None, dim=None, keepdim=False, *, dtype=None)
  >>> # Example:
  >>> input = Tensor(np.array([[[1.0, 2.0], [3.0, 4.0]],
  ...                          [[5.0, 6.0], [7.0, 8.0]]]).astype(np.float32))
  >>> output = ops.norm(input, ord=2, dim=(0, 1))
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.Tensor.norm

Change: The ord parameter function is extended to support multiple forms.

Description: For details, see the example of ops.norm.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  Tensor.norm(axis, p=2, keep_dims=False, epsilon=1e-12)
  </pre>
  </td>
  <td><pre>
  Tensor.norm(ord=None, dim=None, keepdim=False, *, dtype=None)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.dropout

Change: The seed0 and seed1 parameters are deleted and seed=None parameter is added. Instead of returning Tensors and masks, only Tensors are returned. The input parameter training=True is added.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.dropout(x, p=0.5, seed0=0, seed1=0)
  >>> # Example:
  >>> input = Tensor(((20, 16), (50, 50)),
  ...                mindspore.float32)
  >>> output, mask = dropout(x, p=0.5)
  </pre>
  </td>
  <td><pre>
  ops.dropout(input, p=0.5, training=True, seed=None)
  >>> # Example:
  >>> input = Tensor(((20, 16), (50, 50)),
  ...                mindspore.float32)
  >>> output = ops.dropout(input, p=0.5，training=True)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.dropout2d

Change: Return value is changed from Tensor and mask to Tensor only. The input parameter training=True is added.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.dropout2d(x, p=0.5)
  >>> # Example:
  >>> input = Tensor(np.ones([2, 1, 2, 3]),
  ...                mindspore.float32)
  >>> output, mask = dropout2d(input, 0.5)
  </pre>
  </td>
  <td><pre>
  ops.dropout2d(input, p=0.5, training=True)
  >>> # Example:
  >>> input = Tensor(np.ones([2, 1, 2, 3]),
  ...                mindspore.float32)
  >>> output = ops.dropout2d(input, 0.5, training=True)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.dropout3d

Change: Return value is changed from Tensor and mask to Tensor only. The input parameter training=True is added.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.dropout3d(x, p=0.5)
  >>> # Example:
  >>> input = Tensor(np.ones([2, 1, 2, 3]),
  ...                mindspore.float32)
  >>> output, mask = dropout3d(input, 0.5)
  </pre>
  </td>
  <td><pre>
  ops.dropout3d(input, p=0.5, training=True)
  >>> # Example:
  >>> input = Tensor(np.ones([2, 1, 2, 3]),
  ...                mindspore.float32)
  >>> output = ops.dropout3d(input, 0.5, training=True)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.std

Change: The interface is reconstructed, and the interface usage mode is more consistent with user habits.

Description: If parameter `unbiased` has been set, use the following alternative: `unbiased=False` -> `ddof=0`, `unbiased=True` -> `ddof=1`.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.std(input_x, axis=(), unbiased=True, keep_dims=False)
  </pre>
  </td>
  <td><pre>
  ops.std(input, axis=None, ddof=0, keepdims=False)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.load_param_into_net

Change: Parameters that are not loaded in the ckpt are added as return values.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  net_param = load_param_into_net()
  </pre>
  </td>
  <td><pre>
  net_param, ckpt_param = load_param_into_net()
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.nn.BCELoss

Change: The default value of `reduction` is changed from 'none' to 'mean'.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  BCELoss(weight=None, reduction='none')
  >>> # Example:
  >>> weight = Tensor(np.array([[1.0, 2.0, 3.0],
  ...                           [4.0, 3.3, 2.2]]),
  ...                 mindspore.float32)
  >>> loss = nn.BCELoss(weight=weight, reduction='mean')
  >>> logits = Tensor(np.array([[0.1, 0.2, 0.3],
  ...                           [0.5, 0.7, 0.9]]),
  ...                 mindspore.float32)
  >>> labels = Tensor(np.array([[0, 1, 0], [0, 0, 1]]),
  ...                 mindspore.float32)
  >>> output = loss(logits, labels)
  >>> print(output)
  >>> 1.8952923
  </pre>
  </td>
  <td><pre>
  BCELoss(weight=None, reduction='mean')
  >>> # Example:
  >>> weight = Tensor(np.array([[1.0, 2.0, 3.0],
  ...                           [4.0, 3.3, 2.2]]),
  ...                 mindspore.float32)
  >>> loss = nn.BCELoss(weight=weight)
  >>> logits = Tensor(np.array([[0.1, 0.2, 0.3],
  ...                           [0.5, 0.7, 0.9]]),
  ...                 mindspore.float32)
  >>> labels = Tensor(np.array([[0, 1, 0], [0, 0, 1]]),
  ...                 mindspore.float32)
  >>> output = loss(logits, labels)
  >>> print(output)
  >>> 1.8952923
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.split

Change: The interface is reconstructed. The interface usage mode is more suitable for users. The sequence of the second and third parameters is adjusted, and the split_size_or_sections function is modified and extended.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.split(input_x, axis=0, output_num=1)
  >>> # Example:
  >>> input = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]),
  ...                mindspore.int32)
  >>> output = ops.split(input, axis=1, output_num=4)
  </pre>
  </td>
  <td><pre>
  ops.split(tensor, split_size_or_sections, axis=0)
  >>> # Example:
  >>> input = Tensor(np.array([[1, 1, 1, 1], [2, 2, 2, 2]]),
  ...                mindspore.int32)
  >>> output = ops.split(input, split_size_or_sections=1, axis=1)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.Tensor.split

Change: The interface is reconstructed. The interface usage mode is more suitable for users. The positions of the two parameters is adjusted, and the split_size_or_sections function is modified and extended.

Description: For details, see the example of ops.split.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  Tensor.split(axis=0, output_num=1)
  </pre>
  </td>
  <td><pre>
  Tensor.split(split_size_or_sections, axis=0)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.pad

Change: Modify the parameter name paddings to padding, and the mode and value functions are added.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.pad(input_x, paddings)
  >>> # Example:
  >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6],
  ...                            [0.4, 0.5, -3.2]]),
  ...                  mindspore.float32)
  >>> paddings = ((1, 2), (2, 1))
  >>> output = ops.pad(input_x, paddings)
  </pre>
  </td>
  <td><pre>
  ops.pad(input_x, padding, mode='constant', value=None)
  >>> # Example:
  >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6],
  ...                            [0.4, 0.5, -3.2]]),
  ...                  mindspore.float32)
  >>> paddings = (2, 1, 1, 2)
  >>> output = ops.pad(input_x, paddings)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.meshgrid

Change: The input parameter is changed from `inputs` to `*input`.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.meshgrid(inputs, indexing='xy')
  >>> # Example:
  >>> x = Tensor(np.array([1, 2, 3, 4]).astype(np.int32))
  >>> y = Tensor(np.array([5, 6, 7]).astype(np.int32))
  >>> z = Tensor(np.array([8, 9, 0, 1, 2]).astype(np.int32))
  output = ops.meshgrid((x, y, z), indexing='xy')
  </pre>
  </td>
  <td><pre>
  ops.meshgrid(*inputs, indexing='xy')
  >>> # Example:
  >>> x = Tensor(np.array([1, 2, 3, 4]).astype(np.int32))
  >>> y = Tensor(np.array([5, 6, 7]).astype(np.int32))
  >>> z = Tensor(np.array([8, 9, 0, 1, 2]).astype(np.int32))
  output = ops.meshgrid(x, y, z, indexing='xy')
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.max

Change: Return value exchange sequence. The value is changed from "index, value" to "value, index".

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.max(x, axis=0, keep_dims=False)
  >>> # Example:
  >>> input = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]),
  ...                mindspore.float32)
  >>> index, output = ops.max(input)
  >>> print(index, output)
  >>> 3 0.7
  </pre>
  </td>
  <td><pre>
  ops.max(input, axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)
  >>> # Example:
  >>> input = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]),
  ...                mindspore.float32)
  >>> output, index = ops.max(input, axis=0)
  >>> print(output, index)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.min

Change: Return value exchange sequence. The value is changed from "index, value" to "value, index".

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.min(x, axis=0, keep_dims=False)
  >>> # Example:
  >>> input = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]),
  ...                mindspore.float32)
  >>> index, output = ops.min(input)
  >>> 0 0.0
  </pre>
  </td>
  <td><pre>
  ops.min(input, axis=None, keepdims=False, *, initial=None, where=True, return_indices=False)
  >>> # Example:
  >>> input = Tensor(np.array([0.0, 0.4, 0.6, 0.7, 0.1]),
  ...                mindspore.float32)
  >>> output, index = ops.min(input, keepdims=True)
  >>> 0.0 0
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.random_gamma

Change: The seed2 parameter is deleted and seed=0 is changed to None. The framework behavior is unified and complies with the actual application scenarios and habits of users.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.random_gamma(shape, alpha, seed=0, seed2=0)
  </pre>
  </td>
  <td><pre>
  ops.random_gamma(shape, alpha, seed=None)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.standard_laplace

Change: The seed2 parameter is deleted and seed=0 is changed to None. The framework behavior is unified and complies with the actual application scenarios and habits of users.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.standard_laplace(shape, seed=0, seed2=0)
  </pre>
  </td>
  <td><pre>
  ops.standard_laplace(shape, seed=None)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.standard_normal

Change: The seed2 parameter is deleted and seed=0 is changed to None. The framework behavior is unified and complies with the actual application scenarios and habits of users.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.standard_normal(shape, seed=0, seed2=0)
  </pre>
  </td>
  <td><pre>
  ops.standard_normal(shape, seed=None)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.bernoulli

Change: The default value of seed is changed from -1 to None. Meets the actual application scenario.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  ops.bernoulli(x, p=0.5, seed=-1)
  </pre>
  </td>
  <td><pre>
  ops.bernoulli(input, p=0.5, seed=None)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.data_sink

Change: Deleted the steps parameter. Parameter name jit is changed to jit_config, and new input_signature parameter is added. The usability is improved to meet the requirements of actual application scenarios.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  mindspore.data_sink(fn, dataset, steps,
                      sink_size=1, jit=False)
  </pre>
  </td>
  <td><pre>
  mindspore.data_sink(fn, dataset, sink_size=1,
                      jit_config=None, input_signature=None)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.ops.conv2d

Change: Extend Interface Function. Add the bias parameter and modify the parameter name and parameter sequence.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  conv2d(inputs, weight, pad_mode="valid",
         padding=0, stride=1, dilation=1, group=1)
  </pre>
  </td>
  <td><pre>
  conv2d(input, weight, bias=None, stride=1,
         pad_mode="valid", padding=0, dilation=1, groups=1)
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.dataset.vision.Pad

Change: Adjust the input parameter padding of Pad, RandomCrop, and RandomCropWithBbox. When the input length of Padding is 2, the first value is used to fill the left/upper boundary, the second value is used to fill the right/lower boundary, and the first value is used to fill the left/right boundary. Fill the upper/lower boundary with the second value.

Description: The padding parameter whose size is 2 is not compatible with the effect of the earlier version. The padding parameter needs to be explicitly represented (left, right, top, and bottom).

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  mindspore.dataset.vision.Pad(padding=(1,2))
  Indicates that the left/upper part of the image is filled with 1 pixel,
  and the right/down part is filled with 2 pixels.
  </pre>
  </td>
  <td><pre>
  mindspore.dataset.vision.Pad(padding=(1,2,1,2))
  Indicates that the left/upper part of the image is filled with 1 pixel,
  and the right/down part is filled with 2 pixels.
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.dataset.Dataset.map

Change: Delete the column_order parameter. In most cases, output_columns and column_order have the same value. Therefore, column_order does not need to be transferred. To adjust the sequence of data columns, use mindspore.dataset.Dataset.project.

Description:

1. If the column sequence does not need to be changed, delete the column_order parameter.
  2. If you need to specify the data column sequence, delete the column_order parameter and add a project method to the end of the parameter for column transformation (as in the following example).

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  >>> dataset = dataset.map(operations=[transforms],
  ...                       input_columns=["column_a"],
  ...                       output_columns=["column_b", "column_c"],
  ...                       column_order=["column_c", "column_b"])
  </pre>
  </td>
  <td><pre>
  >>> dataset = dataset.map(operations=[transforms],
  ...                       input_columns=["column_a"],
  ...                       output_columns=["column_b", "column_c"])
  >>> dataset = dataset.project(["column_c", column_b"])")
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.dataset.Dataset.batch

Description:

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  >>> dataset = dataset.batch(batch_size=4,
  ...                         input_columns=["column_a"],
  ...                         output_columns=["column_b", "column_c"],
  ...                         column_order=["column_c", "column_b"])
  </pre>
  </td>
  <td><pre>
  >>> dataset = dataset.batch(batch_size=4, input_columns=["column_a"]
  ...                         output_columns=["column_b", "column_c"])
  >>> dataset = dataset.project(["column_c", column_b"])")
  </pre>
  </td>
  </tr>
  </table>

- Interface: mindspore.dataset.Dataset.batch

Change: Split the batch method into two methods: batch and padded_batch. The pad_info parameter is moved from the batch method to the padded_batch method.

Description: To use the pad_info parameter, use the padded_batch method instead.

<table>
  <tr>
  <td style="text-align:center"> Original Interface </td> <td style="text-align:center"> Interface v2.0.0-rc1 </td>
  </tr>
  <tr>
  <td><pre>
  >>> dataset = dataset.batch(batch_size=4,
  ...                         drop_remainder=True, pad_info=...)
  </pre>
  </td>
  <td><pre>
  >>> dataset = dataset.padded_batch(batch_size=4,
  ...                                drop_remainder=True, pad_info=...)
  </pre>
  </td>
  </tr>
  </table>

### Bug fixes

- [I66PE6] fix AssignSub primitive abnormal input leads to coredump.

- [I6F5E6] fix data_sink function timeout on Ascend.

### Others

- Windows support is still being optimized,this version does not support now.It will be available for download in version 2.0.

### Contributors

Thanks goes to these wonderful people:

alashkari,anzhengqi,archer2049,B.L.LAN,baihuawei,bichaoyang,BJ-WANG,Bokai Li,Brian-K,caifubi,caiyimeng,cathwong,changzherui,ChenDonYY,chenfei_mindspore,chengang,chengbin,chenhaozhe,chenjianping,chenkang,chenweifeng,chuht,chujinjin,davidanugraha,DavidFFFan,DeshiChen,douzhixing,emmmmtang,Erpim,Ethan,fangwenyi,fangzehua,fangzhou0329,fary86,fengyixing,gaoshuanglong,Gaoxiong,gaoyong10,gengdongjie,gongdaguo1,Greatpan,GuoZhibin,guozhijian,hangq,hanhuifeng,haozhang,hedongdong,Henry Shi,heterogeneous_to_backoff_2_0,huangbingjian,huanghui,huangxinjing,hujiahui8,hujingsong,huoxinyou,jachua,jiahongQian,jianghui58,jiangzhenguang,jiaorui,jiaoy1224,jijiarong,jjfeing,JoeyLin,json,JuiceZ,jxl,kairui_kou,KevinYi,kisnwang,KXiong,laiyongqiang,lanzhineng,liangchenghui,liangzelang,LiangZhibo,lianliguang,lichen,ligan,lijunbin,limingqi107,ling,linqingke,liubuyu,liuchao,liuchuting,liujunzhu,liuluobin,liutongtong9,liuyang811,lixiao,liyan2022,liyejun,liyuxia,looop5,luochao60,luojianing,luoyang,luoyuan,lyqlola,maning202007,maoyaomin,Margaret_wangrui,mayadong,MaZhiming,melody,mengyuanli,michaelzhu_70ab,Mohammad Motallebi,moran,NaCN,nomindcarry,OwenSec,panfengfeng,panshaowu,panzhihui,pkuliuliu,qinzheng,qiuzhongya,qujianwei,r1chardf1d0,Renyuan Zhang,RobinGrosman,shaojunsong,shenwei41,Soaringfish,tangdezhi_123,tanghuikang,tan-wei-cheng,TinaMengtingZhang,TronZhang,TuDouNi,VectorSL,wang_ziqi,wanghenchang,wangnan39,wangpingan,wangshaocong,wangshengnan123,wangtongyu6,weichaoran,wind-zyx,wqx,wtcheng,wujueying,wYann,XianglongZeng,xiaohanzhang,xiaotianci,xiaoyao,XinDu,xulei,xumengjuan1,xupan,xwkgch,yanghaoran,yangluhang,yangruoqi713,yangshuo,yangsijia,yangzhenzhang,yanzhenxiang2020,Yanzhi_YI,yao_yf,yefeng,yeyunpeng2020,Yi_zhang95,yide12,YijieChen,YingLai Lin,YingtongHu,youshu,yuchaojie,yuedongli,YuJianfeng,zangqx,ZengZitao,zhangbuxue,zhangdanyang,zhangdong,zhangfanghe,zhangqi,zhangqinghua,zhangyanhui,zhangyinxia,zhangyongxian,zhangzhaoju,zhanzhan,zhengzuohe,ZhidanLiu,zhixinaa,zhoufeng,zhouyaqiang0,zhuguodong,zhupuxu,zhuyuxiao,zichun_ye,zjun,zlq2020,zong_shuai,ZPaC,zuochuanyong,zyli2020,陈宇,范吉斌,冯一航,胡彬,宦晓玲,黄勇,雷元哲,李良灿,李林杰,刘崇鸣,刘力力,刘勇琪,吕浩宇,吕昱峰（Nate.River）,没有窗户的小巷,沈竞兴,十六夜,王程浩,王禹程,王振邦,徐安越,徐永飞,杨旭华,于振华,俞涵,张清华,张澍坤,张栩浩,张学同,赵英灼,周超,周洪叶,朱家兴

Contributions of any kind are welcome!

## MindSpore Lite 2.0.0-rc1 Release Notes

### Major Features and Improvements

#### MindSpore Lite Cloud Inference

The original MindSpore Lite is mainly used for edge devices such as mobile phones and head units. Cloud inference is added to support scenarios with multiple backend hardware resources on the cloud, supports Ascend and NVIDIA GPU inference cards, and efficiently utilizes multi-core resources on the cloud.

The original cloud inference integrated through MindSpore training can be changed to MindSpore Lite. For details, see [Quick Start to Cloud-side Inference](https://mindspore.cn/lite/docs/en/r2.0/quick_start/one_hour_introduction_cloud.html). To retain the original integration method, see [Inference](https://mindspore.cn/docs/en/r2.0/faq/inference.html).

- [STABLE] Support MindIR model files.
- [STABLE] Third-party Onnx, TensorFlow, and Caffe models can be converted to MindIR model files using the MindSpore Lite conversion tool.
- [STABLE] One release package supports multiple hardware backends: Ascend 310/310P/910, NVIDIA GPU, CPU.
- [STABLE] Supports the `Model` interface and `ModelParallelRunner` concurrent inference interface.
- [STABLE] Supports C++, Python, and Java inference interfaces.

#### API

- Due to the defects of the original Python API that many configuration parameters and complex usage, the usability of The Python APIs are optimized in version 2.0. The optimizations include class construction methods and class attribute adjustment. In addition, the Python APIs in version 2.0 and later will be integrated into the cloud-side inference scenario, which are incompatible with Python APIs of the earlier versions. For details, see [Python API](https://www.mindspore.cn/lite/api/en/r2.0/mindspore_lite.html).

## MindSpore 2.0.0-alpha Release Notes

### Major Features and Improvements

#### PyNative

- The default mode of MindSpore is switched to PyNative. If you want to manually set the mode, please refer to [Computational Graph](https://www.mindspore.cn/tutorials/en/r2.0.0-alpha/advanced/compute_graph.html).
- Support dynamic shape without padding, three networks are supported as demos: Transformer-GPU, YOLOV5-GPU, ASR-Ascend. Transformer-GPU and YOLOV5-GPU can be downloaded from [models](https://gitee.com/mindspore/models/tree/dynamic_shape). Only the following operators are available on Ascend backend: Add、Assign、BatchMatMul、BiasAdd、BiasAddGrad、Cast、Conv2D、Conv2DBackpropFilter、Conv2DBackpropInput、CTCLoss、Div、Dropout、DropoutDoMask、Equal、ExpandDims、Gather、GetNext、LayerNorm、LayerNormGrad、LessEqual、Load、Log、LogicalAnd、LogicalNot、LogicalOr、LogSoftmax、LogSoftmaxGrad、MatMul、Maximum、Mul、Neg、NotEqual、NPUAllocFloatStatus、NPUClearFloatStatus、OneHot、RealDiv、Reciprocal、ReduceMean、ReduceSum、ReLU、ReluGrad、Reshape、Select、Softmax、StridedSlice、Sub、Tile、Transpose、UnsortedSegmentSum、ZerosLike。The remaining operators have not been fully verified, please use them as appropriate.

#### DataSet

- The TFRecordDataset API can directly read TFRecord files compressed by GZIP or ZLIB.
- The NumpySlicesDataset API can process data of different dimensions at the same time.
- Optimize the structure of error log to display more clear call stack information for debugging.
- Fixed `mindspore.dataset.config.set_seed` does not take effect for random seeds in distributed training scenarios.

#### AutoParallel

- Supports more operators with distributed implements.

Element Wise Operators:AddN, BitwiseAnd, BitwiseOr, BitwiseXor, CumProd, HShrink, HSigmoid, IsFinite, Mish, MulNoNan, Rint, SeLU, SoftShrink, TruncateDiv, TruncateMod, Xdivy Xlogy, InplaceAdd, InplacSub, InplaceUpdate, Cdist, L2Loss, Lerp.

Math Operators:SquaredDifference, Erfinv, MaskedFill, SplitV, Gamma, KLDivLoss, LinSpace.

Scatter Operators:ScatterAdd,ScatterDiv,ScatterMax,ScatterMul,ScatterNdAdd,ScatterNdSub,ScatterNdUpdate,ScatterSub,TensorScatterAdd,TensorScatterDiv,TensorScatterMax,TensorScatterMax,TensorScatterMul,TensorScatterAdd,TensorScatterUpdate.

- Add new apis `transform_checkpoints` and `transform_checkpoint_by_rank` to transfer the distributed checkpoint files by strategy files. Please refer to [Distributed Resilience Training and Inference](https://www.mindspore.cn/tutorials/experts/en/r2.0.0-alpha/parallel/resilience_train_and_predict.html)。

### API Change

#### operator

- [STABLE] Add operator primitive for `mindspore.ops.AdaptiveMaxPool3D`.
- [STABLE] Add operator primitive for `mindspore.ops.AdjustHue`.
- [STABLE] Add operator primitive for `mindspore.ops.BartlettWindow`.
- [STABLE] Add operator primitive for `mindspore.ops.BesselJ0`.
- [STABLE] Add operator primitive for `mindspore.ops.BesselJ1`.
- [STABLE] Add operator primitive for `mindspore.ops.BesselK0`.
- [STABLE] Add operator primitive for `mindspore.ops.BesselK0e`.
- [STABLE] Add operator primitive for `mindspore.ops.BesselK1`.
- [STABLE] Add operator primitive for `mindspore.ops.BesselK1e`.
- [STABLE] Add operator primitive for `mindspore.ops.BesselY0`.
- [STABLE] Add operator primitive for `mindspore.ops.BesselY1`.
- [STABLE] Add operator primitive for `mindspore.ops.Betainc`.
- [STABLE] Add operator primitive for `mindspore.ops.Bincount`.
- [STABLE] Add operator primitive for `mindspore.ops.BlackmanWindow`.
- [STABLE] Add operator primitive for `mindspore.ops.Bucketize`.
- [STABLE] Add operator primitive for `mindspore.ops.CombinedNonMaxSuppression`.
- [STABLE] Add operator primitive for `mindspore.ops.CompareAndBitpack`.
- [STABLE] Add operator primitive for `mindspore.ops.Complex`.
- [STABLE] Add operator primitive for `mindspore.ops.DataFormatVecPermute`.
- [STABLE] Add operator primitive for `mindspore.ops.Eig`.
- [STABLE] Add operator primitive for `mindspore.ops.EuclideanNorm`.
- [STABLE] Add operator primitive for `mindspore.ops.Expand`.
- [STABLE] Add operator primitive for `mindspore.ops.ExtractGlimpse`.
- [STABLE] Add operator primitive for `mindspore.ops.FillDiagonal`.
- [STABLE] Add operator primitive for `mindspore.ops.FractionalAvgPool`.
- [STABLE] Add operator primitive for `mindspore.ops.FractionalMaxPool`.
- [STABLE] Add operator primitive for `mindspore.ops.Gcd`.
- [STABLE] Add operator primitive for `mindspore.ops.HammingWindow`.
- [STABLE] Add operator primitive for `mindspore.ops.Histogram`.
- [STABLE] Add operator primitive for `mindspore.ops.HSVToRGB`.
- [STABLE] Add operator primitive for `mindspore.ops.Lcm`.
- [STABLE] Add operator primitive for `mindspore.ops.LeftShift`.
- [STABLE] Add operator primitive for `mindspore.ops.ListDiff`.
- [STABLE] Add operator primitive for `mindspore.ops.LogSpace`.
- [STABLE] Add operator primitive for `mindspore.ops.Lstsq`.
- [STABLE] Add operator primitive for `mindspore.ops.MatrixDiagPartV3`.
- [STABLE] Add operator primitive for `mindspore.ops.MatrixDiagV3`.
- [STABLE] Add operator primitive for `mindspore.ops.MatrixExp`.
- [STABLE] Add operator primitive for `mindspore.ops.MatrixPower`.
- [STABLE] Add operator primitive for `mindspore.ops.MaxPool3DWithArgmax`.
- [STABLE] Add operator primitive for `mindspore.ops.MaxUnpool2D`.
- [STABLE] Add operator primitive for `mindspore.ops.MultilabelMarginLoss`.
- [STABLE] Add operator primitive for `mindspore.ops.NextAfter`.
- [STABLE] Add operator primitive for `mindspore.ops.Orgqr`.
- [STABLE] Add operator primitive for `mindspore.ops.ReduceStd`.
- [STABLE] Add operator primitive for `mindspore.ops.ResizeNearestNeighborV2`.
- [STABLE] Add operator primitive for `mindspore.ops.RGBToHSV`.
- [STABLE] Add operator primitive for `mindspore.ops.RightShift`.
- [STABLE] Add operator primitive for `mindspore.ops.Roll`.
- [STABLE] Add operator primitive for `mindspore.ops.SampleDistortedBoundingBoxV2`.
- [STABLE] Add operator primitive for `mindspore.ops.ScaleAndTranslate`.
- [STABLE] Add operator primitive for `mindspore.ops.ScatterAddWithAxis`.
- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdDiv`.
- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdMax`.
- [STABLE] Add operator primitive for `mindspore.ops.ScatterNdMul`.
- [STABLE] Add operator primitive for `mindspore.ops.STFT`.
- [STABLE] Add operator primitive for `mindspore.ops.Trace`.
- [STABLE] Add operator primitive for `mindspore.ops.UpsampleNearest3D`.
- [STABLE] Add operator primitive for `mindspore.ops.UpsampleTrilinear3D`.
- [STABLE] Add distributed weight conversion interface `mindspore.parallel.transform_checkpoints`.
- [STABLE] Add distributed weight conversion interface `mindspore.parallel.transform_checkpoint_by_rank`.

#### Backwards Incompatible Change

##### Python API

- The `mindspore.ms_function` interface is renamed to `mindspore.jit`, and `mindspore.ms_function` will be deprecated and removed in a future version.
- The `mindspore.ms_class` interface is renamed to `mindspore.jit_class`, and `mindspore.ms_class` will be deprecated and removed in a future version.
- The `mindspore.ops.ms_kernel` interface is renamed to `mindspore.ops.kernel`, and `mindspore.ops.ms_kernel` will be deprecated and removed in a future version.
- The `mindspore.dataset.map` interface parameter `column_order` does not take effect, use`mindspore.dataset.project`.
- The `mindspore.dataset.close_pool` and `mindspore.dataset.to_device` and `mindspore.dataset.set_dynamic_columns` are deprecated and removed in this version.

### Bug fixes

- Fixed an issue where the mixed precision functional interface could not modify the backend driver in graph mode
- Fixed the problem that users can automatically transfer device_id in the single-P scenario for the following networks:（mobilenetv1/fasterrcnn/yolov3/yolov4/yolov5/unet/openpose/simplepose/crnn/gnmtv2/faceattribute/facequality/facedetection）

### Contributors

Thanks goes to these wonderful people:

AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin,  wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang,  zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.

Contributions of any kind are welcome!

## MindSpore 1.10.1 Release Notes

### Bug fixes

- Fixed the issue that the specified axis is not considered in logsumexp anti-overflow processing
- Fixed the compilation dependency of proto file
- Fixed the issue that the print operator printing result is not normal
- Fixed the issue that the equal operator is out of range
- Fixed the problem that when function wrapped by @jit，the cell id is not correct
- Fixed the GNN scenario data type verification error
- Fixed the problem that the dataset.map multi-process degenerates into threads

### Contributors

Thanks goes to these wonderful people:

archer2049, caifubi, chenfei_mindspore, gaoshuanglong, Greatpan, guozhijian, huoxinyou, Kxiong, lanzhineng, lijunbin, liubuyu, liuchuting, luochao60, lyqlola, nomindcarry, TuDouNi, xiaotianci, xupan, yangshuo, yefeng, YingtongHu, yuchaojie, zhoufeng, ZPaC, 刘勇琪, 吕昱峰, 王禹程, 于振华.

Contributions of any kind are welcome!

## MindSpore 1.10.0 Release Notes

### Major Features and Improvements

#### DataSet

- [STABLE]The timeout waiting time is adjusted in data sinking mode. The default value is 600s after adjusted. This solves the isuses that the GetNext operator may timeout due to environment resource competition and large computing workload when training in sink mode.

### Bug fixes

- Fixed an issue where some Primitive operators in AMP cannot be instantiated in graph mode and the interface is unavailable.
- Fixed an issue of DynamicRNN execution failure in LSTM network under the scenario of computational force segmentation on Ascend platform.
- Fixed DEVICE_ID cannot be set by single card train scripts parameters in mobilenet, fasterrcnn, yolo, etc.

### Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

## MindSpore Lite 1.10.0 Release Notes

### Bug fixes

- Fixed potential accuracy problem of arithmetic type CPU kernels at dynamical shape case.
- Fixed the Incorrect Write Address of the Deconv Quantization Operator.

## MindSpore 1.9.0 Release Notes

### Major Features and Improvements

#### FrontEnd

- [STABLE] Add the object-oriented and functional combination programming paradigm, add mixed-precision APIs for combination programming paradigms such as `mindspore.amp.LossScaler`, `mindspore.amp.DynamicLossScaler`, `mindspore.amp.StaticLossScaler`, `mindspore.amp.auto_mixed_precision` and `mindspore.amp.all_finite`.

### API Change

#### operator

- [STABLE] Add nn interface for `nn.AdaptiveAvgPool3d`.
- [STABLE] Add functional interface for `ops.adaptive_avg_pool3d`.
- [STABLE] Add functional interface for `ops.addcdiv`.
- [STABLE] Add functional interface for `ops.addcmul`.
- [STABLE] Add GPU and CPU support for `ops.approximate_equal`.
- [STABLE] Add GPU support for `ops.atanh`.
- [STABLE] Add GPU support for `ops.bessel_i0`.
- [STABLE] Add Ascend support for `ops.bessel_i0e`.
- [STABLE] Add GPU support for `ops.bessel_i1`.
- [STABLE] Add Ascend and GPU support for `ops.bessel_i1e`.
- [STABLE] Add GPU support for `ops.bessel_j0`.
- [STABLE] Add GPU support for `ops.bessel_j1`.
- [STABLE] Add GPU support for `ops.bessel_k0`.
- [STABLE] Add GPU support for `ops.bessel_k0e`.
- [STABLE] Add GPU support for `ops.bessel_k1`.
- [STABLE] Add GPU support for `ops.bessel_k1e`.
- [STABLE] Add GPU support for `ops.bessel_y0`.
- [STABLE] Add GPU support for `ops.bessel_y1`.
- [STABLE] Add functional interface for `ops.bias_add`.
- [STABLE] Add GPU support for `ops.bitwise_and`.
- [STABLE] Add GPU support for `ops.bitwise_or`.
- [STABLE] Add GPU support for `ops.bitwise_xor`.
- [STABLE] Add Ascend support for `ops.grid_sample`.
- [STABLE] Add CPU support for `ops.inplace_update`.
- [STABLE] Add Ascend and GPU support for `ops.isclose`.
- [STABLE] Add Ascend support for `ops.isnan`.
- [STABLE] Add GPU support for `ops.lerp`.
- [STABLE] Add functional interface for `ops.random_poisson`.
- [STABLE] Add functional interface for `ops.reverse_sequence`.
- [STABLE] Add GPU support for `ops.scatter_mul`.
- [STABLE] Add functional interface for `ops.scatter_nd_max`.
- [STABLE] Add functional interface for `ops.scatter_nd_min`.
- [STABLE] Add GPU support for `ops.SparseToDense`.
- [STABLE] Add functional interface for `ops.square`.
- [STABLE] Add GPU support for `ops.standard_laplace`.
- [STABLE] Add functional interface for `ops.std`.
- [STABLE] Add Ascend and GPU support for `ops.trunc`.
- [STABLE] Add functional interface for `ops.unsorted_segment_sum`.
- [STABLE] Add functional interface for `ops.xdivy`.
- [STABLE] Add GPU support for `ops.xlogy`.
- Deprecate `ops.poisson` and use `ops.random_poisson` instead.
- Deprecate `ops.SparseApplyAdagrad` and use `ops.SparseApplyAdagradV2` instead.

### Bug fixes

- [BUGFIX] The logic of the auto mixed precision (amp) O2 level is revised. In addition to the `BatchNorm1d` and `BatchNorm2d` operators, the other two operators `BatchNorm3d` and `LayerNorm` are added. The four operators still use the float32 data type when calculating.

- [BUGFIX] Fix the problem that when processing string type data, if `output_numpy=True` is specified when calling the `create_dict_iterator` or `create_tuple_iterator` interface, the obtained data will be of type `numpy.bytes_`. After this fixing, these interfaces will directly return `numpy.str_` type data, and users do not need to perform string decoding operations on it. Likewise, when performing user defined processing functions, the received data will also be of type `numpy.str_` directly, matching the original source data type.

### Contributors

Thanks goes to these wonderful people:

AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, liyanliu, lizhenyu, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, panfengfeng, panyifeng, Payne, peixu_ren, Pengyongrong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, Wan, wandongdong, wangdongxu, wangmin,  wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang,  zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanyuan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking, shu-kun-zhang.

Contributions of any kind are welcome!

## MindSpore 1.8.1 Release Notes

### API Change

#### operator

- [STABLE] Add GPU and CPU support for ops.ApplyAdagradDA.
- [STABLE] Add CPU support for ops.ApplyAdagradV2.
- [STABLE] Add Ascend dynamic shape support for ops.ApplyCenteredRmsProp.
- [STABLE] Add CPU support for ops.ApplyFtrl.
- [STABLE] Add CPU support for ops.ApplyGradientDescent.
- [STABLE] Add CPU support for ops.ApplyPowerSign.
- [STABLE] Add GPU and CPU support for ops.ApplyProximalAdagrad.
- [STABLE] Add Ascend dynamic shape support for ops.ApplyRmsProp.
- [STABLE] Add functional interface for ops.max.
- [STABLE] Add functional interface for ops.atan2.
- [STABLE] Add GPU support for ops.cummax.
- [STABLE] Add GPU and CPU support for ops.cummin.
- [STABLE] Add GPU support for ops.diag.
- [STABLE] Add functional interface for ops.expand_dims.
- [STABLE] Add functional interface for ops.gather_elements.
- [STABLE] Add GPU support for ops.grid_sample.
- [STABLE] Add Ascend support for ops.hardswish.
- [BETA] Add GPU support for ops.index_fill.
- [BETA] Add CPU support for ops.inplace_update.
- [BETA] Add GPU support for nn.InstanceNorm1d.
- [BETA] Add GPU support for nn.InstanceNorm2d.
- [BETA] Add GPU support for nn.InstanceNorm3d.
- [STABLE] Add functional interface for ops.log1p.
- [STABLE] Add GPU and CPU support for ops.masked_fill.
- [BETA] Add GPU support for ops.matrix_diag_part.
- [BETA] Add GPU support for ops.matrix_diag.
- [BETA] Add GPU support for ops.matrix_set_diag.
- [STABLE] Add GPU support for ops.max_pool3d.
- [STABLE] Add functional interface for ops.nll_loss.
- [STABLE] Add functional interface for ops.one_hot.
- [STABLE] Add functional interface for ops.pad.
- [STABLE] Add CPU support for ops.random_gamma.
- [STABLE] Add functional interface for ops.amax.
- [STABLE] Add functional interface for ops.mean.
- [STABLE] Add functional interface for ops.amin.
- [STABLE] Add functional interface for ops.prod.
- [STABLE] Add Ascend, GPU, and CPU support for ops.renorm.
- [BETA] Add Ascend, GPU, and CPU support for ops.tensor_scatter_elements.
- [STABLE] Add GPU support for ops.scatter_max.
- [STABLE] Add GPU support for ops.scatter_min.
- [STABLE] Add functional interface for ops.scatter_nd.
- [STABLE] Add GPU support for ops.scatter_nd_max.
- [STABLE] Add functional interface for ops.scatter_update.
- [STABLE] Add CPU support for ops.binary_cross_entropy_with_logits.
- [STABLE] Add functional interface for ops.smooth_l1_loss.
- [STABLE] Add CPU support for ops.space_to_batch_nd.
- [STABLE] Add GPU and CPU support for ops.SparseApplyAdagrad.
- [STABLE] Add GPU and CPU support for ops.sparse_segment_mean.
- [STABLE] Add functional interface for ops.squeeze.
- [STABLE] Add CPU support for ops.standard_laplace.
- [BETA] Add Ascend, GPU, and CPU support for nn.ReflectionPad1d.
- [BETA] Add Ascend, GPU, and CPU support for nn.ReflectionPad2d.
- [STABLE] Add Ascend, GPU, and CPU support for nn.SiLU.
- [STABLE] Add functional interface for ops.transpose.
- [STABLE] Add CPU support for ops.uniform_candidate_sampler.
- [STABLE] Add functional interface for ops.uniform.
- [STABLE] Add GPU support for ops.unique_with_pad.
- [STABLE] Add functional interface for ops.unstack.
- [BETA] Add GPU and CPU support for ops.interpolate.
- [STABLE] Add CPU support for ops.xdivy.
- [STABLE] Add CPU support for ops.xlogy.

## MindSpore 1.8.0 Release Notes

### Major Features and Improvements

#### FrontEnd

- [BETA] Add `mindspore.train.Model.fit` API, add `mindspore.train.callback.EarlyStopping` and `mindspore.train.callback.ReduceLROnPlateau` in Callback.
- [BETA] Support custom operator implemented by Julia.
- [BETA] Support custom operator implemented by MindSpore Hybrid DSL.
- [STABLE] The export() interface supports the export of a model using a custom encryption algorithm, and the load() interface supports the import of a model using a custom decryption algorithm.
- [BETA] [Unified_Dynamic_and_Static_Graphs] [Usability] Constant-type data (tuple/list/dict is supported in Version 1.8) can be set to be variable during graph compiling.
- [BETA] [Unified_Dynamic_and_Static_Graphs] JIT fallback is used to support the control flow capability in the constant scenario.
- [STABLE] [Unified_Dynamic_and_Static_Graphs] The Python raise statement is supported in the graph mode constant scenario.
- [STABLE] [Unified_Dynamic_and_Static_Graphs] The Python assert statement is supported in the graph mode constant scenario.
- [STABLE] [Unified_Dynamic_and_Static_Graphs] The Python print statement is supported in the graph mode constant scenario.
- [STABLE] [Unified_Dynamic_and_Static_Graphs] The str.format() method is supported in the graph mode.
- [STABLE] [Unified_Dynamic_and_Static_Graphs] The slice method can be used to assign a value to the list in the graph mode.
- [STABLE] [Unified_Dynamic_and_Static_Graphs] The instances of custom classes can be created and invoked in the graph mode.
- [STABLE] [Unified_Dynamic_and_Static_Graphs] Obtaining the properties of a class from the Cell array and the custom class array is supported.
- [STABLE] [Unified_Dynamic_and_Static_Graphs] isinstance supports scenario expanding in the graph mode.
- [STABLE] Rename the custom operator decorator 'ms_hybrid' to 'ms_kernel'.
- [BETA] Custom operator Hybrid DSL is supported on the backend of CPU.
- [BETA] Custom operator Ascend backend adds custom scheduling primitive syntax support.

#### PyNative

- [STABLE] Implement the AdamWeightDecay operator to replace the original small operator combination mode.
- [STABLE] In PyNative mode, execute the optimizer by unifying the dynamic and static graphs.
- [STABLE] Optimize the execution performance of PyNative bprop graph and ms_function.

#### Auto Parallel

- [STABLE] Docking the AllToAll single-operator mode. Support AllToAll Operator in the KernelByKernel execution mode.
- [STABLE] Whole-graph offloading supports MPI launching. In Whole-graph offloading, launching with MPI is supported.
- [STABLE] Seeds of model weights provide parallel interface configuration. If you do not set the random number of seeds through the mindspore.set_seed command, the weights initialized by each parameter is determined by the current fragment index. If the random number of seeds are configured, the initialization results of the same shape and weight of the same segmentation policy are the same.
- [STABLE] The HCCL shields internal full-mesh and non-full-mesh connections. Both fully-connected AllToAllv and hierarchical AllToAllv are allowed in one training session.
- [BETA] CPU optimizer fusion. Multiple optimizer operators are combined according to data types through cross-parameter fusion, improving performance. Currently, It has been verified on CPU AdamWeightDecay optimizer. You can use the flatten_weights method in the network cell class to enable this function.

#### Executor

- [STABLE] Provide southbound API.
- [STABLE] Multi-actor fusion execution to optimize the execution performance during runtime.
- [STABLE] Nopop operators (eg. reshape) execute elimination.
- [STABLE] Embedded cache architecture switches unified distributed runtime.
- [STABLE] Parameter Server switches unified distributed runtime.
- [STABLE] Support Parameter Server mode training on CPU.

#### DataSet

- [STABLE] When using the map operation for dataset objects and the parameters like: num_parallel_workers > 1 and python_multiprocessing=True, the multi-process mechanism is optimized, so that the data channel and child processes are mapped one by one, avoiding excessive file handle occupation, and closing_pool interface is also deleted.
- [STABLE] Add a batch of Vision, Text and Audio data augmentation operations.
- [STABLE] Fix a bug where the flat_map method of the Dataset class does not flatten the result.
- [STABLE] Unify import paths of dataset augmentation APIs to provide more easier way to use. Refer to [latest api usages](https://www.mindspore.cn/docs/en/r1.8/api_python/mindspore.dataset.vision.html).

### API Change

#### operator

- [STABLE] Add GPU support for ops.adaptive_avg_pool2d.
- [BETA] Add Ascend, GPU, and CPU support for ops.adaptive_max_pool2d .
- [BETA] Add CPU support for ops.approximate_equal.
- [STABLE] Add CPU support for ops.argmin.
- [BETA] Add CPU support for ops.assign_sub.
- [STABLE] Add GPU support for ops.bernoulli.
- [BETA] Add CPU support for ops.bessel_i0.
- [BETA] Add CPU support for ops.bessel_i0e.
- [BETA] Add CPU support for ops.bessel_i1.
- [BETA] Add CPU support for ops.bessel_i1e Add CPU support.
- [STABLE] Add CPU support for ops.bessel_j0.
- [STABLE] Add CPU support for ops.bessel_j1.
- [STABLE] Add CPU support for ops.bessel_k0.
- [STABLE] Add CPU support for ops.bessel_k0e.
- [BETA] Add CPU support for ops.bessel_k1.
- [BETA] Add CPU support for ops.bessel_k1e.
- [STABLE] Add CPU support for ops.bessel_y0.
- [STABLE] Add CPU support for ops.bessel_y1.
- [STABLE] Add CPU support for ops.bitwise_and.
- [STABLE] Add CPU support for ops.bitwise_or.
- [STABLE] Add CPU support for ops.bitwise_xor.
- [STABLE] Add functional interface for ops.broadcast_to.
- [BETA] Add GPU and CPU support for ops.ceil.
- [BETA] Add GPU support for ops.col2im.
- [BETA] Add functional interface for ops.concat.
- [STABLE] Add GPU support for ops.cosh.
- [STABLE] Add Ascend and CPU support for ops.ctc_greedy_decoder.
- [BETA] Add GPU and CPU support for ops.DataFormatDimMap.
- [BETA] Add GPU and CPU support for ops.dropout2d.
- [BETA] Add CPU support for ops.dropout3d.
- [BETA] Add CPU support for ops.erf.
- [BETA] Add CPU support for ops.erfc.
- [STABLE] Add functional interface for ops.expand_dims.
- [STABLE] Add GPU and CPU support for ops.fast_gelu.
- [STABLE] Add Ascend dynamic shape support for ops.flatten.
- [BETA] Add GPU and CPU support for ops.ger.
- [STABLE] Add Ascend, GPU, and CPU support for ops.gumbel_softmax.
- [BETA] Add GPU and CPU support for ops.hardshrink.
- [BETA] Add CPU support for ops.index_add.
- [BETA] Add CPU support for ops.inplace_add.
- [BETA] Add CPU support for ops.inplace_sub.
- [STABLE] Add CPU support for ops.intopk.
- [STABLE] Add GPU and CPU support for ops.inv.
- [STABLE] Add GPU and CPU support for ops.invert.
- [BETA] Add CPU support for ops.isclose.
- [STABLE] Add CPU support for ops.lerp.
- [BETA] Add CPU support for ops.linspace.
- [BETA] Add functional interface for ops.log_softmax.
- [BETA] Add Ascend, GPU, and CPU support for ops.norm.
- [BETA] Add CPU support for ops.lrn.
- [BETA] Add GPU support for ops.masked_select.
- [BETA] Add GPU and CPU support for ops.matrix_band_part.
- [BETA] Add GPU and CPU support for ops.matrix_solve.
- [BETA] Add CPU support for ops.meshgrid.
- [STABLE] Add CPU support for ops.mish.
- [BETA] Add GPU support forops.nonzero.
- [STABLE] Add GPU and CPU support for ops.padding.
- [BETA] Add Ascend dynamic shape support for ops.pow.
- [BETA] Add functional interface for ops.range.
- [BETA] Add Ascend dynamic shape support for ops.round.
- [STABLE] Add Ascend dynamic shape support for ops.scatter_add.
- [STABLE] Add Ascend dynamic shape support for ops.scatter_div.
- [BETA] Add GPU support for ops.scatter_max.
- [BETA] Add GPU support for ops.scatter_min.
- [BETA] Add CPU support for ops.scatter_nd_add.
- [STABLE] Add GPU and CPU support for ops.scatter_nd_div.
- [STABLE] Add GPU and CPU support for ops.scatter_nd_min.
- [STABLE] Add GPU and CPU support for ops.scatter_nd_mul.
- [BETA] Add CPU support for ops.scatter_nd_sub.
- [STABLE] Add Ascend dynamic shape support for ops.scatter_update.
- [BETA] Add Ascend dynamic shape support for ops.select.
- [BETA] Add GPU and CPU support for ops.selu.
- [BETA] Add GPU and CPU support for ops.soft_shrink.
- [BETA] Add CPU support for ops.softsign.
- [STABLE] Add GPU support for ops.tan.
- [BETA] Add Ascend and CPU support ops.tensor_scatter_add.
- [STABLE] Add GPU and CPU support for ops.tensor_scatter_div.
- [STABLE] Add GPU and CPU support for ops.tensor_scatter_mul.
- [BETA] Add Ascend and CPU support for ops.tensor_scatter_sub.
- [STABLE] Add Ascend, GPU, and CPU support for nn.AdaptiveAvgPool1d.
- [STABLE] Add Ascend, GPU, and CPU support for nn.AdaptiveMaxPool1d.
- [BETA] Add Ascend, GPU, and CPU support for nn.BiDense.
- [STABLE] Add Ascend, GPU, and CPU support for nn.ConstantPad1d.
- [STABLE] Add Ascend, GPU, and CPU support for nn.ConstantPad2d.
- [STABLE] Add Ascend, GPU, and CPU support for nn.ConstantPad3d.
- [STABLE] Add Ascend, GPU, and CPU support for nn.Hardtanh.
- [STABLE] Add Ascend, GPU, and CPU support for nn.HuberLoss.
- [STABLE] Add Ascend, GPU, and CPU support for nn.RReLU.
- [STABLE] Add Ascend, GPU, and CPU support for nn.Tanhshrink.
- [STABLE] Add Ascend, GPU, and CPU support for nn.Threshold.
- [STABLE] Add Ascend, GPU, and CPU support for nn.ZeroPad2d.
- [BETA] Add GPU support for ops.unique_consecutive.
- [STABLE] Add CPU support for ops.unsorted_segment_max.
- [STABLE] Add CPU support for ops.unsorted_segment_min.
- [STABLE] Add GPU support for ops.unsorted_segment_prod.

#### Backwards Incompatible Change

##### Python API

- DVPP simulation algorithm is no longer supported. Remove `mindspore.dataset.vision.c_transforms.SoftDvppDecodeRandomCropResizeJpeg` and `mindspore.dataset.vision.c_transforms.SoftDvppDecodeResizeJpeg` interfaces.
- Add `on_train_epoch_end` method in LossMonitor, which implements printing metric information in the epoch level when it is used in `mindspore.train.Model.fit`.
- TimeMonitor printing content changes, and the printed content is added to "train" or "eval" to distinguish between training and inference phases.
- `filter_prefix` of `mindspore.load_checkpoint` interface: empty string ("") is no longer supported, and the matching rules are changed from strong matching to fuzzy matching.

#### Import Optimization

APIs in `mindspore.context`, `mindspore.parallel`, `mindspore.profiler` and `mindspore.train` can be directly used in `mindspore`. The original usage can still be supported.

For examples:

- `mindspore.context.set_context` can be simplified to `mindspore.set_context`.
- `mindspore.parallel.set_algo_parameters` can be simplified to `mindspore.set_algo_parameters`.
- `mindspore.profiler.Profiler` can be simplified to `mindspore.Profiler`.
- `mindspore.train.callback.Callback` can be simplified to `mindspore.train.Callback`.

The API pages are aggregated to <https://www.mindspore.cn/docs/en/r1.8/api_python/mindspore.html>.

### Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

## MindSpore Lite 1.8.0 Release Notes

### Major Features and Improvements

#### API

- [STABLE] Add C++ and Python APIs for model conversion.
- [STABLE] Add Python APIs for model inference.

#### Post-Training Quantization

- [STABLE] Support perlayer quantization, and built-in CLE to optimize perlayer quantization accuracy.

## MindSpore 1.7.0 Release Notes

### Major Features and Improvements

#### OS

- [STABLE] Support Python 3.8 (Linux/Windows/Mac).
- [STABLE] Installation improved with more detailed install guide and automated shell scripts.
- [STABLE] Support operator computing with multi-thread under Windows.
- [STABLE] Compatible with GCC from version 7.3 to 9.x.

#### FrontEnd

- [STABLE] Support dynamic weight decay for optimizers, that is weight decay value will change according to the increasing step during training.
- [STABLE] Add four methods to create Tensor, which are `mindspore.numpy.rand()`, `mindspore.numpy.randn()`, `mindspore.numpy.randint()`, and `mindspore.ops.arange()`.
- [STABLE] Add `mindspore.train.callback.History` in Callback.
- [BETA] Support custom operator implemented by Julia operator.
- [STABLE] Support accessing attributes and methods of user-defined classes  through `mindspore.ms_class` class decorator.
- [STABLE] Support training when a network has side effect operations and control flow statements at the same time.
- [STABLE] Support for more complex control flow syntax, such as a for loop statement in the body of a while loop.
- [STABLE] Improve the performance of networks with complex syntax control flow statements by decreasing the num of subgraphs.

#### PyNative

- [STABLE] Add Hook functions in PyNative mode, including register_forward_pre_hook, register_forward_hook of the forward hook interface, register_backward_hook of the reverse hook interface.
- [STABLE] Optimize the execution performance of PyNative mode, and execute the front-end Python and the back-end C++ in parallel.

#### Auto Parallel

- [STABLE] Support TopK routing, data parallel and optimizer state parallel when enable MoE.
- [STABLE] Support AllGather/ReduceScatter communication operator fusion. Support AllReuduce fusion by the data volume size in DATA_PARALLEL mode.
- [STABLE] Support ops.clip_by_global_norm in the parallel mode.
- [STABLE] Support AdaSum optimizer in the parallel mode.
- [STABLE] Support automatic optimizer state parallel.
- [STABLE] Support AlltoAll configurable. Support automatically add virtualdataset cell.
- [STABLE] Support automatically inference trainable parameters in pipeline parallel training.
- [STABLE] Support clusters where the device number is not the power of 2.
- [STABLE] Support sharding propagation in auto-parallel mode.
- [STABLE] Support optimizer offload under the unified runtime.
- [STABLE] Support Adafactor operator on CPU.
- [STABLE] Support sharding at H/W axis for Conv2d/Conv2DTranspose operator. Support operators such as ResizeBilinear，ROIAlign, CropAndResize, BoundingBoxEncode, IOU and RandomChoiceWithMask.

#### Executor

- [BETA] [Failure Recovery Under Data Parallel Training](https://www.mindspore.cn/tutorials/experts/en/r1.7/parallel/train_gpu.html#%E5%AE%B9%E7%81%BE%E6%81%A2%E5%A4%8D) Support auto failure recovery under data parallel training mode.
- [BETA] Support searching for the number of threads under the CPU to obtain the optimal number of threads for execution. The entire search process takes 50 steps, and the overall performance will reach a stable state after 50 steps. When testing performance, data after 50 steps need to be used as a standard.

#### DataSet

- [STABLE] Add dataset operations mapping between TensorFlow.data module and MindSpore.dataset module, [check list](https://www.mindspore.cn/docs/en/r1.7/note/api_mapping/tensorflow_api_mapping.html#tf-data).
- [STABLE] Python multiprocessing optimization and make processes exit normally.
- [STABLE] Support [Dataset Autotune](https://www.mindspore.cn/tutorials/experts/en/master/dataset/dataset_autotune.html) for tuning the speed of dataset pipeline automatically.
- [BETA]  [Dataset Offload](https://www.mindspore.cn/tutorials/experts/en/master/dataset/dataset_offload.html) support new data augmentation operations: RandomColorAdjust, RandomSharpness, TypeCast.
- Output a single data column when `__getitem__/__next__` methods of GeneratorDataset return a single NumPy object.
- Use `ulimit -u 10240` to increase the number of threads/processes available to the current user when specify too many processes or threads for loading dataset may cause RuntimeError: can't start new thread.

### API Change

#### Backwards Incompatible Change

##### Python API

- Modify the gradient return value type of the hook corresponding to the register_backward_hook function, and change the gradient return value to the tuple type uniformly.([!31876](https://gitee.com/mindspore/mindspore/pulls/31876))
- Deprecated usage: `import mindspore.dataset.engine.datasets as ds`. Use `import mindspore.dataset as ds` instead as recommended in [mindspore doc](https://www.mindspore.cn/docs/en/r1.7/api_python/mindspore.dataset.html).
- Add `mindspore.ms_class` interface, as class decorator for user-defined classes. It allows MindSpore to identify user-defined classes and access their attributes and methods([!30855](https://gitee.com/mindspore/mindspore/pulls/30855))
- Deprecate `mindspore.SparseTensor` and use `mindspore.COOTensor` instead. ([!28505](https://gitee.com/mindspore/mindspore/pulls/28505))
- Add Tensor init arg `internal` for internal use.

### Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

## MindSpore Lite 1.7.0 Release Notes

### Major Features and Improvements

#### Post quantization

- [STABLE] Support post quantization to run dynamic quantization algorithm.
- [BETA] Support post quantized model to run on NVIDIA GPU.

# MindSpore 1.6.0

## MindSpore 1.6.0 Release Notes

### Major Features and Improvements

#### OS

- [STABLE] Support macOS with CPU(X86)
- [BETA] Supoport macOS with CPU(M1)

#### FrontEnd

- [STABLE] Support JIT Fallback feature in Graph mode.
- [STABLE] Support compile cache feature in Graph mode.
- [STABLE] Add new optimizers, including ASGD and Rprop.
- [STABLE] Add new initializers, including Identity, Orthogonal, Dirac, Sparse and VarianceScaling.
- [STABLE] Support resuming training when an exception occurs in the process.
- [STABLE] Change `mindspore.nn.LSTMCell` from single-layer LSTM to single-cell LSTM.
- [BETA] Introduce `mindspore.ops.Custom` to customize your own operators for Ascend(AICore, AICPU), GPU, CPU backends, and the custom type can be one of TBE, AKG, pure Python function or prebuild binary(called aot operator).

#### PyNative

- [STABLE] Support heterogeneous feature in PyNative mode.
- [STABLE] Optimize memory allocation in PyNative mode.

#### Auto Parallel

- [STABLE] Support configuring the output shard strategy of the MatMul distributed operator.
- [STABLE] Support multi-instances parallel.
- [STABLE] Support activation slice communication and calculation overlap in Transformer.
- [STABLE] Support heterogeneous parallel tensor swap.
- [STABLE] Add implementations of distributed operator of ResizeNearestNeighbor.
- [STABLE] Add a communication operator named NeighborExchangeV2 that supports data exchange between adjacent 8 rank ids.
- [STABLE] Pipeline parallel support GPU platform.
- [STABLE] Add cell-level data parallel interface.
- [STABLE] Support gradient AllReduce fusion according to the amount of data.
- [STABLE] Support a sharding strategy search algorithm called sharding propagation.

#### Executor

- [STABLE] Support multigraph sink and subgraph sink of MindRT.
- [STABLE] Support memory swap to break the device memory size limit on Ascend platform.
- [STABLE] Support dynamic deployment of distributed training cluster(GPU).
- [BETA] Support automatic failover of parameter server.

#### DataSet

- [STABLE] Support overwrite feature in MindRecord.
- [STABLE] Log improvement and more friendly to users.
- [BETA] Support new feature [Dataset Offload](https://www.mindspore.cn/docs/programming_guide/en/r1.6/enable_dataset_offload.html) to speed up data processing by heterogeneous computing.
- [BETA] Support new feature [Dataset Autotune](https://www.mindspore.cn/docs/programming_guide/en/r1.6/enable_auto_tune.html) to adjust parallelism of dataset pipeline automatically.

#### GraphKernel Fusion

- [STABLE] Support kernel fusion and generation for CPU backend.

#### Federated Learning

- [STABLE] FL-Client framework and model decoupling.
- [BETA] Support Cross-silo federated learning framework.

#### Debug

- [STABLE] Support dump in cell level(Ascend).
- [STABLE] Support dump Tensor statistics(Ascend/GPU).
- [STABLE] Support displaying corresponding code lines for fusion nodes.
- [STABLE] Support passing dump flag in Ascend backend in order to dump correct operators after fusion transformation.

### API Change

#### Backwards Incompatible Change

##### Python API

###### `mindspore.dataset.MindDataset` interface changes input parameter dataset_file([!27542](https://gitee.com/mindspore/mindspore/pulls/27542))

`MindDataset` contains the input parameter `dataset_file`, which is in the singular format. It can receive a single file path or a list that stores multiple file paths. Thus It is preferred to change the input parameter `dataset_file` into plural format. In addition, the input parameters of most dataset API, such as `TFRecordDataset`, are in plural formart (`dataset_files`). To ensure consistency, the input parameter `dataset_file` of MindDataset is changed to plural formart as `dataset_files`,  we can see the updated version in api of [mindspore.dataset.MindDataset](https://www.mindspore.cn/docs/en/master/api_python/dataset/mindspore.dataset.MindDataset.html#mindspore.dataset.MindDataset).

###### Delete `mindspore.Tensor`'s property `virtual_flag`([!26989](https://gitee.com/mindspore/mindspore/pulls/26989))

###### Delete `mindspore.Parameter`'s property `is_init`([!26989](https://gitee.com/mindspore/mindspore/pulls/26989))

###### Delete `mindspore.nn.ROC`'s interface `roc`([!25713](https://gitee.com/mindspore/mindspore/pulls/25713))

###### The `shard()` interface of primitive is changed from `shard(strategy)` to `shard(in_strategy=None, out_strategy=None)`

###### The `set_auto_parallel_context()` interface of context is changed from

###### `set_auto_parallel_context(parallel_mode=AUTO_PARALLEL, auto_parallel_search_mode="dynamic_programming")` to `set_auto_parallel_context(parallel_mode=AUTO_PARALLEL, search_mode="dynamic_programming")`

#### Collect Data and Create Landscape

##### Python API

###### `mindspore.train.callback.SummaryCollector` interface's parameter `collect_specified_data` add new operations `collect_landscape` ([!26229](https://gitee.com/mindspore/mindspore/pulls/26229))

`collect_landscape` can collect the parameters needed to create the loss landscape. we can see the updated version in api of [mindspore.train.callback.SummaryCollector](https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.SummaryCollector.html#mindspore.SummaryCollector).

###### `mindspore.train.callback` add new interface `SummaryLandscape` ([!26229](https://gitee.com/mindspore/mindspore/pulls/26229))

`SummaryLandscape` can help you to collect loss landscape information. It can create landscape in PCA direction or random direction by calculating loss. We can see the updated version in api of [mindspore.train.callback.SummaryLandscape](https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.SummaryLandscape.html#mindspore.SummaryLandscape).

### Bug fixes

#### Executor

- Fix process hanging while calling MPI_comm_create in asymmetric pipeline split scenario. ([!28707](https://gitee.com/mindspore/mindspore/pulls/28707))
- Fix the execution error when the weights are shared between graph mode and PyNative mode.([!26635](https://gitee.com/mindspore/mindspore/pulls/26635))
- Fixed the probability coredump when free memory under PyNative mode.([!25472](https://gitee.com/mindspore/mindspore/pulls/25472))

#### Dataset

- Fix memory increase abnormally when running dataset for a long time. ([!26237](https://gitee.com/mindspore/mindspore/pulls/26237))
- Fix saving MindRecord files with Chinese path on Windows. ([!28378](https://gitee.com/mindspore/mindspore/pulls/28378))

## MindSpore Lite

### Major Features and Improvements

#### Converter and runtime

- [STABLE] Add more fusion patterns in the converter tool to improve runtime performance.
- [STABLE] Support take OpenGL texture as input and output of inference.
- [STABLE] Refactor the JAVA API.
- [BETA] Support inference on Ascend310.

#### x86 backend optimization

- [STABLE] Optimize kernels for x86 using Advanced Vector Extensions(AVX512).

#### ARM backend optimization

- [STABLE] Support heterogeneous parallel inference, including splitting operators, constructing heterogeneous subgraphs, and heterogeneous parallel scheduling between CPUs and GPUs.
- [STABLE] Add more FP16 operators.

#### Post quantization

- [STABLE] Post quantization supports debugging.
- [STABLE] Full quantization supports choosing non-quantized nodes.
- [STABLE] Mixed bit quantization supports auto-tune.

#### Training on Device

- [STABLE] Support user-defined algorithm models to access the federated learning framework.

### Contributors

Thanks goes to these wonderful people:

AGroupofProbiotocs, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jiabin Liu, jianghui58, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, liuyongqi, laiyongqiang, leonwanghui, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, lvchangquan, lvliang, lz, maning202007, Margaret_wangrui, mengyuanli, Ming_blue, ms_yan, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, [wangnan39@huawei.com](mailto:wangnan39@huawei.com), wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, [zhanghaibo5@huawei.com](mailto:zhanghaibo5@huawei.com), zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.

Contributions of any kind are welcome!

# MindSpore 1.5.2

## MindSpore 1.5.2 Release Notes

### Bug fixes

- Fix code specification, pclint, codedex alarm.
- Repair NN Abnormal output of graphnorm operator.
- Fixed the problem of poor performance in scenes with dynamic rnngrad batch size of 16 times.

### Contributors

Thanks goes to these wonderful people:

Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiao Tianci, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, Zhenglong Li, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, wangfengwfwf, zymaa, gerayking.

Contributions of any kind are welcome!

# MindSpore 1.5.1

## MindSpore 1.5.1 Release Notes

### Bug fixes

- Fix code specification, pclint, codedex alarm.
- Fix yolov4 network probabilistic segment error.

### Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

# MindSpore 1.5.0

## MindSpore 1.5.0 Release Notes

### Major Features and Improvements

#### NewModels

- [STABLE] Add CV model on Ascend: Fast-SCNN
- [BETA] Add CV models on Ascend: midas_V2, attgan, FairMOT, CenterNet_resnet101, SEResNext, YOLOV3-tiny, RetinaFace
- [STABLE] Add CV models on GPU: ssd_mobilenetv1_fpn, shufflenetv1, tinyDarkNet, CNN-CTC, unet++, DeepText, SqueezeNet
- [STABLE] Add NLP models on GPU: GRU, GNMT2, Bert-Squad
- [STABLE] Add recommend models on GPU: NCF
- [BETA] Add CV models on GPU: FaceAttribute, FaceDetection, FaceRecongnition SENet,
- [BETA] Add Audio models on GPU: DeepSpeech2
- [STABLE]`model_zoo` has been separated to an individual repository`models`

#### FrontEnd

- [STABLE] Support`while` and`break`,`continue` statements of training network in`GRAPH_MODE`.
- [BETA] Support export MindIR file after model training in cloud side and evaluate in edge side by import the MindIR file.
- [STABLE] Support forward mode auto-diff interface Jvp(Jacobian-Vector-Product).
- [STABLE] Support backward mode auto-diff interface Vjp(Vector-Jacobian-Product).

#### Auto Parallel

- [STABLE] Support distributed pipeline inference.
- [STABLE] Add implementation of the sparse attention and its distributed operator.
- [STABLE] Add implementations of distributed operator of Conv2d/Conv2dTranspose/Conv2dBackpropInput/Maxpool/Avgpool/Batchnorm/Gatherd.
- [STABLE] Support configuring the dataset strategy on distributed training and inference mode.
- [STABLE] Add high level API of the Transformer module.

#### Executor

- [STABLE] Support AlltoAll operator.
- [STABLE] CPU operator (Adam) performance optimization increased by 50%.
- [BETA] Support Adam offload feature, reduce the static memory usage of Pangu large model by 50%.
- [STABLE] MindSpore Ascend backend supports configuration operator generation and loading cache path.
- [STABLE] MindSpore Ascend backend supports lazy build in PyNaitve mode and compilation performance improved by 10 times.
- [STABLE] The function or Cell decorated by ms_function supports gradient calculation in PyNative mode.
- [STABLE] The outermost network supports parameters of non tensor type in PyNative mode.

#### DataSet

- [BETA] Add a new method for class Model to support auto data preprocessing in scenario of Ascend 310 inference.
- [STABLE] Add a new drawing tool to visualize detection/segmentation datasets.
- [STABLE] Support a new tensor operation named ConvertColor to support color space transform of images.
- [STABLE] Enhance the following tensor operations to handle multiple columns simultaneously: RandomCrop, RandomHorizontalFlip, RandomResize, RandomResizedCrop, RandomVerticalFlip.
- [STABLE] Support electromagnetic simulation dataset loading and data augmentation.
- [STABLE] Optimize the error logs of Dataset to make them more friendly to users.

#### Federated Learning

- [STABLE] Change the deployment environment of FL-Client.

#### Running Data Recorder

- [STABLE] RDR saves collected data files within directories named by Rank ID on distributed training on Ascend, GPU and CPU.

#### GraphKernel Fusion

### API Change

#### Backwards Incompatible Change

##### Python API

###### New Recomputation Configuration for AutoParallel and SemiAutoParallel Scenarios

Configuring the recomputation of the communication operations generated by the model parallel and optimizer parallel to save the memory on the
devices. Users can pass `mp_comm_recompute` and `parallel_optimizer_comm_recompute` to enable the recomputation of the communication operations.

### Bug fixes

#### FrontEnd

- Fix bug of too many subgraphs when network include`for` statement.([!23669](https://gitee.com/mindspore/mindspore/pulls/23669))

#### Executor

- RunTask failed when parameter_broadcast is enabled in PyNative mode. ([!23255](https://gitee.com/mindspore/mindspore/pulls/23255))
- An illegal memory access was encountered in the dynamic shape net on GPU.
- Fix tune failed for DynamicRnn. ([!21081](https://gitee.com/mindspore/mindspore/pulls/21081))

#### Dataset

- Optimize thread monitoring to solve the problem of running multiple multiprocessesing on Windwos. ([!23232](https://gitee.com/mindspore/mindspore/pulls/23232))
- Fix bugs of Dataset tensor operations in lite mode. ([!21999](https://gitee.com/mindspore/mindspore/pulls/21999))
- Fix memory increasing when using create_dict_iterator in for loop. ([!22529](https://gitee.com/mindspore/mindspore/pulls/22529))([!22529](https://gitee.com/mindspore/mindspore/pulls/22529))

## MindSpore Lite

### Major Features and Improvements

#### Converter and runtime

1. Optimize TDNN-like streaming model by reusing the result of last inference.
2. Support dynamic filter Convolution.
3. Support serializing float32 weight into float16 weight for reducing size of model file.
4. Provide unified runtime API for developer reusing their code between cloud side and end side.
5. Now developer can configure built-in pass as custom passes.
6. Now user can specify format and shape of model inputs while converting model.
7. Support multiple devices inference, includeing CPU, NPU, GPU. User can set devices in mindspore::Context.
8. Support mixed precision inference. User can set inference precision by LoadConfig API.
9. Support custom operator registration and enable inference on third-party hardware.

#### ARM backend optimization

1. Support the nchw data format of some Operators, such as Conv, InstanceNorm, etc. The performance of some models convertered from onnx and caffe is greatly improved.
2. Fix bugs of memory leak on NPU.

#### Post quantization

1. Weight quantization supports mixed bit quantization.
2. Full quantization supports data pre-processing.
3. Adjust the quantization parameters from the command line to the configuration file.

#### Training on Device

1. Unify lite external api with MindSpore.
2. Implement static memory allocator and common workspace for TOD，save memory 10-20%.
3. Provide getgradients and setgradients interface，get and set optimizer params interfaces to support MOE Model.
4. Support user specified output node when export IOD Model.
5. Support more text  networks (tinybert,albert) and operators.

#### Codegen

1. Support kernel register for custom op. Third-party hardware like NNIE can be accessed through it.

### API Change

#### API Incompatible Change

##### C++ API

### Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

# MindSpore 1.4.0

## MindSpore 1.4.0 Release Notes

### Major Features and Improvements

#### NewModels

#### FrontEnd

#### Auto Parallel

- Add distributed operators: Conv2D/Conv2DTranspose/Conv2DBackpropInput/MaxPool/AvgPool/BatchNorm/GatherD
- Support to configure shard strategy for dataset

#### Executor

#### DataSet

- Add SlicePatchesOperation for Remote Sensing feature（[!18179](https://e.gitee.com/mind_spore/repos/mindspore/mindspore/pulls/18179)）

#### FederatedLearning

#### Running Data Recorder

#### GraphKernel Fusion

#### Profiler

- [STABLE]  Support MS_DIAGNOSTIC_DATA_PATH for profiler feature.(Ascend/GPU)

#### Dump

- [STABLE]  Support MS_DIAGNOSTIC_DATA_PATH for dump feature.(Ascend/GPU/CPU)

### API Change

#### Backwards Incompatible Change

##### Python API

##### Command Line Interface

###### Dump Config

Previously, we need to set the dump path in dump config file. To make the dump feature easier to use on cloud, we support new environment parameter `MS_DIAGNOSTIC_DATA_PATH`.

| 1.3.0                          | 1.4.0                                                                                                                                        |
| ------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------- |
| `path` is a mandatory field. | `path` field is optional.  If `path` field is not provided or is empty string, `MS_DIAGNOSTIC_DATA_PATH` should be set in environment. |

### Bug fixes

#### FrontEnd

#### Executor

#### Dataset

- Fix module 'signal' has no attribute 'SIGCHLD' problem under windows platform. ([!21232](https://gitee.com/mindspore/mindspore/pulls/21232))

## MindSpore Lite

### Major Features and Improvements

#### Converter and runtime

#### x86 backend optimization

#### ARM backend optimization

#### Cuda backend optimization

#### OpenCL backend

#### Post quantization

#### Training on Device

#### Codegen

### API Change

#### API Incompatible Change

##### C++ API

#### New features

##### Java API

### Bug fixes

#### Deprecations

### Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

# MindSpore 1.3.0

## MindSpore 1.3.0 Release Notes

### Major Features and Improvements

#### NewModels

- [STABLE] Add CV models on Ascend: CPM, FCN8s, SSD-ResNet50-FPN, EAST, AdvancedEast.
- [STABLE] Add NLP models on Ascend: DGU, TextCNN, SentimentNet(LSTM).
- [STABLE] Add CV models on GPU: Faster-RCNN, FCN8s, CycleGAN, AdvancedEast.
- [BETA] Add CV models on Ascend: CycleGAN, PoseNet, SimCLR.
- [BETA] Add NLP models on Ascend: DGU, EmoTect, Senta, KT-Net.
- [BETA] Add NLP models on GPU: DGU, EmoTect.
- [BETA] Add EPP-MVSNet: a novel deep learning network for 3D reconstruction from multi-view stereo, which has won the first place in Tanks & Temples leaderboard(until April 1, 2021)(GPU).

#### FrontEnd

- [STABLE] The default running mode of MindSpore is changed to Graph mode.
- [STABLE] Support interface `run_check` to check whether MindSpore is working properly or not.
- [STABLE] Support saving custom information in the checkpoint file.
- [STABLE] Normal class adds mean parameter.
- [STABLE] Support export YOLOv3-DarkNet53 and YOLOv4 ONNX model.
- [STABLE] Support 40+ operator export ONNX model.
- [STABLE] The Metric module supports `set_indexes` to select the inputs of `update` in the specified order.
- [STABLE] Switch `_Loss` to an external API `LossBase` as the base class of losses.

#### Auto Parallel

- [STABLE] Add distributed operators: Select/GatherNd/ScatterUpdate/TopK.
- [STABLE] Support basic pipeline parallelism.
- [STABLE] Optimize sharding strategy setting of `Gather`.
- [STABLE] Optimize mix precision and shared parameter scenarios.
- [STABLE] Optimize distributed prediction scenarios.

#### Executor

- [STABLE] Support unified runtime in GPU and CPU backend.
- [STABLE] MindSpore GPU support CUDA11 with cuDNN8.
- [STABLE] MindSpore GPU inference performance optimization by integrating TensorRT.
- [STABLE] MindSpore built on one Linux distribution can now be used on multiple Linux distributions with the same CPU architecture (e.g. EulerOS, Ubuntu, CentOS).
- [STABLE] MindSpore now supports Ascend310 and Ascend910 environments with one single wheel package and provides an alternate binary package for Ascend310 specifically.
- [STABLE] MindSpore Ascend support group convolution.

#### DataSet

- [STABLE] Support caching over MindRecord dataset.
- [STABLE] Support new shuffle mode for MindRecord dataset.
- [STABLE] Support a cropper tool for MindSpore Lite to allow the user to customize MindData binary file according to their script.
- [STABLE] Support share memory mechanism to optimize the multi-processing efficiency of GeneratorDataset/Map/Batch.
- [STABLE] Add features for the GNN dataset to support molecular dynamics simulation scenarios.

#### FederatedLearning

- [STABLE] Support Cross-device federated learning framework.
- [STABLE] Support FL-Server distributed networking including TCP and HTTP communication.
- [STABLE] Support FL-Server distributed federated aggregation，support autoscaling and fault tolerance.
- [STABLE] Develop FL-Client framework.
- [STABLE] Supports local differential privacy algorithms.
- [STABLE] MPC-based security aggregation algorithm.
- [STABLE] MindSpore Lite Device-side Inference & Training Interconnection with FL-Client.

#### Running Data Recorder

- [STABLE] Provide records of multi-stage computational graphs, memory allocation information and graph execution order when a "Launch kernel failed" occurs. (CPU)

#### GraphKernel Fusion

- [STABLE] Add options to control the optimization level.
- [STABLE] Enhance the generalization ability on GPU. GraphKernel is enabled by default in 40+ networks which cover the field of NLP, CV, Recommender, NAS and Audio. The result shows their throughput is significantly improved, and you are Recommended enabling GraphKernel in your network.

#### Debug

- [STABLE] Unified dump function.

### API Change

#### Backwards Incompatible Change

##### Python API

###### `mindspore.dataset.Dataset.device_que` interface removes unused parameter `prefetch_size`([!18973](https://gitee.com/mindspore/mindspore/pulls/18973))

Previously, we have a parameter `prefetch_size` in `device_que` to define the prefetch number of records ahead of the user's request. But indeed this parameter is never used which means it is an ineffective parameter. Therefore, we remove this parameter in 1.3.0 and users can set this configuration by [mindspore.dataset.config.set_prefetch_size](https://www.mindspore.cn/docs/api/en/r1.3/api_python/mindspore.dataset.config.html#mindspore.dataset.config.set_prefetch_size).

```python
device_que(prefetch_size=None, send_epoch_end=True, create_data_info_queue=False)
```

</td>
<td>

```python
device_que(send_epoch_end=True, create_data_info_queue=False)
```

</td>
</tr>
</table>

###### `mindspore.nn.optim.thor` interface changes to lowercase `thor` and adds two parameters `enable_clip_grad` and `frequency`([!17212](https://gitee.com/mindspore/mindspore/pulls/17212))

The parameter `enable_clip_grad` is used for gradient clipping and another parameter `frequency` is used to control the update interval of second order information matrix.

```python
THOR(net, learning_rate, damping, momentum, weight_decay=0.0, loss_scale=1.0, batch_size=32,
     use_nesterov=False, decay_filter=lambda x: x.name not in [], split_indices=None)
```

</td>
<td>

```python
thor(net, learning_rate, damping, momentum, weight_decay=0.0, loss_scale=1.0, batch_size=32,
     use_nesterov=False, decay_filter=lambda x: x.name not in [], split_indices=None, enable_clip_grad=False,
     frequency=100)
```

</td>
</tr>
</table>

##### Dump Config

Previously, we could only dump tensor data for one or all steps. To make the dump feature easier to use, we changed the dump configuration format and dump structure. View the [New Dump Tutorial](https://www.mindspore.cn/tutorials/experts/en/master/debug/dump.html#dump-introduction).

| 1.2.1                                                  | 1.3.0                                                                                       |
| ------------------------------------------------------ | ------------------------------------------------------------------------------------------- |
| `iteration` is an int.                               | `iteration` is a string.                                                                  |
| `op_debug_mode` is in `async_dump_settings` field. | `op_debug_mode` is in `common_dump_settings` field. `async_dump_settings` is removed. |

### Bug fixes

#### FrontEnd

- Fix exception when use import module in while body such as 'F.xxx'.([!17635](https://e.gitee.com/mind_spore/repos/mindspore/mindspore/pulls/17635))
- Fix the exception of 'exceeding limit call depth' in compile graph process when using while expression with grad operation. ([!18662](https://e.gitee.com/mind_spore/repos/mindspore/mindspore/pulls/18662))

#### Executor

- Fix reallocate memory bug for communication op.([!14492](https://gitee.com/mindspore/mindspore/pulls/14492))
- Replace memcpy_async op with tensor_move op.([!15204](https://gitee.com/mindspore/mindspore/pulls/15204))
- Fix the build error when multiple python versions are installed in the environment. ([!19165](https://gitee.com/mindspore/mindspore/pulls/19165))
- The warning when the te/topi/hccl version does not match is optimized, and fix the repeated warning. ([!18704](https://gitee.com/mindspore/mindspore/pulls/18704))
- Fix the error in a cluster with more than 8 pcs in pynative mode. ([!16376](https://gitee.com/mindspore/mindspore/pulls/16376))
- Fix graph ring problem in UB fusion.([!16109](https://gitee.com/mindspore/mindspore/pulls/16109))
- Fix AllGather op select problem when the shape is not divisible by 16. ([!18878](https://gitee.com/mindspore/mindspore/pulls/18878))

#### Dataset

- Fix an out-of-memory error when ImagefolderDataset gets an illegal directory. ([!16196](https://gitee.com/mindspore/mindspore/pulls/16196))
- Fix bugs of vision transformations in lite mode. ([!14722](https://gitee.com/mindspore/mindspore/pulls/14722),[!14774](https://gitee.com/mindspore/mindspore/pulls/14774),[!15050](https://gitee.com/mindspore/mindspore/pulls/15050))
- Fix default numbers of parallel workers of MindData for those CPUs with fewer cores. ([!15921](https://gitee.com/mindspore/mindspore/pulls/15921))
- Fix MindRecord writing failed probabilistically in multiprocessing. ([!15242](https://gitee.com/mindspore/mindspore/pulls/15242))

## MindSpore Lite

### Major Features and Improvements

#### Converter and runtime

1. Support Caffe model running on Hi3516D.
2. Support delegate mechanism to run your models(part or whole) on user specified executor.
3. Support control flow models.
4. Support cross-compiling for iOS, so that we can inference models on iOS devices.

#### x86 backend optimization

1. Optimize kernels for x86 using Advanced Vector Extensions(AVX).

#### ARM backend optimization

1. Optimize fp16 kernels.
2. Support arm32 fp16 instruction acceleration on ARMv8.2.

#### Cuda backend optimization

1. Support NV GPU backend base on delegate mechanism(use TensorRT as delegate).

#### OpenCL backend

1. Optimize the strategy of workgroup and blocksize to improve performance.
2. Support OpenCL dynamic infershape.
3. Support INT32 type ops.

#### Post quantization

1. Support fp32 training model converts to quantization training model.

#### Training on Device

1. Support fp32 training model export to quantization model after training process end.
2. Unify APIs and output package name of training and inference.
3. Simplify implementation of Train Session.
4. Optimize train and infer compile, reduce libmindspore-lite-train.so memory.
5. Training memory optimization:  memory reduce 10-50% compare with  r1.2.
6. Training performance optimization:  for 1*1 special input shape Cov2DGradInput and SparseSoftmaxCrossEntropyWithLogits operator optimization, improved 10%-20%.
7. Support more networks(transformer, albert).

#### Codegen

1. Support deployment on HarmonyOS for device.

### API Change

#### API Incompatible Change

##### C++ API

###### Unify LiteSession and TrainSession, Merge LiteSession And TrainSession.([!17356](https://gitee.com/mindspore/mindspore/pulls/17356))

Previously, Training on Device use TrainSession while Inference on Device use LiteSession. To simplify implementation, we move TrainSession functions to LiteSession as virtual function. and move APIs previous defined in train_session.h to lite_session.h.

```cpp
class MS_API LiteSession {
...
static LiteSession *CreateTrainSession(const std::string &filename, const lite::Context *context,
                                         bool train_mode = false, const lite::TrainCfg *cfg = nullptr);
 static LiteSession *CreateTransferSession(const std::string &filename_backbone, const std::string &filename_head,
                                            const lite::Context *context, bool train_mode = false,
                                            const lite::TrainCfg *cfg = nullptr);
virtual int Train() { return mindspore::lite::RET_ERROR; }
virtual int Eval() { return mindspore::lite::RET_OK; }
virtual int SetupVirtualBatch(int virtual_batch_multiplier, float lr = -1.0f, float momentum = -1.0f) {
    return mindspore::lite::RET_ERROR;
  }
virtual std::vector<tensor::MSTensor *> GetPredictions() const {
    std::vector<tensor::MSTensor *> outputs;
    return outputs;
 }
...
```

###### Add Export API for Training on device, obsolete SaveToFile API.([!17356](https://gitee.com/mindspore/mindspore/pulls/17356))

Previously, Training on Device uses SaveToFile API to save the training model to file. Export API was added in this release to support more format, more model type(train or interface part of the model), and save weight quant model of train.

```cpp
virtual int Export(const std::string &file_name, lite::ModelType model_type = lite::MT_TRAIN,
                     lite::QuantizationType quant_type = lite::QT_DEFAULT, lite::FormatType = lite::FT_FLATBUFFERS) {
    return mindspore::lite::RET_ERROR;
 }
```

###### Add GetFeatureMaps and UpdateFeatureMaps interface for Training on device.([!18344](https://gitee.com/mindspore/mindspore/pulls/18344))

When Training on the device, we may need to update the model featuremap and get model featuremap.particularly in MindSpore Federated Scenario.

```cpp
virtual std::vector<tensor::MSTensor *> GetFeatureMaps() const {
    std::vector<tensor::MSTensor *> features;
    return features;
  }
  virtual int UpdateFeatureMaps(const std::vector<tensor::MSTensor *> &features) { return mindspore::lite::RET_ERROR; }
```

#### New features

##### Java API

###### new static method for creating LiteSession by MSConifg in LiteSession.class

Previously, if we want to create a LiteSession object, we need to call two APIs:

```js
MSConfig config;
// config options ...
LiteSession liteSession = new LiteSession();
boolean ret = liteSession.init(config);
if (!ret) {
  // handle init LiteSession failed ...
}
```

now we can create a LiteSession object with new API just like:

```js
MSConfig config;
// config options ...
LiteSession liteSession = createSession(config);
if (liteSession == null) {
  // handle create LiteSession failed ...
}
```

###### new static method for creating LiteSession byModelBuffer and MSConfig in LiteSession.class

Previously, if we want to inference a model, we need to call APIs like:

```js
MSConfig config;
// config options ...
LiteSession liteSession = new LiteSession();
boolean initSessionRet = liteSession.init(config);
if (!initSessionRet) {
  // handle init LiteSession failed and return ...
}
Model model = new Model();
boolean loadModelRet = model.loadModel(modelMappedByteBuffer);
if (!loadModelRet) {
  // handle load model failed and return ...
}
boolean compileModelRet = liteSession.compileGraph(model);
if (!loadModelRet) {
  // handle compile model failed and return ...
}
model.free();
// liteSession is ready to inference model, call runGraph in LiteSession.class ...
```

now we can use new API just like:

```js
MSConfig config;
// config options ...
LiteSession liteSession = createSession(modelMappedByteBuffer, config);
if (liteSession == null) {
  // handle init LiteSession failed and return ...
}
// liteSession is ready to inference model, call runGraph in LiteSession.class ...
```

New createSession method is an API that integrates four old APIs: LiteSession.init, Model.loadModel, LiteSession.compileGraph and model.free. It is simple and efficient as it reduces one modelBuffer copy operation.

###### new methods getFeaturesMap and updateFeatures for in LiteSession.class

Recently, we add a new C++ api in LiteSession class, Correspondingly we add a new java API in LiteSession.java.

```java
public List<MSTensor> getFeaturesMap() {
         List<Long> ret = this.getFeaturesMap(this.sessionPtr);
                ArrayList<MSTensor> tensors = new ArrayList<MSTensor>();
                for (Long msTensorAddr : ret) {
                    MSTensor msTensor = new MSTensor(msTensorAddr);
                    tensors.add(msTensor);
                }
                return tensors;
   }
   public boolean updateFeatures(List<MSTensor> features) {
            long[] inputsArray = new long[features.size()];
            for (int i = 0; i < features.size(); i++) {
                inputsArray[i] = features.get(i).getMSTensorPtr();
            }
             return this.updateFeatures(this.sessionPtr, inputsArray);
   }
```

###### new methods export to replace saveToFile API in LiteSession.class

Recently, we add a new C++ api in LiteSession class, Correspondingly we add a new java API in LiteSession.java.

```java
public boolean export(String modelFileName, int modelType, int quantizationType) {
        return this.export(this.sessionPtr, modelFileName, modelType, quantizationType);
    }
```

###### new train related  API moved to LiteSession.class from TrainSession.class

Align with update of C++ api in LiteSession class, add new java API to LiteSession.java Correspondingly.

```java
public class LiteSession {
...
public static LiteSession createTrainSession(String modelName, final MSConfig config, boolean trainMode){...}
public boolean train() {...}
public boolean eval() {...}
...
```

### Bug fixes

1. Fix the bug that the train session does not release memory cause of refcount bug.

#### Deprecations

### Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

# MindSpore 1.2.1

## MindSpore 1.2.1 Release Notes

### Major Features and Improvements

#### FrontEnd

- [STABLE] Add MaskedSelect aicpu operation.(Ascend)

#### Auto Parallel

- [STABLE] Support distributed checkpoint loading.(Ascend/GPU)

# MindSpore 1.2.0

## MindSpore 1.2.0 Release Notes

### Major Features and Improvements

#### NewModels

- [STABLE] Add CV models on Ascend: 3D Unet, Unet++, SSD-Resnet50-fpn, SSD-VGG16, crnn_seq2seq_ocr for BSI, CTPN, resnet18, DPN
- [STABLE] Add CV models on GPU: Faster-RCNN
- [STABLE] Add NLP models on Ascend: NAML, Fasttext, GRU, LSTM
- [BETA] Add TPRR: Thinking Path Re-Ranker, an original ranked-base framework for Multi-Hop Question Answering which has won the first place in HotpotQA leaderboard.(Ascend)

#### FrontEnd

- [STABLE] Support side effects expression to ensure that the perform order of user's semantics is correct.(Ascend/GPU/CPU)
- [STABLE] Support calculating the gradient for network that contain non-Tensor input parameters（int, float, bool, mstype,int, mstype.float, mstype.uint, mstype.bool_, tuple, list, dict）.(Ascend/GPU/CPU)
- [STABLE] Support the inverse of a bool Tensor.(Ascend/GPU/CPU)
- [STABLE] Uniform the interface `isinstance`.(Ascend/GPU/CPU)
- [STABLE] Support negative indexes.(Ascend/GPU/CPU)
- [STABLE] Support 110+ Numpy-like interfaces in mindspore.numpy.(Ascend/GPU/CPU)
- [STABLE] Support export/load mindir model with a size greater than 2 GB.
- [STABLE] The optimizer supports gradient centralization.(Ascend)
- [STABLE] Support support auc metric, rou metric, bleu score metric, confusion matrix metric, cosine similarity metric, dice metric, hausdorff distance metric, occlusion sensitivity metric, perplexity metric, mean surface distance metric, root mean surface distance metric.
- [STABLE] Support use EmbeddingLookup with cache.(Ascend)
- [STABLE] Add MaskedSelect aicpu operation.(Ascend)

#### Auto Parallel

- [STABLE] Support AllGather and ReduceScatter fusion.(Ascend)
- [STABLE] Support gradient accumulation feature in auto parallel mode.(Ascend/GPU)
- [STABLE] Support running parallel optimizer with gradient accumulation.(Ascend)
- [STABLE] Add the configuration of communication operators' fusion.(Ascend)
- [STABLE] Support distributed checkpoint loading.(Ascend/GPU)

#### Executor

- [STABLE] Support inference with Nvidia GPU.
- [STABLE] Support data parallelism in PyNative mode.(Ascend/GPU)
- [STABLE] Optimize LSTM inference memory consumption in Graph mode with CPU.

#### Sponge

- [STABLE] Add SPONGE modules for molecular dynamics simulation, including Bond, Angle, Dihedral, Non Bond 14, NeighborList, Particle Mesh Ewald, Langevin MD and LIUJIAN MD.(GPU)

#### DataSet

- [STABLE] If the libnuma library is installed in the environment, you can run `export DATASET_ENABLE_NUMA=True` or `export MS_ENABLE_NUMA=True` to configure NUMA binding. In multi-card training scenarios, the training data processing speed can be improved, thereby improving the network training efficiency.
- [STABLE] Unify API Tensor structure of Training/Inference interfaces in C++ SDK.
- [STABLE] Optimize duplicated Decode in data preprocess using cache, improve preprocess efficiency.
- [STABLE] Support eager mode to run data augmentation in Python & C++.
- [STABLE] Support more data augmentation operators(e.g. Affine, Perspective) in MindSpore-Lite.
- [STABLE] Support light pipeline to process MindData in MindSpore-Lite training.
- [STABLE] Support more data preprossing operators based on DVPP hardware module and can be used on on Ascend310 platform.
- [STABLE] Support copy-free property for data in Ascend310 inference process scenarios.

#### Running Data Recorder

- [STABLE] Support running data recorder (RDR)  for exception demarcation.
- [STABLE] Provide records of multi-stage computational graphs, memory allocation information, graph execution order, stream execution order and task debug information when a "run task error" or "distribute task failed" occurs. (Ascend)
- [STABLE] Provide records of multi-stage computational graphs, memory allocation information and graph execution order when a "SyncStream error" occurs. (GPU)

#### 3D Feature

- [STABLE] Support 3D ops: Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter, Conv3DTranspose, BiasAdd, BiasAddGrad, PReLU, Transpose, Reshape, transdata, StrideSlice, MaxPool3D, MaxPool3DGrad, BinaryCrossEntropy, SigmoidCrossEntropyWithLogits, SigmoidCrossEntropyWithLogitsGrad, SoftmaxCrossEntropyWithLogits, SigmoidCrossEntropyWithLogits, SigmoidCrossEntropyWithLogitsGrad, BatchNorm3d, BatchNorm3dGrad, Dropout3d.
- [STABLE] Support RMSELoss loss function, MAELoss loss function, FocalLoss loss function, DiceLoss binary loss function, and MultiClassDiceLoss multi-type loss function for 2D/3D network.
- [STABLE] Add optimizer: AdamApplyOne(3D), ApplyMomentum(3D), SGD(3D).

### API Change

#### Backwards Incompatible Change

##### Python API

###### `mindspore.numpy.array()`, `mindspore.numpy.asarray()`, `mindspore.numpy.asfarray()`, `mindspore.numpy.copy()` now support GRAPH mode, but cannot accept `numpy.ndarray` as input arguments anymore([!12726](https://gitee.com/mindspore/mindspore/pulls/12726))

Previously, these interfaces can accept numpy.ndarray as arguments and convert numpy.ndarray to Tensor, but cannot be used in GRAPH mode.
However, currently MindSpore Parser cannot parse numpy.ndarray in JIT-graph. To support these interfaces in graph mode, we have to remove `numpy.ndarray` support. With that being said, users can still use `Tensor` to convert `numpy.ndarray` to tensors.

```python
>>> import mindspore.numpy as mnp
>>> import numpy
>>>
>>> nd_array = numpy.array([1,2,3])
>>> tensor = mnp.asarray(nd_array) # this line cannot be parsed in GRAPH mode
```

</td>
<td>

```python
>>> import mindspore.numpy as mnp
>>> import numpy
>>>
>>> tensor = mnp.asarray([1,2,3]) # this line can be parsed in GRAPH mode
```

</td>
</tr>
</table>

###### mindspore.numpy interfaces remove support for keyword arguments `out` and `where`([!12726](https://gitee.com/mindspore/mindspore/pulls/12726))

Previously, we have incomplete support for keyword arguments `out` and `where` in mindspore.numpy interfaces, however, the `out` argument is only functional when `where` argument is also provided, and `out` cannot be used to pass reference to numpy functions. Therefore, we have removed these two arguments to avoid any confusion users may have. Their original functionality can be found in [np.where](https://www.mindspore.cn/docs/en/master/api_python/numpy/mindspore.numpy.where.html#mindspore.numpy.where)

```python
>>> import mindspore.numpy as np
>>>
>>> a = np.ones((3,3))
>>> b = np.ones((3,3))
>>> out = np.zeros((3,3))
>>> where = np.asarray([[True, False, True],[False, False, True],[True, True, True]])
>>> res = np.add(a, b, out=out, where=where) # `out` cannot be used as a reference, therefore it is misleading
```

</td>
<td>

```python
>>> import mindspore.numpy as np
>>>
>>> a = np.ones((3,3))
>>> b = np.ones((3,3))
>>> out = np.zeros((3,3))
>>> where = np.asarray([[True, False, True],[False, False, True],[True, True, True]])
>>> res = np.add(a, b)
>>> out = np.where(where, x=res, y=out) # instead of np.add(a, b, out=out, where=where)
```

</td>
</tr>
</table>

###### Turn `ops.MakeRefKey` into an internal interface ([!12010](https://gitee.com/mindspore/mindspore/pulls/12010))

Previously MakeRefKey is an external interface that is not used, now make it an internal interface with the same usage. We do not recommend users to use this interface, and we will remove the relevant introduction of this interface from the official website.

###### `ops.ApplyFtrl`, `ops.ApplyMomentum`, `ops.ApplyRMSProp`, `ops.ApplyCenteredRMSProp` change the output on Ascend backend from multiple to a single. ([!11895](https://gitee.com/mindspore/mindspore/pulls/11895))

Previously the number of outputs of these operator is different on different backends. To unify their definition we change their output on Ascend backend from multiple to a single.

##### `P.FusedBatchNorm`, `P.FusedBatchNormEx` deleted ([!12115](https://gitee.com/mindspore/mindspore/pulls/12115))

The FusedBatchNorm and FusedBatchNormEx interface has been deleted. Please use the batchnorm operator to replace it.

##### `MetaTensor` deleted ([!10325](https://gitee.com/mindspore/mindspore/pulls/10325))

The MetaTensor interface has been deleted. The function of MetaTensor has been integrated into tensor.

###### `ControlDepend` is deleted, use `Depend` instead. The decorator `@C.add_flags(has_effect=True)` does not work. ([!13793](https://gitee.com/mindspore/mindspore/pulls/13793))

Previously, we used ControlDepend to control the execution order of multiple operators. In version 1.2.0, mindspore introduces the auto-monad side effects expression to ensure that the perform order of user's semantics is correct. Therefore, ControlDepend is deleted and Depend is recommended.

In most scenarios, if operators have IO side effects (such as print) or memory side effects (such as assign), they will be executed according to the user's semantics. In some scenarios, if the two operators A and B have no order dependency, and A must be executed before B, we recommend using Depend to specify their execution order. See the API documentation of the Depend operator for specific usage.

```python
    In some side-effect scenarios, we need to ensure the execution order of operators.
    In order to ensure that operator A is executed before operator B, it is recommended
    to insert the Depend operator between operators A and B.

Previously, the ControlDepend operator was used to control the execution order.
    Since the ControlDepend operator is deprecated from version 1.1, it is recommended
    to use the Depend operator instead. The replacement method is as follows::

a = A(x)                --->        a = A(x)
        b = B(y)                --->        y = Depend(y, a)
        ControlDepend(a, b)     --->        b = B(y)
```

</td>
<td>

```python
    In most scenarios, if operators have IO side effects or memory side effects,
    they will be executed according to the user's semantics. In some scenarios,
    if the two operators A and B have no order dependency, and A must be executed
    before B, we recommend using Depend to specify their execution order. The
    usage method is as follows::

a = A(x)                --->        a = A(x)
        b = B(y)                --->        y = Depend(y, a)
                                --->        b = B(y)
```

</td>
</tr>
</table>

After the introduction of the auto-monad side effect expression feature, the decorator `@C.add_flags(has_effect=True)` does not work. If the decorator is used in the script, please modify. Take the overflow identification operator (without side effects) as an example, the modification method is as follows:

```python
@C.add_flags(has_effect=True)
def construct(self, *inputs):
    ...
    loss = self.network(*inputs)
    init = self.allo_status()
    self.clear_status(init)
    ...
```

</td>
<td>

```python
def construct(self, *inputs):
    ...
    loss = self.network(*inputs)
    init = self.allo_status()
    init = F.depend(init, loss)
    clear_status = self.clear_status(init)
    ...
```

</td>
</tr>
</table>

##### C++ API

###### C++ API support dual ABI now.([!12432](https://gitee.com/mindspore/mindspore/pulls/12432))

1.1.1 supports only the old ABI. Currently, both the new and the old are supported.

```cmake
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
```

</td>
<td>

```cmake
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)  # old ABI are supported
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=1)  # new ABI are supprrted, too
                                                   # write nothing, use new ABI as default
```

</td>
</tr>
</table>

###### Context refactor.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))

The `Context` class is refactored. For details, see the API docs.

```cpp
GlobalContext::SetGlobalDeviceTarget(kDeviceTypeAscend310);       // set device target is ascend310
GlobalContext::SetGlobalDeviceID(0);                              // set device id is 0
auto model_context = std::make_shared<ModelContext>();            // create a model context
ModelContext::SetInsertOpConfigPath(model_context, "./aipp.cfg")  // set aipp config file is ./aipp.cfg
```

</td>
<td>

```cpp
auto model_context = std::make_shared<Context>();                 // create a model context
auto ascend310_info = std::make_shared<Ascend310DeviceInfo>();
model_context.MutableDeviceInfo().push_back(ascend310_info );     // set device target is ascend310
ascend310_info->SetDeviceID(0);                                   // set device id is 0
ascend310_info->SetInsertOpConfigPath("./aipp.cfg");              // set aipp config file is ./aipp.cfg
```

</td>
</tr>
</table>

###### LoadModel interface changes.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))

`LoadModel` is renamed `Load`. No exception is thrown new but the return status should be checked.

```cpp
try {
  auto graph = Serialization::LoadModel(model_file_path, kMindIR);
} catch (...) { ... }
```

</td>
<td>

```cpp
Graph graph;
auto ret = Serialization::Load(model_file_path, kMindIR, &graph);
if (ret != kSuccess) { ... }
```

</td>
</tr>
</table>

###### Model ctor changes.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))

`Model` uses a non-parameter ctor now, and arguments are passed in through `Build`.

```cpp
Model net(net_cell, model_context);
auto ret = net.Build();
if (ret != kSuccess) { ... }
```

</td>
<td>

```cpp
Model net;
auto ret = net.Build(net_cell, model_context);
if (ret != kSuccess) { ... }
```

</td>
</tr>
</table>

###### MSTensor::CreateTensor returns a native pointer now.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))

`MSTensor::CreateTensor` and `MSTensor::CreateRefTensor` returns a native pointer now, need to be destroy by `DestroyTensorPtr`.

```cpp
auto tensor = MSTensor::CreateTensor(xxx, xxx, ...);
auto name = tensor.Name();
```

</td>
<td>

```cpp
auto tensor = MSTensor::CreateTensor(xxx, xxx, ...);
auto name = tensor->Name();
MSTensor::DestroyTensorPtr(tensor);
```

</td>
</tr>
</table>

#### New features

##### Python API

- Add SPONGE functions: `mindspore.ops.operations.BondForceWithAtomEnergy`, `mindspore.ops.operations.AngleForceWithAtomEnergy`, `mindspore.ops.operations.DihedralForceWithAtomEnergy`, `mindspore.ops.operations.Dihedral14LJCFForceWithAtomEnergy`, `mindspore.ops.operations.LJForceWithPMEDirectForce`, `mindspore.ops.operations.PMEExcludedForce`, `mindspore.ops.operations.PMEReciprocalForce`,`mindspore.ops.operations.BondEnergy`, `mindspore.ops.operations.AngleEnergy`,`mindspore.ops.operations.DihedralEnergy`, `mindspore.ops.operations.Dihedral14LJEnergy`, `mindspore.ops.operations.Dihedral14CFEnergy`,`mindspore.ops.operations.LJEnergy`, `mindspore.ops.operations.PMEEnergy`. All operators are supported in `GPU`.

#### Deprecations

##### Python API

###### `nn.MatMul` is now deprecated in favor of `ops.matmul` ([!12817](https://gitee.com/mindspore/mindspore/pulls/12817))

[ops.matmul](https://www.mindspore.cn/docs/en/master/api_python/ops/mindspore.ops.matmul.html#mindspore.ops.matmul) follows the API of [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html) as closely as possible. As a function interface, [ops.matmul](https://www.mindspore.cn/docs/en/master/api_python/ops/mindspore.ops.matmul.html#mindspore.ops.matmul) is applied without instantiation, as opposed to `nn.MatMul`, which should only be used as a class instance.

```python
>>> import numpy as np
>>> from mindspore import Tensor, nn
>>>
>>> x = Tensor(np.ones((2, 3)).astype(onp.float32)
>>> y = Tensor(np.ones((3, 4)).astype(onp.float32)
>>> nn.MatMul()(x, y)
```

</td>
<td>

```python
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>>
>>> x = Tensor(np.ones((2, 3)).astype(onp.float32)
>>> y = Tensor(np.ones((3, 4)).astype(onp.float32)
>>> ops.matmul(x, y)
```

</td>
</tr>
</table>

### Bug fixes

#### FrontEnd

- fix the null pointer problem of evaluator in control flow.([!13312](https://gitee.com/mindspore/mindspore/pulls/13312))
- fix parameter naming conflict bug for CellList and SequentialCell. ([!13260](https://gitee.com/mindspore/mindspore/pulls/13260))

#### Executor

- fix executor pending task not execute in some heterogeneous cases.([!13465](https://gitee.com/mindspore/mindspore/pulls/13465))
- add passes to support frontend IR unification, including following operations: SliceGrad([!11783](https://gitee.com/mindspore/mindspore/pulls/11783)), ApplyFtrl, ApplyMomentum, ApplyRMSProp, CenteredRMSProp([!11895](https://gitee.com/mindspore/mindspore/pulls/11895)), AvgPoolGrad([!12813](https://gitee.com/mindspore/mindspore/pulls/12813)), BatchNorm([!12115](https://gitee.com/mindspore/mindspore/pulls/12115))

#### Dataset

- Fix getter functions(e.g. GetDatasetSize) terminated abnormally when use python multi-processing. ([!13571](https://gitee.com/mindspore/mindspore/pulls/13571), [!13823](https://gitee.com/mindspore/mindspore/pulls/13823))
- Fix unclear error log of data augmentation operators. ([!12398](https://gitee.com/mindspore/mindspore/pulls/12398), [!12883](https://gitee.com/mindspore/mindspore/pulls/12883), [!13176](https://gitee.com/mindspore/mindspore/pulls/13176))
- Fix profiling performs abnormally when sink_size = False, as saving data is later than profiling analysis. ([!13944](https://gitee.com/mindspore/mindspore/pulls/13944))

## MindSpore Lite

### Major Features and Improvements

#### Converter and runtime

1. Support TensorFlow model in Converter except aware-training model.
2. Add fusion pattern for same horizontal operators in Converter.
3. Support Jar in x86_64 system for integrating into server with Java backend conveniently.
4. Provide unified runtime API for developer reusing their code between cloud side and end side.[BETA]
5. Improve control-flow capabilities continually: Support GRU fusion in Converter; Support weight-quant for control-flow model; Support control-flow model inference with half precision; Support nested control-flow model.[BETA]

#### ARM backend optimization

1. Add NLP dependent float16 operators(like lstm) to enhance inference performance.
2. Optimize operators: lstm, gru, depthwise.
3. Add 6 NPU operators(like FullConnection), and fix some bugs about buildIR failed.

#### OpenCL backend

1. Add new ops: add 10+ ops，total 72 ops；
2. Performance optimization: by memory layout optimize，block tiling，Performance improved by 30% compared to version 1.1 at Adreno GPU.
3. Initialization time optimization: initialization time improve 100% vs MSLITE Version1.1 by store kernel cache as binary.
4. Support Java call on Mali or Adreno GPU.

#### Post quantization

1. Support quantization of gather and lstm ops.
2. Support quantizatizing TF Lite models with sub-graph node.
3. Add quantiztion strategy to decide quantize ops or not，less accuracy loss and higher compression rate.

#### Training on Device

1. Virtual batching, use mini-batch to minic large batch in theorical with few RAM consumption.
2. Converter unify, do not compile tod and iod converter separately.
3. Performance optimization to BWD ops.
4. TrainLoop with Off-The-Shelf Functionality blocks, like LR scheduler, Loss Monitor, Ckpt Saver, Accuracy Monitor.
5. Integration of code with Minddata lite.
6. Support more networks (googlenet, densenet, shufflenetv2, nin, vgg) and operators.

#### Codegen

1. Support 79 ops for the ARM platform and all CMSIS ops for Arm Cortex-M Series.
2. Multiplatform support, including Android, IoT Devices.
3. Support offline model weight preprocessing while compiling.
4. Support offline memory reuse computing for minimum runtime buffer size.
5. Support kernel register for custom op. Third-party hardware like NNIE can be accessed through it.

### API Change

#### API Incompatible Change

##### C++ API

###### Add header file named lite_types.h for some common data structs. ([!12262](https://gitee.com/mindspore/mindspore/pulls/12262))

Previously, some common data structs such as `CpuBindMode` and `DeviceType` are in context.h, this may cause cross-dependency between headers. So we create a new header named lite_types.h for some common data structs and move `CpuBindMode` and `DeviceType` from context.h into lite_types.h.

<table>
<tr>
<td style="text-align:center"> lite_types.h </td>
</tr>
<tr>
<td>

```cpp
namespace mindspore::lite {
/// \brief CpuBindMode defined for holding bind cpu strategy argument.
typedef enum {
  NO_BIND,    /**< no bind */
  HIGHER_CPU, /**< bind higher cpu first */
  MID_CPU     /**< bind middle cpu first */
} CpuBindMode;

/// \brief DeviceType defined for holding user's preferred backend.
typedef enum {
  DT_CPU, /**< CPU device type */
  DT_GPU, /**< GPU device type */
  DT_NPU  /**< NPU device type */
} DeviceType;
}  // namespace mindspore::lite
```

</td>
</tr>
</table>

###### Add some new interfaces in ms_tensor.h for unified runtime API.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))

Previously, users could not create `MSTensor` or modify ``MSTensor, all `MSTensor` are created and managed by framework. However users need to create or modify MSTensor sometimes such as pre-processing input data. So we provide two new interfaces in ms_tensor.h: `CreateTensor` interface for creating `MSTensor` by user and `set_shape` interface for modifying the shape of `MSTensor`.

<table>
<tr>
<td style="text-align:center"> CreateTensor </td>
</tr>
<tr>
<td>

```cpp
/// \brief Create a MSTensor.
///
/// \return Pointer to an instance of MindSpore Lite MSTensor.
static MSTensor *CreateTensor(const std::string &name, TypeId type, const std::vector<int> &shape, const void *data,
                                size_t data_len);
```

</td>
</tr>
</table>

<table>
<tr>
<td style="text-align:center"> set_shape </td>
</tr>
<tr>
<td>

```cpp
/// \brief Set the shape of MSTensor.
virtual void set_shape(const std::vector<int> &shape) = 0;
```

</td>
</tr>
</table>

Previously, users could access to data of `MSTensor` by interface named `MutableData`. However `MutableData` is not only returning data of tensor but also allocating data for tensor if its data is nullptr. So we provide a new interfaces in ms_tensor.h named `data` for returning data of tensor without allocating automatically.

```cpp
/// \brief Get the pointer of data in MSTensor.
///
/// \note The data pointer can be used to both write and read data in MSTensor. No memory buffer will be
/// allocated.
///
/// \return the pointer points to data in MSTensor.
virtual void *data() = 0;
```

</td>
</tr>
</table>

###### Delete `DimensionSize()` in ms_tensor.h.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))

The interface named `DimensionSize` is fuinctionally overlapped with the interface named `shape`. For the simplicity of the interface, we delete `DimensionSize` and recommend users to use the new interface named `shape` instead.

<table>
<tr>
<td style="text-align:center"> DimensionSize() </td>
</tr>
<tr>
<td>

```cpp
/// \brief Get size of the dimension of the MindSpore Lite MSTensor index by the parameter index.
///
/// \param[in] index Define index of dimension returned.
///
/// \return Size of dimension of the MindSpore Lite MSTensor.
virtual int DimensionSize(size_t index) const = 0;
```

</td>
</tr>
</table>

###### Move allocator from namespace mindspore::lite to namespace lite for unified runtime API.([!13515](https://gitee.com/mindspore/mindspore/pulls/13515))

Previously, class `Allocator` is in namespace mindspore::lite. Considering unified allocator interface for unified runtime API, we move `Allocator` to namespace mindspore.

```cpp
namespace mindspore::lite {
/// \brief Allocator defined a memory pool for malloc memory and free memory dynamically.
///
/// \note List public class and interface for reference.
class Allocator;
}
```

</td>
<td>

```cpp
namespace mindspore {
/// \brief Allocator defined a memory pool for malloc memory and free memory dynamically.
///
/// \note List public class and interface for reference.
class Allocator;
}
```

</td>
</tr>
</table>

### Bug fixes

1. Fix the bug that the array in kernel registrar is not initialized.
2. Fix segment fault caused by releasing of OpParameter in Crop kernel in mistake.
3. Fix the bug that the MINDIR aware-training model is finally interpreted as weight-quant model.

## Contributors

Thanks goes to these wonderful people:

Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, dong-li001, eric, Eric, fary86, fuzhiye, Gaoxiong, GAO_HYP_XYJ, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Islam Amin, Jesse, , Jiabin Liu, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, Lin Xh, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luopengting, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, Ming_blue, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, qianjiahong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wudenggang, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhanghui_china, zhangxinfeng3, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhiqwang, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa.

Contributions of any kind are welcome!

# MindSpore 1.1.1 Release Notes

## MindSpore

### Major Features and Improvements

#### NewModels

- [STABLE] BGCF: a Bayesian Graph Collaborative Filtering(BGCF) framework used to model the uncertainty in the user-item interaction graph and thus recommend accurate and diverse items on Amazon recommendation dataset.(Ascend)
- [STABLE] GRU: a recurrent neural network architecture like the LSTM(Long-Short Term Memory) on Multi30K dataset.(Ascend)
- [STABLE] FastText: a simple and efficient text classification algorithm on AG's news topic classification dataset, DBPedia Ontology classification dataset and Yelp Review Polarity dataset.(Ascend)
- [STABLE] LSTM: a recurrent neural network architecture used to learn word vectors for sentiment analysis on aclImdb_v1 dataset.(Ascend)
- [STABLE] SimplePoseNet: a convolution-based neural network for the task of human pose estimation and tracking on COCO2017 dataset.(Ascend)

#### FrontEnd

- [BETA] Support Tensor Fancy Index Getitem with tuple and list. (Ascend/GPU/CPU)

### Backwards Incompatible Change

#### Python API

##### `ops.AvgPool`, `ops.MaxPool`, `ops.MaxPoolWithArgmax` change attr name from 'ksize', 'padding' to 'kernel_size', 'pad_mode' ([!11350](https://gitee.com/mindspore/mindspore/pulls/11350))

Previously the kernel size and pad mode attrs of pooling ops are named "ksize" and "padding", which is a little puzzling and inconsistent with convolution ops. So they are rename to "kernel_size" and "pad_mode".

```python
>>> import mindspore.ops as ops
>>>
>>> avg_pool = ops.AvgPool(ksize=2, padding='same')
>>> max_pool = ops.MaxPool(ksize=2, padding='same')
>>> max_pool_with_argmax = ops.MaxPoolWithArgmax(ksize=2, padding='same')
```

</td>
<td>

```python
>>> import mindspore.ops as ops
>>>
>>> avg_pool = ops.AvgPool(kernel_size=2, pad_mode='same')
>>> max_pool = ops.MaxPool(kernel_size=2, pad_mode='same')
>>> max_pool_with_argmax = ops.MaxPoolWithArgmax(kernel_size=2, pad_mode='same')
```

</td>
</tr>
</table>

##### `ops.TensorAdd`, change API name to `ops.Add` ([!11568](https://gitee.com/mindspore/mindspore/pulls/11568))

The operator name TensorAdd is not standardized, it is changed to Add. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.

```python
>>> import mindspore.ops as ops
>>>
>>> add = ops.TensorAdd()
```

</td>
<td>

```python
>>> import mindspore.ops as ops
>>>
>>> add = ops.Add()
```

</td>
</tr>
</table>

##### `ops.Gelu`, `ops.GeluGrad`, `ops.FastGelu`, `ops.FastGeluGrad`, change API name to `ops.GeLU`, `ops.GeLUGrad`, `ops.FastGeLU`, `ops.FastGeLUGrad` ([!11603](https://gitee.com/mindspore/mindspore/pulls/11603))

Gelu, GeluGrad, FastGelu, and FastGeluGrad names are unified into ReLU naming rules, "lu" is changed to the uppercase "LU". The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.

```python
>>> import mindspore.ops as ops
>>>
>>> gelu = ops.Gelu()
>>> gelu_grad = ops.GeluGrad()
>>> fast_gelu = ops.FastGelu()
>>> fast_gelu_grad = ops.FastGeluGrad()
```

</td>
<td>

```python
>>> import mindspore.ops as ops
>>>
>>> gelu = ops.GeLU()
>>> gelu_grad = ops.GeLUGrad()
>>> fast_gelu = ops.FastGeLU()
>>> fast_gelu_grad = ops.FastGeLUGrad()
```

</td>
</tr>
</table>

##### `ops.GatherV2`, change API name to `ops.Gather` ([!11713](https://gitee.com/mindspore/mindspore/pulls/11713))

GatherV2 is changed to Gather. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.

```python
>>> import mindspore.ops as ops
>>>
>>> gather = ops.GatherV2()
```

</td>
<td>

```python
>>> import mindspore.ops as ops
>>>
>>> gather = ops.Gather()
```

</td>
</tr>
</table>

##### `ops.Pack`、`ops.Unpack`, change API name to `ops.Stack`、`ops.Unstack` ([!11828](https://gitee.com/mindspore/mindspore/pulls/11828))

Pack is changed to Stack, and Unpack is changed to Unstack. The old interface can be used continuously, but will be deleted in subsequent versions, it is recommended to use and switch to the latest interface.

```python
>>> import mindspore.ops as ops
>>>
>>> pack= ops.Pack()
>>> unpack= ops.Unpack()
```

</td>
<td>

```python
>>> import mindspore.ops as ops
>>>
>>> stack= ops.Stack()
>>> unstack= ops.Unstack()
```

</td>
</tr>
</table>

##### `ops.ControlDepend`, add deprecated to ControlDepend ([!11844](https://gitee.com/mindspore/mindspore/pulls/11844))

ControlDepend is deprecated and will be removed in a future version, use Depend instead.

```pythonNote:
Note:
    This operation does not work in `PYNATIVE_MODE`.
```

</td>
<td>

```python
Note:
        This operation does not work in `PYNATIVE_MODE`.
        `ControlDepend` is deprecated from version 1.1 and will be removed in a future version, use `Depend` instead.
```

</td>
</tr>
</table>

##### `ops.Depend`, add operator description and use case ([!11815](https://gitee.com/mindspore/mindspore/pulls/11815)), ([!11879](https://gitee.com/mindspore/mindspore/pulls/11879))

Since the ControlDepend operator will be deprecated from version 1.2, it is recommended to use the Depend operator instead.

```python
Depend is used for processing side-effect operations.

Inputs:
    - **value** (Tensor) - the real value to return for depend operator.
    - **expr** (Expression) - the expression to execute with no outputs.

Outputs:
    Tensor, the value passed by last operator.

Supported Platforms:
    ``Ascend`` ``GPU`` ``CPU``
```

</td>
<td>

```python
Depend is used for processing dependency operations.

In some side-effect scenarios, we need to ensure the execution order of operators.
In order to ensure that operator A is executed before operator B, it is recommended
to insert the Depend operator between operators A and B.

Previously, the ControlDepend operator was used to control the execution order.
Since the ControlDepend operator will be deprecated from version 1.2, it is
recommended to use the Depend operator instead. The replacement method is as follows::

a = A(x)                --->        a = A(x)
    b = B(y)                --->        y = Depend(y, a)
    ControlDepend(a, b)     --->        b = B(y)

Inputs:
    - **value** (Tensor) - the real value to return for depend operator.
    - **expr** (Expression) - the expression to execute with no outputs.

Outputs:
    Tensor, the value passed by last operator.

Supported Platforms:
    ``Ascend`` ``GPU`` ``CPU``

Examples:
    >>> import numpy as np
    >>> import mindspore
    >>> import mindspore.nn as nn
    >>> import mindspore.ops.operations as P
    >>> from mindspore import Tensor
    >>> class Net(nn.Cell):
    ...     def __init__(self):
    ...         super(Net, self).__init__()
    ...         self.softmax = P.Softmax()
    ...         self.depend = P.Depend()
    ...
    ...     def construct(self, x, y):
    ...         mul = x - y
    ...         y = self.depend(y, mul)
    ...         ret = self.softmax(y)
    ...         return ret
    ...
    >>> x = Tensor(np.ones([4, 5]), dtype=mindspore.float32)
    >>> y = Tensor(np.ones([4, 5]), dtype=mindspore.float32)
    >>> net = Net()
    >>> output = net(x, y)
    >>> print(output)
    [[0.2 0.2 0.2 0.2 0.2]
     [0.2 0.2 0.2 0.2 0.2]
     [0.2 0.2 0.2 0.2 0.2]
     [0.2 0.2 0.2 0.2 0.2]]
```

</td>
</tr>
</table>

#### C++ API

##### change namespace from `mindspore::api` to `mindspore` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))

```c++
namespace ms = mindspore::api;
```

</td>
<td>

```c++
namespace ms = mindspore;
```

</td>
</tr>
</table>

##### `Context` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))

```c++
ms::Context::Instance().SetDeviceTarget(ms::kDeviceTypeAscend310).SetDeviceID(0);
```

</td>
<td>

```c++
ms::GlobalContext::SetGlobalDeviceTarget(ms::kDeviceTypeAscend310);
ms::GlobalContext::SetGlobalDeviceID(0);
```

</td>
</tr>
</table>

##### rename `Tensor` to `MSTensor` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))

```c++
ms::Tensor a;
```

</td>
<td>

```c++
ms::MSTensor a;
```

</td>
</tr>
</table>

##### `Model` move setting of model options from `Build` to ctor `Model` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))

```c++
ms::Model model(graph_cell);
model.Build(model_options);
```

</td>
<td>

```c++
ms::Model model(graph_cell, model_context);
model.Build();
```

</td>
</tr>
</table>

##### `Model` modify `GetInputsInfo`, `GetOutputsInfo` to `GetInputs`, `GetOutputs` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))

```c++
std::vector<std::string> names;
std::vector<ms::DataType> types;
std::vector<std::vector<int64_t>> shapes;
std::vector<size_t> mem_sizes;
model.GetInputsInfo(&names, &types, &shapes, &mem_sizes);
std::cout << "Input 0 name: " << names[0] << std::endl;
```

</td>
<td>

```c++
auto inputs = model.GetInputs();
std::cout << "Input 0 name: " << inputs[0].Name() << std::endl;
```

</td>
</tr>
</table>

##### `Model` modify `Predict` parameters type from `Buffer` to `MSTensor` ([!11574](https://gitee.com/mindspore/mindspore/pulls/11574))

```c++
std::vector<ms::Buffer> inputs;
std::vector<ms::Buffer> outputs;
model.Predict(inputs, &outputs);
```

</td>
<td>

```c++
std::vector<ms::MSTensor> inputs;
std::vector<ms::MSTensor> outputs;
model.Predict(inputs, &outputs);
```

</td>
</tr>
</table>

### Deprecations

#### Python API

##### `ops.SpaceToBatch`, `ops.BatchToSpace` are deprecated in favor of `ops.SpaceToBatchND`, `ops.BatchToSpaceND`([!11527](https://gitee.com/mindspore/mindspore/pulls/11527))

The `ops.SpaceToBatchND`, `ops.BatchToSpaceND` are more general and have same behavior as `ops.SpaceToBatch`, `ops.BatchToSpace` when `block_shape` is a int.

##### `ops.DepthwiseConv2dNative` is deprecated in favor of `nn.Conv2D`([!11702](https://gitee.com/mindspore/mindspore/pulls/11702))

The `ops.DepthwiseConv2dNative` is only supported by Ascend, it is recommended to directly use `nn.Conv2D`. If `group` is equal to `in_ channels` and `out_channels`, the 2D convolution layer is also a 2D depthwise convolution layer.

## Contributors

Thanks goes to these wonderful people:

Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa

Contributions of any kind are welcome!

# MindSpore 1.1.0 Release Notes

## MindSpore

### Major Features and Improvements

#### NewModels

- [STABLE] GNMT v2: similar to the model described in Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, which is mainly used for corpus translation, on WMT Englis-German dataset.(Ascend)
- [STABLE] MaskRCNN: a conceptually simple, flexible, and general framework for object instance segmentation on COCO2017 dataset.(Ascend)
- [STABLE] YOLOv4: a state-of-the-art detector which is faster and more accurate than all available alternative detectors on MS COCO dataset.(Ascend)
- [STABLE] Openpose: proposes a bottom-up human attitude estimation algorithm using Part Affinity Fields on COCO2017 dataset.(Ascend)
- [STABLE] CNN-CTC: proposes three major contributions to addresses scene text recognition (STR) on MJSynth and SynthText dataset.(Ascend)
- [STABLE] CenterFace: a practical anchor-free face detection and alignment method for edge devices on WiderFace dataset.(Ascend)
- [STABLE] ShuffleNetV2:  a much faster and more accurate network than the previous networks on ImageNet 2012 dataset.(GPU)
- [STABLE] EfficientNet-B0: a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient on ImageNet 2012 dataset.(GPU)
- [BETA] SSD-GhostNet: based on an Ghost module structure which generate more features from cheap operations on Oxford-IIIT Pet dataset.(Ascend)
- [BETA] DS-CNN:  Depthwise separable convolutional neural network on Speech commands dataset.(Ascend)
- [BETA] DeepPotentialH2O: A neural network model for molecular dynamics simulations. (Ascend)
- [BETA] GOMO: A classical numerical method called GOMO for ocean simulation. (GPU)

#### FrontEnd

- [STABLE] Refactor the MINDIR to support 310 inference(Ascend).
- [STABLE] The execution backend of sparse operations in optimizer can be set through 'target'. (Ascend/GPU/CPU)
- [STABLE] Support saving specified network to checkpoint and filtering parameters according to prefix when load checkpoint. (Ascend/GPU/CPU)
- [STABLE] Allow users choose whether to load parameter into network strictly.(Ascend/GPU/CPU)
- [STABLE] Before training, in graph mode, in order to have the same network initialization parameter values for all devices, broadcast the parameters on device 0 to other devices. (Ascend/GPU)
- [STABLE] Support if by if of control flow subgraph. (Ascend/GPU)
- [STABLE] Support the judgment that whether a tensor is in a list. (Ascend/GPU/CPU)
- [STABLE] Support to get a value by using the corresponding key in a dictionary in the network; Support to get keys and values of a dictionary in the network. (Ascend/GPU/CPU)
- [STABLE] Support Tensor in enumerate. (Ascend/GPU/CPU)
- [STABLE] Support multilevel index assignment. (Ascend/GPU/CPU)
- [STABLE] Support the 'expand_as','view','abs','mean' method of Tensor. (Ascend/GPU/CPU)
- [STABLE] Support ResizeBilinear operation transfer ratio. (Ascend)
- [STABLE] nn.Matmul supports matrix-vector product and  batched matrix multiply. (Ascend/GPU)
- [STABLE] nn.Dense supports input tensor whose dimension can be greater than 2. (Ascend/GPU)
- [BETA] Support higher order differentiation for partial operators.(CPU/GPU/Ascend)
- [STABLE] Support Tensor Augassign.(Ascend/GPU)
- [BETA] Support 22 numpy native interfaces.

#### Auto Parallel

- [STABLE] Support parallel optimizer with weight shard. (Ascend/GPU)
- [STABLE] Support distributed operators: element-wise series, UnsortedSegmentSum, UnsortedSegmentMin, Split, BroadcastTo and Unique etc. (Ascend/GPU)
- [STABLE] Support distributed model prediction. (Ascend/GPU)
- [STABLE] Support auto mixed precision level "O2" in auto and semi auto parallel mode. (Ascend/GPU)
- [STABLE] Add MultiFieldEmbeddingLookup high-level interface. (Ascend/GPU)

#### Executor

- [STABLE] ResNet50 performance optimize. (GPU)
- [STABLE] Support modelzoo net in PyNative mode(Ascend 29, GPU 23, CPU 2).(Ascend/GPU/CPU)
- [STABLE] Support PyNative mode on CPU.(CPU)
- [STABLE] Optimize performance in PyNative mode.(Ascend/GPU/CPU)
- [STABLE] Support Safe Optimized Memory Allocation Solver (SOMAS) on Ascend to improve the memory-reuse, the batch size of Bert large model (128 sequence length) is increased from 160 to 208.(Ascend)
- [BETA] Support second order differentiation in PyNative mode.(Ascend/GPU)
- [DEMO] Add distributed trainning in PyNative mode.(Ascend/GPU)

#### MDP

- [STABLE]  Add new operators for Ascend and GPU: IGamma, LGamma, DiGamma;
- [STABLE]  Add new distributions for Ascend and GPU: LogNormal, and Logistic;
- [BETA]  Add new distributions for Ascend only: Gumbel, Cauchy, Gamma, Beta, and Poisson; Add Categorical distribution for GPU;
- [STABLE]  Add new bijectors for Ascend and GPU: GumbelCDF, Invert;
- [STABLE]  Add Bayesian layer realized by local reparameterization method for Ascend and GPU;
- [STABLE]  Add Anomaly Detection Toolbox based on VAE for Ascend and GPU.

#### DataSet

- [STABLE] Support single node multi-p distributed cache data sharing
- [STABLE] Support GPU profiling with data processing
- [STABLE] Support YOLOV3 dynamic shape in sink mode with dataset
- [STABLE] Support unique processing in the data processing pipeline
- [STABLE] Python layer parameter verification error information unified

### API Change

#### Backwards Incompatible Change

##### Python API

###### Delete shape and dtype of class Initializer ([!7373](https://gitee.com/mindspore/mindspore/pulls/7373/files))

Delete shape and dtype attributes of Initializer class.

###### Modify the return type of initializer ([!7373](https://gitee.com/mindspore/mindspore/pulls/7373/files))

Previously, the return type of initializer function may be string, number, instance of class Tensor or subclass of class Initializer.

After modification, initializer function will return instance of class MetaTensor, class Tensor or subclass of class Initializer.

Noted that the MetaTensor is forbidden to initialize parameters, so we recommend that use str, number or subclass of Initializer for parameters initialization rather than the initializer functions.

```python
>>> import mindspore.nn as nn
>>> from mindspore.common import initializer
>>> from mindspore import dtype as mstype
>>>
>>> def conv3x3(in_channels, out_channels)
>>>   weight = initializer('XavierUniform', shape=(3, 2, 32, 32), dtype=mstype.float32)
>>>   return nn.Conv2d(in_channels, out_channels, weight_init=weight, has_bias=False, pad_mode="same")
```

</td>
<td>

```python
>>> import mindspore.nn as nn
>>> from mindspore.common.initializer import XavierUniform
>>>
>>> #1) using string
>>> def conv3x3(in_channels, out_channels)
>>>   return nn.Conv2d(in_channels, out_channels, weight_init='XavierUniform', has_bias=False, pad_mode="same")
>>>
>>> #2) using subclass of class Initializer
>>> def conv3x3(in_channels, out_channels)
>>>   return nn.Conv2d(in_channels, out_channels, weight_init=XavierUniform(), has_bias=False, pad_mode="same")
```

</td>
</tr>
</table>

Advantages:
After modification, we can use the same instance of Initializer to initialize parameters of different shapes, which was not allowed before.

```python
>>> import mindspore.nn as nn
>>> from mindspore.common import initializer
>>> from mindspore.common.initializer import XavierUniform
>>>
>>> weight_init_1 = XavierUniform(gain=1.1)
>>> conv1 = nn.Conv2d(3, 6, weight_init=weight_init_1)
>>> weight_init_2 = XavierUniform(gain=1.1)
>>> conv2 = nn.Conv2d(6, 10, weight_init=weight_init_2)
```

</td>
<td>

```python
>>> import mindspore.nn as nn
>>> from mindspore.common import initializer
>>> from mindspore.common.initializer import XavierUniform
>>>
>>> weight_init = XavierUniform(gain=1.1)
>>> conv1 = nn.Conv2d(3, 6, weight_init=weight_init)
>>> conv2 = nn.Conv2d(6, 10, weight_init=weight_init)
```

</td>
</tr>
</table>

###### Modify get_seed function ([!7429](https://gitee.com/mindspore/mindspore/pulls/7429/files))

Modify get_seed function implementation

Previously, if seed is not set, the value of seed is default, parameters initialized by the normal function are the same every time.

After modification, if seed is not set, the value of seed is generated randomly, the initialized parameters change according to the random seed.

If you want to fix the initial value of parameters, we suggest to set seed.

```python
>>> from mindspore.common import set_seed
>>> set_seed(1)
```

###### `nn.LinSpace` ([!9494](https://gitee.com/mindspore/mindspore/pulls/9494)) has been removed and modify `ops.LinSpace` ([!8920](https://gitee.com/mindspore/mindspore/pulls/8920))

The `nn.LinSpace` interface only support passing the value by args previously. For the convenience, we provided enhancive `ops.LinSpace` interface, which support passing the value by the inputs at the latest version. So there is no need for `nn.LinSpace`.

```python
>>> from mindspore import nn
>>>
>>> start = 1
>>> stop = 10
>>> num = 5
>>> linspace = nn.LinSpace(start, stop, num)
>>> output = linspace()
```

</td>
<td>

```python
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import ops
>>>
>>> linspace = ops.LinSpace()
>>> start = Tensor(1, mindspore.float32)
>>> stop = Tensor(10, mindspore.float32)
>>> num = 5
>>> output = linspace(start, stop, num)
```

</td>
</tr>
</table>

###### Parts of `Optimizer` add target interface ([!6760](https://gitee.com/mindspore/mindspore/pulls/6760/files))

The usage of the sparse optimizer is changed.

The target interface is used to set the execution backend of the sparse operator.

The add_primitive_attr interface is no longer allowed.

The following optimizers add the target interface:  Adam, FTRL, LazyAdam, ProximalAdagrad

```python
>>> from mindspore.nn import Adam
>>>
>>> net = LeNet5()
>>> optimizer = Adam(filter(lambda x: x.requires_grad, net.get_parameters()))
>>> optimizer.sparse_opt.set_device("CPU")
```

</td>
<td>

```python
>>> from mindspore.nn import Adam
>>>
>>> net = LeNet5()
>>> optimizer = Adam(filter(lambda x: x.requires_grad, net.get_parameters()))
>>> optimizer.target = 'CPU'
```

</td>
</tr>
</table>

###### `export` Modify the input parameters and export's file name ([!7385](https://gitee.com/mindspore/mindspore/pulls/7385)， [!9057](https://gitee.com/mindspore/mindspore/pulls/9057/files))

Export the MindSpore prediction model to a file in the specified format.

The reference includes: `net`, `*inputs`, `file_name`, `file_format`, `**kwargs`.

Input parameters can be input according to specific export requirements.

Add the file name extension based on the format.

```python
>>> from mindspore.train.quant import quant
>>>
>>> network = LeNetQuant()
>>> inputs = Tensor(np.ones([1, 1, 32, 32]), mindspore.float32)
>>> quant.export(network, inputs, file_name="lenet_quant.mindir", file_format='MINDIR')
lenet_quant.mindir
```

</td>
<td>

```python
>>> import mindspore as ms
>>>
>>> network = LeNetQuant()
>>> inputs = Tensor(np.ones([1, 1, 32, 32]), mindspore.float32)
>>> ms.export(network, inputs, file_name="lenet_quant", file_format='MINDIR', quant_mode='AUTO')
lenet_quant.mindir
```

</td>
</tr>
</table>

###### `Dense`, `Conv2dBnAct`, `DenseBnAct`, `DenseQuant` support setting the activation attribute as an instance of a class derived from `nn.Cell` or `Primtive` ([!7581](https://gitee.com/mindspore/mindspore/pulls/7581))

activation (Union[str, Cell, Primitive]): activate function applied to the output of the fully connected layer

```python
>>> import mindspore.nn as nn
>>>
>>> dense = nn.Dense(1, 1, activation='relu')
```

</td>
<td>

```python
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>>
>>> dense = nn.Dense(1, 1, activation=nn.ReLU())
>>> dense = nn.Dense(1, 1, activation=ops.ReLU())
```

</td>
</tr>
</table>

###### `tensor.dim()`, `tensor.size()` has been renamed to `tensor.ndim`, `tensor.size` ([!10175](https://gitee.com/mindspore/mindspore/pulls/10175))

Previously, tensor.size() and tensor.dim() were used for checking the total number of elements/dimensions in the tensor.
However, from a user's perspective, tensor.size and tensor.ndim (methods -> properties) are better choices, since they follow the numpy naming convention.

```python
>>> from mindspore import Tensor
>>>
>>> Tensor((1,2,3)).size()
>>> Tensor((1,2,3)).dim()
```

</td>
<td>

```python
>>> from mindspore import Tensor
>>>
>>> Tensor((1,2,3)).size
>>> Tensor((1,2,3)).ndim
```

</td>
</tr>
</table>

###### `EmbeddingLookup` add a config in the interface: sparse ([!8202](https://gitee.com/mindspore/mindspore/pulls/8202))

sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.

```python
>>> from mindspore.nn import EmbeddingLookup
>>>
>>> input_indices = Tensor(np.array([[1, 0], [3, 2]]), mindspore.int32)
>>> result = EmbeddingLookup(4,2)(input_indices)
>>> print(result.shape)
(2, 2, 2)
```

</td>
<td>

</td>
</tr>
</table>

###### `nn.probability.bijector` change types of attributes from (int, float) to (float, list, numpy.ndarray, Tensor) ([!8191](https://gitee.com/mindspore/mindspore/pulls/8191))

Attributes Type change: (int, float) -> (float, list, numpy.ndarray, Tensor).
Int type is not supported anymore. Parameters of all bijectors should be type float, list, numpy.ndarray or Tensor.

```python
>>> import mindspore.nn.probability.bijector as msb
>>>
>>> power = 2
>>> bijector = msb.PowerTransform(power=power)
```

</td>
<td>

```python
>>> import mindspore.nn.probability.bijector as msb
>>>
>>> power = 2.0
>>> bijector = msb.PowerTransform(power=power)
```

</td>
</tr>
</table>

###### `nn.probability.bijector.GumbelCDF` remove a attribute in the interface: dtype ([!8191](https://gitee.com/mindspore/mindspore/pulls/8191))

dtype is removed from GumbelCDF and is no longer an argument of the class.

```python
>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import dtype as mstype
>>>
>>> bijector = msb.GumbelCDF(loc=0.0, scale=1.0, dtype=mstype.float32)
```

</td>
<td>

```python
>>> import mindspore.nn.probability.bijector as msb
>>>
>>> bijector = msb.GumbelCDF(loc=0.0, scale=1.0)
```

</td>
</tr>
</table>

###### `nn.layer.combined.Conv2dBnAct`, `nn.layer.combined.DenseBnAct` move from nn.layer.quant to nn.layer.combined ([!8187](https://gitee.com/mindspore/mindspore/pulls/8187))

Previously Conv2dBnAct and DenseBnAct are in nn.layer.quant, since they are not quant cells, now they are moved to nn.layer.combined. If you import Conv2dBnAct, DenseBnAct from mindspore.nn, then your code doesn't need any change.

```python
>>> from mindspore.nn.layer.quant import Conv2dBnAct, DenseBnAct
```

</td>
<td>

```python
>>> from mindspore.nn import Conv2dBnAct, DenseBnAct
```

</td>
</tr>
</table>

###### `nn.layer.conv.Conv2D`, `nn.layer.quant.Conv2dBnFoldQuant`, `nn.layer.quant.Conv2dBnWithoutFoldQuant` change weight shape when group > 1 in Ascend platform ([!9723](https://gitee.com/mindspore/mindspore/pulls/9723))

In Ascend platform, if group > 1, the weight shape of Conv2D change from [in_channels//group, out_channels, kernel_size, kernel_size] to [out_channels, in_channels//group, kernel_size, kernel_size]. Previously, checkpoints of the networks are used, which use Conv2D with group > 1, such as MobileNet, can not be directly used now, need to transpose the first and second axis of the weight.

### Bug fixes

#### FrontEnd

- [STABLE] Fix the problem of the cse optimization in the situation of control flow. (Ascend/GPU)

#### Auto Parallel

- [STABLE] Resolve the restriction: input and output layouts of Reshape are restricted in tensor redistribution. (Ascend/GPU)
- [STABLE] Resolve the restriction: output strategy should be data parallel in model evaluation. (Ascend/GPU)

#### Executor

- [STABLE] Fix fusion operator compilation cache. (Ascend)
- [STABLE] Fix compilation error of dynamic shape operator. (Ascend)
- [STABLE] Fix bug of pynative cannot insert transdata of node output when node should be spilted in the backend opt.(Ascend)
- [STABLE] Fix the bug of TensorMove and memcpy_async merge to one after backend cse pass (Ascend)

#### DataSet

- [STABLE] Fix cache server hang on RequestFreeTag. (Ascend/GPU/CPU)
- [STABLE] Fix hung when use pyfunc multi-processing. (Ascend/GPU/CPU)
- [STABLE] Fix add multiple parent nodes to tree node cause core dump. (Ascend/GPU/CPU)

## MindSpore Lite

### Major Features and Improvements

#### Converter and runtime

1. Support dynamic shape in MindSpore Lite Converter.
2. Optimize sub-graph mechanism by dynamically splitting the entire graph into multiple subgraphs based on the operator supported, backend hardware and user configuration.
3. Support TensorList and TensorList operators such as TensorListFromTensor, TensorListGetItem and so on.
4. Support BatchMatMul fusion and LSTM fusion in MindSpore Lite Converter.
5. Support converting model and run inference on Windows operator system.
6. Support Model(.ms) visualization on Netron.
7. Support Tensorflow model in MindSpore Lite Converter
8. Add 86 converter parsers.
9. Convert aware training model without user's awareness
10. Support scalar tensor in MindSpore Lite Converter and Runtime
11. Support NPU backend on HUAWEI Kirin SoC.[BETA]
12. Merge timeprofiler into benchmark

#### CPU backend optimization

1. Add 50+ new operators, including new Op type(like Adder, Gru).
2. Enhanced performance on armv8.2 supported platform. For example, utilizing sdot instruction more efficiently.
3. Optimize all operators(fp32, fp16, int8) by implementing multi-thread, SIMD tech as much as possible. Model inference time can reduce at least 20% after these optimizations.
4. Extending to support operators for x86_64 platform based on SSE/AVX instruction set.

#### OpenCL backend

1. Add new ops: add 10+ ops, total 58 ops;
2. Performance optimization: by memory layout optimize, Winograd Convolution select strategyoptimize, SIMT local size optimize, local cache optimize,  GPU performance improvement up to 20+% vs MSLITE Version1.0
3. Add Online Graph optimzation: by fusion Convolution/Matmul/Fullconnection and add/mul/pad/reshape, improve performance up to 50+% for some networks;
4. Add auto tuning: by online tuning in the graph compilation phase, optimize performance up to 10%;
5. Add weight quant: support weight quant
6. Add opencl kernel binary cache: improve Initialization time .

#### Post quantization

MindSpore Lite supports both weight quantization and full quantization. Currently, Weights can be quantized into 1 ~ 16 bits according to user configuration. In internal testing, quantization of networks, such as classification, detection, segmentation and transformer are well supported. To ensure high accuracy of quantized models, MindSpore Lite uses a pipeline quantization method. In the first phase, the weight and activation value are quantized using linear quantization methods, such as MIN-MAX. In the second phase, the quantization error is analyzed, and uses statistical methods to compensate loss caused by fp32 quantization to a fixed point such as Int8 to quantized models. The features of Post-training quantization are:

1. perchannel asymmetric quantization for weights, such as MAX_MIN and KMEANS
2. Perlayer symmetric quantization for activation, such as KL and MAX_MIN.
3. perlayer asymmetrical quantization for activation, such as, RemoveOutlier.
4. accuracy loss compensation, such as BiasCorrection

| mobilenet_v2   | ACC (ImageNet)  |
|---|---|
| FP32  | 71.56%  |
|A8W8   | 71.16%  |
| A8W8(without BiasCorrection)  | 70.74% |
| A8W7  | 71.06%  |
| A7W7  | 70.78%  |

The above table uses the mobilenet_v2 model from TF official website. Using MindSpore Lite quantization, the precision of A8W8 (8-bit activation value quantization and 8-bit weight quantization) decreases from 0.82% to 0.4% after accuracy loss compensation, for 7-bit quantization, the precision loss is still no more than 1%.

#### Training on Device

Within MindSpore 1.1 release, the MindSpore Lite provides the following Training-on-Device (ToD) capabilities:

1. Learning from scratch and Transfer Learning strategies are supported
2. MindSpore based models can be converted and used in training on the device. (Third-party models such as TensorFlow and PyTorch for now cannot be directly imported to the framework)
3. Grad operations are supported for more than 30 operators such as Dense layers, Convolutions and Batch Normalizations. Momentum, SGD, and ADAM optimizers are supported.
4. Supports networks such as LeNet, Alexnet, Resnet, MobileNetV1/V2/V3, and EffectiveNet, and provides complete model loading, conversion, and Python training scripts on the device side.

The MindSpore Lite ToD framework is already in use in the newest Huawei Smart TV, providing a unique and personalized user experience as a family entertainment center.

### API Change

#### API Incompatible Change

##### C++ API

- [Modify] Context now support multi-context configuration.(Context.h)
- [Modify] Callback is move from lite_session.h into ms_tensor.h.
- [Modify] GetInputsByName in lite_session.h is changed into GetInputsByTensorName
- [Add] add static LiteSession *CreateSession(const char*model_buf, size_t size, const lite::Context *context) in lite_session.h
- [Add] add GetErrorInfo interface returning error message in errorcode.h
- [Delete] Remove model_generated.h, ops_generated.h and headers of FlatBuffers library from interfaces

##### Java API

- [Add] Implement JNI layer and add Java api for CPU and GPU backend

#### Deprecations

##### C++ API

Deprecate Interface GetOutputsByNodeName

### Bug fixes

- [BUGFIX] Fix the bug in sub-graph segmentation
- [BUGFIX] Fix the bug in Tensor getitem in which the ellipsis matches the wrong dim-size.
- [BUGFIX] Fix the bug that activation modification after defining Dense will not take effect.

## Contributors

Thanks goes to these wonderful people:

zhouyifengCode, huqi, JulyAi, damon0626, chenbo116, rmdyh, davidmc, gray0v0, doitH, Gogery, zymaa, xinyunfan

Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenbo116, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, damon0626, danish, Danish, davidmc, dayschan, doitH, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, Gogery, gongdaguo, gray0v0, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huqi, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, JulyAi, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, rmdyh, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xinyunfan, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, zhouyifengCode, Zichun, Zirui, Ziyan, zjun, ZPaC, zymaa

Contributions of any kind are welcome!

# MindSpore 1.0.0 Release Notes

## Major Features and Improvements

### MindSpore Training and Inference Framework

#### Ascend 910

- New models
    - DenseNet121: a dense convolutional neural network, which connects each layer to every other layer in a feed-forward fashion for object recognition on ImageNet dataset.
    - UNet2D-Medical: Unet Medical model for 2D image segmentation, Convolutional Networks for Biomedical Image Segmentation on ISBI Challenge database.
- Frontend and user interface
    - Second-Order Optimization
        - Enable second-order optimization for Bert on Ascend 910, which can achieve a masked lm accuracy of 71.3% in 800 seconds using 8 Ascend 910 (Bert-Large @MLPerf v0.7 dataset).
    - New GNN model BGCF
        - Bayesian Graph Convolutional Filtering network which naturally incorporate the uncertainty in the user-item interaction graph shows excellent recommendation performance on Amazon-Beauty dataset.
    - Add append interface for SequentialCell.
    - Add a level `auto` for AMP.
- Executor and performance optimization
    - Support quantitative network (Resnet50 & YoloV3 & MobileNetV2).
    - Project ease of use optimization: project compilation time optimization, CMakelist regularization, cudnn, cuda independent compilation and installation independent.
- Data processing, augmentation, and save format
    - Support GeneratorDataset return string type

#### Other Hardware Support

- GPU platform
    - Enable second-order optimization for resnet50 on GPU, which achieve 30% improvement on training time compared to SGD with Momentum (Resnet50 @ImageNet).

#### User interfaces change log

- Remove global object GradOperation in Autodiff([!5011](https://gitee.com/mindspore/mindspore/pulls/5011))
- Remove useless attribute 'name' in Autodiff([!5172](https://gitee.com/mindspore/mindspore/pulls/5172))
- Rectification distributed init([!5350](https://gitee.com/mindspore/mindspore/pulls/5350))
- Move the setting of ParalleMode from train.parallel_utils to context([!5351](https://gitee.com/mindspore/mindspore/pulls/5351))
- Modification of save_checkpoint([!5482](https://gitee.com/mindspore/mindspore/pulls/5482))
- Wrap numpy random seed into an api([!5634](https://gitee.com/mindspore/mindspore/pulls/5634))
- Delete enable_fused_layernorm in some modelzoo scripts([!5665](https://gitee.com/mindspore/mindspore/pulls/5665))
- Move 'multi-subgraphs' interface to internal([!5696](https://gitee.com/mindspore/mindspore/pulls/5696))
- Rename mirror_mean to gradient_mean([!5700](https://gitee.com/mindspore/mindspore/pulls/5700))
- Remove default value of 'group' of DepthWiseConv2d([!5865](https://gitee.com/mindspore/mindspore/pulls/5865))
- Modify interface for function and remove duplicated def([!5958](https://gitee.com/mindspore/mindspore/pulls/5958))
- Unify Conv2d and DepthwiseConv2d([!5916](https://gitee.com/mindspore/mindspore/pulls/5916))
- Modification of SoftmaxCrossEntropyWithLogits([!5502](https://gitee.com/mindspore/mindspore/pulls/5502))
- Change API set_strategy() to shard()([!5991](https://gitee.com/mindspore/mindspore/pulls/5991))
- Move batch_size from bert_cfg_cfg to cfg([!6233](https://gitee.com/mindspore/mindspore/pulls/6233))
- Remove unused parameters from SummaryRecord __init__([!5548](https://gitee.com/mindspore/mindspore/pulls/5548))
- remove sens parameter of TrainOneStepWithLossScaleCell([!5753](https://gitee.com/mindspore/mindspore/pulls/5753))
- optimize the TrainOneStepCell for user's define([!6159](https://gitee.com/mindspore/mindspore/pulls/6159))
- delete seed0 and seed1 of nn.Dropout([!5735](https://gitee.com/mindspore/mindspore/pulls/5735))
- delete DataWrapper([!6101](https://gitee.com/mindspore/mindspore/pulls/6101))
- LSTM API optimization([!6374](https://gitee.com/mindspore/mindspore/pulls/6374))
- Merge P\C\F of ops([!5645](https://gitee.com/mindspore/mindspore/pulls/5645))
- delete SoftmaxCrossEntropyExpand interface([!6607](https://gitee.com/mindspore/mindspore/pulls/6607))
- Adjust GroupNorm interface([!6329](https://gitee.com/mindspore/mindspore/pulls/6329))
- Modify init interface to internal interface([!6651](https://gitee.com/mindspore/mindspore/pulls/6651))
- Log optimization([!5842](https://gitee.com/mindspore/mindspore/pulls/5842))
- Remove useless API dataset.set_dataset_size（[!5806](https://gitee.com/mindspore/mindspore/pulls/5806))
- Some of Dataset API add usage parameter（[!5605](https://gitee.com/mindspore/mindspore/pulls/5605))
- Change the import path, such as from mindspore.dataset.transforms.vision to mindspore.dataset.vision.transforms（[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
- Rename ImageFolderDatasetV2 to ImageFolderDataset（[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
- Dataset.map parameter optimization（[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
- Add new api dataset.get_col_names（[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
- Add new api dataset.get_col_names（[!5384](https://gitee.com/mindspore/mindspore/pulls/5384))
- Remove useless API MindRecord finish（[!5580](https://gitee.com/mindspore/mindspore/pulls/5580))

### MindSpore Lite

- Converter
    - Add 6 TFLite op, 7 Caffe op, 1 ONNX op.
    - Add support for Windows.
    - Support parallel inference of multiple sessions to adapt to more scenarios
    - Support 8bits only weight-quantization, most main-stream models has small accuracy loss (less than 0.5%) when compared to non-qunantized fp32 model.

- CPU & GPU
    - Add 20 CPU ops，include FP32, int8/uint8, FP16 and int32 ops.
    - Add supporting FP16 for GPU, add 14 GPU ops include FP32/FP16.
    - Add Buffer/Image2D transform op for GPU
    - Performance optimization for CPU ops focus on ARM32.
    - Performance optimization for GPU Convolution using winograd.

- Tool & example
    - Add object detection Android Demo.

## Bugfixes

- Models
    - fix the constant folding problem in multiply.([!6092](https://gitee.com/mindspore/mindspore/pulls/6092))
    - move batch_size from bert_net_cfg to cfg in bert scripts.([!6233](https://gitee.com/mindspore/mindspore/pulls/6233))
    - modify the checkpoint file path.([!6137](https://gitee.com/mindspore/mindspore/pulls/6137))
- Python API
    - fix semi auto parallel parameter of reshape has another user([!5722](https://gitee.com/mindspore/mindspore/pulls/5722))
    - raise ValueError when call hook function in graph mode([!5831](https://gitee.com/mindspore/mindspore/pulls/5831))
- Executor
    - fix pynative mode to build temporary nn objects.（[!6189](https://gitee.com/mindspore/mindspore/pulls/6189))
    - fix the accuracy problem of multiple inputs of multi-card communication operator broadcast.([!6522](https://gitee.com/mindspore/mindspore/pulls/5622))
    - fix the problem that the sample distribution interface categorical does not support graph mode.([!5772](https://gitee.com/mindspore/mindspore/pulls/5772))
    - fix the random seed failure problem of the polynomial downsampling distribution operator.([!5948](https://gitee.com/mindspore/mindspore/pulls/5948))
    - fix unnecessary address binding issues in GPU heterogeneous scenarios.([!6232](https://gitee.com/mindspore/mindspore/pulls/6232))
- GPU platform
    - fix for kernel resource leak([!5315](https://gitee.com/mindspore/mindspore/pulls/5315))
    - fix for insufficient memory for continuous unit test running([!5617](https://gitee.com/mindspore/mindspore/pulls/5617))
    - fix for the memory leak in the sparse slicer([!5578](https://gitee.com/mindspore/mindspore/pulls/5578))
- Data processing
    - fix hang when use pyfunc([!6346](https://gitee.com/mindspore/mindspore/pulls/6346))
    - fix GPU device queue does not release GIL during resource clean up([!5964](https://gitee.com/mindspore/mindspore/pulls/5964))
    - fix hang if scripte exit unnormally([!6441](https://gitee.com/mindspore/mindspore/pulls/6441))
- Third party
    - Sqlite : Update sqlite to 3.32.2 to handle [CVE-2020-11656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11656), [CVE-2020-13871](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13871), [CVE-2020-11655](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655), [CVE-2020-9327](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-9327), [CVE-2020-13630](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13630), [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358), [CVE-2020-13631](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13631), [CVE-2020-13632](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13632), [CVE-2020-13434](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13434), [CVE-2020-13435](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13435), and [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655).
    - Libjpeg-turbo : Update libjpeg-turbo to 2.0.4 to handle [CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).

## Contributors

Thanks goes to these wonderful people:

Adel, AGroupofProbiotocs, anthonyaje, anzhengqi, askmiao, baihuawei, baiyangfan, bai-yangfan, bingyaweng, BowenK, buxue, caifubi, CaoJian, caojian05, caozhou, Cathy, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chenzomi, chenzupeng, chujinjin, cj, cjh9368, Corleone, danish, Danish, dayschan, eric, Eric, fary86, fuzhiye, Gaoxiong, gengdongjie, gongdaguo, gukecai, guoqi, gzhcv, hangq, hanhuifeng2020, Harshvardhan, He, heleiwang, hexia, Hoai, HuangBingjian, huangdongrun, huanghui, huangxinjing, huzhifeng, hwjiaorui, Jesse, jianghui58, jiangzhiwen, Jiaqi, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, jzg, kai00, kingfo, kingxian, kpy, kswang, laiyongqiang, leonwanghui, Li, liangchenghui, liangzelang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, linqingke, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuyang_655, liuzhongkai, Lixia, lixian, liyanliu, liyong, lizhenyu, luoyang, lvchangquan, lvliang, lz, mahdi, Mahdi, maning202007, Margaret_wangrui, mayang, mengyuanli, nhussain, ougongchang, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, Pengyongrong, qianlong, r1chardf1d0, riemann_penn, root, Sheng, shenwei41, simson, Simson, Su, sunsuodong, tao_yunhao, tinazhang, VectorSL, , Wan, wandongdong, wangdongxu, wangmin, wangnan39@huawei.com, wangyue01, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, Xiaoda, xiefangqi, xuanyue, xulei2020, Xun, xuyongfei, yanghaitao, yanghaitao1, yanghaoran, YangLuo, yangruoqi713, yankai, yanzhenxiang2020, yao_yf, yepei6, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zengzitao, Zhang, zhanghaibo5@huawei.com, zhanghuiyao, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaojichen, zhaoting, zhaozhenlong, zhengjun10, zhoufeng, zhousiyi, zhouyaqiang, Zichun, Zirui, Ziyan, zjun, ZPaC

Contributions of any kind are welcome!

# MindSpore 0.7.0-beta Release Notes

## Major Features and Improvements

### MindSpore Training and Inference Framework

#### Ascend 910

- New models
    - TinyBert: a smaller and faster version of BERT using transformer distillation for natural language understanding on GLUE benchmark.
    - SE-ResNet50: add Squeeze-and-Excitation blocks(SE-Blocks) to the resnet50 network to improve channel interdependencies for image classification on ImageNet 2012 dataset.
    - Inception V3: the third version of Inception convolutional architectures for image classification on ImageNet 2012 dataset.
- Frontend and user interface
    - Embedding operator high-level packaging to support segmented by field for Wide&Deep.
    - Load multi-node checkpoint into single-process to support host-device hybrid inference.
    - Support Concat/Tile/Strideslice distributed operators.
    - Support cumulative gradient and batch training split.
    - Support variable parameter input for Cell object.
    - Parameter mixed calculation optimization for pynative mode.
    - Deep Probabilistic Programming
        - Support statistical distributions classes used to generate stochastic tensors.
        - Support probabilistic inference algorithms.
        - Support BNN layers used to construct BNN in Graph mode.
        - Support interfaces for the transformation between BNN and DNN in Graph mode.
        - Support uncertainty estimation to estimate epistemic uncertainty and aleatoric uncertainty.
    - User interfaces change log
        - change base class of parameter([!3473](https://gitee.com/mindspore/mindspore/pulls/3473))
        - change binary to mindir([!4258](https://gitee.com/mindspore/mindspore/pulls/4258))
        - change export from geir to air([!4269](https://gitee.com/mindspore/mindspore/pulls/4269))
        - Init parameter data by default([!3967](https://gitee.com/mindspore/mindspore/pulls/3967))
        - change IndexedSlices to RowTensor([!4031](https://gitee.com/mindspore/mindspore/pulls/4031))
        - Must set or change parallel mode before any Initializer created([!4801](https://gitee.com/mindspore/mindspore/pulls/4801))
- Executor and performance optimization
    - MindSpore graph compilation process performance improved by 20%.
    - Decoupling C++ and Python modules to achieve separate compilation of core modules.
- Data processing, augmentation, and save format
    - Support automatic data augmentation
    - Support GNN distributed cache in single node
    - Support ConcatDataset using distributed sampler

#### Other Hardware Support

- GPU platform
    - New model supported: VGG16, ResNet101, DeepFM.
    - Support some distributed operators in ResNet50 and Wide&Deep.
    - Support automatic parallel for Wide&Deep.
    - Support function funcs[i](*inputs) (such as switch-case).
    - Support distributed training with parameter server.
    - Support GPU operator profiling.
    - Performance optimization of the distributed training with allreduce.
    - Performance optimization of the mixed precision training.
    - Performance optimization of the pynative mode.
    - Performance optimization of the convolution operator, batch normalization operator.
- CPU platform
    - Support MobileNetV2 Re-Training: Re-train the network with different class number.

### MindSpore Lite

- Converter
    - Support third-party models, including TFLite/Caffe/ONNX.
    - Add 93 TFLite op.
    - Add 24 Caffe op.
    - Add 62 ONNX op.
    - Add 11 optimized passes, include fusion/const fold.
    - Support aware-training and Post-training quantization.
- CPU
    - Add 100+ops，support fp32, int8/uint8, FP16 ops
    - Support fast convolution algorithms: Sliding Window, Img2col + Gemm, Strassen, Winograd
    - Support assembly/neon instruction.
    - Support CPU fp16 and sdot on ARM v8.2+.
- GPU
    - Add 20+ ops for OpenCL.
    - Support image2D/buffer format.
    - Optimize online initialization time.
    - add optimized convolution1X1/3X3/depthwise/convolution_transposed for OpenCL.
- Tool & example
    - Add benchmark and TimeProfile tools.
    - Add image classification Android Demo.

## Bugfixes

- Models
    - normalize the readme file([!5410](https://gitee.com/mindspore/mindspore/pulls/5410))
    - fix a sink_size bug for transformer([!5393](https://gitee.com/mindspore/mindspore/pulls/5393))
    - fix bool type optional for resnet50([!5363](https://gitee.com/mindspore/mindspore/pulls/5363))
- Python API
    - improve interface '__bool__' for tensor([!4000](https://gitee.com/mindspore/mindspore/pulls/4000))
    - fix GPU-ResizeNearestNeighbor([!3760](https://gitee.com/mindspore/mindspore/pulls/3760))
    - fix topK multi dimension grad func([!3711](https://gitee.com/mindspore/mindspore/pulls/3711))
    - fix scatterop error msg([!3699](https://gitee.com/mindspore/mindspore/pulls/3699))
    - fix bug of cast dtype when using mix_presion in pynative mode([!3730](https://gitee.com/mindspore/mindspore/pulls/3730))
- Executor
    - fix etsnet train error when UnsegmentSum's first input shape is (1,) ([!4573](https://gitee.com/mindspore/mindspore/pulls/4573))
    - fix bug of result error in while control flow because of unsupporting for value reference ([!4103](https://gitee.com/mindspore/mindspore/pulls/4103))
    - fix bug of the output tensor does not carry device data type ([!3774](https://gitee.com/mindspore/mindspore/pulls/3774))
    - fix bug of avoiding multi attr value are eliminated in pynative mode ([!4225](https://gitee.com/mindspore/mindspore/pulls/4225))
    - fix bug of AssignAdd unable to work normally in multi-cases ([!5171](https://gitee.com/mindspore/mindspore/pulls/5171))
- GPU platform
    - improve the environment variable checking for nvcc compiler path ([!5140](https://gitee.com/mindspore/mindspore/pulls/5140))
    - fix bug of error in cast operator conversion from fp16 to fp32 ([!4147](https://gitee.com/mindspore/mindspore/pulls/4147))
    - fix bug of the array out of bound in case of make_tuple operator ([!5219](https://gitee.com/mindspore/mindspore/pulls/5219))
- Data processing and Pro
    - fix GeneratorDataset time out([!3624](https://gitee.com/mindspore/mindspore/pulls/3624))
    - fix concat operator get_dataset_size error([!4701](https://gitee.com/mindspore/mindspore/pulls/4701))
    - fixing python validator for Repeat Op([!4366](https://gitee.com/mindspore/mindspore/pulls/4366))
- Third party
    - Sqlite : Update sqlite to 3.32.2 to handle [CVE-2020-11656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11656), [CVE-2020-13871](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13871), [CVE-2020-11655](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655), [CVE-2020-9327](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-9327), [CVE-2020-13630](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13630), [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358), [CVE-2020-13631](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13631), [CVE-2020-13632](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13632), [CVE-2020-13434](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13434), [CVE-2020-13435](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13435), and [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655).
    - Libjpeg-turbo : Update libjpeg-turbo to 2.0.4 to handle [CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).

## Contributors

Thanks goes to these wonderful people:

Adel, Alexey, andy, andy_wangrui, anthonyaje, anzhengqi, askmiao, avakh, baihuawei, bingyaweng, BowenK, buxue, caifubi, CaoJian, caozhou, Cathy, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chenzupeng, chujinjin, cjh9368, Corleone, cristoval, danish, dengyutao, eric, Eric, ervinzhang, etone-chan, fangzehua, fary86, fuzhiye, gengdongjie, genglishuai, Giancarlo, gongdaguo, gukecai, guohongzilong, GuoMengHao, hangq, hanhaocheng, hanhuifeng2020, hanjun996, Harshvardhan, He, heleiwang, hesham, hexia, Hoai, hongxing, huangdongrun, huanghui, huangxinjing, islam_amin, Jesse, jianghui58, jiangzhiwen, jin-xiulang, jinyaohui, jjfeing, John, Jonathan, jonyguo, kai00, kingfo, kpy, kswang, laiyongqiang, leilei_snow, leopz, Li, liangzelang, lianliguang, lichen_101010, lichenever, lihongkang, lilei, limingqi107, ling, lingyunli63, linqingke, lirongzhen1, liubuyu, liuwenhao4, liuxiao78, liuxiao93, liuzhongkai, Lixia, lixian, liyong, lizhenyu, looop5, luoyang, lvchangquan, lvliang, lvwenyuan, lyvette, mahdi, Mahdi, mamba_ni, maning202007, Margaret_wangrui, mayang, meixiaowei, meng_chunyang, ms_yan, nhussain, panbingao, panfengfeng, panyifeng, Payne, Peilin, peixu_ren, pengyongrong, Pengyongrong, qianlong, qujianwei, root, shenwei41, shibeiji, simson, songhonglei413, Su, sunsuodong, suteng, tao_yunhao, TFbunny, tinazhang, tom__chen, tony_liu2, tronzhang, VectorSL, wandongdong, wangdongxu, wanghua, wangmin, wangshaocong, wangzhe, wanyiming, Wei, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuweikang, wuxuejian, wuyongkang, xiefangqi, xuanyue, Xun, xutianchun, xuyongfei, yanghaitao, yangjie159, YangLuo, yangruoqi713, yangyongjie, yangzhenzhang, yankai, yao_yf, yelihua, yeyunpeng, Yi, yoni, yoonlee666, yuchaojie, yujianfeng, yuximiao, zhangxuetong, zhaizhiqiang, Zhang, zhangxinfeng3, zhangxuetong, zhangyihui, zhangz0911gm, zhanke, zhanyuan, zhaodezan, zhaoting, zhaozhenlong, zhengjun10, zhongligeng, zhoufeng, zhousiyi, zhouyaqiang, zhouyuanshen, Zichun, Zirui, zjun, zongha, ZPaC, lijiaqi, liangchenghui, wangminggui

Contributions of any kind are welcome!

# MindSpore 0.6.0-beta Release Notes

## Major Features and Improvements

### Ascend 910 Training and Inference Framework

- New models
    - There are official, research and community under modelzoo.
        - Official is maintained  with the newest APIs by MindSpore team,  MaskRCNN are added.
        - Research is uploaded by researchers for official review, and APIs may not  be updated in time.
        - Community reprints the relevant links of partner research results.
    - Hub added on the same level as modelzoo, synchronous storage of materials needed for official hub web pages which will be launched soon.
    - Support pre-trained models, few lines of code can be used to download and load pre-trained models, supporting inference or transfer learning.
- Frontend and user interface
    - Supports user side operator compilation and graph execution error rendering.
    - Uniform definition dynamic learning rate behavior in optimizers.
    - Support IndexSlice in sparse expression.
    - Support use parent construct method during construct.
    - Support asynchronous execution save checkpoint file.
    - Support implicit type conversion in pynative mode.
    - User interfaces change log
        - unform learning rate behavior in optimizers([!2755](https://gitee.com/mindspore/mindspore/pulls/2755))
        - rename operator of sparse optimizer([!3217](https://gitee.com/mindspore/mindspore/pulls/3217))
        - move profiler module from mindinsight to mindspore([!3075](https://gitee.com/mindspore/mindspore/pulls/3075))
        - VOCDataset output change to multi-columns([!3093](https://gitee.com/mindspore/mindspore/pulls/3093))
        - GetDatasize feature([!3212](https://gitee.com/mindspore/mindspore/pulls/3212))
        - dataset: modify config api([!2936](https://gitee.com/mindspore/mindspore/pulls/2936))
- Executor and performance optimization
    - Decouple C++ and python, so make the architecture more extensible.
    - Parameter Server for distributed deep learning supported.
    - Serving: a flexible service deployment framework for deep learning models.
    - Memory reuse is enhanced, and the batch size of Bert large model is increased from 96 to 160 on a single server.
- Data processing, augmentation, and save format
    - Support MindRecord save operator after  date processing
    - Support automatic fusion operator, such as decode/resize/crop
    - Support CSV dataset loading

### Other Hardware Support

- GPU platform
    - New model supported: ResNext50, WarpCTC and GoogLeNet.
    - Support hyperparametric search and data enhanced automl on GPU.
    - Support Resnet50 automatic parallel in GPU backend.

## Bugfixes

- Models
    - Improved the performance and accuracy on ResNet50([!3456](https://gitee.com/mindspore/mindspore/pulls/3456))
    - Fixed the performance test case of bert([!3486](https://gitee.com/mindspore/mindspore/pulls/3486))
- Python API
    - Fix assign used in while loop([!2720](https://gitee.com/mindspore/mindspore/pulls/2720))
    - Revert optimize the graph output of all nop node.([!2857](https://gitee.com/mindspore/mindspore/pulls/2857))
    - Print tensor as numpy.([!2859](https://gitee.com/mindspore/mindspore/pulls/2859))
    - Support weight decay for sparse optimizer([!2668](https://gitee.com/mindspore/mindspore/pulls/2668))
    - Fix BatchToSpaceND([!2741](https://gitee.com/mindspore/mindspore/pulls/2741))
    - Fixing type check mistakes of InplaceAdd and Inplace Sub ops([!2744](https://gitee.com/mindspore/mindspore/pulls/2744]))
    - Change order param only equal to group param([!2748](https://gitee.com/mindspore/mindspore/pulls/2748))
- Executor
    - The performance of graph with control flow is optimized([!2931](https://gitee.com/mindspore/mindspore/pulls/2931))
    - Fix bug of wrong number of tuple layers([!3390](https://gitee.com/mindspore/mindspore/pulls/3390))
    - Fix cpu multi graph memory exception([!3631](https://gitee.com/mindspore/mindspore/pulls/3631))
    - Enable data sync when calling operator without defining a cell([!3081](https://gitee.com/mindspore/mindspore/pulls/3081))
    - Fix argmaxwith value error in pynative mode on GPU([!3082](https://gitee.com/mindspore/mindspore/pulls/3082))
    - Fix precision error with fp16 input on pynative mode([!3196](https://gitee.com/mindspore/mindspore/pulls/3196))
- Data processing
    - Fix bug of RandomColor and RandomSharpness default parameter checking  ([!2833](https://gitee.com/mindspore/mindspore/pulls/2833))
    - Fix process hung when training and eval  ([!3469](https://gitee.com/mindspore/mindspore/pulls/3469))
- Third party
    - Sqlite : Update sqlite to 3.32.2 to handle [CVE-2020-11656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11656), [CVE-2020-13871](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13871), [CVE-2020-11655](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655), [CVE-2020-9327](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-9327), [CVE-2020-13630](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13630), [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358), [CVE-2020-13631](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13631), [CVE-2020-13632](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13632), [CVE-2020-13434](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13434), [CVE-2020-13435](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13435), and [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655).
    - Libjpeg-turbo : Update libjpeg-turbo to 2.0.4 to handle [CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).

## Contributors

Thanks goes to these wonderful people:

Alexey Shevlyakov, avakh, baihuawei, BowenK, buxue, caifubi, caojian05, Cathy Wong, changzherui, chenfei, chengxianbin, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, Danish Farid, dayschan, dengwentao, dinghao, etone-chan, fangzehua, fary86, geekun, Giancarlo Colmenares, gong chen, gukecai, guohongzilong, hangangqiang, heleiwang, hesham, He Wei, hexia, hongxing, huangdongrun, huanghui, islam_amin, Jamie Nisbet, Jesse Lee, jiangjinsheng, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, Jonathan Yan, jonyguo, Junhan Hu, Kang, kingfo, kouzhenzhong, kpy, kswang, laiyongqiang, leopz, liangzelang, lichenever, lihongkang, Li Hongzhang, lilei, limingqi107, lirongzhen1, liubuyu, liuchongming74, liuwenhao4, liuxiao, Lixia Chen, liyanliu, liyong, lizhenyu, lvliang, Mahdi, Margaret_wangrui, meixiaowei, ms_yan, nhussain, ougongchang, panfengfeng, panyifeng, peilinwang, Peilin Wang, pkuliuliu, qianlong, rick_sanchez, shibeiji, Shida He, shijianning, simson, sunsuodong, suteng, Tinazhang, Tron Zhang, unknown, VectorSL, wandongdong, wangcong, wangdongxu, wangdongxu6, wanghua, wangnan39, Wei Luning, wenchunjiang, wenkai, wilfChen, WilliamLian, wukesong, Xian Weizhao, Xiaoda Zhang, xiefangqi, xulei2020, xunxue, xutianchun, Yang, yanghaitao, yanghaitao1, yanghaoran, yangjie, yangjie159, YangLuo, Yanjun Peng, yankai, yanzhenxiang2020, yao_yf, Yi Huaijie, yoonlee666, yuchaojie, yujianfeng, zhangzhongpeng, zhangdengcheng, Zhang Qinghua, zhangyinxia, zhangz0911gm, zhaojichen, zhaoting, zhaozhenlong, zhoufeng, zhouneng, zhousiyi, Zirui Wu, Ziyan, zjun, ZPaC, lihongzhang, wangdongxu

Contributions of any kind are welcome!

# MindSpore 0.5.2-beta Release Notes

## Major Features and Improvements

### Ascend 910 Training and Inference Framework

- New models
    - DenseNet121: a convolution based neural network for the task of image classification on ImageNet 2012 dataset.

## Bugfixes

- Models
    - VGG16,Alexnet,GoogleNet,optimize network for better performance. ([!5539](https://gitee.com/mindspore/mindspore/pulls/5539))
    - YOLOV3, fix yolov3_darknet53 dataset bug. ([!5658](https://gitee.com/mindspore/mindspore/pulls/5658))

## Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

# MindSpore 0.5.0-beta Release Notes

## Major Features and Improvements

### Ascend 910 Training and Inference Framework

- New models
    - ResNext50: a simple, highly modularized network architecture using aggregated resdiual transformations for image classification on ImageNet 2012 dataset.
    - MASS: a pre-training method for sequence to sequence based language generation tasks on Text Summarization and Conversational Response Generation using News Crawls 2007-2017 dataset, Gigaword corpus and Cornell movie dialog corpus.
    - Transformer: a neural network architecture for language understanding on WMT 2014 English-German dataset.
    - GCN: Graph Convolutional Networks for the task of classification of nodes in a graph on Cora and Citeseer datasets.
    - GAT: an attention-based graph neural network for node classification on Cora and CiteSeer dataset.
- Frontend and user interface
    - Support tensor value and assignment of mixed tensor index in graph mode.
    - Support tensor comparison, len operator, constexpr syntax, value and assignment of tensor index in pynative mode.
    - Support converting MindSpore IR to pb format for infer model.
    - Support print operator to write data directly on the hard disk.
    - Add the double recursive programming solution for very high speed parallel strategy search in automatic parallel.
    - User interfaces change log
        - Allow the learning rate of AdamWeightDecayDynamicLR and Lamb to be 0([!1826](https://gitee.com/mindspore/mindspore/pulls/1826))
        - Restricting the entire network input parameter is Tensor([!1967](https://gitee.com/mindspore/mindspore/pulls/1967))
        - Turn shape and dtype into attributes instead of interfaces([!1919](https://gitee.com/mindspore/mindspore/pulls/1919))
        - Delete multitypefungraph([!2116](https://gitee.com/mindspore/mindspore/pulls/2116))
        - Refactor the callback module in an encapsulated way, use _CallbackManager instead of_build_callbacks([!2236](https://gitee.com/mindspore/mindspore/pulls/2236))
        - Delete EmbeddingLookup([!2163](https://gitee.com/mindspore/mindspore/pulls/2163))
        - Checkpoint add model_type([!2517](https://gitee.com/mindspore/mindspore/pulls/2517))
- Executor and performance optimization
    - Heterogeneous execution on CPU and Ascend devices supported, and is verified in Wide&Deep model.
    - Quantitative training of MobileNetV2, Lenet and Resnet50 on Ascend-910 are supported.
    - Support new fusion architecture, which can do fusion optimization across graphs and kernels to improve execution speed.
- Data processing, augmentation, and save format
    - Support data processing pipeline performance profiling.
    - Support public dataset loading, such as CLUE and Coco.
    - Support more text processing, such as more tokenizers and vocab data.
    - Support MindRecord padded data.

### Other Hardware Support

- GPU platform
    - New model supported: Bert / Wide&Deep.
    - Support setting max device memory.
- CPU platform
    - New model supported: LSTM.

## Bugfixes

- Models
    - Bert, Move Bert from `example` to `model_zoo`, optimize network for better performance. ([!1902](https://gitee.com/mindspore/mindspore/pulls/1902))
    - VGG16, Move VGG16 from `example` to `model_zoo`, optimize network for better accuracy. ([!2645](https://gitee.com/mindspore/mindspore/pulls/2645))
    - Alexnet, modify parameter setting to improve accuracy ([!1364](https://gitee.com/mindspore/mindspore/pulls/2370))
    - Wide&Deep, Move Wide&Deep from `example` to `model_zoo`, optimize network for better performance. ([!2221](https://gitee.com/mindspore/mindspore/pulls/2221))
- Python API
    - Fix bug in auto cast([!1766](https://gitee.com/mindspore/mindspore/pulls/1766))
    - Fix bug of register_backward_hook([!2148](https://gitee.com/mindspore/mindspore/pulls/2148))
    - Fix bug of tuple args in pynative mode([!1878](https://gitee.com/mindspore/mindspore/pulls/1878))
    - Fix bug of checking numbers of arguments and graph parameters([!1701](https://gitee.com/mindspore/mindspore/pulls/1701))
- Executor
    - Fix bug of loading input data repeatedly in pynative mode([!1966](https://gitee.com/mindspore/mindspore/pulls/1966))
    - Fix bug of list cannot be used as input in pynative mode([!1765](https://gitee.com/mindspore/mindspore/pulls/1765))
    - Fix bug of kernel select ([!2103](https://gitee.com/mindspore/mindspore/pulls/2103))
    - Fix bug of pattern matching for batchnorm fusion in the case of auto mix precision.([!1851](https://gitee.com/mindspore/mindspore/pulls/1851))
    - Fix bug of generate hccl's kernel info.([!2393](https://gitee.com/mindspore/mindspore/pulls/2393))
- GPU platform
    - Fix bug of summary feature invalid([!2173](https://gitee.com/mindspore/mindspore/pulls/2173))
- Data processing
    - Fix bug of Cifar dataset reading([!2096](https://gitee.com/mindspore/mindspore/pulls/2096))
    - Fix bug of C++ behavior in RandomCropAndResize([!2026](https://gitee.com/mindspore/mindspore/pulls/2026))
    - Fix the bug of mindrecord shuffle([!2420](https://gitee.com/mindspore/mindspore/pulls/2420))
- Third party
    - Sqlite : Update sqlite to 3.32.2 to handle [CVE-2020-11656](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11656), [CVE-2020-13871](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13871), [CVE-2020-11655](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655), [CVE-2020-9327](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-9327), [CVE-2020-13630](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13630), [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15358), [CVE-2020-13631](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13631), [CVE-2020-13632](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13632), [CVE-2020-13434](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13434), [CVE-2020-13435](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13435), and [CVE-2020-15358](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11655).

## Contributors

Thanks goes to these wonderful people:

Contributions of any kind are welcome!

# MindSpore 0.3.1-alpha Release Notes

## Major Features and Improvements

### Ascend 910 Training and Inference Framework

- Frontend and User Interface
    - Independent model init interface.
- Data processing, augmentation, and save format
    - Support sample padding for minddataset.

## Bugfixes

- Python API
    - Fix bugs in the lars optimizer([!1894](https://gitee.com/mindspore/mindspore/pulls/1894))
- Data processing
    - Fix accuracy problem of RandomCropDecodeResize ([!2340](https://gitee.com/mindspore/mindspore/pulls/2340))

# Release 0.3.0-alpha

## Major Features and Improvements

### Ascend 910 Training and Inference Framework

- New models
    - DeepFM: a factorization-machine based neural network for CTR prediction on Criteo dataset.
    - DeepLabV3: significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2007 semantic image segmentation benchmark.
    - Faster-RCNN: towards real-time object detection with region proposal networks on COCO 2017 dataset.
    - SSD: a single stage object detection methods on COCO 2017 dataset.
    - GoogLeNet: a deep convolutional neural network architecture codenamed Inception V1 for classification and detection on CIFAR-10 dataset.
    - Wide&Deep: jointly trained wide linear models and deep neural networks for recommender systems on Criteo dataset.
- Frontend and User Interface
    - Complete numpy advanced indexing method. Supports value and assignment through tensor index.
    - Some optimizers support separating parameter groups. Different parameter groups can set different `learning_rate` and `weight_decay`.
    - Support setting submodule's logging level independently, e.g. you can set logging level of module `A` to warning and set logging level of module `B` to info.
    - Support weights to be compiled according to shape to solve the problem of large memory overhead.
    - Add some operators implement and grammar support in pynative mode. To be consistent with graph mode.
    - User interfaces change log
        - Learning rate and weight decay making group params([!637](https://gitee.com/mindspore/mindspore/pulls/637))
        - Support weights to be compiled according to shape([!1015](https://gitee.com/mindspore/mindspore/pulls/1015))
        - delete some context param([!1100](https://gitee.com/mindspore/mindspore/pulls/1100))
        - ImageSummary/ScalarSummary/TensorSummary/HistogramSummary([!1329](https://gitee.com/mindspore/mindspore/pulls/1329))([!1425](https://gitee.com/mindspore/mindspore/pulls/1425))
- Executor and Performance Optimization
    - Support doing evaluation while in training process, so that the accuracy of training can be easily obtained.
    - Enable second-order optimization for resnet50, which can achieve 75.9% accuracy in 45 epochs (Resnet50 @ImageNet).
    - Optimize pynative implementation and improve it's execution performance.
    - Optimize summary record implementation and improve its performance.
- Data processing, augmentation, and save format
    - Support simple text processing, such as tokenizer/buildvocab/lookup.
    - Support padding batch.
    - Support split or concat dataset.
    - Support MindDataset reading from file list.

### Other Hardware Support

- GPU platform
    - New models supported: MobileNetV2, MobileNetV3.
    - Support mixed precision training.
    - Support device memory swapping.

## Bugfixes

- Python API
    - An exception to the broadcast input data type check([!712](https://gitee.com/mindspore/mindspore/pulls/712))
    - Fix issues assignsub return value 0([!1036](https://gitee.com/mindspore/mindspore/pulls/1036))
    - Fix issue Conv2dBackpropInput bprop should return 3 instead of 2 items([!1001](https://gitee.com/mindspore/mindspore/pulls/1001))
    - Fix sens shape error of TrainOneStepWithLossScaleCell([!1050](https://gitee.com/mindspore/mindspore/pulls/1050))
    - Fix BatchNormGrad operator([!1344](https://gitee.com/mindspore/mindspore/pulls/1344))
- Executor
    - Fix dropout，topK and addn errors in PyNative mode ([!1285](https://gitee.com/mindspore/mindspore/pulls/1285), [!1138](https://gitee.com/mindspore/mindspore/pulls/1138), [!1033](https://gitee.com/mindspore/mindspore/pulls/1033)).
    - Fix memory leaks after execution in PyNatvie mode ([!1201](https://gitee.com/mindspore/mindspore/pulls/1201)).
    - Fix HCCL failure in some special scenes ([!1204](https://gitee.com/mindspore/mindspore/pulls/1204), [!1252](https://gitee.com/mindspore/mindspore/pulls/1252)).
    - Fix SSD network when Select failed, can't find kernel info([!1449](https://gitee.com/mindspore/mindspore/pulls/1449)).
    - Fix Topk operator selection strategy bug between aicore and aicpu([!1367](https://gitee.com/mindspore/mindspore/pulls/1367)).
    - Fix input memory size of 'assign' op unequal in control sink mode when assigning a data from one child graph to another child graph([!802](https://gitee.com/mindspore/mindspore/pulls/802)).
    - Fix allreduce ir inconsistency([!989](https://gitee.com/mindspore/mindspore/pulls/989)).
- GPU platform
    - Fix summary for gradient collection ([!1364](https://gitee.com/mindspore/mindspore/pulls/1364))
    - Fix the slice operator ([!1489](https://gitee.com/mindspore/mindspore/pulls/1489))
- Data processing
    - Fix memory problems of GeneratorDataset of sub-process ([!907](https://gitee.com/mindspore/mindspore/pulls/907))
    - Fix getting data timeout when training the cifar10 dataset under the lenet([!1391](https://gitee.com/mindspore/mindspore/pulls/1391))

## Contributors

Thanks goes to these wonderful people:

Alexey Shevlyakov, Amir Lashkari, anthony, baihuawei, biffex, buxue, caifubi, candanzg, caojian05, Cathy Wong, changzherui, chenfei, chengxianbin, chenhaozhe, chenzomi, chujinjin, cristoval, dengwentao, eric, etone-chan, fary86, gaojing, gengdongjie, gongchen, guohongzilong, guozhijian, heleiwang, hesham, He Wei, Hoai Linh Tran, hongxing, huangdongrun, huanghui, Jamie Nisbet, Jesse Lee, jiangjinsheng, jiangzhiwen, jinyaohui, jjfeing, jonwe, jonyguo, Junhan Hu, Kang, kingfo, kswang, laiyongqiang, leopz, lichenever, lihongkang, limingqi107, liubuyu, liuliyan2, liuwenhao4, liuxiao, liuxiao, liyong, lizhenyu, lvliang, Margaret_wangrui, meixiaowei, ms_yan, Nat Sutyanyong, ougongchang, panfengfeng, panyifeng, Peilin Wang, peixu_ren, qianlong, rick_sanchez, seatea, sheng, shijianning, simson, sunsuodong, Tinazhang, VectorSL, wandongdong, wangcong, wanghua, wangnan39, Wei Luning, wenchunjiang, wilfChen, WilliamLian, wsc, wukesong, wuxuejian, Xiaoda Zhang, xiefangqi, xulei2020, Yang, yangjie159, yangruoqi713, yangyongjie, yangzhenzhang, Yanjun Peng, yanzhenxiang2020, yao_yf, Yi Huaijie, yoonlee666, yujianfeng, YuJianfeng, yvetteliu, zhangdengcheng, Zhang Qinghua, zhangz0911gm, zhaojichen, zhaoting, zhaozhenlong, zhoufeng, zhouneng, zhousiyi, zhouyuanshen, Zirui Wu, Ziyan, zjun, ZPaC, lihongzhang

Contributions of any kind are welcome!

# MindSpore 0.2.0-alpha Release Notes

## Major Features and Improvements

### Ascend 910 Training and Inference Framework

- New models
    - MobileNetV2: Inverted Residuals and Linear Bottlenecks.
    - ResNet101: Deep Residual Learning for Image Recognition.

- Frontend and User Interface
    - Support for all python comparison operators.
    - Support for math operators **,//,%. Support for other python operators like and/or/not/is/is not/ in/ not in.
    - Support for the gradients of function with variable arguments.
    - Support for tensor indexing assignment for certain indexing type.
    - Support for dynamic learning rate.
    - User interfaces change log
        - DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropInput([!424](https://gitee.com/mindspore/mindspore/pulls/424))
        - ReLU6, ReLU6Grad([!224](https://gitee.com/mindspore/mindspore/pulls/224))
        - GeneratorDataset([!183](https://gitee.com/mindspore/mindspore/pulls/183))
        - VOCDataset([!477](https://gitee.com/mindspore/mindspore/pulls/477))
        - MindDataset, PKSampler([!514](https://gitee.com/mindspore/mindspore/pulls/514))
        - map([!506](https://gitee.com/mindspore/mindspore/pulls/506))
        - Conv([!226](https://gitee.com/mindspore/mindspore/pulls/226))
        - Adam([!253](https://gitee.com/mindspore/mindspore/pulls/253))
        - _set_fusion_strategy_by_idx,_set_fusion_strategy_by_size([!189](https://gitee.com/mindspore/mindspore/pulls/189))
        - CheckpointConfig([!122](https://gitee.com/mindspore/mindspore/pulls/122))
        - Constant([!54](https://gitee.com/mindspore/mindspore/pulls/54))
- Executor and Performance Optimization
    - Support parallel execution of data prefetching and forward/backward computing.
    - Support parallel execution of gradient aggregation and forward/backward computing in distributed training scenarios.
    - Support operator fusion optimization.
    - Optimize compilation process and improve the performance.
- Data processing, augmentation, and save format
    - Support multi-process of GeneratorDataset/PyFunc for high performance
    - Support variable batchsize
    - Support new Dataset operators, such as filter,skip,take,TextLineDataset

### Other Hardware Support

- GPU platform
    - Use dynamic memory pool by default on GPU.
    - Support parallel execution of computation and communication.
    - Support continuous address allocation by memory pool.
- CPU platform
    - Support for windows 10 OS.

## Bugfixes

- Models
    - Fix mixed precision bug for VGG16 model ([!629](https://gitee.com/mindspore/mindspore/pulls/629)).
- Python API
    - Fix ControlDepend operator bugs on CPU and GPU ([!396](https://gitee.com/mindspore/mindspore/pulls/396)).
    - Fix ArgMinWithValue operator bugs ([!338](https://gitee.com/mindspore/mindspore/pulls/338)).
    - Fix Dense operator bugs on PyNative mode ([!276](https://gitee.com/mindspore/mindspore/pulls/276)).
    - Fix MatMul operator bugs on PyNative mode ([!288](https://gitee.com/mindspore/mindspore/pulls/288)).
- Executor
    - Fix operator selection bugs and make it general ([!300](https://gitee.com/mindspore/mindspore/pulls/300)).
    - Fix memory reuse bug for GetNext op ([!291](https://gitee.com/mindspore/mindspore/pulls/291)).
- GPU platform
    - Fix memory allocation in multi-graph scenarios ([!444](https://gitee.com/mindspore/mindspore/pulls/444)).
    - Fix bias_add_grad under fp16 precision ([!598](https://gitee.com/mindspore/mindspore/pulls/598)).
    - Fix support for fp16 kernels on nvidia 1080Ti([!571](https://gitee.com/mindspore/mindspore/pulls/571)).
    - Fix parsing of tuple type parameters ([!316](https://gitee.com/mindspore/mindspore/pulls/316)).
- Data processing
    - Fix TypeErrors about can't pickle mindspore._c_dataengine.DEPipeline objects([!434](https://gitee.com/mindspore/mindspore/pulls/434)).
    - Add TFRecord file verification([!406](https://gitee.com/mindspore/mindspore/pulls/406)).

## Contributors

Thanks goes to these wonderful people:

Alexey_Shevlyakov, Cathy, Chong, Hoai, Jonathan, Junhan, JunhanHu, Peilin, SanjayChan, StrawNoBerry, VectorSL, Wei, WeibiaoYu, Xiaoda, Yanjun, YuJianfeng, ZPaC, Zhang, ZhangQinghua, ZiruiWu, amongo, anthonyaje, anzhengqi, biffex, caifubi, candanzg, caojian05, casgj, cathwong, ch-l, chang, changzherui, chenfei, chengang, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, dengwentao, dinghao, fanglei, fary86, flywind, gaojing, geekun, gengdongjie, ghzl, gong, gongchen, gukecai, guohongzilong, guozhijian, gziyan, h.farahat, hesham, huangdongrun, huanghui, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, jonathan_yan, jonyguo, jzw, kingfo, kisnwang, laiyongqiang, leonwanghui, lianliguang, lichen, lichenever, limingqi107, liubuyu, liuxiao, liyong, liyong126, lizhenyu, lupengcheng, lvliang, maoweiyong, ms_yan, mxm, ougongchang, panfengfeng, panyifeng, pengyanjun, penn, qianlong, seatea, simson, suteng, thlinh, vlne-v1, wangchengke, wanghua, wangnan39, wangqiuliang, wenchunjiang, wenkai, wukesong, xiefangqi, xulei, yanghaitao, yanghaoran, yangjie159, yangzhenzhang, yankai10, yanzhenxiang2020, yao_yf, yoonlee666, zhangbuxue, zhangz0911gm, zhangzheng, zhaojichen, zhaoting, zhaozhenlong, zhongligeng, zhoufeng, zhousiyi, zjun, zyli2020, yuhuijun, limingqi107, lizhenyu, chenweifeng.

Contributions of any kind are welcome!

# MindSpore 0.1.0-alpha Release Notes

## Main Features

### Ascend 910 Training and Inference Framework

- Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.5 or EulerOS 2.8
- Python version: 3.7.5
- Preset models
    - ResNet-50: residual structure-based convolutional neural network (CNN) for image classification, which is widely used.
    - AlexNet: classic CNN for image classification, achieving historical results in ImageNet LSVRC-2012.
    - LeNet: classic CNN for image classification, which was proposed by Yann LeCun.
    - VGG16: classic CNN for image classification, which was proposed by Oxford Visual Geometry Group.
    - YoloV3: real-time object detection network.
    - NEZHA: BERT-based Chinese pre-training network produced by Huawei Noah's Ark Laboratory.
- Execution modes
    - Graph mode: provides graph optimization methods such as memory overcommitment, IR fusion, and buffer fusion to achieve optimal execution performance.
    - PyNative mode: single-step execution mode, facilitating process debugging.
- Debugging capability and methods
    - Save CheckPoints and Summary data during training.
    - Support asynchronous printing.
    - Dump the computing data.
    - Support profiling analysis of the execution process performance.
- Distributed execution
    - Support AllReduce, AllGather, and BroadCast collective communication.
    - AllReduce data parallel: Each device obtains different training data, which accelerates the overall training process.
    - Collective communication-based layerwise parallel: Models are divided and allocated to different devices to solve the problem of insufficient memory for large model processing and improve the training speed.
    - Automatic parallel mode: The better data and model parallel mode can be predicted based on the cost model. It is recommended that this mode be used on ResNet series networks.
- Automatic differentiation
    - Implement automatic differentiation based on Source to Source.
    - Support distributed scenarios and automatic insertion of reverse communication operators.
- Data processing, augmentation, and save format
    - Load common datasets such as ImageNet, MNIST, CIFAR-10, and CIFAR-100.
    - Support common data loading pipeline operations, such as shuffle, repeat, batch, map, and sampler.
    - Provide basic operator libraries to cover common CV scenarios.
    - Support users to customize Python data augmentation operators through the Pyfunc mechanism.
    - Support the access of user-defined datasets through the GeneratorDataset mechanism.
    - Provide the MindSpore data format, data aggregation and storage, random access example, data partition, efficient parallel read, user-defined index, and dataset search.
    - Convert user datasets to the MindSpore data format.
    - After data processing and augmentation, provide training applications in feed and graph modes.
- FP32/16 mixed precision computation, supporting automatic and manual configuration
- Provide common operators such as nn, math, and array, which can be customized.

### Inference Deployment

- Deploy models in MindSpore format on the Ascend 310 platform for inference.
- Save models in ONNX format.
- Support saving models in LITE format and running models based on the lightweight inference framework.
    - Recommended OS: Android 4.3 or later
    - Supported network type: LeNet
    - Provide the generalization operators generated by TVM and operators generated after specific networks are tuned.

### Other Hardware Support

- GPU platform training
    - Recommended OS: Ubuntu 16.04
    - CUDA version: 9.2 or 10.1
    - CuDNN version: 7.6 or later
    - Python version: 3.7.5
    - NCCL version: 2.4.8-1
    - OpenMPI version: 3.1.5
    - Supported models: AlexNet, LeNet, and LSTM
    - Supported datasets: MNIST and CIFAR-10
    - Support data parallel.
- CPU platform training
    - Recommended OS: Ubuntu 16.04
    - Python version: 3.7.5
    - Supported model: LeNet
    - Supported dataset: MNIST
    - Provide only the stand-alone operation version.

## Peripherals and Tools

- [MindSpore Official Website](https://www.mindspore.cn/)
- [MindInsight Visualization Debugging and Optimization](https://gitee.com/mindspore/mindinsight)
- [MindArmour Model Security Hardening Package](https://gitee.com/mindspore/mindarmour)
- [GraphEngine Computational Graph Engine](https://gitee.com/mindspore/graphengine)

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