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.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.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
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.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).