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.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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)
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<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=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.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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:
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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`.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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`.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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.
<table>
<tr>
<td style="text-align:center"> data </td>
</tr>
<tr>
<td>
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.
<table>
<tr>
<td style="text-align:center"> 1.1.0 </td> <td style="text-align:center"> 1.2.0 </td>
</tr>
<tr>
<td>
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, wangnan39huawei.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, zhanghaibo5huawei.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!