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v1.7.0

pytorch/pytorch

版本发布时间: 2020-10-28 00:35:58

pytorch/pytorch最新发布版本:v2.4.1(2024-09-05 03:59:29)

PyTorch 1.7.0 Release Notes

Highlights

The PyTorch 1.7 release includes a number of new APIs including support for NumPy-Compatible FFT operations, profiling tools and major updates to both distributed data parallel (DDP) and remote procedure call (RPC) based distributed training. In addition, several features moved to stable including custom C++ Classes, the memory profiler, the creation of custom tensor-like objects, user async functions in RPC and a number of other features in torch.distributed such as Per-RPC timeout, DDP dynamic bucketing and RRef helper.

A few of the highlights include:

To reiterate, starting PyTorch 1.6, features are now classified as stable, beta and prototype. You can see the detailed announcement here. Note that the prototype features listed in this blog are available as part of this release.

Front End APIs

[Beta] NumPy Compatible torch.fft module

FFT-related functionality is commonly used in a variety of scientific fields like signal processing. While PyTorch has historically supported a few FFT-related functions, the 1.7 release adds a new torch.fft module that implements FFT-related functions with the same API as NumPy.

This new module must be imported to be used in the 1.7 release, since its name conflicts with the historic (and now deprecated) torch.fft function.

Example usage:

>>> import torch.fft
>>> t = torch.arange(4)
>>> t
tensor([0, 1, 2, 3])

>>> torch.fft.fft(t)
tensor([ 6.+0.j, -2.+2.j, -2.+0.j, -2.-2.j])

>>> t = tensor([0.+1.j, 2.+3.j, 4.+5.j, 6.+7.j])
>>> torch.fft.fft(t)
tensor([12.+16.j, -8.+0.j, -4.-4.j,  0.-8.j])

[Beta] C++ Support for Transformer NN Modules

Since PyTorch 1.5, we’ve continued to maintain parity between the python and C++ frontend APIs. This update allows developers to use the nn.transformer module abstraction from the C++ Frontend. And moreover, developers no longer need to save a module from python/JIT and load into C++ as it can now be used it in C++ directly.

[Beta] torch.set_deterministic

Reproducibility (bit-for-bit determinism) may help identify errors when debugging or testing a program. To facilitate reproducibility, PyTorch 1.7 adds the torch.set_deterministic(bool) function that can direct PyTorch operators to select deterministic algorithms when available, and to throw a runtime error if an operation may result in nondeterministic behavior. By default, the flag this function controls is false and there is no change in behavior, meaning PyTorch may implement its operations nondeterministically by default.

More precisely, when this flag is true:

Note that this is necessary, but not sufficient, for determinism within a single run of a PyTorch program. Other sources of randomness like random number generators, unknown operations, or asynchronous or distributed computation may still cause nondeterministic behavior.

See the documentation for torch.set_deterministic(bool) for the list of affected operations.

Performance & Profiling

[Beta] Stack traces added to profiler

Users can now see not only operator name/inputs in the profiler output table but also where the operator is in the code. The workflow requires very little change to take advantage of this capability. The user uses the autograd profiler as before but with optional new parameters: with_stack and group_by_stack_n. Caution: regular profiling runs should not use this feature as it adds significant overhead.

Distributed Training & RPC

[Stable] TorchElastic now bundled into PyTorch docker image

Torchelastic offers a strict superset of the current torch.distributed.launch CLI with the added features for fault-tolerance and elasticity. If the user is not be interested in fault-tolerance, they can get the exact functionality/behavior parity by setting max_restarts=0 with the added convenience of auto-assigned RANK and MASTER_ADDR|PORT (versus manually specified in torch.distributed.launch).

By bundling torchelastic in the same docker image as PyTorch, users can start experimenting with torchelastic right-away without having to separately install torchelastic. In addition to convenience, this work is a nice-to-have when adding support for elastic parameters in the existing Kubeflow’s distributed PyTorch operators.

[Beta] Support for uneven dataset inputs in DDP

PyTorch 1.7 introduces a new context manager to be used in conjunction with models trained using torch.nn.parallel.DistributedDataParallel to enable training with uneven dataset size across different processes. This feature enables greater flexibility when using DDP and prevents the user from having to manually ensure dataset sizes are the same across different process. With this context manager, DDP will handle uneven dataset sizes automatically, which can prevent errors or hangs at the end of training.

[Beta] NCCL Reliability - Async Error/Timeout Handling

In the past, NCCL training runs would hang indefinitely due to stuck collectives, leading to a very unpleasant experience for users. This feature will abort stuck collectives and throw an exception/crash the process if a potential hang is detected. When used with something like torchelastic (which can recover the training process from the last checkpoint), users can have much greater reliability for distributed training. This feature is completely opt-in and sits behind an environment variable that needs to be explicitly set in order to enable this functionality (otherwise users will see the same behavior as before).

[Beta] TorchScript remote and rpc_sync

torch.distributed.rpc.rpc_async has been available in TorchScript in prior releases. For PyTorch 1.7, this functionality will be extended the remaining two core RPC APIs, torch.distributed.rpc.rpc_sync and torch.distributed.rpc.remote. This will complete the major RPC APIs targeted for support in TorchScript, it allows users to use the existing python RPC APIs within TorchScript (in a script function or script method, which releases the python Global Interpreter Lock) and could possibly improve application performance in multithreaded environment.

[Beta] Distributed optimizer with TorchScript support

PyTorch provides a broad set of optimizers for training algorithms, and these have been used repeatedly as part of the python API. However, users often want to use multithreaded training instead of multiprocess training as it provides better resource utilization and efficiency in the context of large scale distributed training (e.g. Distributed Model Parallel) or any RPC-based training application). Users couldn’t do this with with distributed optimizer before because we need to get rid of the python Global Interpreter Lock (GIL) limitation to achieve this.

In PyTorch 1.7, we are enabling the TorchScript support in distributed optimizer to remove the GIL, and make it possible to run optimizer in multithreaded applications. The new distributed optimizer has the exact same interface as before but it automatically converts optimizers within each worker into TorchScript to make each GIL free. This is done by leveraging a functional optimizer concept and allowing the distributed optimizer to convert the computational portion of the optimizer into TorchScript. This will help use cases like distributed model parallel training and improve performance using multithreading.

Currently, the only optimizer that supports automatic conversion with TorchScript is Adagrad and all other optimizers will still work as before without TorchScript support. We are working on expanding the coverage to all PyTorch optimizers and expect more to come in future releases. The usage to enable TorchScript support is automatic and exactly the same with existing python APIs, here is an example of how to use this:

import torch.distributed.autograd as dist_autograd
import torch.distributed.rpc as rpc
from torch import optim
from torch.distributed.optim import DistributedOptimizer

with dist_autograd.context() as context_id:
  # Forward pass.
  rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
  rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
  loss = rref1.to_here() + rref2.to_here()

  # Backward pass.
  dist_autograd.backward(context_id, [loss.sum()])

  # Optimizer, pass in optim.Adagrad, DistributedOptimizer will
  # automatically convert/compile it to TorchScript (GIL-free)
  dist_optim = DistributedOptimizer(
     optim.Adagrad,
     [rref1, rref2],
     lr=0.05,
  )
  dist_optim.step(context_id)

[Beta] Enhancements to RPC-based Profiling

Support for using the PyTorch profiler in conjunction with the RPC framework was first introduced in PyTorch 1.6. In PyTorch 1.7, the following enhancements have been made:

User are now able to use familiar profiling tools such as with torch.autograd.profiler.profile() and with torch.autograd.profiler.record_function, and this works transparently with the RPC framework with full feature support, profiles asynchronous functions, and TorchScript functions.

[Prototype] Windows support for Distributed Training

PyTorch 1.7 brings prototype support for DistributedDataParallel and collective communications on the Windows platform. In this release, the support only covers Gloo-based ProcessGroup and FileStore. To use this feature across multiple machines, please provide a file from a shared file system in init_process_group.

# initialize the process group
dist.init_process_group(
    "gloo",
    # multi-machine example:
    # Shared files need six "/"
    # init_method = `"file://////{machine}/{share_folder}/file"`
    # Local file need three "/"
    init_method="file:///{your local file path}",
    rank=rank,
    world_size=world_size
)

model = DistributedDataParallel(local_model, device_ids=[rank])

Mobile

PyTorch Mobile supports both iOS and Android with binary packages available in Cocoapods and JCenter respectively. You can learn more about PyTorch-Mobile here.

[Beta] PyTorch Mobile Caching allocator for performance improvements

On some mobile platforms, such as Pixel, we observed that memory is returned to the system more aggressively. This results in frequent page faults as PyTorch being a functional framework does not maintain state for the operators. Thus outputs are allocated dynamically on each execution of the op, for the most ops. To ameliorate performance penalties due to this, PyTorch 1.7 provides a simple caching allocator for CPU. The allocator caches allocations by tensor sizes and, is currently, available only via the PyTorch C++ API. The caching allocator itself is owned by client and thus the lifetime of the allocator is also maintained by client code. Such a client owned caching allocator can then be used with scoped guard, c10::WithCPUCachingAllocatorGuard, to enable the use of cached allocation within that scope.

Example usage:

#include <c10/mobile/CPUCachingAllocator.h>
.....
c10::CPUCachingAllocator caching_allocator;
  // Owned by client code. Can be a member of some client class so as to tie the
  // the lifetime of caching allocator to that of the class.
.....
{
  c10::optional<c10::WithCPUCachingAllocatorGuard> caching_allocator_guard;
  if (FLAGS_use_caching_allocator) {
    caching_allocator_guard.emplace(&caching_allocator);
  }
  ....
  model.forward(..);
}
.....

NOTE: Caching allocator is only available on mobile builds, thus the use of caching allocator outside of mobile builds won’t be effective.

Backwards Incompatible changes

Python API

torch.conj now returns the input as-is for real Tensors (#43270)

Previously, torch.conj and Tensor.conj were making a clone for Tensors of real dtype. It now returns the Tensor as-is to improve performance. You can recover the original behavior by adding a .clone() for real Tensors. Note that this behavior is different from numpy for which np.conj returns a new ndarray and ndarray.conj returns the ndarray as-is.

1.6.01.7.0
>>> t.is_complex()
False
>>> t.conj() is t
False   
      
>>> t.is_complex()
False
>>> t.conj() is t
True
>>>t.conj().clone() is t
False

torch.tensor, torch.as_tensor, and torch.sparse_coo_tensor now use the input Tensor’s device when it is not specified (#41984)

This will change the device on which the Tensor is created and so the user can start seeing device mismatch errors. It also means for sparse Tensors that both of the provided Tensors must be on the same device if the device is not specified. You can recover the original behavior by passing the device argument.

1.6.01.7.0
>>> t.device
device(type=‘cuda:0’)
>>> # tensor constructor
>>> torch.tensor(t, dtype=torch.float32).device
device(type=‘cpu’)
>>> # sparse constructor
>>> torch.sparse_coo_tensor(
            torch.tensor(([0], [2]), device="cpu"),
            torch.tensor(([1.],), device="cuda"),
            size=(3, 3, 1)).device
device(type='cuda', index=0)    
      
>>> t.device
device(type=‘cuda:0’)
>>> # tensor constructor
>>> torch.tensor(t, dtype=torch.float32).device
device(type=‘cuda:0’)
>>> # Specify the device to get the same behavior as 1.6
>>> torch.tensor(t, dtype=torch.float32, device='cpu').device
device(type=‘cpu’)
>>> # sparse constructor
>>> torch.sparse_coo_tensor(
            torch.tensor(([0], [2]), device="cpu"),
            torch.tensor(([1.],), device="cuda"),
            size=(3, 3, 1)).device
RuntimeError: backend of indices (CPU) must match backend
of values (CUDA)
>>> # Specify the device to get the same behavior as 1.6
>>> torch.sparse_coo_tensor(
            torch.tensor(([0], [2]), device="cpu"),
            torch.tensor(([1.],), device="cuda"),
            size=(3, 3, 1),
            device="cuda:0").device
device(type='cuda', index=0)

torch.nn.utils.pack_padded_sequence: remove hidden cross-device copy for lengths (#41984)

In previous versions, when the lengths argument was a CUDA tensor, it would incorrectly be moved to the CPU silently. This can lead to surprising performances and CPU/GPU sync when using CUDA so this has been removed. You need to make sure that the provided lenghts is a CPU Tensor when it is provided as a Tensor.

1.6.01.7.0
>>> inp = torch.rand(10, 2, 3, device="cuda")
>>> lengths = torch.tensor([10, 7], device="cuda")
>>> torch.nn.utils.rnn.pack_padded_sequence(inp, lengths)
>>> # Implicitly move lengths to the CPU and runs fine
      
>>> inp = torch.rand(10, 2, 3, device="cuda")
>>> lengths = torch.tensor([10, 7], device="cuda")
>>> torch.nn.utils.rnn.pack_padded_sequence(inp, lengths)
RuntimeError: 'lengths' argument should be a 1D CPU int64 tensor,
but got 1D cuda:0 Long tensor
>>> # Ensure the lenghts is already on the right device
>>> lengths = lengths.cpu()
>>> torch.nn.utils.rnn.pack_padded_sequence(inp, lengths)
>>> # Runs fine with no implicit move across device

Improve torch.norm handling of keepdim=True (#41956)

Before this change, when calling torch.norm with keepdim=True and p='fro' or p=number, leaving all other optional arguments as their default values, the keepdim argument would be ignored. It is now properly respected. Also, any time torch.norm was called with p='nuc' and keepdim=True, the result would have one fewer dimension than the input, and the dimensions could be out of order depending on which dimensions were being reduced. It is now properly keeping all the dimensions. You can recover the original behavior by setting keepdim=False. NOTE: this function is now deprecated (see below) and we recommend you use torch.linalg.norm, which follows NumPy’s conventions.

1.6.01.7.0
>>> t.size()
torch.Size([4, 4])
>>> t.norm(p=‘fro’, keepdim=True).size()
torch.size([])
>>> t.norm(p=3, keepdim=True).size()
torch.size([])
>>> t.norm(p=‘nuc’, keepdim=True).size()
torch.size([1]) 
      
>>> t.size()
torch.Size([4, 4])
>>> t.norm(p=‘fro’, keepdim=True).size()
torch.size([1, 1])
>>> t.norm(p=3, keepdim=True).size()
torch.size([1, 1])
>>> t.norm(p=‘nuc’, keepdim=True).size()
torch.size([1, 1])

torch.split and torch.chunk: Fix view tracking for the autograd (#41567)

The autograd system is able to correctly handle modifications through views of Tensors by explicitly tracking known view operations. In prior releases, torch.split and torch.chunk were not marked as known view operations, which could lead to silently wrong gradients.

Note that since v1.5, inplace modification of views created by functions that return multiple views is deprecated. Such case is not properly handled by the autograd and can lead to internal errors or wrong gradients. So, as a side effect of this view fix, inplace modifications of the outputs of torch.split and torch.chunk will now raise a warning and can lead to internal errors or wrong gradients while they were previously silently computing wrong gradients. If you see such a warning, you should replace the inplace operation with an out of place one. You can recover the original behavior by using the new torch.unsafe_split and torch.unsafe_chunk. Note that these functions are only here to ease the transition and will also be removed in a future version.

torch.{argmin,argmax} now always return the first min/max index (#42004)

torch.argmin (torch.argmax) now always returns the index of the first minimum (maximum) element. This choice is consistent with NumPy. Previously if there were multiple minima (maxima) the index returned could be the index of any of them. You cannot recover the original behavior as it was platform dependent and not guaranteed. If your code was relying on a specific index for your specific platform, you should update it to work with the first index and this new code will work on all platforms.

torch.{min,max,median}: Update backward formula when doing full reduction (dim argument not provided) (#43519)

When no dimension is specified, full reduction is performed and the gradient will now flow back evenly towards all the input that realized the output value. The old behavior was to propagate the gradient only for one of such input selected arbitrarily. This should improve stability of training by gradient descent. To recover the previous behavior, you can perform the reduction with the dim= argument. It will ensure that the gradient only flows back for the input whose index was returned.

1.6.01.7.0
>>> a
tensor([3, 2, 3])
>>> a.max().backward()
>>> a.grad
tensor([0, 0, 1])   
      
>>> a
tensor([3, 2, 3])
>>> a.max().backward()
>>> a.grad
tensor([0.5, 0, 0.5])
>>> a.max(dim=0).max(dim=0).max(dim=0).backward()
>>> a.grad
tensor([0, 0, 1])

nn.BCELoss size mismatch warning is now an error (#41426)

This is the end of the deprecation cycle for this op to make sure it does not have different broadcasting semantic compared to numpy’s broadcasting semantic used everywhere else in PyTorch’s codebase. You need to make sure all inputs are the same size to avoid the error.

1.6.01.7.0
>>> bceloss = nn.BCELoss()
>>> a = torch.rand(25)
>>> b = torch.rand(25, 1)
>>> bceloss(a, b)
UserWarning: Using a target size (torch.Size([25, 1]))
that is different to the input size (torch.Size([25]))
is deprecated. Please ensure they have the same size.
tensor(1.0604)  
      
>>> bceloss = nn.BCELoss()
>>> a = torch.rand(25)
>>> b = torch.rand(25, 1)
>>> bceloss(a, b)
ValueError: Using a target size (torch.Size([25, 1]))
that is different to the input size (torch.Size([25]))
is deprecated. Please ensure they have the same size.
>>> b = b.reshape(25)
>>> bceloss(a, b)
tensor(1.0604)

Custom autograd.Function stop materializing None output Tensors (#41490)

To improve performance, the custom autograd.Function will not create a Tensor full of zeros when an input is differentiable but the user’s backward function returns None for it. This means that code for which the .backward() or autograd.grad() final result will now be None while it used to be a Tensor full of zeros. You can recover the previous behavior by having your custom autograd.Function materialize the zero Tensor with torch.zeros_like(input) to replace the None output for the backward method.

import torch

# Custom Function that returns None for the gradient
class GetTwos(torch.autograd.Function):
    @staticmethod
    def forward(ctx, inp):
        return inp.clone().fill_(2)

    @staticmethod
    def backward(ctx, grad_out):
        # To recover the 1.6 behavior, replace the line below with `return torch.zeros_like(grad_out)`
        return None

a = torch.rand(10, requires_grad=True)
b = GetTwos.apply(a)
b.sum().backward()

print(a.grad)
# In PyTorch 1.6 this will print
# tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
# In PyTorch 1.7 this will print
# None

Fix inplace detection for non-differentiable outputs (#41269)

We fixed a bug in the inplace detection code that was preventing the detection of some inplace operations for output that are not differentiable (like integer type Tensors). This can lead to code that used to run fine to throw the error “a Tensor that was needed for backward was modified in an inplace operation”. Such failure is true and the user code must be fixed to compute proper gradients. In general, this involves cloning the Tensor before modifying it inplace to make sure the backward pass can happen safely.

import torch

a = torch.rand(10, requires_grad=True)
with torch.no_grad():
    a[2] = 10

b, ind = a.max(dim=0)
# ind is 2 here

with torch.no_grad():
    t = torch.rand(10)
    t[4] = 10
    res = torch.max(t, dim=0, out=(torch.Tensor(), ind))
    # ind becomes 4 here

# This backward runs in 1.6 but will fail in 1.7
b.sum().backward()
print(a.grad)
# tensor([0., 0., 0., 0., 1., 0., 0., 0., 0., 0.])
# The value is wrong is at index 4 while it should be at index 2

# The issue is avoided by not modifying ind inplace by replacing the line
# above with:
# res = torch.max(t, dim=0, out=(torch.Tensor(), ind.clone()))

Add __torch_functions__ for methods (#37091)

Functions, slicing and Tensor methods will now properly preserve the subclass type when possible.

>>> class SubTensor(torch.Tensor):
...     pass
>>> type(torch.add(SubTensor([0]), SubTensor([1]))).__name__
'SubTensor'
>>> type(torch.add(SubTensor([0]), torch.Tensor([1]))).__name__
'SubTensor'

The old behavior of “any operations on your subclass produces a torch.Tensor instead of the subclass” can be recovered by doing:

from torch._C import _disabled_torch_function_impl
    
class SubTensor(torch.Tensor):
    __torch_function__ = _disabled_torch_function_impl

For all details on how to use this feature, please refer to the doc page for it.

tensor.__iter__: Use torch.unbind instead of a for loop (#40884)

This improves performances significantly but it changes the behavior of in-place operations on the value returned by the iterator. This happens only if either the input Tensor or any argument of the in-place operation is a Tensor that requires gradients. And it will fail with "Output X of UnbindBackward is a view and is being modified inplace". You can recover the previous behavior by manually slicing the Tensor: [t[i] for i in range(t.size(0))] as shown in the example below.

1.6.01.7.0
>>> x = torch.randn(5, 10, requires_grad=True)
>>> for i, v in enumerate(x):
>>>     v.fill_(i)
      
>>> x = torch.randn(5, 10, requires_grad=True)
>>> for i, v in enumerate([x[j] for j in range(x.size(0))]):
>>>   v.fill_(i)

Updated most function that take zero, one or two Tensor arguments and indexing op to check for memory overlap in the Tensor being worked on (#43418, #43419, #43420, #43421, #43423, #43422)

It fixes silent correctness errors: something that used to be silently incorrect now errors out. Code that raises this error must be updated to avoid doing such op that was returning wrong results as shown in the example below:

>>> x = torch.randn(1, 3)
>>> # Create a tensor that has internal memory overlap
>>> y = x.expand(2, 3)

# In 1.6, this would not error out, but in 1.7, this errors out
>>> torch.nn.functional.elu(y, inplace=True)
RuntimeError: unsupported operation: more than one element of the written-to tensor refers to a single m
emory location. Please clone() the tensor before performing the operation.

# Here is the fix in 1.7
>>> torch.nn.functional.elu(y, inplace=False)

c++ API: Any external users of TensorIterator now always get the memory overlap check. The previous behavior can be recovered by setting set_check_mem_overlap(false) when creating the iterator.

TorchScript

TorchScript now correctly supports various exception type and custom exception message (#41907)

TorchScript now supports properties of TorchScript classes and ScriptModules (#42389, #42390)

Quantization

The convolution parameters now support versioning.

Some undocumented functions that were mistakenly made public have been removed

TorchScript Compiler Update

In 1.7, we are enabling a Profiling Executor and a new Tensor-Expressions-based (TE) Fuser. All compilations will now go through one (an adjustable setting) profiling run and one optimization run. For the profiling run, complete tensor shapes are recorded and used by the new Fuser. For the optimization run, the focus is on finding (in torch.jit.ScriptModules) and fusing element-wise operations over CUDA tensors into a single CUDA kernel.

The TE fuser is expected to deliver performance similar to the old fuser used in 1.6. It however unlocks more opportunities for performance improvements in future releases. In rare cases, performance of some models may degrade 5-10%. If you experience any regressions please report it on Github, so we can address them as soon as possible! For 1.7, we are providing an option for our users to revert back to the old fuser by calling torch._C._jit_set_profiling_executor(False) in Python and torch::jit::getExecutorMode()`` = false; in C++. For more information, please see “Graph Executor” section in our documentation.

Deprecations

Python API

torch.norm and torch.functional.norm are deprecated in favor of torch.linalg.norm (#44321)

The new torch.linalg.norm has the same behavior as numpy.linalg.norm Both deprecated functions had odd behaviors for matrix and vector norms. You should refer to the doc here to find the exact behavior they had and how to replicate it with the new API.

Deprecate fft functions in torch. namespace in favor of torch.fft. namespace (#44876)

Please use torch.fft.foo as a drop-in replacement for torch.foo for the following functions: fft, ifft, rfft and irfft.

Warns when some out= functions need to resize an output which is not 0-size (#42079)

This behavior is dangerous and leads to an API that is hard to use. It is being deprecated to be able to fix that API in future versions. You should resize the output before-hand to avoid any issue in the future:

a = torch.rand(5)
b = torch.rand(25)

# This is deprecated
torch.add(a, a, out=b)

# This has the same behavior but will work in future versions
torch.add(a, a, out=b.resize_(0))

torch.optim: Warn for duplicate params in param group (#41597)

Providing multiple times the same Parameter in a single param group is most likely due to user error and is being deprecated. Please open an issue if you have a valid use case that require this feature.

torch.linspace and torch.logspace: Not giving the step argument is deprecated (#43860)

The default steps argument that has been used historically in PyTorch is not consistent with other libraries and so is being removed to avoid confusion. For both functions, passing steps=100 keyword argument can be used to recover the original behavior.

1.6.01.7.0
>>> torch.linspace(0, 10).size()
torch.Size([100])   
      
>>> torch.linspace(0, 10).size()
UserWarning: Not providing a value for linspace's
steps is deprecated and will throw a runtime error
in a future release.
torch.Size([100])
>>> torch.linspace(0, 10, steps=100).size()
torch.Size([100])

Distributed

New features

Python API

New namespaces:

New operators:

API extension:

Autograd

CUDA

C++ API

Mobile

Vulkan

Distributed

Quantization

Misc

Improvements

Python API

torch.nn

Build

Distributed

TorchScript

Mobile

Quantization

ONNX

In PyTorch 1.7, we have continued to add and improve PyTorch operator export to ONNX. We have enabled export of 10 new operators, and further enhanced and optimized export of 10+ torch operators to ONNX. We have also focused on improving export of TorchScript modules, in particular laying some groundwork required for better support in near future. We have also created an API (torch.onnx.utils._find_missing_ops_onnx_export) as a diagnostic tool (preview only) to get a list of operators in a model that are not supported or implemented by ONNX exporter. Support for export of torch.quantization.FakeQuantize has also been added to help enable some QAT workflows.

Misc

Python Type Annotations

Bug fixes

Python API

Torch.nn

C++ API

Distributed

TorchScript

Quantization

ONNX

Misc

Performance

Python API

Distributed

TorchScript

Mobile

Quantization

Misc

Documentation

Python API

Distributed

TorchScript

Mobile

Quantization

Misc

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