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TensorDict 0.10.0: MDS, type annotation and typed `MetaData`

08 Sep 10:08
@vmoens vmoens
v0.10.0
c34264a

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TensorDict 0.10.0 Release Notes

We are excited to announce the release of TensorDict 0.10.0! This release includes significant improvements to type annotations, new features for metadata handling, enhanced tensor operations, and numerous bug fixes that improve the overall stability and usability of the library.

🎉 Highlights

  • Typed MetaData: Complete rewrite of metadata handling with full type support (#1428)
  • TensorCollection Parent Class: New parent class providing better type annotations and enhanced functionality (#1388)
  • Enhanced String Support: to_struct_array now supports string data types (#1410)
  • Improved Type Safety: Comprehensive type annotation improvements across the entire codebase
  • Better TensorClass Support: Enhanced ClassVar support and super() functionality
  • MDS data interface: the to_mds method creates an MDS dataset on your favourite location -- no more painful columns definition etc (#1426).
  • Support for autograd's grad function (#1417)

✨ New Features

Core Functionality

  • [Feature] Typed MetaData (#1428): Complete rewrite of metadata handling system with full type support, enabling better static analysis and runtime type checking
  • [Feature] TensorCollection parent class and better type annotation (#1388): New parent class that provides enhanced type annotations and improved inheritance hierarchy
  • [Feature] to_struct_array with strings (#1410): Extended to_struct_array functionality to handle string data types
  • [Feature] MDS dataset helper functions (#1426): New helper functions for working with MDS (Multi-Dimensional Scaling) datasets
  • [Feature] implement tensor_split (#1386): Added support for tensor_split operation to match PyTorch tensor API
  • [Feature] accept cap-str as input to set_interaction_type (#1387): Enhanced flexibility in interaction type setting by accepting capitalized strings
  • [Feature] Allow in-place modification of lazy stacks (#1384): Enabled in-place modifications for lazy stacked tensors, improving memory efficiency
  • [Feature] Ensure super() works with TensorClass (#1381): Fixed super() functionality in TensorClass inheritance chains
  • [Feature] Add all everywhere (#1389): Added comprehensive __all__ declarations across all modules for better IDE support and import control

Type System Improvements

  • [Typing] @overload for methods that have a reduce arg (#1427): Added proper type overloads for methods with reduce parameters
  • [BE] A bunch of type annotation improvements (#1409): Comprehensive type annotation improvements across the codebase
  • [BE] Better CompatibleType definition (#1404): Enhanced type definitions for better compatibility checking
  • [BE] Add _from_tensordict to TensorClass (#1403): Added internal method for TensorClass construction from TensorDict
  • [BE] Better type annotation for __getitem__ (#1402): Improved type annotations for indexing operations

🐛 Bug Fixes

Critical Fixes

  • [BugFix] Fix stacking typed MetaData (#1429): Fixed issues with stacking operations on typed metadata
  • [BugFix] Call synchronization when using the td.to("cpu") operation on third-party devices (#1425): Fixed potential precision issues when transferring tensors from third-party devices to CPU
  • [BugFix] Fix missing _maybe_broadcast_other in base.py (#1422): Fixed missing broadcast functionality in base operations
  • [BugFix] lock_() consolidated tds to avoid overriding values (#1408): Fixed value override issues in locked TensorDicts during consolidation

TensorClass Fixes

  • [BugFix] Args for TC with ClassVar (#1401): Fixed argument handling for TensorClass with ClassVar annotations
  • [BugFix] Fix ClassVar support in tensorclass (#1398): Enhanced ClassVar support in tensorclass decorator
  • [BugFix] Fix MetaData assignment in tensorclasses (#1394): Fixed metadata assignment issues in TensorClass instances

Type and API Fixes

  • [BugFix,TypeHint] Fix type annotations in tensorclass stub file (#1421): Fixed type hints in stub files for better IDE support
  • [Bugfix] Fix type annotation for tensordict.keys().iter() (#1413): Fixed iterator type annotations for TensorDict keys
  • [Bugfix] Fix TensorDictModuleWrapper forward (#1415): Fixed forward pass in TensorDictModuleWrapper
  • [Bugfix] Improve various typing issues (#1424): General improvements to typing across the codebase

Tensor Operations

  • [BugFix] repeat_interleave supports tensors (#1391): Fixed repeat_interleave to properly support tensor arguments
  • [BugFix] Fix chunk following split fix (#1377): Fixed chunking operations after split functionality improvements
  • [BugFix] Uneven splits (#1376): Fixed handling of uneven tensor splits
  • [BugFix] JSON/orjson compatibility (#1373): Improved compatibility between JSON and orjson serialization

🔄 Deprecations and Breaking Changes

  • [Deprecation] Upgrade set_list_to_stack behavior (#1382): Updated behavior of set_list_to_stack with proper deprecation warnings for the old API

🛠️ Development and Infrastructure

CI and Testing

  • [CI] Better versioning (#1433): Improved versioning system for better release management
  • [CI] Fix benchmark CI upload with conditional PR testing (#1397): Enhanced CI pipeline for benchmark uploads
  • [CI,Tests] Fix tests (#1396): General test fixes and improvements
  • [CI] Update OSX target (#1378): Updated macOS build targets
  • [CI] Downgrade OSX version in builds (#1375): Adjusted macOS version requirements for broader compatibility

Documentation

  • [Doc,CI] Fix installation of the lib for releases in doc CI (#1432): Fixed library installation in documentation CI
  • [Doc] Fix doc errors (#1431): General documentation error fixes
  • [Doc, CI] Fix Doc CI (#1430): Fixed documentation CI pipeline
  • Fix typos in export tutorial (#1405): Corrected typos in export tutorial documentation

👥 Contributors

Special thanks to all the contributors who made this release possible:

  • Vincent Moens (@vmoens) - Lead maintainer, major features and bug fixes
  • Yichao Zhou (@Yichao-Zhou) - Type system improvements and bug fixes
  • Huazhong (@huazhongyang) - Device synchronization fixes
  • Yoann Poupart (@Xmaster6y) - TensorDictModuleWrapper fixes
  • Chi Zhang (@chz8494) - tensor_split implementation
  • Heon Song (@heonsong) - Documentation improvements

📚 Documentation

For comprehensive documentation, tutorials, and examples, visit:

For a complete list of changes, see the full changelog.

If you encounter any issues, please report them on our GitHub Issues page.

Contributors

chz8494, vmoens, and 4 other contributors
Assets 26

v0.9.1: Orjson/Json Interoperability

14 Jul 12:53
@vmoens vmoens
v0.9.1
eaa0248

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v0.9.1: Orjson/Json Interoperability

This minor releases brings the following improvements and bug fixes:

  • Fixing orjson / json interoperability #1373
  • Downgrade OSX build target to 14 #1378
  • Fixing split and chunk #1376 and #1377

Full Changelog: v0.9.0...v0.9.1

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v0.9.0

09 Jul 16:09
@vmoens vmoens
v0.9.0
2ad3f0e

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v0.9.0

TensorDict 0.9.0 Release Notes

Overview

TensorDict 0.9.0 introduces significant improvements in performance, new features for lazy operations, enhanced CUDA graph support, and various bug fixes. This release focuses on stability improvements and new functionality for distributed and lazy tensor operations.

🚀 New Features

Lazy Operations and Stacking

  • to_lazystack(): New method to convert TensorDict instances to lazy stacks (#1351) (a5aab97)
  • Stack name preservation: tensordict.stack now preserves names when stacking TensorDict instances (#1348) (2053031)
  • update_batch_size in where(): Enhanced where() operation now supports update_batch_size parameter (#1365) (847a86c)
  • tolist_first(): New method for converting TensorDict to list with first-level flattening (#1334) (73fe89b)

Torch Function Integration

  • torch.maximum support: Added support for torch.maximum operation in TensorDict (#1362) (85f26e4)
  • Enhanced loss functions: Added support for torch.sum, torch.mean, torch.var and loss functions (l1, smooth_l1, mse) (#1361) (17ca2ff)

CUDA Graph Enhancements

  • CudaGraphModule.state_dict(): New method to access state dictionary of CUDA graph modules (#1346) (909907b)
  • Improved device handling: Better support for CUDA graph operations on non-zero devices (#1315) (89d05a1)
  • Stream management: Enhanced stream handling for CUDA graph operations (#1314) (2fd4843)

Non-Tensor Data Support

  • NonTensorDataBase and MetaData: New base classes for handling non-tensor data in TensorDict (#1324) (8d0241d)
  • Enhanced metadata handling: Improved support for metadata operations

Copy Operations

  • TensorDict.__copy__(): New method for creating shallow copies of TensorDict instances (#1321) (b6feadd)

Distributed tensordicts

  • broadcast tensordicts: New functionality for broadcasting TensorDict instances across different shapes (#1307) (2959863)
  • remote_init with subclasses: Enhanced remote initialization support for TensorDict subclasses (#1308) (5859a2c)
  • return_early for isend: New parameter for early return in send operations (#1306) (4012767)

🐛 Bug Fixes

TensorDict Operations

  • Fixed "none"/"None" environment variable handling (#1372) (cb104a1)
  • Fixed split_size validation in TensorDict.split() (#1370) (0bb94c0)
  • Fixed update_batch_size when source is TD and destination is LTD (#1371) (c8bfda2)
  • Fixed device argument in TensorDict constructor to respect CUDA current device (#1369) (afcbcec)
  • Fixed new_ operations on NonTensorStack (#1366) (5e67c32)
  • Fixed tensor_only construction (#1364) (75e2c26)
  • Fixed missing update_batch_size in lazy stack updates (#1359) (c9f0e40)
  • Fixed context managers update when a key is in _non_tensordict (#1353) (c11a95b)
  • Fixed tensorclass __enter__ and __exit__ methods (#1352) (08abb06)

Stacking and Chunking

  • Fixed tensordict.stack forcing all names to None when no match (#1350) (c90df00)
  • Fixed chunk/split memmap index when dim!=0 (#1345) (297a514)
  • Fixed nested key iterations for lazy stacks within tensorclasses (#1344) (2e616f8)
  • Fixed leaf check in stack function (#1341) (e03c25e)
  • Fixed nested tensorclass maybe_dense_stacks (#1340) (9c8dd2d)
  • Fixed chunk of NJTs (Nested JAX Tensors) (#1339) (3477e96)

Compilation and Device Issues

  • Fixed compilation of TensorClass with non-tensor + batch-size + device (#1337) (5c98749)
  • Fixed new_* operations for Lazy stacks (#1317) (1c8be19)
  • Fixed improper name setting in __setitem__ (#1313) (1d642b0)
  • Fixed CudaGraphModule on devices that are not 0 (#1315) (89d05a1)
  • Fixed lazy stack isend early return (#1316) (8d3c470)

Memory and Performance

  • Fixed memory leak caused by _validate_value (#1310) (a36f7f9)
  • Fixed flatten operation with start=end dim (#1333) (d9972b4)
  • Fixed expansion of lazy stacks (#1331) (56b4493)
  • Fixed return_composite defaults to True only when >1 distribution (#1328) (2c73924)

Distribution and Probabilistic Modules

  • Fixed TDParams compatibility with export (#1285) (ecdde0b)
  • Fixed better list assignment in tensorclasses (#1284) (6d8119c)
  • Fixed method _is_list_tensor_compatible missing return value (#1277) (a9cc632)
  • Fixed .item() warning on tensors that require grad (#1283) (910c953)

⚡ Performance Improvements

  • Faster _get_item: Optimized item retrieval operations (#1288) (1e33a18)
  • Dedicated validation functions: Improved validation performance (#1281) (604b471)
  • tensor_only for tensorclass: Enhanced performance for tensor-only operations (#1280) (d4bc34c)
  • Second attempt at caching validation: Improved caching mechanisms (#1311) (5f26a8b)
  • Better property handling in TC: Optimized property operations in TensorClass

🔧 Setup and CI Improvements

  • Static linking: _C extension now statically linked against Python library (#1304) (af17524)
  • Better version checking: Improved version validation in smoke tests (#1303) (e84d44f)
  • Python 3.13 support: Added support for Python 3.13 nightly builds (#1279) (0eb2ad3)
  • Enhanced CI workflows: Improved continuous integration for various platforms
  • Simplified setup: Streamlined package setup process (#1286) (fffffe5)

🚨 Deprecations and Breaking Changes

Deprecated Features

  • NormalParamWrapper: Deprecated in favor of tensordict.nn.NormalParamExtractor
  • Functional modules: is_functional, make_functional, and get_functional have been removed from tensordict

Future Changes

  • List-to-stack behavior: In version 0.10.0, lists will be automatically stacked by default. A FutureWarning will be raised if lists are assigned to TensorDict without setting the appropriate context manager.

🛠️ Quality of Life Improvements

  • Simplified error handling: Better error messages and handling in TensorDictSequential execution (#1326) (1330b72)
  • Enhanced flatten operations: Made flatten operation idempotent (#1332) (49698e2)
  • Better list handling: Improved list assignment in TensorDict instances (#1282) (6ad496b)
  • Enhanced validation: Better validation functions for different data types

📦 Dependencies

  • Python: Support for Python 3.9, 3.10, 3.11, 3.12, and 3.13
  • PyTorch: Compatible with PyTorch 1.12 and upward
  • Additional: numpy, cloudpickle, packaging, importlib_metadata, orjson (for Python < 3.13)

🔗 Migration Guide

For Users Upgrading from 0.8.0

  1. Update functional module usage: If using is_functional, make_functional, or get_functional, these have been removed
  2. NormalParamWrapper replacement: Use tensordict.nn.NormalParamExtractor instead of NormalParamWrapper
  3. List handling: Consider using the new set_list_to_stack context manager for consistent list behavior

For Developers

  • The new lazy stacking features provide better memory efficiency for large datasets
  • CUDA graph support has been enhanced for better GPU performance
  • Non-tensor data handling has been improved with new base classes

🎯 Contributors

Special thanks to all contributors who made this release possible, including:

  • Vincent Moens
  • Nikolai Karpov
  • Jiahao Li
  • Faury Louis
  • Douglas Boubert
  • Albert Bou

📝 Full Changelog

For a complete list of all changes, please refer to the git log from version 0.8.0 to 0.9.0.

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v0.8.3: Better CudaGraphModule

16 May 15:24
@vmoens vmoens
v0.8.3
8e3fb98

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v0.8.3: Better CudaGraphModule

This minor release provides some fixes to CudaGraphModule, allowing the module to run on different devices than the default.

It also adds __copy__ to the TensorDict ops, such that copy(td) triggers td.copy(), resulting in a copy of the TD stucture without new memory allocation.

Full Changelog: v0.8.2...v0.8.3

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v0.8.2: Fix memory leakage due to validate

05 May 20:49
@vmoens vmoens
v0.8.2
f25e61f

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v0.8.2: Fix memory leakage due to validate

This release fixes an apparent memory leak due to the value validation in tensordict.
The leak is apparent, as in it disappears in gc.collect() is invoked.
See #1309 for context.

Full Changelog: v0.8.1...v0.8.2

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Minor fix: Statically link _C extension against the Python library

30 Apr 12:37
@vmoens vmoens
v0.8.1
554308a

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Minor fix: Statically link _C extension against the Python library

This new minor fixes the _C build pipeline, which was failing on some machines as the extension was build with dynamic linkage against libpython

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v0.8.0: Non-tensor data handling

29 Apr 14:47
@vmoens vmoens
v0.8.0
e84d44f

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v0.8.0: Non-tensor data handling

What's Changed

We're excited to announce a new tensordict release, packed with new features, packaging perks as well as bug fixes.

New features

The interaction with non-tensor data is now much easier to get by ():

set_list_to_stack(True).set() # Ask for new behaviour
td = TensorDict(batch_size=(3, 2))
td["numbers"] = [["0", "1"], ["2", "3"], ["4", "5"]]
print(td)
# TensorDict(
#     fields={
#         numbers: NonTensorStack(
#             [['0', '1'], ['2', '3'], ['4', '5']],
#             batch_size=torch.Size([3, 2]),
#             device=None)},
#     batch_size=torch.Size([3, 2]),
#     device=None,
#     is_shared=False)

Stacks of non-tensor data can also be reshaped 58ccbf5. Using the previous example:

td = td.view(-1)
td["numbers"]
# ['0', '1', '2', '3', '4', '5']

We also made it easier to get values of lazy stacks (f7bc839):

tds = [TensorDict(a=torch.zeros(3)), TensorDict(a=torch.ones(2))]
td = lazy_stack(tds)
print(td.get("a", as_list=True))
# [tensor([0., 0., 0.]), tensor([1., 1.])]
print(td.get("a", as_nested_tensor=True))
# NestedTensor(size=(2, j1), offsets=tensor([0, 3, 5]), contiguous=True)
print(td.get("a", as_padded_tensor=True, padding_value=-1))
# tensor([[ 0.,  0.,  0.],
#         [ 1.,  1., -1.]])

Packaging

You can now install tensordict with any PyTorch version. We only provide test coverage for the latest pytorch (currently 2.7.0), so for any other version you will be on your own in terms of compatibility but there should be no limitations in term of installing the library with older version of pytorch.

New features

Bug Fixes

Performance

Miscellaneous

New Contributors

Full Changelog: v0.7.0...v0.8.0

Contributors

dvmazur, albertbou92, and 3 other contributors
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v0.7.2

27 Feb 11:31
@vmoens vmoens
v0.7.2
3a0a8bb
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v0.7.2

We are pleased to announce the release of tensordict v0.7.2, which includes several bug fixes and backend improvements.

Bug Fixes:

  • Consolidated lazy stacks of non-tensors (#1222, #1224)
  • Passed type directly during reduction (#1225)
  • Fixed non-deterministic key order in stack (#1230)
  • Added _PASSTHROUGH_MEMO for passthrough tensorclass (#1231)
  • Improved performance and safety of non-tensor stack (#1232)
  • Fixed serialization of stacks of Tensorclasses (#1236)
  • Fixed compile during _check_keys (#1239)

Backend Improvements:

Improved errors for TensorDictSequential (#1227)

Documentation Updates:

Improved documentation for TensorDictModuleBase (#1226)

Full Changelog: v0.7.1...v0.7.2

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0.7.1: Fixes and doc improvements

18 Feb 11:03
@vmoens vmoens
v0.7.1
7b24257

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0.7.1: Fixes and doc improvements

We are pleased to announce the release of tensordict v0.7.1, which includes several bug fixes and a deprecation notice.

Bug Fixes

  • Fixed get method for nested keys with default values in TensorClass (#1211)
  • Enforced zip(..., strict=True) in TDModules to prevent potential issues (#1212)
  • Properly handled auto-batch size for unbatched tensors (#1213)
  • Fixed indexing issues in TensorClass (#1217)

Deprecation Notice

Softly deprecated extra-tensors with respect to out_keys (#1215). We make sure a warning is raised when the number of output tensors and output keys do not match.

Full Changelog: v0.7.0...v0.7.1

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v0.7.0: More robust composite distributions, TensorClass superclass

05 Feb 16:39
@vmoens vmoens
v0.7.0
426b38e

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v0.7.0: More robust composite distributions, TensorClass superclass

v0.7.0: More robust composite distributions, TensorClass superclass

v0.7.0 brings a lot of new features and bug fixes. Thanks to the vibrant community to help us keeping this project
alive!

New Contributors

A special thanks to our new contributors (+ people interacting with us on the PyTorch forum, discord or via issues and
social platforms)!

BC-breaking changes

  • In #1180, we use the same object for min and max operations as we do with torch.Tensor.min. Previously, tensorclasses
    were used, but that lead to some undefined behaviors when indexing (in PyTorch, min returns a namedtuple that can be
    indexed to get the values or argmax, whereas indexing a tensorclass indexes it along the batch dimension).
  • In #1166, we introduce broadcasting for pointwise operations between tensors and tensordicts. Now, the following two
    operations on either sides of the == sign are exactly equivalent: 
    td = TensorDict(..., batch_size=[3, 5])
t = torch.randn(5)
td + t == td + t.expand(td.shape)

Announced API changes

CompositeDistribution

TL;DR: We're changing the way log-probs and entropies are collected and written in ProbabilisticTensorDictModule and
in CompositeDistribution. The "sample_log_prob" default key will soon be "<value>_log_prob (or
("path", "to", "<value>_log_prob") for nested keys). For CompositeDistribution, a different log-prob will be
written for each leaf tensor in the distribution. This new behavior is controlled by the
tensordict.nn.set_composite_lp_aggregate(mode: bool) function or by the COMPOSITE_LP_AGGREGATE environment variable.
We strongly encourage users to adopt the new behavior by setting tensordict.nn.set_composite_lp_aggregate(False).set()
at the beginning of their training script.

We've had multiple rounds of refactoring for CompositeDistribution which relied on some very specific assumptions and
resulted in a brittle and painful API. We now settled on the following API that will be enforced in v0.9, unless the
tensordict.nn.set_composite_lp_aggregate(mode) value is explicitly set to True (the current default).

The bulk of the problem was that log-probs were aggregated in a single tensor and registered in td["sample_log_prob"].
This had the following problems:

  • Summing the log-probs isn't a good idea, users should be entitled to user the log-probs as they please.
  • "sample_log_prob" is a generic but inappropriate name (the data may not be a random sample but anything else.)
  • Summing requires reduction (because log-probs may have different shapes), but sometimes we don't want to reduce to the
    shape of the root tensordict (see pytorch/rl#2756 for instance).

What's new

tensorclass

  • Introduction of the TensorClass class to do simple inheritance-style coding, which is accompanied by a stub file
    that encodes all the TensorDict op signatures (we ensure this in the CI). See #1067
  • @Tensorclass shadow attributes: you can now do @tensorclass(shadow=True) or class T(TensorClass["shadow"]): ...
    and you will be able to use dedicated names like get or values as attribute names. This is slightly unsafe when you
    nest the tensorclass, as we can't guarantee that the container won't be calling these methods directly on the
    tensorclass.
  • Similarly, @tensorclass(nocast=True) and TensorClass["nocast"] will deactivate the auto-casting in tensorclasses.
    The behavior is now:
    • No value: tensorclass will cast things like TensorDict (ie, int or np.arrays will be cast to torch.Tensor
      instances for example).
    • autocast=True will cause @tensorclass to go one step further and attempt to cast values to the type indicated
      in the dataclass definition.
    • nocast=True keeps values as they are. All non-tensor (or non-tensordict/tensorclass) values will be wrapped in
      a NonTensorData.

NonTensorData

  • It is not easier to build non-tensor stacks through a simple NonTensorStack(*values).

See the full list of features here:
[Feature] Add __abs__ docstrings, __neg__, __rxor__, __ror__, __invert__, __and__, __rand__, __radd__, __rtruediv__, __rmul__, __rsub__, __rpow__, bitwise_and, logical_and (#1154) (d1363eb) by @vmoens ghstack-source-id: 97ce710b5a4b552d9477182e1836cf3777c2d756
[Feature] Add expln map to NormalParamExtractor (#1204) (e900b24) by @vmoens ghstack-source-id: 9003ceafbe8ecb73c701ea1ce96c0a342d0679b0
[Feature] Add missing __torch_function__ (#1169) (bc6390c) by @vmoens ghstack-source-id: 3dbefb4f5322a944664bbc2d29af7f862cb92342
[Feature] Better list casting in TensorDict.from_any (#1108) (1ffc463) by @vmoens ghstack-source-id: 427d19d5ef7c0d2779e064e64522fc0094a885af
[Feature] Better logs of key errors in assert_close (#1082) (747c593) by @vmoens ghstack-source-id: 46cb41d0da34b17ccc248119c43ddba586d29d80
[Feature] COMPOSITE_LP_AGGREGATE env variable (#1190) (9733d6e) by @vmoens ghstack-source-id: 16b07d0eac582cfd419612f87e38e1a7acffcfc0
[Feature] CompositeDistribution.from_distributions (#1113) (a45c7e3) by @vmoens ghstack-source-id: 04a62439b0fe60422fbc901172df46306e161cc5
[Feature] Ensure all dists work with DETERMINSTIC type without warning (#1182) (8e63112) by @vmoens ghstack-source-id: 63117f9b3ac4125a2be4e3e55719cc718051fc10
[Feature] Expose WrapModule (#1118) (d849756) by @vmoens ghstack-source-id: 55caa5d7c39e0f98c1e0558af2a076fee15f7984
[Feature] Fix type assertion in Seq build (#1143) (eaafc18) by @vmoens ghstack-source-id: 83d3dcafe45568c366207395a22b22fb35f61de1
[Feature] Force log_prob to return a tensordict when kwargs are passed to ProbabilisticTensorDictSequential.log_prob (#1146) (98c57ee) by @vmoens ghstack-source-id: 326d0763c9bbb13b51daac91edca4f0e821adf62
[Feature] Make ProbabilisticTensorDictSequential account for more than one distribution (#1114) (c7bd20c) by @vmoens ghstack-source-id: b62b81b5cfd49168b5875f7ba9b4f35b51cd2423
[Feature] NonTensorData(*sequence_of_any) (#1160) (70d4ed1) by @vmoens ghstack-source-id: 537f3d87b0677a1ae4992ca581a585420a10a284
[Feature] NonTensorStack.data (#1132) (4404abe) by @vmoens ghstack-source-id: 86065377cc1cd7c7283ed0a468f5d5602d60526d
[Feature] NonTensorStack.from_list (#1107) (f924afc) by @vmoens ghstack-source-id: e8f349cb06a72dcb69a639420b14406c9c08aa99
[Feature] Optional in_keys for WrapModule (#1145) (2d37d92) by @vmoens ghstack-source-id: a18dd5dff39937b027243fcebc6ef449b547e0b0
[Feature] OrderedDict for TensorDictSequential (#1142) (7df2062) by @vmoens ghstack-source-id: a8aed1eaefe066dafaa974f5b96190860de2f8f1
[Feature] ProbabilisticTensorDictModule.num_samples (#1117) (978d96c) by @vmoens ghstack-source-id: dc6b1c98cee5fefc891f0d65b66f0d17d10174ba
[Feature] ProbabilisticTensorDictSequential.default_interaction_type (#1123) (68ce9c3) by @vmoens ghstack-source-id: 37d38df36263e8accd84d6cb895269d50354e537
[Feature] Subclass conservation in td ops (#1186) (070ca61) by @vmoens ghstack-source-id: 83e79abda6a4bb6839d99240052323380981855c
[Feature] TensorClass (#1067) (a6a0dd6) by @vmoens ghstack-source-id: c3d4e17599a3204d4ad06bceb45e4fdcd0fd1be5
[Feature] TensorClass shadow attributes (#1159) (c744bcf) by @vmoens ghstack-source-id: b5cc7c7fea2d48394e63d289ee2d6f215c2333bc
[Feature] TensorDict.(dim='feature') (#1121) (ba43159) by @vmoens ghstack-source-id: 68f21aca722895e8a240dbca66e97310c20a6b5d
[Feature] TensorDict.clamp (#1165) (646683c) by @vmoens ghstack-source-id: 44f0937c195d969055de10709402af7c4473df32
[Feature] TensorDict.logsumexp (#1162) (e564b3a) by @vmoens ghstack-source-id: 84148ad9c701029db6d02dfb84ddb0a9b26c9ab7
[Feature] TensorDict.separates (#1120) (674f356) by @vmoens ghstack-source-id: be142a150bf4378a0806347257c3cf64c78e4eda
[Feature] TensorDict.softmax (#1163) (c0c6c14) by @vmoens ghstack-source-id: a88bebc23e6aaa02ec297db72dbda68ec9628ce7
[Feature] TensorDictModule in_keys allowed as Dict[str, tuple | list] to enable multi use of a sample feature (#1101) (e871b7d) by @bachdj-px
[Feature] UnbatchedTensor (#1170) (74cae09) by @vmoens ghstack-source-id: fa25726d61e913a725a71f1579eb06b09455e7c8
[Feature] intersection for assert_close (#1078) (84d31db) by @vmoens ghstack-source-id: 3ae83c4ef90a9377405aebbf1761ace1a39417b1
[Feature] allow tensorclass to be customized (#1080) (31c7330) by @vmoens ghstack-source-id: 0b65b0a2dfb0cd7b5113e245c9444d3a0b55d085
[Feature] broadcast pointwise ops for tensor/tensordict mixed inputs (#1166) (aeff837) by @vmoens ghstack-source-id: bbefbb1a2e9841847c618bb9cf49160ff1a5c36a
[Feature] compatibility of consolidate with compile (quick version) (#1061) (3cf52a0) by @vmoens ghstack-source-id: 1bf3ca550dfe5499b58f878f72c4f1687b0f247e
[Feature] dist_params_keys and dist_sample_keys (#1179) (a728a4f) by @vmoens ghstack-source-id: d1e53e780132d04ddf37d613358b24467520230f
[Feature] flexible return type when indexing prob sequences (#1189) (790bef6) by @vmoens ghstack-source-id: 74d28ee84d965c11c527c60b20d9123ef30007f6
[Feature] from_any with UserDict (#1106) (3485c2c) by @vmoens ghstack-source-id: 420464209cff29c3a1c58ec521fbf4ed69d1355f
[Feature] inplace to method (#1066) (fbb71...

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Contributors

SamAdamDay, priba, and 7 other contributors
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