[Feature] Compressed storage gpu

[AdrianOrenstein]

Description

Replay buffers are used to store a lot of data and are used to feed neural networks with batched samples to learn from. So then ideally we could put this data as close to where the network is being updated. Often raw sensory observations are stored in these buffers, such as images, audio, or text, which consumes many gigabytes of precious memory. CPU memory and accelerator VRAM may be limited, or memory transfer between these devices may be costly. So this PR aims to streamline data compression to aid in efficient storage and memory transfer.

Mainly, creating a compressed storage object will aid in training state-of-the-art RL methods on benchmarks such as the Atari Learning Environment. The ~torchrl.data.replay_buffers.storages.CompressedStorage class provides the memory savings through compression.

closes #3058
closes #2983

• I have raised an issue to propose this change (required for new features and bug fixes)

Types of changes

What types of changes does your code introduce? Remove all that do not apply:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds core functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Documentation (update in the documentation)
• Example (update in the folder of examples)

Checklist

Go over all the following points, and put an x in all the boxes that apply.
If you are unsure about any of these, don't hesitate to ask. We are here to help!

• I have read the CONTRIBUTION guide (required)
• My change requires a change to the documentation.
• I have updated the tests accordingly (required for a bug fix or a new feature).
• I have updated the documentation accordingly.

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/rl/3062

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

❌ 12 New Failures, 6 Unrelated Failures

As of commit 6f97290 with merge base db0e30d ():

NEW FAILURES - The following jobs have failed:

• Build M1 Wheels / pytorch/rl / build-wheel-py3_9-cpu (gh)
  Process completed with exit code 1.
• Build M1 Wheels / pytorch/rl / upload / upload-wheel-py3_9-cpu (gh)
  Unable to download artifact(s): Artifact not found for name: pytorch_rl__3.9_cpu_
• Habitat Tests on Linux / tests (3.9, 12.8) / linux-job (gh)
  RuntimeError: Command docker exec -t c178704da5b33fc7c37484c801348bc9789f4f7588749217956addfbfdd8511f /exec failed with exit code 1
• Lint / python-source-and-configs / linux-job (gh)
  RuntimeError: Command docker exec -t de6659c4147e1682255833e21bb7e11b11f1c066486c1e708ef977b691b42047 /exec failed with exit code 1
• Unit-tests on Linux / tests-cpu (3.10) / linux-job (gh)
  test/test_rb.py::TestCompressedListStorage::test_tensor_to_bytestream_speed[safetensors]
• Unit-tests on Linux / tests-cpu (3.11) / linux-job (gh)
  test/test_rb.py::TestCompressedListStorage::test_tensor_to_bytestream_speed[safetensors]
• Unit-tests on Linux / tests-cpu (3.12) / linux-job (gh)
  test/test_rb.py::TestCompressedListStorage::test_tensor_to_bytestream_speed[safetensors]
• Unit-tests on Linux / tests-cpu (3.9) / linux-job (gh)
  test/test_env.py::TestNonTensorEnv::test_parallel[False-True]
• Unit-tests on Linux / tests-gpu (3.11, 12.8) / linux-job (gh)
  test/test_rb.py::TestCompressedListStorage::test_tensor_to_bytestream_speed[safetensors]
• Unit-tests on Linux / tests-optdeps (3.11, 12.8) / linux-job (gh)
  test/test_rb.py::TestCompressedListStorage::test_tensor_to_bytestream_speed[safetensors]
• Unit-tests on Linux / tests-stable-gpu (3.10, 11.8) / linux-job (gh)
  test/test_rb.py::TestCompressedListStorage::test_tensor_to_bytestream_speed[safetensors]
• Unit-tests on Windows / unittests-cpu (3.10, windows.4xlarge, cpu) / windows-job (gh)
  test/test_rb.py::TestCompressedListStorage::test_compressed_storage_checkpointing

FLAKY - The following job failed but was likely due to flakiness present on trunk:

• Continuous Benchmark (PR) / GPU Pytest benchmark (gh) (similar failure)
  RuntimeError: PassManager::run failed

BROKEN TRUNK - The following jobs failed but was present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• Continuous Benchmark / CPU Pytest benchmark (gh) (trunk failure)
• Continuous Benchmark / GPU Pytest benchmark (gh) (trunk failure)
  RuntimeError: PassManager::run failed
• Libs Tests on Linux / unittests-gym (3.9, 12.8) / linux-job (gh) (trunk failure)
  test/test_libs.py::TestGym::test_gym_fake_td[True-False-3-HalfCheetah-v2]
• LLM Tests on Linux / unittests (3.9, 12.8) / linux-job (gh) (trunk failure)
  ##[error]The operation was canceled.
• Unit-tests on Linux / tests-olddeps (3.9, 11.6) / linux-job (gh) (trunk failure)
  test/test_transforms.py::TestActionDiscretizer::test_transform_env[pendulum-SamplingStrategy.RANDOM-False-True]

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[AdrianOrenstein]

When the tensor is on the CPU numpy is the fastest to convert to a bytestream.

---------------------------- benchmark 'tensor_to_bytestream_speed': 5 tests ----------------------------
Name (time in us)                                                  Mean                     OPS          
---------------------------------------------------------------------------------------------------------
test_tensor_to_bytestream_speed[numpy]                           1.1852 (1.0)      843,727.6370 (1.0)    
test_tensor_to_bytestream_speed[safetensors]                    11.7078 (9.88)      85,413.0849 (0.10)   
test_tensor_to_bytestream_speed[pickle]                         17.5312 (14.79)     57,041.2807 (0.07)   
test_tensor_to_bytestream_speed[torch.save]                     29.3144 (24.73)     34,112.9736 (0.04)   
test_tensor_to_bytestream_speed[tensor.untyped_storage]     37,213.3849 (>1000.0)       26.8721 (0.00)   
---------------------------------------------------------------------------------------------------------