Update

[ghstack-poisoned]

Author: vmoens

.github/scripts/pre-build-script-win.sh

@@ -2,4 +2,4 @@
 
 pip install --upgrade setuptools
 
-export TORCHRL_BUILD_VERSION=0.8.0
+export TORCHRL_BUILD_VERSION=0.9.0

.github/scripts/td_script.sh

@@ -1,6 +1,6 @@
 #!/bin/bash
 
-export TORCHRL_BUILD_VERSION=0.8.0
+export TORCHRL_BUILD_VERSION=0.9.0
 pip install --upgrade setuptools
 
 # Check if ARCH is set to aarch64

.github/scripts/version_script.bat

@@ -1,5 +1,5 @@
 @echo off
-set TORCHRL_BUILD_VERSION=0.8.0
+set TORCHRL_BUILD_VERSION=0.9.0
 echo TORCHRL_BUILD_VERSION is set to %TORCHRL_BUILD_VERSION%
 
 @echo on

.github/unittest/linux/scripts/run_all.sh

@@ -9,7 +9,14 @@ set -v
 
 if [[ $OSTYPE != 'darwin'* ]]; then
   apt-get update && apt-get upgrade -y
-  apt-get install -y vim git wget libsdl2-dev libsdl2-2.0-0 cmake
+  apt-get install -y vim git wget cmake
+
+  # Enable universe repository
+  # apt-get install -y software-properties-common
+  # add-apt-repository universe
+  # apt-get update
+
+  # apt-get install -y libsdl2-dev libsdl2-2.0-0
 
   apt-get install -y libglfw3 libgl1-mesa-glx libosmesa6 libglew-dev
   apt-get install -y libglvnd0 libgl1 libglx0 libegl1 libgles2 xvfb
@@ -208,11 +215,13 @@ pytest test/smoke_test_deps.py -v --durations 200 -k 'test_gym or test_dm_contro
 if [ "${CU_VERSION:-}" != cpu ] ; then
   python .github/unittest/helpers/coverage_run_parallel.py -m pytest test \
     --instafail --durations 200 -vv --capture no --ignore test/test_rlhf.py \
+    --ignore test/llm \
     --timeout=120 --mp_fork_if_no_cuda
 else
   python .github/unittest/helpers/coverage_run_parallel.py -m pytest test \
     --instafail --durations 200 -vv --capture no --ignore test/test_rlhf.py \
     --ignore test/test_distributed.py \
+    --ignore test/llm \
     --timeout=120 --mp_fork_if_no_cuda
 fi
 

.github/unittest/linux_olddeps/scripts_gym_0_13/run_test.sh

@@ -28,7 +28,14 @@ python .github/unittest/helpers/coverage_run_parallel.py -m pytest test/smoke_te
 export DISPLAY=:99
 Xvfb :99 -screen 0 1400x900x24 > /dev/null 2>&1 &
 
-CKPT_BACKEND=torch MUJOCO_GL=egl python .github/unittest/helpers/coverage_run_parallel.py -m pytest --instafail -v --durations 200 --ignore test/test_distributed.py --ignore test/test_rlhf.py
+CKPT_BACKEND=torch MUJOCO_GL=egl python .github/unittest/helpers/coverage_run_parallel.py -m pytest \
+    --instafail -v \
+    --durations 200 \
+    --ignore test/test_distributed.py \
+    --ignore test/test_rlhf.py \
+    --ignore test/llm \
+    --mp_fork_if_no_cuda
+
 #pytest --instafail -v --durations 200
 #python test/test_libs.py
 coverage combine

.github/unittest/linux_optdeps/scripts/run_all.sh

@@ -159,6 +159,7 @@ export BATCHED_PIPE_TIMEOUT=60
 python .github/unittest/helpers/coverage_run_parallel.py -m pytest test \
   --instafail --durations 200 -vv --capture no --ignore test/test_rlhf.py \
   --ignore test/test_distributed.py \
+  --ignore test/llm \
   --timeout=120 --mp_fork_if_no_cuda
 
 coverage combine

docs/source/reference/envs.rst

@@ -1112,6 +1112,7 @@ to be able to create this other composition:
     CenterCrop
     ClipTransform
     Compose
+    ConditionalPolicySwitch
     ConditionalSkip
     Crop
     DataLoadingPrimer

docs/source/reference/index.rst

@@ -7,6 +7,7 @@ API Reference
     collectors
     data
     envs
+    llms
     modules
     objectives
     trainers

docs/source/reference/llms.rst

@@ -0,0 +1,149 @@
+.. currentmodule:: torchrl
+
+LLM interface
+=============
+
+.. _ref_llms:
+
+TorchRL offers a set of tools for LLM post-training, as well as some examples for training or setup.
+
+Collectors
+----------
+
+TorchRL offers a specialized collector class (:class:`~torchrl.collectors.llm.LLMCollector`) that is tailored for LLM
+use cases. We also provide dedicated updaters for some inference engines.
+
+.. currentmodule:: torchrl.collectors.llm
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    vLLMUpdater
+    LLMCollector
+
+
+Data structures
+---------------
+
+To handle text-based data structures (such as conversations etc.), we offer a few data structures dedicated to carrying
+data for LLM post-training.
+
+.. currentmodule:: torchrl.data.llm
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    History
+    LLMData
+
+Environments
+------------
+
+When fine-tuning an LLM using TorchRL, the environment is a crucial component of the inference pipeline, alongside the
+policy and collector. Environments manage operations that are not handled by the LLM itself, such as interacting with
+tools, loading prompts from datasets, computing rewards (when necessary), and formatting data.
+
+The design of environments in TorchRL allows for flexibility and modularity. By framing tasks as environments, users can
+easily extend or modify existing environments using transforms. This approach enables the isolation of individual
+components within specific :class:`~torchrl.envs.EnvBase` or :class:`~torchrl.envs.Transform` subclasses, making it
+simpler to augment or alter the environment logic.
+
+Available Environment Classes and Utilities
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+TorchRL provides various environment classes and utilities for working with LLMs, including:
+
+- Various environment classes (:class:`~torchrl.envs.llm.ChatEnv`, :class:`~torchrl.envs.llm.DatasetChatEnv`,
+  :class:`~torchrl.envs.llm.GSM8KEnv`, etc.)
+- Utility functions (:class:`~torchrl.envs.make_gsm8k_env`, :class:`~torchrl.envs.make_mlgym`, etc.)
+- Transforms and other supporting classes (:class:`~torchrl.envs.KLRewardTransform`,
+  :class:`~torchrl.envs.TemplateTransform`, :class:`~torchrl.envs.Tokenizer`, etc.)
+
+These components can be used to create customized environments tailored to specific use cases and requirements.
+
+.. currentmodule:: torchrl.envs.llm
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    ChatEnv
+    DatasetChatEnv
+    GSM8KEnv
+    make_gsm8k_env
+    GSM8KPrepareQuestion
+    GSM8KEnv
+    IFEvalEnv
+    IfEvalScorer
+    IFEvalScoreData
+    LLMEnv
+    LLMHashingEnv
+    make_mlgym
+    MLGymWrapper
+    GSM8KRewardParser
+    IfEvalScorer
+    as_nested_tensor
+    as_padded_tensor
+    DataLoadingPrimer
+    KLRewardTransform
+    TemplateTransform
+    Tokenizer
+
+Modules
+-------
+
+The :ref:`~torchrl.modules.llm` section provides a set of wrappers and utility functions for popular training and
+inference backends. The main goal of these primitives is to:
+
+- Unify the input / output data format across training and inference pipelines;
+- Unify the input / output data format across backends (to be able to use different backends across losses and
+  collectors, for instance)
+- Give appropriate tooling to construct these objects in typical RL settings (resource allocation, async execution,
+  weight update, etc.)
+
+Wrappers
+~~~~~~~~
+
+.. currentmodule:: torchrl.modules.llm
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    TransformersWrapper
+    vLLMWrapper
+
+Utils
+~~~~~
+
+.. currentmodule:: torchrl.modules.llm
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    CategoricalSequential
+    LLMOnDevice
+    make_vllm_worker
+    stateless_init_process_group
+    vLLMWorker
+
+Objectives
+----------
+
+LLM post training require some appropriate versions of the losses implemented in TorchRL.
+
+GRPO
+~~~~
+
+.. currentmodule:: torchrl.objectives.llm
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    GRPOLoss
+    GRPOLossOutput
+    MCAdvantage

setup.py

@@ -172,7 +172,7 @@ def _main(argv):
     if is_nightly:
         tensordict_dep = "tensordict-nightly"
     else:
-        tensordict_dep = "tensordict>=0.8.1,<0.9.0"
+        tensordict_dep = "tensordict>=0.9.0,<0.10.0"
 
     if is_nightly:
         version = get_nightly_version()