.. _warp-environments: Warp Experimental Environments ============================== .. note:: The warp environment infrastructure lives in ``isaaclab_experimental`` and ``isaaclab_tasks_experimental``. It's an experimental feature. The experimental extensions introduce **warp-first** environment infrastructure with CUDA graph capture support. All environment-side computation (observations, rewards, resets, actions) runs as pure Warp kernels, eliminating Python overhead and enabling CUDA graph capture for maximum throughput. Throughout this page, **stable** refers to the standard Torch-based implementations in the ``isaaclab`` and ``isaaclab_tasks`` packages (the default runtime, used when ``--frontend warp`` is not passed). Their Warp counterparts live in the ``isaaclab_experimental`` and ``isaaclab_tasks_experimental`` packages. Workflows ~~~~~~~~~ Two environment workflows are supported: **Direct workflow** — ``DirectRLEnvWarp`` base class. You implement the step loop, observations, rewards, and resets directly in your env class using Warp kernels. **Manager-based workflow** — ``ManagerBasedRLEnvWarp`` base class. You define MDP terms as standalone Warp-kernel functions and compose them via configuration. Available Environments ~~~~~~~~~~~~~~~~~~~~~~ Direct Warp Environments ^^^^^^^^^^^^^^^^^^^^^^^^ Direct tasks share the stable task configuration. ``--frontend warp`` resolves the stable ``_direct_env:Env`` entry point to the mirrored ``_warp_env:WarpEnv`` implementation and swaps only the environment class. Registrations can use ``warp_entry_point`` as an optional override when the implementation cannot follow this convention. Stable tasks with a Warp implementation: - ``Isaac-Cartpole-Direct`` — Cartpole balance - ``Isaac-Ant-Direct`` — Ant locomotion - ``Isaac-Humanoid-Direct`` — Humanoid locomotion - ``Isaac-Reorient-Cube-Allegro-Direct`` — Allegro hand cube reorient Manager-Based Warp Execution (``--frontend warp``) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Manager-based tasks do not need a parallel warp registration: the shared RL CLI exposes ``--frontend {torch,warp}``, and ``--frontend warp`` adapts the *stable* task configuration onto the warp runtime at build time (swapping each MDP term for its warp twin). Select the Newton solver explicitly with ``presets=newton_mjwarp``. Stable tasks with full twin coverage: - ``Isaac-Cartpole`` - ``Isaac-Ant`` - ``Isaac-Humanoid`` - ``Isaac-Reach-Franka`` - ``Isaac-Reach-UR10`` - ``Isaac-Velocity-Flat-AnymalD`` - ``Isaac-Velocity-Flat-Cassie`` - ``Isaac-Velocity-Flat-G1`` - ``Isaac-Velocity-Flat-H1`` - ``Isaac-Velocity-Flat-UnitreeGo2`` The following contributed tasks also have full twin coverage: - ``IsaacContrib-Velocity-Flat-AnymalB`` - ``IsaacContrib-Velocity-Flat-AnymalC`` - ``IsaacContrib-Velocity-Flat-UnitreeA1`` - ``IsaacContrib-Velocity-Flat-UnitreeGo1`` A missing twin is a hard error listing the affected terms, so a partially covered task fails at build time rather than silently changing behavior. Rough-terrain velocity configurations that use ``height_scan`` cannot currently be adapted: that observation term has no Warp twin. Check the terms used by a task rather than assuming that every terrain configuration has the same limitation. Quick Start ~~~~~~~~~~~ .. code-block:: bash # Direct workflow: stable task, warp env implementation uv run isaaclab train --rl_library rsl_rl \ --task Isaac-Cartpole-Direct --frontend warp presets=newton_mjwarp --num_envs 4096 uv run isaaclab play --rl_library rsl_rl \ --task Isaac-Cartpole-Direct --frontend warp presets=newton_mjwarp \ --num_envs 32 --checkpoint latest --visualizer newton_gl # Manager-based workflow: stable task on the warp runtime uv run isaaclab train --rl_library rsl_rl \ --task Isaac-Velocity-Flat-AnymalD --frontend warp presets=newton_mjwarp --num_envs 4096 uv run isaaclab play --rl_library rsl_rl \ --task Isaac-Velocity-Flat-AnymalD --frontend warp presets=newton_mjwarp \ --num_envs 32 --checkpoint latest --visualizer newton_gl All RL libraries with warp-compatible wrappers are supported: RSL-RL, RL Games, SKRL, and Stable-Baselines3. .. note:: ``--video`` is rejected on the warp path, for both ``train`` and ``play``: video recording requires the standard torch frontend. To record a rollout, replay the same checkpoint with ``--frontend torch``; see :ref:`how_to_record_video`. Performance Comparison ~~~~~~~~~~~~~~~~~~~~~~ Historical step time comparison between the stable (torch/manager) and warp (CUDA graph captured) variants, both running on the Newton physics backend. Measured over 300 iterations with 4096 environments. The table covers the measured tasks, not every currently supported task. These figures are retained as the original benchmark record, not a measurement of the latest ``develop`` revision. For updated results, record the hardware and Isaac Lab, Newton, and Warp revisions, and compare both frontends on those same revisions. Improvements in shared base libraries can benefit both frontends and must not be attributed solely to the Warp frontend. .. note:: The warp migration is an ongoing effort. Several components (e.g. scene write, actuator models) have not yet been migrated to Warp kernels and still run through torch. Further performance improvements are expected as these components are migrated. .. list-table:: :header-rows: 1 :widths: 42 10 14 14 12 * - Env - Type - Stable Step (us) - Warp Step (us) - Change * - Isaac-Cartpole-Direct - Direct - 5,274 - 4,331 - -17.88% * - Isaac-Ant-Direct - Direct - 6,368 - 3,128 - -50.88% * - Isaac-Humanoid-Direct - Direct - 13,937 - 10,783 - -22.63% * - Isaac-Reorient-Cube-Allegro-Direct - Direct - 82,950 - 74,570 - -10.10% * - Isaac-Cartpole - Manager - 7,971 - 3,642 - -54.31% * - Isaac-Ant - Manager - 9,781 - 4,672 - -52.23% * - Isaac-Humanoid - Manager - 17,653 - 12,505 - -29.16% * - Isaac-Reach-Franka - Manager - 11,458 - 7,813 - -31.83% * - Isaac-Velocity-Flat-AnymalD - Manager - 32,294 - 23,977 - -25.75% * - Isaac-Velocity-Flat-Cassie - Manager - 17,320 - 10,706 - -38.19% * - Isaac-Velocity-Flat-G1 - Manager - 34,487 - 27,300 - -20.84% * - Isaac-Velocity-Flat-H1 - Manager - 22,202 - 15,864 - -28.55% * - Isaac-Velocity-Flat-UnitreeGo2 - Manager - 15,221 - 9,966 - -34.52% Which Workflows Benefit Most ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The savings come from eliminating Python / torch overhead in the env's step loop, so envs gain in proportion to how much of their step time was previously dominated by per-kernel CPU overhead. Reading the table above: - **Manager-based classic RL** (Cartpole, Ant) — biggest gains (-52% to -54%). Many small reward / observation terms with low compute per term, so per-launch CPU overhead dominated the stable baseline. - **Manager-based locomotion** (AnymalD, G1, H1, Cassie, UnitreeGo2) — consistent -21% to -38% range. The MDP has more terms but the underlying physics step is heavier, so the relative Python savings shrink. - **Direct workflow** — gains scale with how much the env's step body was Python (Ant -51%, Cartpole -18%, Allegro hand -10%). Direct envs that already wrote most of their work as GPU kernels see modest gains; ones with substantial Python state machinery see large ones. - **Compute-heavy / scene-write-heavy envs** (Allegro hand, large humanoids) — see smaller relative gains because the warp-side savings are amortised over a heavier step. Components that still go through torch (scene write, actuator models) currently bound the floor; this is expected to improve as remaining components migrate to warp. If your env's step time is dominated by physics or scene I/O, expect modest gains. If it has many small MDP terms or a lot of Python in the step loop, expect large ones. Use the benchmarking workflow below to measure on your task before committing to a migration. Limitations ~~~~~~~~~~~ The warp env path is experimental and has the following known constraints. These are specific to warp envs; for Newton physics maturity and specialist guides see :ref:`physics-backends-newton`. **Physics backend** - **Newton only.** PhysX is not supported under the warp env path. Asset and sensor ``class_type`` fields resolve to ``isaaclab_physx.*`` classes that depend on ``omni.physics.tensors`` (a Kit module the warp runtime does not initialise), and several warp APIs (env-mask reset, CUDA graph capture) require the Newton articulation. Configure the cfg with a Newton physics block (or the typed selector ``physics=newton_mjwarp``, which fails loudly if the task has no Newton physics preset). **MDP coverage** - Only the terms listed under :ref:`Available Warp MDP Terms ` are implemented. Stable envs that depend on un-migrated terms cannot be run on the warp path until those terms are ported. - Some scene-side operations (asset write, actuator models, certain sensor types) still go through torch. They participate in the step but are not yet captured into the graph; they set the lower bound on observed step time. - Sensors that depend on the Kit RTX renderer (camera-based observations) cannot be combined with the warp env path — they need Kit, which the warp runtime does not initialise. **API differences vs stable** - Reset events use a boolean ``env_mask`` (``wp.array(dtype=wp.bool)``) instead of an ``env_ids`` list. This is required for capture safety: variable-length indexing changes graph topology and breaks replay. - All buffers must be pre-allocated in ``__init__``. There is no dynamic allocation inside the captured step loop, so observation / reward / termination output dimensions must be known at env init. - Term functions write into a pre-allocated ``out`` buffer rather than returning a tensor. See :ref:`warp-env-migration` for the kernel + launch pattern. - Code inside the captured step loop must follow capture-safety rules (no ``wp.to_torch``, no torch arithmetic, no lazy-evaluated properties, no Python branching on GPU data). See :ref:`warp-env-capture-safety` for the full set of rules. Benchmarking Your Environment ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The performance table above was produced with ``isaaclab benchmark training``, which runs a fixed iteration count and reports step-time statistics. Use the same command to estimate the gain for your own task before committing to a migration. **Single-task A/B** Run the same stable task id twice, differing only in ``--frontend``. Pass ``presets=newton_mjwarp`` to *both* runs so the physics backend is identical and the measured difference isolates the frontend (manager pipeline + CUDA graph capture): .. code-block:: bash # Torch frontend (stable managers) uv run isaaclab benchmark training \ --rl_library rsl_rl \ --task \ --num_envs 4096 \ --max_iterations 500 \ --benchmark_formatter summary \ --output_path benchmarks/torch \ presets=newton_mjwarp # Warp frontend (warp managers, CUDA-graph captured) — same task id uv run isaaclab benchmark training \ --rl_library rsl_rl \ --task \ --frontend warp \ --num_envs 4096 \ --max_iterations 500 \ --benchmark_formatter summary \ --output_path benchmarks/warp \ presets=newton_mjwarp The ``summary`` formatter prints step time (min / mean / max) and total throughput. Compare "step time" between the two runs to estimate the gain per env step. **Sweep across all available tasks** Run ``isaaclab benchmark training`` for each task in the stable set (cartpole, ant, humanoid, locomotion, manipulation) and again with ``--frontend warp``, then diff the two output directories. **What to look at in the output** - *Step time (min / mean / max)*: the headline number — what each env step costs. - *Iteration time*: includes policy update; useful for end-to-end training throughput. - *Capture overhead*: for warp runs, the first few iterations include CUDA graph capture cost; exclude those when comparing steady-state numbers. **Estimating before you migrate** If you can't run the warp variant yet (e.g. the task isn't ported), measure the stable step time and look at where it's spent: - ``num_envs * step_time`` dominated by physics → expect modest warp gains. - ``step_time`` dominated by ``manager.compute_*`` calls → expect large gains, since those are exactly what the warp managers replace with captured kernel launches. Use ``--num_steps`` on ``runtime.py`` for a no-policy step-time microbenchmark when you want to isolate env overhead from policy compute. .. _warp-env-migration: Migrating Existing Environments ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This section covers the key conventions and patterns used by the warp-first environment infrastructure, useful for migrating existing torch environments or creating new ones natively: project layout, the kernel + launch pattern shared by observations / rewards / events / terminations / actions, capture-safety rules, and parity testing. Design Rationale ^^^^^^^^^^^^^^^^ The warp environment path is built around `CUDA graph capture `_. A CUDA graph records a sequence of GPU operations (kernel launches, memory copies) during a capture phase, then replays the entire sequence with a single launch. This eliminates per-kernel CPU overhead — the parameter validation, kernel selection, and buffer setup that normally costs 20–200 μs per operation is performed once during graph instantiation and reused on every replay (~10 μs total). All CPU-side code (Python logic, torch dispatching) executed during capture is completely bypassed during replay. See the `Warp concurrency documentation `_ for Warp's graph capture API (``wp.ScopedCapture``). All design decisions in the warp infrastructure follow from this constraint: every operation in the step loop must be a GPU kernel launch with stable memory pointers so that the captured graph can be replayed without modification. Key consequences: - All buffers are **pre-allocated** — no dynamic allocation inside the step loop - Data flows through **persistent ``wp.array`` pointers** — never replaced, only overwritten - MDP terms are **pure ``@wp.kernel`` functions** — no Python branching on GPU data - Reset uses **boolean masks** (``env_mask``) instead of index lists (``env_ids``) to avoid variable-length indexing that changes graph topology Project Structure ^^^^^^^^^^^^^^^^^ Warp-specific implementations that diverge from the torch-based managers and env classes live in the ``_experimental`` packages: - ``isaaclab_experimental`` — warp managers, base env classes, warp MDP terms - ``isaaclab_tasks_experimental`` — warp task configs and task-specific MDP terms Any new warp implementation that differs from the torch-based managers or env classes belongs in these packages. Warp task configs reference Newton physics directly (no ``PresetCfg``) since the warp path is Newton-only. Writing Warp MDP Terms ^^^^^^^^^^^^^^^^^^^^^^ Imports """"""" Warp task configs import from the experimental packages: .. code-block:: python # Warp from isaaclab_experimental.managers import ObservationTermCfg, RewardTermCfg, SceneEntityCfg import isaaclab_experimental.envs.mdp as mdp The term config classes have the same interface — only the import path changes. Common Pattern """""""""""""" All warp MDP terms (observations, rewards, terminations, events, actions) follow the same **kernel + launch** pattern. Torch terms use torch tensors and return results; warp terms write into pre-allocated ``wp.array`` output buffers via ``@wp.kernel`` functions: .. code-block:: python # Torch — returns a tensor def lin_vel_z_l2(env, asset_cfg) -> torch.Tensor: return torch.square(asset.data.root_lin_vel_b[:, 2]) # Warp — writes into pre-allocated output @wp.kernel def _lin_vel_z_l2_kernel(vel: wp.array(...), out: wp.array(dtype=wp.float32)): i = wp.tid() out[i] = vel[i][2] * vel[i][2] def lin_vel_z_l2(env, out, asset_cfg) -> None: wp.launch(_lin_vel_z_l2_kernel, dim=env.num_envs, inputs=[..., out]) The output buffer shapes differ by term type: - **Observations**: ``(num_envs, D)`` where D is the observation dimension - **Rewards**: ``(num_envs,)`` - **Terminations**: ``(num_envs,)`` with dtype ``bool`` - **Events**: ``(num_envs,)`` mask — events don't produce output, they modify sim state Observation Terms """"""""""""""""" Since warp terms write into pre-allocated buffers, the observation manager must know each term's output dimension at initialization to allocate the correct ``(num_envs, D)`` output array. This is resolved via a fallback chain (see ``ObservationManager._infer_term_dim_scalar`` in ``isaaclab_experimental/managers/observation_manager.py``): .. warning:: The IO descriptor decorators are deprecated in Isaac Lab 3.0 and will be removed in Isaac Lab 3.2. Their output-dimension metadata remains available temporarily for Warp-first environments while a replacement runtime configuration is developed. 1. **Explicit ``out_dim`` in decorator** (preferred): .. code-block:: python @generic_io_descriptor_warp(out_dim=3, observation_type="RootState") def base_lin_vel(env, out, asset_cfg) -> None: ... ``out_dim`` can be an integer, or a string that resolves at initialization: - ``"joint"`` — number of selected joints from ``asset_cfg`` - ``"body:N"`` — N components per selected body from ``asset_cfg`` - ``"command"`` — dimension from command manager - ``"action"`` — dimension from action manager 2. **``axes`` metadata**: Dimension equals the number of axes listed: .. code-block:: python @generic_io_descriptor_warp(axes=["X", "Y", "Z"], observation_type="RootState") def projected_gravity(env, out, asset_cfg) -> None: ... # → dimension = 3 3. **Legacy params**: ``term_dim``, ``out_dim``, or ``obs_dim`` keys in ``term_cfg.params``. 4. **Asset config fallback**: Count of ``asset_cfg.joint_ids`` (or ``joint_ids_wp``) for joint-level terms. Event Terms """"""""""" Events use ``env_mask`` (boolean ``wp.array``) instead of ``env_ids``, and each kernel checks the mask to skip non-selected environments: .. code-block:: python def reset_joints_by_offset(env, env_mask, ...): wp.launch(_kernel, dim=env.num_envs, inputs=[env_mask, ...]) @wp.kernel def _kernel(env_mask: wp.array(dtype=wp.bool), ...): i = wp.tid() if not env_mask[i]: return # ... modify state for selected envs only - RNG uses per-env ``env.rng_state_wp`` (``wp.uint32``) instead of ``torch.rand`` - **Startup/prestartup** events use the torch convention ``(env, env_ids, **params)`` - **Reset/interval** events use the warp convention ``(env, env_mask, **params)`` Action Terms """""""""""" Actions follow a **two-stage execution**: ``process_actions`` (called once per env step) scales and clips raw actions, and ``apply_actions`` (called once per sim step) writes targets to the asset. Both stages use warp kernels with pre-allocated ``_raw_actions`` and ``_processed_actions`` buffers. .. _warp-env-capture-safety: Capture Safety """""""""""""" When writing terms that run inside the captured step loop, keep in mind: - **No ``wp.to_torch``** or torch arithmetic — stay in warp throughout - **No lazy-evaluated properties** — use sim-bound (Tier 1) data directly; if a derived quantity is needed, compute it inline in the kernel - **No dynamic allocation** — all buffers must be pre-allocated in ``__init__`` Parity Testing ^^^^^^^^^^^^^^ Two levels of parity testing are used to validate warp terms: **1. Implementation parity (torch vs warp)** — verifies that the warp kernel produces the same result as the torch implementation. This is optional for terms that have no torch counterpart (e.g. new terms written directly in warp). .. code-block:: python import isaaclab.envs.mdp.observations as torch_obs import isaaclab_experimental.envs.mdp.observations as warp_obs # Torch baseline expected = torch_obs.joint_pos(torch_env, asset_cfg=cfg) # Warp (uncaptured) out = wp.zeros((num_envs, num_joints), dtype=wp.float32, device=device) warp_obs.joint_pos(warp_env, out, asset_cfg=cfg) actual = wp.to_torch(out) torch.testing.assert_close(actual, expected) **2. Capture parity (warp vs warp-captured)** — verifies that the term produces identical results when replayed from a CUDA graph vs launched directly. A mismatch here indicates capture-unsafe code (e.g. stale pointers, dynamic allocation, or lazy property access that doesn't replay). This test should always be run, even for terms without a torch counterpart. .. code-block:: python # Warp uncaptured out_uncaptured = wp.zeros((num_envs, num_joints), dtype=wp.float32, device=device) warp_obs.joint_pos(warp_env, out_uncaptured, asset_cfg=cfg) # Warp captured (graph replay) out_captured = wp.zeros((num_envs, num_joints), dtype=wp.float32, device=device) with wp.ScopedCapture() as cap: warp_obs.joint_pos(warp_env, out_captured, asset_cfg=cfg) wp.capture_launch(cap.graph) torch.testing.assert_close(wp.to_torch(out_captured), wp.to_torch(out_uncaptured)) See ``source/isaaclab_experimental/test/envs/mdp/`` for complete parity test examples. .. _warp-env-mdp-terms: Available Warp MDP Terms ^^^^^^^^^^^^^^^^^^^^^^^^ .. list-table:: :header-rows: 1 :widths: 20 80 * - Category - Available Terms * - Observations (11) - | ``base_pos_z`` | ``base_lin_vel`` | ``base_ang_vel`` | ``projected_gravity`` | ``joint_pos`` | ``joint_pos_rel`` | ``joint_pos_limit_normalized`` | ``joint_vel`` | ``joint_vel_rel`` | ``last_action`` | ``generated_commands`` * - Rewards (16) - | ``is_alive`` | ``is_terminated`` | ``lin_vel_z_l2`` | ``ang_vel_xy_l2`` | ``flat_orientation_l2`` | ``joint_torques_l2`` | ``joint_vel_l1`` | ``joint_vel_l2`` | ``joint_acc_l2`` | ``joint_deviation_l1`` | ``joint_pos_limits`` | ``action_rate_l2`` | ``action_l2`` | ``undesired_contacts`` | ``track_lin_vel_xy_exp`` | ``track_ang_vel_z_exp`` * - Events (6) - | ``reset_joints_by_offset`` | ``reset_joints_by_scale`` | ``reset_root_state_uniform`` | ``push_by_setting_velocity`` | ``apply_external_force_torque`` | ``randomize_rigid_body_com`` * - Terminations (4) - | ``time_out`` | ``root_height_below_minimum`` | ``joint_pos_out_of_manual_limit`` | ``illegal_contact`` * - Actions (2) - | ``JointPositionAction`` | ``JointEffortAction`` Terms not listed here remain in torch only. When using an env that requires unlisted terms, those terms must be implemented in warp first.