Add PPO kernels - #5
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Adds vortx::linalg::{Activation (tanh + tanh_backward), Adam} and their shaders,
the GPU building blocks for MLP training (used by nexus RL demos / zealot-rl).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
New `ppo` op (host + shader) producing the per-sample OUTPUT gradients that feed the generic GEMM/elu_backward backward backbone: - gpu_ppo_actor_grad: clipped-surrogate actor gradient (logp over the action dims, ratio = exp(logp - logp_old), clip mask) -> g_mean plus the state-independent log_std gradient contribution. - gpu_ppo_value_grad: clipped value-loss gradient. Both are an exact port of zealot-rl's minibatch_step. Every per-sample tensor is row-major [rows x M] (M = minibatch columns); one thread handles one sample column and loops over the (small) action dim. No Shape uniform -- dims ride in PpoActorParams/PpoValueParams. Exports Ppo, PpoActorParams, PpoValueParams (host) and GpuPpoActorGrad, GpuPpoValueGrad (shaders). Verified vs CPU minibatch_step (~1e-7, ~25% of samples on the clip branch). Note: the one-line glamx Cargo.toml dependency is shared with the ELU and GEMM-vec4 branches; whichever lands first, the others need a trivial rebase of that line. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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This is being merged as part of #11 |
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Aug 27, 2026
* feat: move inferi shaders into an optional ml module for vortx * chore: cargo fmt * feat(ml): add tanh activation + Adam optimizer GPU ops Replaces #5 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * refactor(ml): move the tanh-backward + Adam kernels into vortx::ml and drop the duplicated tanh forward Completes #5 * feat(ml): add ELU activation GPU ops (fwd/backward/vec4) Replaces #6 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * refactor(ml): keep only the ELU backward pass, the forward is already UnaryOp::Elu Completes #6 * feat(ml): add PPO loss-gradient GPU kernels Replaces #5 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * refactor(ml): wire the PPO kernels to vortx::ml and use StepRng for uniform control flow Completes #5 * perf(linalg): vec4 GEMM (compute-FMA inner loop + vec4 global-load variant) Replaces #7 Co-Authored-By: Haixuan Xavier Tao <tao.xavier@outlook.com> * perf(linalg): keep the vec4 FMA inner loop, drop the unmeasured vec4 global-load GEMM variant Completes #7 * feat(ml): gpu_ppo_stage_batch, building the PPO minibatch on device * chore(ml): comment cleanups * chore: CI fixes --------- Co-authored-by: Haixuan Xavier Tao <tao.xavier@outlook.com>
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Add PPO gradient kernel for RL training