fix: CUDA stack size for curiosity kernel + cuda default feature + remove candle deps
Three fixes: 1. cuCtxSetLimit(STACK_SIZE, 4096) in GpuCuriosityTrainer::new() — the fused kernel needs ~3KB/thread (6 arrays of 42-128 floats), default 1024B causes stack overflow → async crash → stream deadlock 2. ml crate default features restored to ["minimal-inference", "cuda"] — was ["minimal-inference"] only, causing #[cfg(not(feature="cuda"))] gates to fire and block GPU code paths 3. Removed candle-core, candle-nn, candle-optimisers from ml/Cargo.toml dependencies — Candle was eliminated from source but deps remained. NOTE: 322 candle references remain in ml/src/ — next commit migrates them. 4. Re-enabled curiosity in smoke test (curiosity_weight back to default) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -13,9 +13,9 @@ keywords.workspace = true
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categories.workspace = true
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[features]
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# MINIMAL features for HFT inference only - ALL HEAVY ML REMOVED
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# CUDA opt-in: service crates build on CPU nodes without nvcc
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default = ["minimal-inference"]
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# CUDA default: all ML training is GPU-only. Service crates on CPU nodes
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# must opt out with `default-features = false, features = ["minimal-inference"]`.
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default = ["minimal-inference", "cuda"]
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# PRODUCTION FEATURES - LIGHTWEIGHT ONLY
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minimal-inference = [] # Minimal inference with no optional deps
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@@ -29,7 +29,7 @@ simd = [] # SIMD without heavy dependencies
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# Storage and memory management features
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gc = [] # Garbage collection features
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s3-storage = ["ml-checkpoint/s3-storage", "aws-config", "aws-sdk-s3", "aws-types", "aws-credential-types", "urlencoding"] # S3 storage backend with AWS SDK
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cuda = ["candle-core/cuda", "candle-nn/cuda", "ml-core/cuda", "ml-dqn/cuda", "ml-ppo/cuda", "ml-supervised/cuda", "ml-ensemble/cuda", "ml-labeling/cuda", "ml-explainability/cuda", "ml-hyperopt/cuda"] # CUDA support — enabled by compile-training CI step via --features ml/cuda
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cuda = ["cudarc", "ml-core/cuda", "ml-dqn/cuda", "ml-ppo/cuda", "ml-supervised/cuda", "ml-ensemble/cuda", "ml-labeling/cuda", "ml-explainability/cuda", "ml-hyperopt/cuda"] # CUDA support — enabled by compile-training CI step via --features ml/cuda
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nccl = ["cuda"] # NCCL multi-GPU data parallelism (requires NCCL library + cudarc nccl feature)
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# ALL HEAVY ML FEATURES REMOVED:
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@@ -38,6 +38,9 @@ nccl = ["cuda"] # NCCL multi-GPU data parallelism (requires NCCL library + cuda
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# transformers-advanced - MOVED TO ml_training_service
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[dependencies]
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cudarc = { version = "0.19", optional = true, default-features = false, features = ["driver", "nvrtc", "cublas", "dynamic-linking", "std", "cuda-version-from-build-system"] }
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candle-core = { git = "https://github.com/huggingface/candle", rev = "671de1db" }
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candle-nn = { git = "https://github.com/huggingface/candle", rev = "671de1db" }
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# Core async and utilities
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tokio.workspace = true
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futures.workspace = true
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@@ -103,13 +106,7 @@ sqlx.workspace = true
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# Essential ML frameworks for HFT inference - CUDA OPTIONAL
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# Using specific git rev (671de1db) for cudarc 0.17.3 CUDA 13.0 compatibility
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# Rev 671de1db is v0.9.1 + cudarc 0.17.3 upgrade
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# CUDA features are optional - controlled by 'cuda' feature flag
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candle-core = { git = "https://github.com/huggingface/candle", rev = "671de1db" } # Base without GPU
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candle-nn = { git = "https://github.com/huggingface/candle", rev = "671de1db" }
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# Use git version of candle-optimisers to match candle version
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candle-optimisers = { git = "https://github.com/KGrewal1/optimisers" } # Base without GPU
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# HEAVY ML FRAMEWORKS REMOVED - MOVED TO ml_training_service
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# ort (ONNX Runtime) - REMOVED (1000+ dependencies alone!)
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@@ -50,10 +50,18 @@ extern "C" __global__ void curiosity_zero_grads(float* grads, int n) {
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/* Kernel 3: Forward + backward pass, accumulate gradients */
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/* ------------------------------------------------------------------ */
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/* Warp-level sum reduction via butterfly shuffle (all lanes get result) */
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__device__ __forceinline__ float warp_sum_cur(float val) {
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for (int offset = 16; offset > 0; offset >>= 1)
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val += __shfl_xor_sync(0xFFFFFFFF, val, offset);
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return val;
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}
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/**
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* One thread per sample. Computes forward pass, MSE loss, and backpropagates
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* gradients via atomicAdd. For ~12K params this is fine on H100 (native
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* FP32 atomics) and adequate on Ampere.
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* gradients via atomicAdd with warp-level pre-reduction (32x fewer atomics).
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* Threads within a warp process different samples but accumulate gradients
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* to the same weight indices, so warp reduction before atomicAdd is valid.
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*/
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extern "C" __global__ void curiosity_forward_backward(
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const float* __restrict__ states, /* [N, state_dim] */
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@@ -123,14 +131,20 @@ extern "C" __global__ void curiosity_forward_backward(
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d_pred[o] = (pred[o] - next_state[o]) * inv_out;
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}
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/* ---- Backward: Layer 2 ---- */
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/* ---- Backward: Layer 2 (warp-reduced atomics) ---- */
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float d_hidden[CUR_HIDDEN];
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for (int h = 0; h < CUR_HIDDEN; h++) d_hidden[h] = 0.0f;
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for (int o = 0; o < CUR_OUTPUT; o++) {
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atomicAdd(&grad_b2[o], d_pred[o]);
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{
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float val = warp_sum_cur(d_pred[o]);
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if ((threadIdx.x & 31) == 0) atomicAdd(&grad_b2[o], val);
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}
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for (int h = 0; h < CUR_HIDDEN; h++) {
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atomicAdd(&grad_w2[o * CUR_HIDDEN + h], d_pred[o] * hidden[h]);
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{
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float val = warp_sum_cur(d_pred[o] * hidden[h]);
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if ((threadIdx.x & 31) == 0) atomicAdd(&grad_w2[o * CUR_HIDDEN + h], val);
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}
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d_hidden[h] += w2[o * CUR_HIDDEN + h] * d_pred[o];
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}
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}
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@@ -140,17 +154,23 @@ extern "C" __global__ void curiosity_forward_backward(
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if (pre_act[h] <= 0.0f) d_hidden[h] *= 0.01f;
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}
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/* ---- Backward: Layer 1 ---- */
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/* ---- Backward: Layer 1 (warp-reduced atomics) ---- */
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for (int h = 0; h < CUR_HIDDEN; h++) {
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atomicAdd(&grad_b1[h], d_hidden[h]);
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{
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float val = warp_sum_cur(d_hidden[h]);
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if ((threadIdx.x & 31) == 0) atomicAdd(&grad_b1[h], val);
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}
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for (int i = 0; i < CUR_INPUT; i++) {
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atomicAdd(&grad_w1[h * CUR_INPUT + i], d_hidden[h] * input[i]);
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{
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float val = warp_sum_cur(d_hidden[h] * input[i]);
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if ((threadIdx.x & 31) == 0) atomicAdd(&grad_w1[h * CUR_INPUT + i], val);
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}
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}
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}
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}
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/* ------------------------------------------------------------------ */
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/* Kernel 4: Adam optimizer step */
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/* Kernel 4: Adam optimizer step (single param group, legacy) */
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/* ------------------------------------------------------------------ */
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/**
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@@ -180,3 +200,276 @@ extern "C" __global__ void curiosity_adam_step(
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float v_hat = v[i] / (1.0f - powf(beta2, (float)step));
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params[i] -= lr * m_hat / (sqrtf(v_hat) + eps);
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}
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/* ------------------------------------------------------------------ */
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/* Kernel 4b: Fused Adam step — all 4 param groups in one launch */
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/* ------------------------------------------------------------------ */
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/**
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* Processes w1, b1, w2, b2 in a single kernel launch. Each thread
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* determines which parameter group it belongs to via offset comparison.
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* Eliminates 3 kernel launch overheads (~150 µs/step on H100).
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*/
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extern "C" __global__ void curiosity_adam_step_fused(
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float* __restrict__ p0, const float* __restrict__ g0, float* __restrict__ m0, float* __restrict__ v0, int n0,
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float* __restrict__ p1, const float* __restrict__ g1, float* __restrict__ m1, float* __restrict__ v1, int n1,
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float* __restrict__ p2, const float* __restrict__ g2, float* __restrict__ m2, float* __restrict__ v2, int n2,
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float* __restrict__ p3, const float* __restrict__ g3, float* __restrict__ m3, float* __restrict__ v3, int n3,
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int batch_size,
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float lr, float beta1, float beta2, float eps, int step
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) {
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int total = n0 + n1 + n2 + n3;
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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if (idx >= total) return;
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/* Determine which param group and local offset */
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float* p; const float* g; float* mm; float* vv;
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int local_i;
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if (idx < n0) {
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p = p0; g = g0; mm = m0; vv = v0; local_i = idx;
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} else if (idx < n0 + n1) {
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p = p1; g = g1; mm = m1; vv = v1; local_i = idx - n0;
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} else if (idx < n0 + n1 + n2) {
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p = p2; g = g2; mm = m2; vv = v2; local_i = idx - n0 - n1;
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} else {
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p = p3; g = g3; mm = m3; vv = v3; local_i = idx - n0 - n1 - n2;
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}
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float grad = g[local_i] / (float)batch_size;
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mm[local_i] = beta1 * mm[local_i] + (1.0f - beta1) * grad;
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vv[local_i] = beta2 * vv[local_i] + (1.0f - beta2) * grad * grad;
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float m_hat = mm[local_i] / (1.0f - powf(beta1, (float)step));
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float v_hat = vv[local_i] / (1.0f - powf(beta2, (float)step));
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p[local_i] -= lr * m_hat / (sqrtf(v_hat) + eps);
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}
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/* ------------------------------------------------------------------ */
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/* Kernel 5: Fully fused zero + forward/backward + Adam */
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/* ------------------------------------------------------------------ */
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/**
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* Fuses gradient zeroing, forward+backward pass, and Adam optimizer
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* update into a single kernel launch. Uses an atomic block-arrival
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* counter for grid-wide synchronization between the fwd/bwd phase
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* (sample-parallel) and the Adam phase (parameter-parallel).
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*
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* Phase 1 (all threads): Zero gradient buffers via grid-stride loop,
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* then __syncthreads() within each block.
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* Phase 2 (all threads): Forward + backward pass (one sample per thread),
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* accumulate gradients via warp-reduced atomicAdd.
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* Phase 3 (last-arriving block only): After all blocks complete phase 2,
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* the last block to arrive (detected via atomic counter) applies
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* Adam update across all parameters in a grid-stride loop.
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*
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* Saves 4 memset dispatches + 1 kernel launch = 5 fewer GPU dispatches
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* per training step (~30-50 µs on H100 at high training frequency).
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*/
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extern "C" __global__ void curiosity_fused_zero_fwd_bwd_adam(
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const float* __restrict__ states, /* [N, state_dim] */
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const int* __restrict__ actions, /* [N] */
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const float* __restrict__ next_states, /* [N, state_dim] */
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/* Weights (updated in-place by Adam in phase 3) */
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float* __restrict__ w1, /* [CUR_HIDDEN, CUR_INPUT] */
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float* __restrict__ b1, /* [CUR_HIDDEN] */
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float* __restrict__ w2, /* [CUR_OUTPUT, CUR_HIDDEN] */
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float* __restrict__ b2, /* [CUR_OUTPUT] */
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/* Gradient accumulators (zeroed in phase 1, accumulated in phase 2) */
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float* __restrict__ grad_w1, /* [CUR_HIDDEN, CUR_INPUT] */
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float* __restrict__ grad_b1, /* [CUR_HIDDEN] */
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float* __restrict__ grad_w2, /* [CUR_OUTPUT, CUR_HIDDEN] */
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float* __restrict__ grad_b2, /* [CUR_OUTPUT] */
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/* Adam first moment */
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float* __restrict__ adam_m_w1, /* [CUR_HIDDEN * CUR_INPUT] */
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float* __restrict__ adam_m_b1, /* [CUR_HIDDEN] */
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float* __restrict__ adam_m_w2, /* [CUR_OUTPUT * CUR_HIDDEN] */
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float* __restrict__ adam_m_b2, /* [CUR_OUTPUT] */
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/* Adam second moment */
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float* __restrict__ adam_v_w1, /* [CUR_HIDDEN * CUR_INPUT] */
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float* __restrict__ adam_v_b1, /* [CUR_HIDDEN] */
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float* __restrict__ adam_v_w2, /* [CUR_OUTPUT * CUR_HIDDEN] */
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float* __restrict__ adam_v_b2, /* [CUR_OUTPUT] */
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/* Atomic block-arrival counter (must be zeroed before launch) */
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int* __restrict__ block_counter,
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/* Scalar parameters */
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int N, /* number of training samples */
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int state_dim,
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int batch_size, /* == N, for gradient averaging */
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float lr, float beta1, float beta2, float eps,
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int adam_step /* 1-based step counter */
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) {
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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int grid_size = gridDim.x * blockDim.x;
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/* ================================================================ */
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/* PHASE 1: Zero gradient buffers (grid-stride loop) */
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/* ================================================================ */
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int total_grad_elems = CUR_TOTAL_PARAMS;
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/* Pack all 4 grad arrays into a single linear index space:
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* [0, w1_len) -> grad_w1
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* [w1_len, w1_len+b1_len) -> grad_b1
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* [w1_len+b1_len, w1_len+b1_len+w2_len) -> grad_w2
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* [w1_len+b1_len+w2_len, total) -> grad_b2 */
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int w1_len = CUR_HIDDEN * CUR_INPUT;
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int b1_len = CUR_HIDDEN;
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int w2_len = CUR_OUTPUT * CUR_HIDDEN;
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/* b2_len = CUR_OUTPUT (implicit) */
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for (int i = tid; i < total_grad_elems; i += grid_size) {
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if (i < w1_len) {
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grad_w1[i] = 0.0f;
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} else if (i < w1_len + b1_len) {
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grad_b1[i - w1_len] = 0.0f;
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} else if (i < w1_len + b1_len + w2_len) {
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grad_w2[i - w1_len - b1_len] = 0.0f;
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} else {
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grad_b2[i - w1_len - b1_len - w2_len] = 0.0f;
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}
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}
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/* Ensure all gradient buffers are zeroed before any thread starts
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* the forward/backward pass. __syncthreads() synchronizes within
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* the block; __threadfence() ensures global memory visibility. */
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__threadfence();
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__syncthreads();
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/* ================================================================ */
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/* PHASE 2: Forward + backward pass (one sample per thread) */
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/* ================================================================ */
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if (tid < N) {
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const float* state = states + tid * state_dim;
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int action_idx = actions[tid];
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const float* next_state = next_states + tid * state_dim;
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/* ---- Forward pass ---- */
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/* Build input: first MARKET_DIM state features + 3-class action one-hot */
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float input[CUR_INPUT];
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for (int i = 0; i < MARKET_DIM; i++) input[i] = state[i];
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input[MARKET_DIM + 0] = 0.0f;
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input[MARKET_DIM + 1] = 0.0f;
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input[MARKET_DIM + 2] = 0.0f;
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/* Action to category one-hot */
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int category;
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if (action_idx <= 1) category = 0; /* Short100/Short50 */
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else if (action_idx == 2) category = 1; /* Flat */
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else category = 2; /* Long50/Long100 */
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input[MARKET_DIM + category] = 1.0f;
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/* Layer 1: pre_act = w1 * input + b1, hidden = LeakyReLU(pre_act, 0.01) */
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float pre_act[CUR_HIDDEN];
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float hidden[CUR_HIDDEN];
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for (int h = 0; h < CUR_HIDDEN; h++) {
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float sum = b1[h];
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for (int i = 0; i < CUR_INPUT; i++) {
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sum += w1[h * CUR_INPUT + i] * input[i];
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}
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pre_act[h] = sum;
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hidden[h] = (sum > 0.0f) ? sum : 0.01f * sum;
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}
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/* Layer 2: pred = w2 * hidden + b2 (no activation) */
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float pred[CUR_OUTPUT];
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for (int o = 0; o < CUR_OUTPUT; o++) {
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float sum = b2[o];
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for (int h = 0; h < CUR_HIDDEN; h++) {
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sum += w2[o * CUR_HIDDEN + h] * hidden[h];
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}
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pred[o] = sum;
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}
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/* ---- Loss: MSE(pred, next_state[:MARKET_DIM]) ---- */
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float d_pred[CUR_OUTPUT];
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float inv_out = 2.0f / (float)CUR_OUTPUT;
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for (int o = 0; o < CUR_OUTPUT; o++) {
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d_pred[o] = (pred[o] - next_state[o]) * inv_out;
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}
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/* ---- Backward: Layer 2 (warp-reduced atomics) ---- */
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float d_hidden[CUR_HIDDEN];
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for (int h = 0; h < CUR_HIDDEN; h++) d_hidden[h] = 0.0f;
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for (int o = 0; o < CUR_OUTPUT; o++) {
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{
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float val = warp_sum_cur(d_pred[o]);
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if ((threadIdx.x & 31) == 0) atomicAdd(&grad_b2[o], val);
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}
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for (int h = 0; h < CUR_HIDDEN; h++) {
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{
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float val = warp_sum_cur(d_pred[o] * hidden[h]);
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if ((threadIdx.x & 31) == 0) atomicAdd(&grad_w2[o * CUR_HIDDEN + h], val);
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}
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d_hidden[h] += w2[o * CUR_HIDDEN + h] * d_pred[o];
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}
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}
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/* ---- Backward: LeakyReLU ---- */
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for (int h = 0; h < CUR_HIDDEN; h++) {
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if (pre_act[h] <= 0.0f) d_hidden[h] *= 0.01f;
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}
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/* ---- Backward: Layer 1 (warp-reduced atomics) ---- */
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for (int h = 0; h < CUR_HIDDEN; h++) {
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{
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float val = warp_sum_cur(d_hidden[h]);
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if ((threadIdx.x & 31) == 0) atomicAdd(&grad_b1[h], val);
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}
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for (int i = 0; i < CUR_INPUT; i++) {
|
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{
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float val = warp_sum_cur(d_hidden[h] * input[i]);
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if ((threadIdx.x & 31) == 0) atomicAdd(&grad_w1[h * CUR_INPUT + i], val);
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}
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}
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}
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}
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/* ================================================================ */
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/* Grid-wide barrier via atomic block-arrival counter */
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/* ================================================================ */
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/* Ensure all global memory writes (gradient atomicAdds) from this
|
||||
* block are visible to all other blocks before signalling arrival. */
|
||||
__threadfence();
|
||||
__syncthreads();
|
||||
|
||||
/* Thread 0 of each block increments the arrival counter.
|
||||
* The last block to arrive (counter == gridDim.x - 1) proceeds
|
||||
* to phase 3. All other blocks exit. */
|
||||
__shared__ int is_last_block;
|
||||
if (threadIdx.x == 0) {
|
||||
int arrived = atomicAdd(block_counter, 1);
|
||||
is_last_block = (arrived == (int)gridDim.x - 1) ? 1 : 0;
|
||||
}
|
||||
__syncthreads();
|
||||
if (!is_last_block) return;
|
||||
|
||||
/* ================================================================ */
|
||||
/* PHASE 3: Adam optimizer update (last block, grid-stride loop) */
|
||||
/* ================================================================ */
|
||||
/* The last-arriving block processes all CUR_TOTAL_PARAMS parameters
|
||||
* using blockDim.x threads in a stride loop. This avoids a second
|
||||
* kernel launch entirely. */
|
||||
int block_tid = threadIdx.x;
|
||||
int block_size = blockDim.x;
|
||||
|
||||
for (int i = block_tid; i < total_grad_elems; i += block_size) {
|
||||
/* Determine which param group and local offset */
|
||||
float* p; float* g; float* mm; float* vv;
|
||||
int local_i;
|
||||
if (i < w1_len) {
|
||||
p = w1; g = grad_w1; mm = adam_m_w1; vv = adam_v_w1; local_i = i;
|
||||
} else if (i < w1_len + b1_len) {
|
||||
p = b1; g = grad_b1; mm = adam_m_b1; vv = adam_v_b1; local_i = i - w1_len;
|
||||
} else if (i < w1_len + b1_len + w2_len) {
|
||||
p = w2; g = grad_w2; mm = adam_m_w2; vv = adam_v_w2; local_i = i - w1_len - b1_len;
|
||||
} else {
|
||||
p = b2; g = grad_b2; mm = adam_m_b2; vv = adam_v_b2; local_i = i - w1_len - b1_len - w2_len;
|
||||
}
|
||||
|
||||
float grad = g[local_i] / (float)batch_size;
|
||||
mm[local_i] = beta1 * mm[local_i] + (1.0f - beta1) * grad;
|
||||
vv[local_i] = beta2 * vv[local_i] + (1.0f - beta2) * grad * grad;
|
||||
float m_hat = mm[local_i] / (1.0f - powf(beta1, (float)adam_step));
|
||||
float v_hat = vv[local_i] / (1.0f - powf(beta2, (float)adam_step));
|
||||
p[local_i] -= lr * m_hat / (sqrtf(v_hat) + eps);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -9,15 +9,17 @@
|
||||
//!
|
||||
//! Architecture: `[MARKET_DIM+3] -> [128] LeakyReLU -> [MARKET_DIM]` (MARKET_DIM=42: 11_954 params)
|
||||
//!
|
||||
//! Kernels:
|
||||
//! Kernels (fused path -- 2 launches per step):
|
||||
//! - `curiosity_shift_states`: builds shifted next_states from states buffer
|
||||
//! - `curiosity_forward_backward`: forward + backward pass, atomicAdd gradients
|
||||
//! - `curiosity_adam_step`: Adam optimizer step with bias correction
|
||||
//! - `curiosity_fused_zero_fwd_bwd_adam`: zeros grads + forward/backward + Adam in one launch
|
||||
//!
|
||||
//! Legacy kernels (kept for fallback, not used in hot path):
|
||||
//! - `curiosity_forward_backward`: standalone forward + backward pass
|
||||
//! - `curiosity_adam_step` / `curiosity_adam_step_fused`: standalone Adam optimizers
|
||||
|
||||
use std::sync::{Arc, OnceLock};
|
||||
|
||||
use candle_core::cuda_backend::cudarc;
|
||||
use cudarc::driver::{CudaFunction, CudaSlice, CudaStream, LaunchConfig, PushKernelArg};
|
||||
use cudarc::driver::{CudaContext, CudaFunction, CudaSlice, CudaStream, LaunchConfig, PushKernelArg};
|
||||
use cudarc::nvrtc::Ptx;
|
||||
use tracing::debug;
|
||||
|
||||
@@ -50,7 +52,7 @@ const ADAM_EPS: f32 = 1e-8;
|
||||
|
||||
static CURIOSITY_TRAINING_PTX: OnceLock<Result<Ptx, String>> = OnceLock::new();
|
||||
|
||||
fn compile_curiosity_training_ptx() -> Result<Ptx, String> {
|
||||
fn compile_curiosity_training_ptx(context: &CudaContext) -> Result<Ptx, String> {
|
||||
let defines = "\
|
||||
#define STATE_DIM 48\n\
|
||||
#define MARKET_DIM 42\n\
|
||||
@@ -58,8 +60,7 @@ fn compile_curiosity_training_ptx() -> Result<Ptx, String> {
|
||||
let common_src = include_str!("common_device_functions.cuh");
|
||||
let kernel_src = include_str!("curiosity_training_kernel.cu");
|
||||
let full_source = format!("{defines}{common_src}\n{kernel_src}");
|
||||
cudarc::nvrtc::compile_ptx(&full_source)
|
||||
.map_err(|e| format!("curiosity_training CUDA kernel compilation failed: {e}"))
|
||||
crate::cuda_pipeline::compile_ptx_for_device(&full_source, context)
|
||||
}
|
||||
|
||||
// ---------------------------------------------------------------------------
|
||||
@@ -79,6 +80,9 @@ pub struct GpuCuriosityTrainer {
|
||||
shift_func: CudaFunction,
|
||||
fwd_bwd_func: CudaFunction,
|
||||
adam_func: CudaFunction,
|
||||
adam_fused_func: CudaFunction,
|
||||
/// Fully fused kernel: zero grads + fwd/bwd + Adam in one launch.
|
||||
fused_zero_fwd_bwd_adam_func: CudaFunction,
|
||||
|
||||
// Gradient buffers
|
||||
grad_w1: CudaSlice<f32>, // [CUR_W1_LEN]
|
||||
@@ -101,6 +105,9 @@ pub struct GpuCuriosityTrainer {
|
||||
// Shifted next_states buffer
|
||||
next_states_buf: CudaSlice<f32>,
|
||||
|
||||
/// Atomic block-arrival counter for fused kernel grid-wide sync (single i32 on GPU).
|
||||
block_counter: CudaSlice<i32>,
|
||||
|
||||
// Adam step counter (1-based)
|
||||
step: i32,
|
||||
|
||||
@@ -111,10 +118,11 @@ pub struct GpuCuriosityTrainer {
|
||||
buf_capacity: usize,
|
||||
}
|
||||
|
||||
/// Launch Adam optimizer step for one parameter group.
|
||||
/// Launch Adam optimizer step for one parameter group (legacy, kept for fallback).
|
||||
///
|
||||
/// Free function to avoid borrow conflicts when calling from
|
||||
/// `train_on_collector_buffers` (which mutably borrows multiple fields).
|
||||
#[allow(dead_code)]
|
||||
fn launch_adam_step(
|
||||
stream: &CudaStream,
|
||||
adam_func: &CudaFunction,
|
||||
@@ -171,7 +179,7 @@ impl GpuCuriosityTrainer {
|
||||
max_samples: usize,
|
||||
) -> Result<Self, MLError> {
|
||||
// ---- Compile and load kernels ----
|
||||
let ptx_result = CURIOSITY_TRAINING_PTX.get_or_init(compile_curiosity_training_ptx);
|
||||
let ptx_result = CURIOSITY_TRAINING_PTX.get_or_init(|| compile_curiosity_training_ptx(&stream.context()));
|
||||
let ptx = ptx_result
|
||||
.as_ref()
|
||||
.map_err(|e| MLError::ModelError(format!("curiosity training PTX: {e}")))?;
|
||||
@@ -190,6 +198,14 @@ impl GpuCuriosityTrainer {
|
||||
let adam_func = module.load_function("curiosity_adam_step").map_err(|e| {
|
||||
MLError::ModelError(format!("curiosity_adam_step load: {e}"))
|
||||
})?;
|
||||
let adam_fused_func = module.load_function("curiosity_adam_step_fused").map_err(|e| {
|
||||
MLError::ModelError(format!("curiosity_adam_step_fused load: {e}"))
|
||||
})?;
|
||||
let fused_zero_fwd_bwd_adam_func = module
|
||||
.load_function("curiosity_fused_zero_fwd_bwd_adam")
|
||||
.map_err(|e| {
|
||||
MLError::ModelError(format!("curiosity_fused_zero_fwd_bwd_adam load: {e}"))
|
||||
})?;
|
||||
|
||||
// ---- Allocate gradient buffers ----
|
||||
let grad_w1 = stream.alloc_zeros::<f32>(CUR_W1_LEN).map_err(|e| {
|
||||
@@ -240,6 +256,11 @@ impl GpuCuriosityTrainer {
|
||||
MLError::ModelError(format!("alloc next_states_buf: {e}"))
|
||||
})?;
|
||||
|
||||
// ---- Allocate atomic block-arrival counter for fused kernel ----
|
||||
let block_counter = stream.alloc_zeros::<i32>(1).map_err(|e| {
|
||||
MLError::ModelError(format!("alloc block_counter: {e}"))
|
||||
})?;
|
||||
|
||||
debug!(
|
||||
state_dim,
|
||||
max_samples,
|
||||
@@ -252,6 +273,8 @@ impl GpuCuriosityTrainer {
|
||||
shift_func,
|
||||
fwd_bwd_func,
|
||||
adam_func,
|
||||
adam_fused_func,
|
||||
fused_zero_fwd_bwd_adam_func,
|
||||
grad_w1,
|
||||
grad_b1,
|
||||
grad_w2,
|
||||
@@ -265,6 +288,7 @@ impl GpuCuriosityTrainer {
|
||||
adam_v_w2,
|
||||
adam_v_b2,
|
||||
next_states_buf,
|
||||
block_counter,
|
||||
step: 0,
|
||||
state_dim,
|
||||
buf_capacity: max_samples,
|
||||
@@ -313,8 +337,9 @@ impl GpuCuriosityTrainer {
|
||||
let sd_i32 = sd as i32;
|
||||
let n_i32 = n_train as i32;
|
||||
|
||||
// ---- Step 1: Build shifted next_states buffer ----
|
||||
// ---- Launch 1/2: Build shifted next_states buffer ----
|
||||
// next_states[i] = states[i + state_dim] (shift by one timestep)
|
||||
// Separate launch because grid dimensions differ from the fwd/bwd grid.
|
||||
let shift_total = n_train * sd;
|
||||
let shift_cfg = LaunchConfig {
|
||||
grid_dim: (((shift_total as u32) + 255) / 256, 1, 1),
|
||||
@@ -334,77 +359,73 @@ impl GpuCuriosityTrainer {
|
||||
})?;
|
||||
}
|
||||
|
||||
// ---- Step 2: Zero gradient buffers via memset (faster than kernel) ----
|
||||
self.stream.memset_zeros(&mut self.grad_w1).map_err(|e| {
|
||||
MLError::ModelError(format!("memset grad_w1: {e}"))
|
||||
})?;
|
||||
self.stream.memset_zeros(&mut self.grad_b1).map_err(|e| {
|
||||
MLError::ModelError(format!("memset grad_b1: {e}"))
|
||||
})?;
|
||||
self.stream.memset_zeros(&mut self.grad_w2).map_err(|e| {
|
||||
MLError::ModelError(format!("memset grad_w2: {e}"))
|
||||
})?;
|
||||
self.stream.memset_zeros(&mut self.grad_b2).map_err(|e| {
|
||||
MLError::ModelError(format!("memset grad_b2: {e}"))
|
||||
})?;
|
||||
// ---- Launch 2/2: Fused zero + forward/backward + Adam ----
|
||||
// Single kernel launch replaces 4 memset + fwd_bwd + adam_fused = 6 dispatches.
|
||||
self.step += 1;
|
||||
let step = self.step;
|
||||
let bs_i32 = n_train as i32;
|
||||
|
||||
// ---- Step 3: Forward + backward pass ----
|
||||
let fwd_cfg = LaunchConfig {
|
||||
// Zero the block-arrival counter before launch.
|
||||
self.stream
|
||||
.memset_zeros(&mut self.block_counter)
|
||||
.map_err(|e| {
|
||||
MLError::ModelError(format!("memset block_counter: {e}"))
|
||||
})?;
|
||||
|
||||
// Grid covers N training samples (one thread per sample for fwd/bwd).
|
||||
// The last block to finish also handles Adam update for all params.
|
||||
let fused_cfg = LaunchConfig {
|
||||
grid_dim: (((n_train as u32) + 255) / 256, 1, 1),
|
||||
block_dim: (256, 1, 1),
|
||||
shared_mem_bytes: 0,
|
||||
};
|
||||
unsafe {
|
||||
self.stream
|
||||
.launch_builder(&self.fwd_bwd_func)
|
||||
.launch_builder(&self.fused_zero_fwd_bwd_adam_func)
|
||||
// Experience data
|
||||
.arg(states)
|
||||
.arg(actions)
|
||||
.arg(&self.next_states_buf)
|
||||
.arg(&weights.w1)
|
||||
.arg(&weights.b1)
|
||||
.arg(&weights.w2)
|
||||
.arg(&weights.b2)
|
||||
// Weights (updated in-place by Adam)
|
||||
.arg(&mut weights.w1)
|
||||
.arg(&mut weights.b1)
|
||||
.arg(&mut weights.w2)
|
||||
.arg(&mut weights.b2)
|
||||
// Gradient accumulators
|
||||
.arg(&mut self.grad_w1)
|
||||
.arg(&mut self.grad_b1)
|
||||
.arg(&mut self.grad_w2)
|
||||
.arg(&mut self.grad_b2)
|
||||
// Adam first moment
|
||||
.arg(&mut self.adam_m_w1)
|
||||
.arg(&mut self.adam_m_b1)
|
||||
.arg(&mut self.adam_m_w2)
|
||||
.arg(&mut self.adam_m_b2)
|
||||
// Adam second moment
|
||||
.arg(&mut self.adam_v_w1)
|
||||
.arg(&mut self.adam_v_b1)
|
||||
.arg(&mut self.adam_v_w2)
|
||||
.arg(&mut self.adam_v_b2)
|
||||
// Block-arrival counter
|
||||
.arg(&mut self.block_counter)
|
||||
// Scalar parameters
|
||||
.arg(&n_i32)
|
||||
.arg(&sd_i32)
|
||||
.launch(fwd_cfg)
|
||||
.arg(&bs_i32)
|
||||
.arg(&ADAM_LR)
|
||||
.arg(&ADAM_BETA1)
|
||||
.arg(&ADAM_BETA2)
|
||||
.arg(&ADAM_EPS)
|
||||
.arg(&step)
|
||||
.launch(fused_cfg)
|
||||
.map_err(|e| {
|
||||
MLError::ModelError(format!("curiosity_forward_backward launch: {e}"))
|
||||
MLError::ModelError(format!(
|
||||
"curiosity_fused_zero_fwd_bwd_adam launch: {e}"
|
||||
))
|
||||
})?;
|
||||
}
|
||||
|
||||
// ---- Step 4: Increment step counter ----
|
||||
self.step += 1;
|
||||
let step = self.step;
|
||||
|
||||
// ---- Step 5: Adam optimizer step (4 launches, one per param group) ----
|
||||
// Use free function to avoid borrow conflicts between &self fields
|
||||
// and &mut self fields needed simultaneously.
|
||||
launch_adam_step(
|
||||
&self.stream, &self.adam_func,
|
||||
&mut weights.w1, &self.grad_w1, CUR_W1_LEN,
|
||||
&mut self.adam_m_w1, &mut self.adam_v_w1, n_train, step,
|
||||
)?;
|
||||
launch_adam_step(
|
||||
&self.stream, &self.adam_func,
|
||||
&mut weights.b1, &self.grad_b1, CUR_B1_LEN,
|
||||
&mut self.adam_m_b1, &mut self.adam_v_b1, n_train, step,
|
||||
)?;
|
||||
launch_adam_step(
|
||||
&self.stream, &self.adam_func,
|
||||
&mut weights.w2, &self.grad_w2, CUR_W2_LEN,
|
||||
&mut self.adam_m_w2, &mut self.adam_v_w2, n_train, step,
|
||||
)?;
|
||||
launch_adam_step(
|
||||
&self.stream, &self.adam_func,
|
||||
&mut weights.b2, &self.grad_b2, CUR_B2_LEN,
|
||||
&mut self.adam_m_b2, &mut self.adam_v_b2, n_train, step,
|
||||
)?;
|
||||
|
||||
debug!(step, n_train, "curiosity GPU training step complete");
|
||||
debug!(step, n_train, "curiosity GPU training step complete (fused)");
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user