feat(ml-alpha): Mamba2AdamW optimizer + end-to-end training-loop validation (Phase 1d.1, session 4)
GPU-pure AdamW for Mamba2Block's nine parameter tensors with bias-corrected
moment updates, decoupled weight decay, and host-side L2 grad clipping
(reads all 9 grad norms once, multiplies a single scale factor into the
kernel). Adam state (m, v) allocated once at optimizer construction;
reused across all training steps.
New kernel `mamba2_alpha_adamw_step` added to ml-alpha's cubin (no
cross-crate cubin loading; ml-alpha stays self-contained per its crate
invariant).
Borrow-checker gotcha worth flagging: `step()` mutably borrows each of
the 9 per-param `AdamState` fields in turn, plus the param itself.
Tried `apply()` as a method on `&self` — conflicts with `&mut self.s_*`.
Resolved by extracting `adamw_apply` as a free function taking (stream,
kernel, config) by reference; lets the caller mutably borrow distinct
state fields while sharing immutable references to the surroundings.
**The end-to-end training-loop test is the analytical-gradient validation:**
- 20 AdamW steps on a fixed batch (n_batch=4, seq_len=8, in_dim=4,
hidden=8, state=4) with binary labels (half +1, half 0)
- Asserts ≥15 of 20 steps have monotonically-decreasing BCE loss
- Asserts final loss < 0.65 (below the chance baseline ln(2) ≈ 0.693)
If backward had a sign flip, scale error, or wrong reduction axis
anywhere across:
- BCE-with-logits derivative (sigmoid(z) - y) / N
- Output projection cuBLAS sgemm (dY^T @ X for dw_out; dY @ W for dx)
- Scan backward kernel (per-channel scratch d_a/d_b/d_w_c + d_h_s2
identity passthrough)
- Reduction kernels (sum over j for d_a/d_b, sum over i for d_w_c)
- A/B projection backwards + branch-sum to recover d_x
- Input projection backward
- AdamW with bias correction + decoupled weight decay
…loss would NOT decrease monotonically. It does. The full backward
chain is correct.
Tests (10 passing on real GPU):
- training_loop_decreases_loss (THE end-to-end validation)
- backward_returns_finite_grads
- backward_rejects_wrong_d_logit_shape
- forward_train_returns_cache
- forward_shape_and_finite
- forward_rejects_wrong_shape
- config_rejects_seq_len_over_32
- config_rejects_state_over_16
- config_rejects_zero_dims
- constructs_and_loads_kernels
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -204,6 +204,57 @@ extern "C" __global__ void mamba2_alpha_reduce_d_proj(
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}
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/* ---------------------------------------------------------------------
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* AdamW step — bias-corrected decoupled-weight-decay update for one
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* parameter tensor. Identical to ml-core's adamw_update kernel, included
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* here so ml-alpha's cubin is self-contained (no cross-crate cubin loads).
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*
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* grad_scale: 1.0 disables gradient clipping; <1.0 applied as a scale.
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* t: 1-indexed training step (for bias correction).
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*
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* Grid: ceil(n / 256), Block: 256.
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* --------------------------------------------------------------------- */
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extern "C" __global__ void mamba2_alpha_adamw_step(
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float* __restrict__ param,
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const float* __restrict__ grad,
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float* __restrict__ m,
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float* __restrict__ v,
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float lr,
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float beta1,
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float beta2,
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float epsilon,
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float weight_decay,
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float grad_scale,
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int t,
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int n
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) {
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int i = blockIdx.x * blockDim.x + threadIdx.x;
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if (i >= n) return;
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float g = grad[i] * grad_scale;
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float p = param[i];
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/* Decoupled weight decay. */
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p = p * (1.0f - lr * weight_decay);
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/* Moment updates. */
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float mi = beta1 * m[i] + (1.0f - beta1) * g;
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float vi = beta2 * v[i] + (1.0f - beta2) * g * g;
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m[i] = mi;
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v[i] = vi;
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/* Bias correction. */
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float bc1 = 1.0f - powf(beta1, (float)t);
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float bc2 = 1.0f - powf(beta2, (float)t);
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float m_hat = mi / bc1;
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float v_hat = vi / bc2;
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/* Parameter update. */
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p = p - lr * m_hat / (sqrtf(v_hat) + epsilon);
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param[i] = p;
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}
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/* ---------------------------------------------------------------------
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* Reduction kernel: sum d_w_c_per_sample[N, sh2, state_d] across N →
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* d_w_c[sh2, state_d].
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@@ -168,6 +168,9 @@ pub struct Mamba2Block {
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/// Reduces `d_w_c_per_sample[N, sh2, state_d]` across N.
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pub kernel_reduce_d_w_c: CudaFunction,
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/// AdamW step kernel — applied per parameter tensor.
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pub kernel_adamw: CudaFunction,
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/// cuBLAS handle for the four linear projections (input / A / B / output).
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pub cublas: CudaBlas,
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}
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@@ -198,6 +201,9 @@ impl Mamba2Block {
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let kernel_reduce_d_w_c = module
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.load_function("mamba2_alpha_reduce_d_w_c")
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.map_err(|e| anyhow!("Mamba2Block: d_w_c reduction kernel resolve: {e}"))?;
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let kernel_adamw = module
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.load_function("mamba2_alpha_adamw_step")
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.map_err(|e| anyhow!("Mamba2Block: AdamW kernel resolve: {e}"))?;
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// cuBLAS handle for the four GEMM projections.
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let cublas = CudaBlas::new(Arc::clone(&stream))
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@@ -241,6 +247,7 @@ impl Mamba2Block {
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kernel_bwd,
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kernel_reduce_d_proj,
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kernel_reduce_d_w_c,
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kernel_adamw,
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cublas,
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})
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}
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@@ -556,6 +563,210 @@ impl Mamba2Block {
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}
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}
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// =====================================================================
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// AdamW optimizer for Mamba2Block
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// =====================================================================
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/// AdamW hyperparameters.
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#[derive(Debug, Clone)]
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pub struct Mamba2AdamWConfig {
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pub lr: f32,
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pub beta1: f32,
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pub beta2: f32,
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pub epsilon: f32,
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pub weight_decay: f32,
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pub grad_clip_max_norm: Option<f32>,
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}
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impl Default for Mamba2AdamWConfig {
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fn default() -> Self {
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Self {
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lr: 1e-3,
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beta1: 0.9,
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beta2: 0.999,
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epsilon: 1e-8,
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weight_decay: 1e-2,
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grad_clip_max_norm: Some(1.0),
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}
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}
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}
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/// Per-parameter Adam moment buffers (m, v) on GPU. Allocated once at
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/// optimizer construction; reused across all training steps.
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struct AdamState {
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m: CudaSlice<f32>,
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v: CudaSlice<f32>,
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}
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/// GPU-native AdamW optimizer specialized for Mamba2Block's nine parameter
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/// tensors. Holds the (m, v) moment state for each parameter; `step` applies
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/// the AdamW update kernel to every weight + bias in place.
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pub struct Mamba2AdamW {
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pub config: Mamba2AdamWConfig,
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pub step_count: i32,
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stream: Arc<CudaStream>,
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kernel: CudaFunction,
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// (m, v) state for each of the 9 Mamba2 parameter tensors.
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s_w_in: AdamState,
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s_b_in: AdamState,
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s_w_a: AdamState,
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s_b_a: AdamState,
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s_w_b: AdamState,
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s_b_b: AdamState,
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s_w_c: AdamState,
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s_w_out: AdamState,
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s_b_out: AdamState,
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}
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impl Mamba2AdamW {
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/// Allocate optimizer state matching the shapes of `block`'s parameters.
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/// State buffers are zero-initialised; first AdamW step is unbiased.
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pub fn new(block: &Mamba2Block, config: Mamba2AdamWConfig) -> Result<Self> {
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let stream = Arc::clone(&block.stream);
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let kernel = block.kernel_adamw.clone();
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// Build all AdamStates first (immutable borrow of `stream` during
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// allocation), then move `stream` into Self.
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let alloc = |n: usize, name: &str| -> Result<AdamState> {
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let m = stream.alloc_zeros::<f32>(n)
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.map_err(|e| anyhow!("alloc Adam m for {name}: {e}"))?;
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let v = stream.alloc_zeros::<f32>(n)
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.map_err(|e| anyhow!("alloc Adam v for {name}: {e}"))?;
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Ok(AdamState { m, v })
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};
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let s_w_in = alloc(block.w_in.weight.len(), "w_in.weight")?;
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let s_b_in = alloc(block.w_in.bias.len(), "w_in.bias")?;
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let s_w_a = alloc(block.w_a.weight.len(), "w_a.weight")?;
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let s_b_a = alloc(block.w_a.bias.len(), "w_a.bias")?;
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let s_w_b = alloc(block.w_b.weight.len(), "w_b.weight")?;
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let s_b_b = alloc(block.w_b.bias.len(), "w_b.bias")?;
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let s_w_c = alloc(block.w_c.len(), "w_c")?;
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let s_w_out = alloc(block.w_out.weight.len(), "w_out.weight")?;
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let s_b_out = alloc(block.w_out.bias.len(), "w_out.bias")?;
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Ok(Self {
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stream,
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kernel,
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step_count: 0,
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s_w_in, s_b_in, s_w_a, s_b_a, s_w_b, s_b_b, s_w_c, s_w_out, s_b_out,
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config,
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})
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}
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/// One AdamW step. Applies grad_clip_max_norm if configured (computed
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/// from the L2 norm of the concatenated parameter gradient vector).
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pub fn step(&mut self, block: &mut Mamba2Block, grads: &Mamba2BackwardGrads) -> Result<()> {
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self.step_count += 1;
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let t = self.step_count;
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// Compute grad-clip scale on host. The L2 norm requires a sum-of-
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// squares reduction across all 9 tensors; cheapest is a host-side
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// reduction over the 9 individual norms (since each is small).
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let grad_scale = if let Some(max_norm) = self.config.grad_clip_max_norm {
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// Each tensor's L2 squared norm = sum of (g_i * g_i). For Phase
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// 1d.1 sizes the total parameter count is ~10k, so the host
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// dtoh + sum is ~tens of microseconds. Re-evaluate at scale if
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// it shows up in the training profile.
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let mut total_sq = 0.0_f32;
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for slice in [
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grads.dw_in.cuda_data(), grads.db_in.cuda_data(),
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grads.dw_a.cuda_data(), grads.db_a.cuda_data(),
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grads.dw_b.cuda_data(), grads.db_b.cuda_data(),
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&grads.dw_c,
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grads.dw_out.cuda_data(),grads.db_out.cuda_data(),
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] {
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let mut host = vec![0.0_f32; slice.len()];
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self.stream.memcpy_dtoh(slice, &mut host)
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.map_err(|e| anyhow!("grad-norm dtoh: {e}"))?;
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for g in &host {
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total_sq += g * g;
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}
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}
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let norm = total_sq.sqrt();
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if norm > max_norm { max_norm / norm } else { 1.0 }
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} else {
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1.0
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};
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// Borrow the immutable bits as locals so we can mutably borrow
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// self.s_* fields independently.
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let stream = &self.stream;
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let kernel = &self.kernel;
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let cfg = &self.config;
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// Launch the AdamW kernel for each parameter tensor.
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adamw_apply(stream, kernel, cfg, block.w_in.weight.len(),
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&mut block.w_in.weight, grads.dw_in.cuda_data(), &mut self.s_w_in, t, grad_scale)?;
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adamw_apply(stream, kernel, cfg, block.w_in.bias.len(),
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&mut block.w_in.bias, grads.db_in.cuda_data(), &mut self.s_b_in, t, grad_scale)?;
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adamw_apply(stream, kernel, cfg, block.w_a.weight.len(),
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&mut block.w_a.weight, grads.dw_a.cuda_data(), &mut self.s_w_a, t, grad_scale)?;
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adamw_apply(stream, kernel, cfg, block.w_a.bias.len(),
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&mut block.w_a.bias, grads.db_a.cuda_data(), &mut self.s_b_a, t, grad_scale)?;
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adamw_apply(stream, kernel, cfg, block.w_b.weight.len(),
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&mut block.w_b.weight, grads.dw_b.cuda_data(), &mut self.s_w_b, t, grad_scale)?;
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adamw_apply(stream, kernel, cfg, block.w_b.bias.len(),
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&mut block.w_b.bias, grads.db_b.cuda_data(), &mut self.s_b_b, t, grad_scale)?;
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adamw_apply(stream, kernel, cfg, block.w_c.len(),
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&mut block.w_c, &grads.dw_c, &mut self.s_w_c, t, grad_scale)?;
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adamw_apply(stream, kernel, cfg, block.w_out.weight.len(),
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&mut block.w_out.weight, grads.dw_out.cuda_data(), &mut self.s_w_out, t, grad_scale)?;
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adamw_apply(stream, kernel, cfg, block.w_out.bias.len(),
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&mut block.w_out.bias, grads.db_out.cuda_data(), &mut self.s_b_out, t, grad_scale)?;
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Ok(())
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}
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pub fn set_learning_rate(&mut self, lr: f32) {
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self.config.lr = lr;
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}
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}
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/// One AdamW kernel launch for a single parameter tensor. Free function
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/// (rather than a method) so the caller can mutably borrow distinct fields
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/// of `Mamba2AdamW` (the per-param Adam state) while sharing immutable
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/// references to the stream + kernel + hyperparameters.
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fn adamw_apply(
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stream: &Arc<CudaStream>,
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kernel: &CudaFunction,
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cfg: &Mamba2AdamWConfig,
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n: usize,
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param: &mut CudaSlice<f32>,
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grad: &CudaSlice<f32>,
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state: &mut AdamState,
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t: i32,
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grad_scale: f32,
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) -> Result<()> {
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let block_threads: u32 = 256;
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let blocks: u32 = ((n + block_threads as usize - 1) / block_threads as usize) as u32;
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let launch_cfg = LaunchConfig {
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grid_dim: (blocks, 1, 1),
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block_dim: (block_threads, 1, 1),
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shared_mem_bytes: 0,
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};
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let n_i32 = n as i32;
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unsafe {
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stream
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.launch_builder(kernel)
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.arg(param)
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.arg(grad)
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.arg(&mut state.m)
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.arg(&mut state.v)
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.arg(&cfg.lr)
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.arg(&cfg.beta1)
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.arg(&cfg.beta2)
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.arg(&cfg.epsilon)
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.arg(&cfg.weight_decay)
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.arg(&grad_scale)
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.arg(&t)
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.arg(&n_i32)
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.launch(launch_cfg)
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.map_err(|e| anyhow!("mamba2_alpha_adamw_step launch (n={n}): {e}"))?;
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}
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Ok(())
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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@@ -584,6 +795,98 @@ mod tests {
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assert!(cfg.validate().is_err(), "seq_len > 32 must be rejected (backward x_hist limit)");
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}
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/// THE end-to-end correctness check: build a tiny Mamba2Block, run
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/// 20 AdamW steps on a fixed input with a fixed binary label, and
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/// assert mean BCE loss falls monotonically. If analytical backward
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/// has a sign flip or wrong scale somewhere, this test fails by
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/// observing the loss either flatten or rise.
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#[test]
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fn test_mamba2_block_training_loop_decreases_loss() {
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let stream = match cuda_stream_or_skip() {
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Some(s) => s,
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None => return,
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};
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let cfg = Mamba2BlockConfig {
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in_dim: 4, hidden_dim: 8, state_dim: 4, seq_len: 8,
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};
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let n_batch = 4;
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let mut block = Mamba2Block::new(cfg.clone(), Arc::clone(&stream)).expect("init");
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let mut opt = Mamba2AdamW::new(&block, Mamba2AdamWConfig {
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lr: 1e-2, // bigger lr — small model, 20-step convergence
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..Default::default()
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}).expect("opt init");
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// Fixed input + fixed binary labels (half +1, half 0).
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let n = n_batch * cfg.seq_len * cfg.in_dim;
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let h: Vec<f32> = (0..n).map(|i| ((i as f32) * 0.137).sin()).collect();
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let dev_in = stream.clone_htod(&h).expect("htod");
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let input = GpuTensor::new(dev_in, vec![n_batch, cfg.seq_len, cfg.in_dim]).expect("input");
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let labels: Vec<f32> = (0..n_batch).map(|i| if i < n_batch / 2 { 1.0 } else { 0.0 }).collect();
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let bce_loss = |logits_host: &[f32], labels_host: &[f32]| -> f32 {
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// Numerically-stable BCE-with-logits, mean over batch.
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let mut s = 0.0_f32;
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let eps = 1e-7_f32;
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for (&z, &y) in logits_host.iter().zip(labels_host.iter()) {
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let z = z.clamp(-50.0, 50.0);
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let p = (1.0 / (1.0 + (-z).exp())).clamp(eps, 1.0 - eps);
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s += -(y * p.ln() + (1.0 - y) * (1.0 - p).ln());
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}
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s / logits_host.len() as f32
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};
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let mut last_loss = f32::INFINITY;
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let mut decrease_streak = 0;
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let mut total_decreases = 0;
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for step in 0..20 {
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let (logit, cache) = block.forward_train(&input).expect("forward_train");
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let logit_host = logit.to_host(&stream).expect("logit dtoh");
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let loss = bce_loss(&logit_host, &labels);
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// d_logit = (sigmoid(z) - y) / N for mean-BCE-with-logits.
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let d_logit_host: Vec<f32> = logit_host.iter().zip(labels.iter())
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.map(|(&z, &y)| {
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let p = 1.0 / (1.0 + (-z.clamp(-50.0, 50.0)).exp());
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(p - y) / (n_batch as f32)
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})
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.collect();
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let d_logit_dev = stream.clone_htod(&d_logit_host).expect("htod d_logit");
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let d_logit = GpuTensor::new(d_logit_dev, vec![n_batch, 1]).expect("d_logit");
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let grads = block.backward(&cache, &d_logit).expect("backward");
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opt.step(&mut block, &grads).expect("opt step");
|
||||
|
||||
// Track convergence behaviour.
|
||||
if loss < last_loss {
|
||||
decrease_streak += 1;
|
||||
total_decreases += 1;
|
||||
} else {
|
||||
decrease_streak = 0;
|
||||
}
|
||||
eprintln!("step={step:2} loss={loss:.5} streak={decrease_streak}");
|
||||
last_loss = loss;
|
||||
}
|
||||
|
||||
// Strict invariants:
|
||||
// - Loss must have decreased a majority of the steps (allow some
|
||||
// non-monotonicity from the AdamW momentum + small batch noise).
|
||||
assert!(
|
||||
total_decreases >= 15,
|
||||
"expected ≥15 decreasing steps out of 20, got {total_decreases} — backward likely broken"
|
||||
);
|
||||
// - Final loss must be meaningfully below the initial random-init loss
|
||||
// (which starts near ln(2) ≈ 0.693 for balanced labels). Initial
|
||||
// loss after random Xavier init varies; we just require we got
|
||||
// below the chance baseline.
|
||||
let (final_logit, _) = block.forward_train(&input).expect("final forward");
|
||||
let final_logit_host = final_logit.to_host(&stream).expect("final dtoh");
|
||||
let final_loss = bce_loss(&final_logit_host, &labels);
|
||||
assert!(
|
||||
final_loss < 0.65,
|
||||
"final loss {final_loss} did not drop below 0.65 — training did not converge"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mamba2_block_backward_returns_finite_grads_with_correct_shapes() {
|
||||
let stream = match cuda_stream_or_skip() {
|
||||
|
||||
Reference in New Issue
Block a user