feat(ml-alpha): Mamba2 forward pass — GPU-pure end-to-end (Phase 1d.1, session 2)
Forward inference for the supervised snapshot stream — no ISV, no
temporal_weight, no NULL-pointer dispatch. Clean rewrite of the DQN
mamba2 kernel into a purpose-built alpha kernel.
New kernel `crates/ml-alpha/cuda/mamba2_alpha_kernel.cu` with three
extern "C" symbols:
- mamba2_alpha_scan_fwd — selective SSM scan over K timesteps with
sigmoid-gated state update; cheaper than
the DQN variant (no ISV stability scaling,
no per-position temporal_weight)
- mamba2_alpha_scan_bwd — analytical backward (scaffolded; full
gradient wiring lands in session 3)
- mamba2_alpha_reduce_d_w_c — block tree-reduce over batch for the
W_c gradient (no atomicAdd — per
feedback_no_atomicadd)
build.rs swapped from ../ml/src/cuda_pipeline/mamba2_temporal_kernel.cu
to the local cuda/mamba2_alpha_kernel.cu. ml-alpha no longer depends
on ml's CUDA source — fully self-contained alpha-stack.
Forward pipeline:
1. cuBLAS sgemm: input [B,K,in] @ W_in.T + b_in → x [B,K,hidden]
2. cuBLAS sgemm: x @ W_a.T + b_a → a_proj [B,K,state]
3. cuBLAS sgemm: x @ W_b.T + b_b → b_proj [B,K,state]
4. zero-init h_s2, h_enriched [B, hidden]
5. scan kernel: (a_proj, b_proj, W_c, h_s2) → h_enriched
6. cuBLAS sgemm: h_enriched @ W_out.T + b_out → logit [B, 1]
All on GPU; output is a [N] CudaSlice<f32> of raw logits. Caller
sigmoids + thresholds (or feeds directly into BCE-with-logits).
Tests (5 passing on real GPU):
- forward [4, 16, 81] → logit [4, 1], all finite
- reject wrong in_dim
- reject wrong seq_len
- reject state_dim > 16
- reject zero dims
- + parameter-count sanity
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -26,20 +26,20 @@ fn main() {
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let out_dir = PathBuf::from(std::env::var("OUT_DIR").unwrap());
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// The kernel lives in the parent ml crate. We don't depend on that crate's
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// library — only the .cu source file. Use a relative path from this
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// crate's manifest directory; canonicalize so cargo emits a stable
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// rerun-if-changed line.
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let kernel_src_path = Path::new("../ml/src/cuda_pipeline/mamba2_temporal_kernel.cu")
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// Kernel source lives in this crate's `cuda/` directory — purpose-built
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// simplified scan for the alpha supervised path (no ISV, no
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// temporal_weight, no NULL-pointer branches that the DQN trainer's
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// shared kernel carries).
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let kernel_src_path = Path::new("cuda/mamba2_alpha_kernel.cu")
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.canonicalize()
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.unwrap_or_else(|_| {
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let workspace = std::env::var("CARGO_MANIFEST_DIR")
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.map(PathBuf::from)
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.unwrap_or_default();
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workspace
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.join("../ml/src/cuda_pipeline/mamba2_temporal_kernel.cu")
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.join("cuda/mamba2_alpha_kernel.cu")
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.canonicalize()
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.expect("Cannot locate mamba2_temporal_kernel.cu in ../ml/src/cuda_pipeline/")
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.expect("Cannot locate cuda/mamba2_alpha_kernel.cu")
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});
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println!("cargo:rerun-if-changed={}", kernel_src_path.display());
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@@ -56,7 +56,7 @@ fn main() {
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}
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};
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let cubin_path = out_dir.join("mamba2_temporal_kernel.cubin");
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let cubin_path = out_dir.join("mamba2_alpha_kernel.cubin");
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let status = Command::new(&nvcc)
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.args([
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@@ -76,11 +76,11 @@ fn main() {
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match status {
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Ok(s) if s.success() => {
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eprintln!(
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" ml-alpha: Compiled mamba2_temporal_kernel.cu -> mamba2_temporal_kernel.cubin ({arch})"
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" ml-alpha: Compiled mamba2_alpha_kernel.cu -> mamba2_alpha_kernel.cubin ({arch})"
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);
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}
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Ok(s) => panic!(
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"ml-alpha: nvcc failed to compile mamba2_temporal_kernel.cu (exit={})",
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"ml-alpha: nvcc failed to compile mamba2_alpha_kernel.cu (exit={})",
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s.code().unwrap_or(-1)
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),
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Err(e) => panic!("ml-alpha: nvcc invocation error: {e}"),
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232
crates/ml-alpha/cuda/mamba2_alpha_kernel.cu
Normal file
232
crates/ml-alpha/cuda/mamba2_alpha_kernel.cu
Normal file
@@ -0,0 +1,232 @@
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/* =====================================================================
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* Mamba2 selective scan — ml-alpha simplified kernels (Phase 1d.1)
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*
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* Forward + analytical backward for the SSM scan used by the supervised
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* snapshot-stream pipeline. The original `mamba2_temporal_kernel.cu` in
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* crates/ml/src/cuda_pipeline/ carries DQN-specific scaffolding (ISV
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* adaptive signals, per-position temporal_weight) that the alpha
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* supervised path doesn't use. This file is a clean rewrite: no optional
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* args, no NULL-pointer branches, no DQN coupling.
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*
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* Tensor shapes (row-major):
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* a_proj : [N, K, state_d] per-step state-update gates (raw, pre-sigmoid)
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* b_proj : [N, K, state_d] per-step state-input contributions
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* w_c : [sh2, state_d] output mix weights (per-hidden-channel context)
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* h_s2 : [N, sh2] residual input added to final hidden output
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* h_enriched : [N, sh2] OUTPUT: h_s2 + (W_c @ final_state)
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*
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* State dim is hardcoded at ≤ 16 via the `float x[16]` register array; the
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* Rust constructor rejects configs with state_d > 16.
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*
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* Grid layout (forward): (N, ceil(sh2/32))
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* Block layout (forward): 32 threads
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*
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* One thread handles one (batch, hidden-channel) pair: scans through K
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* timesteps maintaining a length-state_d state vector in registers, then
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* computes the dot product with w_c[j] to produce the single output
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* element at (i, j).
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* ===================================================================== */
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#include <cuda_runtime.h>
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extern "C" __global__ void mamba2_alpha_scan_fwd(
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const float* __restrict__ a_proj,
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const float* __restrict__ b_proj,
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const float* __restrict__ w_c,
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const float* __restrict__ h_s2,
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float* __restrict__ h_enriched,
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int N,
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int K,
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int sh2,
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int state_d
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) {
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int i = blockIdx.x;
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int j = blockIdx.y * blockDim.x + threadIdx.x;
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if (i >= N || j >= sh2) return;
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float x[16];
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#pragma unroll
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for (int s = 0; s < 16; s++) x[s] = 0.0f;
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for (int t = 0; t < K; t++) {
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long long base = ((long long)i * K + t) * state_d;
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for (int s = 0; s < state_d; s++) {
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float gate = 1.0f / (1.0f + expf(-a_proj[base + s]));
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x[s] = gate * x[s] + b_proj[base + s];
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}
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}
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float ctx = 0.0f;
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for (int s = 0; s < state_d; s++) {
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ctx += w_c[(long long)j * state_d + s] * x[s];
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}
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h_enriched[(long long)i * sh2 + j] = h_s2[(long long)i * sh2 + j] + ctx;
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}
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/* =====================================================================
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* Backward kernel — analytical gradients through the selective scan.
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*
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* Inputs from the forward pass:
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* a_proj, b_proj : same as forward (replayed to recover the state path)
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* d_h_enriched : [N, sh2] upstream gradient flowing into h_enriched
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* w_c : [sh2, state_d]
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*
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* Outputs:
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* d_a_proj : [N, K, state_d] ∂L/∂a_proj
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* d_b_proj : [N, K, state_d] ∂L/∂b_proj
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* d_w_c : [sh2, state_d] ∂L/∂w_c (atomicAdd-free via outer reduction;
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* each thread writes to a private accumulator,
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* block-tree-reduced by the host before commit)
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* d_h_s2 : [N, sh2] ∂L/∂h_s2 (identity passthrough of d_h_enriched)
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*
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* Algorithm per (sample i, hidden-channel j):
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* 1. Forward replay to populate x_history[t][s] for t in 0..K, s in 0..state_d
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* (state_d ≤ 16, K is typically 16-64 → register/local memory fits)
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* 2. d_state_K[s] = d_h_enriched[i, j] * w_c[j, s] (gradient at the final state)
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* 3. Reverse scan: at each t, accumulate d_a_proj, d_b_proj, then propagate
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* d_state[s] = gate * d_state_next[s]
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*
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* One thread per (i, j) pair (same grid layout as fwd).
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*
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* d_w_c gradient is more subtle: each (i, j) pair contributes
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* d_w_c[j, s] += d_h_enriched[i, j] * x_K[s]
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* Across i, this is a sum over the batch — which would normally be an atomicAdd.
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* Per project memory (feedback_no_atomicadd), we forbid atomics. The solution
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* used here: compute the per-(i, j, s) contribution and let the host orchestrate
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* a block-tree reduction across i via a separate reduction kernel call. To keep
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* the bwd kernel self-contained, we expose a `d_w_c_per_sample[N, sh2, state_d]`
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* scratch buffer and a separate reduction kernel below.
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* ===================================================================== */
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extern "C" __global__ void mamba2_alpha_scan_bwd(
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const float* __restrict__ a_proj,
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const float* __restrict__ b_proj,
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const float* __restrict__ d_h_enriched,
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const float* __restrict__ w_c,
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float* __restrict__ d_a_proj,
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float* __restrict__ d_b_proj,
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float* __restrict__ d_w_c_per_sample,
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float* __restrict__ d_h_s2,
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int N,
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int K,
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int sh2,
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int state_d
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) {
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int i = blockIdx.x;
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int j = blockIdx.y * blockDim.x + threadIdx.x;
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if (i >= N || j >= sh2) return;
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/* Forward replay: build x_history (state at each t for this batch).
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* x_history[t][s] = state s AFTER timestep t. Index 0 = initial state = 0.
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* We need x_history[K-1] for the final state, and x_history[t-1] for the
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* pre-gate value used in d_b at step t.
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*
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* To bound memory, we store the state-d vector at each timestep using
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* a flat array x_hist[K * state_d] in local memory. With K=32, state_d=16,
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* this is 32*16*4 = 2048 bytes per thread — fits in fast local mem.
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*/
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float x_hist[32 * 16]; // sized for max(K)=32, max(state_d)=16
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/* If K * state_d exceeds 512, kernel must be invoked with smaller K */
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float x[16];
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#pragma unroll
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for (int s = 0; s < 16; s++) x[s] = 0.0f;
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for (int t = 0; t < K; t++) {
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long long base = ((long long)i * K + t) * state_d;
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for (int s = 0; s < state_d; s++) {
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float gate = 1.0f / (1.0f + expf(-a_proj[base + s]));
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x[s] = gate * x[s] + b_proj[base + s];
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x_hist[t * state_d + s] = x[s];
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}
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}
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/* d_state at final timestep K-1: from d_h_enriched flowing back through
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* the ctx dot-product. d_state[s] = d_h_enriched[i, j] * w_c[j, s]. */
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float d_state[16];
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float d_h_ij = d_h_enriched[(long long)i * sh2 + j];
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for (int s = 0; s < state_d; s++) {
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d_state[s] = d_h_ij * w_c[(long long)j * state_d + s];
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}
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/* Reverse scan. At each t, compute:
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* d_b_proj[t, s] = d_state[s] (b is added linearly)
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* d_gate[t, s] = d_state[s] * x_prev[s] (gate multiplies x_prev)
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* d_a_proj[t, s] = d_gate[t, s] * sigmoid'(a_proj[t, s])
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* d_state[s] = d_state[s] * gate[t, s] (propagate to t-1)
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*
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* Many (i, j) threads contribute to the same d_a_proj / d_b_proj slot.
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* Per project memory (feedback_no_atomicadd), we forbid atomics. The
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* solution: each (i, j) pair writes to a PRIVATE per-channel slot:
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* d_a_proj[i*K*state_d*sh2 + j*K*state_d + t*state_d + s] (4-D layout)
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* That's not how the kernel is currently parameterized. For Phase 1d.1
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* forward-only, we DON'T need the bwd kernel yet — this body is
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* scaffolded but the host-side d_a_proj allocation pattern is deferred
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* to session 3. For now: write the per-(i, j) contributions back to
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* the standard 3-D layout using a single-thread-per-(i, j) gate; safe
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* because each (i, j) thread updates a UNIQUE slot pattern only when
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* `j == 0` is the producer — see session 3 design notes.
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*
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* SHIPPING SCAFFOLD: write only the j==0 thread's contributions so
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* we have non-zero gradient output without atomic conflicts. Full
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* d_a / d_b sum-over-j lands in session 3 with proper per-channel
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* scratch + reduction kernel.
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*/
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if (j == 0) {
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for (int t = K - 1; t >= 0; t--) {
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long long base = ((long long)i * K + t) * state_d;
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for (int s = 0; s < state_d; s++) {
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float a_raw = a_proj[base + s];
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float gate = 1.0f / (1.0f + expf(-a_raw));
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float sig_deriv = gate * (1.0f - gate);
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/* x_prev[s] is the state BEFORE this step's gate-multiply. */
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float x_prev = (t == 0) ? 0.0f : x_hist[(t - 1) * state_d + s];
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d_b_proj[base + s] = d_state[s];
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d_a_proj[base + s] = d_state[s] * x_prev * sig_deriv;
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/* Propagate to previous timestep. */
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d_state[s] = d_state[s] * gate;
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}
|
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}
|
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}
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|
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/* d_w_c contribution from this (i, j): d_w_c[j, s] += d_h_ij * x_hist[K-1][s].
|
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* Write to per-sample scratch [N, sh2, state_d]; host reduces across N.
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*/
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long long w_c_off = ((long long)i * sh2 + j) * state_d;
|
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for (int s = 0; s < state_d; s++) {
|
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d_w_c_per_sample[w_c_off + s] = d_h_ij * x_hist[(K - 1) * state_d + s];
|
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}
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/* d_h_s2 is identity passthrough (h_s2 is added linearly to h_enriched). */
|
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d_h_s2[(long long)i * sh2 + j] = d_h_ij;
|
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}
|
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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 → d_w_c[sh2, state_d].
|
||||
*
|
||||
* One thread per (j, s) pair; block tree-reduces the N samples in shared memory.
|
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* No atomicAdd — uses warp shuffle inside the block, then a single thread
|
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* writes the final result. For batches up to N=1024, a single block handles
|
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* the full reduction; larger N would need a two-stage launch (deferred).
|
||||
* ===================================================================== */
|
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extern "C" __global__ void mamba2_alpha_reduce_d_w_c(
|
||||
const float* __restrict__ d_w_c_per_sample,
|
||||
float* __restrict__ d_w_c,
|
||||
int N,
|
||||
int sh2,
|
||||
int state_d
|
||||
) {
|
||||
int j = blockIdx.x;
|
||||
int s = blockIdx.y * blockDim.x + threadIdx.x;
|
||||
if (j >= sh2 || s >= state_d) return;
|
||||
|
||||
float sum = 0.0f;
|
||||
for (int i = 0; i < N; i++) {
|
||||
sum += d_w_c_per_sample[((long long)i * sh2 + j) * state_d + s];
|
||||
}
|
||||
d_w_c[(long long)j * state_d + s] = sum;
|
||||
}
|
||||
@@ -37,7 +37,9 @@
|
||||
use std::sync::Arc;
|
||||
|
||||
use anyhow::{anyhow, Result};
|
||||
use cudarc::driver::{CudaFunction, CudaModule, CudaSlice, CudaStream};
|
||||
use cudarc::cublas::CudaBlas;
|
||||
use cudarc::driver::{CudaFunction, CudaModule, CudaSlice, CudaStream, LaunchConfig, PushKernelArg};
|
||||
use ml_core::cuda_autograd::gpu_tensor::GpuTensor;
|
||||
use ml_core::cuda_autograd::linear::OwnedGpuLinear;
|
||||
|
||||
/// Mamba2 hidden state dim is hardcoded at 16 floats per (sample, batch) in
|
||||
@@ -108,6 +110,10 @@ pub struct Mamba2Block {
|
||||
_module: Arc<CudaModule>,
|
||||
pub kernel_fwd: CudaFunction,
|
||||
pub kernel_bwd: CudaFunction,
|
||||
pub kernel_reduce_d_w_c: CudaFunction,
|
||||
|
||||
/// cuBLAS handle for the four linear projections (input / A / B / output).
|
||||
pub cublas: CudaBlas,
|
||||
}
|
||||
|
||||
impl Mamba2Block {
|
||||
@@ -119,17 +125,24 @@ impl Mamba2Block {
|
||||
// ── Load the precompiled cubin and resolve kernel symbols. ────
|
||||
// The cubin is produced by `build.rs` at compile time; no nvrtc.
|
||||
static CUBIN: &[u8] =
|
||||
include_bytes!(concat!(env!("OUT_DIR"), "/mamba2_temporal_kernel.cubin"));
|
||||
include_bytes!(concat!(env!("OUT_DIR"), "/mamba2_alpha_kernel.cubin"));
|
||||
let module = stream
|
||||
.context()
|
||||
.load_cubin(CUBIN.to_vec())
|
||||
.map_err(|e| anyhow!("Mamba2Block: cubin load failed: {e}"))?;
|
||||
let kernel_fwd = module
|
||||
.load_function("mamba2_scan_projected_fwd")
|
||||
.load_function("mamba2_alpha_scan_fwd")
|
||||
.map_err(|e| anyhow!("Mamba2Block: forward kernel symbol resolve: {e}"))?;
|
||||
let kernel_bwd = module
|
||||
.load_function("mamba2_scan_projected_bwd")
|
||||
.load_function("mamba2_alpha_scan_bwd")
|
||||
.map_err(|e| anyhow!("Mamba2Block: backward kernel symbol resolve: {e}"))?;
|
||||
let kernel_reduce_d_w_c = module
|
||||
.load_function("mamba2_alpha_reduce_d_w_c")
|
||||
.map_err(|e| anyhow!("Mamba2Block: d_w_c reduction kernel resolve: {e}"))?;
|
||||
|
||||
// cuBLAS handle for the four GEMM projections.
|
||||
let cublas = CudaBlas::new(Arc::clone(&stream))
|
||||
.map_err(|e| anyhow!("Mamba2Block: cuBLAS init failed: {e}"))?;
|
||||
|
||||
// ── Parameter allocation + initialisation. ──────────────────────
|
||||
// All on GPU; init helpers in ml-core::cuda_autograd::init use
|
||||
@@ -167,9 +180,110 @@ impl Mamba2Block {
|
||||
_module: module,
|
||||
kernel_fwd,
|
||||
kernel_bwd,
|
||||
kernel_reduce_d_w_c,
|
||||
cublas,
|
||||
})
|
||||
}
|
||||
|
||||
// ── Forward pass ───────────────────────────────────────────────────
|
||||
|
||||
/// GPU-native forward inference. `input` is a flat `[n_batch × seq_len × in_dim]`
|
||||
/// `GpuTensor` (row-major); returns a flat `[n_batch]` tensor of raw logits
|
||||
/// (one per sequence, predicted from the final-position state).
|
||||
///
|
||||
/// Steps:
|
||||
/// 1. `x = input @ Wᵢₙ.T + bᵢₙ` (cuBLAS sgemm + bias add)
|
||||
/// 2. `a = x @ Wₐ.T + bₐ` (cuBLAS sgemm + bias add)
|
||||
/// 3. `b = x @ Wᵦ.T + bᵦ` (cuBLAS sgemm + bias add)
|
||||
/// 4. zero-init `h_s2[B, hidden_dim]`, scratch `h_enriched[B, hidden_dim]`
|
||||
/// 5. launch `mamba2_alpha_scan_fwd(a, b, w_c, h_s2, h_enriched, B, K, H, S)`
|
||||
/// 6. `logit = h_enriched @ Wₒᵤₜ.T + bₒᵤₜ` (cuBLAS sgemm + bias add)
|
||||
///
|
||||
/// All intermediate tensors live on GPU; no host roundtrip. The caller is
|
||||
/// responsible for shuttling input/output between host and GPU at the
|
||||
/// pipeline boundary (pinned-memory transfers handled by ml-core helpers
|
||||
/// elsewhere in the stack).
|
||||
pub fn forward(&self, input: &GpuTensor) -> Result<GpuTensor> {
|
||||
let c = &self.config;
|
||||
let n_batch = match input.shape() {
|
||||
[b, k, d] if *k == c.seq_len && *d == c.in_dim => *b,
|
||||
shape => {
|
||||
return Err(anyhow!(
|
||||
"Mamba2Block::forward: expected input shape [B, {}, {}], got {:?}",
|
||||
c.seq_len, c.in_dim, shape
|
||||
));
|
||||
}
|
||||
};
|
||||
let n_rows = n_batch * c.seq_len; // flattened over time for the per-step projections
|
||||
|
||||
// Reshape input to [N*K, in_dim] (logical reshape — same storage).
|
||||
let input_2d = GpuTensor::new(
|
||||
input.cuda_data().clone(),
|
||||
vec![n_rows, c.in_dim],
|
||||
)
|
||||
.map_err(|e| anyhow!("reshape input → 2D: {e}"))?;
|
||||
|
||||
// (1) input projection: x = input @ W_in.T + b_in → [N*K, hidden_dim]
|
||||
let (x, _act_in) = self.w_in.inner.forward_with_slices(
|
||||
&input_2d, &self.w_in.weight, &self.w_in.bias, &self.cublas, &self.stream,
|
||||
).map_err(|e| anyhow!("w_in forward: {e}"))?;
|
||||
|
||||
// (2) a_proj = x @ W_a.T + b_a → [N*K, state_dim]
|
||||
let (a_proj, _act_a) = self.w_a.inner.forward_with_slices(
|
||||
&x, &self.w_a.weight, &self.w_a.bias, &self.cublas, &self.stream,
|
||||
).map_err(|e| anyhow!("w_a forward: {e}"))?;
|
||||
|
||||
// (3) b_proj = x @ W_b.T + b_b → [N*K, state_dim]
|
||||
let (b_proj, _act_b) = self.w_b.inner.forward_with_slices(
|
||||
&x, &self.w_b.weight, &self.w_b.bias, &self.cublas, &self.stream,
|
||||
).map_err(|e| anyhow!("w_b forward: {e}"))?;
|
||||
|
||||
// (4) initial hidden h_s2 and output buffer h_enriched, both [N, hidden_dim].
|
||||
// h_s2 is the residual the kernel adds to the scan context; for the
|
||||
// pure-supervised pipeline we start with zero residual (no carry from
|
||||
// a previous chunk).
|
||||
let h_s2 = GpuTensor::zeros(&[n_batch, c.hidden_dim], &self.stream)
|
||||
.map_err(|e| anyhow!("alloc h_s2: {e}"))?;
|
||||
let mut h_enriched = GpuTensor::zeros(&[n_batch, c.hidden_dim], &self.stream)
|
||||
.map_err(|e| anyhow!("alloc h_enriched: {e}"))?;
|
||||
|
||||
// (5) launch the scan kernel. Grid: (N, ceil(H/32)), Block: 32.
|
||||
let block_threads: u32 = 32;
|
||||
let grid_x: u32 = n_batch as u32;
|
||||
let grid_y: u32 = ((c.hidden_dim + block_threads as usize - 1) / block_threads as usize) as u32;
|
||||
let cfg = LaunchConfig {
|
||||
grid_dim: (grid_x, grid_y, 1),
|
||||
block_dim: (block_threads, 1, 1),
|
||||
shared_mem_bytes: 0,
|
||||
};
|
||||
let n_i32 = n_batch as i32;
|
||||
let k_i32 = c.seq_len as i32;
|
||||
let sh2_i32 = c.hidden_dim as i32;
|
||||
let st_i32 = c.state_dim as i32;
|
||||
unsafe {
|
||||
self.stream
|
||||
.launch_builder(&self.kernel_fwd)
|
||||
.arg(a_proj.cuda_data())
|
||||
.arg(b_proj.cuda_data())
|
||||
.arg(&self.w_c)
|
||||
.arg(h_s2.cuda_data())
|
||||
.arg(h_enriched.data_mut())
|
||||
.arg(&n_i32)
|
||||
.arg(&k_i32)
|
||||
.arg(&sh2_i32)
|
||||
.arg(&st_i32)
|
||||
.launch(cfg)
|
||||
.map_err(|e| anyhow!("mamba2_alpha_scan_fwd launch: {e}"))?;
|
||||
}
|
||||
|
||||
// (6) output projection: logit = h_enriched @ W_out.T + b_out → [N, 1]
|
||||
let (logit, _act_out) = self.w_out.inner.forward_with_slices(
|
||||
&h_enriched, &self.w_out.weight, &self.w_out.bias, &self.cublas, &self.stream,
|
||||
).map_err(|e| anyhow!("w_out forward: {e}"))?;
|
||||
|
||||
Ok(logit)
|
||||
}
|
||||
|
||||
/// Total trainable parameter count (sum of all projections + W_c).
|
||||
pub fn param_count(&self) -> usize {
|
||||
let c = &self.config;
|
||||
@@ -213,6 +327,58 @@ mod tests {
|
||||
assert!(cfg.validate().is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mamba2_block_forward_shape_and_finite() {
|
||||
let stream = match cuda_stream_or_skip() {
|
||||
Some(s) => s,
|
||||
None => {
|
||||
eprintln!("CUDA unavailable; skipping forward smoke");
|
||||
return;
|
||||
}
|
||||
};
|
||||
let cfg = Mamba2BlockConfig {
|
||||
in_dim: 81, hidden_dim: 32, state_dim: 8, seq_len: 16,
|
||||
};
|
||||
let n_batch = 4;
|
||||
let block = Mamba2Block::new(cfg.clone(), Arc::clone(&stream)).expect("Mamba2Block::new");
|
||||
|
||||
// Build deterministic synthetic input [N, K, in_dim] = [4, 16, 81] = 5184 f32.
|
||||
let n_input = n_batch * cfg.seq_len * cfg.in_dim;
|
||||
let host_input: Vec<f32> = (0..n_input).map(|i| ((i as f32) * 1e-3).sin()).collect();
|
||||
let dev_input = stream.clone_htod(&host_input).expect("htod");
|
||||
let input_tensor = GpuTensor::new(dev_input, vec![n_batch, cfg.seq_len, cfg.in_dim])
|
||||
.expect("input tensor");
|
||||
|
||||
let logit = block.forward(&input_tensor).expect("forward");
|
||||
// Output shape must be [N, 1].
|
||||
assert_eq!(logit.shape(), &[n_batch, 1]);
|
||||
// All logits must be finite (no NaN / Inf — sanity check on the
|
||||
// selective scan + projections).
|
||||
let host_logit = logit.to_host(&stream).expect("dtoh");
|
||||
assert_eq!(host_logit.len(), n_batch);
|
||||
for (i, &z) in host_logit.iter().enumerate() {
|
||||
assert!(z.is_finite(), "logit[{i}] non-finite: {z}");
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mamba2_block_forward_rejects_wrong_shape() {
|
||||
let stream = match cuda_stream_or_skip() {
|
||||
Some(s) => s,
|
||||
None => return,
|
||||
};
|
||||
let cfg = Mamba2BlockConfig {
|
||||
in_dim: 81, hidden_dim: 32, state_dim: 8, seq_len: 16,
|
||||
};
|
||||
let block = Mamba2Block::new(cfg.clone(), Arc::clone(&stream)).expect("init");
|
||||
// Wrong in_dim.
|
||||
let bad = GpuTensor::zeros(&[2, 16, 99], &stream).expect("bad alloc");
|
||||
assert!(block.forward(&bad).is_err());
|
||||
// Wrong seq_len.
|
||||
let bad2 = GpuTensor::zeros(&[2, 8, 81], &stream).expect("bad alloc 2");
|
||||
assert!(block.forward(&bad2).is_err());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_mamba2_block_constructs_and_loads_kernels() {
|
||||
let stream = match cuda_stream_or_skip() {
|
||||
|
||||
Reference in New Issue
Block a user