ops.rs wraps each Tensor op as a Var node with its backward closure (forward
caches captured by move). swiglu = mul(silu(gate), up); attention is composed
(matmul+scale+softmax+matmul), no fused kernel. tests/autograd.rs grad-checks
every op via the L=sum(W∘out) template, plus a fan-out grad-accumulation test
(dL/dx=4x) and an end-to-end composed-attention grad-check (dQ/dK/dV). Adds
xtrain-cuda dev-dep for device selection in tests.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New xtrain-autodiff crate with a reusable central finite-difference
gradient check: grad_check(x, shape, f, analytic_grad, cfg) compares an
analytic gradient against (f(x+ε)-f(x-ε))/2ε per element with a relative
tolerance. Host-only (no CUDA): the loss closure owns any GPU work, so
T4's per-op backward checks can reuse it directly. Includes host unit
tests (sum(x²) grad 2x passes; a wrong grad is rejected).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New xtrain-tensor crate: DType (F32), shape/stride helpers, Arc-counted
host/device Storage with CPU↔CUDA copy, and a contiguous Tensor with
creation, host↔device transfer, and a scale() op driving the elementwise
kernel. GPU integration tests (host↔device roundtrip + scale correctness)
gated behind not(no_cuda); a thin build.rs emits the no_cuda cfg so the
kernel call sites compile out locally.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Stand up the xtrain project skeleton: a Cargo workspace mirroring xserv's
csrc/ + crates/ layout, with a single xtrain-cuda crate that wraps the CUDA
Runtime over hand-written extern "C" FFI. build.rs compiles csrc/test/vecadd.cu
via the cc crate targeting sm_120 (RTX 5090) and links cudart.
A gated integration test runs the vector-add kernel on the GPU and asserts the
result. When nvcc is absent (local GPU-less machine), build.rs skips CUDA
compilation and sets a `no_cuda` cfg so host-side cargo check still works.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
