Gahow Wang
e7ce504b1f
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>
12 lines
281 B
TOML
12 lines
281 B
TOML
[package]
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name = "xtrain-autodiff"
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version.workspace = true
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edition.workspace = true
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[dependencies]
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xtrain-tensor = { path = "../xtrain-tensor" }
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[dev-dependencies]
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# Acceptance tests need device selection (set_device) to drive the GPU.
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xtrain-cuda = { path = "../xtrain-cuda" }
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