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chore: mark EnzymeCore.augmented_primal as codecov excluded (#1467)
* chore: mark EnzymeCore.augmented_primal as codecov excluded * ci: codecov status reporting
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.codecov.yml

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codecov:
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require_ci_to_pass: false
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notify:
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wait_for_ci: false

ext/LuxLossFunctionsExt.jl

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@@ -44,6 +44,8 @@ function CRC.rrule(
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return LossFunctionImpl.fused_agg(sum, lfn, x, y), ∇fused_agg
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end
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# COV_EXCL_START
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function EnzymeRules.augmented_primal(
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cfg::EnzymeRules.RevConfigWidth{1},
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func::EnzymeCore.Const{typeof(LossFunctionImpl.fused_agg)},
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return ntuple(Returns(nothing), 4)
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end
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# COV_EXCL_STOP
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end

lib/LuxLib/src/impl/batched_mul.jl

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@@ -338,6 +338,8 @@ function CRC.rrule(
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return batched_matmul_fallback(x, y), ∇batched_matmul
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end
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# COV_EXCL_START
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# This is type-piracy but needed to fix a blocking issue. TODO: upstream to NNlib
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# Enzyme causes a "active variables passed by value to jl_new_task are not yet supported"
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# warning without this patch.
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end
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end
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end
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# COV_EXCL_STOP

lib/LuxLib/src/impl/dense.jl

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@@ -133,6 +133,8 @@ function CRC.rrule(
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return z, ∇fused_dense
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end
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# COV_EXCL_START
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# TODO: We can optimize these a bit further by checking for cases where the forward pass
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# is not needed. We skip such optimizations for now
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function EnzymeRules.augmented_primal(
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return ntuple(Returns(nothing), 6)
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end
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# COV_EXCL_STOP
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∇matmul_bias(∂y, weight, x, bias) = ∇matmul_bias(∂y, ∇bias_add(bias, ∂y), weight, x, bias)
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∇matmul_bias(∂y, ∂b, weight, x, _) = matmul(∂y, x'), matmul(weight', ∂y), ∂b

lib/LuxLib/src/impl/matmul.jl

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return matmuladd(A, B, bias), ∇matmuladd
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end
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# COV_EXCL_START
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# EnzymeRules
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function EnzymeRules.augmented_primal(
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cfg,
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return ntuple(Returns(nothing), 5)
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end
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# COV_EXCL_STOP
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@enzyme_alternative matmul_octavian! matmul_linalg_default!
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@enzyme_alternative serial_matmul_loopvec! matmul_linalg_default!
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@enzyme_alternative matmul_loopvec! matmul_linalg_default!

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