TorchLean API

FloatLib.Floats.Formats.BinaryInterchange.Configured.Rounding.Proof

Packing correctness for explicitly rounded configured binary operations #

The decoding bridge theorems toModel_add through toModel_sqrt and their IEEEOutcome.toModel_*WithStatus companions state that decoding a configured result recovers the single descriptor-model operation used to compute it. They let proofs transfer results from the model without depending on the configured storage plan.

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Repacking and then decoding a model outcome preserves both its value and status.

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Decoding configured addition gives model addition with the same rounding mode.

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Decoding configured subtraction gives model subtraction with the same rounding mode.

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Decoding configured multiplication gives model multiplication with the same rounding mode.

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Decoding configured division gives model division with the same rounding mode.

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Decoding configured FMA gives model FMA with the same rounding mode.

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Decoding configured square root gives model square root with the same rounding mode.

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Decoding configured addition preserves the model result and exception flags.

@[simp]

Decoding configured subtraction preserves the model result and exception flags.

@[simp]

Decoding configured multiplication preserves the model result and exception flags.

@[simp]

Decoding configured division preserves the model result and exception flags.

@[simp]

Decoding configured FMA preserves the model result and exception flags.

@[simp]

Decoding configured square root preserves the model result and exception flags.