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mixed_precision_ops.Linear.forward only quantized activations that were 2D, or 3D (reshaped to 2D). Inputs with rank >= 4 (e.g. Anima's MLP activations, which are not reshaped to 3D the way the attention path is) fell through the `input_reshaped.ndim == 2` guard and reached scaled_mm as bf16, silently dispatching a bf16 kernel instead of FP8. Since MLP is roughly half the compute, the FP8 speedup was far below expectation. Generalize the existing 3D->2D reshape to any rank >= 3 (flatten the leading dims, keep the contraction dim) and reshape the output back to the original leading dims. 2D and 3D inputs are handled exactly as before; only rank >= 4 inputs change (now quantized instead of skipped). This matches the rank-agnostic handling already used by the training path (flatten(0, -2) / unflatten). Fixes #14595.
70 KiB
70 KiB