* Handle odd shapes and non-float scales in FP8BlockQuantLinear
Small fp8 checkpoints (e.g. tiny test models) break the block-quantized
linear in three ways: weight scales stored in a float8 dtype such as
float8_e8m0fnu have no triton dtype mapping; activations whose hidden dim is
not a multiple of the activation quant block fail act_quant's divisibility
assert; and weights whose dims are not multiples of the weight block cannot
be tiled by the triton dequant kernel.
Cast non-float scales to float32 on entry, and when the hidden dim does not
divide into the activation block, dequantize the weight and run a plain
matmul instead of the fp8 block matmul. The dequant goes through a new
shape-safe helper that falls back to a torch-native scale expansion when the
weight does not tile evenly; backward uses the same helper so the gradient
path works for every shape the forward accepts. Full-size checkpoints are
unaffected.
* Add tiny / e8m0 fp8 block-quant regression test
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* Fix FP8 block-quant fallback: real block size in dequant and scalar-scale fast path
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* Route rectangular fp8 blocks through torch dequant and keep block_size across e8m0 upcast
The triton weight_dequant kernel uses one BLOCK_SIZE for both axes, so
rectangular blocks (block_size[0] != block_size[1]) mis-index the column
scale and corrupt grad_X. Route those through the torch scale expansion,
which handles each dimension independently, and keep the triton path for
square blocks only.
Also preserve a block_size attribute carried on the scale tensor across the
e8m0 -> float32 upcast so the later lookup no longer falls back to [128, 128].
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