float8 upcoming feature tracker

[vkuzo]

configurability

• [planned] support rowwise/blockwise scaling granularity, configurable separately for each gemm
• [planned] configure settings for each of the three gemms in linear fwd/bwd separately
• [planned] support more fine grained configuration of how to apply Float8Linear to individual modules
• [planned] inference support (see [RFC] Float8 Inference pytorch-labs/float8_experimental#314)

performance

• [in progress] torch._scaled_mm support for rowwise scaled float8 gemm
  • [done] eager mode support
  • [planned] torch.compile support, backed by triton/cutlass
• [in progress] optimize torch.compile performance for float8 scaling/casting kernels
  • [fixed behind a flag, off by default] Improve the Inductor generated kernel for the pattern of output1 = pointwise(intput); output2 = transpose(output1) pytorch#130015
  • [planned] Improve inductor codegen for writing out tensor and tensor.t() in the same kernel pytorch#133242
  • [planned] [Inductor] Fusion of Tiled Point-Wise and Reduction Operators pytorch#128063
  • [planned] PT2 should leverage partial reductions to speed up larger reductions pytorch#136267

distributed

• [in progress] integrate with FSDP2 with 16-bit or 8-bit all-gather with delayed scaling for weights
  • POC is done, performance optimizations are ongoing
• [planned] verify integration with PP

other

• weight gradient accumulation in float32
• add use_fast_accum (float8 accumulation of gemm) option to UX - Allow for modifying the scaled_mm compute pytorch-labs/float8_experimental#144
• improve saturated casting performance

copied from pytorch-labs/float8_experimental#187