[MXFP] mxfp conversions speedup

[farzad-openai]

This PR improves the throughput of mxfp8 upcast and downcast operations. I included a commit from @jongsoo-openai (original PR here) and added improvements below on top of it. The PR is functionally a no-op, which is verified by the tests in python/triton_kernels/tests/test_mxfp.py.

Upcast improvements:

• Added native packed e2m1 conversion to fp16 (for Blackwell+).
• Added tensor descriptors to utilize TMA for reading the input mxfp value tensor and writing the output.
  • Note that this addition required adding padding for the innermost dimension for IO tensors not adhering to tensor descriptor specification requirements, and unpadding the output afterwards.
• Tuned tile dimensions and num_warps.

Downcast improvements:

• Enabled grouped store of mxfp4 value tensors, as opposed to byte-level stores.
• Tuned the tile dimensions as well as num_warps.
• Unfortunately, as opposed to upcast, tensor descriptors were unable to give a consistent performance improvement.

I left performance tuning as a TODO for a subsequent PR.

Performance comparison (BW, in GBps)

Done via python/triton_kernels/tests/test_mxfp.py.

Before -- GB200

MXFP8 (e4m3fn):
   M     N  quant_dtype            quant_bw_bfloat16    quant_bw_float16    dequant_bw_bfloat16    dequant_bw_float16
----  ----  -------------------  -------------------  ------------------  ---------------------  --------------------
1024  8192  torch.float8_e4m3fn              1985.94             2053.35                2154.61               2347.56
4096  8192  torch.float8_e4m3fn              3479.79             3518.71                3243.02               3753.85

MXFP4 (e2m1):
   M     N  quant_dtype      quant_bw_bfloat16    quant_bw_float16    dequant_bw_bfloat16    dequant_bw_float16
----  ----  -------------  -------------------  ------------------  ---------------------  --------------------
1024  8192  torch.uint8                808.089             815.124                647.589               713.9
4096  8192  torch.uint8               1045.23             1041.91                 811.089               888.624

After -- GB200

MXFP8 (e4m3fn):
   M     N  quant_dtype            quant_bw_bfloat16    quant_bw_float16    dequant_bw_bfloat16    dequant_bw_float16
----  ----  -------------------  -------------------  ------------------  ---------------------  --------------------
1024  8192  torch.float8_e4m3fn              2259.86             2404.99                2119.76               2361.66
4096  8192  torch.float8_e4m3fn              4106.69             4268.29                4038.16               4059

MXFP4 (e2m1):
   M     N  quant_dtype      quant_bw_bfloat16    quant_bw_float16    dequant_bw_bfloat16    dequant_bw_float16
----  ----  -------------  -------------------  ------------------  ---------------------  --------------------
1024  8192  torch.uint8                1334.75             1332.03                1424.7                1397.36
4096  8192  torch.uint8                2027.41             2028.98                2097.15               2275.56

Before -- H100

MXFP8 (e4m3fn):
   M     N  quant_dtype            quant_bw_bfloat16    quant_bw_float16    dequant_bw_bfloat16    dequant_bw_float16
----  ----  -------------------  -------------------  ------------------  ---------------------  --------------------
1024  8192  torch.float8_e4m3fn              1250.29             1244.35                1595.2                1588.75
4096  8192  torch.float8_e4m3fn              1805.81             1799.62                2080.51               2118.34

MXFP4 (e2m1):
   M     N  quant_dtype      quant_bw_bfloat16    quant_bw_float16    dequant_bw_bfloat16    dequant_bw_float16
----  ----  -------------  -------------------  ------------------  ---------------------  --------------------
1024  8192  torch.uint8                418.493             416.102                572.367               627.739
4096  8192  torch.uint8                489.531             490.08                 687.861               758.08

After -- H100

MXFP8 (e4m3fn):
   M     N  quant_dtype            quant_bw_bfloat16    quant_bw_float16    dequant_bw_bfloat16    dequant_bw_float16
----  ----  -------------------  -------------------  ------------------  ---------------------  --------------------
1024  8192  torch.float8_e4m3fn              1604.96             1624.86                1732.23               1751.52
4096  8192  torch.float8_e4m3fn              2347.56             2337.09                2386.74               2292.8

MXFP4 (e2m1):
   M     N  quant_dtype      quant_bw_bfloat16    quant_bw_float16    dequant_bw_bfloat16    dequant_bw_float16
----  ----  -------------  -------------------  ------------------  ---------------------  --------------------
1024  8192  torch.uint8                731.429             745.575                892.861               917.871
4096  8192  torch.uint8                882.343             894.995               1102.37               1165.08

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