update float8 readme with more recent performance numbers (#2580)

1. run roofline script for tensorwise and rowwise recipes on recent torch and torchao
2. add section for rowwise_with_gw_hp

Author: vkuzo

torchao/float8/README.md

@@ -97,30 +97,32 @@ on using `torchao.float8` in a distributed setting.
 
 A common question about float8 training is "when is float8 linear faster vs bfloat16?".  Given the M, K, N of the forward pass through your linear, you can reference the tables below for a microbenchmark based speedup estimate on NVIDIA H100:
 
-### Tensorwise scaling
+### tensorwise scaling
 
-<img width="805" alt="float8_speedup" src="https://github.com/user-attachments/assets/5c5f2817-7eb7-4cab-bd03-49fe70cd31a8">
+<img width="753" height="773" alt="Image" src="https://github.com/user-attachments/assets/e46c671a-ed35-41b4-b17c-50caf1629ecb" />
 
-Example 1 (small shapes):
-* forward input tensor size 1024x2048, linear weight size 2048x1024; M, K, N = 1024, 2048, 1024
-* benchmark speedup is 0.80
-* recommendation: leave this linear in bfloat16, the shapes are too small to benefit from float8 compute
+```lang=shell
+# reproduction: run the script below
+python benchmarks/float8/float8_roofline.py your_output_filename.csv --shape_gen_name sweep
+```
 
-Example 2 (large shapes):
-* forward input tensor size 4096x8192, linear weight size 8192x16384; M, K, N = 4096, 8192, 16384
-* benchmark speedup is 1.39
-* recommendation: enable float8 for this linear to get a speedup
+### rowwise scaling
 
-To reproduce the raw data for table above, you can run the following script
+<img width="755" height="778" alt="Image" src="https://github.com/user-attachments/assets/7d70ba36-f480-459f-b5c0-797895332631" />
 
 ```lang=shell
-python benchmarks/float8/float8_roofline.py your_output_filename.csv --shape_gen_name sweep
+# reproduction: run the script below
+python benchmarks/float8/float8_roofline.py your_output_filename.csv --shape_gen_name sweep --float8_recipe_name rowwise
 ```
 
-### Rowwise scaling
+### rowwise_with_gw_hp scaling
 
-<img width="805" alt="float8_rowwise_speedup" src="../../docs/static/fp8-rowwise-perf.png" />
+<img width="750" height="797" alt="Image" src="https://github.com/user-attachments/assets/e4479abc-1aca-436d-a142-60e5e804ff10" />
 
+```lang=shell
+# reproduction: run the script below
+python benchmarks/float8/float8_roofline.py your_output_filename.csv --shape_gen_name sweep --float8_recipe_name rowwise_with_gw_hp
+```
 
 ## Derivation