float8 training: add README.md entry for rowwise scaling (#1733)

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Author: vkuzo

torchao/float8/README.md

@@ -17,9 +17,9 @@ throughput speedups of up to 1.5x on 128 GPU LLaMa 3 70B pretraining jobs.
 
 We provide three per-tensor scaling strategies: dynamic, delayed and static.  See https://arxiv.org/pdf/2209.05433.pdf, Section 4.3 for more details. These strategies are configurable separately for activations (`input`), weights (`weight`) and gradients (`grad_output`).
 
-## float8 linear with dynamic scaling for `input`, `weight` and `grad_output`
+## float8 linear with dynamic tensorwise scaling
 
-This is the most accurate recipe as every tensor is scaled dynamically.
+This is the default recipe, with a good balance of performance and accuracy.
 
 ```python
 import torch
@@ -63,6 +63,57 @@ for _ in range(10):
     optimizer.step()
 ```
 
+## float8 linear with rowwise scaling
+
+This is a more accurate recipe compared to tensorwise, with more granular scaling.
+
+:warning: <em>The composability of float8 with rowwise scaling with Tensor Parallelism is WIP, please see https://github.com/pytorch/ao/issues/1732 for more details.</em>
+
+```python
+import torch
+import torch.nn as nn
+from torchao.float8 import convert_to_float8_training, Float8LinearConfig
+from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
+
+if not TORCH_VERSION_AT_LEAST_2_5:
+    raise AssertionError("torchao.float8 requires PyTorch version 2.5 or greater")
+
+# create model and sample input
+m = nn.Sequential(
+    nn.Linear(2048, 4096),
+    nn.Linear(4096, 128),
+).bfloat16().cuda()
+x = torch.randn(4096, 2048, device="cuda", dtype=torch.bfloat16)
+optimizer = torch.optim.SGD(m.parameters(), lr=0.1)
+
+# optional: filter modules from being eligible for float8 conversion
+def module_filter_fn(mod: torch.nn.Module, fqn: str):
+    # don't convert the last module
+    if fqn == "1":
+        return False
+    # don't convert linear modules with weight dimensions not divisible by 16
+    if isinstance(mod, torch.nn.Linear):
+        if mod.in_features % 16 != 0 or mod.out_features % 16 != 0:
+            return False
+    return True
+
+# configure rowwise scaling
+config = Float8LinearConfig.from_recipe_name("rowwise")
+
+# convert specified `torch.nn.Linear` modules to `Float8Linear`
+convert_to_float8_training(m, config=config, module_filter_fn=module_filter_fn)
+
+# enable torch.compile for competitive performance
+m = torch.compile(m)
+
+# toy training loop
+for _ in range(10):
+    optimizer.zero_grad()
+    y = m(x)
+    y.sum().backward()
+    optimizer.step()
+```
+
 ## float8 linear with delayed scaling
 
 :warning: <em>We plan to deprecate delayed scaling in a future release, see https://github.com/pytorch/ao/issues/1680 for more details.</em>