[not for land yet] example of float8 with rowwise scaling

Summary:

This is an example of how to call float8 training with rowwise scaling
from torchao.

TODO: finalize API in torchao, and finalize how we want to expose it in
torchtitan, and optimize performance.

```
// baseline (bf16 + compile)
> with-proxy CONFIG_FILE="./train_configs/llama3_8b.toml" ./run_llama_train.sh --training.compile
...
step: 20  loss:  8.4931  memory: 47.65GiB(50.16%)  tps: 5,760  mfu: 33.73%

// experiment (rowwise float8 + compile)
> with-proxy CONFIG_FILE="./train_configs/llama3_8b.toml" ./run_llama_train.sh --float8.enable_float8_linear --training.compile
...
// torchao main branch
step: 40  loss:  7.3818  memory: 66.81GiB(70.33%)  tps: 6,412  mfu: 37.55%
// torchao with pytorch/ao#1629
step: 20  loss:  8.3823  memory: 58.55GiB(61.63%)  tps: 6,424  mfu: 37.62%

// for comparison, tensorwise float8 with float8 all-gather (on main branch)
with-proxy CONFIG_FILE="./train_configs/llama3_8b.toml" ./run_llama_train.sh --float8.enable_float8_linear --training.compile --float8.enable_fsdp_float8_all_gather --float8.precompute_float8_dynamic_scale_for_fsdp
...
step: 20  loss:  8.4258  memory: 47.32GiB(49.81%)  tps: 7,186  mfu: 42.08%

```

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Tags:

torchtitan/config_manager.py

@@ -577,6 +577,12 @@ def __init__(self):
             action="store_true",
             help="Whether precompute float8 scales dynamically for FSDP",
         )
+        self.parser.add_argument(
+            "--float8.recipe_name",
+            type=str,
+            default=None,
+            help="If specified, creates float8 config from recipe name, choices are `all_axiswise` and `lw_axiswise_with_gw_hp`",
+        )
 
         # communications library settings
         self.parser.add_argument(

torchtitan/float8.py

@@ -42,29 +42,39 @@ def __init__(self, job_config: JobConfig, parallel_dims: ParallelDims):
             return
         try:
             from torchao.float8 import Float8LinearConfig
+            # we should update this code after torchao exposes this publically
+            from torchao.float8.config import Float8LinearRecipeName, recipe_name_to_linear_config
         except ImportError as e:
             raise ImportError(
                 "torchao is not installed. Please install it to use float8 linear layers."
             ) from e
 
-        # Mutates the model inplace replacing instances of torch.nn.Linear with Float8Linear
-        enable_fsdp_float8_all_gather = (
-            parallel_dims.dp_shard_enabled
-            and float8_config.enable_fsdp_float8_all_gather
-        )
-        self.config = Float8LinearConfig(
-            enable_fsdp_float8_all_gather=enable_fsdp_float8_all_gather,
-        )
-
         self.enabled = True
 
-        # for precompute_float8_dynamic_scale_for_fsdp
-        self.precompute_scale = (
-            enable_fsdp_float8_all_gather
-            and float8_config.precompute_float8_dynamic_scale_for_fsdp
-        )
-
-        logger.info("Float8 training active")
+        if float8_config.recipe_name is not None:
+            # TODO(future PR): the recipe lookup by name is currently a private API, we'll need
+            # to expose it publically in torchao before a PR similar to this one can be
+            # landed in torchtitan
+            recipe = Float8LinearRecipeName(float8_config.recipe_name)
+            self.config = recipe_name_to_linear_config(recipe)
+            self.precompute_scale = False
+            logger.info(f"Float8 training active with recipe {recipe}")
+
+        else:
+            # Mutates the model inplace replacing instances of torch.nn.Linear with Float8Linear
+            enable_fsdp_float8_all_gather = (
+                parallel_dims.dp_shard_enabled
+                and float8_config.enable_fsdp_float8_all_gather
+            )
+            self.config = Float8LinearConfig(
+                enable_fsdp_float8_all_gather=enable_fsdp_float8_all_gather,
+            )
+            # for precompute_float8_dynamic_scale_for_fsdp
+            self.precompute_scale = (
+                enable_fsdp_float8_all_gather
+                and float8_config.precompute_float8_dynamic_scale_for_fsdp
+            )
+            logger.info("Float8 tensorwise scaled training active")
 
     def convert_to_float8_training(self, model: nn.Module):
         """