add per-gemm config to Float8LinearConfig (#334)

Summary:
Pull Request resolved: #334

Previously the per-gemm configuration had to be hardcoded in library
code. This PR exposes it to the top-level UX by adding a
`Float8GemmConfig` field to `Float8LinearConfig`.

Note that today the only supported configuration option is
`use_fast_accum`.  In the future, configuring output_dtype
and whether to keep a gemm in higher precision would go here.

Reviewed By: weifengpy

Differential Revision: D60252069

fbshipit-source-id: bca34eb49e1bf046f937e32b11b2369b535d56e6

float8_experimental/__init__.py

@@ -6,6 +6,7 @@
 # Lets define a few top level things here
 from float8_experimental.config import (
     DelayedScalingConfig,
+    Float8GemmConfig,
     Float8LinearConfig,
     Float8TensorCastConfig,
     TensorScalingType,
@@ -33,6 +34,7 @@
     # configuration
     "DelayedScalingConfig",
     "TensorScalingType",
+    "Float8GemmConfig",
     "Float8LinearConfig",
     "Float8TensorCastConfig",
     # top level UX

float8_experimental/config.py

@@ -53,6 +53,17 @@ def __post_init__(self):
         ), f"{self.scale_fn_name} is not implemented yet. Only max is supported for now."
 
 
+@dataclass(frozen=True)
+class Float8GemmConfig:
+    """
+    Configuration for a float8 gemm.
+    """
+
+    # If True, fast accumulation in lower precision is used.
+    # Note: this flag is currently a no-op if emulation is turned on.
+    use_fast_accum: bool = False
+
+
 @dataclass(frozen=True)
 class Float8LinearConfig:
     """
@@ -67,6 +78,14 @@ class Float8LinearConfig:
     cast_config_weight: Float8TensorCastConfig = Float8TensorCastConfig()
     cast_config_grad_output: Float8TensorCastConfig = Float8TensorCastConfig()
 
+    #
+    # Per-gemm configuration for gemms calculating `output`, `grad_input` and
+    # `grad_weight`
+    #
+    gemm_config_output: Float8GemmConfig = Float8GemmConfig(use_fast_accum=True)
+    gemm_config_grad_input: Float8GemmConfig = Float8GemmConfig()
+    gemm_config_grad_weight: Float8GemmConfig = Float8GemmConfig()
+
     #
     # Per-linear configuration
     #

float8_experimental/float8_linear.py

@@ -168,24 +168,28 @@ def __init__(self, *args, **kwargs):
 
         self.create_buffers()
 
-        # TODO(future): user level configuration of gemms
         self.linear_mm_config = LinearMMConfig(
-            # input
+            # output
             ScaledMMConfig(
                 emulate,
-                True if not emulate else False,
+                self.config.gemm_config_output.use_fast_accum,
                 False,
                 self.config.pad_inner_dim,
             ),
-            # weight
+            # grad_input
             ScaledMMConfig(
                 emulate,
-                True if not emulate else False,
+                self.config.gemm_config_grad_input.use_fast_accum,
+                False,
+                self.config.pad_inner_dim,
+            ),
+            # grad_weight
+            ScaledMMConfig(
+                emulate,
+                self.config.gemm_config_grad_weight.use_fast_accum,
                 False,
                 self.config.pad_inner_dim,
             ),
-            # grad_output
-            ScaledMMConfig(emulate, False, False, self.config.pad_inner_dim),
         )
 
         # Note: is_amax_initialized is not a buffer to avoid data dependent