Addressed the reviewer's comment. Avoided changing the existing

Fp8DotGeneralOp API.

flax/linen/__init__.py

@@ -86,7 +86,7 @@
 )
 from .batch_apply import BatchApply as BatchApply
 from .combinators import Sequential as Sequential
-from .fp8_ops import OCPFp8DotGeneralOp as OCPFp8DotGeneralOp
+from .fp8_ops import Fp8DotGeneralOp as Fp8DotGeneralOp
 from .fp8_ops import NANOOFp8DotGeneralOp as NANOOFp8DotGeneralOp
 from .initializers import (
     ones_init as ones_init,

flax/linen/fp8_ops.py

@@ -17,6 +17,9 @@
 import warnings
 from functools import partial
 
+from typing import Any
+DType = Any
+
 import jax
 from jax import custom_jvp, custom_vjp, lax, random
 from jax import numpy as jnp
@@ -269,8 +272,9 @@ def dot_general_with_precision_jvp(
 
 
 class Fp8DotGeneralOp(module.Module):
-  fp8_genre: str = 'OCP'
   amax_history_length: int = 1024
+  e4m3_dtype: DType = jnp.float8_e4m3fn
+  e5m2_dtype: DType = jnp.float8_e5m2
 
   def setup(self) -> None:
     scale_args = (
@@ -317,27 +321,23 @@ def __call__(self, *args, **kwargs):
     comp_dtype = k.dtype
     x = jnp.asarray(x, comp_dtype)
 
-    e4m3_dtype, e5m2_dtype = get_fp8_dtypes(self.fp8_genre)
-
     x_qdq = in_qdq(
-      comp_dtype, e4m3_dtype, x, self.input_scale.value, self.input_amax_history.value
+      comp_dtype, self.e4m3_dtype, x, self.input_scale.value, self.input_amax_history.value
     )
     k_qdq = in_qdq(
-      comp_dtype, e4m3_dtype, k, self.kernel_scale.value, self.kernel_amax_history.value
+      comp_dtype, self.e4m3_dtype, k, self.kernel_scale.value, self.kernel_amax_history.value
     )
     y_qdq = dot_general_with_precision(x_qdq, k_qdq, dimension_numbers)  # type: ignore
     y = out_qdq(
       comp_dtype,
-      e5m2_dtype,
+      self.e5m2_dtype,
       y_qdq,
       self.output_grad_scale.value,
       self.output_grad_amax_history.value,
     )
 
     return y  # type: ignore
 
-class OCPFp8DotGeneralOp(Fp8DotGeneralOp):
-  fp8_genre: str = 'OCP'
-
 class NANOOFp8DotGeneralOp(Fp8DotGeneralOp):
-  fp8_genre: str = 'NANOO'
+  e4m3_dtype: DType = jnp.float8_e4m3fnuz
+  e5m2_dtype: DType = jnp.float8_e5m2fnuz

tests/linen/linen_test.py

@@ -1271,7 +1271,7 @@ def run(fp8_injection, expected_shapes):
       p = nn.DenseGeneral(features=64, name='dense')
       if fp8_injection:
         if fp8_genre == 'OCP':
-          p.dot_general_cls = nn.OCPFp8DotGeneralOp
+          p.dot_general_cls = nn.Fp8DotGeneralOp
         else:
           p.dot_general_cls = nn.NANOOFp8DotGeneralOp
 
@@ -1293,7 +1293,7 @@ def _train(variables, x):
       'params': {'kernel': (32, 64), 'bias': (64,)},
     }
     if fp8_genre == 'OCP':
-      fp8_op_name = 'OCPFp8DotGeneralOp_0'
+      fp8_op_name = 'Fp8DotGeneralOp_0'
     else:
       fp8_op_name = 'NANOOFp8DotGeneralOp_0'
     expected_shapes_new = {
@@ -1326,8 +1326,8 @@ def test_fp8_train_state(self, fp8_genre):
     key, init_key, random_key = random.split(random.PRNGKey(seed=123), 3)
     x = random.uniform(random_key, (16, 16), dtype=jnp.float32)
     if fp8_genre == 'OCP':
-      fp8_dot_op = nn.OCPFp8DotGeneralOp
-      fp8_op_name = 'OCPFp8DotGeneralOp_0'
+      fp8_dot_op = nn.Fp8DotGeneralOp
+      fp8_op_name = 'Fp8DotGeneralOp_0'
     else:
       fp8_dot_op = nn.NANOOFp8DotGeneralOp
       fp8_op_name = 'NANOOFp8DotGeneralOp_0'