[pallas:mosaic_gpu] Addressed a todo in broadcasted_iota lowering

PiperOrigin-RevId: 709303687

jax/_src/pallas/mosaic_gpu/primitives.py

@@ -16,6 +16,7 @@
 
 from __future__ import annotations
 
+from collections.abc import Sequence
 import enum
 import math
 from typing import Any, Literal
@@ -25,7 +26,6 @@
 from jax._src import state
 from jax._src import tree_util
 from jax._src import util
-from jax._src.interpreters import mlir
 from jax._src.lib.mlir import ir
 from jax._src.lib.mlir.dialects import arith as arith_dialect
 from jax._src.lib.mlir.dialects import llvm as llvm_dialect
@@ -36,7 +36,8 @@
 from jax._src.state import discharge
 from jax._src.state import indexing
 from jax._src.state import primitives as state_primitives
-import jax.experimental.mosaic.gpu as mgpu
+from jax.experimental.mosaic import gpu as mgpu
+from jax.experimental.mosaic.gpu import utils as mgpu_utils
 import jax.numpy as jnp
 
 
@@ -703,38 +704,40 @@ def _broadcasted_iota_abstract_eval(dtype, shape, dimension, layout):
   del layout, dimension
   return jax_core.ShapedArray(shape, dtype)
 
-@lowering.register_lowering_rule(broadcasted_iota_p)
-def _broadcasted_iota_lowering(ctx: lowering.LoweringRuleContext, dtype, shape, dimension, layout):
-  del ctx
-  # Unsigned integers (as opposed to signless) cause MLIR verification
-  # errors so we only use signless like Mosaic GPU does.
-  #
-  # TODO(cperivol): use mgpu.utils.dtype_to_ir_type() instead.
-  mlir_dtype = (
-      ir.IntegerType.get_signless(dtype.itemsize * 8)
-      if jnp.issubdtype(dtype, jnp.integer)
-      else mlir.dtype_to_ir_type(dtype)
-  )
-  undef = llvm_dialect.mlir_undef(mlir_dtype)
-  is_signed = (
-      jnp.issubdtype(dtype, jnp.signedinteger)
-      if jnp.issubdtype(dtype, jnp.integer)
-      else None
-  )
 
-  i32 = ir.IntegerType.get_signless(32)
-  def _cast(x):
-    if ir.FloatType.isinstance(mlir_dtype):
-      x = arith_dialect.index_cast(i32, x)
-      return arith_dialect.uitofp(mlir_dtype, x)
-    else:
-      return arith_dialect.index_cast(mlir_dtype, x)
+@lowering.register_lowering_rule(broadcasted_iota_p)
+def _broadcasted_iota_lowering(
+    ctx: lowering.LoweringRuleContext, dtype, shape, dimension, layout
+):
+  del ctx  # Unused.
+  mlir_dtype = mgpu_utils.dtype_to_ir_type(dtype)
+  if ir.FloatType.isinstance(mlir_dtype):
+    i32 = ir.IntegerType.get_signless(32)
+    cast = lambda x: arith_dialect.uitofp(
+        mlir_dtype, arith_dialect.index_cast(i32, x)
+    )
+  else:
+    cast = lambda x: arith_dialect.index_cast(mlir_dtype, x)
+  is_signed = mgpu_utils.is_signed(dtype)
   return mgpu.FragmentedArray.splat(
-      undef, shape, layout.value, is_signed=is_signed
+      llvm_dialect.mlir_undef(mlir_dtype),
+      shape,
+      layout.value,
+      is_signed=is_signed,
   ).foreach(
-      lambda _, idx: _cast(idx[dimension]), create_array=True, is_signed=is_signed
+      lambda _, idx: cast(idx[dimension]),
+      create_array=True,
+      is_signed=is_signed,
   )
 
 
-def broadcasted_iota(dtype, shape, dimension, *, layout: Layout | None = None):
-  return broadcasted_iota_p.bind(dtype=jnp.dtype(dtype), shape=shape, dimension=dimension, layout=layout)
+def broadcasted_iota(
+    dtype: jax.typing.DTypeLike,
+    shape: Sequence[int],
+    dimension: int,
+    *,
+    layout: Layout | None = None,
+) -> jax.Array:
+  return broadcasted_iota_p.bind(
+      dtype=jnp.dtype(dtype), shape=shape, dimension=dimension, layout=layout
+  )