[JAX] Allow pallas to accept scalar shape semaphores.

PiperOrigin-RevId: 727091407

jax/_src/pallas/BUILD

@@ -40,10 +40,15 @@ py_library(
     deps = [
         "//jax",
         "//jax:ad_util",
+        "//jax:api_util",
+        "//jax:config",
         "//jax:core",
+        "//jax:dtypes",
+        "//jax:effects",
         "//jax:mlir",
         "//jax:partial_eval",
         "//jax:pretty_printer",
+        "//jax:source_info_util",
         "//jax:tree_util",
         "//jax:util",
         "//jax/_src/lib",

jax/_src/pallas/core.py

@@ -1191,3 +1191,5 @@ def lower_as_mlir(
     stablehlo = lowered.compiler_ir(dialect="stablehlo")
 
   return stablehlo  # type: ignore[return-value]
+
+_out_shape_to_aval_mapping = {}

jax/_src/pallas/mosaic/core.py

@@ -46,6 +46,7 @@
 AbstractMemoryRef = pallas_core.AbstractMemoryRef
 no_block_spec = pallas_core.no_block_spec
 _convert_block_spec_to_block_mapping = pallas_core._convert_block_spec_to_block_mapping
+_out_shape_to_aval_mapping = pallas_core._out_shape_to_aval_mapping
 split_list = util.split_list
 
 _ENABLE_RUNTIME_ASSERT = config.bool_state(
@@ -278,3 +279,14 @@ def _tensorcore_mesh_discharge_rule(
 pallas_core._core_map_mesh_rules[TensorCoreMesh] = (
     _tensorcore_mesh_discharge_rule
 )
+
+
+def _convert_semaphore_type_to_aval(
+    out_shape: SemaphoreType,
+) -> jax_core.AbstractValue:
+  return out_shape.get_array_aval()
+
+
+pallas_core._out_shape_to_aval_mapping[SemaphoreType] = (
+    _convert_semaphore_type_to_aval
+)

jax/_src/pallas/pallas_call.py

@@ -38,9 +38,9 @@
 from jax._src.interpreters import mlir
 from jax._src.interpreters import partial_eval as pe
 from jax._src.pallas import core as pallas_core
-from jax._src.pallas import primitives
 from jax._src.pallas import helpers as pallas_helpers
 from jax._src.pallas import hlo_interpreter
+from jax._src.pallas import primitives
 from jax._src.state import discharge as state_discharge
 from jax._src.state import types as state_types
 from jax._src.util import (
@@ -1337,6 +1337,10 @@ def _convert_out_shape_to_aval(out_shape: Any) -> jax_core.AbstractValue:
     case pallas_core.MemoryRef():
       return out_shape.get_array_aval()
     case _:
+      if type(out_shape) in pallas_core._out_shape_to_aval_mapping:
+        return pallas_core._out_shape_to_aval_mapping[type(out_shape)](
+            out_shape
+        )
       if not (hasattr(out_shape, "shape") and hasattr(out_shape, "dtype")):
         raise ValueError(f"Invalid out_shape type: {type(out_shape)}")
       return jax_core.ShapedArray(shape=out_shape.shape, dtype=out_shape.dtype)

tests/pallas/tpu_pallas_test.py

@@ -1188,6 +1188,55 @@ def body(sem):
     )(x)
     np.testing.assert_array_equal(y, x)
 
+  def test_output_dma_semaphore_ref(self):
+    if self.INTERPRET:
+      self.skipTest('TODO(sharadmv, justinfu): Add interpret support for DMA.')
+
+    def kernel(x_hbm_ref, y_hbm_ref, sem_out):
+      pltpu.make_async_copy(
+          x_hbm_ref.at[pl.ds(8), :], y_hbm_ref.at[:, pl.ds(128)], sem_out
+      ).start()
+
+    def kernel2(x_hbm_ref, y_hbm_ref, sem_in, y_hbm_out):
+      del y_hbm_out
+      pltpu.make_async_copy(
+          x_hbm_ref.at[pl.ds(8), :], y_hbm_ref.at[:, pl.ds(128)], sem_in
+      ).wait()
+
+    x = jnp.arange(8 * 128.0).reshape((8, 128))
+
+    @jax.jit
+    def body(x):
+      y, sem_out = self.pallas_call(
+          kernel,
+          in_specs=[
+              pl.BlockSpec(memory_space=pl.ANY),
+          ],
+          out_specs=[
+              pl.BlockSpec(memory_space=pl.ANY),
+              pl.BlockSpec(memory_space=pltpu.SEMAPHORE),
+          ],
+          out_shape=[
+              jax.ShapeDtypeStruct((8, 128), jnp.float32),
+              pltpu.SemaphoreType.DMA,
+          ],
+      )(x)
+
+      y = self.pallas_call(
+          kernel2,
+          in_specs=[
+              pl.BlockSpec(memory_space=pl.ANY),
+              pl.BlockSpec(memory_space=pl.ANY),
+              pl.BlockSpec(memory_space=pltpu.SEMAPHORE),
+          ],
+          out_specs=pl.BlockSpec(memory_space=pl.ANY),
+          out_shape=jax.ShapeDtypeStruct((8, 128), jnp.float32),
+          input_output_aliases={1: 0},
+      )(x, y, sem_out)
+      return y
+
+    np.testing.assert_array_equal(body(x), x)
+
   def test_hbm_hbm_grid_dma(self):
     # When using the grid, we have to emit Mosaic window_params. Test that they
     # work correctly with ANY memory space operands.