[mgpu] FragentedArray.foreach() can now optionally return a new array

PiperOrigin-RevId: 700002715

jax/experimental/mosaic/gpu/fragmented_array.py

@@ -1243,15 +1243,29 @@ def select(self, on_true, on_false):
         lambda t, p, f: arith.select(p, t, f), self, on_false,
     )
 
-  def foreach(self, fn: Callable[[ir.Value, tuple[ir.Value, ...]], None]):
+  def foreach(
+      self,
+      fn: Callable[[ir.Value, tuple[ir.Value, ...]], ir.Value | None],
+      *,
+      create_array=False,
+      is_signed=None,
+  ):
     """Call a function for each value and index."""
     index = ir.IndexType.get()
-    for idx, reg in zip(self.layout.thread_idxs(self.shape), self.registers.flat, strict=True):
-      assert len(idx) == len(self.shape), (idx, self.shape)
+    new_regs = None
+    if create_array:
+      new_regs = np.full_like(self.registers, llvm.mlir_undef(self.registers.flat[0].type))
+    for mlir_idx, reg_idx in zip(self.layout.thread_idxs(self.shape), np.ndindex(self.registers.shape), strict=True):
+      reg = self.registers[reg_idx]
+      assert len(mlir_idx) == len(self.shape), (mlir_idx, self.shape)
       [elems] = ir.VectorType(reg.type).shape
       for i in range(elems):
         i = c(i, index)
-        fn(vector.extractelement(reg, position=i), (*idx[:-1], arith.addi(idx[-1], i)))
+        val = fn(vector.extractelement(reg, position=i), (*mlir_idx[:-1], arith.addi(mlir_idx[-1], i)))
+        if create_array:
+          new_regs[reg_idx] = vector.insertelement(val, new_regs[reg_idx], position=i)
+
+    return FragmentedArray(_registers=new_regs, _layout=self.layout, _is_signed=is_signed)
 
   def store_untiled(self, ref: ir.Value):
     if not ir.MemRefType.isinstance(ref.type):

tests/mosaic/gpu_test.py

@@ -1361,6 +1361,39 @@ def kernel(ctx, dst, _):
     rhs = rhs = 0 if rhs_is_literal else iota + 1
     np.testing.assert_array_equal(result, op(iota, rhs))
 
+  def test_foreach(self):
+    dtype = jnp.int32
+    swizzle = 128
+    tile = 64, swizzle // jnp.dtype(dtype).itemsize
+    shape = 128, 192
+    tiled_shape = mgpu.tile_shape(shape, tile)
+    mlir_dtype = utils.dtype_to_ir_type(dtype)
+    cst = 9999
+    def causal(val, idx):
+      row, col = idx
+      mask = arith.cmpi(arith.CmpIPredicate.uge, row, col)
+      return arith.select(mask, val, c(cst, mlir_dtype))
+
+    tiling = mgpu.TileTransform(tile)
+    def kernel(ctx, dst, smem):
+      x = iota_tensor(shape[0], shape[1], dtype)
+      x.foreach(causal, create_array=True, is_signed=False).store_untiled(smem)
+      mgpu.commit_shared()
+      ctx.async_copy(src_ref=smem, dst_ref=dst)
+      ctx.await_async_copy(0)
+
+    iota = np.arange(np.prod(shape), dtype=dtype).reshape(*shape)
+    result = mgpu.as_gpu_kernel(
+        kernel,
+        (1, 1, 1),
+        (128, 1, 1),
+        (),
+        jax.ShapeDtypeStruct(shape=shape, dtype=dtype),
+        jax.ShapeDtypeStruct(shape=shape, dtype=dtype),
+    )()
+    expected = jnp.tril(iota) + jnp.triu(jnp.ones(shape), k=1) * cst
+    np.testing.assert_array_equal(result, expected)
+
   @parameterized.product(
       op=[operator.and_, operator.or_, operator.xor],
       dtype=[jnp.uint32],