Removing deprecated XLA translation rules

src/jax_finufft/lowering.py

@@ -0,0 +1,107 @@
+import numpy as np
+from jax.interpreters.mlir import ir
+from jax.lib import xla_client
+from jaxlib.hlo_helpers import custom_call, hlo_const
+
+from . import jax_finufft_cpu
+
+try:
+    from . import jax_finufft_gpu
+
+    for _name, _value in jax_finufft_gpu.registrations().items():
+        xla_client.register_custom_call_target(_name, _value, platform="gpu")
+except ImportError:
+    jax_finufft_gpu = None
+
+for _name, _value in jax_finufft_cpu.registrations().items():
+    xla_client.register_custom_call_target(_name, _value, platform="cpu")
+
+
+def default_layouts(*shapes):
+    return [range(len(shape) - 1, -1, -1) for shape in shapes]
+
+
+def lowering(platform, ctx, source, *points, output_shape, iflag, eps):
+    del ctx
+
+    if platform not in ["cpu", "gpu"]:
+        raise ValueError(f"Unrecognized platform '{platform}'")
+
+    if platform == "gpu" and jax_finufft_gpu is None:
+        raise ValueError("jax-finufft was not compiled with GPU support")
+
+    ndim = len(points)
+    assert 1 <= ndim <= 3
+    if platform == "gpu" and ndim == 1:
+        raise ValueError("1-D transforms are not yet supported on the GPU")
+
+    source_type = ir.RankedTensorType(source.type)
+    points_type = [ir.RankedTensorType(x.type) for x in points]
+
+    # Check supported and consistent dtypes
+    f32 = ir.F32Type.get()
+    f64 = ir.F64Type.get()
+    source_dtype = source_type.element_type
+    single = source_dtype == ir.ComplexType.get(f32) and all(
+        x.element_type == f32 for x in points_type
+    )
+    double = source_dtype == ir.ComplexType.get(f64) and all(
+        x.element_type == f64 for x in points_type
+    )
+    assert single or double
+    suffix = "f" if single else ""
+
+    # Check shapes
+    source_shape = source_type.shape
+    points_shape = tuple(x.shape for x in points_type)
+    n_tot = source_shape[0]
+    n_transf = source_shape[1]
+    n_j = points_shape[0][1]
+    if output_shape is None:
+        op_name = f"nufft{ndim}d2{suffix}".encode("ascii")
+        n_k = np.array(source_shape[2:], dtype=np.int64)
+        full_output_shape = tuple(source_shape[:2]) + (n_j,)
+    else:
+        op_name = f"nufft{ndim}d1{suffix}".encode("ascii")
+        n_k = np.array(output_shape, dtype=np.int64)
+        full_output_shape = tuple(source_shape[:2]) + tuple(output_shape)
+
+    # The backend expects the output shape in Fortran order so we'll just
+    # fake it here, by sending in n_k and x in the reverse order.
+    n_k_full = np.zeros(3, dtype=np.int64)
+    n_k_full[:ndim] = n_k[::-1]
+
+    # Build the descriptor containing the transform parameters
+    opaque = getattr(jax_finufft_cpu, f"build_descriptor{suffix}")(
+        eps, iflag, n_tot, n_transf, n_j, *n_k_full
+    )
+
+    if platform == "cpu":
+        opaque_arg = hlo_const(np.frombuffer(opaque, dtype=np.uint8))
+        opaque_shape = ir.RankedTensorType(opaque_arg.type).shape
+        return custom_call(
+            op_name,
+            result_types=[
+                ir.RankedTensorType.get(full_output_shape, source_type.element_type)
+            ],
+            # Reverse points because backend uses Fortran order
+            operands=[opaque_arg, source, *points[::-1]],
+            backend_config=opaque,
+            operand_layouts=default_layouts(
+                opaque_shape, source_shape, *points_shape[::-1]
+            ),
+            result_layouts=default_layouts(full_output_shape),
+        ).results
+
+    else:
+        return custom_call(
+            op_name,
+            result_types=[
+                ir.RankedTensorType.get(full_output_shape, source_type.element_type)
+            ],
+            # Reverse points because backend uses Fortran order
+            operands=[source, *points[::-1]],
+            backend_config=opaque,
+            operand_layouts=default_layouts(source_shape, *points_shape[::-1]),
+            result_layouts=default_layouts(full_output_shape),
+        ).results

src/jax_finufft/ops.py

@@ -6,9 +6,9 @@
 from jax import core
 from jax import jit
 from jax import numpy as jnp
-from jax.interpreters import ad, batching, xla
+from jax.interpreters import ad, batching, xla, mlir
 
-from . import shapes, translation
+from . import shapes, lowering
 
 
 @partial(jit, static_argnums=(0,), static_argnames=("iflag", "eps"))
@@ -181,13 +181,9 @@ def batch(args, axes, *, output_shape, **kwargs):
 nufft1_p = core.Primitive("nufft1")
 nufft1_p.def_impl(partial(xla.apply_primitive, nufft1_p))
 nufft1_p.def_abstract_eval(shapes.abstract_eval)
-xla.register_translation(
-    nufft1_p, partial(translation.translation_rule, "cpu"), platform="cpu"
-)
-if translation.jax_finufft_gpu is not None:
-    xla.register_translation(
-        nufft1_p, partial(translation.translation_rule, "gpu"), platform="cuda"
-    )
+mlir.register_lowering(nufft1_p, partial(lowering.lowering, "cpu"), platform="cpu")
+if lowering.jax_finufft_gpu is not None:
+    mlir.register_lowering(nufft1_p, partial(lowering.lowering, "cpu"), platform="gpu")
 ad.primitive_jvps[nufft1_p] = partial(jvp, nufft1_p)
 ad.primitive_transposes[nufft1_p] = transpose
 batching.primitive_batchers[nufft1_p] = batch
@@ -196,13 +192,9 @@ def batch(args, axes, *, output_shape, **kwargs):
 nufft2_p = core.Primitive("nufft2")
 nufft2_p.def_impl(partial(xla.apply_primitive, nufft2_p))
 nufft2_p.def_abstract_eval(shapes.abstract_eval)
-xla.register_translation(
-    nufft2_p, partial(translation.translation_rule, "cpu"), platform="cpu"
-)
-if translation.jax_finufft_gpu is not None:
-    xla.register_translation(
-        nufft2_p, partial(translation.translation_rule, "gpu"), platform="cuda"
-    )
+mlir.register_lowering(nufft2_p, partial(lowering.lowering, "cpu"), platform="cpu")
+if lowering.jax_finufft_gpu is not None:
+    mlir.register_lowering(nufft2_p, partial(lowering.lowering, "cpu"), platform="gpu")
 ad.primitive_jvps[nufft2_p] = partial(jvp, nufft2_p)
 ad.primitive_transposes[nufft2_p] = transpose
 batching.primitive_batchers[nufft2_p] = batch