Add cudnn_fusion decorator lowering computations to XLA cuDNN fusions.

jax/_src/cudnn/__init__.py

@@ -11,3 +11,5 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+from .fusion import cudnn_fusion

jax/_src/cudnn/fusion.py

@@ -0,0 +1,86 @@
+# Copyright 2024 The JAX Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import functools
+import jax
+from jax import core as jax_core
+from jax.interpreters import mlir
+from jax.interpreters.mlir import hlo
+from jax.interpreters.mlir import ir
+
+
+
+def _cudnn_fusion_impl(*args, jaxpr, **unused_kwargs):
+ del unused_kwargs
+ return jax_core.jaxpr_as_fun(jaxpr)(*args)
+
+
+def _custom_abstract_eval(*args, jaxpr, **unused_kwargs):
+ del unused_kwargs
+ del args
+ return jaxpr.out_avals
+
+
+cudnn_fusion_p = jax_core.Primitive("cudnn_fusion")
+cudnn_fusion_p.multiple_results = True
+cudnn_fusion_p.def_abstract_eval(_custom_abstract_eval)
+cudnn_fusion_p.def_impl(_cudnn_fusion_impl)
+
+
+def call_cudnn_fusion(f, *args, **kwargs):
+ """Creates a new cudnn_fusion corresponding to calling the given function f with args and kwargs."""
+ jaxpr, out_shapes = jax.make_jaxpr(
+ functools.partial(f, **kwargs), return_shape=True
+ )(*args)
+ flat_args = jax.tree.leaves(args)
+ out_tree = jax.tree.structure(out_shapes)
+ out_flat = cudnn_fusion_p.bind(*flat_args, name=f.__name__, jaxpr=jaxpr)
+ return jax.tree.unflatten(out_tree, out_flat)
+
+
+def _cudnn_fusion_stablehlo_lowering(
+ ctx,
+ *args,
+ name,
+ jaxpr,
+):
+ """Make cudnn_fusion which calls the implementation function.
+ Currently this leaks a CallOp since we're using the `core_call_lowering`
+ function, but this should get cleaned up by DCE easily.
+ """
+ impl = mlir.core_call_lowering(ctx, *args, name=name + ".impl", call_jaxpr=jaxpr)
+ call_op = impl[0].owner
+ called_fn = call_op.attributes["callee"]
+ cudnn_fusion = hlo.CustomCallOp(
+ [r.type for r in call_op.results],
+ call_op.operands,
+ call_target_name="__cudnn$fusion",
+ called_computations=ir.ArrayAttr.get([called_fn]),
+ )
+ return cudnn_fusion.results
+
+
+mlir.register_lowering(cudnn_fusion_p, _cudnn_fusion_stablehlo_lowering)
+
+
+def cudnn_fusion(f):
+ """Makes a function become a cuDNN kernel. Relies on XLA's handling of
+ custom fusions with __cudnn$fusion backend. Currently limited to GEMM
+ fusions. For example - batch matmul with mixed types and addition:
+
+ @cudnn_fusion
+ def fn(x, y, z):
+ return jnp.float32(jax.lax.batch_matmul(jnp.bfloat16(x), y)) + z
+ """
+ return functools.partial(call_cudnn_fusion, f)

tests/BUILD

@@ -1530,6 +1530,19 @@ py_test(
  ],
 )
 
+jax_test(
+ name = "cudnn_fusion_test",
+ srcs = ["cudnn_fusion_test.py"],
+ disable_backends = [
+ "cpu",
+ "tpu",
+ ],
+ enable_configs = [
+ "gpu_h100",
+ ],
+ tags = ["multiaccelerator"],
+)
+
 exports_files(
  [
  "api_test.py",

tests/cudnn_fusion_test.py

@@ -0,0 +1,62 @@
+# Copyright 2024 The JAX Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from absl.testing import absltest, parameterized
+from unittest import SkipTest
+from jax._src import test_util as jtu
+import jax
+import jax.numpy as jnp
+from jax._src.cudnn import cudnn_fusion
+
+
+jax.config.parse_flags_with_absl()
+
+
+class CudnnFusionTest(jtu.JaxTestCase):
+ @parameterized.parameters(["", "pmap"])
+ def test_cudnn_fusion(self, mode):
+ batch_size = 2
+ if mode == "pmap" and jax.device_count() < batch_size:
+ raise SkipTest("pmap test requires 2 GPUs")
+
+ @cudnn_fusion
+ def comp1(x, y, z):
+ return jnp.float32(jax.lax.batch_matmul(jnp.bfloat16(x), y)) + z
+
+ k = jax.random.key(0)
+ s = batch_size, 16, 16
+ x = jnp.int8(jax.random.normal(k, shape=s))
+ y = jnp.bfloat16(jax.random.normal(k, shape=s))
+ z = jnp.float32(jax.random.normal(k, shape=s))
+
+ fn = jax.pmap(comp1) if mode == "pmap" else comp1
+ jitted = jax.jit(comp1)
+ lowered = jitted.lower(x, y, z)
+ stablehlo = lowered.as_text("stablehlo")
+ self.assertIn("func.func private @comp1", stablehlo)
+ self.assertIn("__cudnn$fusion", stablehlo)
+
+ hlo = lowered.as_text("hlo")
+ self.assertIn('custom_call_target="__cudnn$fusion"', hlo)
+ self.assertIn("called_computations=", hlo)
+
+ hlo_after_opt = lowered.compile().as_text()
+ self.assertIn("kind=kCustom", hlo_after_opt)
+ self.assertIn("plan_id", hlo_after_opt)
+
+ self.assertAllClose(jitted(x, y, z), fn(x, y, z))
+
+
+if __name__ == '__main__':
+ absltest.main(testLoader=jtu.JaxTestLoader())