Merge pull request #18795 from gnecula:test_export_grad

PiperOrigin-RevId: 587730171

jax/_src/pjit.py

@@ -1816,7 +1816,7 @@ def _resource_typing_pjit(avals, params, source_info, resource_env, named_axis_r
         s._original_sharding, '_parsed_pspec'):
       parsed_pspec = s._original_sharding._parsed_pspec
     else:
-      if resource_env is not None:
+      if resource_env is not None and not resource_env.physical_mesh.empty:
         parsed_pspec = parse_flatten_op_sharding(
             s._hlo_sharding, resource_env.physical_mesh)[0]
       else:
@@ -1838,7 +1838,7 @@ def _resource_typing_pjit(avals, params, source_info, resource_env, named_axis_r
         s._original_sharding, '_parsed_pspec'):
       parsed_pspec = s._original_sharding._parsed_pspec
     else:
-      if resource_env is not None:
+      if resource_env is not None and not resource_env.physical_mesh.empty:
         parsed_pspec = parse_flatten_op_sharding(
             s._hlo_sharding, resource_env.physical_mesh)[0]
       else:

jax/experimental/jax2tf/tests/sharding_test.py

@@ -22,6 +22,7 @@
 import contextlib
 from functools import partial
 import logging
+import math
 import os
 import re
 from typing import Any
@@ -40,6 +41,7 @@
 from jax.experimental import pjit
 from jax.experimental.maps import xmap
 from jax.experimental.shard_map import shard_map
+from jax.sharding import NamedSharding
 from jax.sharding import Mesh
 from jax.sharding import PartitionSpec as P
 import jax.numpy as jnp
@@ -382,25 +384,38 @@ def f_jax(x):  # x: f32[10, 20], optionally some axes as polymorphic
       for in_shardings in ("missing", None, "P")
       for out_shardings in ("missing", None, "P")
   ])
-  @jtu.with_mesh([("x", 2)])
   def test_grad_pjit(self, in_shardings="P", out_shardings=None):
+    if not config.jax2tf_default_native_serialization.value:
+      self.skipTest("TODO: failure in non-native serialization")
+    local_devices = list(jax.local_devices())
+    size = 2
+    if len(local_devices) < size:
+      raise unittest.SkipTest(f"Test requires {size} local devices")
+    mesh_devices = np.array(local_devices[:size]).reshape((2,))
+    mesh = jax.sharding.Mesh(mesh_devices, ("x",))
     def f_jax(x):  # x: f32[10,20] -> f32[20,10]
       return jnp.sin(x.T)
 
     pjit_kwargs = {}
     if in_shardings != "missing":
-      pjit_kwargs["in_shardings"] = (P(None, "x") if in_shardings == "P" else None)
+      pjit_kwargs["in_shardings"] = (
+        NamedSharding(mesh, P(None, "x")) if in_shardings == "P" else None)
     if out_shardings != "missing":
-      pjit_kwargs["out_shardings"] = (P("x", None) if out_shardings == "P" else None)
+      pjit_kwargs["out_shardings"] = (
+        NamedSharding(mesh, P("x", None)) if out_shardings == "P" else None)
     f_jax = pjit.pjit(f_jax, **pjit_kwargs)
     x_shape = (10, 20)
     x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)
 
     def f_grad_tf(x_v, res_ct):
       with tf.GradientTape(persistent=True) as tape:
         tape.watch(x_v)
-        res_tf = jax2tf.convert(f_jax)(x_v)
-        return tape.gradient(res_tf, x_v, output_gradients=res_ct)
+        with tf.GradientTape() as tape2:
+          tape2.watch(x_v)
+          res_tf = jax2tf.convert(f_jax)(x_v)
+        dy_dx = tape.gradient(res_tf, x_v, output_gradients=res_ct)
+      d2y_dx2 = tape.gradient(dy_dx, x_v)
+      return d2y_dx2
 
     # Annotation count for the primal input and the grad output
     count_in_P = self.GEQ(2) if in_shardings == "P" else 0

tests/export_test.py

@@ -28,6 +28,7 @@
 from jax import tree_util
 from jax.experimental.export import export
 from jax.experimental import pjit
+from jax.sharding import NamedSharding
 from jax.sharding import Mesh
 from jax.sharding import PartitionSpec as P
 
@@ -755,30 +756,50 @@ def f_jax(b):  # b: f32[16 // DEVICES, 4]
       )(a)
 
   @jtu.parameterized_filterable(
+    one_containing="in_shardings_None_out_shardings_P_with_mesh_False",
     kwargs=[
-      dict(testcase_name=f"_in_shardings={in_shardings}_out_shardings={out_shardings}",
-           in_shardings=in_shardings, out_shardings=out_shardings)
+      dict(in_shardings=in_shardings, out_shardings=out_shardings,
+           with_mesh=with_mesh)
       for in_shardings in ("missing", None, "P")
       for out_shardings in ("missing", None, "P")
+      for with_mesh in (True, False)
   ])
-  def test_grad_with_sharding(self, in_shardings="P", out_shardings=None):
+  def test_grad_with_sharding(self, in_shardings="P", out_shardings=None,
+                              with_mesh=False):
     if len(jax.devices()) < 2:
       self.skipTest("Test requires at least 2 devices")
     x_shape = (10, 20)
     x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)
     def f_jax(x):  # x: f32[10,20] -> f32[20,10]
       return jnp.sin(x.T)
 
+    mesh = Mesh(jax.devices()[:2], "d")
     pjit_kwargs = {}
+    # Use NamedShardings if we don't have a mesh_context
+    if with_mesh:
+      sharding_None_d = P(None, "d")
+      sharding_d_None = P("d", None)
+    else:
+      sharding_None_d = NamedSharding(mesh, P(None, "d"))
+      sharding_d_None = NamedSharding(mesh, P("d", None))
+
     if in_shardings != "missing":
-      pjit_kwargs["in_shardings"] = (P(None, "x") if in_shardings == "P" else None)
+      pjit_kwargs["in_shardings"] = (
+        sharding_None_d if in_shardings == "P" else None)
     if out_shardings != "missing":
-      pjit_kwargs["out_shardings"] = (P("x", None) if out_shardings == "P" else None)
-    f_jax = pjit.pjit(f_jax, **pjit_kwargs)
+      pjit_kwargs["out_shardings"] = (
+        sharding_d_None if out_shardings == "P" else None)
+    f_jax_pjit = pjit.pjit(f_jax, **pjit_kwargs)
+
+    with contextlib.ExitStack() as stack:
+      if with_mesh:
+        stack.enter_context(mesh)
+      # Serialize higher-order gradiends
+      exp = export.export(f_jax_pjit)(x)
 
-    with Mesh(jax.devices()[:2], "x"):
-      exp = export.export(f_jax)(x)
       exp_vjp = exp.vjp()
+      # Try 2nd order grad as well
+      exp_vjp2 = exp_vjp.vjp()
 
     vjp_module_str = str(exp_vjp.mlir_module())
 
@@ -812,13 +833,41 @@ def f_jax(x):  # x: f32[10,20] -> f32[20,10]
 
     # Custom calls for the primal output shape all match primal_out_sharding
     primal_out_calls = re.findall(
-      r"custom_call @Sharding.* {mhlo.sharding = \"(.+)\".*:.*tensor<20x10xf32>",
+      r"custom_call @Sharding.*mhlo.sharding = \"(.+)\".*:.*tensor<20x10xf32>",
       vjp_module_str)
     self.assertTrue(
       all(s == primal_out_sharding for s in primal_out_calls),
       primal_in_calls
     )
 
+    # Call the exported gradient functions. In order to set the device context
+    # we replicate the inputs. If we don't use a mesh context and there are
+    # no shardings on inputs or outputs, then we have serialized for one
+    # device.
+    if in_shardings != "P" and out_shardings != "P" and not with_mesh:
+      self.assertEqual(exp_vjp.nr_devices, 1)
+      self.assertEqual(exp_vjp2.nr_devices, 1)
+      call_mesh = Mesh(jax.devices()[:1], "e")
+    else:
+      self.assertEqual(exp_vjp.nr_devices, 2)
+      self.assertEqual(exp_vjp2.nr_devices, 2)
+      call_mesh = Mesh(jax.devices()[:2], "e")
+
+    g1 = pjit.pjit(export.call_exported(exp_vjp),
+                   in_shardings=(NamedSharding(call_mesh, None),
+                                 NamedSharding(call_mesh, None)))(x, x.T)
+    _, f_jax_vjp = jax.vjp(f_jax, x)
+    xbar = f_jax_vjp(x.T)
+    self.assertAllClose(xbar, g1)
+
+    g2 = pjit.pjit(export.call_exported(exp_vjp2),
+                   in_shardings=(NamedSharding(call_mesh, None),
+                                 NamedSharding(call_mesh, None),
+                                 NamedSharding(call_mesh, None)))(x, x.T, x)
+    _, f_jax_vjp2 = jax.vjp(f_jax_vjp, x.T)
+    xbar2, = f_jax_vjp2((x,))
+    self.assertAllClose(xbar2, g2[1])
+
   def test_multi_platform(self):
     x = np.arange(8, dtype=np.float32)
     exp = export.export(_testing_multi_platform_func,