Merge pull request #13603 from gnecula:native_unused

PiperOrigin-RevId: 494769977

jax/experimental/jax2tf/README.md

@@ -381,6 +381,10 @@ lowered with the batch dimension polymorphic and the remaining dimensions concre
 
 It is reasonable to expect that there will be JAX programs for which there is a
 shape-polymorphic TensorFlow graph, but which will give an error when lowering with jax2tf.
+In general, you should expect that shape polymorphism can handle those programs for which
+all the intermediate shapes can be expressed as polynomials in the dimension variables
+appearing in the input shapes. In particular, this does not include programs whose
+intermediate shapes depend on the data.
 
 ### Details
 
@@ -613,6 +617,38 @@ jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)),
                polymorphic_shapes=["(2*b, ...)"])(np.ones((4, 5, 7)))
 ```
 
+### Dimension variables must be solvable from the input shapes
+
+`jax2tf` will generate code to derive the values of the dimension variables
+from the input shapes. This works only if dimension polynomials in the input shapes are linear.
+For example, the following `polymorphic_shapes` will result in errors:
+
+```python
+polymorphic_shapes = ["a * a"]  # Not a linear polynomial
+polymorphic_shapes = ["a + b"]  # Too few equations to derive both `a` and `b`
+```
+
+If you are using native lowering, the restrictions are stronger: every dimension
+variable must occur as the value of some dimension of some input, e.g.,
+the following will work:
+
+```python
+polymorphic_shapes = ["a, 2*a, b"]
+polymorphic_shapes = ["a * a, a"]
+```
+
+Furthermore, when using the native lowering the inputs that are not needed in the computation
+are ignored, so the dimension variables must be derivable only from used inputs.
+In the following example, the `x_unused` is not part of the computation so its
+input shapes cannot be used for deriving the dimension variables, and you will
+get an error that `a` cannot be derived:
+
+```python
+jax2tf.convert(lambda x_unused, y: y * 2.,
+               polymorphic_shapes=["b, a", "b, 2 * a"])(x, y)
+```
+
+
 ## Known issues
 
 `jax2tf` has been in use since 2020 and the vast majority of users encounter

jax/experimental/jax2tf/jax2tf.py

@@ -595,46 +595,28 @@ def _lower_native_and_run(fun_jax: Callable,
   Special care must be taken in presence of shape polymorphism.
   """
   # Look for shape polymorphism
-
-  # For each dimension variable, encode how to compute its value from the
-  # shape of the explicit arguments. E.g., "2.1" denotes args_tf[2].shape[1].
-  # The order of the dimension variables must match the order of the first N
-  # arguments of the lowered function.
-
   # We now have two implementations for the native lowering. If --jax_dynamic_shapes
-  # then we use JAX's in-progress support for native dynamic shapes. In that
-  # case we assume that the dimension variables are listed in the order in which
-  # they are encountered by scanning the arguments and their shapes in order.
-  # If we don't use --jax_dynamic_shapes then the dimension variables are passed
-  # in the alphabetical order of their names.
-  abstracted_axes: Sequence[Dict[int, str]] = []
-  dim_args_spec_dict: Dict[str, str] = {}  # map dim var name to dim_args_spec
-  dim_vars_seen: List[str] = []  # the dim var names in order
-  for arg_idx, aval in enumerate(args_avals):
-    one_abstract_axes = {}
-    for axis_idx, d in enumerate(aval.shape):
-      if not core.is_constant_dim(d):
-        d_var = d.to_var()
-        if d_var is None:
-          raise ValueError(f"Only simple dimension variables supported: {aval.shape}")
-        if not d_var in dim_vars_seen:
-          dim_args_spec_dict[d_var] = f"{arg_idx}.{axis_idx}"
-          dim_vars_seen.append(d_var)
-        one_abstract_axes[axis_idx] = d_var
-    abstracted_axes.append(one_abstract_axes)
-
-  if any(abstracted_axes):
-    if config.jax_dynamic_shapes:
+  # then we use JAX's in-progress support for native dynamic shapes, and we pass
+  # abstracted_axes to lowering functions. Otherwise, we just lower using
+  # abstract values whose shapes may include polynomials (already in args_avals).
+  if config.jax_dynamic_shapes:
+    abstracted_axes: Sequence[Dict[int, str]] = []
+    for arg_idx, aval in enumerate(args_avals):
+      one_abstract_axes = {}
+      for axis_idx, d in enumerate(aval.shape):
+        if not core.is_constant_dim(d):
+          d_var = d.to_var()
+          if d_var is None:
+            raise ValueError(f"Only trivial dimension polynomials on input: {aval.shape}")
+          one_abstract_axes[axis_idx] = d_var
+      abstracted_axes.append(one_abstract_axes)
+
+    if any(abstracted_axes):
       abstracted_axes = tuple(abstracted_axes)
-      # In the order we have seen them
-      dim_args_spec = [dim_args_spec_dict[d_var] for d_var in dim_vars_seen]
     else:
       abstracted_axes = None  # type: ignore
-      # In sorted order by name
-      dim_args_spec = [dim_args_spec_dict[d_var] for d_var in sorted(dim_vars_seen)]
   else:
     abstracted_axes = None  # type: ignore
-    dim_args_spec = []
 
   arg_specs_jax = [
     jax.ShapeDtypeStruct(aval.shape, aval.dtype, named_shape=aval.named_shape)
@@ -647,7 +629,6 @@ def _lower_native_and_run(fun_jax: Callable,
     # convert(f_jax), in which case a "jit" is implied. We also add a jit when
     # we need to pass the abstracted axes.
     fun_jax_lower = jax.jit(fun_jax, backend=backend,
-                            keep_unused=True,  # TODO: allow dropping unused
                             abstracted_axes=abstracted_axes).lower
   else:
     fun_jax_lower = fun_jax.lower
@@ -658,10 +639,6 @@ def _lower_native_and_run(fun_jax: Callable,
   else:
     mhlo_module = lowered.mhlo()
     xla_call_module_version = 1
-  if logging.vlog_is_on(3):
-    mhlo_module_text = mlir.module_to_string(mhlo_module)
-    logging.vlog(3, "XlaCallModule (version=%d)\n%s", xla_call_module_version,
-                 mhlo_module_text)
 
   mhlo_serialized_module = mlir.module_to_bytecode(mhlo_module)
   # Figure out the result types and shapes
@@ -685,6 +662,62 @@ def _out_type(jax_type):
     return jax_type
   out_types = tuple(_out_type(out_aval.dtype) for out_aval in out_avals)
 
+  module_kept_var_idx = lowered.compile_args["kept_var_idx"]
+  # We must compute the dim_args_spec: for each dimension variable, encode how
+  # to compute its value from the shape of the explicit arguments. E.g., "2.1"
+  # denotes args_tf[2].shape[1]. The order of the dimension variables must match
+  # the order of the first N arguments of the lowered function.
+  # If we use --jax_dynamic_shapes, the dimension variables are listed in the
+  # order in which they are encountered by scanning the arguments and their
+  # shapes in order. Otherwise, the dimension variables are passed in the
+  # alphabetical order of their names.
+  dim_args_spec_dict: Dict[str, str] = {}  # map dim var name to dim_args_spec
+  dim_vars_order: List[str] = []
+  all_dim_vars: Set[str] = set()
+  current_kept_arg_idx = -1  # The index among the kept arguments
+  for arg_idx, aval in enumerate(args_avals):
+    is_kept = arg_idx in module_kept_var_idx
+    if is_kept:
+      current_kept_arg_idx += 1
+
+    for axis_idx, d in enumerate(aval.shape):
+      if not core.is_constant_dim(d):
+        # We collect dimension variables even from dropped args
+        all_dim_vars = all_dim_vars.union(d.get_vars())
+        if not is_kept: continue
+        d_var = d.to_var()
+        # We can compute dim vars only from trivial polynomials
+        if d_var is None: continue
+        if not d_var in dim_args_spec_dict:
+          dim_vars_order.append(d_var)
+          dim_args_spec_dict[d_var] = f"{current_kept_arg_idx}.{axis_idx}"
+
+  if all_dim_vars:
+    dim_args_spec_set = set(dim_vars_order)
+    if dim_args_spec_set != all_dim_vars:
+      missing = all_dim_vars.difference(dim_args_spec_set)
+      args_list = [f"  Arg[{arg_idx}] - {'KEPT   ' if arg_idx in module_kept_var_idx else 'DROPPED'}: {aval}"
+                   for arg_idx, aval in enumerate(args_avals)]
+      raise ValueError(
+          "The following dimension variables cannot be computed from the static "
+          f"shapes of the kept lowered arguments: {missing}. These are the "
+          "argument shapes:\n" +
+          "\n".join(args_list) +
+          "\n"
+          "Please see https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md#dimension-variables-must-be-solvable-from-the-input-shapes for more details.")
+
+    if config.jax_dynamic_shapes:
+      # In the order we have seen them
+      dim_args_spec = [dim_args_spec_dict[d_var] for d_var in dim_vars_order]
+    else:
+      # In sorted order by name
+      dim_args_spec = [dim_args_spec_dict[d_var] for d_var in sorted(dim_vars_order)]
+  else:
+    dim_args_spec = []
+
+  args_avals = [aval for i, aval in enumerate(args_avals) if i in module_kept_var_idx]
+  args_tf = [atf for i, atf in enumerate(args_tf) if i in module_kept_var_idx]
+
   # Apply the shardings on arguments and results for pjit. This is redundant
   # because the mhlo_module_text will already contain the shardings, but it
   # makes it easier for tools like the TPU inference converter to see the
@@ -694,6 +727,11 @@ def _out_type(jax_type):
     args_tf = tuple(
       map(_shard_value, args_tf, args_avals, lowered.compile_args["in_shardings"]))
 
+  if logging.vlog_is_on(3):
+    mhlo_module_text = mlir.module_to_string(mhlo_module)
+    logging.vlog(3, "XlaCallModule (version=%d, dim_args_spec=%s)\n%s",
+                 xla_call_module_version, ", ".join(dim_args_spec),
+                 mhlo_module_text)
   res = tfxla.call_module(
       args_tf,
       version=xla_call_module_version,

jax/experimental/jax2tf/shape_poly.py

@@ -886,5 +886,7 @@ def process_one_eqn(eqn: DimEquation) -> bool:
   err_msg = (
       f"Cannot solve for values of dimension variables {unsolved_vars} from "
       f"the remaining dimension polynomials\n  {eqns_str}.{_shapeenv_to_str()} "
-      "Dimension variables can be solved only from linear polynomials.")
+      "Dimension variables can be solved only from linear polynomials.\n"
+      "\n"
+      "Please see https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md#dimension-variables-must-be-solvable-from-the-input-shapes for more details.")
   raise ValueError(err_msg)

jax/experimental/jax2tf/tests/jax2tf_test.py

@@ -818,6 +818,28 @@ def inner1(y):
 
     jax2tf.convert(func)(2.)  # No error
 
+  def test_jit_unused(self):
+    def f_jax(x, y_unused):
+      return x * np.float32(2.)
+    x, y_unused = np.float32(5.), np.arange(7, dtype=np.int32)
+    res_tf = jax2tf.convert(jax.jit(f_jax, keep_unused=False))(x, y_unused)
+    self.assertAllClose(f_jax(x, None), res_tf)
+
+  def test_jit_unused_grad(self):
+    def f_jax(x, y_unused):
+      return x * np.float32(2.)
+
+    x, y_unused = np.float32(5.), np.arange(7, dtype=np.int32)
+    f_tf = jax2tf.convert(jax.jit(f_jax, keep_unused=False))
+    xv, y_unused_v = tf.Variable(x), tf.Variable(y_unused)
+    with tf.GradientTape() as tape:
+      res_tf = f_tf(xv, y_unused_v)
+      grad_tf_x, grad_tf_y = tape.gradient(res_tf, (xv, y_unused_v))
+
+    self.assertAllClose(f_jax(x, None), res_tf)
+    self.assertAllClose(np.float32(2.), grad_tf_x)
+    self.assertIsNone(grad_tf_y)
+
   def test_nested_convert_error(self):
     def outer(y):
       return jax2tf.convert(jnp.sin)(y)  # Inner convert takes tracer args

jax/experimental/jax2tf/tests/shape_poly_test.py

@@ -20,7 +20,6 @@
 import collections
 import functools
 from functools import partial
-import logging
 import operator
 import re
 
@@ -722,6 +721,70 @@ def f_jax(x):  # x: f32[w, h]
         input_signature=[tf.TensorSpec([None, None])],
         polymorphic_shapes=["w, h"])
 
+  def test_non_trivial_polynomials(self):
+    if config.jax_dynamic_shapes:
+      raise unittest.SkipTest("--jax_dynamic_shapes supports only trivial polynomials")
+    # We can handle non-trivial polynomials in the input shape,
+    # as long as all variables also occur in trivial polynoamials
+    self.CheckShapePolymorphism(
+        lambda x, y: x + y.reshape((-1,)),
+        input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None, None])],
+        polymorphic_shapes=["b * b", "b, b"])
+
+  def test_unused_args(self):
+    # Tests with functions that do not use their inputs.
+
+    # First arg unused, not polymorphic
+    self.CheckShapePolymorphism(
+        lambda x_unused, y: y * 2.0,
+        input_signature=[tf.TensorSpec([]), tf.TensorSpec([None])],
+        polymorphic_shapes=[None, "b"])
+
+    # Some args unused, not polymorphic
+    self.CheckShapePolymorphism(
+        lambda x_unused, y, z_unused, w: jnp.concatenate([y, w]),
+        input_signature=[tf.TensorSpec([]), tf.TensorSpec([None]),
+                         tf.TensorSpec([]), tf.TensorSpec([None])],
+        polymorphic_shapes=[None, "b1", None, "b2"])
+
+    # A polymorphic arg is not used, but the dimension var appears
+    # in a used arg also
+    self.CheckShapePolymorphism(
+        lambda x_unused, y: y * 2.0,
+        input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
+        polymorphic_shapes=["b", "b"])
+
+    # A polymorphic arg is not used, and the dimension var does not appear
+    # elsewhere.
+    if config.jax2tf_default_experimental_native_lowering:
+      with self.assertRaisesRegex(ValueError,
+                                  "The following dimension variables cannot be computed"):
+        self.CheckShapePolymorphism(
+            lambda x_unused, y: y * 2.0,
+            input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
+            polymorphic_shapes=["b1", "b2"])
+    else:
+      self.CheckShapePolymorphism(
+          lambda x_unused, y: y * 2.0,
+          input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
+          polymorphic_shapes=["b1", "b2"])
+
+    # A polymorphic arg is not used, and the dimension var does appear
+    # elsewhere but not as a trivial monomial.
+    if config.jax2tf_default_experimental_native_lowering:
+      with self.assertRaisesRegex(ValueError,
+                                  "The following dimension variables cannot be computed"):
+        self.CheckShapePolymorphism(
+            lambda x_unused, y: y * 2.0,
+            input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
+            polymorphic_shapes=["b1", "b1 * b1"])
+    else:
+      self.CheckShapePolymorphism(
+          lambda x_unused, y: y * 2.0,
+          input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])],
+          polymorphic_shapes=["b1", "b1 * b1"])
+
+
   def test_with_custom_vjp(self):
     """Shape-polymorphic custom VJP."""
 
@@ -1065,6 +1128,11 @@ def f_jax(x):
         jax2tf.convert(f_jax, polymorphic_shapes=["b, b"])).get_concrete_function(tf.TensorSpec([None, None], dtype=np.float32))
     self.assertEqual(1, f_tf(x45))
 
+    x = np.ones((5,), dtype=np.float32)
+    with self.assertRaisesRegex(ValueError,
+                                "Cannot solve for values of dimension variables"):
+      jax2tf.convert(lambda x: x, polymorphic_shapes=["a + b"])(x)
+
 
 class DimAsValueTest(tf_test_util.JaxToTfTestCase):
   """Dimension polynomials used as values in the JAX computation."""