# Copyright 2020 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. """Tests for the shape-polymorphic jax2tf conversion.""" import contextlib import math import unittest from absl import logging from absl.testing import absltest, parameterized from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union import collections import functools from functools import partial import operator as op import re import jax from jax import core from jax.experimental import jax2tf from jax.experimental.jax2tf import shape_poly from jax.experimental import pjit from jax import lax import jax.numpy as jnp from jax import random from jax import tree_util from jax._src import test_util as jtu from jax._src import util from jax._src.lax import lax as lax_internal from jax._src.lax import control_flow as lax_control_flow from jax._src.lib import xla_client import numpy as np from jax.experimental.jax2tf.tests import tf_test_util import tensorflow as tf # type: ignore[import] from jax import config from jax._src.config import numpy_dtype_promotion config.parse_flags_with_absl() # Import after parsing flags from jax.experimental.jax2tf.tests import primitive_harness from jax.experimental.jax2tf.tests.primitive_harness import Harness, CustomArg, RandArg, StaticArg from jax.experimental.jax2tf.tests.jax2tf_limitations import Jax2TfLimitation PS = jax2tf.PolyShape _f32 = np.float32 _i32 = np.int32 expect_error_associative_scan = ( (None, None) if (not config.jax2tf_default_native_serialization or jtu.device_under_test() == "tpu") else (NotImplementedError, "associative scan over axis of non-constant size")) class DimExprTest(tf_test_util.JaxToTfTestCase): def test_parse_shape(self): self.assertEqual((), shape_poly._parse_spec("", ())) self.assertEqual((), shape_poly._parse_spec("()", ())) self.assertEqual((2, 3), shape_poly._parse_spec(None, (2, 3))) self.assertEqual((2, 3), shape_poly._parse_spec("2, 3,", (2, 3))) self.assertEqual((2, 3), shape_poly._parse_spec("2, _", (2, 3))) self.assertEqual((2, 3), shape_poly._parse_spec("2, ...", (2, 3))) self.assertEqual((2, 3), shape_poly._parse_spec("...", (2, 3))) self.assertEqual((2, 3), shape_poly._parse_spec(" ( 2 , 3 ) ", (2, 3))) a, b = shape_poly._parse_spec("a, b", (2, 3)) self.assertEqual((a, 3), shape_poly._parse_spec("(a, ...) ", (None, 3))) tshape = tf.TensorShape([None, 3]) self.assertEqual((a, 3), shape_poly._parse_spec("(a, ...) ", tshape)) a, b = shape_poly._parse_spec("a, b", (2, 3)) @parameterized.named_parameters( dict(testcase_name=f"_{dim_spec}", dim_spec=dim_spec, dim_poly=dim_poly) for dim_spec, dim_poly in [ ("2*a*b", 2 * a * b), ("-2 * a^2 * b + b^2", -2 * a * a * b + b * b), ("-2 * a^2 * b + -1 *b^2*a", -2 * a * a * b - a * b * b), ("3 * a * b * a + -2", 3 * a * b * a - 2), ("a + 1 ,", a + 1), ("a - 1", a - 1), ("a + -1", a - 1), ("3 * a * mod(a + 2, b + 2)", 3 * a * ((a + 2) % (b + 2))), ("3 * floordiv(a + 2, b + 2) * 2", 3 * ((a + 2) // (b + 2)) * 2), ]) def test_parse_dim(self, dim_spec="-2 * a^2 * b + b^2", dim_poly=-2 * a * a * b + b * b): self.assertEqual((dim_poly,), shape_poly._parse_spec(dim_spec, (None,))) self.assertEqual((dim_poly,), shape_poly._parse_spec(str(dim_poly), (None,))) @parameterized.named_parameters( dict(testcase_name=f"_{shape_spec=}", shape_spec=shape_spec) for shape_spec in [ "2.5", "a + a a", "a ^ a", "a, a", "_", "...", "a ;", ")(", "2a", "a@", "'a'", "('a', ...)", "mod(a)", "floordiv(a, b, c)", "..., 3" ]) def test_parse_error(self, shape_spec="a + a a"): with self.assertRaisesRegex(ValueError, "syntax error in polymorphic shape"): shape_poly._parse_spec(shape_spec, (None,)) @parameterized.named_parameters( dict(testcase_name=f"_{shape_spec=}", shape_spec=shape_spec, arg_shape=arg_shape) for shape_spec, arg_shape in [ ("3", (4,)), ("b, 3", (None, 4)), ]) def test_parse_mismatch_error(self, shape_spec="3", arg_shape=(4,)): with self.assertRaisesRegex(ValueError, "syntax error in polymorphic shape .* different size"): shape_poly._parse_spec(shape_spec, arg_shape) def test_dim_vars(self): a, b, a1 = shape_poly._parse_spec("a, b, a", (2, 3, 2)) self.assertEqual(True, a == a) self.assertEqual(True, a == a1) self.assertEqual(False, a != a) self.assertFalse(a == b) self.assertTrue(a != b) self.assertLen({a, a}, 1) self.assertLen({a, b}, 2) self.assertIn(a, {a, b}) self.assertIn(b, {a, b}) self.assertIn(a, [a, b]) self.assertIn(b, [a, b]) def test_get_vars(self): a, b = shape_poly._parse_spec("a, b", (2, 3)) self.assertEqual({"a"}, a.get_vars()) self.assertEqual({"a", "b"}, (a * b * a).get_vars()) def test_evaluate(self): a, b = shape_poly._parse_spec("a, b", (2, 3)) self.assertEqual(1, (a * a - b).evaluate(dict(a=2, b=3))) self.assertEqual(1, ((a * a) // b).evaluate(dict(a=2, b=3))) self.assertEqual(4, ((a * a) % b).evaluate(dict(a=5, b=7))) def test_dim_vars_symbolic_equal(self): a, b = shape_poly._parse_spec("a, b", (2, 3)) self.assertTrue(core.symbolic_equal_dim(a, a)) self.assertFalse(core.symbolic_equal_dim(a, 1)) self.assertFalse(core.symbolic_equal_dim(a, b)) self.assertTrue(core.symbolic_equal_one_of_dim(a, [2, a])) self.assertFalse(core.symbolic_equal_one_of_dim(a, [2, b])) self.assertFalse(core.symbolic_equal_one_of_dim(a, [])) self.assertTrue(core.symbolic_equal_one_of_dim(2, [a, 3, 2])) self.assertFalse(core.symbolic_equal_one_of_dim(1, [2, b])) self.assertFalse(core.symbolic_equal_one_of_dim(3, [])) self.assertTrue(core.symbolic_equal_dim(1, jnp.add(0, 1))) # A DeviceArray with self.assertRaisesRegex(TypeError, re.escape("Shapes must be 1D sequences of concrete values of integer type, got (1, 'a').")): self.assertTrue(core.symbolic_equal_dim(1, "a")) def test_poly_bounds(self): a, b = shape_poly._parse_spec("a, b", (2, 3)) bounded_le4 = 5 - a bounded_ge2 = b + 1 bounded_ge0_le4 = a % 5 self.assertEqual(a.bounds(), (1, np.PINF)) self.assertEqual(bounded_le4.bounds(), (np.NINF, 4)) self.assertEqual(bounded_ge2.bounds(), (2, np.PINF)) self.assertEqual(bounded_ge0_le4.bounds(), (0, 4)) # Additions self.assertEqual((bounded_ge0_le4 + bounded_le4).bounds(), (np.NINF, 8)) self.assertEqual((bounded_ge0_le4 + bounded_ge2).bounds(), (2, np.PINF)) self.assertEqual((bounded_le4 + bounded_ge2).bounds(), (np.NINF, np.PINF)) # Subtractions self.assertEqual((bounded_ge0_le4 - bounded_le4).bounds(), (-4, np.PINF)) self.assertEqual((- bounded_ge0_le4 + bounded_le4).bounds(), (np.NINF, 4)) self.assertEqual((bounded_ge0_le4 - bounded_ge2).bounds(), (np.NINF, 2)) self.assertEqual((- bounded_ge0_le4 + bounded_ge2).bounds(), (-2, np.PINF)) self.assertEqual((bounded_le4 - bounded_ge2).bounds(), (np.NINF, 2)) self.assertEqual((- bounded_le4 + bounded_ge2).bounds(), (-2, np.PINF)) # Multiplications self.assertEqual((2 * a - 3).bounds(), (-1, np.PINF)) self.assertEqual((-2 * a - 3).bounds(), (np.NINF, -5)) self.assertEqual((3 * a * b * b + 5 * a - 7).bounds(), (1, np.PINF)) self.assertEqual((3 * a * b * b - 5 * a - 7).bounds(), (np.NINF, np.PINF)) self.assertEqual((a + b - a * b + a * b * a).bounds(), (np.NINF, np.PINF)) self.assertEqual((a + 2 * b - a).bounds(), (2, np.PINF)) self.assertEqual((a + 2 * b - a).bounds(), (2, np.PINF)) # mod self.assertEqual(((b + 1) % 2).bounds(), (0, 1)) self.assertEqual(((b + 1) % -2).bounds(), (-1, 0)) self.assertEqual(((b - 4) % 2).bounds(), (0, 1)) self.assertEqual(((b + 1) % a).bounds(), (0, np.PINF)) self.assertEqual((11 % (a + 1)).bounds(), (0, np.PINF)) self.assertEqual((-11 % (a + 1)).bounds(), (0, np.PINF)) self.assertEqual((b % (a - 2)).bounds(), (np.NINF, np.PINF)) # floordiv self.assertEqual(((a + 4) // 2).bounds(), (2, np.PINF)) self.assertEqual(((a + 4) // -2).bounds(), (np.NINF, -3)) self.assertEqual(((a + 5) // 2).bounds(), (3, np.PINF)) self.assertEqual(((a + 5) // -2).bounds(), (np.NINF, -3)) self.assertEqual((11 // (a + 1)).bounds(), (0, 5)) self.assertEqual((-11 // (a + 1)).bounds(), (-6, -1)) self.assertEqual((-11 // (- a)).bounds(), (0, 11)) # finite negative dividend, infinite divisor self.assertEqual(((b + 1) // (a + 1)).bounds(), (0, np.PINF)) self.assertEqual((-b // (a + 1)).bounds(), (np.NINF, -1)) # Generate test cases for floordiv and mod: (a + N) // +-2, (N - a) // +-2 # and then evaluate them for a = 1, 5, 10000 div_mod_atoms = [ operation(op1 + n, div) for op1 in (a, a + 10, a + 11, -a, -a + 10, -a + 11) for n in (-3, -1, 0, 1, 3) for div in (-2, 2, a + 4, -4 - a) # Either negative, or positive for operation in (op.floordiv, op.mod) ] for atom in div_mod_atoms: lb, ub = atom.bounds() self.assertLessEqual(lb, ub) for a_val in (1, 5, 10000): atom_val = atom.evaluate(dict(a=a_val)) self.assertGreaterEqual(atom_val, lb) self.assertLessEqual(atom_val, ub) # Inequalities involving mod and floordiv self.assertEqual((5 - a % 5).bounds(), (1, 5)) self.assertEqual((-5 - a % (-5)).bounds(), (-5, -1)) self.assertEqual((a - 5 % a).bounds(), (1, np.PINF)) self.assertEqual((a - 5 % a).bounds(), (1, np.PINF)) self.assertEqual((3 * (a + b) - 5 % (3 * (a + b))).bounds(), (1, np.PINF)) self.assertEqual((- a + (b - 5) % a).bounds(), (np.NINF, -1)) def test_poly_equal(self): a, b = shape_poly._parse_spec("a, b", (2, 3)) poly3 = a + 3 - a self.assertTrue(poly3 == 3) self.assertTrue(poly3 == np.array(3, np.int64)) self.assertTrue(poly3 == np.array(3, np.int64)[()]) self.assertFalse((poly3 + 1) == 3) self.assertFalse(poly3 == poly3 + 1) self.assertTrue((2 * a * b * a + 3).eq(1 + b * a * a + a * a * b + 2)) self.assertFalse((2 * a * b * a + 3).eq(a * b * a + 3)) self.assertFalse((a * b * a + 3).eq(a * b * a + 4)) self.assertFalse((2 * a * b * a).eq(a * b * a)) self.assertFalse((2 * a * b * a + 1).eq(a * b * a)) self.assertFalse((3 * a * b * a - 1).eq(a * b * a)) self.assertFalse((3 * a * b * a - 2).eq(a * b * a)) self.assertTrue(a % b == a % b) self.assertTrue(a % b - a % b == 0) self.assertTrue(a // b == a // b) self.assertTrue(a // b - a // b == 0) self.assertTrue(a % b == (2 * a // 2) % (a + b - a)) self.assertTrue(a // b == (2 * a // 2) // (a + b - a)) self.assertTrue(a, a + (a + b) // b - (b + a) // b) # Test the normalization (a // b) * b == a - a % b self.assertTrue((a // 2) * 2 == a - a % 2) self.assertTrue((a // 2) + (a // 2) == a - a % 2) self.assertTrue((a // 2) * 6 == 3 * a - 3 * (a % 2)) self.assertTrue((a // b) * b == a - a % b) self.assertTrue(2 * (a // b) * b * b == 2 * b * a - 2 * b * (a % b)) self.assertTrue(a // (2 * b) * 2 * b == a - a % (2 * b)) self.assertTrue(a // (2 * b) * 2 * b + 2 * a == 3 * a - a % (2 * b)) def test_poly_compare(self): a, b = shape_poly._parse_spec("a, b", (2, 3)) poly = 4 * a + b + 3 self.assertTrue(poly.ge(0)) self.assertTrue(poly.ge(8)) self.assertTrue(poly.ge(poly)) self.assertTrue(poly.ge(poly - 1)) with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "inconclusive"): poly.ge(9) with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "inconclusive"): (4 * a - b).ge(0) def test_poly_compare_overload(self): a, b = shape_poly._parse_spec("a, b", (2, 3)) poly = 4 * a + b + 3 self.assertTrue(poly >= 0) self.assertTrue(poly >= 8) self.assertTrue(poly > 7) self.assertTrue(poly >= poly) self.assertTrue(poly >= poly - 1) with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "inconclusive"): poly >= 9 with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "inconclusive"): (4 * a - b) >= 0 def test_core_greater_equal(self): a, b = shape_poly._parse_spec("a, b", (2, 3)) self.assertTrue(core.greater_equal_dim(a, a)) self.assertTrue(core.greater_equal_dim(a, 0)) self.assertTrue(core.greater_equal_dim(a, 1)) self.assertTrue(core.greater_equal_shape((a, 2), (1, 1))) with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "Symbolic dimension comparison .* is inconclusive"): core.greater_equal_dim(a, 2) with self.assertRaisesRegex(core.InconclusiveDimensionOperation, "Symbolic dimension comparison .* is inconclusive"): core.greater_equal_dim(a, b) def test_poly_int_results(self): # Whenever the result is an integer, it should be represented as an # Python integer, not a symbolic dimension. a, b = shape_poly._parse_spec("a, b", (2, 3)) self.assertEqual(a + 2 - a, 2) self.assertIsInstance(a + 2 - a, int) self.assertEqual(a + (2 - a), 2) self.assertIsInstance(a + (2 - a), int) self.assertEqual(a * 2 // a, 2) self.assertIsInstance(a * 2 // a, int) @parameterized.named_parameters( dict(testcase_name=f"_D={dividend}_d={divisor}_q={quotient}_r={remainder}", dividend=dividend, divisor=divisor, quotient=quotient, remainder=remainder) for dividend, divisor, quotient, remainder in [ (a, 1, a, 0), (3 * a, 3, a, 0), (3 * a + 3, 3, a + 1, 0), (3 * a + 2, 3, a, 2), (3 * a + 5, 3, a + 1, 2), (3 * a - 2, 3, a - 1, 1), (3 * a * a * b + 2 * b * b * a, a * b, 3 * a + 2 * b, 0), (a * a - b * b, a + b, a - b, 0), (a, b, "floordiv(a, b)", "mod(a, b)"), (3 * a, 2, "floordiv(3*a, 2)", "mod(3*a, 2)"), (2 * a * b + b * b, a + b, "floordiv(2*a*b + b^2, a + b)", "mod(2*a*b + b^2, a + b)"), (3, a, "floordiv(3, a)", "mod(3, a)"), ]) def test_poly_divmod(self, *, dividend, quotient, divisor, remainder): if isinstance(quotient, str): d1, d2 = divmod(dividend, divisor) self.assertEqual((quotient, remainder), (str(d1), str(d2))) else: self.assertEqual((quotient, remainder), divmod(dividend, divisor)) def test_dilate_shape(self): """0 if d == 0 else 1 + dilation * (d - 1))""" a, = shape_poly._parse_spec("a,", (2,)) self.assertEqual((4, 7), core.dilate_shape((2, 3), (3, 3))) self.assertEqual((0, 7), core.dilate_shape((0, 3), (3, 3))) self.assertEqual((a, 7), core.dilate_shape((a, 3), (1, 3))) self.assertEqual((2 * a - 1, 7), core.dilate_shape((a, 3), (2, 3))) def test_stride_shape(self): """(s - window_size) // window_stride + 1""" a, stride = shape_poly._parse_spec("a, s", (2, 3)) self.assertEqual((8, 9), core.stride_shape((10, 20), window_size=(3, 3), window_stride=(1, 2))) self.assertEqual((a, 9), core.stride_shape((a, 20), (1, 3), (1, 2))) self.assertEqual((a - 1, 9), core.stride_shape((a, 20), (2, 3), (1, 2))) self.assertEqual((a + 1, 9), core.stride_shape((a * stride + 2, 20), (2, 3), (stride, 2))) (stride0, stride1) = core.stride_shape((a, 20), (1, 3), (2, 2)) self.assertEqual("floordiv(a + -1, 2) + 1", str(stride0)) self.assertEqual(9, stride1) class PolyHarness(Harness): """Tests a function with shape polymorphism. Converts `fun` with shape polymorphism, creates a `tf.ConcreteFunction` given `input_signature` and checks the inferred output shapes to match `expected_output_shapes`, then checks that the JAX and the TF functions produce the same results. """ def __init__(self, group_name: str, name: str, fun: Callable, *, arg_descriptors: Sequence[primitive_harness.ArgDescriptor] = (), polymorphic_shapes: Optional[Sequence[Any]] = None, input_signature: Optional[Sequence[tf.TensorSpec]] = None, poly_axes: Optional[Sequence[Optional[Union[int, Sequence[int]]]]] = None, expected_output_signature: Optional[tf.TensorSpec] = None, enable_xla: bool = True, expect_error: Tuple[Optional[Any], Optional[str]] = (None, None), skip_jax_run: bool = False, check_result: bool = True, tol: Optional[float] = None, limitations: Sequence[Jax2TfLimitation] = (), override_jax_config_flags: Dict[str, Any] = {}): """Args: group_name, name: The name for the harness. See `Harness.__init__`. fun: the function to be converted, possbily after partial application to static arguments from `arg_descriptors`. See `Harness.__init__`. arg_descriptors: The argument descriptors. See `Harness.__init__`. May be missing, in which case `skip_jax_run` should be `True` and `poly_axes` cannot be used. polymorphic_shapes: For `jax2tf.convert`. If missing, generated from `poly_axes`. input_signature: For `tf.function.get_concrete_function`. If missing, generated from `poly_axes`. poly_axes: If present, used to generate `polymorphic_shapes` and `input_signature`. Must correspond to the non-static arguments, and for each one it must specify which axes are polymorphic: None, or an int (for the index of the polymorphic axis), or a tuple of ints (for multiple polymorphic axes). For each argument, we use its `poly_axes` entry to generate the polymorphic_shapes specification, creating dimension variables `b0`, `b1, ..., for each of its polymorphic axes. This means that separate arguments will share the same dimension variable names, in the order in which the axes are listed in `poly_axes`. We also generate the input_signature from `poly_axes`. expected_output_signature: the expected inferred output shape. enable_xla: For `jax2tf.convert`. expect_error: a pair of an Exception type and a regular expression to match the expected exception string. skip_jax_run: If True, then neither the JAX nor the TF functions are executed. check_result: specifies if we want to check that the result of the shape polymorphic conversion produces the same result and the JAX function. tol: the tolerance to use for checking results. limitations: if given, then apply the custom_assert and tolerance from the Jax2TfLimitations. override_jax_config_flags: jax.config flags to override for the duration of the test. """ super().__init__(group_name, name, fun, arg_descriptors, dtype=np.float32) self.poly_axes = poly_axes self.polymorphic_shapes = polymorphic_shapes self.input_signature = input_signature self.expected_output_signature = expected_output_signature self.skip_jax_run = skip_jax_run self.expect_error = expect_error self.enable_xla = enable_xla self.tol = tol self.check_result = check_result self.limitations = limitations self.override_jax_config_flags = override_jax_config_flags # Replicate the harness for both enable and disable xla def both_enable_and_disable_xla(self) -> Tuple["PolyHarness", "PolyHarness"]: assert self.enable_xla other = PolyHarness(self.group_name, f"{self.name}_enable_xla=False", self.fun, arg_descriptors=self.arg_descriptors, poly_axes=self.poly_axes, polymorphic_shapes=self.polymorphic_shapes, input_signature=self.input_signature, expected_output_signature=self.expected_output_signature, expect_error=self.expect_error, tol=self.tol, enable_xla=False) self.name = f"{self.name}_enable_xla=True" return (self, other) def run_test(self, tst: tf_test_util.JaxToTfTestCase): def log_message(extra: str): return f"[{tst._testMethodName}]: {extra}" # Check that we have overriden the jax.config flags for fname, fvalue in self.override_jax_config_flags.items(): tst.assertEqual(getattr(jax.config, fname), fvalue, ( f"Flag {fname} current value {getattr(jax.config, fname)} != {fvalue}")) # Make polymorphic_shapes and input_signature from poly_axes. if self.poly_axes is None: polymorphic_shapes = self.polymorphic_shapes input_signature = self.input_signature assert input_signature is not None if not self.skip_jax_run: args = self.dyn_args_maker(tst.rng()) else: assert isinstance(self.poly_axes, Sequence) # Make poly_axes: Sequence[Sequence[int]], one top-level element for each argument poly_axes = tuple(map(lambda pa: pa if isinstance(pa, Sequence) or pa is None else (pa,), self.poly_axes)) args = self.dyn_args_maker(tst.rng()) assert self.polymorphic_shapes is None assert self.input_signature is None assert args is not None and len(args) == len(poly_axes) # Make the polymorphic_shapes and input_signature polymorphic_shapes = [] input_signature = [] for arg, poly_axis in zip(args, poly_axes): if poly_axis is None: polymorphic_shapes.append(None) input_signature.append(tf.TensorSpec(np.shape(arg), arg.dtype)) else: def make_arg_polymorphic_shapes(poly_axis: Sequence[int]) -> Tuple[str, tf.TensorSpec]: idx = -1 dims = [] tensorspec_dims: List[Optional[int]] = [] for i, d in enumerate(arg.shape): if i in poly_axis: idx += 1 dims.append(f"b{idx}") tensorspec_dims.append(None) else: dims.append(str(d)) tensorspec_dims.append(d) return ", ".join(dims), tf.TensorSpec(tensorspec_dims, arg.dtype) arg_polymorphic_shapes, arg_tensorspec = make_arg_polymorphic_shapes(poly_axis) polymorphic_shapes.append(arg_polymorphic_shapes) input_signature.append(arg_tensorspec) expect_error_type, expect_error_regex = self.expect_error if self.skip_jax_run and not self.arg_descriptors: f_jax = self.fun else: f_jax = self.dyn_fun with contextlib.ExitStack() as stack: if expect_error_type is not None: stack.enter_context(tst.assertRaisesRegex(expect_error_type, expect_error_regex)) f_tf = jax2tf.convert(f_jax, polymorphic_shapes=polymorphic_shapes, enable_xla=self.enable_xla) # Run in tf.Eager mode first, because it is friendlier to debuggers res_tf = f_tf(*args) if not self.skip_jax_run else None f_tf_func = tf.function( f_tf, autograph=False, input_signature=input_signature) # Create tf.ConcreteFunction and check inferred output signature concrete_f_tf = f_tf_func.get_concrete_function(*input_signature) if expect_error_type is not None: return if self.expected_output_signature: # Strangely, output_shapes can be a single shape for a function with a # single result, or a list/tuple of shapes. expected_output_signature = self.expected_output_signature concrete_output_tf_shape = concrete_f_tf.output_shapes if not isinstance(concrete_output_tf_shape, (tuple, list)): # Single result assert not isinstance(self.expected_output_signature, (tuple, list)) expected_output_signature = [self.expected_output_signature] concrete_output_tf_shape = [concrete_output_tf_shape] for expected, found in util.safe_zip(expected_output_signature, concrete_output_tf_shape): tst.assertEqual(tuple(expected.shape), tuple(found)) # Run the JAX and the TF functions and compare the results if not self.skip_jax_run: res_jax = f_jax(*args) if self.check_result: res_tf = tf.nest.map_structure(lambda t: t.numpy(), res_tf) # type: ignore custom_assert_lims = [ l for l in self.limitations if l.custom_assert is not None] assert len(custom_assert_lims) <= 1, custom_assert_lims tol = None if self.tol is not None: tol = self.tol elif self.limitations: max_lim = self.limitations[0].get_max_tolerance_limitation( self.limitations) if max_lim is not None: tol = max_lim.tol if not custom_assert_lims: tst.assertAllClose(res_jax, res_tf, atol=tol, rtol=tol) else: logging.info(log_message( f"Running custom_assert with tol={tol} due " f"to {custom_assert_lims[0]}")) custom_assert_lims[0].custom_assert(tst, res_jax, res_tf, args=args, # type: ignore tol=tol, err_msg=None) def check_shape_poly(tst, f_jax: Callable, *, arg_descriptors: Sequence[primitive_harness.ArgDescriptor] = (), skip_jax_run: bool = False, poly_axes = None, polymorphic_shapes: Optional[Sequence[Any]] = None, input_signature: Optional[Sequence[tf.TensorSpec]] = None, expected_output_signature: Optional[tf.TensorSpec] = None, expect_error=(None, None)): # Makes and tests a harness. See PolyHarness documentation. h = PolyHarness("", "", f_jax, arg_descriptors=arg_descriptors, skip_jax_run=skip_jax_run, poly_axes=poly_axes, polymorphic_shapes=polymorphic_shapes, input_signature=input_signature, expected_output_signature=expected_output_signature, expect_error=expect_error) h.run_test(tst) class ShapePolyTest(tf_test_util.JaxToTfTestCase): def test_simple_unary(self): """Test shape polymorphism for a simple case, unary function.""" def f_jax(x): return x + jnp.sin(x) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((2, 3), _f32)], input_signature=[tf.TensorSpec([2, 3])], polymorphic_shapes=None, expected_output_signature=tf.TensorSpec([2, 3])) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((2, 3), _f32)], input_signature=[tf.TensorSpec([2, None])], polymorphic_shapes=["_, h"], expected_output_signature=tf.TensorSpec([2, None])) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3, 3), _f32)], input_signature=[tf.TensorSpec([None, None])], polymorphic_shapes=["h, h"], expected_output_signature=tf.TensorSpec([None, None])) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3, 3), _f32)], input_signature=[tf.TensorSpec([None, None])], polymorphic_shapes="h, h", expected_output_signature=tf.TensorSpec([None, None])) def test_simple_binary(self): """Test shape polymorphism for a simple case, binary function.""" def f_jax(x, y): return x + jnp.sin(y) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((2, 3), _f32), RandArg((2, 3), _f32)], input_signature=[tf.TensorSpec([2, 3]), tf.TensorSpec([2, 3])], polymorphic_shapes=None, expected_output_signature=tf.TensorSpec([2, 3])) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((2, 3), _f32), RandArg((2, 3), _f32)], input_signature=[tf.TensorSpec([2, None]), tf.TensorSpec([2, 3])], polymorphic_shapes="_, h", expected_output_signature=( # for native serialization we cannot refine the inferred shape of the # output if the input is more specific than polymorphic_shapes. tf.TensorSpec([2, 3]) if not config.jax2tf_default_native_serialization else tf.TensorSpec([2, None]))) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3, 3), _f32), RandArg((3, 3), _f32)], input_signature=[tf.TensorSpec([None, None]), tf.TensorSpec([None, None])], polymorphic_shapes=PS("h", "h"), expected_output_signature=tf.TensorSpec([None, None])) @parameterized.named_parameters([ dict(testcase_name=f"_{name}", make_args=make_args) for name, make_args in [ # make_args invoked with op.shape[0]: start, stop, step, dtype ("b", lambda b: (b, None, None, None)), ("0_b+1", lambda b: (0, b + 1, None, None)), ("0_5b_2", lambda b: (0, 5 * b, 2, None)), ("0_5b+1_2", lambda b: (0, 5 * b + 1, 2, None)), ("b_5b+2_2", lambda b: (b, 5 * b + 2, 2, None)), ("0_b-1_2", lambda b: (0, b - 1, 2, None)), ("0_b-2_2", lambda b: (0, b - 2, 2, None)), ("0_-b_2", lambda b: (0, -b, 2, None)), ("0_1-b_2", lambda b: (0, 1 - b, 2, None)), # Negative step ("b_0_-1", lambda b: (b, 0, -1, None)), ("b_1_-2", lambda b: (b, 1, -2, None)), ("b_-1_-1", lambda b: (b, -1, -1, None)), ("5b+1_0_-2", lambda b: (5 * b + 1, 0, -2, None)), ("5b+2_0_-2", lambda b: (5 * b + 2, 0, -2, None)), # Symbolic step ("0_10_b", lambda b: (0, 10, b)), ("0_0_b", lambda b: (0, 0, b)), ("10_0_-b", lambda b: (10, 0, -b)), ("b_1_-b", lambda b: (b, 1, -b)), # Float return type ("0_b_1_f32", lambda b: (0, b, 1, np.float32)) ] ]) def test_arange(self, make_args=lambda b: (0, -b, 2, None)): def f_jax(x): # x: i32[b] return x[0] + jnp.arange(*(make_args(x.shape[0]))) x = np.ones((3,), dtype=np.int32) self.assertAllClose(jax2tf.convert(f_jax, polymorphic_shapes="b")(x), f_jax(x)) @parameterized.named_parameters([ dict(testcase_name=f"_{name}", make_args=make_args, expect_error=expect_error, expect_msg=expect_msg) for name, make_args, expect_error, expect_msg in [ # make_args invoked with op.shape[0]: start, stop, step, dtype ("float_start", lambda b: (0., b, None), ValueError, "must be either dimension expressions or integers"), ("float_step", lambda b: (0, b, 0.5), ValueError, "must be either dimension expressions or integers"), ("step_0", lambda b: (0, b, 0), ValueError, "has step == 0"), ("inconclusive_step_sign", lambda b: (0, b, b - 2), core.InconclusiveDimensionOperation, "must be resolved statically if it is > 0 or < 0"), ("inconclusive_distance", lambda b: (0, b - 3, 2), core.InconclusiveDimensionOperation, "must be resolved statically if it is >= -1 or >= 1"), ] ]) def test_arange_error(self, make_args=lambda b: (0., b, 2), expect_error=ValueError, expect_msg="must be either dimension expressions or integers"): def f_jax(x): # x: i32[b] return x[0] + jnp.arange(*(make_args(x.shape[0]))) x = np.ones((3,), dtype=np.int32) with self.assertRaisesRegex(expect_error, expect_msg): jax2tf.convert(f_jax, polymorphic_shapes="b")(x) def test_argmax(self): def f_jax(x): # x: f32[b, 4, 5] return lax.argmax(x, axis=1, index_dtype=np.int32) x = np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5)) self.assertAllClose(jax2tf.convert(f_jax, polymorphic_shapes="(b, _, _)")(x), f_jax(x)) @parameterized.named_parameters([ dict(testcase_name=f"_expr={name}", expr=expr) for name, expr in [ ("d + 2", lambda d: d + 2), ("2 - d", lambda d: 2 - d), ("d * 2", lambda d: d * 2), ("d * d", lambda d: d * d), ("(- d) * d", lambda d: (- d) * d), ("d * d - d", lambda d: d * d - d), # Division ("d // 2", lambda d: d // 2), ("(d + 1) // 2", lambda d: (d + 1) // 2), ("d // -2", lambda d: d // -2), ("(d + 1) // -2", lambda d: (d + 1) // -2), ("(-d) // 2", lambda d: (-d) // 2), ("(-d - 1) // 2", lambda d: (-d - 1) // 2), ("(-d) // -2", lambda d: (-d) // -2), ("(-d - 1) // -2", lambda d: (-d - 1) // -2), # Remainder ("d % 2", lambda d: d % 2), ("(d + 1) % 2", lambda d: (d + 1) % 2), ("d % -2", lambda d: d % -2), ("(d + 1) % -2", lambda d: (d + 1) % -2), ("(-d) % 2", lambda d: (-d) % 2), ("(-d - 1) % 2", lambda d: (-d - 1) % 2), ("(-d) % -2", lambda d: (-d) % -2), ("(-d - 1) % -2", lambda d: (-d - 1) % -2), ] ]) def test_non_trivial_dim_expr(self, expr=lambda d: d % -2): # Check the lowering for shape expressions check_shape_poly( self, lambda x: x[0] * 0 + expr(x.shape[0]), arg_descriptors=[RandArg((3,), np.int64)], poly_axes=[0]) def test_static_shape_result(self): """The result has static shape.""" def f_jax(x): return jnp.sum(x + jnp.sin(x), axis=0) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((2, 3), _f32)], input_signature=[tf.TensorSpec([2, 3])], polymorphic_shapes=None, expected_output_signature=tf.TensorSpec([3])) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((2, 3), _f32)], input_signature=[tf.TensorSpec([None, 3])], polymorphic_shapes="b, _", expected_output_signature=tf.TensorSpec([3])) def test_forgot_polymorphic_shapes_error(self): msg_re = "syntax error in polymorphic shape" with self.assertRaisesRegex(ValueError, msg_re): check_shape_poly(self, jnp.sin, arg_descriptors=[RandArg((1, 3,), _f32)], input_signature=[tf.TensorSpec([1, None])], polymorphic_shapes=None) def test_kwargs(self): """Test shape polymorphism for a function with kwargs.""" x = np.ones(3, dtype=np.float32) y = np.ones(1, dtype=np.float32) def f_jax(x, *, y): return x + jnp.sin(y) f_tf: Callable[..., Any] = jax2tf.convert(f_jax, polymorphic_shapes=["b, ..."]) self.assertAllClose(f_jax(x, y=y), f_tf(x, y=y)) def test_arg_avals(self): """Test conversion of actual arguments to abstract values.""" def check_avals(*, arg_shapes: Sequence[Sequence[Optional[int]]], polymorphic_shapes: Sequence[Optional[Union[str, PS]]], expected_avals: Optional[Sequence[core.ShapedArray]] = None, expected_shapeenv: Optional[Dict[str, int]] = None, eager_mode: bool = False): # Use eager mode only for when all arg_shapes are known, in order to # check expected_shapeenv. arg_dtypes = (_f32,) * len(arg_shapes) def f_tf(*args_tf): avals = tuple(map(shape_poly.arg_aval, arg_shapes, arg_dtypes, polymorphic_shapes)) dim_vars = shape_poly.all_dim_vars(avals) dim_values, _ = jax2tf.jax2tf._interpret_fun_jax( partial(shape_poly.compute_dim_vars_from_arg_shapes, avals, args_kwargs_tree=tree_util.tree_flatten((avals, {}))[1]), args_tf, avals, "") if expected_avals is not None: self.assertEqual(expected_avals, avals) return dict(zip(dim_vars, dim_values)) if eager_mode: # If we want to check the shape_env then all arg_shapes must be known assert all(all(d is not None for d in a_s) for a_s in arg_shapes) shape_env = f_tf(*[tf.ones(a_s, dtype=_f32) for a_s in arg_shapes]) if expected_shapeenv is not None: for v, val in expected_shapeenv.items(): self.assertEqual(val, shape_env.get(v)) else: f_tf = tf.function(autograph=False)(f_tf) f_tf.get_concrete_function(*[tf.TensorSpec(a_s, _f32) for a_s in arg_shapes]) assert not expected_shapeenv, "Should use eager_mode=True" def shaped_array(shape_spec: str, actual_shape: core.Shape): return core.ShapedArray( shape_poly._parse_spec(shape_spec, actual_shape), np.float32) # Known shapes for the arguments check_avals( arg_shapes=[(2, 3)], polymorphic_shapes=[None], expected_avals=(shaped_array("2, 3", [2, 3]),)) check_avals( arg_shapes=[(2, 3)], polymorphic_shapes=["(2, 3)"], expected_avals=(shaped_array("2, 3", [2, 3]),)) check_avals( arg_shapes=[(2, 3)], polymorphic_shapes=["(_, 3)"], expected_avals=(shaped_array("2, 3", [2, 3]),)) check_avals( arg_shapes=[(2, 3)], polymorphic_shapes=[PS("_", 3)], expected_avals=(shaped_array("2, 3", [2, 3]),)) check_avals( arg_shapes=[(2, 3)], polymorphic_shapes=["..."], expected_avals=(shaped_array("2, 3", [2, 3]),)) check_avals( arg_shapes=[(2, 3)], polymorphic_shapes=[PS(...)], expected_avals=(shaped_array("2, 3", [2, 3]),)) # Partially known shapes for the arguments check_avals( arg_shapes=[(None, 3)], polymorphic_shapes=[PS("b", ...)], expected_avals=(shaped_array("(b, 3)", (2, 3)),)) check_avals( arg_shapes=[(None, None)], polymorphic_shapes=["h, h"], expected_avals=(shaped_array("(h, h)", (2, 2)),)) check_avals( arg_shapes=[(2, None)], polymorphic_shapes=["h, h"], expected_avals=(shaped_array("(h, h)", (2, 2)),)) check_avals( arg_shapes=[(None, 3, 4)], polymorphic_shapes=["(c, b, a)"], expected_avals=(shaped_array("(c, b, a)", (2, 3, 4)),), ) # Check cases when the specifications are polynomials check_avals( arg_shapes=[(2, 3)], polymorphic_shapes=[PS("a + 1", "b + 2")], eager_mode=True, expected_shapeenv=dict(a=1, b=1)) check_avals( arg_shapes=[(7, 5)], polymorphic_shapes=[PS("2 * a + b", "b + 2")], eager_mode=True, expected_shapeenv=dict(a=2, b=3)) check_avals( arg_shapes=[(7, 11, 4)], polymorphic_shapes=[PS("2 * a + b", "b * b + 2", "b + 1")], eager_mode=True, expected_shapeenv=dict(a=2, b=3)) check_avals( arg_shapes=[(7, 11, 19, 7)], polymorphic_shapes=[PS("2 * a + b", "b * b + 2", "b + c * c", "2 * c + -1")], eager_mode=True, expected_shapeenv=dict(a=2, b=3, c=4)) with self.assertRaisesRegex(ValueError, "Cannot solve for values of dimension variables {'b'}"): check_avals( arg_shapes=[(4, 36, 3)], polymorphic_shapes=[PS("b * b", "b * d * d", "d")]) with self.assertRaisesRegex(ValueError, "Dimension variable 'b' must have integer value >= 1"): check_avals( arg_shapes=[(5, 36)], polymorphic_shapes=[PS("3 * b", ...)], eager_mode=True) with self.assertRaisesRegex(ValueError, "Dimension variable 'b' must have integer value >= 1"): check_avals( arg_shapes=[(10, 3)], polymorphic_shapes=[PS("3 * b + 10", ...)], eager_mode=True) with self.assertRaisesRegex(ValueError, "Dimension variable 'b' must have integer value >= 1"): check_avals( arg_shapes=[(7, 3)], polymorphic_shapes=[PS("3 * b + 10", ...)], eager_mode=True) for invalid_syntax in [5.0, ["a list"], ("a tuple",), re.compile(".")]: with self.assertRaisesRegex(ValueError, re.escape("Invalid polymorphic shape element")): check_avals( arg_shapes=[(2,)], polymorphic_shapes=[PS([invalid_syntax])]) with self.assertRaisesRegex( ValueError, "Found inconsistency 3 != 2 when solving.*"): check_avals( arg_shapes=[(2, 3)], polymorphic_shapes=["(a, a)"], eager_mode=True) # Same error across multiple arguments with self.assertRaisesRegex( ValueError, "Found inconsistency 5 != 2 when solving.*"): check_avals( arg_shapes=[(2, 3), (5,)], polymorphic_shapes=["a, ...", "a"], eager_mode=True) def test_pytree(self): """Arguments and polymorphic_shapes are pytrees.""" # Arguments are of the form [([x00, x01], [x10]), dict(a=ya, b=yb)] def add_all_jax(x_pair_of_list, y_dict): x_list_0, x_list_1 = x_pair_of_list return functools.reduce(op.add, x_list_0 + x_list_1 + [y_dict["a"], y_dict["b"]]) input_signature = [([tf.TensorSpec([None]), tf.TensorSpec([None])], [tf.TensorSpec([None])]), dict(a=tf.TensorSpec([None]), b=tf.TensorSpec([None]))] check_shape_poly(self, add_all_jax, skip_jax_run=True, input_signature=input_signature, polymorphic_shapes=[(["v", "v"], ["v"]), dict(a="v", b="v")], expected_output_signature=tf.TensorSpec([None])) # Prefix polymorphic shapes check_shape_poly(self, add_all_jax, skip_jax_run=True, input_signature=input_signature, polymorphic_shapes="v", expected_output_signature=tf.TensorSpec([None])) check_shape_poly(self, add_all_jax, skip_jax_run=True, input_signature=input_signature, polymorphic_shapes=["v", "v"], expected_output_signature=tf.TensorSpec([None])) check_shape_poly(self, add_all_jax, skip_jax_run=True, input_signature=input_signature, polymorphic_shapes=[("v", "v"), "v"], expected_output_signature=tf.TensorSpec([None])) # Now partial polymorphic_shapes; the parts of the polymorphic_shapes that # are not specified must have full input_signatures. check_shape_poly(self, add_all_jax, skip_jax_run=True, input_signature=[([tf.TensorSpec([4]), tf.TensorSpec([4])], [tf.TensorSpec([4])]), dict(a=tf.TensorSpec([4]), b=tf.TensorSpec([4]))], polymorphic_shapes=((["(4,)", "(_,)"], [("4,")]), dict(a="(_,)", b="(4,)")), expected_output_signature=tf.TensorSpec([4])) @parameterized.named_parameters( dict(testcase_name=f"_{name}", polymorphic_shapes=polymorphic_shapes) for name, polymorphic_shapes in [ ("1", ("b", "b", "b")), ("2", dict(a="b")), ("3", (dict(a="b"), "b")), ] ) def test_pytree_errors(self, polymorphic_shapes=("b", "b", "b")): """Arguments and polymorphic_shapes are not-matching pytrees.""" # Arguments are of the form [([x00, x01], [x10]), dict(a=ya, b=yb)] x = np.arange(4, dtype=_f32) args = (([x, x], [x]), dict(a=x, b=x)) def add_all_jax(x_pair_of_list, y_dict): x_list_0, x_list_1 = x_pair_of_list return functools.reduce(op.add, x_list_0 + x_list_1 + [y_dict["a"], y_dict["b"]]) with self.assertRaisesRegex(ValueError, "pytree structure error"): jax2tf.convert(add_all_jax, polymorphic_shapes=polymorphic_shapes)(*args) def test_with_nested_jit(self): def f_jax(x): # x: f32[w, h] # x + (np.sin(x) + np.broadcast_to(np.arange(x.shape[1]), x.shape)) return jnp.sin(x) + jnp.arange(x.shape[1], dtype=x.dtype) check_shape_poly(self, lambda x: x + jax.jit(f_jax)(x), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[(0, 1)]) @parameterized.named_parameters( dict(testcase_name=f"_{str(polymorphic_shapes)}", polymorphic_shapes=polymorphic_shapes) # The polymorphic_shapes should have three comma-separated DimExpr matching # 16, 24, 32 for polymorphic_shapes in [ "b1+6,b1+14,b2", # b1=10, b2=32 "2*b1,4*b2,b1+b2+18", # b1=8,b2=6 "b1+2*b2,4*b2,b1*b1+16", # b1=4,b2=6 ]) def test_non_trivial_polynomials_spec(self, polymorphic_shapes="2*b1,4*b2,b1+b2+18"): # We can handle non-trivial polynomials in the input shape, # as long as all variables also occur in trivial expressions check_shape_poly(self, lambda x: 2 * x.shape[0] + 3 * x.shape[1] + 4 * x.shape[2], arg_descriptors=[RandArg((16, 24, 32), _f32)], input_signature=[tf.TensorSpec([None, None, None])], polymorphic_shapes=polymorphic_shapes) def test_unused_args(self): # Tests with functions that do not use their inputs. # First arg unused, not polymorphic check_shape_poly(self, lambda x_unused, y: y * 2.0, arg_descriptors=[RandArg((2, 3), _f32), RandArg((3,), _f32)], input_signature=[tf.TensorSpec([]), tf.TensorSpec([None])], polymorphic_shapes=[None, "b"]) # Some args unused, not polymorphic check_shape_poly(self, lambda x_unused, y, z_unused, w: jnp.concatenate([y, w]), arg_descriptors=[RandArg((3,), _f32), RandArg((4,), _f32), RandArg((5,), _f32), RandArg((6,), _f32)], 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 check_shape_poly(self, lambda x_unused, y: y * 2.0, arg_descriptors=[RandArg((3,), _f32), RandArg((3,), _f32)], 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. check_shape_poly(self, lambda x_unused, y: y * 2.0, arg_descriptors=[RandArg((4,), _f32), RandArg((3,), _f32)], 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. check_shape_poly(self, lambda x_unused, y: y * 2.0, arg_descriptors=[RandArg((3,), _f32), RandArg((9,), _f32)], input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])], polymorphic_shapes=["b1", "b1 * b1"]) # It is not sufficient to just use the shape of an input; it is still unused check_shape_poly(self, lambda x_unused, y: y + x_unused.shape[0], arg_descriptors=[RandArg((3,), _f32), RandArg((9,), _f32)], input_signature=[tf.TensorSpec([None]), tf.TensorSpec([None])], polymorphic_shapes=["b1", "b2"]) def test_with_custom_vjp(self): """Shape-polymorphic custom VJP.""" @jax.custom_vjp def f(x): # x: [b1, b2, d1, d2] (a batch of matrices) # res: [b1, b2, d1, d1] return jnp.matmul(x, jnp.transpose(x, axes=(0, 1, 3, 2))) # f_fwd: a -> (b, residual) def f_fwd(x): # x: [b1, b2, d1, d2] # b: [b1, b2, d1, d1] # res: [b1, b2, d1, d1] # residual: [b1, b2, d1, d2] return f(x), 3. * x # f_bwd: (residual, CT b) -> [CT a] def f_bwd(residual, ct_b): # residual: [b1, b2, d1, d2] # ct_b: [b1, b2, d1, d1] # ct_a: [b1, b2, d1, d2] return jnp.matmul(ct_b, residual), f.defvjp(f_fwd, f_bwd) x = np.ones((2, 3, 4, 5), dtype=np.float32) res_jax = f(x) res_jax_grad = jax.grad(lambda x: jnp.sum(f(x)))(x) f_tf = jax2tf.convert(f, polymorphic_shapes=["(batch1, batch2, d1, d2)"]) self.assertAllClose(res_jax, f_tf(x)) xv = tf.Variable(x, dtype=np.float32) def tf_value_and_grad(xv): with tf.GradientTape() as tape: tape.watch(xv) res_tf = f_tf(xv) res_tf_grad = tape.gradient(res_tf, xv) return res_tf, res_tf_grad res_tf, res_tf_grad = tf_value_and_grad(xv) self.assertAllClose(res_jax, res_tf) self.assertAllClose(res_jax_grad, res_tf_grad) # Now use TF tracing for the gradient tf_grad = tf.function( tf_value_and_grad, autograph=False).get_concrete_function( tf.TensorSpec([3, 4, 8, 9])) # for native serialization we cannot refine the inferred shape of the # output if the input is more specific than polymorphic_shapes. if config.jax2tf_default_native_serialization: self.assertEqual((None, None, None, None), tuple(tf_grad.output_shapes[0])) self.assertEqual((None, None, None, None), tuple(tf_grad.output_shapes[1])) else: self.assertEqual((3, 4, 8, 8), tuple(tf_grad.output_shapes[0])) self.assertEqual((3, 4, 8, 9), tuple(tf_grad.output_shapes[1])) def test_gradients_pytree(self): """Shape polymorphism with gradients and pytrees for inputs and outputs.""" def f(x): # x: dict(x=[b, 3, 4]) # res: dict(res=[b, 3, 4]) return dict(res=x["x"] * 2.) check_shape_poly(self, f, skip_jax_run=True, input_signature=[dict(x=tf.TensorSpec([None, 3, 4]))], polymorphic_shapes=[dict(x=("b, 3, 4"))]) f_tf = jax2tf.convert(f, polymorphic_shapes=[dict(x=("b, 3, 4"))]) x = dict(x=np.ones((2, 3, 4), dtype=np.float32)) xv = tf.Variable(x["x"], dtype=np.float32) def tf_value_and_grad(xv): # xv: [b, 3, 4] # res_value: dict(res=[b, 3, 4]) # res_grad: dict(grad=[b, 3, 4]) with tf.GradientTape() as tape: tape.watch(xv) res_tf = f_tf(dict(x=xv)) res_tf_grad = tape.gradient(res_tf, xv) return res_tf, dict(grad=res_tf_grad) res_tf, res_tf_grad = tf_value_and_grad(xv) # Now use TF tracing for the gradient tf_grad = tf.function( tf_value_and_grad, autograph=False).get_concrete_function(tf.TensorSpec([None, 3, 4])) # The shape of the value self.assertEqual((None, 3, 4), tuple(tf_grad.output_shapes[0]["res"])) # The shape of the gradient should match the input self.assertEqual((None, 3, 4), tuple(tf_grad.output_shapes[1]["grad"])) def test_grad_not_var_output(self): def f_jax(x): # :[b, 3] return jnp.reshape(x, (-1,)) # : [3b] x = np.arange(12, dtype=np.float32).reshape((4, 3)) xv = tf.Variable(x) f_tf = jax2tf.convert(f_jax, with_gradient=True, polymorphic_shapes=["b, ..."]) with tf.GradientTape() as tape: res_tf = f_tf(xv) grad_tf = tape.gradient(res_tf, xv) self.assertAllClose(np.ones(x.shape, dtype=np.float32), grad_tf.numpy()) def test_cond(self): # Test the primitive under conditional def f(x, y): # x: f32[B, H], y : f32[H] return lax.cond( jnp.sum(x) > 0., lambda _: x + y, lambda _: jnp.zeros_like(x), operand=None) x = np.ones((2, 3)) y = np.ones((3,)) res_jax = f(x, y) self.assertAllClose( res_jax, jax2tf.convert(f, polymorphic_shapes=["(b, h)", "h"])(x, y)) def test_while(self): def f(x): # x: f32[B], iter: i32 return lax.while_loop(lambda x_iter: x_iter[1] < 5, lambda x_iter: (x_iter[0] + jnp.arange(x_iter[0].shape[0], dtype=np.float32), x_iter[1] + 1), (x, 0)) x = np.ones((3,), dtype=np.float32) res_tf = jax2tf.convert(f, polymorphic_shapes=["(b,)"])(x) self.assertAllClose(f(x), res_tf) @jtu.sample_product(with_function=[False, True]) def test_grad_int(self, with_function=False): # https://github.com/google/jax/issues/7093 # Also issue #6975. x_shape = (2, 3, 4) xi = np.arange(math.prod(x_shape), dtype=np.int16).reshape(x_shape) yf = xi.astype(np.float32) xi_yf = (xi, yf) zb = np.array([True, False], dtype=np.bool_) def f_jax(xi_yf, zb): # xi: s16[2, 3, 4], yf: f32[2, 3, 4], zb: bool[2] # results: f32[2, 3, 4], s16[2, 3, 4], bool[2], f32[2, 3, 4] xi, yf = xi_yf # Return a tuple: # (1) float constant, with 0 tangent; # (2) a tuple with: # (2.1) the integer input; # (2.2) the boolean input; # (2.3) a float depending on both inputs. # TODO: there is a problem if we add a None output return (jnp.zeros(xi.shape, dtype=jnp.float32), (xi, zb, xi.astype(np.float32) * 2. * yf)) args = (xi_yf, zb) f_tf = jax2tf.convert(f_jax, polymorphic_shapes=[("b1, b2, 4", "b1, b2, 4"), "b1"]) if with_function: f_tf = tf.function(f_tf, autograph=False) res_tf, g_tf = tf_test_util.ComputeTfValueAndGrad(f_tf, args) self.assertAllClose(g_tf[0][0], np.zeros_like(xi)) self.assertAllClose(g_tf[0][1], (xi * 2).astype(yf.dtype)) self.assertAllClose(g_tf[1], np.zeros_like(zb)) def test_prng(self): # The PRNG implementation uses opaque types, test shape polymorphism try: prev_custom_prng = config.jax_enable_custom_prng config.update("jax_enable_custom_prng", True) def f_jax(x): # x: f32[b1, b2] key = random.PRNGKey(123) # key: key[] # Exercise key operations that have custom lowering rules broadcast_keys = lax.broadcast_in_dim(key, x.shape, ()) # key[b1, b2] gather_keys = lax.broadcast_in_dim(broadcast_keys[0], (1, x.shape[1]), (1,)) # : key[1, b2] slice_keys1 = lax.slice(broadcast_keys, (0, 0), (1, x.shape[1]), (1, 1)) # key[1, b2] slice_keys2 = lax.dynamic_slice(broadcast_keys, (0, 0), slice_sizes=(1, x.shape[1])) # key[1, b2] upd1 = lax.dynamic_update_slice(slice_keys2, slice_keys1, start_indices=(0, 0)) # key[1, b2] _ = lax.dynamic_update_slice(upd1, gather_keys, start_indices=(0, 0)) # We need to test the special case for vmap(while) xs = broadcast_keys counts = jnp.arange(broadcast_keys.shape[0], dtype=np.int32) def f_vmap_jax(counts, xs): # counts: i32[b1], xs: key[b1, b2] def inner(count, x): # count i32, x: key[b2] return lax.fori_loop(0, count, lambda _, acc: acc, x) return jax.vmap(inner)(counts, xs) _ = f_vmap_jax(counts, xs) return x check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3, 4), _f32)], input_signature=[tf.TensorSpec([None, None], dtype=tf.float32)], polymorphic_shapes=["b1, b2"]) finally: config.update("jax_enable_custom_prng", prev_custom_prng) def test_saved_model(self): f_jax = jnp.sin f_tf = jax2tf.convert(f_jax, polymorphic_shapes=["(b, ...)"]) x = np.array([0.7, 0.8], dtype=np.float32) restored_f, _ = tf_test_util.SaveAndLoadFunction( f_tf, input_signature=[tf.TensorSpec([None], x.dtype)]) self.assertAllClose(f_jax(x), restored_f(x)) # Ensure that restored_f works at other batch size as well y = np.concatenate([x, x]) self.assertAllClose(f_jax(y), restored_f(y)) def test_saved_model_int_function(self): def f_jax(x): # x:s32[b, 3, 4] return jnp.reshape(x, (-1,)) # : s32[b * 12] f_tf = jax2tf.convert(f_jax, polymorphic_shapes=["(b, ...)"]) f_tf = tf.function(f_tf, autograph=False) x_shape = (2, 3, 4) x = np.arange(math.prod(x_shape), dtype=np.int32).reshape(x_shape) # When saving the model with gradients, we trace the gradient function # and we used to get an error when creating zeros_like_aval for a # polymorphic shape restored_f, _ = tf_test_util.SaveAndLoadFunction( f_tf, input_signature=[tf.TensorSpec((None,) + x.shape[1:], x.dtype)]) f_jax_rt = jax2tf.call_tf(restored_f) res_jax_rt = f_jax_rt(x) self.assertAllClose(f_jax(x), res_jax_rt) def test_saved_model_constant_gradient(self): def f_jax(x): # A function whose gradient is a constant return x f_tf = jax2tf.convert(f_jax, polymorphic_shapes=["(b, ...)"]) x = np.array([0.7, 0.8], dtype=np.float32) restored_f, _ = tf_test_util.SaveAndLoadFunction( f_tf, input_signature=[tf.TensorSpec([None], x.dtype)]) self.assertAllClose(f_jax(x), restored_f(x)) def test_readme_examples(self): """Some of the examples from the README.""" jax2tf.convert(lambda x: jnp.reshape(x, (x.shape[0] * x.shape[1],)), polymorphic_shapes=["(b, 4)"])(np.ones((3, 4))) jax2tf.convert(lambda x: jnp.reshape(x, (math.prod(x.shape),)), polymorphic_shapes=["(b, 4)"])(np.ones((3, 4))) jax2tf.convert(lambda x: x + x.shape[0] + jnp.sin(x.shape[0]), polymorphic_shapes=["b"])(np.ones(3)) jax2tf.convert(lambda x: jnp.sum(x, axis=0) / x.shape[0], polymorphic_shapes=["(v, _)"])(np.ones((3, 4))) with self.assertRaisesRegex(TypeError, "prod requires ndarray or scalar arguments"): jax2tf.convert(lambda x: jnp.prod(x.shape) + x, polymorphic_shapes=["(b, 4)"])(np.ones((3, 4))) jax2tf.convert(lambda x: jnp.prod(jnp.array(x.shape)) + x, polymorphic_shapes=["(b, 4)"])(np.ones((3, 4))) four_ones = np.ones((4,)) with self.assertRaisesRegex( TypeError, re.escape("add got incompatible shapes for broadcasting: (v,), (4,)")): jax2tf.convert(lambda x, y: x + y, polymorphic_shapes=["(v,)", "(4,)"])(four_ones, four_ones) # We get the error even if we use correct actual arguments with self.assertRaisesRegex( TypeError, re.escape("add got incompatible shapes for broadcasting: (v,), (4,)")): jax2tf.convert( lambda x, y: x + y, polymorphic_shapes=["(v,)", "(4,)"])(four_ones, four_ones) with self.assertRaisesRegex(TypeError, re.escape("dot_general requires contracting dimensions to have the same shape, got (4,) and (v,)")): jax2tf.convert(lambda x: jnp.matmul(x, x), polymorphic_shapes=["(v, 4)"])(np.ones((4, 4))) with self.assertRaisesRegex(core.InconclusiveDimensionOperation, re.compile("Cannot divide evenly the sizes of shapes \\(b, 5, 7\\) and \\(2, -1\\)", re.DOTALL)): jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)), polymorphic_shapes=["(b, _, _)"])(np.ones((4, 5, 7))) jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)), polymorphic_shapes=["(b, _, _)"])(np.ones((4, 5, 6))) jax2tf.convert(lambda x: jnp.reshape(x, (-1, x.shape[0])), polymorphic_shapes=["(b1, b2, ...)"])(np.ones((4, 5, 6))) jax2tf.convert(lambda x: jnp.reshape(x, (2, -1)), polymorphic_shapes=["(2*b, ...)"])(np.ones((4, 5, 7))) with self.assertRaisesRegex( core.InconclusiveDimensionOperation, re.escape("Symbolic dimension comparison 'a + 1' >= 'b' is inconclusive")): jax2tf.convert(lambda x: 0 if x.shape[0] + 1 >= x.shape[1] else 1, polymorphic_shapes=["(a, b)"])(np.ones((4, 4))) # Unsoundness: not checking that the dimension variable is 0 def f1_jax(x): # f32[b] # We have to use "x" return jnp.concatenate([x, jnp.array([0. if x.shape[0] == 0 else 1.], dtype=np.float32)]) x0 = np.array([], np.float32) # JAX with static shapes sees that the x.shape[0] == 0 self.assertEqual(jnp.array([0.], dtype=np.float32), f1_jax(x0)) with self.assertRaisesRegex( ValueError, "Dimension variable 'b' must have integer value >= 1. Found 0"): jax2tf.convert(f1_jax, polymorphic_shapes=["b"], native_serialization=False)(x0) # In native serialization, or if we trace to a TF graph, we miss this res1_tf = jax2tf.convert(f1_jax, polymorphic_shapes=["b"], native_serialization=True)(x0) self.assertEqual(jnp.array([1.], dtype=np.float32), res1_tf) f1_tf = tf.function( jax2tf.convert(f1_jax, polymorphic_shapes=["b"], native_serialization=False) ).get_concrete_function(tf.TensorSpec([None], dtype=np.float32)) self.assertEqual(jnp.array([1.], dtype=np.float32), f1_tf(x0)) # Unsoundness: not checking that the actual dimensions denoted by the same # dimension variables have equal sizes. def f2_jax(x): # f32[b, b] # We have to use "x" return jnp.sum(x) + (0. if x.shape[0] != x.shape[1] else 1.) x45 = np.ones((4, 5), dtype=np.float32) # JAX with static shapes sees that x.shape[0] != x.shape[1] self.assertEqual(jnp.sum(x45), f2_jax(x45)) # jax2tf catches the broken assumption b >= 1 if the converted function is executed # eagerly. with self.assertRaisesRegex( ValueError, r"Found inconsistency 5 != 4 when solving b == args\[0\].shape\[1\]"): jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"], native_serialization=False)(x45) # In native serialization, or if we trace to a TF graph, we miss this res2_tf = jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"], native_serialization=True)(x45) self.assertEqual(1. + jnp.sum(x45), res2_tf) f2_tf = tf.function( jax2tf.convert(f2_jax, polymorphic_shapes=["b, b"], native_serialization=False) ).get_concrete_function(tf.TensorSpec([None, None], dtype=np.float32)) self.assertEqual(1. + jnp.sum(x45), f2_tf(x45)) x = np.ones((5,), dtype=np.float32) with self.assertRaisesRegex( ValueError, "Cannot solve for values of dimension variables"): jax2tf.convert(lambda x: jnp.sum(x), polymorphic_shapes=["a + b"])(x) def test_dynamic_shapes(self): # Test dim_as_value with dynamic shapes. def f(x): return jnp.sum(x, axis=0) * x.shape[0] x = np.arange(3.) self.assertAllClose(9., jax2tf.convert(f, polymorphic_shapes=["(b,)"])(x)) self.assertAllClose( 9., jax2tf.convert(jax.jit(f), polymorphic_shapes=["(b,)"])(x)) self.assertAllClose( 9., tf.function(jax2tf.convert(f, polymorphic_shapes=["(b,)"]))(x)) res_primal, res_tangent = jax2tf.convert( lambda x, xt: jax.jvp(f, (x,), (xt,)), polymorphic_shapes=["b", "b"])(x, np.array([0.1, 0.2, 0.3])) self.assertAllClose((9., 1.8), (res_primal, res_tangent)) self.assertAllClose( np.array([3., 3., 3.]), jax2tf.convert(jax.grad(f), polymorphic_shapes=["b"])(x)) xv = np.arange(24.).reshape((2, 3, 4)) res_vmap = jax.vmap(f, in_axes=1)(xv) # Implement by iteration res_iter = jnp.stack([f(xv[:, i, :]) for i in range(xv.shape[1])]) self.assertAllClose(res_iter, res_vmap) res_vmap_tf = jax2tf.convert(jax.vmap(f, in_axes=1), polymorphic_shapes=["b1, b2, ..."])(xv) self.assertAllClose(res_iter, res_vmap_tf.numpy()) def test_with_hash_collision_vmap(self): # Batching caches based on Jaxpr, and Jaxpr include _DimExpr. If we have # a collision for the hashing of a _DimExpr, then Python will call the # equality, which will raise InconclusiveDimensionOperation. def f_jax(x): return jnp.reshape(x, (2, -1,)) try: # Override the hashing to create collisions orig_hash = getattr(shape_poly._DimExpr, "__hash__") def collision_hash(obj): return hash(5) setattr(shape_poly._DimExpr, "__hash__", collision_hash) xs = np.ones((3, 5, 6), dtype=np.float32) f_toconvert = jax.vmap(pjit.pjit(f_jax)) res_1 = jax2tf.convert(f_toconvert)(xs) res_2 = jax2tf.convert(f_toconvert, polymorphic_shapes = "b1, b2, ...")(xs) self.assertAllClose(res_1, res_2) finally: setattr(shape_poly._DimExpr, "__hash__", orig_hash) @parameterized.named_parameters([ dict(testcase_name=f"_{op_name}", op=op) for op, op_name in [ (jnp.array, "array"), (jnp.sin, "sin"), (lambda x: x, "id"), (core.dimension_as_value, "dimension_as_value"), ] ]) def test_poly_unary_op(self, *, op=jnp.array): def f_jax(x): # x: f32[b] poly = 2 * x.shape[0] return (op(poly), x) # Make sure we are using x check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3,), _f32)], poly_axes=[0], expected_output_signature=(tf.TensorSpec([]), tf.TensorSpec((None,), _f32))) @parameterized.named_parameters([ dict(testcase_name=f"_{op.__name__}_other={other}:{type(other)}{'_other_jnp_array' if other_jnp_array else ''}{'_swap' if swap else ''}", op=op, other=other, other_jnp_array=other_jnp_array, swap=swap) for op in [op.add, op.mul, op.sub, op.mod, op.floordiv, op.truediv] for other in [ 2, np.int32(2), 2., np.float32(2), np.array(2, dtype=np.int32), np.arange(1, 5, dtype=np.int32), np.array(2., dtype=np.float32), np.arange(1., 7., dtype=np.float32) ] for other_jnp_array in ( [True, False] if np.shape(other) == (7,) else [False]) # type: ignore for swap in [False, True] # The poly is the left op by default ]) def test_poly_binary_op(self, *, op=op.add, other=np.arange(2, dtype=np.int32), other_jnp_array=False, swap=True): # Test arithmetic operations with poly and a variety of other operand types def f_jax(x): # x: f32[b] poly = 2 * x.shape[0] # This will allow divisions with 2 other_wrapped = jnp.array(other) if other_jnp_array else other ops = (poly, other_wrapped) if not swap else (other_wrapped, poly) res = op(*ops) # If the other op is an integer then the result is a symbolic dim try: op.index(other) other_isint = True except Exception: other_isint = False if (hasattr(poly, "dimension_as_value") and other_isint and op.__name__ != "truediv"): # If we running under jax2tf and "other" is an integer the result # should be a symbolic dimension self.assertTrue(isinstance(res, int) or hasattr(res, "dimension_as_value")) if config.jax_enable_x64: # Outside jax2tf, x.shape[0] is a Python (64-bit) integer and for most # operations here JAX is not involved at all because the other operand # is a Python or NumPy constant. So the result will be 64-bits. But under # jax2tf, x.shape[0] is rewritten to jnp.array(x.shape[0]) which when # used with int32 or float32 values will produce 32-bit values. return (lax.convert_element_type(res, np.float32), x) return (res, x) # Make sure we are using x check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3,), np.int32)], poly_axes=[0]) def test_mean0(self): def f_jax(x): # x: f32[b, 4] return jnp.sum(x, axis=0) / x.shape[0] check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0], expected_output_signature=tf.TensorSpec([4])) def test_shape_as_array(self): def f_jax(x): # The entire x.shape is passed to jnp.array return x + jnp.sum(jnp.array(x.shape)).astype(np.int32) check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]) def test_dim_as_value_weak_type(self): def f_jax(x): # x: f32[b] d0 = jnp.array(x.shape[0]) # in JAX should have weak_type=True if isinstance(d0, core.Tracer): self.assertTrue(d0.aval.weak_type), d0 # And an implicit conversion to array d1 = x.shape[0] + jnp.array(4) if isinstance(d1, core.Tracer): self.assertTrue(d1.aval.weak_type), d1 return d0 + np.array(5., dtype=np.float32) + d1 + x[0] with numpy_dtype_promotion("strict"): # strict type promotion is sensitive to weak_types check_shape_poly(self, f_jax, arg_descriptors=[RandArg((3,), _f32)], poly_axes=[0]) def test_vmap_while(self): def cond_func(x): # x: f32[3] return jnp.sum(x) >= 0. def body_func(x): # x: f32[3] return x - 1. def f_jax(x): return lax.while_loop(cond_func, body_func, x) check_shape_poly(self, jax.vmap(f_jax), arg_descriptors=[RandArg((3,), _f32)], input_signature=[tf.TensorSpec((None, 3), dtype=tf.float32)], polymorphic_shapes=["b, ..."], expected_output_signature=tf.TensorSpec((None, 3), dtype=tf.float32) ) def test_vmap_error(self): # vmap is careful to give nice error messages when mapped axes have # different sizes, but this can be foiled by InconsistentDimensionOperation x = y = np.ones((3, 5), dtype=np.float32) with self.assertRaisesRegex(ValueError, "vmap got inconsistent sizes for array axes to be mapped"): jax2tf.convert(jax.vmap(lambda x, y: x + y), polymorphic_shapes=["b, ...", None])(x, y) z = x with self.assertRaisesRegex(ValueError, "vmap got inconsistent sizes for array axes to be mapped"): jax2tf.convert(jax.vmap(lambda x, y, z: x + y + z), polymorphic_shapes=["b, ...", "c, ...", None])(x, y, z) def test_reshape_compiled(self): # We compile the result of conversion for two shapes, hence we need to # involve the TF compiler twice, but we trace only once with shape polymorphism traced = False def f_jax(x): nonlocal traced traced = True y = jnp.sin(x) return y.reshape([x.shape[0], -1]) x = self.rng().rand(4, 2, 3) res_jax = f_jax(x) traced = False # If we get_concrete_function we trace once f_tf = tf.function( jax2tf.convert(f_jax, polymorphic_shapes=[PS("b", ...)]), autograph=False, jit_compile=True).get_concrete_function( tf.TensorSpec([None, 2, 3], x.dtype)) self.assertTrue(traced) traced = False self.assertAllClose(res_jax, f_tf(x)) self.assertFalse(traced) # We are not tracing again x = self.rng().rand(6, 2, 3) res_jax = f_jax(x) traced = False self.assertAllClose(res_jax, f_tf(x)) self.assertFalse(traced) # We are not tracing again def test_eval_poly_shapes(self): def f1(x, y): # x: f32[a, 5] y: f[a, 5] -> f32[a, 10] return jnp.concatenate([x, y], axis=1) def f2(x, z): # x: f32[a, 5] z: f32[a, 10] return jnp.concatenate([x, jax.lax.slice_in_dim(z, 0, 5, axis=1)], axis=1), x = np.arange(np.prod((3, 5)), dtype=np.float32).reshape((3, 5)) y = x x_polymorphic_shape = "a, _" y_polymorphic_shape = x_polymorphic_shape z_spec, z_polymorphic_shape = jax2tf.eval_polymorphic_shape( f1, polymorphic_shapes=[x_polymorphic_shape, y_polymorphic_shape])(x, y) self.assertEqual(np.float32, z_spec.dtype) self.assertEqual("(a, 10)", z_polymorphic_shape) # We can use the z_polymorphic_shape for jax2tf.convert z = jax2tf.convert( f1, polymorphic_shapes=[x_polymorphic_shape, y_polymorphic_shape])(x, y) res = jax2tf.convert( f2, polymorphic_shapes=[x_polymorphic_shape, z_polymorphic_shape])(x, z) self.assertAllClose(f2(x, f1(x, y)), res) def test_eval_poly_shapes_tuple_output(self): def f1(x, y): # x: f32[a, 5] y: f[b, 5] -> (f32[a, 5], f32[a + b, 5]) return (x, jnp.concatenate([x, y], axis=0)) def f2(z, w): # z: f32[a, 5] w: f32[a + b, 5] -> f32[2*a + b, 10] return jnp.concatenate([z, w], axis=0) x = np.arange(np.prod((3, 5)), dtype=np.float32).reshape((3, 5)) y = np.arange(np.prod((4, 5)), dtype=np.float32).reshape((4, 5)) x_polymorphic_shape = "a, _" y_polymorphic_shape = "b, _" zw_specs, zw_polymorphic_shapes = jax2tf.eval_polymorphic_shape( f1, polymorphic_shapes=[x_polymorphic_shape, y_polymorphic_shape])(x, y) self.assertEqual(np.float32, zw_specs[0].dtype) self.assertEqual(np.float32, zw_specs[1].dtype) self.assertEqual(("(a, 5)", "(a + b, 5)"), zw_polymorphic_shapes) # We can use the zw_polymorphic_shapes for jax2tf.convert z, w = jax2tf.convert( f1, polymorphic_shapes=[x_polymorphic_shape, y_polymorphic_shape])(x, y) res = jax2tf.convert(f2, polymorphic_shapes=zw_polymorphic_shapes)(z, w) self.assertAllClose(f2(* f1(x, y)), res) def test_gather_1d(self): operand = jnp.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], np.float32) rand_idxs = np.random.randint(0, high=max(operand.shape), size=(3, 1), dtype=np.int32) slice_x = np.zeros((10,), dtype=jnp.float32) dnums = lax.GatherDimensionNumbers( offset_dims=(1,), collapsed_slice_dims=(), start_index_map=(0,) ) @jax.jit def f_jax(operand, start_indices, x): return lax.gather( operand, start_indices, dimension_numbers=dnums, slice_sizes=x.shape, mode="promise_in_bounds", ) res = f_jax(operand, rand_idxs, slice_x) f_tf = jax2tf.convert( f_jax, native_serialization=True, polymorphic_shapes=["(t, )", "(3, 1)", "(t)"], ) res_tf = f_tf(operand, rand_idxs, slice_x) self.assertAllClose(res, res_tf) # List containing either harnesses, or lists of harnesses _POLY_SHAPE_TEST_HARNESSES = [ PolyHarness("add", "", jnp.add, arg_descriptors=[RandArg((3, 4), _f32), RandArg((2, 3, 4), _f32)], poly_axes=[0, 1]), PolyHarness("add_transpose", "", jax.grad(lambda x: jnp.sum(jnp.sum(x, axis=0, keepdims=False) + jnp.sin(x))), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), # Reduce the poly dimension PolyHarness("argmax", "0", lambda op: lax.argmax(op, axis=0, index_dtype=np.int32), arg_descriptors=[RandArg((3, 4, 5), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), # Reduce the non-poly dimension PolyHarness("argmax", "1", lambda op: lax.argmax(op, axis=1, index_dtype=np.int32), arg_descriptors=[RandArg((3, 4, 5), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), PolyHarness("jnp.argsort", "", lambda op: jnp.argsort(op), arg_descriptors=[RandArg((3, 4, 5), _f32)], poly_axes=[0]), [ PolyHarness("average", f"{axis=}_weights=None", lambda x, axis: jnp.average(x, axis=axis, returned=False, weights=None), arg_descriptors=[RandArg((7, 8, 4), _f32), StaticArg(axis)], poly_axes=[0]) for axis in [None, 0, 1] ], [ PolyHarness("average", f"{axis=}_weights=Some", lambda x, weights, axis: jnp.average(x, axis=axis, returned=False, weights=weights), arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 8, 4), _f32), StaticArg(axis)], poly_axes=[0, 0]) for axis in [None, 0, 1] ], PolyHarness("jnp.bincount", "length=constant", lambda x: jnp.bincount(x % 2, length=4), arg_descriptors=[RandArg((12,), np.int32)], poly_axes=[0]), PolyHarness("jnp.bincount", "length=poly", lambda x: jnp.bincount(x % 4, length=x.shape[0]), arg_descriptors=[RandArg((12,), np.int32)], poly_axes=[0]), PolyHarness("broadcast_to", "", lambda x: jnp.broadcast_to(x, [x.shape[0], x.shape[0], 4]), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("broadcast_in_dim", "0", lambda x: lax.broadcast_in_dim(x, [x.shape[0], 4, 5, 6], broadcast_dimensions=(0, 2, 3)), arg_descriptors=[RandArg((3, 1, 6), _f32)], poly_axes=[0]), PolyHarness("broadcast_in_dim", "poly", lambda x: lax.broadcast_in_dim(x, [x.shape[0], x.shape[0] + x.shape[0], 4], broadcast_dimensions=(0, 1, 2)), arg_descriptors=[RandArg((3, 1, 4), _f32)], poly_axes=[0]), PolyHarness("broadcast_in_dim", "poly2", lambda x: lax.broadcast_in_dim(x, [x.shape[0], 5, 6, x.shape[2], 4], broadcast_dimensions=(0, 2, 3)), arg_descriptors=[RandArg((3, 1, 4), _f32)], poly_axes=[(0, 2)]), PolyHarness("broadcast_in_dim", "transpose", jax.grad(lambda x: jnp.sum( lax.broadcast_in_dim(jnp.sin(x), [2, x.shape[0], 5, x.shape[2], 4], broadcast_dimensions=(1, 2, 3)))), arg_descriptors=[RandArg((3, 1, 4), _f32)], poly_axes=[(0, 2)]), PolyHarness("clamp", "", lax.clamp, arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((3, 4, 5), _f32), RandArg((3, 4, 5), _f32)], poly_axes=[0, 0, 0]), PolyHarness("collapse", "", lambda x: lax.collapse(x, 1, 4), arg_descriptors=[RandArg((3, 4, 5, 6, 7), _f32)], poly_axes=[(0, 1, 3)]), PolyHarness("concatenate", "", lambda x: jnp.concatenate([x, x], axis=0), arg_descriptors=[RandArg((3, 4, 5), _f32)], poly_axes=[(0, 1)]), PolyHarness("concatenate", "grad", jax.grad(lambda x: jnp.sum(jnp.concatenate([x, jnp.sin(x)], axis=0))), arg_descriptors=[RandArg((3, 4, 5), _f32)], poly_axes=[(0, 1)]), PolyHarness("conv_general_dilated", "1d_stride=1", lambda lhs, rhs: lax.conv_general_dilated( lhs, rhs, window_strides=(1,), padding="SAME", rhs_dilation=None, dimension_numbers=lax.ConvDimensionNumbers(lhs_spec=(0, 2, 1), rhs_spec=(2, 1, 0), out_spec=(0, 2, 1))), arg_descriptors=[RandArg((1, 12, 16), _f32), RandArg((4, 16, 16), _f32)], poly_axes=[1, None]).both_enable_and_disable_xla(), # The same example from above, but with stride=2. PolyHarness("conv_general_dilated", "1d_stride=2_even", lambda lhs, rhs: lax.conv_general_dilated( lhs, rhs, window_strides=(2,), padding="SAME", rhs_dilation=None, dimension_numbers=lax.ConvDimensionNumbers(lhs_spec=(0, 2, 1), rhs_spec=(2, 1, 0), out_spec=(0, 2, 1))), arg_descriptors=[RandArg((1, 12, 16), _f32), RandArg((4, 16, 16), _f32)], poly_axes=[1, None], ).both_enable_and_disable_xla(), # The same example from above, but with stride=2 and odd input size. PolyHarness("conv_general_dilated", "1d_stride=2_odd", lambda lhs, rhs: lax.conv_general_dilated( lhs, rhs, window_strides=(2,), padding="SAME", rhs_dilation=None, dimension_numbers=lax.ConvDimensionNumbers(lhs_spec=(0, 2, 1), rhs_spec=(2, 1, 0), out_spec=(0, 2, 1))), arg_descriptors=[RandArg((1, 13, 16), _f32), RandArg((4, 16, 16), _f32)], poly_axes=[1, None], ).both_enable_and_disable_xla(), # Issue #11402 PolyHarness("conv_general_dilated", "1d_2", lambda lhs, rhs: lax.conv_transpose(lhs, rhs, strides=(2,), padding="SAME", rhs_dilation=None, transpose_kernel=False), arg_descriptors=[RandArg((5, 12, 16), _f32), RandArg((4, 16, 16), _f32)], poly_axes=[0, None], tol=1e-5).both_enable_and_disable_xla(), # Issue #11402 PolyHarness("conv_general_dilated", "1d_3", lambda lhs, rhs: lax.conv_transpose(lhs, rhs, strides=(2,), padding="SAME", rhs_dilation=None, transpose_kernel=False), arg_descriptors=[RandArg((5, 12, 16), _f32), RandArg((4, 16, 16), _f32)], poly_axes=[1, None], tol=1e-5).both_enable_and_disable_xla(), PolyHarness("conv_general_dilated", "", lambda lhs, rhs: lax.conv_general_dilated( lhs, rhs, window_strides=(2, 3), padding=((0, 0), (0, 0)), lhs_dilation=(1, 1), rhs_dilation=(1, 2), dimension_numbers=("NCHW", "OIHW", "NCHW"), feature_group_count=1, batch_group_count=1, precision=None), arg_descriptors=[RandArg((7, 3, 9, 10), _f32), RandArg((3, 3, 4, 5), _f32)], poly_axes=[0, None]).both_enable_and_disable_xla(), PolyHarness("cummax", "", lambda x: lax_control_flow.cummax(x, axis=1, reverse=False), arg_descriptors=[RandArg((3, 4, 5), _f32)], poly_axes=[0]), PolyHarness("jnp.cumsum", "reduce_axis=poly", lambda x: jnp.cumsum(x, axis=0), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0], expect_error=( (None, None) if (not config.jax2tf_default_native_serialization or jtu.device_under_test() == "tpu") else (NotImplementedError, "associative scan over axis of non-constant size"))), PolyHarness("jnp.cumsum", "reduce_axis=static", lambda x: jnp.cumsum(x, axis=1), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]), PolyHarness("delta", "0", lambda x: lax_internal._delta(_f32, x.shape, axes=(0, 1)) + x, arg_descriptors=[RandArg((3, 1), _f32)], poly_axes=[0]), PolyHarness("dot_general", "", lambda lhs, rhs: lax.dot_general(lhs, rhs, dimension_numbers=(((2,), (1,)), ((0,), (0,)))), arg_descriptors=[RandArg((3, 4, 4), _f32), RandArg((3, 4), _f32)], poly_axes=[0, 0]), PolyHarness("dynamic_slice", "idx=tuple_int", # x:shape: (b, 4) lambda x: lax.dynamic_slice(x, (0, 1), (x.shape[0], 2)), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), PolyHarness("dynamic_slice", "idx=tuple_arg", # x:shape: (b, 4) lambda x, i0: lax.dynamic_slice(x, (i0, np.int32(1)), (x.shape[0], 2)), arg_descriptors=[RandArg((3, 4), _f32), np.array(-2, dtype=np.int32)], poly_axes=[0, None]).both_enable_and_disable_xla(), PolyHarness("dynamic_slice", "idx=array", # x:shape: (b, 4) lambda x, idx: lax.dynamic_slice(x, idx, (x.shape[0], 2)), arg_descriptors=[RandArg((3, 4), _f32), np.array([-2, -1], dtype=np.int32)], poly_axes=[0, None]).both_enable_and_disable_xla(), PolyHarness("dynamic_slice_in_dim", "idx=0", # x:shape: (b, 4) lambda x: lax.dynamic_slice_in_dim(x, 0, x.shape[0], axis=0), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), PolyHarness("dynamic_update_slice", "idx=tuple_int", # x:shape: (b, 4) lambda x: lax.dynamic_update_slice(x, x, (0, 0)), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), PolyHarness("dynamic_update_slice", "idx=tuple_arg", # x:shape: (b, 4) lambda x, i0: lax.dynamic_update_slice(x, x, (i0, np.int32(0))), arg_descriptors=[RandArg((3, 4), _f32), np.array(-2, dtype=np.int32)], poly_axes=[0, None]).both_enable_and_disable_xla(), PolyHarness("dynamic_update_slice", "idx=array", # x:shape: (b, 4) lambda x, idx: lax.dynamic_update_slice(x, x, idx), arg_descriptors=[RandArg((3, 4), _f32), np.array([-2, -1], dtype=np.int32)], poly_axes=[0, None]).both_enable_and_disable_xla(), PolyHarness("einsum", "0", lambda x: jnp.einsum("...i->...", x), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("einsum", "0_alt", lambda x: jnp.einsum(x, (..., 1), [...]), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("einsum", "1", lambda x, y: jnp.einsum("...ij,...jk->...ik", x, y), arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((3, 5, 6), _f32)], poly_axes=[0, 0]), PolyHarness("einsum", "1_alt", lambda x, y: jnp.einsum(x, [..., 0, 1], y, (..., 1, 2), [..., 0, 2]), arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((3, 5, 6), _f32)], poly_axes=[0, 0]), PolyHarness("einsum", "2", lambda x, y: jnp.einsum("...ij,jk->...ik", x, y), arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((5, 6), _f32)], poly_axes=[0, None]), PolyHarness("einsum", "2_alt", lambda x, y: jnp.einsum(x, [..., 0, 1], y, [1, 2], [..., 0, 2]), arg_descriptors=[RandArg((3, 4, 5), _f32), RandArg((5, 6), _f32)], poly_axes=[0, None]), PolyHarness("einsum", "3", # Reduced dimension is polymorphic lambda x, y: jnp.einsum("ij,jk->ik", x, y), arg_descriptors=[RandArg((3, 4), _f32), RandArg((4, 5), _f32)], poly_axes=[1, 0]), PolyHarness("einsum", "3_alt", # Reduced dimension is polymorphic lambda x, y: jnp.einsum(x, [0, 1], y, [1, 2], [0, 2]), arg_descriptors=[RandArg((3, 4), _f32), RandArg((4, 5), _f32)], poly_axes=[1, 0]), PolyHarness("einsum", "4", # Reduced dimension is polymorphic, and is 2*b lambda x, y: jnp.einsum("ij,jk->ik", jnp.concatenate([x, x], axis=1), jnp.concatenate([y, y], axis=0)), arg_descriptors=[RandArg((3, 4), _f32), RandArg((4, 5), _f32)], poly_axes=[1, 0]), PolyHarness("einsum", "4_alt", # Reduced dimension is polymorphic, and is 2*b lambda x, y: jnp.einsum(jnp.concatenate([x, x], axis=1), [0, 1], jnp.concatenate([y, y], axis=0), [1, 2], [0, 2]), arg_descriptors=[RandArg((3, 4), _f32), RandArg((4, 5), _f32)], poly_axes=[1, 0]), PolyHarness("einsum", "multiple_contractions", lambda x, y, z: jnp.einsum("ab,bc,cd->ad", x, y, z), arg_descriptors=[RandArg((3, 2), _f32), RandArg((2, 3), _f32), RandArg((3, 4), _f32)], poly_axes=[0, None, None]), PolyHarness("einsum", "incompatible_contractions_error", lambda x, y: jnp.einsum("ab,cb->ac", x, y), arg_descriptors=[RandArg((2, 3), _f32), RandArg((2, 3), _f32)], polymorphic_shapes=["(2, b0)", "(2, b1)"], input_signature=[tf.TensorSpec((2, None)), tf.TensorSpec((2, None))], expect_error=(AssertionError, "Incompatible reduction dimensions")), PolyHarness("eye", "N=poly_M=None", lambda x: jnp.eye(x.shape[0]) + x, arg_descriptors=[RandArg((3, 1), _f32)], poly_axes=[0]), PolyHarness("eye", "N=poly_M=poly", lambda x: jnp.eye(x.shape[0], M=x.shape[0] + 2) + x, arg_descriptors=[RandArg((3, 1), _f32)], poly_axes=[0]), [ PolyHarness("fft", f"{fft_type=}_{nr_fft_lengths=}", lambda x, fft_type, nr_fft_lengths: lax.fft_p.bind( x, fft_type=fft_type, fft_lengths=tuple( x.shape[-nr_fft_lengths:] if fft_type != xla_client.FftType.IRFFT else [(x.shape[-1] - 1) * 2])), arg_descriptors=[ RandArg((3, 4, 5, 6), np.float32 if fft_type == xla_client.FftType.RFFT else np.complex64), StaticArg(fft_type), StaticArg(nr_fft_lengths)], # All axes but the last one are dynamic. This means that the test # with nr_fft_lengths==1 will not have dynamic fft_lengths. poly_axes=[(0, 1, 2)], tol=1e-4) for fft_type in (xla_client.FftType.FFT, xla_client.FftType.IFFT, xla_client.FftType.RFFT, xla_client.FftType.IRFFT) for nr_fft_lengths in (1, 2) ], PolyHarness("full", "", lambda x: lax.full((x.shape[0], 2), 3.) + x, arg_descriptors=[RandArg((3, 1), _f32)], poly_axes=[0]), # operand is non-poly, index is poly PolyHarness("getitem", "op=static_idx=poly", lambda a, i: a[i], arg_descriptors=[RandArg((3, 4), _f32), np.array([2, 2], np.int32)], poly_axes=[None, 0]).both_enable_and_disable_xla(), # operand is poly, index is integer PolyHarness("getitem", "op=poly_idx=const", lambda a: a[1], arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), # operand is poly, index is dim poly PolyHarness("getitem", "op=poly_idx=dim", lambda a: a[jnp.array(a.shape[0] - 2)], arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), # Both the operand and the index are poly PolyHarness("getitem", "op=poly_idx=poly", lambda a, i: a[i], arg_descriptors=[RandArg((3, 4), _f32), np.array([1, 2, 0], np.int32)], poly_axes=[0, 0]).both_enable_and_disable_xla(), # op is poly and index is an entire slice PolyHarness("getitem", "op=poly_idx=slice-all", lambda a: a[:], arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), # op is poly and index is a partial slice PolyHarness("getitem", "op=poly_idx=slice-ct-1", lambda a: a[:2], arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0], expect_error=(IndexError, "Cannot use NumPy slice indexing on an array dimension") ).both_enable_and_disable_xla(), PolyHarness("getitem", "op=poly_idx=slice-ct-2", lambda a: a[:, :2], arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), PolyHarness("getitem", "op=poly_idx=slice-None-1", lambda a: a[:a.shape[0]], arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), PolyHarness("getitem", "op=poly_idx=slice-poly", lambda a: a[:a.shape[0] - 1], arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0], expect_error=(IndexError, "Array slice indices must have static")).both_enable_and_disable_xla(), PolyHarness("image_resize", "linear_0", lambda x: jax.image.resize(x, (x.shape[0], 2 * x.shape[1], 2 * x.shape[2], x.shape[3]), method="linear"), arg_descriptors=[RandArg((3, 16, 32, 3), _f32)], poly_axes=[(1, 2)]), PolyHarness("image_resize", "linear_to_fixed_dim", lambda x: jax.image.resize(x, (x.shape[0], 64, 64, x.shape[3]), method="linear"), arg_descriptors=[RandArg((3, 16, 32, 3), _f32)], poly_axes=[(1, 2)]), PolyHarness("image_resize", "nearest_0", lambda x: jax.image.resize(x, (x.shape[0], 2 * x.shape[1], 2 * x.shape[2], x.shape[3]), method="nearest"), arg_descriptors=[RandArg((3, 5, 7, 3), _f32)], poly_axes=[(1, 2)]), PolyHarness("index_in_dim", "0", lambda x: lax.index_in_dim(x, -1, axis=0, keepdims=False), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("index_in_dim", "idx=neg", lambda x: lax.index_in_dim(x, -1, axis=0, keepdims=False), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("index_in_dim", "idx=last", lambda x: lax.index_in_dim(x, x.shape[0] - 1, axis=0, keepdims=False), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("jnp.insert", "insert=constant", lambda x: jnp.insert(x, jnp.arange(3, dtype=_i32), np.array([3, 4, 5], dtype=_i32)), arg_descriptors=[RandArg((12,), _i32)], poly_axes=[0], expect_error=expect_error_associative_scan), PolyHarness("jnp.insert", "insert=poly", lambda x: jnp.insert(x, jnp.arange(x.shape[0], dtype=_i32), x, axis=0), arg_descriptors=[RandArg((12, 3), _i32)], poly_axes=[(0, 1)], expect_error=expect_error_associative_scan), PolyHarness("iota", "", lambda x: x + lax.iota(_f32, x.shape[0]), arg_descriptors=[RandArg((3,), _f32)], poly_axes=[0]), PolyHarness("matmul", "0", jnp.matmul, arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 4, 5), _f32)], poly_axes=[0, 0], tol=1e-5), PolyHarness("matmul", "1", jnp.matmul, arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((4, 5), _f32)], poly_axes=[0, None], tol=1e-5), [ PolyHarness("mean", f"{axis=}_{keepdims=}_where=None", lambda x, axis, keepdims: jnp.mean(x, axis=axis, keepdims=keepdims, where=None), arg_descriptors=[RandArg((7, 8, 4), _f32), StaticArg(axis), StaticArg(keepdims)], poly_axes=[0]) for keepdims in [False, True] for axis in [None, (0,), (0, 1), (1,)] ], [ PolyHarness("mean", f"{axis=}_{keepdims=}_where=Some", lambda x, where, axis, keepdims: jnp.mean(x, axis=axis, keepdims=keepdims, where=where), arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 8, 4), np.bool_), StaticArg(axis), StaticArg(keepdims)], poly_axes=[0, 0]) for keepdims in [False, True] for axis in [None, (0,), (0, 1), (1,)] ], PolyHarness("jnp.nonzero", "size=constant", lambda x: jnp.nonzero(x % 3, size=10, fill_value=100), arg_descriptors=[RandArg((3, 2, 4), _i32)], poly_axes=[0], expect_error=expect_error_associative_scan), PolyHarness("jnp.nonzero", "size=poly", lambda x: jnp.nonzero(x % 3, size=x.shape[0] * 2, fill_value=100), arg_descriptors=[RandArg((3, 2, 4), _i32)], poly_axes=[0], expect_error=expect_error_associative_scan), PolyHarness("one_hot", "poly_num_classes", lambda x, y: jax.nn.one_hot(x, y.shape[0]), arg_descriptors=[np.arange(16, dtype=_f32), RandArg((16,), _f32)], poly_axes=[None, 0]), PolyHarness("one_hot", "all_poly", lambda x, y: jax.nn.one_hot(x, y.shape[0]), arg_descriptors=[np.arange(16, dtype=_f32), RandArg((16,), _f32)], poly_axes=[0, 0]), PolyHarness("ones", "", lambda x: jnp.ones(x.shape, dtype=_f32) + x, arg_descriptors=[RandArg((3, 2, 4), _f32)], poly_axes=[0]), PolyHarness("pad", "", lax.pad, arg_descriptors=[RandArg((3, 2, 5), _f32), np.float32(5.), StaticArg(((0, 0, 0), (0, 0, 0), (1, 1, 1)))], poly_axes=[0, None]), PolyHarness("pad", "poly_padding_config", lambda x: lax.pad(x, _f32(0.), ((x.shape[0], x.shape[1], x.shape[0]), (0, 0, 0))), arg_descriptors=[RandArg((3, 2), _f32)], poly_axes=[0]), PolyHarness("jnp.pad", "mode=constant", lambda x: jnp.pad(x, [[x.shape[0], 0], [x.shape[1], 1]], mode="constant"), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]), PolyHarness("jnp.pad", "mode=constant_bminus1", # We slice first the unknown dimension to make it of size b - 1 # which may be 0. lambda x: jnp.pad(lax.dynamic_slice_in_dim(x, 1, x.shape[0] - 1, axis=0), [[x.shape[0], 0], [x.shape[1], 1]], mode="constant"), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]), PolyHarness("jnp.pad", "mode=edge", lambda x: jnp.pad(x, [[x.shape[0], 0], [x.shape[1], 1]], mode="edge"), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]), PolyHarness("percentile", "axis=None", lambda x: jnp.percentile(x, 50, axis=None), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]), PolyHarness("nanquantile", "axis=None", lambda x: jnp.nanquantile(x, .5, axis=None), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]), PolyHarness("percentile", "axis=0", lambda x: jnp.percentile(x, 50, axis=0), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]), PolyHarness("nanquantile", "axis=0", lambda x: jnp.nanquantile(x, .5, axis=0), arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]), [ # The random primitive tests, with threefry (both partitionable and # non-partitionable), and unsafe_rbg. [ PolyHarness("random_gamma", f"{flags_name}", lambda key, a: jax.random.gamma(key, a), arg_descriptors=[RandArg((3, key_size), np.uint32), RandArg((3, 4, 5), _f32)], poly_axes=[0, (0, 1)], override_jax_config_flags=override_jax_config_flags), # type: ignore # The known dimensions product must be even. PolyHarness("random_categorical", f"axis=0_{flags_name}", lambda key, a: jax.random.categorical(key, a, axis=0), arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 8), _f32)], poly_axes=[None, 0], override_jax_config_flags=override_jax_config_flags), # type: ignore PolyHarness("random_categorical", f"axis=1_{flags_name}", lambda key, a: jax.random.categorical(key, a, axis=1), arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 5, 8), _f32)], poly_axes=[None, (0, 1)], override_jax_config_flags=override_jax_config_flags), # type: ignore PolyHarness("random_categorical", f"axis=1_then_reshape_{flags_name}", lambda key, a: jax.random.categorical(key, a, axis=1).reshape((-1)), arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 5, 8), _f32)], poly_axes=[None, (0, 1)], override_jax_config_flags=override_jax_config_flags), # type: ignore PolyHarness("random_categorical", f"0_dim_{flags_name}", # One axis has 0 size lambda key, a: jax.random.categorical(key, a, axis=1), arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 5, 0), _f32)], poly_axes=[None, (0, 1)], override_jax_config_flags=override_jax_config_flags), # type: ignore PolyHarness("random_split", f"{flags_name}", lambda key, a: jax.random.key_data(jax.random.split(key, 2 * a.shape[0])), arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 4), _f32)], poly_axes=[None, (0,)], override_jax_config_flags=override_jax_config_flags), # type: ignore # Works when the known dimensions are known to be even or odd. PolyHarness("random_uniform", f"even_1_{flags_name}", lambda key, a: jax.random.uniform(key, a.shape, dtype=_f32), arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 4, 5), _f32)], poly_axes=[None, 0], override_jax_config_flags=override_jax_config_flags), # type: ignore PolyHarness("random_uniform", f"even_2_{flags_name}", lambda key, a: jax.random.uniform(key, (2 * a.shape[0], a.shape[1]), dtype=_f32), arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 4), _f32)], poly_axes=[None, (0, 1)], override_jax_config_flags=override_jax_config_flags), # type: ignore PolyHarness("random_uniform", f"error_not_even_{flags_name}", lambda key, a: jax.random.uniform(key, a.shape, dtype=_f32), arg_descriptors=[RandArg((key_size,), np.uint32), RandArg((3, 5), _f32)], poly_axes=[None, 0], expect_error=( (core.InconclusiveDimensionOperation, "the product of the known dimensions must be even") if flags_name == "threefry_non_partitionable" else (None, None)), override_jax_config_flags=override_jax_config_flags) # type: ignore ] for key_size, flags_name, override_jax_config_flags in [ (2, "threefry_non_partitionable", dict(jax_default_prng_impl="threefry2x32", jax_threefry_partitionable=False)), (2, "threefry_partitionable", dict(jax_default_prng_impl="threefry2x32", jax_threefry_partitionable=True)), (4, "unsafe_rbg", dict(jax_default_prng_impl="unsafe_rbg")) ] ], PolyHarness("reduce_window", "min", # x.shape = (b, 8) lambda x: lax.reduce_window(x, np.array(1., _f32), lax.min, (2, 2), (1, 1), "VALID"), arg_descriptors=[RandArg((3, 8), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), PolyHarness("reduce_window", "add_0", # x.shape = (b, 8) lambda x: lax.reduce_window(x, 0, lax.add, (2, 2), (1, 1), "VALID"), arg_descriptors=[RandArg((3, 8), _f32)], poly_axes=[0]).both_enable_and_disable_xla(), # https://github.com/google/jax/issues/11804 # Use the reshape trick to simulate a polymorphic dimension of 16*b. # (See test "conv_general_dilated.1d_1" above for more details.) PolyHarness("reduce_window", "add_1", # x.shape = (1, 16*b, 1) lambda x: lax.reduce_window( jnp.reshape(x, (1, -1, 1)), 0., lax.add, (1, 4, 1), (1, 2, 1), "SAME"), arg_descriptors=[RandArg((1, 128, 16), _f32)], poly_axes=[1]).both_enable_and_disable_xla(), # TODO(necula): not yet supported, but also unlikely to come up. # PolyHarness("random_uniform", "odd", # lambda key, a: jax.random.uniform(key, (2 * a.shape[0] + 1, a.shape[1]), # dtype=_f32), # [RandArg((2,), np.uint32), RandArg((3, 5), _f32)], # poly_axes=[None, 0]), [ PolyHarness("reduce", reduce_op.__name__, lambda x: reduce_op(x, axis=-1, keepdims=True), # type: ignore arg_descriptors=[RandArg((3, 5), _f32)], poly_axes=[0]) for reduce_op in [jnp.all, jnp.any, jnp.max, jnp.min, jnp.prod, jnp.sum] ], # Repeat f32[b, 2] * 3 PolyHarness("repeat", "repeats=int_axis=0", lambda x: jnp.repeat(x, repeats=3, axis=0), arg_descriptors=[RandArg((3, 2), _f32)], poly_axes=[0]), # Repeat f32[b, 2] * b PolyHarness("repeat", "repeats=poly_axis=0", lambda x: jnp.repeat(x, repeats=x.shape[0], axis=0), arg_descriptors=[RandArg((3, 2), _f32)], poly_axes=[0]), # Repeat f32[b, 2] * b PolyHarness("repeat", "repeats=poly_axis=None", lambda x: jnp.repeat(x, repeats=x.shape[0], axis=None), arg_descriptors=[RandArg((3, 2), _f32)], poly_axes=[0]), # Repeat f32 * b PolyHarness("repeat", "repeats=poly_axis=None_scalar", lambda x, y: jnp.repeat(x, repeats=y.shape[0], axis=None) + y, arg_descriptors=[RandArg((), _f32), RandArg((3, 1), _f32)], poly_axes=[None, 0]), PolyHarness("repeat", "repeats=poly_axis=None_total_repeat_length1", lambda x: jnp.repeat(x, repeats=x.shape[0], axis=None, total_repeat_length=8), arg_descriptors=[RandArg((3, 2), _f32)], poly_axes=[0], expect_error=(ValueError, "jnp.repeat with a non-constant `repeats` is supported only .*")), PolyHarness("reshape", "0", lambda x: x.reshape([x.shape[0], -1]), arg_descriptors=[RandArg((3, 2, 3), _f32)], poly_axes=[0]), PolyHarness("reshape", "1", lambda x: x.reshape([x.shape[0], -1]), arg_descriptors=[RandArg((3, 2, 3), _f32)], poly_axes=[(0, 1)]), PolyHarness("reshape", "2", lambda x: x.reshape([x.shape[0], -1, x.shape[3], x.shape[2]]), arg_descriptors=[RandArg((3, 4, 5, 6, 7), _f32)], poly_axes=[(0, 2, 3)]), PolyHarness("reshape", "3", lambda x: jnp.reshape(x, [2, -1]), arg_descriptors=[RandArg((3, 4, 5, 6, 7), _f32)], poly_axes=[(0, 2)]), PolyHarness("reshape", "_issue_9975", # The newshape is a scalar lambda x: jnp.reshape(x, x.shape[0] * x.shape[1]), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("reshape", "error", lambda x: x.reshape([x.shape[0], -1, 3]), arg_descriptors=[RandArg((3, 2, 4), _f32)], poly_axes=[0], skip_jax_run=True, expect_error=(core.InconclusiveDimensionOperation, re.escape( "Cannot divide evenly the sizes of shapes (b0, 2, 4) and (b0, -1, 3)"))), PolyHarness("roll", "axis=0", lambda x: jnp.roll(x, 2, axis=0), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("roll", "axis=None", lambda x: jnp.roll(x, 2), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("scatter_add", "", partial(lax.scatter_add, indices_are_sorted=False, unique_indices=True), arg_descriptors=[RandArg((7, 4), _f32), np.array([[1], [2]], np.int32), # indices: [2, 1] RandArg((7, 2), _f32), # updates: [7, 2] StaticArg(lax.ScatterDimensionNumbers((0,), (1,), (1,)))], poly_axes=[0, None, 0]), PolyHarness("scatter_add", "clip0", partial(lax.scatter_add, indices_are_sorted=False, unique_indices=True, mode=lax.GatherScatterMode.CLIP), arg_descriptors=[RandArg((7, 4), _f32), # [b, 4] np.array([[1], [2]], np.int32), # indices: [2, 1] RandArg((7, 2), _f32), # updates: [b, 2] StaticArg(lax.ScatterDimensionNumbers((0,), (1,), (1,)))], poly_axes=[0, None, 0]), PolyHarness("scatter_add", "clip1", partial(lax.scatter_add, indices_are_sorted=False, unique_indices=True, mode=lax.GatherScatterMode.CLIP), arg_descriptors=[RandArg((7, 4), _f32), # [b, 4] np.array([[1, 2], [-2, 0], [6, 4], [7, -1], [1, 0], [3, 0], [0, 5]], np.int32), # indices: [b, 2] RandArg((7, 1), _f32), # updates: [b, 1] StaticArg(lax.ScatterDimensionNumbers((1,), (0,), (0, 1,)))], poly_axes=[0, 0, 0]), PolyHarness("select", "0", # x.shape = (b, 3) lambda x: lax.select(x > 5., x, x), arg_descriptors=[RandArg((7, 3), _f32)], poly_axes=[0]), PolyHarness("select", "1", # x.shape = (b, 3); y.shape = (3,) jax.vmap(lambda x, y: lax.select(x > 5., x, y), in_axes=[0, None]), arg_descriptors=[RandArg((7, 3), _f32), RandArg((3,), _f32)], poly_axes=[0, None]), PolyHarness("slice", "entire_axis", lambda x: lax.slice(x, start_indices=(0, 1), limit_indices=(x.shape[0], 3)), arg_descriptors=[RandArg((7, 3), _f32)], poly_axes=[0]), PolyHarness("slice_in_dim", "entire_axis", lambda x: lax.slice_in_dim(x, 0, x.shape[0], stride=1, axis=0), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("slice_in_dim", "start=neg", lambda x: lax.slice_in_dim(x, -1, x.shape[0], stride=1, axis=0), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("slice_in_dim", "limit=neg", lambda x: lax.slice_in_dim(x, 0, -1, stride=1, axis=0), arg_descriptors=[RandArg((3, 4), _f32)], poly_axes=[0]), PolyHarness("slice_in_dim", "stride=2_even", lambda x: lax.slice_in_dim(x, 0, x.shape[0], stride=2, axis=0), arg_descriptors=[RandArg((12, 4), _f32)], poly_axes=[0]), PolyHarness("slice_in_dim", "stride=2_odd", lambda x: lax.slice_in_dim(x, 0, x.shape[0], stride=2, axis=0), arg_descriptors=[RandArg((13, 4), _f32)], poly_axes=[0]), # Not yet, the slice_in_dim does int(stride) # PolyHarness("slice_in_dim", "stride=sym", # lambda x: lax.slice_in_dim(x, 0, x.shape[0], stride=x.shape[0] // 4, axis=0), # arg_descriptors=[RandArg((13, 4), _f32)], # poly_axes=[0]), PolyHarness("squeeze", "axis=empty", jnp.squeeze, arg_descriptors=[RandArg((5,), _f32), StaticArg(())], poly_axes=[0]), PolyHarness("squeeze", "axis=None", jnp.squeeze, arg_descriptors=[RandArg((5,), _f32), StaticArg(None)], poly_axes=[0], expect_error=(ValueError, "jnp.squeeze with axis=None is not supported with shape polymorphism")), PolyHarness("squeeze", "axis=1", jnp.squeeze, arg_descriptors=[RandArg((4, 1), _f32), StaticArg((1,))], poly_axes=[0]), PolyHarness("squeeze", "axis=1_2", jnp.squeeze, arg_descriptors=[RandArg((4, 1, 1), _f32), StaticArg((1, 2))], poly_axes=[0]), PolyHarness("squeeze", "error", jnp.squeeze, arg_descriptors=[RandArg((3, 33), _f32), StaticArg(-1)], poly_axes=[(0, 1)], skip_jax_run=True, expect_error=(ValueError, re.escape( "cannot select an axis to squeeze out which has size not equal to one, got shape=(b0, b1) and dimensions=(1,)")) ), PolyHarness("take", "", lambda a, i: jnp.take(a, i, axis=1), arg_descriptors=[RandArg((3, 4, 5), _f32), np.array([1, 2], np.int32)], poly_axes=[0, None]).both_enable_and_disable_xla(), PolyHarness("take_along_axis", "0", lambda x, y: jnp.take_along_axis(x, y, axis=0), arg_descriptors=[RandArg((5, 2), _f32), RandArg((5, 1), np.int32)], poly_axes=[0, 0]), PolyHarness("take_along_axis", "1", lambda x, y: jnp.take_along_axis(x, y, axis=1), arg_descriptors=[RandArg((5, 2), _f32), RandArg((5, 1), np.int32)], poly_axes=[0, 0]), PolyHarness("tile", "0", lambda x: jnp.tile(x, (1, 2)), arg_descriptors=[RandArg((4, 3), _f32)], poly_axes=[0]), PolyHarness("tile", "1", # The repetitions are polys lambda x: jnp.tile(x, (1, x.shape[0])), arg_descriptors=[RandArg((4, 2), _f32)], poly_axes=[0]), PolyHarness("lax_top_k", "", lambda x: jax.lax.top_k(x, x.shape[-1] - 1), arg_descriptors=[RandArg((16,), _f32)], poly_axes=[0]), PolyHarness("tri", "N=poly_M=None", lambda x: jnp.tri(x.shape[0]) + x, arg_descriptors=[RandArg((3, 1), _f32)], poly_axes=[0]), PolyHarness("tri", "N=poly_M=poly", lambda x: jnp.tri(x.shape[0], M=x.shape[0] + 2) + x, arg_descriptors=[RandArg((3, 1), _f32)], poly_axes=[0]), [ PolyHarness("var", f"{axis=}_{keepdims=}_where=None", lambda x, axis, keepdims: jnp.var(x, axis=axis, keepdims=keepdims, where=None), arg_descriptors=[RandArg((7, 8, 4), _f32), StaticArg(axis), StaticArg(keepdims)], poly_axes=[0]) for keepdims in [False, True] for axis in [None, (0,), (0, 1), (1,)] ], [ PolyHarness("var", f"{axis=}_{keepdims=}_where=Some", lambda x, where, axis, keepdims: jnp.var(x, axis=axis, keepdims=keepdims, where=where), arg_descriptors=[RandArg((7, 8, 4), _f32), RandArg((7, 8, 4), np.bool_), StaticArg(axis), StaticArg(keepdims)], poly_axes=[0, 0]) for keepdims in [False, True] for axis in [None, (0,), (0, 1), (1,)] ], PolyHarness("where", "", jnp.where, arg_descriptors=[RandArg((2,), np.bool_), RandArg((), _f32), RandArg((2,), _f32)], poly_axes=[0, None, 0]), ] def _get_jax2tf_limitations( device, h: primitive_harness.Harness) -> Sequence[Jax2TfLimitation]: # And the jax2tf limitations def applicable_jax2tf_limitation(l: Jax2TfLimitation) -> bool: # The CheckShapePolymorphism uses tf.function, so we care about "graph" return l.filter(device=device, dtype=h.dtype, mode="graph") limitations = Jax2TfLimitation.limitations_for_harness(h) return tuple(filter(applicable_jax2tf_limitation, limitations)) ### We add to the test harnesses some that are obtained from the ### primitive harnesses by applying vmap to the function and then asserting ### that we can convert shape polymorphically the result. def _make_vmap_primitive_harnesses() -> Sequence[PolyHarness]: """For each harness group, pick a single dtype. See PolyHarness for documentation. Ignore harnesses that fail in graph mode in jax2tf. """ all_h = primitive_harness.all_harnesses res = [] # Index by group harness_groups: Dict[ str, Sequence[primitive_harness.Harness]] = collections.defaultdict(list) device = jtu.device_under_test() for h in all_h: # Drop the JAX limitations if not h.filter(device_under_test=device, include_jax_unimpl=False): continue # And the jax2tf limitations that are known to result in TF error. if any(l.expect_tf_error for l in _get_jax2tf_limitations(device, h)): continue harness_groups[h.group_name].append(h) selected_harnesses = [] for group_name, hlist in harness_groups.items(): # Pick the dtype with the most harnesses in this group. Some harness # groups only test different use cases at a few dtypes. c = collections.Counter([h.dtype for h in hlist]) (dtype, _), = c.most_common(1) selected_harnesses.extend([h for h in hlist if h.dtype == dtype]) batch_size = 3 for h in selected_harnesses: if h.group_name in [ "tridiagonal_solve", # batching not implemented in JAX ]: continue def make_batched_arg_descriptor( ad: primitive_harness.ArgDescriptor) -> Optional[primitive_harness.ArgDescriptor]: if isinstance(ad, RandArg): return RandArg((batch_size,) + ad.shape, ad.dtype) elif isinstance(ad, CustomArg): def wrap_custom(rng): arg = ad.make(rng) return np.stack([arg] * batch_size) return CustomArg(wrap_custom) else: assert isinstance(ad, np.ndarray), ad return np.stack([ad] * batch_size) new_args = [make_batched_arg_descriptor(ad) for ad in h.arg_descriptors if not isinstance(ad, StaticArg)] # This test does not make sense for nullary functions if not new_args: continue limitations = [ l for l in _get_jax2tf_limitations(device, h) if not l.skip_comparison and (l.custom_assert or l.tol is not None)] vmap_harness = PolyHarness("vmap_" + h.group_name, h.name, jax.vmap(h.dyn_fun, in_axes=0, out_axes=0), arg_descriptors=new_args, poly_axes=[0] * len(new_args), limitations=limitations) vmap_harness.original_harness = h res.append(vmap_harness) return res _POLY_SHAPE_TEST_HARNESSES.append(_make_vmap_primitive_harnesses()) def _flatten_harnesses(harnesses): res = [] for h in harnesses: if isinstance(h, Sequence): res.extend(_flatten_harnesses(h)) else: res.append(h) return res class ShapePolyPrimitivesTest(tf_test_util.JaxToTfTestCase): """Tests for primitives that take shape values as parameters.""" # This test runs for all _POLY_SHAPE_PRIMITIVE_HARNESSES. # For each primitive "xxx" the test will be called "test_harness_xxx_...". # If you want to run this test for only one harness that includes "foo" # in the name (after test_harness), add parameter `one_containing="foo"` # to parameterized below. @primitive_harness.parameterized( _flatten_harnesses(_POLY_SHAPE_TEST_HARNESSES), #one_containing="", ) def test_harness(self, harness: PolyHarness): # Exclude some harnesses that are known to fail for native serialization # FOR GRAPH SERIALIZATION if config.jax2tf_default_native_serialization: if not harness.enable_xla: raise unittest.SkipTest("disabled for native_serialization and enable_xla=False") # Set of harness.group_name:platform that are implemented with custom call custom_call_harnesses = { "vmap_cholesky:cpu", "vmap_cholesky:gpu", "vmap_eig:cpu", "vmap_fft:cpu", "fft:cpu", "householder_product:cpu", "householder_product:gpu", "vmap_geqrf:cpu", "vmap_geqrf:gpu", "vmap_lu:cpu", "vmap_lu:gpu", "vmap_lu:tpu", # custom_linear_solve uses lu "vmap_custom_linear_solve:cpu", "vmap_custom_linear_solve:gpu", "vmap_custom_linear_solve:tpu", "vmap_qr:cpu", "vmap_qr:gpu", "vmap_svd:cpu", "vmap_svd:gpu", } if f"{harness.group_name}:{jtu.device_under_test()}" in custom_call_harnesses: raise unittest.SkipTest("native serialization with shape polymorphism not implemented for custom calls; b/261671778") if "fft_fft_type" in harness.fullname: if "nr_fft_lengths=2" in harness.fullname: raise unittest.SkipTest("native serialization with shape polymorphism not implemented for fft with non-constant fft_lengths on GPU and TPU") if harness.group_name == "vmap_eigh" and jtu.device_under_test() == "gpu": # For eigh on GPU with shape polymorphism under native serialization, # we use a different lowering for small matrices. See README.md. shape = harness.original_harness.params["shape"] if 0 < shape[-1] <= 32: harness.check_result = False if harness.group_name == "vmap_tan": # Tan (b/274462307) require support for custom call mhlo.tan. raise unittest.SkipTest( "native lowering with shape polymorphism requires additional StableHLO feature support") if "_unsafe_rbg" in harness.fullname: # https://github.com/openxla/stablehlo/issues/1344: need DynamicRngBitGenerator raise unittest.SkipTest("native lowering with shape polymorphism not implemented for rng_bit_generator") if "top_k" in harness.fullname: # https://github.com/openxla/stablehlo/issues/1255: need DynamicTopK raise unittest.SkipTest("native lowering with shape polymorphism not implemented for top_k") if (jtu.device_under_test() in ["tpu", "gpu"] and harness.fullname in [ "jnp.cumsum_reduce_axis=poly", "jnp.insert_insert=constant", "jnp.insert_insert=poly", "jnp.nonzero_size=constant", "jnp.nonzero_size=poly"]): # https://github.com/openxla/stablehlo/issues/1258: need DynamicReduceWindowOp raise unittest.SkipTest( "native serialization with shape polymorphism not implemented for window_reductions") # FOR GRAPH SERIALIZATION if not config.jax2tf_default_native_serialization: if ("random_gamma_threefry_non_partitionable" in harness.fullname and jtu.device_under_test() == "cpu"): harness.tol = 1e-6 if harness.group_name == "vmap_cumsum": # For cumsum we use a different implementation than JAX native # See README.md for associative scan reductions harness.tol = 1e-5 if "vmap_" in harness.group_name: # For non-native serialization, it seems that we cannot just use # the custom_asserts; we get too many errors. if [l for l in harness.limitations if l.custom_assert]: harness.check_result = False if "vmap_integer_pow" in harness.group_name: # For non-native serialization the overflow behavior is different. harness.check_result = False # FOR BOTH NATIVE AND GRAPH SERIALIZATION if harness.group_name == "vmap_conv_general_dilated": # https://github.com/openxla/stablehlo/issues/1268 raise unittest.SkipTest("Need more dynamism for DynamicConvOp") prev_jax_config_flags = { fname: getattr(jax.config, fname) for fname, fvalue in harness.override_jax_config_flags.items() } try: for fname, fvalue in harness.override_jax_config_flags.items(): jax.config.update(fname, fvalue) harness.run_test(self) finally: for fname, _ in harness.override_jax_config_flags.items(): jax.config.update(fname, prev_jax_config_flags[fname]) if __name__ == "__main__": absltest.main(testLoader=jtu.JaxTestLoader())