# Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections from functools import partial import itertools import operator import re import unittest from absl.testing import absltest from absl.testing import parameterized import numpy as np import jax from jax import core from jax.errors import UnexpectedTracerError from jax import lax from jax import random from jax._src import test_util as jtu from jax import tree_util from jax._src.util import unzip2 from jax.experimental import maps from jax.ad_checkpoint import checkpoint as new_checkpoint, checkpoint_policies import jax.numpy as jnp # scan tests use numpy import jax.scipy as jsp from jax._src.lax.control_flow import for_loop from jax.config import config config.parse_flags_with_absl() # Some tests are useful for testing both lax.cond and lax.switch. This function # provides a lax.cond-compatible interface to a two-branch lax.switch. Several # tests in this file are parameterized such that they either call into lax.cond # or into this function. def cond_via_switch(pred, true_fun, false_fun, op, *args): if len(args) > 0: assert len(args) == 1 true_op, _true_fun, false_op, _false_fun = true_fun, false_fun, op, args[0] op = (false_op, true_op) false_fun = lambda op: _false_fun(op[0]) true_fun = lambda op: _true_fun(op[1]) index = lax.convert_element_type(pred, np.int32) return lax.switch(index, [false_fun, true_fun], op) def cond_with_new_checkpoint(pred, true_fun, false_fun, op, *args): if args: true_op, _true_fun, false_op, _false_fun = true_fun, false_fun, op, args[0] op = (false_op, true_op) false_fun = lambda op: _false_fun(op[0]) true_fun = lambda op: _true_fun(op[1]) index = lax.convert_element_type(pred, np.int32) fn = lambda index, op: lax.switch(index, [false_fun, true_fun], op) return new_checkpoint(fn)(index, op) COND_IMPLS = [ (lax.cond, 'cond'), (cond_via_switch, 'switch'), (cond_with_new_checkpoint, 'new_checkpoint'), ] # We wanted to try all scan tests with the scan partial evaluation rule that # happens under ad_checkpoint.checkpoint, so we make a scan wrapper which # wraps a ad_checkpoint.checkpoint around the computation. def scan_with_new_checkpoint(f, *args, **kwargs): return new_checkpoint(partial(lax.scan, f, **kwargs), policy=checkpoint_policies.nothing_saveable)(*args) def scan_with_new_checkpoint2(f, *args, **kwargs): return new_checkpoint(partial(lax.scan, f, **kwargs), policy=checkpoint_policies.everything_saveable)(*args) def scan_with_for(f, *args, **kwargs): return for_loop.scan(f, *args, **kwargs) SCAN_IMPLS = [ (lax.scan, 'unroll1'), (partial(lax.scan, unroll=2), 'unroll2'), (scan_with_new_checkpoint , 'new_checkpoint'), (scan_with_new_checkpoint2, 'new_checkpoint2'), ] SCAN_IMPLS_WITH_FOR = [ (lax.scan, 'unroll1'), (partial(lax.scan, unroll=2), 'unroll2'), (scan_with_new_checkpoint , 'new_checkpoint'), (scan_with_new_checkpoint2, 'new_checkpoint2'), (scan_with_for, 'for'), ] def while_loop_new_checkpoint(cond_fun, body_fun, init_val): return new_checkpoint(partial(lax.while_loop, cond_fun, body_fun))(init_val) WHILE_LOOP_IMPLS = [ (lax.while_loop, 'while_loop'), (while_loop_new_checkpoint, 'new_checkpoint'), ] def while_loop_reference(cond, body, carry): while cond(carry): carry = body(carry) return carry def scan_reference(f, init, xs): carry = init ys = [] for x in xs: (carry, y) = f(carry, x) ys.append(lax.reshape(y, (1,) + np.shape(y))) ys = lax.concatenate(ys, 0) return carry, ys ignore_jit_of_pmap_warning = partial( jtu.ignore_warning, message=".*jit-of-pmap.*") class LaxControlFlowTest(jtu.JaxTestCase): def setUp(self): super().setUp() jax._src.lax.control_flow._initial_style_open_jaxpr.cache_clear() jax._src.lax.control_flow._initial_style_jaxpr.cache_clear() jax._src.lax.control_flow._initial_style_jaxprs_with_common_consts.cache_clear() def testCallableErrors(self): not_callable = 42 with self.assertRaisesRegex(TypeError, "lax.fori_loop.*callable.*"): lax.fori_loop(0, 1, not_callable, 0) with self.assertRaisesRegex(TypeError, "lax.while_loop.*callable.*"): lax.while_loop(not_callable, not_callable, 0) with self.assertRaisesRegex(TypeError, "lax.switch:.*callable.*"): lax.switch(0, [not_callable]) with self.assertRaisesRegex(TypeError, "lax.cond.*callable.*"): lax.cond(0, not_callable, not_callable) with self.assertRaisesRegex(TypeError, "lax.scan.*callable.*"): lax.scan(not_callable, 0, 1) with self.assertRaisesRegex(TypeError, "lax.associative_scan.*callable.*"): lax.associative_scan(not_callable, 0) def testWhileWithTuple(self): limit = 10 def loop_cond(state): pos, _ = state return lax.lt(pos, limit) def loop_body(state): pos, count = state return (lax.add(pos, 1), lax.add(count, 1)) def loop(init): result = lax.while_loop(loop_cond, loop_body, (init, 0)) _, count = result return count cloop = jax.jit(loop) self.assertEqual(loop(2), limit - 2) self.assertEqual(cloop(2), limit - 2) self.assertEqual(cloop(2), limit - 2) self.assertEqual(cloop(3), limit - 3) def testWhileWithManyArgs(self): nargs = 256 def loop_cond(state): return lax.lt(state[0], 2) def loop_body(state): return tuple(lax.add(s, 1) for s in state) _ = lax.while_loop(loop_cond, loop_body, (0,) * nargs) def testNestedWhile(self): def outer_loop(num): # pylint: disable=missing-docstring def cond_fun(state): num, i, _ = state return lax.lt(i, num) def body_fun(state): num, i, count = state return (num, lax.add(i, 1), inner_loop(i, count)) init_val = (num, 0, 0) _, i, count = lax.while_loop(cond_fun, body_fun, init_val) return (i, count) def inner_loop(i, count): # pylint: disable=missing-docstring def cond_fun(state): i, j, _ = state return lax.le(j, i) def body_fun(state): i, j, count = state return (i, lax.add(j, 1), lax.add(count, 1)) init_val = (i, 0, count) _, _, count = lax.while_loop(cond_fun, body_fun, init_val) return count cloop = jax.jit(outer_loop) self.assertEqual(outer_loop(3), (3, 6)) self.assertEqual(cloop(3), (3, 6)) self.assertEqual(cloop(3), (3, 6)) self.assertEqual(cloop(2), (2, 3)) self.assertEqual(cloop(4), (4, 10)) def testWhileWithClosure(self): def loop(init, local_limit, inc): def loop_cond(state): pos, _ = state return lax.lt(pos, local_limit) def loop_body(state): effect[0] = True pos, count = state return (lax.add(pos, 1), lax.add(count, inc)) result = lax.while_loop(loop_cond, loop_body, (init, 0)) _, count = result return count cloop = jax.jit(loop) limit = 10 effect = [False] self.assertEqual(loop(2, limit, 1), limit - 2) assert effect[0] effect[0] = False self.assertEqual(cloop(2, limit, 1), limit - 2) assert effect[0] effect[0] = False self.assertEqual(cloop(2, limit, 1), limit - 2) self.assertEqual(cloop(3, limit, 1), limit - 3) assert not effect[0] def testWhileWithClosureJit(self): def loop(init, local_limit, inc): def loop_cond(state): pos, _ = state return lax.lt(pos, local_limit) def loop_body(state): effect[0] = True pos, count = state f = lambda pos, inc: (lax.add(pos, 1), lax.add(count, inc)) return jax.jit(f)(pos, inc) result = lax.while_loop(loop_cond, loop_body, (init, 0)) _, count = result return count cloop = jax.jit(loop) limit = 10 effect = [False] self.assertEqual(loop(2, limit, 1), limit - 2) assert effect[0] effect[0] = False self.assertEqual(cloop(2, limit, 1), limit - 2) assert effect[0] effect[0] = False self.assertEqual(cloop(2, limit, 1), limit - 2) self.assertEqual(cloop(3, limit, 1), limit - 3) assert not effect[0] def testWhileTypeErrors(self): """Test typing error messages for while.""" tuple_treedef = tree_util.tree_structure((1., 1.)) leaf_treedef = tree_util.tree_structure(0.) with self.assertRaisesRegex(TypeError, re.escape(f"cond_fun must return a boolean scalar, but got pytree {tuple_treedef}.")): lax.while_loop(lambda c: (1., 1.), lambda c: c, 0.) with self.assertRaisesRegex(TypeError, re.escape("cond_fun must return a boolean scalar, but got output type(s) [ShapedArray(float32[])].")): lax.while_loop(lambda c: np.float32(1.), lambda c: c, np.float32(0.)) with self.assertRaisesRegex(TypeError, re.escape("body_fun output and input must have same type structure, " f"got {tuple_treedef} and {leaf_treedef}.")): lax.while_loop(lambda c: True, lambda c: (1., 1.), 0.) with self.assertRaisesWithLiteralMatch(TypeError, ("body_fun output and input must have identical types, got\n" "('ShapedArray(bool[])', " "'DIFFERENT ShapedArray(bool[]) vs. " "ShapedArray(float32[])').")): lax.while_loop(lambda c: True, lambda c: (True, True), (np.bool_(True), np.float32(0.))) def testNestedWhileWithDynamicUpdateSlice(self): num = 5 def update_entry(arr, val, i, j): val = lax.reshape(val, [1, 1]) return lax.dynamic_update_slice(arr, val, (i, j)) def outer_loop(arr): # pylint: disable=missing-docstring def cond_fun(state): i, num, _, _ = state return lax.lt(i, num) def body_fun(state): i, num, arr, out = state return (lax.add(i, 1), num, arr, inner_loop(i, arr, out)) out = np.zeros(arr.shape, dtype=arr.dtype) init_val = (0, num, arr, out) _, _, _, out = lax.while_loop(cond_fun, body_fun, init_val) return out def inner_loop(i, arr, out): # pylint: disable=missing-docstring def cond_fun(state): i, j, _, _ = state return lax.le(j, i) def body_fun(state): i, j, arr, out = state arr_i = lax.dynamic_index_in_dim(arr, i, 0, False) arr_i_j = lax.dynamic_index_in_dim(arr_i, j, 0, False) out = update_entry(out, arr_i_j, i, j) return (i, lax.add(j, 1), arr, out) init_val = (i, 0, arr, out) _, _, _, out = lax.while_loop(cond_fun, body_fun, init_val) return out cloop = jax.jit(outer_loop) arr = self.rng().randn(5, 5) self.assertAllClose(outer_loop(arr), np.tril(arr), check_dtypes=False) self.assertAllClose(cloop(arr), np.tril(arr), check_dtypes=False) self.assertAllClose(cloop(arr), np.tril(arr), check_dtypes=False) def testLoopWithConjunctionCondition(self): def sum_first_n(arr, num): # pylint: disable=missing-docstring def cond_fun(state): arr, num, i, _ = state return lax.bitwise_and(lax.lt(i, num), lax.lt(i, arr.shape[0])) def body_fun(state): arr, num, i, total = state arr_i = lax.dynamic_index_in_dim(arr, i, 0, False) return (arr, num, lax.add(i, 1), lax.add(total, arr_i)) init_val = (arr, num, 0, 0.) _, _, _, total = lax.while_loop(cond_fun, body_fun, init_val) return total cfun = jax.jit(sum_first_n) x = self.rng().randn(10).astype(jnp.float_) for num in [0, 5, 10, 15]: self.assertAllClose(sum_first_n(x, num), np.sum(x[:num]), check_dtypes=False) self.assertAllClose(cfun(x, num), np.sum(x[:num]), check_dtypes=False) self.assertAllClose(cfun(x, num), np.sum(x[:num]), check_dtypes=False) def testWhileLoopBatched(self): def fun(x): return lax.while_loop(lambda x: x < 3, lambda x: x + 2, x) ans = jax.vmap(fun)(np.array([0, 1, 2, 3])) expected = np.array([4, 3, 4, 3]) self.assertAllClose(ans, expected, check_dtypes=False) fun = jax.jit(fun) ans = jax.vmap(fun)(np.array([0, 1, 2, 3])) expected = np.array([4, 3, 4, 3]) self.assertAllClose(ans, expected, check_dtypes=False) def testWhileLoopAxisIndexBatched(self): def fun(x): return lax.while_loop(lambda x: x < lax.axis_index('i'), lambda x: x + 2, x) ans = jax.vmap(fun, axis_name='i')(np.array([0, 0, 0, 0], dtype='int32')) expected = np.array([0, 2, 2, 4]) self.assertAllClose(ans, expected, check_dtypes=False) fun = jax.jit(fun) ans = jax.vmap(fun, axis_name='i')(np.array([0, 0, 0, 0], dtype='int32')) expected = np.array([0, 2, 2, 4]) self.assertAllClose(ans, expected, check_dtypes=False) ans = jax.vmap(lambda _, x: fun(x), axis_name='i', in_axes=(0, None))( np.array([0, 0, 0, 0]), 0) expected = np.array([0, 2, 2, 4], dtype='int32') self.assertAllClose(ans, expected, check_dtypes=False) def testWhileLoopBatchedWithConstBody(self): def f(x): def body_fn(_): return jnp.asarray(0., dtype=jnp.float32) def cond_fn(_): return jnp.logical_not(False) == False return jax.lax.while_loop(cond_fn, body_fn, x) x = jnp.arange(5, dtype=jnp.float32) self.assertAllClose(jax.vmap(f)(x), x) def testWhileLoopCondConstsBatched(self): def fun(x, y): return lax.while_loop(lambda x: x < y, lambda x: x + 2, x) ans = jax.vmap(fun, in_axes=(None, 0))(0, np.array([2, 3])) expected = np.array([2, 4]) self.assertAllClose(ans, expected, check_dtypes=False) def testWhileLoopBodyConstsBatched(self): def fun(x, y): return lax.while_loop(lambda x: x < 3, lambda x: x + y, x) ans = jax.vmap(fun, in_axes=(None, 0))(0, jnp.array([2, 3])) expected = np.array([4, 3]) self.assertAllClose(ans, expected, check_dtypes=False) def testWhileLoopTupleBatched(self): def cond_fun(loop_carry): x, y = loop_carry return x + y < 5 def body_fun(loop_carry): x, y = loop_carry x = x + 1 return x, y def fun(x, y): return lax.while_loop(cond_fun, body_fun, (x, y)) ans = jax.vmap(fun)(np.array([0, 0]), np.array([1, 2])) expected = (np.array([4, 3]), np.array([1, 2])) self.assertAllClose(ans, expected, check_dtypes=False) def test_issue_3204(self): # Error during XLA code generation for vmap of nested loops def test(a, b): val = 0 i = 0 j = 0 condfun_1 = lambda inp: inp[1] < a + 1 condfun_2 = lambda inp: inp[2] < b + 1 def bodyfun_1(inp): val, i, j = inp j = 0 def bodyfun_2(inp): val, i, j = inp val += i + j j += 1 return (val, i, j) result = lax.while_loop(condfun_2, bodyfun_2, (val, i, j)) val = result[0] i += 1 return (val, i, j) result = lax.while_loop(condfun_1, bodyfun_1, (val, i, j)) return result[0] arr = np.arange(5) vmap_test = jax.vmap(test, (0, 0)) vmap_test(arr, arr) def testForiLoopErrors(self): """Test typing error messages for while.""" with self.assertRaisesRegex( TypeError, "arguments to fori_loop must have equal types"): lax.fori_loop(np.int16(0), jnp.int32(10), (lambda i, c: c), jnp.float32(7)) def testForiLoopBatched(self): def body_fun(i, loop_carry): x, y = loop_carry x = x + 1 y = y + 2 return x, y def fun(x): return lax.fori_loop(0, 10, body_fun, (x, 0)) ans = jax.vmap(fun)(np.array([0, 1])) expected = (np.array([10, 11]), np.array([20, 20])) self.assertAllClose(ans, expected, check_dtypes=False) def testForiLoopBatchedIssue1190(self): cond_fun = lambda carry: carry[0] < 4 body_fun = lambda carry: (carry[0] + 1, carry[1] + 1) f = lambda x: lax.while_loop(cond_fun, body_fun, (0, x)) jaxpr = jax.make_jaxpr(jax.vmap(f))(jnp.arange(3)) eqn = jaxpr.jaxpr.eqns[0] self.assertIs(eqn.primitive, lax.while_p) self.assertEqual(eqn.params['cond_jaxpr'].in_avals[0].shape, ()) def testForiLoopBasic(self): def body_fun(i, tot): return lax.add(tot, i) def count(num): return lax.fori_loop(0, num, body_fun, 0) self.assertEqual(count(2), 1) self.assertEqual(count(3), 3) self.assertEqual(count(4), 6) for args_maker in [lambda: [2], lambda: [3], lambda: [4]]: self._CompileAndCheck(count, args_maker) def testForiLoopClosure(self): def count(num): def body_fun(i, tot): return lax.add(num, lax.add(tot, i)) return lax.fori_loop(0, num, body_fun, 0) cfun = jax.jit(count) self.assertEqual(count(2), 1 + 2**2) self.assertEqual(count(2), cfun(2)) self.assertEqual(count(3), 3 + 3**2) self.assertEqual(count(3), cfun(3)) self.assertEqual(count(4), 6 + 4**2) self.assertEqual(count(4), cfun(4)) def testForiLoopTupleState(self): def sum_first_n(arr, num): def body_fun(i, state): arr, total = state arr_i = lax.dynamic_index_in_dim(arr, i, 0, False) return (arr, lax.add(total, arr_i)) init_val = (arr, 0.) _, total = lax.fori_loop(0, lax.min(arr.shape[0], num), body_fun, init_val) return total cfun = jax.jit(sum_first_n) x = self.rng().randn(10).astype(jnp.float_) for num in [0, 5, 10, 15]: self.assertAllClose(sum_first_n(x, num), np.sum(x[:num]), check_dtypes=False) self.assertAllClose(cfun(x, num), np.sum(x[:num]), check_dtypes=False) self.assertAllClose(cfun(x, num), np.sum(x[:num]), check_dtypes=False) def testForiLoopDictState(self): def sum_first_n(arr, num): def body_fun(i, state): arr, total = state['arr'], state['total'] arr_i = lax.dynamic_index_in_dim(arr, i, 0, False) return {'arr': arr, 'total': lax.add(total, arr_i)} init_val = {'arr': arr, 'total': 0.} out_val = lax.fori_loop(0, lax.min(arr.shape[0], num), body_fun, init_val) return out_val['total'] cfun = jax.jit(sum_first_n) x = self.rng().randn(10).astype(jnp.float_) for num in [0, 5, 10, 15]: self.assertAllClose(sum_first_n(x, num), np.sum(x[:num]), check_dtypes=False) self.assertAllClose(cfun(x, num), np.sum(x[:num]), check_dtypes=False) self.assertAllClose(cfun(x, num), np.sum(x[:num]), check_dtypes=False) def testForiLoopEmptyTupleInState(self): def sum_first_n(arr, num): def body_fun(i, state): arr, total, _ = state arr_i = lax.dynamic_index_in_dim(arr, i, 0, False) return (arr, lax.add(total, arr_i), ()) init_val = (arr, 0., ()) _, tot, _ = lax.fori_loop(0, lax.min(arr.shape[0], num), body_fun, init_val) return tot cfun = jax.jit(sum_first_n) x = self.rng().randn(10).astype(jnp.float_) for num in [0, 5, 10, 15]: self.assertAllClose(sum_first_n(x, num), np.sum(x[:num]), check_dtypes=False) self.assertAllClose(cfun(x, num), np.sum(x[:num]), check_dtypes=False) self.assertAllClose(cfun(x, num), np.sum(x[:num]), check_dtypes=False) def testForiLoopIssue8152(self): y = lax.fori_loop(lower=0, upper=0, body_fun=lambda x, i: x + i, init_val=1.) self.assertAllClose(y, 1., check_dtypes=False) # trivial fori_loop should work - even when jit is disabled with jax.disable_jit(): y = lax.fori_loop(lower=0, upper=0, body_fun=lambda x, i: x + i, init_val=1.) self.assertAllClose(y, 1., check_dtypes=False) # scan with length 0 should work with jit, but raise an error without def should_raise_wo_jit(): carry, out = lax.scan(lambda c, x: (c + x, x), 0., np.array([])) return carry self.assertAllClose(should_raise_wo_jit(), 0., check_dtypes=False) with jax.disable_jit(): self.assertRaises(ValueError, should_raise_wo_jit) def testCond(self): def fun(x): if x < 3: return (x, x) else: y = lax.mul(2, x) return y, lax.mul(2, y) @jax.jit def cfun(x): def false_fun(x): y = lax.mul(2, x) return y, lax.mul(2, y) return lax.cond(lax.lt(x, 3), lambda x: (x, x), false_fun, x) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(0), (0, 0)) self.assertEqual(fun(1), cfun(1)) self.assertEqual(fun(1), (1, 1)) self.assertEqual(fun(2), cfun(2)) self.assertEqual(fun(2), (2, 2)) self.assertEqual(fun(3), cfun(3)) self.assertEqual(fun(3), (6, 12)) self.assertEqual(fun(4), cfun(4)) self.assertEqual(fun(4), (8, 16)) def testCondPredIsNone(self): # see https://github.com/google/jax/issues/11574 def f(pred, x): return lax.cond(pred, lambda x: x + 1, lambda x: x + 2, x) self.assertRaisesRegex(TypeError, "cond predicate is None", lambda: f(None, 1.)) self.assertRaisesRegex(TypeError, "cond predicate is None", lambda: jax.jit(f)(None, 1.)) def testCondTwoOperands(self): # see https://github.com/google/jax/issues/8469 add, mul = lax.add, lax.mul def fun(x): return add(x, x) if x == 0 else mul(x, x) def cfun(x): return lax.cond(x == 0, add, mul, x, x) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(1), cfun(1)) cfun = jax.jit(cfun) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(1), cfun(1)) def testCondThreeOperands(self): add = lambda x, y, z: x + y + z mul = lambda x, y, z: x * y * z def fun(x): return add(x, x, x) if x == 0 else mul(x, x, x) def cfun(x): return lax.cond(x == 0, add, mul, x, x, x) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(1), cfun(1)) cfun = jax.jit(cfun) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(1), cfun(1)) def testSwitch(self): def branch(x): y = lax.mul(2, x) return y, lax.mul(2, y) branches = [lambda x: (x, x), branch, lambda x: (x, -x)] def fun(x): if x <= 0: return branches[0](x) elif x == 1: return branches[1](x) else: return branches[2](x) def cfun(x): return lax.switch(x, branches, x) self.assertEqual(fun(-1), cfun(-1)) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(1), cfun(1)) self.assertEqual(fun(2), cfun(2)) self.assertEqual(fun(3), cfun(3)) cfun = jax.jit(cfun) self.assertEqual(fun(-1), cfun(-1)) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(1), cfun(1)) self.assertEqual(fun(2), cfun(2)) self.assertEqual(fun(3), cfun(3)) def testSwitchMultiOperands(self): branches = [lax.add, lax.mul] def fun(x): i = 0 if x <= 0 else 1 return branches[i](x, x) def cfun(x): return lax.switch(x, branches, x, x) self.assertEqual(fun(-1), cfun(-1)) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(1), cfun(1)) self.assertEqual(fun(2), cfun(2)) cfun = jax.jit(cfun) self.assertEqual(fun(-1), cfun(-1)) self.assertEqual(fun(0), cfun(0)) self.assertEqual(fun(1), cfun(1)) self.assertEqual(fun(2), cfun(2)) def testSwitchResidualsMerge(self): def get_conds(fun): jaxpr = jax.make_jaxpr(jax.grad(fun))(0., 0) return [eqn for eqn in jaxpr.jaxpr.eqns if eqn.primitive.name == 'cond'] def branch_invars_len(cond_eqn): lens = [len(jaxpr.jaxpr.invars) for jaxpr in cond_eqn.params['branches']] assert len(set(lens)) == 1 return lens[0] def branch_outvars_len(cond_eqn): lens = [len(jaxpr.jaxpr.outvars) for jaxpr in cond_eqn.params['branches']] assert len(set(lens)) == 1 return lens[0] branches1 = [ lambda x: jnp.sin(x), lambda x: jnp.cos(x)] # branch residuals overlap, should be reused branches2 = branches1 + [ lambda x: jnp.sinh(x)] # another overlapping residual, expect reuse branches3 = branches2 + [ lambda x: jnp.sin(x) + jnp.cos(x)] # requires one more residual slot def fun1(x, i): return lax.switch(i + 1, branches1, x) def fun2(x, i): return lax.switch(i + 1, branches2, x) def fun3(x, i): return lax.switch(i + 1, branches3, x) fwd1, bwd1 = get_conds(fun1) fwd2, bwd2 = get_conds(fun2) fwd3, bwd3 = get_conds(fun3) fwd1_num_out = branch_outvars_len(fwd1) fwd2_num_out = branch_outvars_len(fwd2) fwd3_num_out = branch_outvars_len(fwd3) assert fwd1_num_out == fwd2_num_out assert fwd3_num_out == fwd2_num_out + 1 bwd1_num_in = branch_invars_len(bwd1) bwd2_num_in = branch_invars_len(bwd2) bwd3_num_in = branch_invars_len(bwd3) assert bwd1_num_in == bwd2_num_in assert bwd3_num_in == bwd2_num_in + 1 def testOneBranchSwitch(self): branch = lambda x: -x f = lambda i, x: lax.switch(i, [branch], x) x = 7. self.assertEqual(f(-1, x), branch(x)) self.assertEqual(f(0, x), branch(x)) self.assertEqual(f(1, x), branch(x)) cf = jax.jit(f) self.assertEqual(cf(-1, x), branch(x)) self.assertEqual(cf(0, x), branch(x)) self.assertEqual(cf(1, x), branch(x)) cf = jax.jit(f, static_argnums=0) self.assertEqual(cf(-1, x), branch(x)) self.assertEqual(cf(0, x), branch(x)) self.assertEqual(cf(1, x), branch(x)) def testIssue1379(self): def fun(pred): return lax.cond(pred, lambda x: (True, x), lambda x: (False, x), pred) @jax.jit def cfun(pred): return fun(pred) self.assertEqual(fun(0), cfun(0), (False,0)) self.assertEqual(fun(0.), cfun(0.), (False,0.)) self.assertEqual(fun(1), cfun(1), (True,1)) self.assertEqual(fun(1.), cfun(1.), (True,1.)) # test that proper errors are raised for wrong types for pred in ["abc", [], [1,2]]: for f in [fun, cfun]: self.assertRaises(TypeError, f, pred) @parameterized.named_parameters( {"testcase_name": f"_{name}", "cond": cond} for cond, name in COND_IMPLS) def testNestedCond(self, cond): def fun(x): if x < 2: return lax.mul(2, x) else: if x < 5: return lax.mul(3, x) else: return lax.mul(4, x) @jax.jit def cfun(x): return cond( lax.lt(x, 2), lambda x: lax.mul(2, x), lambda x: cond(lax.lt(x, 5), x, lambda x: lax.mul(3, x), 4, lambda y: lax.mul(y, x)), x) self.assertEqual(cfun(1), 2) self.assertEqual(cfun(3), 9) self.assertEqual(cfun(6), 24) self.assertEqual(cfun(1), fun(1)) self.assertEqual(cfun(3), fun(3)) self.assertEqual(cfun(6), fun(6)) def testCondTypeErrors(self): """Test typing error messages for cond.""" with self.assertRaisesRegex(TypeError, re.escape("Pred type must be either boolean or number, got ")): lax.cond("foo", lambda top: 2., lambda fop: 3., 1.) with self.assertRaisesRegex(TypeError, re.escape("Pred must be a scalar, got (1.0, 1.0) of type ")): lax.cond((1., 1.), lambda top: 2., lambda fop: 3., 1.) with self.assertRaisesRegex(TypeError, re.escape("true_fun and false_fun output must have same type structure, " f"got {tree_util.tree_structure(2.)} and {tree_util.tree_structure((3., 3.))}.")): lax.cond(True, lambda top: 2., lambda fop: (3., 3.), 1.) with self.assertRaisesRegex( TypeError, "true_fun and false_fun output must have identical types, got\n" r"DIFFERENT ShapedArray\(float32\[1\]\) vs. " r"ShapedArray\(float32\[\].*\)."): lax.cond(True, lambda top: jnp.array([1.], jnp.float32), lambda fop: jnp.float32(1.), 1.) def testSwitchErrors(self): """Test typing error messages for switch.""" with self.assertRaisesRegex(TypeError, re.escape("Index type must be an integer, got 0, f_1, f_2, x) def g(x): return jnp.where(x > 0, f_1(x), f_2(x)) x = jnp.linspace(-1, 1, 20) ans = jax.vmap(jax.grad(f))(x) expected = jax.vmap(jax.grad(g))(x) self.assertAllClose(ans, expected, check_dtypes=False) def testIssue1263(self): def f(rng, x): cond = random.bernoulli(rng) return lax.cond(cond, x, lambda x: x, jnp.abs(x) - 1., lambda x: x) def body_fn(i, state): rng, x = state key, subkey = random.split(rng) return key, f(subkey, x) def g(rng, x): return lax.fori_loop(0, 10, body_fn, (rng, x)) jax.vmap(g)(random.split(random.PRNGKey(0), 3), jnp.ones((3, 4))) def testIssue514(self): # just check this doesn't crash lax.cond(True, (0, 0), lambda x: (x[0], 0), (1, 1), lambda x: x) def testIssue649(self): from jax import lax def body(x): a, b = x return (7, b + 1) def cond(x): a, b = x return b < 10 out = lax.while_loop(cond, body, (33, 4)) self.assertEqual(out, (7, 10)) @parameterized.named_parameters( {"testcase_name": "_jit_scan={}_jit_f={}_impl={}".format( jit_scan, jit_f, scan_name), "jit_scan": jit_scan, "jit_f": jit_f, "scan": scan_impl, "impl_name": scan_name} for jit_scan in [False, True] for jit_f in [False, True] for scan_impl, scan_name in SCAN_IMPLS_WITH_FOR) def testScanImpl(self, jit_scan, jit_f, scan, impl_name): rng = self.rng() d = rng.randn(2) def f(c, a): assert a.shape == (3,) assert c.shape == (4,) b = jnp.cos(jnp.sum(jnp.sin(a)) + jnp.sum(jnp.cos(c)) + jnp.sum(jnp.tan(d))) c = jnp.sin(c * b) assert b.shape == () return c, b if jit_f: f = jax.jit(f) if jit_scan: scan = jax.jit(scan, static_argnums=(0,)) as_ = rng.randn(5, 3) c = rng.randn(4) ans = scan(f, c, as_) expected = scan_reference(f, c, as_) rtol = {np.float64: 1.4e-15} atol = {np.float64: 8e-15} if impl_name == "for": rtol[np.float32] = 8e-5 atol[np.float32] = 3e-5 self.assertAllClose( ans, expected, check_dtypes=False, rtol=rtol, atol=atol) @parameterized.named_parameters( {"testcase_name": "_jit_scan={}_jit_f={}_impl={}".format( jit_scan, jit_f, scan_name), "jit_scan": jit_scan, "jit_f": jit_f, "scan": scan_impl} for jit_scan in [False, True] for jit_f in [False, True] for scan_impl, scan_name in SCAN_IMPLS_WITH_FOR) def testScanJVP(self, jit_scan, jit_f, scan): rng = self.rng() d = rng.randn(2) def f(c, a): assert a.shape == (3,) assert c.shape == (4,) b = jnp.cos(jnp.sum(jnp.sin(a)) + jnp.sum(jnp.cos(c)) + jnp.sum(jnp.tan(d))) c = jnp.sin(c * b) assert b.shape == () return c, b if jit_f: f = jax.jit(f) if jit_scan: scan = jax.jit(scan, static_argnums=(0,)) as_ = rng.randn(5, 3) c = rng.randn(4) ans = jax.jvp( lambda c, as_: scan(f, c, as_), (c, as_), (c, as_)) expected = jax.jvp(lambda c, as_: scan_reference(f, c, as_), (c, as_), (c, as_)) tol = {np.float64: 1e-12, np.float32: 1e-4} self.assertAllClose(ans, expected, check_dtypes=False, rtol=tol, atol=tol) jtu.check_grads(partial(scan, f), (c, as_), order=2, modes=["fwd"]) @parameterized.named_parameters( {"testcase_name": "_jit_scan={}_jit_f={}_impl={}".format( jit_scan, jit_f, scan_name), "jit_scan": jit_scan, "jit_f": jit_f, "scan": scan_impl} for jit_scan in [False, True] for jit_f in [False, True] for scan_impl, scan_name in SCAN_IMPLS_WITH_FOR) def testScanLinearize(self, jit_scan, jit_f, scan): rng = self.rng() d = rng.randn(2) def f(c, a): assert a.shape == (3,) assert c.shape == (4,) b = jnp.cos(jnp.sum(jnp.sin(a)) + jnp.sum(jnp.cos(c)) + jnp.sum(jnp.tan(d))) c = jnp.sin(c * b) assert b.shape == () return c, b if jit_f: f = jax.jit(f) if jit_scan: scan = jax.jit(scan, static_argnums=(0,)) as_ = rng.randn(5, 3) c = rng.randn(4) if scan is scan_with_new_checkpoint2: rtol = {np.float64: 1e-12, np.float32: 1e-4} else: rtol = {np.float64: 1e-14, np.float32: 1e-4} ans = jax.linearize(lambda c, as_: scan(f, c, as_), c, as_)[1](c, as_) expected = jax.linearize(lambda c, as_: scan_reference(f, c, as_), c, as_)[1](c, as_) self.assertAllClose(ans, expected, check_dtypes=False, rtol=rtol) @parameterized.named_parameters( {"testcase_name": "_jit_scan={}_jit_f={}_impl={}".format( jit_scan, jit_f, scan_name), "jit_scan": jit_scan, "jit_f": jit_f, "scan": scan_impl} for jit_scan in [False, True] for jit_f in [False, True] for scan_impl, scan_name in SCAN_IMPLS) @jtu.skip_on_flag("jax_skip_slow_tests", True) def testScanGrad(self, jit_scan, jit_f, scan): rng = self.rng() d = rng.randn(2) def f(c, a): assert a.shape == (3,) assert c.shape == (4,) b = jnp.sum(jnp.sin(a)) + jnp.sum(jnp.sin(c)) + jnp.sum(jnp.sin(d)) c = jnp.sin(c * b) assert b.shape == () return c, b if jit_f: f = jax.jit(f) if jit_scan: scan = jax.jit(scan, static_argnums=(0,)) as_ = rng.randn(5, 3) c = rng.randn(4) ans = jax.grad(lambda c, as_: list( scan(f, c, as_))[0].sum())(c, as_) expected = jax.grad(lambda c, as_: list(scan_reference(f, c, as_))[0].sum())(c, as_) if scan is scan_with_new_checkpoint: rtol = {np.float32: 5e-5, np.float64: 1e-13} else: rtol = {np.float32: 2e-5, np.float64: 1e-13} self.assertAllClose(ans, expected, check_dtypes=False, rtol=rtol, atol=1e-5) rtol = 5e-3 if scan is not scan_with_new_checkpoint2 else 5e-2 jtu.check_grads(partial(scan, f), (c, as_), order=2, modes=["rev"], atol=1e-3, rtol=rtol) @jtu.skip_on_devices("tpu") # TPU lacks precision for this test. @jtu.skip_on_flag("jax_skip_slow_tests", True) def testScanRnn(self): r = self.rng() n_in = 4 n_hid = 2 n_out = 1 length = 3 W_trans = r.randn(n_hid, n_hid + n_in).astype(jnp.float_) W_out = r.randn(n_out, n_hid + n_in).astype(jnp.float_) params = W_trans, W_out inputs = r.randn(length, n_in).astype(jnp.float_) targets = r.randn(length, n_out).astype(jnp.float_) def step(params, state, input): W_trans, W_out = params stacked = jnp.concatenate([state, input]) output = jnp.tanh(jnp.dot(W_out, stacked)) next_state = jnp.tanh(jnp.dot(W_trans, stacked)) return next_state, output def rnn(params, inputs): init_state = jnp.zeros(n_hid) _, outputs = lax.scan(partial(step, params), init_state, inputs) return outputs @jax.jit def loss(params, inputs, targets): predictions = rnn(params, inputs) return jnp.sum((predictions - targets)**2) # evaluation doesn't crash loss(params, inputs, targets) # jvp evaluation doesn't crash jax.jvp(lambda params: loss(params, inputs, targets), (params,), (params,)) # jvp numerical check passes jtu.check_grads(loss, (params, inputs, targets), order=2, modes=["fwd"], rtol={np.float32: 2e-2, np.float64: 1e-6}) # linearize works _, expected = jax.jvp(loss, (params, inputs, targets), (params, inputs, targets)) _, linfun = jax.linearize(loss, params, inputs, targets) ans = linfun(params, inputs, targets) self.assertAllClose(ans, expected, check_dtypes=False) # gradient evaluation doesn't crash jax.grad(loss)(params, inputs, targets) # gradient check passes jtu.check_grads(loss, (params, inputs, targets), order=2, rtol=2e-2) # we can vmap to batch things batch_size = 7 batched_inputs = r.randn(batch_size, length, n_in).astype(jnp.float_) batched_targets = r.randn(batch_size, length, n_out).astype(jnp.float_) batched_loss = jax.vmap(lambda x, y: loss(params, x, y)) losses = batched_loss(batched_inputs, batched_targets) expected = np.stack(list(map(lambda x, y: loss(params, x, y), batched_inputs, batched_targets))) self.assertAllClose(losses, expected, check_dtypes=False, rtol=1e-2) @parameterized.named_parameters( {"testcase_name": "_impl={}".format(scan_name), "scan": scan_impl} for scan_impl, scan_name in SCAN_IMPLS) def testIssue711(self, scan): # Tests reverse-mode differentiation through a scan for which the scanned # function also involves reverse-mode differentiation. # See https://github.com/google/jax/issues/711 def harmonic_bond(conf, params): return jnp.sum(conf * params) def minimize_structure(test_params): energy_fn = partial(harmonic_bond, params=test_params) def apply_carry(carry, _): i, x = carry new_x = x - 0.1 * jax.grad(energy_fn)(x) new_carry = (i+1, new_x) return new_carry, _ x0 = jnp.array([1., 2., 3.]) carry_final, _ = scan(apply_carry, (0, x0), jnp.zeros((75, 0))) _, x_final = carry_final return x_final initial_params = 0.5 minimize_structure(initial_params) # doesn't crash def loss(test_params): x_final = minimize_structure(test_params) return jnp.sum(jnp.sin(1.0 - x_final)) jax.grad(loss)(0.25) # doesn't crash def testIssue744(self): Point = collections.namedtuple('Point', ['x', 'y']) p0 = Point(x=jnp.array(1), y=jnp.array(2)) def plus_one(p, iter_idx): return Point(p.x+1, p.y+1), iter_idx self.assertRaisesRegex( ValueError, 'scan got value with no leading axis to scan over.*', lambda: lax.scan(plus_one, p0, list(range(5)))) def testScanTypeErrors(self): """Test typing error messages for scan.""" a = jnp.arange(5) # Body output not a tuple with self.assertRaisesRegex(TypeError, re.escape("scan body output must be a pair, got ShapedArray(float32[]).")): lax.scan(lambda c, x: np.float32(0.), 0, a) with self.assertRaisesRegex(TypeError, re.escape("scan carry output and input must have same type structure, " f"got {tree_util.tree_structure((0, 0, 0,))} " f"and {tree_util.tree_structure((1, (2, 3)))}")): lax.scan(lambda c, x: ((0, 0, 0), x), (1, (2, 3)), a) with self.assertRaisesRegex(TypeError, re.escape("scan carry output and input must have same type structure, " f"got {tree_util.tree_structure(a)} and {tree_util.tree_structure(None)}.")): lax.scan(lambda c, x: (0, x), None, a) with self.assertRaisesWithLiteralMatch( TypeError, "scan carry output and input must have identical types, got\n" "DIFFERENT ShapedArray(int32[]) vs. ShapedArray(float32[])."): lax.scan(lambda c, x: (np.int32(0), x), np.float32(1.0), a) with self.assertRaisesRegex(TypeError, re.escape("scan carry output and input must have same type structure, " f"got {tree_util.tree_structure(a)} and {tree_util.tree_structure((1, 2))}.")): lax.scan(lambda c, x: (0, x), (1, 2), a) @parameterized.named_parameters( {"testcase_name": f"_{scan_name}", "scan": scan_impl} for scan_impl, scan_name in SCAN_IMPLS) def testScanHigherOrderDifferentiation(self, scan): d = 0.75 def f(c, a): b = jnp.sin(c * jnp.sum(jnp.cos(d * a))) c = 0.9 * jnp.cos(d * jnp.sum(jnp.sin(c * a))) return c, b as_ = jnp.arange(6.).reshape((3, 2)) c = 1. jtu.check_grads(lambda c, as_: scan(f, c, as_), (c, as_), modes=["rev"], order=2, rtol={np.float32: 6e-3}) @parameterized.named_parameters( {"testcase_name": "_jit_scan={}_jit_f={}_in_axes={}_impl={}".format( jit_scan, jit_f, in_axes, scan_name), "jit_scan": jit_scan, "jit_f": jit_f, "in_axes": in_axes, "scan": scan_impl} for jit_scan in [False, True] for jit_f in [False, True] for scan_impl, scan_name in SCAN_IMPLS for in_axes in itertools.product([None, 0, 1], [None, 0, 1, 2]) if in_axes != (None, None)) def testScanVmap(self, jit_scan, jit_f, in_axes, scan): rng = self.rng() d = rng.randn(2) def f(c, a): assert a.shape == (3,) assert c.shape == (4,) b = jnp.cos(jnp.sum(jnp.sin(a)) + jnp.sum(jnp.cos(c)) + jnp.sum(jnp.tan(d))) c = jnp.sin(c * b) assert b.shape == () return c, b if jit_f: f = jax.jit(f) if jit_scan: scan = jax.jit(scan, static_argnums=(0,)) as_shape = [5, 3] c_shape = [4] c_bdim, as_bdim = in_axes if c_bdim is not None: c_shape.insert(c_bdim, 7) if as_bdim is not None: as_shape.insert(as_bdim, 7) as_ = rng.randn(*as_shape) c = rng.randn(*c_shape) ans = jax.vmap(lambda c, as_: scan(f, c, as_), in_axes)(c, as_) expected = jax.vmap(lambda c, as_: scan_reference(f, c, as_), in_axes)(c, as_) self.assertAllClose(ans, expected, check_dtypes=False, rtol=1e-5, atol=1e-5) def testScanVmapTuples(self): def f(c, a): a1, a2 = a c1, c2 = c b = jnp.sum(jnp.cos(a1)) * jnp.sum(jnp.tan(c2 * a2)) c = c1 * jnp.sin(jnp.sum(a1 * a2)), c2 * jnp.cos(jnp.sum(a1)) return c, b in_axes = (0, (1, 2)) r = self.rng() as_ = (r.randn(3, 7), r.randn(3, 4, 7)) c = (r.randn(7, 2), r.randn(7)) expected_c_out, expected_bs = [], [] for i in range(7): c_out, bs = lax.scan(f, (c[0][i], c[1][i]), (as_[0][:,i], as_[1][:,:,i])) expected_c_out.append(c_out) expected_bs.append(bs) expected_c_out_0, expected_c_out_1 = unzip2(expected_c_out) expected_c_out = (jnp.stack(expected_c_out_0), jnp.stack(expected_c_out_1)) expected_bs = jnp.stack(expected_bs) expected = expected_c_out, expected_bs ans = jax.vmap(lambda c, as_: lax.scan(f, c, as_), in_axes)(c, as_) self.assertAllClose(ans, expected, check_dtypes=False) @parameterized.named_parameters( {"testcase_name": "_impl={}".format(scan_name), "scan": scan_impl} for scan_impl, scan_name in SCAN_IMPLS) def testScanVmapFixpoint(self, scan): def f(carry_init): def scan_body(c, x): # The carry is a 4-tuple, the last element starts batched, # and the carry is shifted left at each iteration. return ((c[1], c[2], c[3], 0.), None) return scan(scan_body, (0., 1., 2., carry_init), jnp.zeros(2)) carry_init = jnp.array([3., 4., 5.]) carry_out, _ = jax.vmap(f)(carry_init) self.assertAllClose(carry_out[3], jnp.array([0., 0., 0.]), check_dtypes=False) self.assertAllClose(carry_out[2], jnp.array([0., 0., 0.]), check_dtypes = False) # After two shifts, we get the carry_init self.assertAllClose(carry_out[1], carry_init, check_dtypes=False) self.assertAllClose(carry_out[0], jnp.array([2., 2., 2.]), check_dtypes = False) def testIssue757(self): # code from https://github.com/google/jax/issues/757 def fn(a): return jnp.cos(a) def loop(val): iterations = 10 def apply_carry(x, i): return jax.grad(fn, argnums=(0,))(x)[0], i final_val, _ = lax.scan(apply_carry, val, jnp.arange(iterations)) return final_val arg = 0.5 jax.jit(jax.jacfwd(loop, argnums=(0,)))(arg) # doesn't crash def testIssue804(self): num_devices = jax.device_count() f = partial(lax.scan, lambda c, x: (c + lax.psum(x, "i") , c), 0.) jax.pmap(f, axis_name="i")(jnp.ones((num_devices, 4))) # doesn't crash def testMap(self): f = lambda x: x ** 2 xs = jnp.arange(10) expected = xs ** 2 actual = lax.map(f, xs) self.assertAllClose(actual, expected) def testMapEmpty(self): # https://github.com/google/jax/issues/2412 ans = lax.map(lambda x: x * x, jnp.array([])) expected = jnp.array([]) self.assertAllClose(ans, expected) def testCaching(self): def cond(x): assert python_should_be_executing return x < 5 def body(x): assert python_should_be_executing return x + 2 python_should_be_executing = True lax.while_loop(cond, body, 0) python_should_be_executing = False lax.while_loop(cond, body, 0) # This second caching test shows a different kind of caching that we haven't # implemented (but could!), namely that Python functions that are distinct # objects but are equivalent functions trigger cache hits. This kind of # caching could be salient when using lambda functions with control flow: # # lax.while_loop(lambda x: x < 5, lambda x: x + 2, 0) # lax.while_loop(lambda x: x < 5, lambda x: x + 2, 0) # # To get a cache hit on the second line we'd need to form a jaxpr and # compare them for equality (including the literals on identity). We could # implement that by adding a __hash__/__eq__ to core.Jaxpr and # core.ClosedJaxpr (see #1221). @unittest.skip("not implemented") def testCaching2(self): def cond(x): assert python_should_be_executing return x < 5 def body(x): assert python_should_be_executing return x + 2 python_should_be_executing = True lax.while_loop(cond, body, 0) def cond(x): assert python_should_be_executing return x < 5 def body(x): assert python_should_be_executing return x + 2 python_should_be_executing = False lax.while_loop(cond, body, 0) def test_caches_depend_on_axis_env(self): # https://github.com/google/jax/issues/9187 scanned_f = lambda _, __: (lax.psum(1, 'i'), None) f = lambda: lax.scan(scanned_f, 0, None, length=1)[0] ans = jax.vmap(f, axis_name='i', axis_size=2, out_axes=None)() self.assertEqual(ans, 2) ans = jax.vmap(f, axis_name='i', axis_size=3, out_axes=None)() self.assertEqual(ans, 3) def testWhileCondConstant(self): out = lax.while_loop(lambda _: False, lambda _: (), ()) # doesn't crash self.assertEqual(out, ()) @parameterized.named_parameters( {"testcase_name": "_jit_loop={}_jit_body={}_jit_cond={}".format( jit_loop, jit_body, jit_cond), "jit_loop": jit_loop, "jit_body": jit_body, "jit_cond": jit_cond} for jit_loop in [False, True] for jit_body in [False, True] for jit_cond in [False, True]) def testWhileJVP(self, jit_loop=True, jit_body=False, jit_cond=True): cond = lambda x: x[0, 2] <= 8 body = lambda x: x * x if jit_cond: cond = jax.jit(cond) if jit_body: body = jax.jit(body) loop = partial(lax.while_loop, cond, body) if jit_loop: loop = jax.jit(loop) loop_ref = partial(while_loop_reference, cond, body) x = jnp.arange(9.).reshape((3, 3)) ans = jax.jvp(loop, (x,), (x,)) expected = jax.jvp(loop_ref, (x,), (x,)) self.assertAllClose(ans, expected, check_dtypes=False) jtu.check_grads(loop, (x,), order=2, modes=["fwd"]) @parameterized.named_parameters( {"testcase_name": "_jit_loop={}_jit_body={}_jit_cond={}_impl={}".format( jit_loop, jit_body, jit_cond, while_name), "jit_loop": jit_loop, "jit_body": jit_body, "jit_cond": jit_cond, "while_loop": while_impl} for jit_loop in [False, True] for jit_body in [False, True] for jit_cond in [False, True] for while_impl, while_name in WHILE_LOOP_IMPLS) def testWhileLinearize(self, while_loop, jit_loop=True, jit_body=False, jit_cond=True): cond = lambda x: x[0, 2] <= 8 body = lambda x: x * x if jit_cond: cond = jax.jit(cond) if jit_body: body = jax.jit(body) loop = partial(while_loop, cond, body) if jit_loop: loop = jax.jit(loop) loop_ref = partial(while_loop_reference, cond, body) x = jnp.arange(9.).reshape((3, 3)) y, f_lin = jax.linearize(loop, x) ydot = f_lin(x) y_expected, ydot_expected = jax.jvp(loop_ref, (x,), (x,)) self.assertAllClose(y, y_expected, check_dtypes=False) self.assertAllClose(ydot, ydot_expected, check_dtypes=False) def testWhileJVPViaForiLoop(self): f = lambda x: lax.fori_loop(0, 3, lambda i, x: x * 2, x) self.assertAllClose(f(2.), 16., check_dtypes=False) self.assertAllClose(jax.jvp(f, (2.,), (1.,)), (16., 8.), check_dtypes=False) jtu.check_grads(f, (2.,), order=2, modes=["fwd"]) f = lambda x: lax.fori_loop(0, 3, lambda i, x: x * (i + 1), x) self.assertAllClose(f(2.), 12., check_dtypes=False) self.assertAllClose(jax.jvp(f, (2.,), (1.,)), (12., 6.), check_dtypes=False) jtu.check_grads(f, (2.,), order=2, modes=["fwd"]) def testWhileJVPWithGrowingNonzeroTangents(self): rng = self.rng() def cond(state): i, x, y, z = state return i < 2 def body(state): i, x, y, z = state y = x * x z = y * y return i + 1, x, y, z y, z = rng.randn(2), rng.randn(2) def loop(loop_impl, x): return loop_impl(cond, body, (0, x, y, z))[1] loop_lax = partial(loop, lax.while_loop) loop_ref = partial(loop, while_loop_reference) x = rng.randn(2) ans = jax.jvp(loop_lax, (x,), (x,)) expected = jax.jvp(loop_ref, (x,), (x,)) self.assertAllClose(ans, expected, check_dtypes=False) jtu.check_grads(loop_lax, (x,), order=2, modes=["fwd"]) def testStaticForiGrad(self): func = lambda x: lax.fori_loop(x, x + 2., lambda i, c: c, x) jax.grad(func)(1.) # doesn't crash jax.linearize(func, 1.) # doesn't crash @parameterized.named_parameters( dict(testcase_name=f"_loop={loop}", loop=loop) for loop in ["while", "fori_inside_cond", "fori_inside_scan"]) def testWhileGradError(self, loop: str = "fori_inside_scan"): # Raise error for vjp for loops if loop == "while": func = lambda x: lax.while_loop(lambda i: i < 5., lambda i: i + 1., x) elif loop == "fori_inside_jit": func = jax.jit(lambda x: lax.fori_loop(x, x + 2., lambda i, c: c, x)) elif loop == "fori_inside_cond": func = lambda x: lax.cond( True, x, lambda x: lax.fori_loop(x, x + 2., lambda i, c: c, x), 1., lambda x: x) elif loop == "fori_inside_scan": func = lambda x: lax.scan( lambda c, x: (lax.fori_loop(x, x + 2., lambda i, c1: c1 * c, x), None), x, np.ones(2))[0] else: assert False with self.assertRaisesRegex(ValueError, "Reverse-mode differentiation does not work for lax.while_loop"): jax.grad(func)(1.) jax.linearize(func, 1.) # Linearization works def testIssue1316(self): def f(carry, _): c, key = carry key, _ = random.split(key) return (c, key), () key = random.PRNGKey(0) jax.grad(lambda c: lax.scan(f, (c, key), np.ones(3))[0][0])(0.) # doesn't crash def testIssue1361(self): @jax.jit def jit_run_scan(x): def fun(carry, _): x, _ = carry return (2 * x, 0.), None (x, _), _ = lax.scan(fun, (x, 0.), jnp.arange(3)) return x jax.grad(lambda x: jit_run_scan(x))(0.) # doesn't crash def testIssue810(self): def loss(A): def step(x, i): return jnp.matmul(A, x), None init_x = jnp.zeros(A.shape[-1:]) last_x, _ = lax.scan(step, init_x, jnp.arange(10)) return jnp.sum(last_x) A = jnp.zeros((3, 3)) # The second DUS was unnecessarily replicating A across time. # We check XLA because _scan_impl is "underneath" the jaxpr language. s = str(jax.xla_computation(jax.grad(loss))(A).as_hlo_text()) assert s.count("dynamic-update-slice(") < 2 def testScanLengthArg(self): def arange(n): return lax.scan(lambda c, _: (c + 1, c), 0, None, length=n)[1] ans = arange(10) expected = np.arange(10) self.assertAllClose(ans, expected, check_dtypes=False) @ignore_jit_of_pmap_warning() def test_while_loop_of_pmap(self): # code from jsnoek@ def body(i, x): result = jax.pmap(lambda z: lax.psum(jnp.sin(z), 'i'), axis_name='i')(x) return result + x f_loop = lambda x: lax.fori_loop(0, 3, body, x) # noqa: F821 ans = f_loop(jnp.ones(jax.device_count())) del body, f_loop def body2(i, x): result = jnp.broadcast_to(jnp.sin(x).sum(), x.shape) return result + x g_loop = lambda x: lax.fori_loop(0, 3, body2, x) expected = g_loop(jnp.ones(jax.device_count())) self.assertAllClose(ans, expected, check_dtypes=False) @ignore_jit_of_pmap_warning() def test_while_loop_of_pmap_error_message(self): def body(i, x): result = jax.pmap(lambda z: lax.psum(jnp.sin(z), 'i'), axis_name='i')(x) return result + x f_loop = lambda x: lax.fori_loop(0, 3, body, x) too_big = 2 * jax.device_count() self.assertRaisesRegex( ValueError, re.escape( "compiling computation `scan` that requires {} " "replicas, but only {} XLA devices are available." .format(too_big, jax.device_count())), lambda: f_loop(jnp.ones(too_big))) @parameterized.named_parameters( {"testcase_name": f"_{scan_name}", "scan": scan_impl} for scan_impl, scan_name in SCAN_IMPLS_WITH_FOR) def test_scan_reverse(self, scan): def cumsum(x, reverse): return scan(lambda c, x: (c + x, c + x), 0, x, reverse=reverse)[1] x = np.array([3, 1, 4, 1, 5, 9]) self.assertAllClose(np.cumsum(x), cumsum(x, False), check_dtypes=False) self.assertAllClose(np.cumsum(x[::-1])[::-1], cumsum(x, True), check_dtypes=False) with jax.disable_jit(): self.assertAllClose(np.cumsum(x), cumsum(x, False), check_dtypes=False) with jax.disable_jit(): self.assertAllClose(np.cumsum(x[::-1])[::-1], cumsum(x, True), check_dtypes=False) def test_scan_unroll(self): d = jnp.ones(2) def f(c, a): assert a.shape == (3,) assert c.shape == (4,) b = jnp.cos(jnp.sum(jnp.sin(a)) + jnp.sum(jnp.cos(c)) + jnp.sum(jnp.tan(d))) c = jnp.sin(c * b) assert b.shape == () return c, b xs = jnp.ones((5, 3)) c = jnp.ones(4) scan = lambda c, xs: lax.scan(f, c, xs) scan_unrolled = lambda c, xs: lax.scan(f, c, xs, unroll=2) # jaxprs should be the same size self.assertEqual( len(str(jax.make_jaxpr(scan)(c, xs))), len(str(jax.make_jaxpr(scan_unrolled)(c, xs)))) # but HLO should grow due to unrolling self.assertLess( len(str(jax.xla_computation(scan)(c, xs).as_hlo_text())), len(str(jax.xla_computation(scan_unrolled)(c, xs).as_hlo_text()))) def test_disable_jit_cond_with_vmap(self): # https://github.com/google/jax/issues/3093 def fn(t): return lax.cond(t > 0, 0, lambda x: 0, 0, lambda x: 1) fn = jax.vmap(fn) with jax.disable_jit(): _ = fn(jnp.array([1])) # doesn't crash def test_disable_jit_while_loop_with_vmap(self): # https://github.com/google/jax/issues/2823 def trivial_while(y): return lax.while_loop(lambda x: x < 10.0, lambda x: x + 1.0, y) with jax.disable_jit(): jax.vmap(trivial_while)(jnp.array([3.0,4.0])) # doesn't crash def test_vmaps_of_while_loop(self): # https://github.com/google/jax/issues/3164 def f(x, n): return lax.fori_loop(0, n, lambda _, x: x + 1, x) x, n = jnp.arange(3), jnp.arange(4) jax.vmap(jax.vmap(f, (None, 0)), (0, None))(x, n) # doesn't crash @parameterized.named_parameters( {"testcase_name": f"_{shape}_axis={axis}", "shape": shape, "axis": axis} for shape in [ [0], [1], [2], [3], [5], [10], [1000], [2, 3], [7, 5], [5, 6, 7] ] for axis in range(-len(shape), len(shape) - 1)) def testAssociativeScanUnstructured(self, shape, axis): data = np.arange(np.prod(shape)).reshape(shape) + 7 expected = np.cumsum(data, axis=axis) result = lax.associative_scan(operator.add, data, axis=axis) self.assertAllClose(result, expected, check_dtypes=False) def testAssociativeScanUnstructured1000Reverse(self): data = np.arange(1000) + 32 expected = np.cumsum(data[::-1])[::-1] result = lax.associative_scan(operator.add, data, reverse=True) self.assertAllClose(result, expected, check_dtypes=False) def testAssociativeScanStructured3(self): pair = collections.namedtuple('pair', ('first', 'second')) data = pair(first=np.array([0., 1., 2.]), second=np.array([0., 10., 20.])) def fn(a, b): return pair(first=a.first + b.first, second=a.second + b.second) result = lax.associative_scan(fn, elems=data) self.assertAllClose(result.first, np.array([0., 1., 3.]), check_dtypes=False) self.assertAllClose(result.second, np.array([0., 10., 30.]), check_dtypes=False) def testAssociativeScanOfBools(self): x = jnp.array([False, True, True, True, False, True]) y = lax.associative_scan(lax.bitwise_xor, x) self.assertArraysEqual(np.array([False, True, False, True, True, False]), y) @parameterized.named_parameters({"testcase_name": f"_{shape}", "shape": shape} for shape in [2, 43, 100]) def testAssociativeScanSolvingRegressionTest(self, shape): # This test checks that the batching rule doesn't raise for a batch # sensitive function (solve). ms = np.repeat(np.eye(2).reshape(1, 2, 2), shape, axis=0) vs = np.ones((shape, 2)) @jax.vmap def fn(a, b): m1, v1 = a m2, v2 = b return m1 + m2, jsp.linalg.solve(m1, v2) + jsp.linalg.solve(m2, v1) _ = lax.associative_scan(fn, elems=(ms, vs)) def test_scan_typecheck_param(self): d = jnp.ones(2) def f(c, a): b = jnp.cos(jnp.sum(a) + jnp.sum(c) + jnp.sum(d)) c = jnp.sin(c * b) return c, b xs = jnp.ones((5, 3)) c = jnp.ones(4) scan_fun = lambda c, xs: lax.scan(f, c, xs) def new_jaxpr(): jaxpr = jax.make_jaxpr(scan_fun)(c, xs).jaxpr scan = next(eqn for eqn in jaxpr.eqns if eqn.primitive.name == 'scan') return jaxpr, scan jaxpr, eqn = new_jaxpr() eqn.params['reverse'] = 4 self.assertRaisesRegex( core.JaxprTypeError, re.escape('invalid scan param reverse of type int, bool required: 4'), lambda: core.check_jaxpr(jaxpr)) jaxpr, eqn = new_jaxpr() eqn.params['num_consts'] = -3 self.assertRaisesRegex( core.JaxprTypeError, re.escape('invalid scan param num_consts of type int, ' 'non-negative int required: -3'), lambda: core.check_jaxpr(jaxpr)) def test_cond_typecheck_param(self): def new_jaxpr(): jaxpr = jax.make_jaxpr( lambda x: lax.switch(0, [jnp.sin, jnp.cos], x))(1.).jaxpr cond = next(eqn for eqn in jaxpr.eqns if eqn.primitive.name == 'cond') return jaxpr, cond jaxpr, eqn = new_jaxpr() eqn.params['branches'] = (4, 2) self.assertRaisesRegex( core.JaxprTypeError, re.escape('invalid cond param branches of type tuple, ' 'tuple of ClosedJaxpr required: (4, 2)'), lambda: core.check_jaxpr(jaxpr)) jaxpr, eqn = new_jaxpr() eqn.params['linear'] = (4, 2) self.assertRaisesRegex( core.JaxprTypeError, re.escape('invalid cond param linear of type tuple, ' 'tuple of bool required: (4, 2)'), lambda: core.check_jaxpr(jaxpr)) jaxpr, eqn = new_jaxpr() eqn.params['linear'] = 'multi\nline' self.assertRaisesRegex( core.JaxprTypeError, r'invalid cond param linear of type str, ' r'tuple of bool required:\nmulti\nline', lambda: core.check_jaxpr(jaxpr)) def test_cond_transformation_rule_with_consts(self): # https://github.com/google/jax/pull/9731 @jax.custom_jvp def f(x): return x @f.defjvp def f_jvp(primals, tangents): (x,), (xdot,) = primals, tangents const = np.arange(3, dtype=x.dtype) return x * const, xdot * const g = lambda x: jax.lax.cond(True, f, lambda x: x, x) x = np.arange(3, dtype='float32') jax.jvp(g, (x,), (x,)) # doesn't crash @parameterized.named_parameters( {"testcase_name": f"_dtype={dtype.__name__}", "dtype": dtype} for dtype in jtu.dtypes.all_integer) def test_scan_init_weak_type(self, dtype): def func(carry, x): return carry + x, x init_weak = 0 # Python scalars are weakly-typed. x = jnp.ones(5, dtype=dtype) carry, result = lax.scan(func, init_weak, x) self.assertEqual(carry, x.sum(dtype=carry.dtype)) self.assertArraysEqual(result, x) @parameterized.named_parameters( {"testcase_name": f"_dtype={dtype.__name__}", "dtype": dtype} for dtype in jtu.dtypes.all_integer) def test_while_loop_init_weak_type(self, dtype): # This tests whether lax.while_loop can properly handle weakly-typed # initial values. def cond_fun(val): return val < 2 def body_fun(val): return val + increment increment = jnp.array(1, dtype=dtype) init_weak = 0 # Python scalars are weakly-typed. result = lax.while_loop(cond_fun, body_fun, init_weak) self.assertArraysEqual(result, jnp.full_like(increment, 2)) @parameterized.named_parameters( {"testcase_name": f"{suffix}", "remat": remat} for suffix, remat in [ ('', None), ('new_remat', new_checkpoint), ]) def test_scan_vjp_forwards_extensive_residuals(self, remat): # https://github.com/google/jax/issues/4510 def cumprod(x): s = jnp.ones((2, 32), jnp.float32) return lax.scan(lambda s, x: (x*s, s), s, x) if remat is not None: cumprod = remat(cumprod) rng = self.rng() x = jnp.asarray(rng.randn(32, 2, 32).astype('float32')) _, vjp_fun = jax.vjp(cumprod, x) # Need to spelunk into vjp_fun. This is fragile, and if it causes problems # just skip this test and make an issue for mattjj. *_, ext_res = vjp_fun.args[0].args[0] self.assertIs(ext_res, x) if remat is not None: # TODO(mattjj): make the numpy.ndarray test pass w/ remat raise unittest.SkipTest("new-remat-of-scan doesn't convert numpy.ndarray") x = rng.randn(32, 2, 32).astype('float32') # numpy.ndarray, not DeviceArray _, vjp_fun = jax.vjp(cumprod, x) *_, ext_res = vjp_fun.args[0].args[0] self.assertIsInstance(ext_res, jnp.DeviceArray) def test_scan_vmap_collectives(self): def scan_f(state, x): s = lax.psum(state, 'i') * x return state, s def scan(state, xs): return lax.scan(scan_f, state, xs) scan_v = jax.vmap(scan, in_axes=0, out_axes=0, axis_name='i') self.assertAllClose( scan_v(jnp.ones([1]), jnp.arange(5.).reshape((1, 5))), (jnp.array([1.]), jnp.array([[0., 1., 2., 3., 4.]]))) def test_xla_cpu_gpu_loop_cond_bug(self): # https://github.com/google/jax/issues/5900 def deriv(f): return lambda x, *args: jax.linearize(lambda x: f(x, *args), x)[1](1.0) def _while_loop(cond_fun, body_fun, init_val, max_iter): def _iter(val): next_val = body_fun(val) next_cond = True return next_val, next_cond def _fun(tup, _): val, cond = tup return jax.lax.cond(cond, _iter, lambda x: (x, False), val), _ init = (init_val, cond_fun(init_val)) return jax.lax.scan(_fun, init, None, length=max_iter)[0][0] def my_pow(x, y): def body_fun(val): return val * x def cond_fun(val): return True return _while_loop(cond_fun, body_fun, 1.0, y) self.assertAllClose(deriv(my_pow)(3.0, 1), 1.0, check_dtypes=False) def test_unexpected_tracer_error(self): with self.assertRaisesRegex(UnexpectedTracerError, "for while_loop"): lst = [] def side_effecting_body(val): lst.append(val) return val+1 lax.while_loop(lambda x: x < 2, side_effecting_body, 1) lst[0] += 1 with self.assertRaisesRegex(UnexpectedTracerError, "for scan"): lst = [] def side_effecting_scan(carry, val): lst.append(val) return carry, val+1 lax.scan(side_effecting_scan, None, jnp.ones((2, 2))) lst[0] += 1 def test_while_loop_fixed_point_with_nested_named_axes(self): def f(x): z = x + lax.axis_index('a').astype(x.dtype) y = x + lax.axis_index('b').astype(x.dtype) def cond(carry): i, x = carry return x < 5 def body(carry): i, x = carry return i + 1, x + lax.psum(y, 'b') return lax.while_loop(cond, body, (0, z))[1] maps.xmap(f, axis_sizes=dict(a=2, b=10), out_axes=(['a']), in_axes={})(1.) def test_while_loop_fixed_point_with_batched_pred_and_consts(self): def f(i, x): def cond(carry): i, x = carry return i < 5 def body(carry): i, z = carry # Close over const with batch dim = 1 return i + 1, z + x return lax.while_loop(cond, body, (i, jnp.ones(3)))[1] jax.vmap(f, in_axes=(0, 1))(jnp.arange(4), jnp.ones((3, 4))) def test_cond_ad_batched_unit(self): # see issue #9985 def cond_id(x): return lax.cond(x < 0., lambda x: x, lambda x: x, x) jax.vmap(jax.jacrev(lambda x: cond_id(cond_id(x))))(jnp.ones(1)) @parameterized.named_parameters( {"testcase_name": "impl={}".format(scan_name), "scan": scan_impl} for scan_impl, scan_name in SCAN_IMPLS) def test_scan_hoisting_consts(self, scan): A = jnp.arange(4.).reshape(2, 2) B = jnp.arange(4.).reshape(2, 2) + 1. def f(x): def body(c, _): c1, c2, c3 = c return (jnp.dot(A, c1), jnp.dot(B, c2), jnp.dot(jnp.sin(B), c3)), None init_carry = (x * jnp.ones(2), x * jnp.ones(2), x * jnp.ones(2)) (c1, c2, c3), _ = scan(body, init_carry, None, length=3) return jnp.sum(c1) + jnp.sum(c2) + jnp.sum(c3) jax.grad(f)(1.) # doesn't crash class ForLoopTest(jtu.JaxTestCase): def test_for_loop_impl_trivial(self): out = for_loop.for_loop(5, lambda i, _: None, None) self.assertEqual(out, None) def test_for_loop_can_write_to_ref(self): def body(_, x_ref): x_ref[()] = jnp.float32(1.) out = for_loop.for_loop(1, body, jnp.float32(0.)) self.assertEqual(out, 1.) def body2(i, x_ref): x_ref[()] = jnp.float32(i) out = for_loop.for_loop(2, body2, jnp.float32(0.)) self.assertEqual(out, 1.) def body3(i, x_ref): x_ref[()] = jnp.float32(i) * 2. out = for_loop.for_loop(2, body3, jnp.float32(0.)) self.assertEqual(out, 2.) def test_for_loop_can_write_to_multiple_refs(self): def body(_, refs): x_ref, y_ref = refs x_ref[()] = jnp.float32(1.) y_ref[()] = jnp.float32(2.) x, y = for_loop.for_loop(1, body, (jnp.float32(0.), jnp.float32(0.))) self.assertEqual(x, 1.) self.assertEqual(y, 2.) def test_for_loop_can_read_from_ref(self): def body(_, x_ref): x_ref[()] x = for_loop.for_loop(1, body, jnp.float32(0.)) self.assertEqual(x, 0.) def test_for_loop_can_read_from_and_write_to_ref(self): def body(_, x_ref): x = x_ref[()] x_ref[()] = x + jnp.float32(1.) x = for_loop.for_loop(5, body, jnp.float32(0.)) self.assertEqual(x, 5.) def test_for_loop_can_read_from_and_write_to_refs(self): def body2(_, refs): x_ref, y_ref = refs x = x_ref[()] y_ref[()] = x + 1. x_ref[()] = x + 1. x, y = for_loop.for_loop(5, body2, (0., 0.)) self.assertEqual(x, 5.) self.assertEqual(y, 5.) def test_for_loop_can_read_from_and_write_to_ref_slice(self): def body(i, x_ref): x = x_ref[i] x_ref[i] = x + jnp.float32(1.) x = for_loop.for_loop(4, body, jnp.ones(4, jnp.float32)) np.testing.assert_allclose(x, 2 * jnp.ones(4, jnp.float32)) def body2(i, x_ref): x = x_ref[i, 0] x_ref[i, 1] = x + x_ref[i, 1] x = for_loop.for_loop(4, body2, jnp.arange(8.).reshape((4, 2))) np.testing.assert_allclose( x, jnp.array([[0., 1.], [2., 5.], [4., 9.], [6., 13.]])) def test_for_loop_can_implement_cumsum(self): def cumsum(x): def body(i, refs): x_ref, accum_ref = refs accum_ref[i + 1] = accum_ref[i] + x_ref[i] accum = jnp.zeros(x.shape[0] + 1, x.dtype) _, accum_out = for_loop.for_loop(x.shape[0], body, (x, accum)) return accum_out[1:] key = jax.random.PRNGKey(0) x = jax.random.normal(key, (8,)) np.testing.assert_allclose(cumsum(x), jnp.cumsum(x)) def for_body_swap(i, refs): a_ref, b_ref = refs a, b = a_ref[i], b_ref[i] b_ref[i] = a a_ref[i] = b def swap_ref(a, b): return b, a def for_body_swap_swap(i, refs): for_body_swap(i, refs) for_body_swap(i, refs) swap_swap_ref = lambda a, b: (a, b) def for_body_sincos(i, refs): a_ref, b_ref = refs a = a_ref[i] b_ref[i] = jnp.sin(jnp.cos(a)) sincos_ref = lambda x, y: (x, jnp.sin(jnp.cos(x))) def for_body_sincostan(i, refs): a_ref, b_ref = refs a = a_ref[i] b_ref[i] = jnp.tan(jnp.sin(jnp.cos(a))) sincostan_ref = lambda x, y: (x, jnp.tan(jnp.sin(jnp.cos(x)))) def for_body_accum(i, refs): x_ref, accum_ref = refs accum_ref[i + 1] = accum_ref[i] + x_ref[i] def accum_ref(x, accum): for i in range(x.shape[0] - 1): accum = accum.at[i + 1].set(accum[i] + x[i]) return x, accum def for_body_sin_sq(i, refs): x_ref, y_ref = refs x = x_ref[i] y = x y_ref[i] = y y = y_ref[i] y_ref[i] = jnp.sin(y * y) sin_sq_ref = lambda x, y: (x, jnp.sin(x * x)) def for_body_reverse(i, refs): x_ref, y_ref = refs j = y_ref.shape[0] - i - 1 y_ref[i] = x_ref[j] reverse_ref = lambda x, y: (x, x[::-1]) identity = lambda x, y: (x, y) for_reference = for_loop.discharged_for_loop class ForLoopTransformationTest(jtu.JaxTestCase): @parameterized.named_parameters( {"testcase_name": "_jit_for={}_f={}_nsteps={}".format( jit_for, for_body_name, nsteps), "jit_for": jit_for, "f": for_body, "body_shapes": body_shapes, "ref": ref, "n": nsteps} for jit_for in [False, True] for for_body_name, for_body, ref, body_shapes, nsteps in [ ("swap", for_body_swap, swap_ref, [(4,), (4,)], 4), ("swap_swap", for_body_swap_swap, swap_swap_ref, [(4,), (4,)], 4), ("sincos", for_body_sincos, sincos_ref, [(4,), (4,)], 4), ("sincostan", for_body_sincostan, sincostan_ref, [(4,), (4,)], 4), ("accum", for_body_accum, accum_ref, [(4,), (4,)], 3), ("sin_sq", for_body_sin_sq, sin_sq_ref, [(4,), (4,)], 4), ("reverse", for_body_reverse, reverse_ref, [(4,), (4,)], 4), ]) def test_for_jvp(self, jit_for, f, ref, body_shapes, n): for_ = for_loop.for_loop rng = self.rng() args = [rng.randn(*s) for s in body_shapes] if jit_for: for_ = jax.jit(for_, static_argnums=(0, 1)) tol = {np.float64: 1e-12, np.float32: 1e-4} ans = jax.jvp( lambda *args: for_( n, f, args), args, args) ans_discharged = jax.jvp(lambda *args: for_reference(n, f, args), args, args) expected = jax.jvp(ref, args, args) self.assertAllClose(ans, ans_discharged, check_dtypes=True, rtol=tol, atol=tol) self.assertAllClose(ans, expected, check_dtypes=True, rtol=tol, atol=tol) jtu.check_grads(partial(for_, n, f), (args,), order=3, modes=["fwd"]) @parameterized.named_parameters( {"testcase_name": "_jit_for={}_f={}_nsteps={}".format( jit_for, for_body_name, nsteps), "jit_for": jit_for, "f": for_body, "body_shapes": body_shapes, "ref": ref, "n": nsteps} for jit_for in [False, True] for for_body_name, for_body, ref, body_shapes, nsteps in [ ("swap", for_body_swap, swap_ref, [(4,), (4,)], 4), ("swap_swap", for_body_swap_swap, swap_swap_ref, [(4,), (4,)], 4), ("sincos", for_body_sincos, sincos_ref, [(4,), (4,)], 4), ("sincostan", for_body_sincostan, sincostan_ref, [(4,), (4,)], 4), ("accum", for_body_accum, accum_ref, [(4,), (4,)], 3), ("sin_sq", for_body_sin_sq, sin_sq_ref, [(4,), (4,)], 4), ("reverse", for_body_reverse, reverse_ref, [(4,), (4,)], 4), ]) def test_for_linearize(self, jit_for, f, ref, body_shapes, n): for_ = for_loop.for_loop rng = self.rng() args = [rng.randn(*s) for s in body_shapes] if jit_for: for_ = jax.jit(for_, static_argnums=(0, 1)) tol = {np.float64: 1e-12, np.float32: 1e-4} ans = jax.linearize(lambda *args: for_( n, f, args), *args)[1](*args) ans_discharged = jax.linearize(lambda *args: for_reference(n, f, args), *args)[1](*args) expected = jax.linearize(ref, *args)[1](*args) self.assertAllClose(ans, ans_discharged, check_dtypes=True, rtol=tol, atol=tol) self.assertAllClose(ans, expected, check_dtypes=True, rtol=tol, atol=tol) def test_for_loop_invar(self): def f(x): s = jnp.ones((2, 32), x.dtype) def body(i, refs): x_ref, y_ref = refs y_ref[i] = s * x_ref[i] * jnp.cos(s) # We should save `s` and `jnp.cos(s)` as residuals and not broadcast # them. return for_loop.for_loop(x.shape[0], body, (x, jnp.zeros_like(x))) _, f_vjp = jax.linearize(f, jnp.ones((5, 2, 32))) jaxpr = jax.make_jaxpr(f_vjp)(jnp.ones((5, 2, 32))) consts = [v.aval for v in jaxpr.jaxpr.constvars if v.aval.shape == (2, 32)] self.assertLen(consts, 2) def test_for_loop_fixpoint_correctly_identifies_loop_varying_residuals(self): def body(i, refs): a_ref, b_ref, c_ref = refs a = a_ref[i] b = b_ref[()] x = jnp.sin(a) b_ref[()] = jnp.sin(b * x) c_ref[i] = x * b def f(a, b): c = jnp.zeros_like(a) _, b, c = for_loop.for_loop(5, body, (a, b, c)) return b, c a = jnp.arange(5.) + 1. b = 1. _, f_lin = jax.linearize(f, a, b) expected_tangents = f_lin(a, b) _, actual_tangents = jax.jvp(f, (a, b), (a, b)) np.testing.assert_allclose(actual_tangents[0], expected_tangents[0]) np.testing.assert_allclose(actual_tangents[1], expected_tangents[1]) if __name__ == '__main__': absltest.main(testLoader=jtu.JaxTestLoader())