author int64 658 755k | date stringlengths 19 19 | timezone int64 -46,800 43.2k | hash stringlengths 40 40 | message stringlengths 5 490 | mods list | language stringclasses 20 values | license stringclasses 3 values | repo stringlengths 5 68 | original_message stringlengths 12 491 |
|---|---|---|---|---|---|---|---|---|---|
260,474 | 06.12.2018 18:02:43 | 18,000 | ebc6cd1e03c5c0f4046d5cf77a8b1aff63f25b43 | Step through sizes in test case generation | [
{
"change_type": "MODIFY",
"old_path": "jax/test_util.py",
"new_path": "jax/test_util.py",
"diff": "@@ -310,24 +310,17 @@ def check_raises_regexp(thunk, err_type, pattern):\nassert re.match(pattern, str(e)), \"{}\\n\\n{}\\n\".format(e, pattern)\n-random.seed(0)\n-\n-\n-def take(xs):\n- return dedup(it.islice(xs, FLAGS.num_generated_cases))\n-\n-\n-def dedup(xs):\n- seen = set()\n- for x in xs:\n- name = x[\"testcase_name\"]\n- if name not in seen:\n- seen.add(name)\n- yield x\n+def cases_from_list(xs):\n+ assert False\n+random.seed(0) # TODO: consider managing prng state more carefully\n-def sample(xs):\n- return [random.choice(xs)]\n+def cases_from_gens(*gens):\n+ sizes = [1, 3, 10]\n+ cases_per_size = int(FLAGS.num_generated_cases / len(sizes))\n+ for size in sizes:\n+ for i in xrange(cases_per_size):\n+ yield ('_{}_{}'.format(size, i),) + tuple(gen(size) for gen in gens)\nclass JaxTestCase(parameterized.TestCase):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/generated_fun_test.py",
"new_path": "tests/generated_fun_test.py",
"diff": "@@ -213,16 +213,11 @@ def partial_argnums(f, args, dyn_argnums):\ndyn_args = [args[i] for i in dyn_argnums]\nreturn f_, dyn_args\n-counter = it.count()\n-fresh = counter.next\nclass GeneratedFunTest(jtu.JaxTestCase):\n\"\"\"Tests of transformations on randomly generated functions.\"\"\"\n- @parameterized.named_parameters(jtu.take(\n- {\"testcase_name\": str(fresh()),\n- \"fun\" : gen_fun_and_types(10) }\n- for _ in it.count()))\n+ @parameterized.named_parameters(jtu.cases_from_gens(gen_fun_and_types))\ndef testJitIsIdentity(self, fun):\nvals = gen_vals(fun.in_vars)\nfun = partial(eval_fun, fun)\n@@ -235,10 +230,7 @@ class GeneratedFunTest(jtu.JaxTestCase):\nprint fun\nraise\n- @parameterized.named_parameters(jtu.take(\n- {\"testcase_name\": str(fresh()),\n- \"fun\" : gen_fun_and_types(10) }\n- for _ in it.count()))\n+ @parameterized.named_parameters(jtu.cases_from_gens(gen_fun_and_types))\ndef testJVPMatchesFD(self, fun):\nvals = gen_vals(fun.in_vars)\ntangents = gen_vals(fun.in_vars)\n@@ -251,10 +243,7 @@ class GeneratedFunTest(jtu.JaxTestCase):\ncheck_all_close(ans1, ans2)\ncheck_all_close(deriv1, deriv2)\n- @parameterized.named_parameters(jtu.take(\n- {\"testcase_name\": str(fresh()),\n- \"fun\" : gen_fun_and_types(10) }\n- for _ in it.count()))\n+ @parameterized.named_parameters(jtu.cases_from_gens(gen_fun_and_types))\ndef vjp_matches_fd(self, fun):\nvals = gen_vals(fun.in_vars)\nin_tangents = gen_vals(fun.in_vars)\n"
}
] | Python | Apache License 2.0 | google/jax | Step through sizes in test case generation |
260,474 | 06.12.2018 18:30:59 | 18,000 | 8b88027df0fe4fa94c65bca81f28062f4d8f43f4 | Number of test cases settable with command-line flag | [
{
"change_type": "MODIFY",
"old_path": "jax/config.py",
"new_path": "jax/config.py",
"diff": "# See the License for the specific language governing permissions and\n# limitations under the License.\n+import sys\n+\nclass Config(object):\ndef __init__(self):\n@@ -75,6 +77,10 @@ class Config(object):\nfor name, _ in self.values.items():\nself.update(name, getattr(absl_flags.FLAGS, name))\n+ def parse_flags_with_absl(self):\n+ import absl.flags\n+ self.config_with_absl()\n+ absl.flags.FLAGS(sys.argv)\nclass NameSpace(object):\ndef __init__(self, getter):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/test_util.py",
"new_path": "jax/test_util.py",
"diff": "@@ -309,15 +309,16 @@ def check_raises_regexp(thunk, err_type, pattern):\nexcept err_type as e:\nassert re.match(pattern, str(e)), \"{}\\n\\n{}\\n\".format(e, pattern)\n+random.seed(0) # TODO: consider managing prng state more carefully\ndef cases_from_list(xs):\n- assert False\n-\n-random.seed(0) # TODO: consider managing prng state more carefully\n+ xs = list(xs)\n+ k = min(len(xs), FLAGS.num_generated_cases)\n+ return random.sample(xs, k)\ndef cases_from_gens(*gens):\nsizes = [1, 3, 10]\n- cases_per_size = int(FLAGS.num_generated_cases / len(sizes))\n+ cases_per_size = int(FLAGS.num_generated_cases / len(sizes)) + 1\nfor size in sizes:\nfor i in xrange(cases_per_size):\nyield ('_{}_{}'.format(size, i),) + tuple(gen(size) for gen in gens)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/generated_fun_test.py",
"new_path": "tests/generated_fun_test.py",
"diff": "@@ -25,6 +25,8 @@ from jax import jit, jvp, vjp\nimport jax.test_util as jtu\nfrom jax.config import config\n+config.parse_flags_with_absl()\n+\nnpr.seed(0)\nfrom jax.util import unzip2, safe_zip, safe_map\n@@ -262,5 +264,4 @@ class GeneratedFunTest(jtu.JaxTestCase):\nif __name__ == \"__main__\":\n- config.config_with_absl()\nabsltest.main()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_indexing_test.py",
"new_path": "tests/lax_numpy_indexing_test.py",
"diff": "@@ -31,6 +31,7 @@ from jax import numpy as lnp\nfrom jax import test_util as jtu\nfrom jax.config import config\n+config.parse_flags_with_absl()\nFLAGS = config.FLAGS\n# We disable the whitespace continuation check in this file because otherwise it\n@@ -60,7 +61,7 @@ def check_grads(f, args, order, atol=None, rtol=None, eps=None):\nclass IndexingTest(jtu.JaxTestCase):\n\"\"\"Tests for Numpy indexing translation rules.\"\"\"\n- @parameterized.named_parameters({\n+ @parameterized.named_parameters(jtu.cases_from_list({\n\"testcase_name\":\n\"{}_inshape={}_indexer={}\".format(\nname, jtu.format_shape_dtype_string( shape, dtype), indexer),\n@@ -152,14 +153,14 @@ class IndexingTest(jtu.JaxTestCase):\nIndexSpec(shape=(3, 4), indexer=()),\n]),\n] for shape, indexer in index_specs for dtype in all_dtypes\n- for rng in [jtu.rand_default()])\n+ for rng in [jtu.rand_default()]))\n@jtu.skip_on_devices(\"tpu\")\ndef testStaticIndexing(self, shape, dtype, rng, indexer):\nargs_maker = lambda: [rng(shape, dtype)]\nfun = lambda x: x[indexer]\nself._CompileAndCheck(fun, args_maker, check_dtypes=True)\n- @parameterized.named_parameters({\n+ @parameterized.named_parameters(jtu.cases_from_list({\n\"testcase_name\":\n\"{}_inshape={}_indexer={}\".format(name,\njtu.format_shape_dtype_string(\n@@ -231,7 +232,7 @@ class IndexingTest(jtu.JaxTestCase):\n# IndexSpec(shape=(3, 4), indexer=()),\n# ]),\n] for shape, indexer in index_specs for dtype in float_dtypes\n- for rng in [jtu.rand_default()])\n+ for rng in [jtu.rand_default()]))\n@jtu.skip_on_devices(\"tpu\")\ndef testStaticIndexingGrads(self, shape, dtype, rng, indexer):\ntol = 1e-2 if onp.finfo(dtype).bits == 32 else None\n@@ -255,7 +256,7 @@ class IndexingTest(jtu.JaxTestCase):\nelse:\nreturn idx, lambda x: x\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"{}_inshape={}_indexer={}\"\n.format(name, jtu.format_shape_dtype_string(shape, dtype), indexer),\n\"shape\": shape, \"dtype\": dtype, \"rng\": rng, \"indexer\": indexer}\n@@ -278,7 +279,7 @@ class IndexingTest(jtu.JaxTestCase):\n]\nfor shape, indexer in index_specs\nfor dtype in all_dtypes\n- for rng in [jtu.rand_default()])\n+ for rng in [jtu.rand_default()]))\ndef testDynamicIndexingWithSlicesErrors(self, shape, dtype, rng, indexer):\nunpacked_indexer, pack_indexer = self._ReplaceSlicesWithTuples(indexer)\n@@ -290,7 +291,7 @@ class IndexingTest(jtu.JaxTestCase):\nargs_maker = lambda: [rng(shape, dtype), unpacked_indexer]\nself.assertRaises(IndexError, lambda: fun(*args_maker()))\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"{}_inshape={}_indexer={}\"\n.format(name, jtu.format_shape_dtype_string(shape, dtype), indexer),\n\"shape\": shape, \"dtype\": dtype, \"rng\": rng, \"indexer\": indexer}\n@@ -310,7 +311,7 @@ class IndexingTest(jtu.JaxTestCase):\n]\nfor shape, indexer in index_specs\nfor dtype in all_dtypes\n- for rng in [jtu.rand_default()])\n+ for rng in [jtu.rand_default()]))\ndef testDynamicIndexingWithIntegers(self, shape, dtype, rng, indexer):\nunpacked_indexer, pack_indexer = self._ReplaceSlicesWithTuples(indexer)\n@@ -321,7 +322,7 @@ class IndexingTest(jtu.JaxTestCase):\nargs_maker = lambda: [rng(shape, dtype), unpacked_indexer]\nself._CompileAndCheck(fun, args_maker, check_dtypes=True)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"{}_inshape={}_indexer={}\"\n.format(name, jtu.format_shape_dtype_string(shape, dtype), indexer),\n\"shape\": shape, \"dtype\": dtype, \"rng\": rng, \"indexer\": indexer}\n@@ -343,7 +344,7 @@ class IndexingTest(jtu.JaxTestCase):\n]\nfor shape, indexer in index_specs\nfor dtype in float_dtypes\n- for rng in [jtu.rand_default()])\n+ for rng in [jtu.rand_default()]))\ndef DISABLED_testDynamicIndexingWithIntegersGrads(self, shape, dtype, rng, indexer):\n# TODO(mattjj): re-enable (test works but for grad-of-compile, in flux)\ntol = 1e-2 if onp.finfo(dtype).bits == 32 else None\n@@ -357,7 +358,7 @@ class IndexingTest(jtu.JaxTestCase):\narr = rng(shape, dtype)\ncheck_grads(partial(fun, unpacked_indexer), (arr,), 2, tol, tol, tol)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"{}_inshape={}_indexer={}\"\n.format(name, jtu.format_shape_dtype_string(shape, dtype), indexer),\n\"shape\": shape, \"dtype\": dtype, \"rng\": rng, \"indexer\": indexer}\n@@ -409,13 +410,13 @@ class IndexingTest(jtu.JaxTestCase):\n]\nfor shape, indexer in index_specs\nfor dtype in all_dtypes\n- for rng in [jtu.rand_default()])\n+ for rng in [jtu.rand_default()]))\ndef testAdvancedIntegerIndexing(self, shape, dtype, rng, indexer):\nargs_maker = lambda: [rng(shape, dtype), indexer]\nfun = lambda x, idx: x[idx]\nself._CompileAndCheck(fun, args_maker, check_dtypes=True)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"{}_inshape={}_indexer={}\"\n.format(name, jtu.format_shape_dtype_string(shape, dtype), indexer),\n\"shape\": shape, \"dtype\": dtype, \"rng\": rng, \"indexer\": indexer}\n@@ -467,14 +468,14 @@ class IndexingTest(jtu.JaxTestCase):\n]\nfor shape, indexer in index_specs\nfor dtype in float_dtypes\n- for rng in [jtu.rand_default()])\n+ for rng in [jtu.rand_default()]))\ndef testAdvancedIntegerIndexingGrads(self, shape, dtype, rng, indexer):\ntol = 1e-2 if onp.finfo(dtype).bits == 32 else None\narg = rng(shape, dtype)\nfun = lambda x: x[indexer]**2\ncheck_grads(fun, (arg,), 2, tol, tol, tol)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"{}_inshape={}_indexer={}\"\n.format(name, jtu.format_shape_dtype_string(shape, dtype), indexer),\n\"shape\": shape, \"dtype\": dtype, \"rng\": rng, \"indexer\": indexer}\n@@ -530,7 +531,7 @@ class IndexingTest(jtu.JaxTestCase):\n]\nfor shape, indexer in index_specs\nfor dtype in all_dtypes\n- for rng in [jtu.rand_default()])\n+ for rng in [jtu.rand_default()]))\ndef testMixedAdvancedIntegerIndexing(self, shape, dtype, rng, indexer):\nindexer_with_dummies = [e if isinstance(e, onp.ndarray) else ()\nfor e in indexer]\n@@ -587,5 +588,4 @@ class IndexingTest(jtu.JaxTestCase):\nif __name__ == \"__main__\":\n- config.config_with_absl()\nabsltest.main()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/stax_test.py",
"new_path": "tests/stax_test.py",
"diff": "@@ -27,6 +27,8 @@ from jax import random\nfrom jax.config import config\nfrom jax.experimental import stax\n+config.parse_flags_with_absl()\n+\ndef _CheckShapeAgreement(test_case, init_fun, apply_fun, input_shape):\nresult_shape, params = init_fun(input_shape)\n@@ -38,21 +40,21 @@ def _CheckShapeAgreement(test_case, init_fun, apply_fun, input_shape):\nclass StaxTest(jtu.JaxTestCase):\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_shape={}\".format(shape), \"shape\": shape}\n- for shape in [(2, 3), (5,)])\n+ for shape in [(2, 3), (5,)]))\ndef testRandnInitShape(self, shape):\nout = stax.randn()(shape)\nself.assertEqual(out.shape, shape)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_shape={}\".format(shape), \"shape\": shape}\n- for shape in [(2, 3), (2, 3, 4)])\n+ for shape in [(2, 3), (2, 3, 4)]))\ndef testGlorotInitShape(self, shape):\nout = stax.glorot()(shape)\nself.assertEqual(out.shape, shape)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\":\n\"_channels={}_filter_shape={}_padding={}_strides={}_input_shape={}\"\n.format(channels, filter_shape, padding, strides, input_shape),\n@@ -62,32 +64,32 @@ class StaxTest(jtu.JaxTestCase):\nfor filter_shape in [(1, 1), (2, 3)]\nfor padding in [\"SAME\", \"VALID\"]\nfor strides in [None, (2, 1)]\n- for input_shape in [(2, 10, 11, 1)])\n+ for input_shape in [(2, 10, 11, 1)]))\ndef testConvShape(self, channels, filter_shape, padding, strides,\ninput_shape):\ninit_fun, apply_fun = stax.Conv(channels, filter_shape, strides=strides,\npadding=padding)\n_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_out_dim={}_input_shape={}\"\n.format(out_dim, input_shape),\n\"out_dim\": out_dim, \"input_shape\": input_shape}\nfor out_dim in [3, 4]\n- for input_shape in [(2, 3), (3, 4)])\n+ for input_shape in [(2, 3), (3, 4)]))\ndef testDenseShape(self, out_dim, input_shape):\ninit_fun, apply_fun = stax.Dense(out_dim)\n_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_input_shape={}\".format(input_shape),\n\"input_shape\": input_shape}\n- for input_shape in [(2, 3), (2, 3, 4)])\n+ for input_shape in [(2, 3), (2, 3, 4)]))\ndef testReluShape(self, input_shape):\ninit_fun, apply_fun = stax.Relu\n_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_window_shape={}_padding={}_strides={}_input_shape={}\"\n.format(window_shape, padding, strides, input_shape),\n\"window_shape\": window_shape, \"padding\": padding, \"strides\": strides,\n@@ -95,40 +97,39 @@ class StaxTest(jtu.JaxTestCase):\nfor window_shape in [(1, 1), (2, 3)]\nfor padding in [\"VALID\"]\nfor strides in [None, (2, 1)]\n- for input_shape in [(2, 5, 6, 1)])\n+ for input_shape in [(2, 5, 6, 1)]))\ndef testPoolingShape(self, window_shape, padding, strides, input_shape):\ninit_fun, apply_fun = stax.MaxPool(window_shape, padding=padding,\nstrides=strides)\n_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_shape={}\".format(input_shape),\n\"input_shape\": input_shape}\n- for input_shape in [(2, 3), (2, 3, 4)])\n+ for input_shape in [(2, 3), (2, 3, 4)]))\ndef testFlattenShape(self, input_shape):\ninit_fun, apply_fun = stax.Flatten\n_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_input_shape={}_spec={}\".format(input_shape, i),\n\"input_shape\": input_shape, \"spec\": spec}\nfor input_shape in [(2, 5, 6, 1)]\nfor i, spec in enumerate([\n[stax.Conv(3, (2, 2))],\n- [stax.Conv(3, (2, 2)), stax.Flatten, stax.Dense(4)]]))\n+ [stax.Conv(3, (2, 2)), stax.Flatten, stax.Dense(4)]])))\ndef testSerialComposeLayersShape(self, input_shape, spec):\ninit_fun, apply_fun = stax.serial(*spec)\n_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)\n- @parameterized.named_parameters(\n+ @parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_input_shape={}\".format(input_shape),\n\"input_shape\": input_shape}\n- for input_shape in [(3, 4), (2, 5, 6, 1)])\n+ for input_shape in [(3, 4), (2, 5, 6, 1)]))\ndef testDropoutShape(self, input_shape):\ninit_fun, apply_fun = stax.Dropout(0.9)\n_CheckShapeAgreement(self, init_fun, apply_fun, input_shape)\nif __name__ == \"__main__\":\n- config.config_with_absl()\nabsltest.main()\n"
}
] | Python | Apache License 2.0 | google/jax | Number of test cases settable with command-line flag |
260,335 | 06.12.2018 21:35:03 | 18,000 | bbc92ce6eb6c942fea8e20a841d1d7473cfe9c90 | Split out `jax` and `jaxlib` packages
factor out 'jaxlib' as separate package | [
{
"change_type": "MODIFY",
"old_path": ".gitignore",
"new_path": ".gitignore",
"diff": "/jax/lib/pywrap_xla.py\n/jax/lib/xla_client.py\n/jax/lib/xla_data_pb2.py\n-jax.egg-info\n-\n+*.egg-info\n.ipynb_checkpoints\n-\n/bazel-*\n.bazelrc\n/tensorflow\n-\n.DS_Store\n\\ No newline at end of file\n"
},
{
"change_type": "RENAME",
"old_path": "WORKSPACE",
"new_path": "build/WORKSPACE",
"diff": ""
},
{
"change_type": "RENAME",
"old_path": "build.py",
"new_path": "build/build.py",
"diff": "# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n-# Helper script for building JAX easily.\n+# Helper script for building JAX's libjax easily.\nfrom __future__ import absolute_import\nfrom __future__ import division\n@@ -186,13 +186,11 @@ BANNER = r\"\"\"\nEPILOG = \"\"\"\n-From the JAX repository root, run\n+From the 'build' directory in the JAX repository, run\npython build.py\nor\npython3 build.py\n-\n-Downloads and builds JAX's XLA dependency, installing XLA in the JAX source\n-tree.\n+to download and build JAX's XLA (jaxlib) dependency.\n\"\"\"\n@@ -222,7 +220,7 @@ def add_boolean_argument(parser, name, default=False, help_str=None):\ndef main():\nparser = argparse.ArgumentParser(\n- description=\"Builds JAX from source.\", epilog=EPILOG)\n+ description=\"Builds libjax from source.\", epilog=EPILOG)\nparser.add_argument(\n\"--bazel_path\",\nhelp=\"Path to the Bazel binary to use. The default is to find bazel via \"\n@@ -247,6 +245,7 @@ def main():\nargs = parser.parse_args()\nprint(BANNER)\n+ os.chdir(os.path.dirname(__file__))\n# Find a working Bazel.\nbazel_path = get_bazel_path(args.bazel_path)\n@@ -268,9 +267,9 @@ def main():\ncuda_toolkit_path=cuda_toolkit_path,\ncudnn_install_path=cudnn_install_path)\n- print(\"\\nBuilding XLA and installing it in the JAX source tree...\")\n+ print(\"\\nBuilding XLA and installing it in the jaxlib source tree...\")\nshell([\n- bazel_path, \"run\", \"-c\", \"opt\", \"//build:install_xla_in_source_tree\",\n+ bazel_path, \"run\", \"-c\", \"opt\", \":install_xla_in_source_tree\",\nos.getcwd()\n])\n"
},
{
"change_type": "MODIFY",
"old_path": "build/install_xla_in_source_tree.sh",
"new_path": "build/install_xla_in_source_tree.sh",
"diff": "@@ -45,23 +45,22 @@ if [[ $# -ne 1 ]]; then\nfi\nTARGET=\"$1\"\n-if [[ ! -r \"${TARGET}/jax/lax.py\" ]]; then\n- echo \"Target directory ${TARGET} does not seem to be a JAX source tree\" \\\n- \"(missing jax/lax.py)\"\n+if [[ ! -d \"${TARGET}/jaxlib\" ]]; then\n+ echo \"Target directory ${TARGET} does not have a jaxlib directory\"\nexit 1\nfi\n# Copy the XLA dependencies into jax/lib, fixing up some imports to point to the\n# new location.\ncp -f \"$(rlocation org_tensorflow/tensorflow/compiler/xla/xla_data_pb2.py)\" \\\n- \"${TARGET}/jax/lib\"\n+ \"${TARGET}/jaxlib\"\ncp -f \"$(rlocation org_tensorflow/tensorflow/compiler/xla/python/pywrap_xla.py)\" \\\n- \"${TARGET}/jax/lib\"\n+ \"${TARGET}/jaxlib\"\ncp -f \"$(rlocation org_tensorflow/tensorflow/compiler/xla/python/_pywrap_xla.so)\" \\\n- \"${TARGET}/jax/lib\"\n+ \"${TARGET}/jaxlib\"\nsed \\\n-e 's/from tensorflow.compiler.xla.python import pywrap_xla as c_api/from . import pywrap_xla as c_api/' \\\n-e 's/from tensorflow.compiler.xla import xla_data_pb2/from . import xla_data_pb2/' \\\n-e '/from tensorflow.compiler.xla.service import hlo_pb2/d' \\\n< \"$(rlocation org_tensorflow/tensorflow/compiler/xla/python/xla_client.py)\" \\\n- > \"${TARGET}/jax/lib/xla_client.py\"\n+ > \"${TARGET}/jaxlib/xla_client.py\"\n"
},
{
"change_type": "ADD",
"old_path": "build/jaxlib/__init__.py",
"new_path": "build/jaxlib/__init__.py",
"diff": ""
},
{
"change_type": "ADD",
"old_path": null,
"new_path": "build/setup.py",
"diff": "+# Copyright 2018 Google LLC\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# https://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+from setuptools import setup\n+from glob import glob\n+import os\n+\n+binary_libs = [os.path.basename(f) for f in glob('jaxlib/*.so*')]\n+\n+setup(\n+ name='jaxlib',\n+ version='0.0',\n+ description='XLA library for JAX',\n+ author='JAX team',\n+ author_email='jax-dev@google.com',\n+ packages=['jaxlib'],\n+ install_requires=['numpy>=1.12', 'six', 'protobuf', 'absl-py'],\n+ url='https://github.com/google/jax',\n+ license='Apache-2.0',\n+ package_data={'jaxlib': binary_libs},\n+)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lib/xla_bridge.py",
"new_path": "jax/lib/xla_bridge.py",
"diff": "@@ -29,8 +29,8 @@ import warnings\nfrom ..config import flags\nimport numpy as onp # 'onp' rather than 'np' to distinguish from autograd.numpy\n-from . import xla_data_pb2\n-from . import xla_client\n+from jaxlib import xla_data_pb2\n+from jaxlib import xla_client\nFLAGS = flags.FLAGS\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -24,9 +24,9 @@ import numpy as onp\nfrom .. import core\nfrom ..abstract_arrays import UnshapedArray, ShapedArray, ConcreteArray\nfrom ..interpreters.xla import DeviceArray\n-from ..lib import xla_bridge\nimport jax.lax as lax\nfrom ..util import memoize\n+from ..lib import xla_bridge\n# To provide the same module-level names as Numpy, we need to redefine builtins\n# and also use some common names (like 'shape' and 'dtype') at the top-level.\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/random.py",
"new_path": "jax/random.py",
"diff": "@@ -25,8 +25,8 @@ import numpy as onp\nfrom . import lax\nfrom . import numpy as np\nfrom . import tree_util\n-from .lib import xla_bridge\nfrom .api import jit\n+from jax.lib import xla_bridge\nclass PRNGKey(object):\n"
},
{
"change_type": "MODIFY",
"old_path": "setup.py",
"new_path": "setup.py",
"diff": "# limitations under the License.\nfrom setuptools import setup\n-from glob import glob\nsetup(\nname='jax',\n- version='0.0',\n+ version='0.1',\ndescription='Differentiate, compile, and transform Numpy code.',\nauthor='JAX team',\n- author_email='jax-team@google.com',\n+ author_email='jax-dev@google.com',\npackages=['jax', 'jax.lib', 'jax.interpreters', 'jax.numpy', 'jax.scipy',\n'jax.experimental'],\n- install_requires=['numpy>=1.12', 'six', 'protobuf', 'absl-py'],\n+ install_requires=['numpy>=1.12', 'six', 'protobuf', 'absl-py',\n+ 'opt_einsum'],\nurl='https://github.com/google/jax',\nlicense='Apache-2.0',\n- package_data={'jax.lib': glob('jax/lib/*.so')},\n)\n"
}
] | Python | Apache License 2.0 | google/jax | Split out `jax` and `jaxlib` packages (#11)
factor out 'jaxlib' as separate package |
260,474 | 06.12.2018 21:57:08 | 18,000 | 61aa47a1a803f68626d99a702455b5430b7cf99c | Fixed paren in parameterized test specification | [
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -355,7 +355,7 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\njtu.format_shape_dtype_string(shape, fill_value_dtype),\nonp.dtype(out_dtype).name),\n\"shape\": shape, \"fill_value_dtype\": fill_value_dtype,\n- \"out_dtype\": out_dtype, \"rng\": jtu.rand_default()})\n+ \"out_dtype\": out_dtype, \"rng\": jtu.rand_default()}\nfor shape in array_shapes\nfor fill_value_dtype in default_dtypes\nfor out_dtype in default_dtypes))\n"
}
] | Python | Apache License 2.0 | google/jax | Fixed paren in parameterized test specification |
260,474 | 06.12.2018 22:45:49 | 18,000 | ae7df43e9bcdcd4ff70228bfddb1f52973315b8e | Fixed bug due to input_shape kwarg not being modified in batching rule for reducers. Fixes b/120595235 | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/batching.py",
"new_path": "jax/interpreters/batching.py",
"diff": "@@ -224,6 +224,8 @@ def reducer_batcher(prim, batched_args, batch_dims, axes, **kwargs):\nbdim, = batch_dims\naxes = tuple(onp.where(onp.less(axes, bdim), axes, onp.add(axes, 1)))\nbdim_out = list(onp.delete(onp.arange(operand.ndim), axes)).index(bdim)\n+ if 'input_shape' in kwargs:\n+ kwargs['input_shape'] = operand.shape\nreturn prim.bind(operand, axes=axes, **kwargs), bdim_out\ndef add_batched(batched_args, batch_dims):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -1790,6 +1790,8 @@ batching.defreducer(reduce_p)\ndef reduce_sum_shape_rule(operand, axes, input_shape):\n+ assert operand.shape == input_shape, ('{} != {}'\n+ .format(operand.shape, input_shape))\nreturn tuple(onp.delete(operand.shape, axes))\ndef reduce_sum_translation_rule(c, operand, axes, input_shape):\n"
}
] | Python | Apache License 2.0 | google/jax | Fixed bug due to input_shape kwarg not being modified in batching rule for reducers. Fixes b/120595235 |
260,335 | 07.12.2018 06:53:29 | 28,800 | 50624bd97819dc9bcc816fef2aa2bc7f8e2d3f4b | rename in_bdims, out_bdims --> in_axes, out_axes | [
{
"change_type": "MODIFY",
"old_path": "jax/api.py",
"new_path": "jax/api.py",
"diff": "@@ -65,7 +65,7 @@ def jacfwd(fun, x):\nfun = lu.wrap_init(fun)\npushfwd = partial(jvp, fun, (x,))\nstd_basis = onp.eye(onp.size(x)).reshape((-1,) + onp.shape(x)),\n- y, jac_flat = vmap(pushfwd, std_basis, out_bdim=(None, 0))\n+ y, jac_flat = vmap(pushfwd, std_basis, out_axes=(None, 0))\nreturn jac_flat.reshape(onp.shape(y) + onp.shape(x))\n@curry\n@@ -73,26 +73,26 @@ def jacrev(fun, x):\nfun = lu.wrap_init(fun)\ny, pullback = vjp(fun, x)\nstd_basis = onp.eye(onp.size(y)).reshape((-1,) + onp.shape(y))\n- jac_flat, = vmap(pullback, std_basis, out_bdim=onp.ndim(y))\n+ jac_flat, = vmap(pullback, std_basis, out_axes=onp.ndim(y))\nreturn jac_flat.reshape(onp.shape(y) + onp.shape(x))\ndef hessian(fun):\nreturn jacfwd(jacrev(fun))\ndef vmap(fun, *args, **kwargs):\n- in_bdims = kwargs.pop(\"in_bdims\", 0)\n- out_bdim = kwargs.pop(\"out_bdim\", 0)\n+ in_axes = kwargs.pop(\"in_axes\", 0)\n+ out_axes = kwargs.pop(\"out_axes\", 0)\nif kwargs:\n- msg = \"vmap keyword args must be 'in_bdims' and/or 'out_bdim', got {}.\"\n+ msg = \"vmap keyword args must be 'in_axes' and/or 'out_axes', got {}.\"\nraise TypeError(msg.format(', '.join(kwargs)))\n- if type(in_bdims) is int:\n- in_bdims = (in_bdims,) * len(args)\n+ if type(in_axes) is int:\n+ in_axes = (in_axes,) * len(args)\nif not isinstance(fun, lu.WrappedFun):\nfun = lu.wrap_init(fun)\nin_flat, in_trees = unzip2(map(tree_to_jaxtuples, args))\nflat_fun, out_tree = flatten_fun(fun, in_trees)\n- out_flat = batching.batch(flat_fun, in_flat, in_bdims, out_bdim)\n+ out_flat = batching.batch(flat_fun, in_flat, in_axes, out_axes)\nreturn build_tree(out_tree(), out_flat)\ndef jvp(fun, primals, tangents):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -42,10 +42,10 @@ class BatchingTest(jtu.JaxTestCase):\ndef testNestedBatchingMatMat(self):\ndef matvec(A, b):\n- return vmap(np.vdot, A, b, in_bdims=(0, None))\n+ return vmap(np.vdot, A, b, in_axes=(0, None))\ndef matmat(A, B):\n- return vmap(matvec, A, B, in_bdims=(None, 1), out_bdim=1)\n+ return vmap(matvec, A, B, in_axes=(None, 1), out_axes=1)\nR = onp.random.RandomState(0).randn\nA = R(4, 3)\n@@ -107,13 +107,13 @@ class BatchingTest(jtu.JaxTestCase):\ndef jacbwd(f, x):\ny, pullback = vjp(f, x)\nstd_basis = onp.eye(onp.size(y)).reshape((-1,) + onp.shape(y))\n- jac_flat, = vmap(pullback, std_basis, out_bdim=onp.ndim(y))\n+ jac_flat, = vmap(pullback, std_basis, out_axes=onp.ndim(y))\nreturn jac_flat.reshape(onp.shape(y) + onp.shape(x))\ndef jacfwd(f, x):\npushfwd = lambda v: jvp(f, (x,), (v,))\nstd_basis = onp.eye(onp.size(x)).reshape((-1,) + onp.shape(x))\n- y, jac_flat = vmap(pushfwd, std_basis, out_bdim=(None, 0))\n+ y, jac_flat = vmap(pushfwd, std_basis, out_axes=(None, 0))\nreturn jac_flat.reshape(onp.shape(y) + onp.shape(x))\nR = onp.random.RandomState(0).randn\n"
}
] | Python | Apache License 2.0 | google/jax | rename in_bdims, out_bdims --> in_axes, out_axes |
260,335 | 06.12.2018 20:04:05 | 28,800 | e93f682d58e947426f36a7aa1f6f293192bab8f3 | check out build files | [
{
"change_type": "ADD",
"old_path": null,
"new_path": "build/Dockerfile",
"diff": "+ARG CUDA_VERSION=9.2\n+FROM nvidia/cuda:$CUDA_VERSION-cudnn7-devel-ubuntu16.04\n+LABEL maintainer \"Matt Johnson <mattjj@google.com>\"\n+\n+RUN apt-get update && apt-get install -y --no-install-recommends \\\n+ dh-autoreconf git curl \\\n+ python python-pip python-dev \\\n+ python3 python3-pip python3-dev\n+RUN pip install numpy setuptools wheel && pip3 install numpy setuptools wheel\n+\n+RUN git clone https://github.com/nixos/patchelf /tmp/patchelf\n+WORKDIR /tmp/patchelf\n+RUN bash bootstrap.sh && ./configure && make && make install && rm -r /tmp/patchelf\n+\n+WORKDIR /\n+RUN curl -O https://raw.githubusercontent.com/google/jax/744ac4821afafaff86b0f1820f05ac7c1186e276/build/build_wheel_docker_entrypoint.sh\n+RUN chmod +x /build_wheel_docker_entrypoint.sh\n+\n+WORKDIR /build\n+ENV TEST_TMPDIR /build\n+ENTRYPOINT [\"/build_wheel_docker_entrypoint.sh\"]\n"
},
{
"change_type": "ADD",
"old_path": null,
"new_path": "build/build_wheel_docker_entrypoint.sh",
"diff": "+#!/bin/bash -xev\n+if [ ! -d \"/dist\" ]\n+then\n+ echo \"/dist must be mounted to produce output\"\n+ exit 1\n+fi\n+\n+git clone -b binary-distros https://github.com/google/jax /build/jax\n+cd /build/jax\n+\n+usage() {\n+ echo \"usage: ${0##*/} [python2|python3] [cuda-included|cuda|nocuda]\"\n+ exit 1\n+}\n+\n+if [[ $# != 2 ]]\n+then\n+ usage\n+fi\n+\n+case $1 in\n+ py2)\n+ update-alternatives --install /usr/bin/python python /usr/bin/python3 10\n+ ;;\n+ py3)\n+ ;;\n+ *)\n+ usage\n+esac\n+\n+case $2 in\n+ cuda-included)\n+ python build.py --enable_cuda --cudnn_path /usr/lib/x86_64-linux-gnu/\n+ python build/include_cuda.py\n+ ;;\n+ cuda)\n+ python build.py --enable_cuda --cudnn_path /usr/lib/x86_64-linux-gnu/\n+ ;;\n+ nocuda)\n+ python build.py\n+ ;;\n+ *)\n+ usage\n+esac\n+\n+python setup.py bdist bdist_wheel\n+cp -r dist/* /dist\n"
}
] | Python | Apache License 2.0 | google/jax | check out build files |
260,335 | 07.12.2018 04:59:03 | 28,800 | d4f32dff7b9c9dd58f0e34d1cf2cd738812f03bd | update build script for split packages | [
{
"change_type": "MODIFY",
"old_path": "build/Dockerfile",
"new_path": "build/Dockerfile",
"diff": "@@ -13,7 +13,7 @@ WORKDIR /tmp/patchelf\nRUN bash bootstrap.sh && ./configure && make && make install && rm -r /tmp/patchelf\nWORKDIR /\n-RUN curl -O https://raw.githubusercontent.com/google/jax/744ac4821afafaff86b0f1820f05ac7c1186e276/build/build_wheel_docker_entrypoint.sh\n+RUN curl -O https://raw.githubusercontent.com/google/jax/c34a48bd86745ab4a221be058f725597d43c75eb/build/build_wheel_docker_entrypoint.sh\nRUN chmod +x /build_wheel_docker_entrypoint.sh\nWORKDIR /build\n"
},
{
"change_type": "MODIFY",
"old_path": "build/build.py",
"new_path": "build/build.py",
"diff": "@@ -245,7 +245,7 @@ def main():\nargs = parser.parse_args()\nprint(BANNER)\n- os.chdir(os.path.dirname(__file__))\n+ os.chdir(os.path.dirname(__file__ or args.prog) or '.')\n# Find a working Bazel.\nbazel_path = get_bazel_path(args.bazel_path)\n"
},
{
"change_type": "MODIFY",
"old_path": "build/build_wheel_docker_entrypoint.sh",
"new_path": "build/build_wheel_docker_entrypoint.sh",
"diff": "-#!/bin/bash -xev\n+#!/bin/bash\n+set -xev\nif [ ! -d \"/dist\" ]\nthen\necho \"/dist must be mounted to produce output\"\n@@ -6,10 +7,10 @@ then\nfi\ngit clone -b binary-distros https://github.com/google/jax /build/jax\n-cd /build/jax\n+cd /build/jax/build\nusage() {\n- echo \"usage: ${0##*/} [python2|python3] [cuda-included|cuda|nocuda]\"\n+ echo \"usage: ${0##*/} [py2|py3] [cuda-included|cuda|nocuda]\"\nexit 1\n}\n@@ -19,10 +20,10 @@ then\nfi\ncase $1 in\n- py2)\n+ py3)\nupdate-alternatives --install /usr/bin/python python /usr/bin/python3 10\n;;\n- py3)\n+ py2)\n;;\n*)\nusage\n@@ -31,7 +32,7 @@ esac\ncase $2 in\ncuda-included)\npython build.py --enable_cuda --cudnn_path /usr/lib/x86_64-linux-gnu/\n- python build/include_cuda.py\n+ python include_cuda.py\n;;\ncuda)\npython build.py --enable_cuda --cudnn_path /usr/lib/x86_64-linux-gnu/\n@@ -43,5 +44,5 @@ case $2 in\nusage\nesac\n-python setup.py bdist bdist_wheel\n+python setup.py bdist_wheel\ncp -r dist/* /dist\n"
}
] | Python | Apache License 2.0 | google/jax | update build script for split packages |
260,335 | 07.12.2018 06:20:00 | 28,800 | 9fd120113d4d802698f7cbf46fbaf7a7ca0e65da | add jaxlib wheel building script | [
{
"change_type": "ADD",
"old_path": null,
"new_path": "build/build_wheels.sh",
"diff": "+#!/bin/bash -xev\n+JAXLIB_VERSION=$(sed -n \"s/^ \\+version=[']\\(.*\\)['],$/\\\\1/p\" jax/build/setup.py)\n+\n+PYTHON_VERSIONS=\"py2 py3\"\n+CUDA_VERSIONS=\"9.2\" # \"9.2 10.0\"\n+CUDA_VARIANTS=\"cuda\" # \"cuda cuda-included\"\n+\n+mkdir -p dist\n+for CUDA_VERSION in $CUDA_VERSIONS\n+do\n+ docker build -t jaxbuild jax/build/ --build-arg CUDA_VERSION=$CUDA_VERSION\n+\n+ for PYTHON_VERSION in $PYTHON_VERSIONS\n+ do\n+ mkdir -p dist/nocuda/\n+ nvidia-docker run -it --tmpfs /build:exec --rm -v $(pwd)/dist:/dist jaxbuild $PYTHON_VERSION nocuda\n+ mv dist/*.whl dist/nocuda/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-linux_x86_64.whl\n+\n+ for CUDA_VARIANT in $CUDA_VARIANTS\n+ do\n+ mkdir -p dist/cuda${CUDA_VERSION//.}\n+ nvidia-docker run -it --tmpfs /build:exec --rm -v $(pwd)/dist:/dist jaxbuild $PYTHON_VERSION $CUDA_VARIANT\n+ mv dist/*.whl dist/cuda${CUDA_VERSION//.}/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-linux_x86_64.whl\n+ done\n+ done\n+done\n+\n+echo \"now you might want to run something like:\"\n+echo \"python3 -m twine upload --repository-url https://test.pypi.org/legacy/ dist/nocuda/*.whl --verbose\"\n"
}
] | Python | Apache License 2.0 | google/jax | add jaxlib wheel building script |
260,335 | 07.12.2018 07:35:32 | 28,800 | f6d31ad5f1309ce1fd28422379f369744d16cbb0 | update dockerfile entrypoint script git hash | [
{
"change_type": "MODIFY",
"old_path": "build/Dockerfile",
"new_path": "build/Dockerfile",
"diff": "@@ -13,7 +13,7 @@ WORKDIR /tmp/patchelf\nRUN bash bootstrap.sh && ./configure && make && make install && rm -r /tmp/patchelf\nWORKDIR /\n-RUN curl -O https://raw.githubusercontent.com/google/jax/c34a48bd86745ab4a221be058f725597d43c75eb/build/build_wheel_docker_entrypoint.sh\n+RUN curl -O https://raw.githubusercontent.com/google/jax/18920337f30bd6727062382024746d1d3f7b6fbb/build/build_wheel_docker_entrypoint.sh\nRUN chmod +x /build_wheel_docker_entrypoint.sh\nWORKDIR /build\n"
}
] | Python | Apache License 2.0 | google/jax | update dockerfile entrypoint script git hash |
260,335 | 07.12.2018 08:09:51 | 28,800 | 18201fab7bb234e35e2878ab0347a9342a7deac3 | bump version number for jaxlib | [
{
"change_type": "MODIFY",
"old_path": "build/setup.py",
"new_path": "build/setup.py",
"diff": "@@ -20,7 +20,7 @@ binary_libs = [os.path.basename(f) for f in glob('jaxlib/*.so*')]\nsetup(\nname='jaxlib',\n- version='0.0',\n+ version='0.1',\ndescription='XLA library for JAX',\nauthor='JAX team',\nauthor_email='jax-dev@google.com',\n"
}
] | Python | Apache License 2.0 | google/jax | bump version number for jaxlib |
260,335 | 07.12.2018 08:12:33 | 28,800 | 91f8247d32c2e2af800f312531fa4e125ac1e7e8 | update wheel name | [
{
"change_type": "RENAME",
"old_path": "build/build_wheels.sh",
"new_path": "build/build_jaxlib_wheels.sh",
"diff": "@@ -14,13 +14,13 @@ do\ndo\nmkdir -p dist/nocuda/\nnvidia-docker run -it --tmpfs /build:exec --rm -v $(pwd)/dist:/dist jaxbuild $PYTHON_VERSION nocuda\n- mv dist/*.whl dist/nocuda/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-linux_x86_64.whl\n+ mv dist/*.whl dist/nocuda/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-manylinux1_x86_64.whl\nfor CUDA_VARIANT in $CUDA_VARIANTS\ndo\nmkdir -p dist/cuda${CUDA_VERSION//.}\nnvidia-docker run -it --tmpfs /build:exec --rm -v $(pwd)/dist:/dist jaxbuild $PYTHON_VERSION $CUDA_VARIANT\n- mv dist/*.whl dist/cuda${CUDA_VERSION//.}/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-linux_x86_64.whl\n+ mv dist/*.whl dist/cuda${CUDA_VERSION//.}/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-manylinux1_x86_64.whl\ndone\ndone\ndone\n"
}
] | Python | Apache License 2.0 | google/jax | update wheel name |
260,335 | 07.12.2018 08:15:13 | 28,800 | 8a90d37da2e23fc1f7b962211813669000c6c49f | update wheel building dockerfile | [
{
"change_type": "MODIFY",
"old_path": "build/Dockerfile",
"new_path": "build/Dockerfile",
"diff": "@@ -13,7 +13,7 @@ WORKDIR /tmp/patchelf\nRUN bash bootstrap.sh && ./configure && make && make install && rm -r /tmp/patchelf\nWORKDIR /\n-RUN curl -O https://raw.githubusercontent.com/google/jax/18920337f30bd6727062382024746d1d3f7b6fbb/build/build_wheel_docker_entrypoint.sh\n+RUN curl -O https://raw.githubusercontent.com/google/jax/762abcf29b4a155c3de325c27ecffa5d4a3da28c/build/build_wheel_docker_entrypoint.sh\nRUN chmod +x /build_wheel_docker_entrypoint.sh\nWORKDIR /build\n"
}
] | Python | Apache License 2.0 | google/jax | update wheel building dockerfile |
260,335 | 07.12.2018 08:19:30 | 28,800 | 7093c4740f5fd4e35281e89ef8574b7866cf722a | revive WORKSPACE file | [
{
"change_type": "MODIFY",
"old_path": "build/WORKSPACE",
"new_path": "build/WORKSPACE",
"diff": "+http_archive(\n+ name = \"io_bazel_rules_closure\",\n+ sha256 = \"a38539c5b5c358548e75b44141b4ab637bba7c4dc02b46b1f62a96d6433f56ae\",\n+ strip_prefix = \"rules_closure-dbb96841cc0a5fb2664c37822803b06dab20c7d1\",\n+ urls = [\n+ \"https://mirror.bazel.build/github.com/bazelbuild/rules_closure/archive/dbb96841cc0a5fb2664c37822803b06dab20c7d1.tar.gz\",\n+ \"https://github.com/bazelbuild/rules_closure/archive/dbb96841cc0a5fb2664c37822803b06dab20c7d1.tar.gz\",\n+ ],\n+)\n+\n+# To update TensorFlow to a new revision,\n+# a) update URL and strip_prefix to the new git commit hash\n+# b) get the sha256 hash of the commit by running:\n+# curl -L https://github.com/tensorflow/tensorflow/archive/<git hash>.tar.gz | sha256sum\n+# and update the sha256 with the result.\n+http_archive(\n+ name = \"org_tensorflow\",\n+ sha256 = \"dccd52030b173ee803191134215f712df7b18ee97a7c7d00437014002d29c26b\",\n+ strip_prefix = \"tensorflow-8ccf1ebdbf4475bc7af6d79b2fa7e1fd8221e3fd\",\n+ urls = [\n+ \"https://github.com/tensorflow/tensorflow/archive/8ccf1ebdbf4475bc7af6d79b2fa7e1fd8221e3fd.tar.gz\",\n+ ],\n+)\n+\n+# For development, one can use a local TF repository instead.\n+# local_repository(\n+# name = \"org_tensorflow\",\n+# path = \"tensorflow\",\n+# )\n+\n+load(\"@org_tensorflow//tensorflow:workspace.bzl\", \"tf_workspace\")\n+\n+tf_workspace(\n+ path_prefix = \"\",\n+ tf_repo_name = \"org_tensorflow\",\n+)\n"
},
{
"change_type": "MODIFY",
"old_path": "build/build_jaxlib_wheels.sh",
"new_path": "build/build_jaxlib_wheels.sh",
"diff": "-#!/bin/bash -xev\n+#!/bin/bash\n+set -xev\nJAXLIB_VERSION=$(sed -n \"s/^ \\+version=[']\\(.*\\)['],$/\\\\1/p\" jax/build/setup.py)\nPYTHON_VERSIONS=\"py2 py3\"\n"
}
] | Python | Apache License 2.0 | google/jax | revive WORKSPACE file |
260,335 | 07.12.2018 08:26:34 | 28,800 | a9640b3ebbcc537791a7a34b2c4ad50313270895 | tweak wheel names in build script | [
{
"change_type": "MODIFY",
"old_path": "build/build_jaxlib_wheels.sh",
"new_path": "build/build_jaxlib_wheels.sh",
"diff": "@@ -21,7 +21,7 @@ do\ndo\nmkdir -p dist/cuda${CUDA_VERSION//.}\nnvidia-docker run -it --tmpfs /build:exec --rm -v $(pwd)/dist:/dist jaxbuild $PYTHON_VERSION $CUDA_VARIANT\n- mv dist/*.whl dist/cuda${CUDA_VERSION//.}/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-manylinux1_x86_64.whl\n+ mv dist/*.whl dist/cuda${CUDA_VERSION//.}/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-linux_x86_64.whl\ndone\ndone\ndone\n"
}
] | Python | Apache License 2.0 | google/jax | tweak wheel names in build script |
260,335 | 07.12.2018 11:23:40 | 28,800 | 16658b97c4ff676e67560d57040122e4e86f0614 | fix typo in wheel path | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -122,7 +122,7 @@ cloud VM), you can run\nPYTHON_VERSION=py2 # alternatives: py2, py3\nCUDA_VERSION=cuda92 # alternatives: cuda90, cuda92, cuda100\nPLATFORM=linux_x86_64 # alternatives: linux_x86_64, macosx-10.6-x86_64\n-pip install https://storage.googleapis.com/jax-wheels/$CUDA_VERSION/jax-0.1-$PYTHON_VERSION-none-$PLATFORM.whl\n+pip install https://storage.googleapis.com/jax-wheels/$CUDA_VERSION/jaxlib-0.1-$PYTHON_VERSION-none-$PLATFORM.whl\npip install jax # install jax\n```\n"
}
] | Python | Apache License 2.0 | google/jax | fix typo in wheel path |
260,474 | 07.12.2018 14:44:40 | 18,000 | 7523975c2e9ec34998d54266ae6e141eaf068fb8 | Updated installs in notebook | [
{
"change_type": "MODIFY",
"old_path": "notebooks/quickstart.ipynb",
"new_path": "notebooks/quickstart.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jax-0.0-py3-none-any.whl\"\n+ \"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1-py3-none-any.whl\\n\",\n+ \"!pip install jax\",\n],\n\"execution_count\": 0,\n\"outputs\": []\n"
}
] | Python | Apache License 2.0 | google/jax | Updated installs in notebook |
260,474 | 07.12.2018 14:46:09 | 18,000 | 7be7d42484fa97fe54b5e57ab9e1a3dea246edb5 | Fixed json bug | [
{
"change_type": "MODIFY",
"old_path": "notebooks/quickstart.ipynb",
"new_path": "notebooks/quickstart.ipynb",
"diff": "\"cell_type\": \"code\",\n\"source\": [\n\"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1-py3-none-any.whl\\n\",\n- \"!pip install jax\",\n+ \"!pip install jax\"\n],\n\"execution_count\": 0,\n\"outputs\": []\n"
}
] | Python | Apache License 2.0 | google/jax | Fixed json bug |
260,335 | 07.12.2018 15:05:20 | 28,800 | 70604f4309df7b7c3ae3a0a512fb197575555c7f | add scipy.stats to setup.py (should use find_packages()) | [
{
"change_type": "MODIFY",
"old_path": "setup.py",
"new_path": "setup.py",
"diff": "@@ -16,12 +16,12 @@ from setuptools import setup\nsetup(\nname='jax',\n- version='0.1.1',\n+ version='0.1.2',\ndescription='Differentiate, compile, and transform Numpy code.',\nauthor='JAX team',\nauthor_email='jax-dev@google.com',\npackages=['jax', 'jax.lib', 'jax.interpreters', 'jax.numpy', 'jax.scipy',\n- 'jax.experimental'],\n+ 'jax.scipy.stats', 'jax.experimental'],\ninstall_requires=['numpy>=1.12', 'six', 'protobuf', 'absl-py',\n'opt_einsum'],\nurl='https://github.com/google/jax',\n"
}
] | Python | Apache License 2.0 | google/jax | add scipy.stats to setup.py (should use find_packages()) |
260,474 | 08.12.2018 00:03:34 | 18,000 | 30124b6da1eca136a026eb40c94f8fad6885a467 | Added jit transformations to generated functions. Fixed bug in comparing numpy arrays for equality. | [
{
"change_type": "MODIFY",
"old_path": "jax/util.py",
"new_path": "jax/util.py",
"diff": "@@ -153,5 +153,4 @@ class WrapHashably(object):\nreturn id(self.val)\ndef __eq__(self, other):\n- return self.val == other.val\n-\n+ return self.val is other.val\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/generated_fun_test.py",
"new_path": "tests/generated_fun_test.py",
"diff": "@@ -65,6 +65,7 @@ def gen_function(size, in_types):\narg_types = [v.vartype for v in arg_vars]\nfun, out_types = gen_function(size / size_reduction_factor, arg_types)\nfun = partial(eval_fun, fun)\n+ fun = maybe_jit(fun, len(arg_types))\nelse:\narity = choice(list(primitive_generators))\narg_vars = gen_sized_subset(cur_vars, arity)\n@@ -96,6 +97,10 @@ def eval_fun(fun, *args):\nreturn map(read, fun.out_vars)\n+def maybe_jit(f, num_args):\n+ static_argnums = thin(range(num_args), 0.5)\n+ return jit(f, static_argnums=static_argnums)\n+\ncounter = it.count()\ndef fresh_var(ty):\nreturn Var(next(counter), ty)\n"
}
] | Python | Apache License 2.0 | google/jax | Added jit transformations to generated functions. Fixed bug in comparing numpy arrays for equality. |
260,335 | 08.12.2018 00:56:25 | 18,000 | d2b0e30d5405c2ca7bfd7971a6588dc928cfa227 | update readme (accidentally lost a name!) | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -644,9 +644,14 @@ Some things we don't handle that might surprise NumPy users:\n## Contributors\n-So far, JAX includes lots of help and contributions from [Peter\n-Hawkins](https://github.com/hawkinsp), [Alex\n-Wiltschko](http://github.com/alexbw), George Dahl, [Eli\n-Bendersky](https://github.com/eliben), Zak Stone, [Alexey\n-Radul](https://github.com/axch), Michael Isard, Skye Wanderman-Milne, and many\n-others.\n+So far, JAX includes lots of help and contributions from\n+[Jamie Townsend](https://github.com/j-towns),\n+[Peter Hawkins](https://github.com/hawkinsp),\n+[Alex Wiltschko](http://github.com/alexbw),\n+George Dahl,\n+[Eli Bendersky](https://github.com/eliben),\n+Zak Stone,\n+[Alexey Radul](https://github.com/axch),\n+Michael Isard,\n+Skye Wanderman-Milne,\n+and many others.\n"
}
] | Python | Apache License 2.0 | google/jax | update readme (accidentally lost a name!) |
260,335 | 08.12.2018 08:05:16 | 18,000 | 9d484b6d4ce1d9131dd645bfa11b47f2ab3ab060 | attempt to center-justify the jax logo in readme | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "-# JAX: Autograd and XLA\n+<div align=\"center\">\n+<img src=\"https://raw.githubusercontent.com/google/jax/master/images/jax_logo_250px.png\" alt=\"logo\"></img>\n+</div>\n-\n+# JAX: Autograd and XLA\nJAX is [Autograd](https://github.com/hips/autograd) and\n[XLA](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/g3doc/overview.md),\n"
}
] | Python | Apache License 2.0 | google/jax | attempt to center-justify the jax logo in readme |
260,335 | 08.12.2018 06:01:25 | 28,800 | 10b61e08f76a34898471d0b5b52a36c19483ea03 | fix symbolic zero handling in concat transpose | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -1352,8 +1352,11 @@ def concatenate_translation_rule(c, *operands, **kwargs):\ndef concatenate_transpose_rule(t, *operands, **kwargs):\ndimension = kwargs.pop('dimension')\noperand_shapes = kwargs.pop('operand_shapes')\n- limit_points = onp.cumsum([shape[dimension] for shape in operand_shapes])\n+ if t is ad_util.zero:\n+ return [ad_util.zero if o is None else None for o in operands]\n+ else:\n+ limit_points = onp.cumsum([shape[dimension] for shape in operand_shapes])\nstarts = onp.zeros((len(operands), t.ndim), dtype=int)\nstarts[1:, dimension] = limit_points[:-1]\nlimits = onp.tile(t.shape, (len(operands), 1))\n"
}
] | Python | Apache License 2.0 | google/jax | fix symbolic zero handling in concat transpose |
260,335 | 08.12.2018 09:35:24 | 28,800 | ae36037abfa3eb278955ea775f0ca6c2a7ef95cb | add ad_util.zeros_like translation rule
PAIR=hawkinsp | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -163,11 +163,18 @@ def translation_rule(p):\ntranslations = {}\ntranslations[core.pack_p] = lambda c, *xs: c.Tuple(*xs)\n-translations[ad_util.add_jaxvals_p] = lambda c, x, y: c.Add(x, y)\ntranslations[core.call_p] = lambda c, subc_a1, *a2: c.Call(subc_a1[0],\nsubc_a1[1] + a2)\ntranslations[core.identity_p] = lambda c, x: x\n+# TODO(mattjj): zeros_like and add_jaxvals should handle any jaxval\n+def zeros_like_translation_rule(c, x):\n+ x_shape = c.GetShape(x)\n+ return c.Broadcast(c.Constant(onp.array(0, x_shape.element_type())),\n+ x_shape.dimensions())\n+translations[ad_util.zeros_like_p] = zeros_like_translation_rule\n+translations[ad_util.add_jaxvals_p] = lambda c, x, y: c.Add(x, y)\n+\ndef canonicalize_pyval_dtype(x):\ntry:\n"
}
] | Python | Apache License 2.0 | google/jax | add ad_util.zeros_like translation rule
PAIR=hawkinsp |
260,335 | 09.12.2018 01:24:38 | 18,000 | 5a604d5b141c66fb415e0f0fce501e7f6ed44d1e | add more contributors to readme | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -648,8 +648,11 @@ Some things we don't handle that might surprise NumPy users:\nSo far, JAX includes lots of help and contributions from\n[Jamie Townsend](https://github.com/j-towns),\n[Peter Hawkins](https://github.com/hawkinsp),\n+[Jonathan Ragan-Kelley](https://people.eecs.berkeley.edu/~jrk/),\n[Alex Wiltschko](http://github.com/alexbw),\nGeorge Dahl,\n+[Stephan Hoyer](http://stephanhoyer.com/),\n+Sam Schoenholz,\n[Eli Bendersky](https://github.com/eliben),\nZak Stone,\n[Alexey Radul](https://github.com/axch),\n"
}
] | Python | Apache License 2.0 | google/jax | add more contributors to readme |
260,335 | 09.12.2018 06:47:38 | 28,800 | b7f6adaa90b0ed858b240619da6a12565efda61d | add dot_general batching rule | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -1231,14 +1231,41 @@ def dot_general_transpose_rhs(g, x, dimension_numbers):\nreturn dot_general_transpose_lhs(g, x, swapped_dimension_numbers, True)\n-# def dot_general_batch_rule(batched_args, batch_dims, dimension_numbers):\n-# assert False # TODO\n+def dot_general_batch_rule(batched_args, batch_dims, dimension_numbers):\n+ (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\n+ lhs, rhs = batched_args\n+ lbd, rbd = batch_dims\n+ assert lbd is not None or rbd is not None\n+\n+ if lbd is not None:\n+ if lbd != 0:\n+ lhs = batching.move_dim_to_front(lhs, lbd)\n+ lbd = 0\n+ else:\n+ assert rbd is not None\n+ lhs = broadcast(lhs, (rhs.shape[rbd],))\n+ lhs_contract = tuple(onp.add(1, lhs_contract))\n+ lhs_batch = (0,) + tuple(onp.add(1, lhs_batch))\n+\n+ if rbd is not None:\n+ if rbd != 0:\n+ rhs = batching.move_dim_to_front(rhs, rbd)\n+ rbd = 0\n+ else:\n+ assert lbd is not None\n+ rhs = broadcast(rhs, (lhs.shape[lbd],))\n+ rhs_contract = tuple(onp.add(1, rhs_contract))\n+ rhs_batch = (0,) + tuple(onp.add(1, rhs_batch))\n+\n+ new_dimension_numbers = [(lhs_contract, rhs_contract), (lhs_batch, rhs_batch)]\n+ batched_out = dot_general(lhs, rhs, new_dimension_numbers)\n+ return batched_out, 0\ndot_general_p = standard_primitive(dot_general_shape_rule,\ndot_general_dtype_rule, 'dot_general')\nad.defbilinear(dot_general_p,\ndot_general_transpose_lhs, dot_general_transpose_rhs)\n-# batching.primitive_batchers[dot_general_p] = dot_general_batch_rule\n+batching.primitive_batchers[dot_general_p] = dot_general_batch_rule\ndef broadcast_shape_rule(operand, sizes):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -194,6 +194,39 @@ class BatchingTest(jtu.JaxTestCase):\nself.assertAllClose(ans[i], expected_ans, check_dtypes=False)\n+ def testDotGeneral(self):\n+ R = onp.random.RandomState(0).randn\n+\n+ x = R(10, 3, 4, 5)\n+ y = R(10, 3, 5, 6)\n+ ans = vmap(lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))]), x, y)\n+ expected = lax.dot_general(x, y, [((3,), (2,)), ((0, 1), (0, 1))])\n+ self.assertAllClose(ans, expected, check_dtypes=True)\n+\n+ x = R(3, 4, 10, 5)\n+ y = R(3, 10, 5, 6)\n+ ans = vmap(lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))]), x, y,\n+ in_axes=(2, 1))\n+ fun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\n+ expected = onp.stack([fun(x[..., i, :], y[:, i, ...]) for i in range(10)])\n+ self.assertAllClose(ans, expected, check_dtypes=True)\n+\n+ x = R(3, 4, 5, 10)\n+ y = R(3, 5, 6)\n+ ans = vmap(lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))]), x, y,\n+ in_axes=(3, None))\n+ fun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\n+ expected = onp.stack([fun(x[..., i], y) for i in range(10)])\n+ self.assertAllClose(ans, expected, check_dtypes=True)\n+\n+ x = R(3, 4, 5)\n+ y = R(3, 5, 10, 6)\n+ ans = vmap(lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))]), x, y,\n+ in_axes=(None, 2))\n+ fun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\n+ expected = onp.stack([fun(x, y[..., i, :]) for i in range(10)])\n+ self.assertAllClose(ans, expected, check_dtypes=True)\n+\nif __name__ == '__main__':\nconfig.config_with_absl()\n"
}
] | Python | Apache License 2.0 | google/jax | add dot_general batching rule |
260,335 | 09.12.2018 07:27:03 | 28,800 | 499ea19e44ad68626d3ce10550f27feaf03812df | fix 'unreachable' bug in dot batching rule | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -1112,6 +1112,7 @@ def dot_batch_rule(batched_args, batch_dims):\nlbd, rbd = batch_dims\nT = lambda x: transpose(x, onp.arange(onp.ndim(x))[::-1])\n+ # in some cases, we can call dot instead of dot_general\nif max(onp.ndim(lhs), onp.ndim(rhs)) <= 2:\nif rbd is None:\nassert lbd in (0, 1)\n@@ -1127,7 +1128,13 @@ def dot_batch_rule(batched_args, batch_dims):\nelse:\nreturn dot(rhs, T(lhs)), 0\n- assert False # unreachable\n+ assert lbd is not None and rbd is not None\n+ assert lhs.ndim == rhs.ndim == 2 # dot only supports rank 1 and above\n+ if lbd != 0:\n+ batching.move_dim_to_front(lhs, lbd)\n+ if rbd != 0:\n+ batching.move_dim_to_front(rhs, rbd)\n+ return dot_general(lhs, rhs, [((1,), (1,)), ((0,), (0,))])\nif lbd is None:\nassert rbd is not None\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -29,7 +29,7 @@ from jax.api import vmap\nfrom jax.config import config\nfrom jax.core import unit\nfrom jax.interpreters import partial_eval as pe\n-from jax.util import partial\n+from jax.util import partial, curry\nimport functools as fn\n@@ -227,6 +227,23 @@ class BatchingTest(jtu.JaxTestCase):\nexpected = onp.stack([fun(x, y[..., i, :]) for i in range(10)])\nself.assertAllClose(ans, expected, check_dtypes=True)\n+ def testDot(self):\n+ # these tests are based on @shoyer's notebook studying gufuncs\n+ curried_vmap = curry(vmap)\n+\n+ def vecvec(a, b):\n+ dot = np.dot\n+ for ndim in range(1, max(a.ndim, b.ndim)):\n+ a_ax = 0 if a.ndim > ndim else None\n+ b_ax = 0 if b.ndim > ndim else None\n+ dot = curried_vmap(dot, in_axes=(a_ax, b_ax))\n+ return dot(a, b)\n+\n+ assert vecvec(np.zeros((3,)), np.zeros((3,))).shape == ()\n+ assert vecvec(np.zeros((2, 3)), np.zeros((3,))).shape == (2,)\n+ # TODO(mattjj): this fails due to an xla error in dot_general\n+ # assert vecvec(np.zeros((4, 2, 3)), np.zeros((3,))).shape == (4, 2)\n+\nif __name__ == '__main__':\nconfig.config_with_absl()\n"
}
] | Python | Apache License 2.0 | google/jax | fix 'unreachable' bug in dot batching rule |
260,335 | 09.12.2018 07:46:24 | 28,800 | f50996df640ae7a8fb51c73b1cd8570ebdaecf6a | update gufunc notebook w/ dot_general batch rule | [
{
"change_type": "MODIFY",
"old_path": "notebooks/gufuncs.ipynb",
"new_path": "notebooks/gufuncs.ipynb",
"diff": "\"assert matmat(np.zeros((2, 3)), np.zeros((3, 4))).shape == (2, 4)\\n\",\n\"assert matmat(np.zeros((2, 3)), np.zeros((1, 3, 4))).shape == (1, 2, 4)\\n\",\n\"assert matmat(np.zeros((5, 2, 3)), np.zeros((1, 3, 4))).shape == (5, 2, 4)\\n\",\n- \"# raises: NotImplementedError: Batching rule for 'dot_general' not implemented\\n\",\n- \"# assert matmat(np.zeros((6, 5, 2, 3)), np.zeros((3, 4))).shape == (6, 5, 2, 4)\\n\",\n+ \"assert matmat(np.zeros((6, 5, 2, 3)), np.zeros((3, 4))).shape == (6, 5, 2, 4)\\n\",\n\"\\n\",\n\"assert matvec(np.zeros((2, 3)), np.zeros((3,))).shape == (2,)\\n\",\n\"assert matvec(np.zeros((2, 3)), np.zeros((1, 3))).shape == (1, 2)\\n\",\n\"assert matvec(np.zeros((4, 2, 3)), np.zeros((1, 3))).shape == (4, 2)\\n\",\n- \"# raises: NotImplementedError: Batching rule for 'dot_general' not implemented\\n\",\n- \"# assert matvec(np.zeros((5, 4, 2, 3)), np.zeros((1, 3))).shape == (5, 4, 2)\\n\",\n+ \"assert matvec(np.zeros((5, 4, 2, 3)), np.zeros((1, 3))).shape == (5, 4, 2)\\n\",\n\"\\n\",\n\"assert vecvec(np.zeros((3,)), np.zeros((3,))).shape == ()\\n\",\n- \"# these raise: AssertionError -- assert False # unreachable\\n\",\n+ \"# these next two don't work yet\\n\",\n\"# assert vecvec(np.zeros((2, 3)), np.zeros((3,))).shape == (2,)\\n\",\n\"# assert vecvec(np.zeros((4, 2, 3)), np.zeros((3,))).shape == (4, 2) \"\n],\n\"cell_type\": \"code\",\n\"source\": [\n\"assert magnitude(np.arange(3.0)).shape == ()\\n\",\n- \"# these also raise (\\\"unreachable\\\")\\n\",\n+ \"# these next two don't work yet\\n\",\n\"# assert magnitude(np.arange(6.0).reshape(2, 3)).shape == (2,)\\n\",\n\"# assert magnitude(np.arange(6.0).reshape(1, 2, 3)).shape == (1, 2,)\"\n],\n\"metadata\": {\n\"id\": \"MSyxOrUPixDI\",\n\"colab_type\": \"code\",\n- \"outputId\": \"e8d05c0c-a337-4a9c-bdc8-8645616b4b93\",\n+ \"outputId\": \"65ff7d75-6658-402b-85f7-558924ebe934\",\n\"colab\": {\n\"base_uri\": \"https://localhost:8080/\",\n- \"height\": 35\n+ \"height\": 34\n}\n},\n\"cell_type\": \"code\",\n\"metadata\": {\n\"id\": \"7UZGOS8FGT_D\",\n\"colab_type\": \"code\",\n- \"outputId\": \"fdb7dad9-721c-439a-ffd6-c469445f371a\",\n+ \"outputId\": \"28dd7afe-2bbe-485a-ac6e-f8ca6bac9f37\",\n\"colab\": {\n\"base_uri\": \"https://localhost:8080/\",\n- \"height\": 71\n+ \"height\": 68\n}\n},\n\"cell_type\": \"code\",\n\"metadata\": {\n\"id\": \"n2Fz91ptjM_7\",\n\"colab_type\": \"code\",\n- \"outputId\": \"0a75386a-8238-4fca-c0c8-1d8ac9573e48\",\n+ \"outputId\": \"9ea07d50-7cbc-423c-bffb-92047dcca62c\",\n\"colab\": {\n\"base_uri\": \"https://localhost:8080/\",\n- \"height\": 71\n+ \"height\": 68\n}\n},\n\"cell_type\": \"code\",\n\"metadata\": {\n\"id\": \"G-AyCkAK4RKT\",\n\"colab_type\": \"code\",\n- \"outputId\": \"b4738597-d189-43b9-ad52-67a527b4c179\",\n+ \"outputId\": \"dccce2b9-a7ec-4634-d45d-d440ddc1e1fe\",\n\"colab\": {\n\"base_uri\": \"https://localhost:8080/\",\n- \"height\": 71\n+ \"height\": 68\n}\n},\n\"cell_type\": \"code\",\n\"metadata\": {\n\"id\": \"_FhnjYMUjZgI\",\n\"colab_type\": \"code\",\n- \"outputId\": \"b81b93ac-5f6a-43ce-ce85-17d6c237891a\",\n+ \"outputId\": \"96c71fea-b30e-4057-9634-22f4d62218e2\",\n\"colab\": {\n\"base_uri\": \"https://localhost:8080/\",\n- \"height\": 71\n+ \"height\": 68\n}\n},\n\"cell_type\": \"code\",\n"
}
] | Python | Apache License 2.0 | google/jax | update gufunc notebook w/ dot_general batch rule |
260,335 | 09.12.2018 08:56:54 | 28,800 | b907bcd5ae8f0161303ca474d9d1e121f4cd6237 | improve jaxlib wheel building script | [
{
"change_type": "MODIFY",
"old_path": "build/build_jaxlib_wheels.sh",
"new_path": "build/build_jaxlib_wheels.sh",
"diff": "@@ -3,28 +3,31 @@ set -xev\nJAXLIB_VERSION=$(sed -n \"s/^ \\+version=[']\\(.*\\)['],$/\\\\1/p\" jax/build/setup.py)\nPYTHON_VERSIONS=\"py2 py3\"\n-CUDA_VERSIONS=\"9.2\" # \"9.2 10.0\"\n+CUDA_VERSIONS=\"9.0 9.2 10.0\"\nCUDA_VARIANTS=\"cuda\" # \"cuda cuda-included\"\nmkdir -p dist\n-for CUDA_VERSION in $CUDA_VERSIONS\n-do\n- docker build -t jaxbuild jax/build/ --build-arg CUDA_VERSION=$CUDA_VERSION\n+# build the pypi linux packages, tagging with manylinux1 for pypi reasons\n+docker build -t jaxbuild jax/build/\nfor PYTHON_VERSION in $PYTHON_VERSIONS\ndo\nmkdir -p dist/nocuda/\nnvidia-docker run -it --tmpfs /build:exec --rm -v $(pwd)/dist:/dist jaxbuild $PYTHON_VERSION nocuda\nmv dist/*.whl dist/nocuda/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-manylinux1_x86_64.whl\n+done\n+# build the cuda linux packages, tagging with linux_x86_64\n+for CUDA_VERSION in $CUDA_VERSIONS\n+do\n+ docker build -t jaxbuild jax/build/ --build-arg CUDA_VERSION=$CUDA_VERSION\n+ for PYTHON_VERSION in $PYTHON_VERSIONS\n+ do\nfor CUDA_VARIANT in $CUDA_VARIANTS\ndo\n- mkdir -p dist/cuda${CUDA_VERSION//.}\n+ mkdir -p dist/${CUDA_VARIANT}${CUDA_VERSION//.}\nnvidia-docker run -it --tmpfs /build:exec --rm -v $(pwd)/dist:/dist jaxbuild $PYTHON_VERSION $CUDA_VARIANT\n- mv dist/*.whl dist/cuda${CUDA_VERSION//.}/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-linux_x86_64.whl\n+ mv dist/*.whl dist/${CUDA_VARIANT}${CUDA_VERSION//.}/jaxlib-${JAXLIB_VERSION}-${PYTHON_VERSION}-none-linux_x86_64.whl\ndone\ndone\ndone\n-\n-echo \"now you might want to run something like:\"\n-echo \"python3 -m twine upload --repository-url https://test.pypi.org/legacy/ dist/nocuda/*.whl --verbose\"\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/api.py",
"new_path": "jax/api.py",
"diff": "@@ -57,7 +57,6 @@ def grad(fun, argnums=0):\ng = vjp_py(onp.ones((), onp.result_type(ans)))\nreturn g[0] if isinstance(argnums, int) else g\n-\nreturn grad_f\n@curry\n"
}
] | Python | Apache License 2.0 | google/jax | improve jaxlib wheel building script |
260,560 | 09.12.2018 10:05:39 | 28,800 | 2bbc046d9dfa2f282e0ef7912f4d6274eba15289 | Require protobuf 3.6.0 or later | [
{
"change_type": "MODIFY",
"old_path": "build/setup.py",
"new_path": "build/setup.py",
"diff": "@@ -25,7 +25,7 @@ setup(\nauthor='JAX team',\nauthor_email='jax-dev@google.com',\npackages=['jaxlib'],\n- install_requires=['numpy>=1.12', 'six', 'protobuf', 'absl-py'],\n+ install_requires=['numpy>=1.12', 'six', 'protobuf>=3.6.0', 'absl-py'],\nurl='https://github.com/google/jax',\nlicense='Apache-2.0',\npackage_data={'jaxlib': binary_libs},\n"
},
{
"change_type": "MODIFY",
"old_path": "setup.py",
"new_path": "setup.py",
"diff": "@@ -22,7 +22,7 @@ setup(\nauthor_email='jax-dev@google.com',\npackages=['jax', 'jax.lib', 'jax.interpreters', 'jax.numpy', 'jax.scipy',\n'jax.scipy.stats', 'jax.experimental'],\n- install_requires=['numpy>=1.12', 'six', 'protobuf', 'absl-py',\n+ install_requires=['numpy>=1.12', 'six', 'protobuf>=3.6.0', 'absl-py',\n'opt_einsum'],\nurl='https://github.com/google/jax',\nlicense='Apache-2.0',\n"
}
] | Python | Apache License 2.0 | google/jax | Require protobuf 3.6.0 or later |
260,335 | 09.12.2018 10:54:37 | 28,800 | 38c7a07248fbccf5f9a8bde5cd88972c747c52aa | download mnist example data w/ six.moves.request
fixes | [
{
"change_type": "MODIFY",
"old_path": "examples/datasets.py",
"new_path": "examples/datasets.py",
"diff": "@@ -23,7 +23,7 @@ import gzip\nimport os\nfrom os import path\nimport struct\n-import urllib2\n+from six.moves.urllib.request import urlretrieve\nimport numpy as np\n@@ -37,8 +37,7 @@ def _download(url, filename):\nos.makedirs(_DATA)\nout_file = path.join(_DATA, filename)\nif not path.isfile(out_file):\n- with open(out_file, \"wb\") as f:\n- f.write(urllib2.urlopen(url, out_file).read())\n+ urlretrieve(url, out_file)\nprint(\"downloaded {} to {}\".format(url, _DATA))\n"
}
] | Python | Apache License 2.0 | google/jax | download mnist example data w/ six.moves.request
fixes #28 |
260,335 | 10.12.2018 07:13:51 | 28,800 | 0f8710a6e341f5e7b130867ce4e8d48091e12a76 | add another link to Autograd in the readme | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -8,7 +8,8 @@ JAX is [Autograd](https://github.com/hips/autograd) and\n[XLA](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/g3doc/overview.md),\nbrought together for high-performance machine learning research.\n-With its updated version of Autograd, JAX can automatically differentiate native\n+With its updated version of [Autograd](https://github.com/hips/autograd),\n+JAX can automatically differentiate native\nPython and NumPy functions. It can differentiate through loops, branches,\nrecursion, and closures, and it can take derivatives of derivatives of\nderivatives. It supports reverse-mode differentiation (a.k.a. backpropagation)\n"
}
] | Python | Apache License 2.0 | google/jax | add another link to Autograd in the readme |
260,335 | 10.12.2018 08:55:17 | 28,800 | 72b77b5bbe3f181d0efb1cfd9cb88b477f4a4a84 | fix typo in notebook text (closes | [
{
"change_type": "MODIFY",
"old_path": "notebooks/gufuncs.ipynb",
"new_path": "notebooks/gufuncs.ipynb",
"diff": "\"\\n\",\n\"1. Write the inner loops yourself in C.\\n\",\n\"2. [`np.vectorize`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.vectorize.html) creates something kind of like a gufunc, but it's painfully slow: the outer loop is performed in Python.\\n\",\n- \"3. [`numba.guvectorize`](https://numba.pydata.org/numba-doc/dev/user/vectorize.html) can work well, if you don't need further code transformations like automatic differention.\\n\",\n+ \"3. [`numba.guvectorize`](https://numba.pydata.org/numba-doc/dev/user/vectorize.html) can work well, if you don't need further code transformations like automatic differentiation.\\n\",\n\"\\n\",\n\"JAX's `vmap` contains all the core functionality we need to write functions that work like gufuncs. JAX gufuncs play nicely with other transformations like `grad` and `jit`.\\n\",\n\"\\n\",\n"
}
] | Python | Apache License 2.0 | google/jax | fix typo in notebook text (closes #22) |
260,446 | 11.12.2018 13:58:33 | -28,800 | 7180eb031b4c7ddaf82e61b51b43ba6f6984f25d | More informative error on trying to concatenate 0-dim arrays. | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -633,6 +633,8 @@ def stack(arrays):\ndef concatenate(arrays, axis=0):\nif not arrays:\nraise ValueError(\"Need at least one array to concatenate.\")\n+ if ndim(arrays[0]) == 0:\n+ raise ValueError(\"Zero-dimensional arrays cannot be concatenated.\")\nreturn lax.concatenate(_promote_dtypes(*arrays), axis % ndim(arrays[0]))\n"
}
] | Python | Apache License 2.0 | google/jax | More informative error on trying to concatenate 0-dim arrays. |
260,474 | 09.12.2018 13:33:15 | 18,000 | 1350db2b799430e55a73d84c1bc551ecaa9ab4f7 | Added higher-order differentiation checks in lax_test and fixed some bugs. Conv tests currently failing. | [
{
"change_type": "ADD",
"old_path": "WORKSPACE",
"new_path": "WORKSPACE",
"diff": ""
},
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "@@ -28,9 +28,9 @@ from .util import unzip2, safe_zip, safe_map, partial\nfrom .pprint_util import pp, vcat, hcat, pp_kv_pairs\n# TODO(dougalm): the trace cache breaks the leak detector. Consisder solving.\n-# TODO(mattjj): put each of these behind flags\ncheck_leaks = False\n-skip_checks = True # not __debug__ # google doesn't use -O\n+# TODO(dougalm): put this behind a flag that's enabled during testing\n+skip_checks = False # not __debug__ # google doesn't use -O\nzip = safe_zip\nmap = safe_map\n@@ -435,7 +435,9 @@ pytype_aval_mappings = {}\nclass JaxTuple(tuple):\ndef __new__(cls, xs):\n- assert skip_checks or all(map(valid_jaxtype, xs)), xs\n+ if not skip_checks:\n+ xs = list(xs)\n+ assert all(map(valid_jaxtype, xs)), xs\nreturn tuple.__new__(cls, xs)\ndef __repr__(self):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -1799,10 +1799,12 @@ def index_untake_jvp(primals, tangents, axes, jaxpr, consts):\ndef index_untake_transpose_rule(t, src, dst, *idxs, **kwargs):\naxes = kwargs['axes']\n+ t_src = t_dst = None\nif src is None:\nt_src = index_take(t, idxs, axes)\nif dst is None:\nt_dst = t\n+\nreturn [t_src, t_dst] + [None] * len(idxs)\nindex_untake_p = standard_primitive(\n@@ -2123,7 +2125,7 @@ def sort_key_val_jvp(primals, tangents, dimension):\nvalues_tangents_out = sort_jvp_rule(values_tangents, keys, dimension)\ntangents_out = keys_tangents_out, values_tangents_out\n- return core.pack(val_out), core.pack(tangents_out)\n+ return core.pack(val_out), ad.TangentTuple(tangents_out)\ndef sort_key_val_transpose_rule(t, keys, values, dimension):\nt_keys, t_values = t\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/test_util.py",
"new_path": "jax/test_util.py",
"diff": "@@ -37,7 +37,7 @@ from .tree_util import tree_multimap, tree_all, tree_map, tree_reduce\nFLAGS = flags.FLAGS\nflags.DEFINE_enum(\n'jax_test_dut',\n- None,\n+ 'cpu',\nenum_values=['cpu', 'gpu', 'tpu'],\nhelp=\n'Describes the device under test in case special consideration is required.'\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/build_defs.bzl",
"new_path": "tests/build_defs.bzl",
"diff": "@@ -31,9 +31,7 @@ def jax_test(\nfail(\"Only one test source file is currently supported.\")\n# Deps that are linked into all test target variants.\n- all_test_deps = [\n- \":libjax\",\n- ]\n+ all_test_deps = []\ndisabled_tags = [\"manual\", \"notap\", \"disabled\"]\nnative.py_test(\nname = name + \"_cpu\",\n@@ -45,33 +43,33 @@ def jax_test(\nargs = args + [\"--jax_enable_x64=true --jax_test_dut=cpu --jax_platform_name=Host\"],\ntags = (disabled_tags if \"cpu\" in disable else []),\n)\n- native.py_test(\n- name = name + \"_cpu_x32\",\n- main = main,\n- srcs = srcs,\n- data = data,\n- deps = deps + all_test_deps,\n- shard_count = shard_count.get(\"cpu\") if shard_count else None,\n- args = args + [\"--jax_enable_x64=false --jax_test_dut=cpu --jax_platform_name=Host\"],\n- tags = (disabled_tags if \"cpu\" in disable else []),\n- )\n- native.py_test(\n- name = name + \"_gpu\",\n- main = main,\n- srcs = srcs,\n- data = data,\n- args = args + [\"--jax_test_dut=gpu --jax_enable_x64=true --jax_platform_name=CUDA\"],\n- deps = deps + all_test_deps,\n- tags = [\"requires-gpu-sm35\"] + (disabled_tags if \"gpu\" in disable else []),\n- shard_count = shard_count.get(\"gpu\") if shard_count else None,\n- )\n- native.py_test(\n- name = name + \"_gpu_x32\",\n- main = main,\n- srcs = srcs,\n- data = data,\n- args = args + [\"--jax_test_dut=gpu --jax_enable_x64=false --jax_platform_name=CUDA\"],\n- deps = deps + all_test_deps,\n- tags = [\"requires-gpu-sm35\"] + (disabled_tags if \"gpu\" in disable else []),\n- shard_count = shard_count.get(\"gpu\") if shard_count else None,\n- )\n+ # native.py_test(\n+ # name = name + \"_cpu_x32\",\n+ # main = main,\n+ # srcs = srcs,\n+ # data = data,\n+ # deps = deps + all_test_deps,\n+ # shard_count = shard_count.get(\"cpu\") if shard_count else None,\n+ # args = args + [\"--jax_enable_x64=false --jax_test_dut=cpu --jax_platform_name=Host\"],\n+ # tags = (disabled_tags if \"cpu\" in disable else []),\n+ # )\n+ # native.py_test(\n+ # name = name + \"_gpu\",\n+ # main = main,\n+ # srcs = srcs,\n+ # data = data,\n+ # args = args + [\"--jax_test_dut=gpu --jax_enable_x64=true --jax_platform_name=CUDA\"],\n+ # deps = deps + all_test_deps,\n+ # tags = [\"requires-gpu-sm35\"] + (disabled_tags if \"gpu\" in disable else []),\n+ # shard_count = shard_count.get(\"gpu\") if shard_count else None,\n+ # )\n+ # native.py_test(\n+ # name = name + \"_gpu_x32\",\n+ # main = main,\n+ # srcs = srcs,\n+ # data = data,\n+ # args = args + [\"--jax_test_dut=gpu --jax_enable_x64=false --jax_platform_name=CUDA\"],\n+ # deps = deps + all_test_deps,\n+ # tags = [\"requires-gpu-sm35\"] + (disabled_tags if \"gpu\" in disable else []),\n+ # shard_count = shard_count.get(\"gpu\") if shard_count else None,\n+ # )\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -1411,7 +1411,13 @@ LAX_GRAD_OPS = [\ndef check_grads(f, args, order, atol=None, rtol=None, eps=None):\n- # TODO(mattjj,dougalm): add higher-order check\n+ if order > 1:\n+ def f_vjp(*args):\n+ out_primal_py, vjp_py = api.vjp(f, *args)\n+ return vjp_py(out_primal_py)\n+\n+ check_grads(f_vjp, args, order - 1, atol=atol, rtol=rtol, eps=eps)\n+ else:\ndefault_tol = 1e-6 if FLAGS.jax_enable_x64 else 1e-2\natol = atol or default_tol\nrtol = rtol or default_tol\n"
}
] | Python | Apache License 2.0 | google/jax | Added higher-order differentiation checks in lax_test and fixed some bugs. Conv tests currently failing. |
260,335 | 10.12.2018 17:18:56 | 28,800 | 0d64aea6bb0cd55a2abcc4e2958633764fdcdc74 | clean up conv dimension_numbers handling | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -44,12 +44,6 @@ from .lib import xla_bridge\n_max = builtins.max\n_min = builtins.max\n-if six.PY3:\n- def maketrans(s1, s2):\n- return s1.maketrans(s1, s2)\n-else:\n- maketrans = string.maketrans\n-\n### traceables\ndef neg(x): return neg_p.bind(x)\n@@ -126,28 +120,20 @@ def concatenate(operands, dimension):\nreturn concatenate_p.bind(*operands, dimension=dimension,\noperand_shapes=tuple(o.shape for o in operands))\n-def conv(lhs, rhs, window_strides, padding):\n- pads = padtype_to_pads(lhs.shape[2:], rhs.shape[2:], window_strides, padding)\n- return conv_general_dilated_p.bind(\n- lhs, rhs, window_strides=tuple(window_strides), padding=tuple(pads),\n- lhs_dilation=(), rhs_dilation=(), dimension_numbers=None,\n- lhs_shape=lhs.shape, rhs_shape=rhs.shape)\n-\n-def conv_with_general_padding(lhs, rhs, window_strides, padding,\n- lhs_dilation, rhs_dilation):\n- return conv_general_dilated_p.bind(\n- lhs, rhs, window_strides=tuple(window_strides), padding=tuple(padding),\n- lhs_dilation=(), rhs_dilation=(), dimension_numbers=None,\n- lhs_shape=lhs.shape, rhs_shape=rhs.shape)\n-\n-def conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation,\n- rhs_dilation, dimension_numbers):\n+def conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation=None,\n+ rhs_dilation=None, dimension_numbers=None):\n+ if type(dimension_numbers) is not ConvDimensionNumbers:\n+ dimension_numbers = conv_dimension_numbers(\n+ lhs.shape, rhs.shape, dimension_numbers)\nif isinstance(padding, str):\n- perms = conv_general_permutations(dimension_numbers)\n- lhs_perm, rhs_perm, _ = perms\n- padding = padtype_to_pads(onp.take(lhs.shape, lhs_perm)[2:],\n- onp.take(rhs.shape, rhs_perm)[2:],\n+ lhs_perm, rhs_perm, _ = dimension_numbers\n+ padding = padtype_to_pads(\n+ onp.take(lhs.shape, lhs_perm)[2:], onp.take(rhs.shape, rhs_perm)[2:],\nwindow_strides, padding)\n+ if lhs_dilation is None:\n+ lhs_dilation = (1,) * (lhs.ndim - 2)\n+ if rhs_dilation is None:\n+ rhs_dilation = (1,) * (rhs.ndim - 2)\nreturn conv_general_dilated_p.bind(\nlhs, rhs, window_strides=tuple(window_strides), padding=tuple(padding),\nlhs_dilation=tuple(lhs_dilation), rhs_dilation=tuple(rhs_dilation),\n@@ -405,6 +391,17 @@ opaque_param_ids = itertools.count()\n### convenience wrappers around traceables\n+def conv(lhs, rhs, window_strides, padding):\n+ pads = padtype_to_pads(lhs.shape[2:], rhs.shape[2:], window_strides, padding)\n+ return conv_general_dilated(lhs, rhs, window_strides, padding)\n+\n+def conv_with_general_padding(lhs, rhs, window_strides, padding,\n+ lhs_dilation, rhs_dilation):\n+ return conv_general_dilated(\n+ lhs, rhs, window_strides, padding, lhs_dilation=lhs_dilation,\n+ rhs_dilation=rhs_dilation)\n+\n+\ndef full_like(x, fill_value, dtype=None, shape=None):\n\"\"\"Create a full array like np.full based on the example array `x`.\n@@ -952,31 +949,9 @@ batching.defvectorized(bitcast_convert_type_p)\ndef conv_general_dilated_shape_rule(\nlhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\n- dimension_numbers=None, **unused_kwargs):\n- if dimension_numbers is None:\n- lhs_dilated = _dilate_shape(lhs.shape, lhs_dilation)\n- rhs_dilated = _dilate_shape(rhs.shape, rhs_dilation)\n- _check_conv_shapes('conv_general_dilated', lhs_dilated, rhs_dilated,\n- window_strides)\n- return conv_shape_tuple(lhs_dilated, rhs_dilated, window_strides, padding)\n- else:\n- if not isinstance(dimension_numbers, (tuple, list)):\n- msg = \"conv_general_dilated dimension_numbers must be tuple/list, got {}.\"\n- raise TypeError(msg.format(type(dimension_numbers)))\n- if len(dimension_numbers) != 3:\n- msg = \"conv_general_dilated dimension_numbers must be length 3, got {}.\"\n- raise TypeError(msg.format(len(dimension_numbers)))\n- if not all(isinstance(elt, str) for elt in dimension_numbers):\n- msg = (\"conv_general_dilated dimension_numbers elements must be strings, \"\n- \"got {}.\")\n- raise TypeError(msg.format(tuple(map(type, dimension_numbers))))\n- msg = (\"conv_general_dilated dimension_numbers[{}] must have len equal to \"\n- \"the ndim of lhs and rhs, got {} for lhs and rhs shapes {} and {}.\")\n- for i, elt in enumerate(dimension_numbers):\n- if len(elt) != lhs.ndim:\n- raise TypeError(msg.format(i, len(elt), lhs.shape, rhs.shape))\n-\n- lhs_perm, rhs_perm, out_perm = conv_general_permutations(dimension_numbers)\n+ dimension_numbers, **unused_kwargs):\n+ assert type(dimension_numbers) is ConvDimensionNumbers\n+ lhs_perm, rhs_perm, out_perm = dimension_numbers\nlhs_trans = _dilate_shape(onp.take(lhs.shape, lhs_perm), lhs_dilation)\nrhs_trans = _dilate_shape(onp.take(rhs.shape, rhs_perm), rhs_dilation)\nout_trans = conv_shape_tuple(lhs_trans, rhs_trans, window_strides, padding)\n@@ -988,19 +963,17 @@ def conv_general_dilated_dtype_rule(\nreturn binop_dtype_rule(_input_dtype, [_f32, _f32], 'conv_general_dilated',\nlhs, rhs)\n+_conv_transpose = lambda spec: (spec[1], spec[0]) + spec[2:]\n+_conv_sdims = lambda spec: spec[2:]\n+\ndef conv_general_dilated_transpose_lhs(\ng, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\ndimension_numbers, lhs_shape, rhs_shape):\n- if dimension_numbers is None:\n- nd = len(lhs_shape)\n- lhs_sdims = rhs_sdims = out_sdims = list(range(2, nd))\n- trans_dimension_numbers = ConvolutionDimensionNumbers(\n- tuple(range(nd)), (1, 0) + tuple(range(2, nd)), tuple(range(nd)))\n- else:\n- lhs_sdims, rhs_sdims, out_sdims = _get_sdims(dimension_numbers)\n+ assert type(dimension_numbers) is ConvDimensionNumbers\n+ lhs_sdims, rhs_sdims, out_sdims = map(_conv_sdims, dimension_numbers)\nlhs_spec, rhs_spec, out_spec = dimension_numbers\n- trans_dimension_numbers = out_spec, _charswap(\"I\", \"O\", rhs_spec), lhs_spec\n-\n+ t_rhs_spec = _conv_transpose(rhs_spec)\n+ trans_dimension_numbers = ConvDimensionNumbers(lhs_spec, t_rhs_spec, out_spec)\npadding = _conv_general_vjp_lhs_padding(\nonp.take(lhs_shape, lhs_sdims), onp.take(rhs_shape, rhs_sdims),\nwindow_strides, onp.take(g.shape, out_sdims), padding, lhs_dilation,\n@@ -1011,24 +984,13 @@ def conv_general_dilated_transpose_lhs(\nlhs_dilation=window_strides, rhs_dilation=rhs_dilation,\ndimension_numbers=trans_dimension_numbers)\n-\ndef conv_general_dilated_transpose_rhs(\ng, lhs, window_strides, padding, lhs_dilation, rhs_dilation,\ndimension_numbers, lhs_shape, rhs_shape):\n- if dimension_numbers is None:\n- nd = len(lhs_shape)\n- lhs_sdims = rhs_sdims = out_sdims = list(range(2, nd))\n- trans_dimension_numbers = ConvolutionDimensionNumbers(\n- (1, 0) + tuple(range(2, nd)),\n- (1, 0) + tuple(range(2, nd)),\n- (1, 0) + tuple(range(2, nd)))\n- else:\n- lhs_sdims, rhs_sdims, out_sdims = _get_sdims(dimension_numbers)\n- lhs_spec, rhs_spec, out_spec = dimension_numbers\n- trans_dimension_numbers = (_charswap(\"C\", \"N\", lhs_spec),\n- out_spec.translate(maketrans(\"NC\", \"IO\")),\n- rhs_spec.translate(maketrans(\"IO\", \"NC\")))\n-\n+ assert type(dimension_numbers) is ConvDimensionNumbers\n+ lhs_sdims, rhs_sdims, out_sdims = map(_conv_sdims, dimension_numbers)\n+ transposed = map(_conv_transpose, dimension_numbers)\n+ trans_dimension_numbers = ConvDimensionNumbers(*transposed)\npadding = _conv_general_vjp_rhs_padding(\nonp.take(lhs_shape, lhs_sdims), onp.take(rhs_shape, rhs_sdims),\nwindow_strides, onp.take(g.shape, out_sdims), padding, lhs_dilation,\n@@ -1038,11 +1000,10 @@ def conv_general_dilated_transpose_rhs(\nlhs_dilation=lhs_dilation, rhs_dilation=window_strides,\ndimension_numbers=trans_dimension_numbers)\n-\ndef conv_general_dilated_translation_rule(\nc, lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\ndimension_numbers, **unused_kwargs):\n- if isinstance(dimension_numbers, ConvolutionDimensionNumbers):\n+ assert type(dimension_numbers) is ConvDimensionNumbers\ndimension_numbers = _conv_general_proto(dimension_numbers)\nreturn c.ConvGeneralDilated(lhs, rhs, window_strides, padding, lhs_dilation,\nrhs_dilation, dimension_numbers)\n@@ -2285,35 +2246,6 @@ def _check_shapelike(fun_name, arg_name, obj):\nraise TypeError(msg.format(fun_name, arg_name, obj))\n-def conv_general_permutations(dimension_numbers):\n- \"\"\"Utility for convolution dimension permutations relative to Conv HLO.\"\"\"\n- lhs_spec, rhs_spec, out_spec = dimension_numbers\n- lhs_char, rhs_char, out_char = charpairs = (\"N\", \"C\"), (\"O\", \"I\"), (\"N\", \"C\")\n- for i, (a, b) in enumerate(charpairs):\n- if not dimension_numbers[i].count(a) == dimension_numbers[i].count(b) == 1:\n- msg = (\"convolution dimension_numbers[{}] must contain the characters \"\n- \"'{}' and '{}' exatly once, got {}.\")\n- raise TypeError(msg.format(i, a, b, dimension_numbers[i]))\n- if len(dimension_numbers[i]) != len(set(dimension_numbers[i])):\n- msg = (\"convolution dimension_numbers[{}] cannot have duplicate \"\n- \"characters, got {}.\")\n- raise TypeError(msg.format(i, dimension_numbers[i]))\n- if not (set(lhs_spec) - set(lhs_char) == set(rhs_spec) - set(rhs_char) ==\n- set(out_spec) - set(out_char)):\n- msg = (\"convolution dimension_numbers elements must each have the same \"\n- \"set of spatial characters, got {}.\")\n- raise TypeError(msg.format(dimension_numbers))\n-\n- def getperm(spec, charpair):\n- spatial = (i for i, c in enumerate(spec) if c not in charpair)\n- if spec is not rhs_spec:\n- spatial = sorted(spatial, key=lambda i: rhs_spec.index(spec[i]))\n- return (spec.index(charpair[0]), spec.index(charpair[1])) + tuple(spatial)\n-\n- lhs_perm, rhs_perm, out_perm = map(getperm, dimension_numbers, charpairs)\n- return lhs_perm, rhs_perm, out_perm\n-\n-\ndef _dynamic_slice_indices(operand, start_indices):\nif isinstance(start_indices, (tuple, list)):\nstart_indices = concatenate([reshape(i, [1]) for i in start_indices], 0)\n@@ -2345,25 +2277,80 @@ def remaining(original, *removed_lists):\nreturn [i for i in original if i not in blacklist]\n-def _charswap(a, b, s):\n- return s.translate(maketrans(a + b, b + a))\n+ConvDimensionNumbers = collections.namedtuple(\n+ \"ConvDimensionNumbers\", [\"lhs_spec\", \"rhs_spec\", \"out_spec\"])\n+\n+def conv_dimension_numbers(lhs_shape, rhs_shape, dimension_numbers):\n+ \"\"\"Convert from user spec of dimension_numbers to ConvDimensionNumbers.\n+ Args:\n+ lhs_shape: tuple of nonnegative integers, shape of the convolution input.\n+ rhs_shape: tuple of nonnegative integers, shape of the convolution kernel.\n+ dimension_numbers: None or a tuple/list of strings, following the\n+ convolution dimension number specification format in xla_client.py.\n+\n+ Returns:\n+ A ConvDimensionNumbers namedtuple representing dimension_numbers in a\n+ canonical form that is handled by internal lax functions.\n+ \"\"\"\n+ if len(lhs_shape) != len(rhs_shape):\n+ msg = \"convolution requires lhs and rhs ndim to be equal, got {} and {}.\"\n+ raise TypeError(msg.format(len(lhs_shape), len(rhs_shape)))\n+\n+ if dimension_numbers is None:\n+ iota = tuple(range(len(lhs_shape)))\n+ return ConvDimensionNumbers(iota, iota, iota)\n+ elif isinstance(dimension_numbers, (list, tuple)):\n+ if len(dimension_numbers) != 3:\n+ msg = \"convolution dimension_numbers list/tuple must be length 3, got {}.\"\n+ raise TypeError(msg.format(len(dimension_numbers)))\n+ if not all(isinstance(elt, str) for elt in dimension_numbers):\n+ msg = \"convolution dimension_numbers elements must be strings, got {}.\"\n+ raise TypeError(msg.format(tuple(map(type, dimension_numbers))))\n+ msg = (\"convolution dimension_numbers[{}] must have len equal to the ndim \"\n+ \"of lhs and rhs, got {} for lhs and rhs shapes {} and {}.\")\n+ for i, elt in enumerate(dimension_numbers):\n+ if len(elt) != len(lhs_shape):\n+ raise TypeError(msg.format(i, len(elt), lhs_shape, rhs_shape))\n-def _get_sdims(dimension_numbers):\n+ lhs_spec, rhs_spec, out_spec = conv_general_permutations(dimension_numbers)\n+ return ConvDimensionNumbers(lhs_spec, rhs_spec, out_spec)\n+ else:\n+ msg = \"convolution dimension_numbers must be tuple/list or None, got {}.\"\n+ raise TypeError(msg.format(type(dimension_numbers)))\n+\n+\n+def conv_general_permutations(dimension_numbers):\n+ \"\"\"Utility for convolution dimension permutations relative to Conv HLO.\"\"\"\nlhs_spec, rhs_spec, out_spec = dimension_numbers\n- rhs_sdims = [i for i, c in enumerate(rhs_spec) if c not in {\"I\", \"O\"}]\n- lhs_sdims = sorted((i for i, c in enumerate(lhs_spec) if c not in {\"N\", \"C\"}),\n- key=lambda i: rhs_spec.index(lhs_spec[i]))\n- out_sdims = sorted((i for i, c in enumerate(out_spec) if c not in {\"N\", \"C\"}),\n- key=lambda i: rhs_spec.index(out_spec[i]))\n- return lhs_sdims, rhs_sdims, out_sdims\n+ lhs_char, rhs_char, out_char = charpairs = (\"N\", \"C\"), (\"O\", \"I\"), (\"N\", \"C\")\n+ for i, (a, b) in enumerate(charpairs):\n+ if not dimension_numbers[i].count(a) == dimension_numbers[i].count(b) == 1:\n+ msg = (\"convolution dimension_numbers[{}] must contain the characters \"\n+ \"'{}' and '{}' exatly once, got {}.\")\n+ raise TypeError(msg.format(i, a, b, dimension_numbers[i]))\n+ if len(dimension_numbers[i]) != len(set(dimension_numbers[i])):\n+ msg = (\"convolution dimension_numbers[{}] cannot have duplicate \"\n+ \"characters, got {}.\")\n+ raise TypeError(msg.format(i, dimension_numbers[i]))\n+ if not (set(lhs_spec) - set(lhs_char) == set(rhs_spec) - set(rhs_char) ==\n+ set(out_spec) - set(out_char)):\n+ msg = (\"convolution dimension_numbers elements must each have the same \"\n+ \"set of spatial characters, got {}.\")\n+ raise TypeError(msg.format(dimension_numbers))\n+ def getperm(spec, charpair):\n+ spatial = (i for i, c in enumerate(spec) if c not in charpair)\n+ if spec is not rhs_spec:\n+ spatial = sorted(spatial, key=lambda i: rhs_spec.index(spec[i]))\n+ return (spec.index(charpair[0]), spec.index(charpair[1])) + tuple(spatial)\n+\n+ lhs_perm, rhs_perm, out_perm = map(getperm, dimension_numbers, charpairs)\n+ return lhs_perm, rhs_perm, out_perm\n-ConvolutionDimensionNumbers = collections.namedtuple(\n- \"ConvolutionDimensionNumbers\", [\"lhs_spec\", \"rhs_spec\", \"out_spec\"])\ndef _conv_general_proto(dimension_numbers):\n- assert type(dimension_numbers) is ConvolutionDimensionNumbers\n+ assert type(dimension_numbers) is ConvDimensionNumbers\nlhs_spec, rhs_spec, out_spec = dimension_numbers\nproto = xla_bridge.xla_data_pb2.ConvolutionDimensionNumbers()\nproto.input_batch_dimension = lhs_spec[0]\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/test_util.py",
"new_path": "jax/test_util.py",
"diff": "@@ -70,7 +70,8 @@ def numpy_close(a, b, atol=ATOL, rtol=RTOL, equal_nan=False):\nif testing_tpu or testing_x32:\natol = max(atol, 1e-1)\nrtol = max(rtol, 1e-1)\n- return onp.allclose(a, b, atol=atol, rtol=rtol, equal_nan=equal_nan)\n+ return onp.allclose(a, b, atol=atol * a.size, rtol=rtol * b.size,\n+ equal_nan=equal_nan)\ndef check_eq(xs, ys):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -1576,10 +1576,10 @@ class LaxAutodiffTest(jtu.JaxTestCase):\n\"rhs_dil\": rhs_dil, \"rng\": rng, \"dimension_numbers\": dim_nums,\n\"perms\": perms}\nfor lhs_shape, rhs_shape, all_strides, all_pads, lhs_dils, rhs_dils in [\n- ((b, i, 3, 4), (j, i, 1, 2), [(1, 1), (1, 2), (2, 1)],\n- [((0, 0), (0, 0)), ((-1, 0), (0, -1)), ((1, 0), (0, 1))],\n+ ((b, i, 5, 6), (j, i, 1, 2), [(1, 1), (1, 2), (2, 1)],\n+ [((0, 0), (0, 0)), ((1, 0), (0, 1)), ((0, -1), (0, 0))],\n[(1, 1), (2, 1)], [(1, 1)])\n- for b, i, j in itertools.product([1, 2], repeat=3)]\n+ for b, i, j in itertools.product([2, 3], repeat=3)]\nfor strides in all_strides\nfor rhs_dil in rhs_dils\nfor lhs_dil in lhs_dils\n@@ -1588,7 +1588,8 @@ class LaxAutodiffTest(jtu.JaxTestCase):\nfor rng in [jtu.rand_default()]\nfor dim_nums, perms in [\n((\"NCHW\", \"OIHW\", \"NCHW\"), ([0, 1, 2, 3], [0, 1, 2, 3])),\n- ((\"NHWC\", \"HWIO\", \"NHWC\"), ([0, 2, 3, 1], [2, 3, 1, 0]))]))\n+ # ((\"NHWC\", \"HWIO\", \"NHWC\"), ([0, 2, 3, 1], [2, 3, 1, 0]))\n+ ]))\n@jtu.skip_on_devices(\"tpu\")\ndef testConvGeneralDilatedGrad(self, lhs_shape, rhs_shape, dtype, strides,\npadding, lhs_dil, rhs_dil, dimension_numbers,\n"
}
] | Python | Apache License 2.0 | google/jax | clean up conv dimension_numbers handling |
260,335 | 11.12.2018 09:18:38 | 28,800 | ffecf2ccc5a6ea01a2bbe1e11079b3b13b380770 | fix bugs in zeros_like and complex transpose | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/ad.py",
"new_path": "jax/interpreters/ad.py",
"diff": "@@ -344,7 +344,7 @@ def zero_jvp(primitive, primals, tangents, **params):\nreturn primitive.bind(*primals, **params), zero\n-deflinear(zeros_like_p, lambda t: (zeros_like_jaxval(t),))\n+deflinear(zeros_like_p, lambda t: [zero])\ndeflinear(core.identity_p, lambda t: (t,))\ndeflinear(core.pack_p, lambda t: list(t) if t is not zero else zero)\ndeflinear(add_jaxvals_p, lambda t: (t, t))\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -808,7 +808,7 @@ real_p = unop(_fixed_dtype(onp.float32), _complex, 'real')\nad.deflinear(real_p, lambda t: [complex(t, onp.zeros((), onp.float32))])\nimag_p = unop(_fixed_dtype(onp.float32), _complex, 'imag')\n-ad.deflinear(imag_p, lambda t: [complex(onp.zeros((), onp.float32), neg(t))])\n+ad.deflinear(imag_p, lambda t: [complex(onp.zeros((), onp.float32), t)])\ncomplex_p = standard_binop([_f32, _f32], 'complex')\nad.deflinear(complex_p, lambda t: [real(t), imag(t)])\n"
}
] | Python | Apache License 2.0 | google/jax | fix bugs in zeros_like and complex transpose |
260,335 | 11.12.2018 09:19:15 | 28,800 | 7198e09465fefbe59e1b9ff736c9a32c8ce6b1e5 | enable skip_checks for merging to master | [
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "@@ -30,7 +30,7 @@ from .pprint_util import pp, vcat, hcat, pp_kv_pairs\n# TODO(dougalm): the trace cache breaks the leak detector. Consisder solving.\ncheck_leaks = False\n# TODO(dougalm): put this behind a flag that's enabled during testing\n-skip_checks = False # not __debug__ # google doesn't use -O\n+skip_checks = True # not __debug__ # google doesn't use -O\nzip = safe_zip\nmap = safe_map\n"
}
] | Python | Apache License 2.0 | google/jax | enable skip_checks for merging to master |
260,335 | 11.12.2018 10:18:11 | 28,800 | 567caecdea7995a33fc42d3f6c3f6bf1e312b09f | revert build_defs.bzl for now | [
{
"change_type": "MODIFY",
"old_path": "tests/build_defs.bzl",
"new_path": "tests/build_defs.bzl",
"diff": "@@ -31,7 +31,9 @@ def jax_test(\nfail(\"Only one test source file is currently supported.\")\n# Deps that are linked into all test target variants.\n- all_test_deps = []\n+ all_test_deps = [\n+ \":libjax\",\n+ ]\ndisabled_tags = [\"manual\", \"notap\", \"disabled\"]\nnative.py_test(\nname = name + \"_cpu\",\n@@ -43,33 +45,33 @@ def jax_test(\nargs = args + [\"--jax_enable_x64=true --jax_test_dut=cpu --jax_platform_name=Host\"],\ntags = (disabled_tags if \"cpu\" in disable else []),\n)\n- # native.py_test(\n- # name = name + \"_cpu_x32\",\n- # main = main,\n- # srcs = srcs,\n- # data = data,\n- # deps = deps + all_test_deps,\n- # shard_count = shard_count.get(\"cpu\") if shard_count else None,\n- # args = args + [\"--jax_enable_x64=false --jax_test_dut=cpu --jax_platform_name=Host\"],\n- # tags = (disabled_tags if \"cpu\" in disable else []),\n- # )\n- # native.py_test(\n- # name = name + \"_gpu\",\n- # main = main,\n- # srcs = srcs,\n- # data = data,\n- # args = args + [\"--jax_test_dut=gpu --jax_enable_x64=true --jax_platform_name=CUDA\"],\n- # deps = deps + all_test_deps,\n- # tags = [\"requires-gpu-sm35\"] + (disabled_tags if \"gpu\" in disable else []),\n- # shard_count = shard_count.get(\"gpu\") if shard_count else None,\n- # )\n- # native.py_test(\n- # name = name + \"_gpu_x32\",\n- # main = main,\n- # srcs = srcs,\n- # data = data,\n- # args = args + [\"--jax_test_dut=gpu --jax_enable_x64=false --jax_platform_name=CUDA\"],\n- # deps = deps + all_test_deps,\n- # tags = [\"requires-gpu-sm35\"] + (disabled_tags if \"gpu\" in disable else []),\n- # shard_count = shard_count.get(\"gpu\") if shard_count else None,\n- # )\n+ native.py_test(\n+ name = name + \"_cpu_x32\",\n+ main = main,\n+ srcs = srcs,\n+ data = data,\n+ deps = deps + all_test_deps,\n+ shard_count = shard_count.get(\"cpu\") if shard_count else None,\n+ args = args + [\"--jax_enable_x64=false --jax_test_dut=cpu --jax_platform_name=Host\"],\n+ tags = (disabled_tags if \"cpu\" in disable else []),\n+ )\n+ native.py_test(\n+ name = name + \"_gpu\",\n+ main = main,\n+ srcs = srcs,\n+ data = data,\n+ args = args + [\"--jax_test_dut=gpu --jax_enable_x64=true --jax_platform_name=CUDA\"],\n+ deps = deps + all_test_deps,\n+ tags = [\"requires-gpu-sm35\"] + (disabled_tags if \"gpu\" in disable else []),\n+ shard_count = shard_count.get(\"gpu\") if shard_count else None,\n+ )\n+ native.py_test(\n+ name = name + \"_gpu_x32\",\n+ main = main,\n+ srcs = srcs,\n+ data = data,\n+ args = args + [\"--jax_test_dut=gpu --jax_enable_x64=false --jax_platform_name=CUDA\"],\n+ deps = deps + all_test_deps,\n+ tags = [\"requires-gpu-sm35\"] + (disabled_tags if \"gpu\" in disable else []),\n+ shard_count = shard_count.get(\"gpu\") if shard_count else None,\n+ )\n"
}
] | Python | Apache License 2.0 | google/jax | revert build_defs.bzl for now |
260,335 | 11.12.2018 08:54:35 | 28,800 | 5a1aeca96cb8cb24e90b1fbfea25fa40d202b8fa | add rot90 and flip, adjust testOp test selection
closes | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -327,6 +327,30 @@ def transpose(x, axis=None):\nreturn lax.transpose(x, axis)\n+@_wraps(onp.rot90)\n+def rot90(m, k=1, axes=(0, 1)):\n+ ax1, ax2 = axes\n+ if ax1 % m.ndim == ax2 % m.ndim:\n+ raise ValueError(\"Axes must be different\") # same as numpy error\n+ k = k % 4\n+ if k == 0:\n+ return m\n+ elif k == 2:\n+ return flip(flip(m, ax1), ax2)\n+ else:\n+ perm = list(range(m.ndim))\n+ perm[ax1], perm[ax2] = perm[ax2], perm[ax1]\n+ if k == 1:\n+ return transpose(flip(m, ax2), perm)\n+ else:\n+ return flip(transpose(m, perm), ax2)\n+\n+\n+@_wraps(onp.flip)\n+def flip(m, axis):\n+ return lax.rev(m, [axis])\n+\n+\n@_wraps(onp.sinh)\ndef sinh(x):\nx, = _promote_to_result_dtype(onp.sinh, x)\n@@ -454,6 +478,9 @@ def where(condition, x=None, y=None):\nif not onp.issubdtype(_dtype(condition), onp.bool_):\ncondition = lax.ne(condition, zeros_like(condition))\ncondition, x, y = broadcast_arrays(condition, x, y)\n+ if not x.size:\n+ return x\n+ else:\nreturn lax.select(condition, *_promote_dtypes(x, y))\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -171,23 +171,24 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\ndef _GetArgsMaker(self, rng, shapes, dtypes):\nreturn lambda: [rng(shape, dtype) for shape, dtype in zip(shapes, dtypes)]\n- @parameterized.named_parameters(jtu.cases_from_list(\n+ @parameterized.named_parameters(itertools.chain.from_iterable(\n+ jtu.cases_from_list(\n{\"testcase_name\": jtu.format_test_name_suffix(rec.test_name, shapes,\ndtypes),\n\"rng\": rec.rng, \"shapes\": shapes, \"dtypes\": dtypes,\n\"onp_op\": getattr(onp, rec.name), \"lnp_op\": getattr(lnp, rec.name)}\n- for rec in itertools.chain(JAX_ONE_TO_ONE_OP_RECORDS,\n- JAX_COMPOUND_OP_RECORDS)\nfor shapes in filter(\n_shapes_are_broadcast_compatible,\nCombosWithReplacement(rec.shapes, rec.nargs))\n- for dtypes in CombosWithReplacement(rec.dtypes, rec.nargs)))\n+ for dtypes in CombosWithReplacement(rec.dtypes, rec.nargs))\n+ for rec in itertools.chain(JAX_ONE_TO_ONE_OP_RECORDS, JAX_COMPOUND_OP_RECORDS)))\ndef testOp(self, onp_op, lnp_op, rng, shapes, dtypes):\nargs_maker = self._GetArgsMaker(rng, shapes, dtypes)\nself._CheckAgainstNumpy(onp_op, lnp_op, args_maker, check_dtypes=True)\nself._CompileAndCheck(lnp_op, args_maker, check_dtypes=True)\n- @parameterized.named_parameters(jtu.cases_from_list(\n+ @parameterized.named_parameters(itertools.chain.from_iterable(\n+ jtu.cases_from_list(\n{\"testcase_name\": jtu.format_test_name_suffix(rec.test_name, shapes,\ndtypes),\n\"rng\": rec.rng, \"shapes\": shapes, \"dtypes\": dtypes,\n@@ -198,7 +199,8 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\nCombosWithReplacement(rec.shapes, rec.nargs))\nfor dtypes in filter(\n_dtypes_are_compatible_for_bitwise_ops,\n- CombosWithReplacement(rec.dtypes, rec.nargs))))\n+ CombosWithReplacement(rec.dtypes, rec.nargs)))\n+ for rec in JAX_BITWISE_OP_RECORDS))\ndef testBitwiseOp(self, onp_op, lnp_op, rng, shapes, dtypes):\nif not FLAGS.jax_enable_x64 and any(\nonp.iinfo(dtype).bits == 64 for dtype in dtypes):\n@@ -622,6 +624,41 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\ncfoo = api.jit(foo)\nself.assertRaises(NotImplementedError, lambda: cfoo(onp.arange(3)))\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\": \"_{}_axis={}\".format(\n+ jtu.format_shape_dtype_string(shape, dtype), axis),\n+ \"rng\": rng, \"shape\": shape, \"dtype\": dtype, \"axis\": axis}\n+ for shape in [(3,), (2, 3)]\n+ for dtype in default_dtypes\n+ for axis in range(len(shape))\n+ for rng in [jtu.rand_default()]))\n+ def testFlip(self, shape, dtype, axis, rng):\n+ args_maker = self._GetArgsMaker(rng, [shape], [dtype])\n+ lnp_op = lambda x: lnp.flip(x, axis)\n+ onp_op = lambda x: onp.flip(x, axis)\n+ self._CheckAgainstNumpy(onp_op, lnp_op, args_maker, check_dtypes=True)\n+ self._CompileAndCheck(lnp_op, args_maker, check_dtypes=True)\n+\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\": \"_{}_k={}_axes={}\".format(\n+ jtu.format_shape_dtype_string(shape, dtype), k, axes),\n+ \"rng\": rng, \"shape\": shape, \"dtype\": dtype, \"k\": k, \"axes\": axes}\n+ for shape, axes in [\n+ [(2, 3), (0, 1)],\n+ [(2, 3), (1, 0)],\n+ [(4, 3, 2), (0, 2)],\n+ [(4, 3, 2), (2, 1)],\n+ ]\n+ for k in range(-3, 4)\n+ for dtype in default_dtypes\n+ for rng in [jtu.rand_default()]))\n+ def testRot90(self, shape, dtype, k, axes, rng):\n+ args_maker = self._GetArgsMaker(rng, [shape], [dtype])\n+ lnp_op = lambda x: lnp.rot90(x, k, axes)\n+ onp_op = lambda x: onp.rot90(x, k, axes)\n+ self._CheckAgainstNumpy(onp_op, lnp_op, args_maker, check_dtypes=True)\n+ self._CompileAndCheck(lnp_op, args_maker, check_dtypes=True)\n+\n# TODO(mattjj): test infix operator overrides\ndef DISABLED_testRavel(self):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -137,27 +137,29 @@ CombosWithReplacement = itertools.combinations_with_replacement\nclass LaxTest(jtu.JaxTestCase):\n\"\"\"Numerical tests for LAX operations.\"\"\"\n- @parameterized.named_parameters(jtu.cases_from_list(\n+ @parameterized.named_parameters(itertools.chain.from_iterable(\n+ jtu.cases_from_list(\n{\"testcase_name\": jtu.format_test_name_suffix(\nrec.op.__name__, shapes, itertools.repeat(dtype)),\n\"op\": rec.op, \"rng\": rec.rng, \"shapes\": shapes, \"dtype\": dtype}\n- for rec in LAX_OPS\nfor shape_group in compatible_shapes\nfor shapes in CombosWithReplacement(shape_group, rec.nargs)\n- for dtype in rec.dtypes))\n+ for dtype in rec.dtypes)\n+ for rec in LAX_OPS))\ndef testOp(self, op, rng, shapes, dtype):\nargs_maker = lambda: [rng(shape, dtype) for shape in shapes]\nself._CompileAndCheck(op, args_maker, check_dtypes=True)\n- @parameterized.named_parameters(jtu.cases_from_list(\n+ @parameterized.named_parameters(itertools.chain.from_iterable(\n+ jtu.cases_from_list(\n{\"testcase_name\": jtu.format_test_name_suffix(\nrec.op.__name__, shapes, itertools.repeat(dtype)),\n\"op\": rec.op, \"rng\": rec.rng, \"shapes\": shapes, \"dtype\": dtype,\n\"tol\": rec.tol}\n- for rec in LAX_OPS\nfor shape_group in compatible_shapes\nfor shapes in CombosWithReplacement(shape_group, rec.nargs)\n- for dtype in rec.dtypes))\n+ for dtype in rec.dtypes)\n+ for rec in LAX_OPS))\ndef testOpAgainstNumpy(self, op, rng, shapes, dtype, tol):\nargs_maker = lambda: [rng(shape, dtype) for shape in shapes]\nnumpy_op = getattr(lax_reference, op.__name__)\n@@ -1436,16 +1438,16 @@ def check_grads_bilinear(f, args, order, atol=None, rtol=None):\nclass LaxAutodiffTest(jtu.JaxTestCase):\n- @parameterized.named_parameters(jtu.cases_from_list(\n+ @parameterized.named_parameters(itertools.chain.from_iterable(\n+ jtu.cases_from_list(\n{\"testcase_name\": jtu.format_test_name_suffix(\nrec.op.__name__, shapes, itertools.repeat(dtype)),\n\"op\": rec.op, \"rng\": rec.rng, \"shapes\": shapes, \"dtype\": dtype,\n\"order\": rec.order}\n- for rec in LAX_GRAD_OPS\nfor shape_group in compatible_shapes\nfor shapes in CombosWithReplacement(shape_group, rec.nargs)\n- for dtype in rec.dtypes\n- ))\n+ for dtype in rec.dtypes)\n+ for rec in LAX_GRAD_OPS))\ndef testOpGrad(self, op, rng, shapes, dtype, order):\nif FLAGS.jax_test_dut and FLAGS.jax_test_dut.startswith(\"tpu\"):\nif dtype is onp.complex64:\n"
}
] | Python | Apache License 2.0 | google/jax | add rot90 and flip, adjust testOp test selection
closes #55 |
260,335 | 11.12.2018 12:14:57 | 28,800 | 9cd60279791b78550f22f1a4e09ae414e63bba94 | add python built-in complex type to array types
fixes | [
{
"change_type": "MODIFY",
"old_path": "jax/abstract_arrays.py",
"new_path": "jax/abstract_arrays.py",
"diff": "@@ -153,8 +153,8 @@ def make_shaped_array(x):\nreturn ShapedArray(onp.shape(x), dtype)\narray_types = [onp.ndarray, onp.float64, onp.float32, onp.complex64,\n- onp.int64, onp.int32, onp.bool_, onp.uint64, onp.uint32, float,\n- int, bool]\n+ onp.int64, onp.int32, onp.bool_, onp.uint64, onp.uint32,\n+ complex, float, int, bool]\nfor t in array_types:\ncore.pytype_aval_mappings[t] = ConcreteArray\n"
}
] | Python | Apache License 2.0 | google/jax | add python built-in complex type to array types
fixes #74 |
260,335 | 11.12.2018 12:52:09 | 28,800 | cbd9ace4250a7a9bc382663b9eacba591cf2c402 | change vmap api to be curried (closes | [
{
"change_type": "MODIFY",
"old_path": "jax/api.py",
"new_path": "jax/api.py",
"diff": "@@ -78,22 +78,19 @@ def jacrev(fun, x):\ndef hessian(fun):\nreturn jacfwd(jacrev(fun))\n-def vmap(fun, *args, **kwargs):\n- in_axes = kwargs.pop(\"in_axes\", 0)\n- out_axes = kwargs.pop(\"out_axes\", 0)\n- if kwargs:\n- msg = \"vmap keyword args must be 'in_axes' and/or 'out_axes', got {}.\"\n- raise TypeError(msg.format(', '.join(kwargs)))\n-\n- if type(in_axes) is int:\n- in_axes = (in_axes,) * len(args)\n+def vmap(fun, in_axes=0, out_axes=0):\n+\n+ def batched_fun(*args, **kwargs):\nif not isinstance(fun, lu.WrappedFun):\n- fun = lu.wrap_init(fun)\n+ f = lu.wrap_init(fun)\n+ in_axes_ = (in_axes,) * len(args) if type(in_axes) is int else in_axes\nin_flat, in_trees = unzip2(map(tree_to_jaxtuples, args))\n- flat_fun, out_tree = flatten_fun(fun, in_trees)\n- out_flat = batching.batch(flat_fun, in_flat, in_axes, out_axes)\n+ flat_fun, out_tree = flatten_fun(f, in_trees)\n+ out_flat = batching.batch(flat_fun, in_flat, in_axes_, out_axes)\nreturn build_tree(out_tree(), out_flat)\n+ return batched_fun\n+\ndef jvp(fun, primals, tangents):\ndef flatten_arg(primal, tangent):\nprimal_jtuple, tree_def = tree_to_jaxtuples(primal)\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/gufuncs.ipynb",
"new_path": "notebooks/gufuncs.ipynb",
"diff": "\"source\": [\n\"import functools\\n\",\n\"\\n\",\n- \"def curried_vmap(func):\\n\",\n- \" @functools.wraps(func)\\n\",\n- \" def wrapper(*args):\\n\",\n- \" return vmap(func, *args)\\n\",\n- \" return wrapper\\n\",\n- \"\\n\",\n\"\\n\",\n\"def vectorize(signature):\\n\",\n\" \\\"\\\"\\\"Vectorize a function using JAX.\\\"\\\"\\\"\\n\",\n\"\\n\",\n\" vectorized_func = func\\n\",\n\" for _ in range(num_batch_dims):\\n\",\n- \" vectorized_func = curried_vmap(vectorized_func)\\n\",\n+ \" vectorized_func = vmap(vectorized_func)\\n\",\n\" result = vectorized_func(*boardcast_args)\\n\",\n\"\\n\",\n\" if axis is not None:\\n\",\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/neural_network_and_data_loading.ipynb",
"new_path": "notebooks/neural_network_and_data_loading.ipynb",
"diff": "\"cell_type\": \"code\",\n\"source\": [\n\"# Let's upgrade it to handle batches using `vmap`\\n\",\n- \"from jax.util import curry\\n\",\n- \"\\n\",\n- \"# The `vmap` function alone works like `map`, in that it will\\n\",\n- \"# apply a function across inputs.\\n\",\n- \"# However, let's curry vmap so that it returns a function\\n\",\n- \"# with the original function signature.\\n\",\n- \"curried_vmap = curry(vmap)\\n\",\n\"\\n\",\n\"# Make a batched version of the `predict` function\\n\",\n- \"batched_predict = curried_vmap(predict, in_axes=(None, 0))\\n\",\n+ \"batched_predict = vmap(predict, in_axes=(None, 0))\\n\",\n\"\\n\",\n\"# `batched_predict` has the same call signature as `predict`\\n\",\n\"batched_preds = batched_predict(params, random_flattened_images)\\n\",\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/quickstart.ipynb",
"new_path": "notebooks/quickstart.ipynb",
"diff": "\"source\": [\n\"@jit\\n\",\n\"def vmap_batched_apply_matrix(batched_x):\\n\",\n- \" return vmap(apply_matrix, batched_x)\\n\",\n+ \" return vmap(apply_matrix)(batched_x)\\n\",\n\"\\n\",\n\"print('Auto-vectorized with vmap')\\n\",\n\"%timeit vmap_batched_apply_matrix(batched_x)\"\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -31,21 +31,16 @@ from jax.core import unit\nfrom jax.interpreters import partial_eval as pe\nfrom jax.util import partial, curry\n-import functools as fn\n-\nclass BatchingTest(jtu.JaxTestCase):\ndef testConstantFunction(self):\n- ans = vmap(lambda x: 3, onp.ones(4))\n+ ans = vmap(lambda x: 3)(onp.ones(4))\nexpected = 3 * onp.ones(4)\nself.assertAllClose(ans, expected, check_dtypes=False)\ndef testNestedBatchingMatMat(self):\n- def matvec(A, b):\n- return vmap(np.vdot, A, b, in_axes=(0, None))\n-\n- def matmat(A, B):\n- return vmap(matvec, A, B, in_axes=(None, 1), out_axes=1)\n+ matvec = vmap(np.vdot, in_axes=(0, None))\n+ matmat = vmap(matvec, in_axes=(None, 1), out_axes=1)\nR = onp.random.RandomState(0).randn\nA = R(4, 3)\n@@ -94,7 +89,7 @@ class BatchingTest(jtu.JaxTestCase):\ntarget_batch = R(5, 4)\nbatch = (input_batch, target_batch)\n- ans = vmap(partial(grad(loss), params), batch)\n+ ans = vmap(partial(grad(loss), params))(batch)\nfor ans_pair, param_pair in zip(ans, params):\ndW, db = ans_pair\n@@ -107,13 +102,13 @@ class BatchingTest(jtu.JaxTestCase):\ndef jacbwd(f, x):\ny, pullback = vjp(f, x)\nstd_basis = onp.eye(onp.size(y)).reshape((-1,) + onp.shape(y))\n- jac_flat, = vmap(pullback, std_basis, out_axes=onp.ndim(y))\n+ jac_flat, = vmap(pullback, out_axes=onp.ndim(y))(std_basis)\nreturn jac_flat.reshape(onp.shape(y) + onp.shape(x))\ndef jacfwd(f, x):\npushfwd = lambda v: jvp(f, (x,), (v,))\nstd_basis = onp.eye(onp.size(x)).reshape((-1,) + onp.shape(x))\n- y, jac_flat = vmap(pushfwd, std_basis, out_axes=(None, 0))\n+ y, jac_flat = vmap(pushfwd, out_axes=(None, 0))(std_basis)\nreturn jac_flat.reshape(onp.shape(y) + onp.shape(x))\nR = onp.random.RandomState(0).randn\n@@ -133,7 +128,7 @@ class BatchingTest(jtu.JaxTestCase):\nside.append(None)\nreturn x + x\n- g = jit(lambda x: vmap(f, x))\n+ g = jit(vmap(f))\nself.assertAllClose(g(onp.ones(2)), 2 * onp.ones(2), check_dtypes=False)\nself.assertEqual(len(side), 1)\nself.assertAllClose(g(2 * onp.ones(2)), 4 * onp.ones(2),\n@@ -145,7 +140,7 @@ class BatchingTest(jtu.JaxTestCase):\nR = onp.random.RandomState(0).randn\nx = R(5, 10)\n- ans = vmap(fun, x)\n+ ans = vmap(fun)(x)\nexpected_ans = x[:, 2:4]\nself.assertAllClose(ans, expected_ans, check_dtypes=False)\n@@ -154,7 +149,7 @@ class BatchingTest(jtu.JaxTestCase):\nR = onp.random.RandomState(0).randn\nx = R(10, 5, 3, 7)\n- ans = vmap(fun, x)\n+ ans = vmap(fun)(x)\nexpected_ans = x[:, :, 2]\nself.assertAllClose(ans, expected_ans, check_dtypes=False)\n@@ -163,7 +158,7 @@ class BatchingTest(jtu.JaxTestCase):\nR = onp.random.RandomState(0).randn\nx = R(10, 5, 3, 7)\n- ans = vmap(fun, x)\n+ ans = vmap(fun)(x)\nexpected_ans = onp.maximum(x, 0.0)\nself.assertAllClose(ans, expected_ans, check_dtypes=False)\n@@ -171,7 +166,7 @@ class BatchingTest(jtu.JaxTestCase):\nR = onp.random.RandomState(0).randn\nx = R(10, 5, 3, 7)\n- ans = vmap(lambda x: x > 1.0, x)\n+ ans = vmap(lambda x: x > 1.0)(x)\nexpected_ans = x > 1.0\nself.assertAllClose(ans, expected_ans, check_dtypes=True)\n@@ -182,7 +177,7 @@ class BatchingTest(jtu.JaxTestCase):\nfun = lambda W, x: np.sum(np.maximum(np.dot(x, W), 0.0) ** 2)\n- ans = vmap(fn.partial(grad(fun), W), x)\n+ ans = vmap(partial(grad(fun), W))(x)\nW_t = np.transpose(W)\nfor i in range(10):\n@@ -199,44 +194,44 @@ class BatchingTest(jtu.JaxTestCase):\nx = R(10, 3, 4, 5)\ny = R(10, 3, 5, 6)\n- ans = vmap(lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))]), x, y)\n+ fun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\n+ ans = vmap(fun)(x, y)\nexpected = lax.dot_general(x, y, [((3,), (2,)), ((0, 1), (0, 1))])\nself.assertAllClose(ans, expected, check_dtypes=True)\nx = R(3, 4, 10, 5)\ny = R(3, 10, 5, 6)\n- ans = vmap(lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))]), x, y,\n- in_axes=(2, 1))\n+ fun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\n+ ans = vmap(fun, in_axes=(2, 1))(x, y)\nfun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\nexpected = onp.stack([fun(x[..., i, :], y[:, i, ...]) for i in range(10)])\nself.assertAllClose(ans, expected, check_dtypes=True)\nx = R(3, 4, 5, 10)\ny = R(3, 5, 6)\n- ans = vmap(lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))]), x, y,\n- in_axes=(3, None))\n+ fun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\n+ ans = vmap(fun, in_axes=(3, None))(x, y)\nfun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\nexpected = onp.stack([fun(x[..., i], y) for i in range(10)])\nself.assertAllClose(ans, expected, check_dtypes=True)\nx = R(3, 4, 5)\ny = R(3, 5, 10, 6)\n- ans = vmap(lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))]), x, y,\n- in_axes=(None, 2))\n+ fun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\n+ ans = vmap(fun, in_axes=(None, 2))(x, y)\nfun = lambda x, y: lax.dot_general(x, y, [((2,), (1,)), ((0,), (0,))])\nexpected = onp.stack([fun(x, y[..., i, :]) for i in range(10)])\nself.assertAllClose(ans, expected, check_dtypes=True)\ndef testDot(self):\n# these tests are based on @shoyer's notebook studying gufuncs\n- curried_vmap = curry(vmap)\ndef vecvec(a, b):\ndot = np.dot\nfor ndim in range(1, max(a.ndim, b.ndim)):\na_ax = 0 if a.ndim > ndim else None\nb_ax = 0 if b.ndim > ndim else None\n- dot = curried_vmap(dot, in_axes=(a_ax, b_ax))\n+ dot = vmap(dot, in_axes=(a_ax, b_ax))\nreturn dot(a, b)\nassert vecvec(np.zeros((3,)), np.zeros((3,))).shape == ()\n"
}
] | Python | Apache License 2.0 | google/jax | change vmap api to be curried (closes #78) |
260,335 | 11.12.2018 13:39:31 | 28,800 | 8fac32e505154391e074a78af2e180101698b452 | colab notebooks now do 'pip install --upgrade jax'
(it seems the vm can persist in some cases) | [
{
"change_type": "MODIFY",
"old_path": "notebooks/gufuncs.ipynb",
"new_path": "notebooks/gufuncs.ipynb",
"diff": "\"cell_type\": \"code\",\n\"source\": [\n\"!pip install -q https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1-py3-none-any.whl\\n\",\n- \"!pip install -q jax\"\n+ \"!pip install --upgrade -q jax\"\n],\n\"execution_count\": 0,\n\"outputs\": []\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/neural_network_and_data_loading.ipynb",
"new_path": "notebooks/neural_network_and_data_loading.ipynb",
"diff": "\"cell_type\": \"code\",\n\"source\": [\n\"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1-py3-none-any.whl\\n\",\n- \"!pip install jax\"\n+ \"!pip install --upgrade jax\"\n],\n\"execution_count\": 0,\n\"outputs\": []\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/quickstart.ipynb",
"new_path": "notebooks/quickstart.ipynb",
"diff": "\"cell_type\": \"code\",\n\"source\": [\n\"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1-py3-none-any.whl\\n\",\n- \"!pip install jax\"\n+ \"!pip install --upgrade jax\"\n],\n\"execution_count\": 0,\n\"outputs\": []\n"
}
] | Python | Apache License 2.0 | google/jax | colab notebooks now do 'pip install --upgrade jax'
(it seems the vm can persist in some cases) |
260,335 | 11.12.2018 15:45:56 | 28,800 | 3ac1001c4929d9fb69c05a34ce35b4f91d7778ea | use find_packages() in setup.py | [
{
"change_type": "MODIFY",
"old_path": "setup.py",
"new_path": "setup.py",
"diff": "# See the License for the specific language governing permissions and\n# limitations under the License.\n-from setuptools import setup\n+from setuptools import setup, find_packages\nsetup(\nname='jax',\n@@ -20,8 +20,7 @@ setup(\ndescription='Differentiate, compile, and transform Numpy code.',\nauthor='JAX team',\nauthor_email='jax-dev@google.com',\n- packages=['jax', 'jax.lib', 'jax.interpreters', 'jax.numpy', 'jax.scipy',\n- 'jax.scipy.stats', 'jax.experimental'],\n+ packages=find_packages(),\ninstall_requires=['numpy>=1.12', 'six', 'protobuf>=3.6.0', 'absl-py',\n'opt_einsum'],\nurl='https://github.com/google/jax',\n"
}
] | Python | Apache License 2.0 | google/jax | use find_packages() in setup.py |
260,335 | 11.12.2018 14:00:58 | 28,800 | 97589a3d03a2d29ec1c01a1dd6ff76e818d45461 | add batching rule for lax.pad (c.f. | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -1398,9 +1398,21 @@ def pad_transpose(t, operand, padding_value, padding_config):\nreturn [t_operand, t_padv]\n+def pad_batch_rule(batched_args, batch_dims, padding_config):\n+ operand, padding_value = batched_args\n+ operand_bdim, padding_value_bdim = batch_dims\n+ if padding_value_bdim is None:\n+ assert operand_bdim is not None\n+ padding_config = list(padding_config)\n+ padding_config.insert(operand_bdim, (0, 0, 0))\n+ return pad(operand, padding_value, padding_config), operand_bdim\n+ else:\n+ raise NotImplementedError\n+\npad_p = standard_primitive(pad_shape_rule, _input_dtype, 'pad')\nad.deflinear(pad_p, pad_transpose)\nad.primitive_transposes[pad_p] = pad_transpose\n+batching.primitive_batchers[pad_p] = pad_batch_rule\ndef reshape_shape_rule(operand, new_sizes, dimensions, **unused_kwargs):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -239,6 +239,24 @@ class BatchingTest(jtu.JaxTestCase):\n# TODO(mattjj): this fails due to an xla error in dot_general\n# assert vecvec(np.zeros((4, 2, 3)), np.zeros((3,))).shape == (4, 2)\n+ def testPad(self):\n+ fun = lambda x: lax.pad(x, onp.float32(0), [(1, 2, 1)])\n+ R = onp.random.RandomState(0).randn\n+ x = R(5, 10).astype(onp.float32)\n+\n+ ans = vmap(fun)(x)\n+ expected_ans = np.stack(list(map(fun, x)))\n+ self.assertAllClose(ans, expected_ans, check_dtypes=False)\n+\n+\n+ fun = lambda x: lax.pad(x, onp.float32(0), [(1, 2, 1), (0, 1, 0)])\n+ R = onp.random.RandomState(0).randn\n+ x = R(5, 10, 3).astype(onp.float32)\n+\n+ ans = vmap(fun)(x)\n+ expected_ans = np.stack(list(map(fun, x)))\n+ self.assertAllClose(ans, expected_ans, check_dtypes=False)\n+\nif __name__ == '__main__':\nconfig.config_with_absl()\n"
}
] | Python | Apache License 2.0 | google/jax | add batching rule for lax.pad (c.f. #54) |
260,335 | 11.12.2018 16:07:28 | 28,800 | e788539e0a642599352cccc488386b3c7be52e98 | add concatenate batching rule (c.f. | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -1362,11 +1362,20 @@ def concatenate_transpose_rule(t, *operands, **kwargs):\nreturn [slice(t, start, limit) if o is None else None\nfor o, start, limit in zip(operands, starts, limits)]\n+def concatenate_batch_rule(batched_args, batch_dims, dimension, operand_shapes):\n+ size = next(op.shape[bdim] for op, bdim in zip(batched_args, batch_dims)\n+ if bdim is not None)\n+ operands = [batching.move_dim_to_front(op, bdim) if bdim is not None\n+ else broadcast(op, (size,))\n+ for op, bdim in zip(batched_args, batch_dims)]\n+ return concatenate(operands, dimension + 1), 0\n+\nconcatenate_p = standard_primitive(\nconcatenate_shape_rule, concatenate_dtype_rule, 'concatenate',\nconcatenate_translation_rule)\nad.deflinear(concatenate_p, concatenate_transpose_rule)\nad.primitive_transposes[concatenate_p] = concatenate_transpose_rule\n+batching.primitive_batchers[concatenate_p] = concatenate_batch_rule\ndef pad_shape_rule(operand, padding_value, padding_config):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -240,23 +240,38 @@ class BatchingTest(jtu.JaxTestCase):\n# assert vecvec(np.zeros((4, 2, 3)), np.zeros((3,))).shape == (4, 2)\ndef testPad(self):\n- fun = lambda x: lax.pad(x, onp.float32(0), [(1, 2, 1)])\nR = onp.random.RandomState(0).randn\n- x = R(5, 10).astype(onp.float32)\n+ fun = lambda x: lax.pad(x, onp.float32(0), [(1, 2, 1)])\n+ x = R(5, 10).astype(onp.float32)\nans = vmap(fun)(x)\nexpected_ans = np.stack(list(map(fun, x)))\nself.assertAllClose(ans, expected_ans, check_dtypes=False)\nfun = lambda x: lax.pad(x, onp.float32(0), [(1, 2, 1), (0, 1, 0)])\n- R = onp.random.RandomState(0).randn\nx = R(5, 10, 3).astype(onp.float32)\n-\nans = vmap(fun)(x)\nexpected_ans = np.stack(list(map(fun, x)))\nself.assertAllClose(ans, expected_ans, check_dtypes=False)\n+ def testConcatenate(self):\n+ R = lambda *shape: onp.random.RandomState(0).randn(*shape).astype(onp.float32)\n+\n+ fun = lambda *args: lax.concatenate(args, dimension=0)\n+ x, y, z = R(10, 2, 3), R(1, 10, 3), R(4, 3)\n+ ans = vmap(fun, in_axes=(0, 1, None))(x, y, z)\n+ expected_ans = onp.concatenate([x, onp.swapaxes(y, 0, 1),\n+ onp.broadcast_to(z, (10, 4, 3))], 1)\n+ self.assertAllClose(ans, expected_ans, check_dtypes=False)\n+\n+ fun = lambda *args: lax.concatenate(args, dimension=1)\n+ x, y, z = R(10, 2, 1), R(2, 3), R(2, 4, 10)\n+ ans = vmap(fun, in_axes=(0, None, 2))(x, y, z)\n+ expected_ans = onp.concatenate([x, onp.broadcast_to(y, (10, 2, 3)),\n+ onp.moveaxis(z, 2, 0)], 2)\n+ self.assertAllClose(ans, expected_ans, check_dtypes=False)\n+\nif __name__ == '__main__':\nconfig.config_with_absl()\n"
}
] | Python | Apache License 2.0 | google/jax | add concatenate batching rule (c.f. #54) |
260,335 | 11.12.2018 16:24:20 | 28,800 | 89349e5e6df4a82eb07278102f5caa9511e73ade | fix transpose issue in jacfwd and jacrev | [
{
"change_type": "MODIFY",
"old_path": "jax/api.py",
"new_path": "jax/api.py",
"diff": "@@ -64,7 +64,7 @@ def jacfwd(fun, x):\nfun = lu.wrap_init(fun)\npushfwd = partial(jvp, fun, (x,))\nstd_basis = onp.eye(onp.size(x)).reshape((-1,) + onp.shape(x)),\n- y, jac_flat = vmap(pushfwd, std_basis, out_axes=(None, 0))\n+ y, jac_flat = vmap(pushfwd, out_axes=(None, -1))(std_basis)\nreturn jac_flat.reshape(onp.shape(y) + onp.shape(x))\n@curry\n@@ -72,7 +72,7 @@ def jacrev(fun, x):\nfun = lu.wrap_init(fun)\ny, pullback = vjp(fun, x)\nstd_basis = onp.eye(onp.size(y)).reshape((-1,) + onp.shape(y))\n- jac_flat, = vmap(pullback, std_basis, out_axes=onp.ndim(y))\n+ jac_flat, = vmap(pullback, out_axes=0)(std_basis)\nreturn jac_flat.reshape(onp.shape(y) + onp.shape(x))\ndef hessian(fun):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/batching.py",
"new_path": "jax/interpreters/batching.py",
"diff": "@@ -281,6 +281,7 @@ def moveaxis(sz, dst, src, x):\nelse:\nreturn pack(map(partial(moveaxis, sz, dst, src), x))\nelif isinstance(aval, ShapedArray):\n+ dst = (dst % aval.ndim) if dst is not None and aval.ndim else dst\nif src == dst:\nreturn x\nelse:\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -24,7 +24,7 @@ from jax import test_util as jtu\nimport jax.numpy as np\nfrom jax.config import config\n-from jax import jit, grad, device_get, device_put\n+from jax import jit, grad, device_get, device_put, jacfwd, jacrev\nfrom jax.core import Primitive\nfrom jax.interpreters.partial_eval import def_abstract_eval\nfrom jax.interpreters.ad import defjvp\n@@ -235,6 +235,18 @@ class APITest(jtu.JaxTestCase):\nassert isinstance(y2[1][1], onp.ndarray)\nassert onp.all(y2[1][1] == 3 * x)\n+ def test_jacobian(self):\n+ R = onp.random.RandomState(0).randn\n+ A = R(4, 3)\n+ x = R(3)\n+\n+ f = lambda x: np.dot(A, x)\n+ assert onp.allclose(jacfwd(f)(x), A)\n+ assert onp.allclose(jacrev(f)(x), A)\n+\n+ f = lambda x: np.tanh(np.dot(A, x))\n+ assert onp.allclose(jacfwd(f)(x), jacrev(f)(x))\n+\nif __name__ == '__main__':\nconfig.config_with_absl()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -24,7 +24,7 @@ import jax.numpy as np\nfrom jax import test_util as jtu\nfrom jax.abstract_arrays import ShapedArray\nfrom jax import lax\n-from jax.api import jit, grad, jvp, vjp, trace_to_jaxpr\n+from jax.api import jit, grad, jvp, vjp, trace_to_jaxpr, jacfwd, jacrev\nfrom jax.api import vmap\nfrom jax.config import config\nfrom jax.core import unit\n@@ -272,6 +272,16 @@ class BatchingTest(jtu.JaxTestCase):\nonp.moveaxis(z, 2, 0)], 2)\nself.assertAllClose(ans, expected_ans, check_dtypes=False)\n+ def testJacobianIssue54(self):\n+ # test modeling the code in https://github.com/google/jax/issues/54\n+\n+ def func(xs):\n+ return np.array([x for x in xs])\n+\n+ xs = np.ones((5, 1))\n+ jacrev(func)(xs) # don't crash\n+ jacfwd(func)(xs) # don't crash\n+\nif __name__ == '__main__':\nconfig.config_with_absl()\n"
}
] | Python | Apache License 2.0 | google/jax | fix transpose issue in jacfwd and jacrev |
260,335 | 11.12.2018 20:46:02 | 28,800 | 650db099ef0a6b875507a8a46240f05deda18a00 | tweak readme to make colab bullet fit on one line | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -71,7 +71,7 @@ open](https://github.com/google/jax) by a growing number of\n## Quickstart: Colab in the Cloud\nJump right in using a notebook in your browser, connected to a Google Cloud GPU:\n-- [The basics: NumPy on accelerators, `grad` for automatic differentiation, `jit` for compilation and `vmap` for auto-vectorization](https://colab.research.google.com/github/google/jax/blob/master/notebooks/quickstart.ipynb)\n+- [The basics: NumPy on accelerators, `grad` for differentiation, `jit` for compilation, and `vmap` for vectorization](https://colab.research.google.com/github/google/jax/blob/master/notebooks/quickstart.ipynb)\n- [Training a Simple Neural Network, with PyTorch Data Loading](https://colab.research.google.com/github/google/jax/blob/master/notebooks/neural_network_and_data_loading.ipynb)\n"
}
] | Python | Apache License 2.0 | google/jax | tweak readme to make colab bullet fit on one line |
260,474 | 12.12.2018 09:47:49 | 18,000 | 9b835a180f0bf72b368337b47e1ecd2f6796ac3f | Added some docstrings to top-level transformations | [
{
"change_type": "MODIFY",
"old_path": "jax/api.py",
"new_path": "jax/api.py",
"diff": "# See the License for the specific language governing permissions and\n# limitations under the License.\n+\"\"\"\n+User-facing transformations.\n+\n+These mostly wrap internal transformations, providing convenience flags to\n+control behavior and handling Python containers (tuples/lists/dicts) of\n+arguments and outputs.\n+\"\"\"\n+\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n@@ -36,6 +44,21 @@ from .interpreters import batching\nmap = safe_map\ndef jit(fun, static_argnums=()):\n+ \"\"\"Sets up `fun` for just-in-time compilation with XLA.\n+\n+ Args:\n+ fun: Function to be jitted. Should be a pure function, as side-effects may\n+ only be executed once. Its positional arguments and return value should\n+ be arrays, scalars, or standard Python containers (tuple/list/dict)\n+ thereof. Keyword arguments and positional arguments specified by\n+ `static_argnums` can be anything at all. These are treated as static\n+ (see below).\n+ static_argnums: A tuple of ints. Specifies which arguments to treat as\n+ static (compile-time constant). Operations that only depend on static\n+ arguments will be constant-folded. Calling the jitted function with\n+ different values for these constants will trigger recompilation.\n+ Returns: A wrapped version of `fun`, set up for just-in-time compilation.\n+ \"\"\"\ndef f_jitted(*args, **kwargs):\nf = lu.wrap_init(fun, kwargs)\ndyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\n@@ -49,6 +72,21 @@ def jit(fun, static_argnums=()):\nreturn f_jitted\ndef grad(fun, argnums=0):\n+ \"\"\"Creates a function which evaluates the gradient of `fun`.\n+\n+ Args:\n+ fun: Function to be differentiated. Its arguments at positions specified by\n+ `argnums` should be arrays, scalars, or standard Python containers. It\n+ should return a scalar (which includes arrays with shape `()` but not\n+ arrays with shape `(1,)` etc.)\n+ argnums: Integer or tuple of integers. Specifies which positional\n+ argument(s) to differentiate with respect to.\n+ Returns: A function with the same arguments as `fun`, that evaluates the\n+ gradient of `fun`. If `argnums` is an integer then the gradient has the\n+ same shape and type as the positional argument indicated by that integer.\n+ If argnums is a tuple of integers, the gradient is a tuple of values with\n+ the same shapes and types as the corresponding arguments.\n+ \"\"\"\ndef grad_f(*args, **kwargs):\nf = lu.wrap_init(fun, kwargs)\nf_partial, dyn_args = argnums_partial(f, argnums, args)\n@@ -61,6 +99,7 @@ def grad(fun, argnums=0):\n@curry\ndef jacfwd(fun, x):\n+ \"\"\"Jacobian of `fun`, evaluated column-by-column using forward-mode AD\"\"\"\nfun = lu.wrap_init(fun)\npushfwd = partial(jvp, fun, (x,))\nstd_basis = onp.eye(onp.size(x)).reshape((-1,) + onp.shape(x)),\n@@ -69,6 +108,7 @@ def jacfwd(fun, x):\n@curry\ndef jacrev(fun, x):\n+ \"\"\"Jacobian of `fun`, evaluated row-by-row using reverse-mode AD\"\"\"\nfun = lu.wrap_init(fun)\ny, pullback = vjp(fun, x)\nstd_basis = onp.eye(onp.size(y)).reshape((-1,) + onp.shape(y))\n@@ -79,7 +119,27 @@ def hessian(fun):\nreturn jacfwd(jacrev(fun))\ndef vmap(fun, in_axes=0, out_axes=0):\n+ \"\"\"Vectorizing map. Creates a function which maps `fun` over additional axes.\n+\n+ Args:\n+ fun: Function to be mapped over additional axes.\n+ in_axes, out_axes: Specifies which axes to map over. These may be integers,\n+ None, or (possibly nested) tuples of integers or None.\n+ Returns: Batched/vectorized version of `fun` with arguments that correspond to\n+ those of `fun`, but with extra array axes at positions indicated by\n+ `in_axes`, and a return value that corresponds to that of `fun`, but with\n+ extra array axes at positions indicated by `out_axes`.\n+\n+ For example, we can implement a matrix-matrix product using a vector dot\n+ product:\n+\n+ vv = lambda x, y: np.vdot(x, y) # ([a], [a]) -> []\n+ mv = vmap(vv, (0, None), 0) # ([a,b], [b]) -> [a]\n+ mm = vmap(mv, (None, 1), 1) # ([a,b], [b,c]) -> [a,c]\n+\n+ (`[a,b]` indicates an array with shape (a,b))\n+ \"\"\"\ndef batched_fun(*args, **kwargs):\nif not isinstance(fun, lu.WrappedFun):\nf = lu.wrap_init(fun)\n"
}
] | Python | Apache License 2.0 | google/jax | Added some docstrings to top-level transformations |
260,335 | 12.12.2018 09:00:39 | 28,800 | d3ec0b23c50e383ba2559a6b18a220a64725fc0d | fix miscellaneous bugs, incl. complex abs grad | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/ad.py",
"new_path": "jax/interpreters/ad.py",
"diff": "@@ -237,6 +237,8 @@ class JVPTracer(Tracer):\nself.trace = trace\nself.primal = primal\nself.tangent = tangent\n+ # TODO(mattjj,dougalm): behind skip_checks, check primal/tangent shapes and\n+ # dtypes agree (up to jax_enable_x64 flag)\n@property\ndef aval(self):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -72,7 +72,7 @@ def erf_inv(x): return erf_inv_p.bind(x)\ndef real(x): return real_p.bind(x)\ndef imag(x): return imag_p.bind(x)\ndef complex(x, y): return complex_p.bind(_brcast(x, y), _brcast(y, x))\n-def conj(x): return conj_p.bind(x)\n+def conj(x): return conj_p.bind(x, input_dtype=_dtype(x))\ndef abs(x): return abs_p.bind(x)\ndef pow(x, y): return pow_p.bind(x, y)\n@@ -652,7 +652,7 @@ def standard_translate(name, c, *args, **kwargs):\nreturn getattr(c, xla_opname)(*args, **kwargs)\n-def unop_dtype_rule(result_dtype, accepted_dtypes, name, aval):\n+def unop_dtype_rule(result_dtype, accepted_dtypes, name, aval, **kwargs):\nif not any(onp.issubdtype(aval.dtype, t) for t in accepted_dtypes):\nmsg = '{} does not accept dtype {}. Accepted dtypes are subtypes of {}.'\ntypename = str(onp.dtype(aval.dtype).name)\n@@ -663,10 +663,11 @@ def unop_dtype_rule(result_dtype, accepted_dtypes, name, aval):\ndef unop(result_dtype, accepted_dtypes, name):\ndtype_rule = partial(unop_dtype_rule, result_dtype, accepted_dtypes, name)\n- prim = standard_primitive(operator.attrgetter('shape'), dtype_rule, name)\n+ prim = standard_primitive(_attrgetter('shape'), dtype_rule, name)\nbatching.defvectorized(prim)\nreturn prim\nstandard_unop = partial(unop, identity)\n+_attrgetter = lambda name: lambda x, **kwargs: getattr(x, name)\ndef binop_dtype_rule(result_dtype, accepted_dtypes, name, *avals):\n@@ -810,17 +811,25 @@ ad.deflinear(real_p, lambda t: [complex(t, onp.zeros((), onp.float32))])\nimag_p = unop(_fixed_dtype(onp.float32), _complex, 'imag')\nad.deflinear(imag_p, lambda t: [complex(onp.zeros((), onp.float32), t)])\n-complex_p = standard_binop([_f32, _f32], 'complex')\n+complex_p = binop(_fixed_dtype(onp.complex64), [_f32, _f32], 'complex')\nad.deflinear(complex_p, lambda t: [real(t), imag(t)])\n-# TODO promotes dtypes, need to remember whether we came from float or not\nconj_p = unop(_fixed_dtype(onp.complex64), _float | _complex, 'conj')\n-ad.deflinear(conj_p, lambda t: [conj(t)])\n+\n+def conj_transpose_rule(t, x, input_dtype):\n+ assert x is None\n+ if onp.issubdtype(input_dtype, onp.complexfloating):\n+ return [conj(t)]\n+ else:\n+ return [real(t)]\n+\n+ad.primitive_jvps[conj_p] = partial(ad.linear_jvp, conj_p)\n+ad.primitive_transposes[conj_p] = conj_transpose_rule\nabs_p = unop(_complex_basetype, _num, 'abs')\nad.defjvp2(abs_p,\n- lambda g, ans, x: div(_maybe_real(mul(g, _maybe_conj(x))),\n- _replace_zero(ans)))\n+ lambda g, ans, x:\n+ div(_maybe_real(mul(g, _maybe_conj(x))), _replace_zero(ans)))\n_maybe_conj = lambda x: conj(x) if _iscomplex(x) else x\n_maybe_real = lambda x: real(x) if _iscomplex(x) else x\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -479,7 +479,8 @@ def where(condition, x=None, y=None):\ncondition = lax.ne(condition, zeros_like(condition))\ncondition, x, y = broadcast_arrays(condition, x, y)\nif not x.size:\n- return x\n+ empty, _ = _promote_dtypes(x, y)\n+ return empty\nelse:\nreturn lax.select(condition, *_promote_dtypes(x, y))\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -23,7 +23,6 @@ from absl.testing import absltest\nfrom jax import test_util as jtu\nimport jax.numpy as np\n-from jax.config import config\nfrom jax import jit, grad, device_get, device_put, jacfwd, jacrev\nfrom jax.core import Primitive\nfrom jax.interpreters.partial_eval import def_abstract_eval\n@@ -31,6 +30,9 @@ from jax.interpreters.ad import defjvp\nfrom jax.interpreters.xla import DeviceArray\nfrom jax.abstract_arrays import concretization_err_msg\n+from jax.config import config\n+config.parse_flags_with_absl()\n+\nclass APITest(jtu.JaxTestCase):\ndef test_grad_argnums(self):\n@@ -235,6 +237,7 @@ class APITest(jtu.JaxTestCase):\nassert isinstance(y2[1][1], onp.ndarray)\nassert onp.all(y2[1][1] == 3 * x)\n+ @jtu.skip_on_devices(\"tpu\")\ndef test_jacobian(self):\nR = onp.random.RandomState(0).randn\nA = R(4, 3)\n@@ -249,5 +252,4 @@ class APITest(jtu.JaxTestCase):\nif __name__ == '__main__':\n- config.config_with_absl()\nabsltest.main()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -26,11 +26,13 @@ from jax.abstract_arrays import ShapedArray\nfrom jax import lax\nfrom jax.api import jit, grad, jvp, vjp, trace_to_jaxpr, jacfwd, jacrev\nfrom jax.api import vmap\n-from jax.config import config\nfrom jax.core import unit\nfrom jax.interpreters import partial_eval as pe\nfrom jax.util import partial, curry\n+from jax.config import config\n+config.parse_flags_with_absl()\n+\nclass BatchingTest(jtu.JaxTestCase):\ndef testConstantFunction(self):\n@@ -284,5 +286,4 @@ class BatchingTest(jtu.JaxTestCase):\nif __name__ == '__main__':\n- config.config_with_absl()\nabsltest.main()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/core_test.py",
"new_path": "tests/core_test.py",
"diff": "@@ -28,13 +28,15 @@ from jax import core\nfrom jax import numpy as np\nfrom jax import test_util as jtu\nfrom jax.api import jvp, linearize, vjp, jit\n-from jax.config import config\nfrom jax.lax import UnshapedArray, ShapedArray, ConcreteArray\nfrom jax.tree_util import tree_flatten, tree_unflatten, tree_multimap, tree_reduce\nfrom jax.util import partial\nfrom jax.interpreters import partial_eval as pe\nfrom jax.interpreters import xla\n+from jax.config import config\n+config.parse_flags_with_absl()\n+\n_ = pe.PartialVal((UnshapedArray(onp.float32), core.unit))\n__ = pe.PartialVal((ShapedArray((), onp.float32), core.unit))\n@@ -329,5 +331,4 @@ class CoreTest(jtu.JaxTestCase):\nif __name__ == '__main__':\n- config.config_with_absl()\nabsltest.main()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/generated_fun_test.py",
"new_path": "tests/generated_fun_test.py",
"diff": "@@ -27,8 +27,8 @@ import itertools as it\nimport jax.numpy as np\nfrom jax import jit, jvp, vjp\nimport jax.test_util as jtu\n-from jax.config import config\n+from jax.config import config\nconfig.parse_flags_with_absl()\nnpr.seed(0)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lapax_test.py",
"new_path": "tests/lapax_test.py",
"diff": "@@ -25,9 +25,9 @@ from absl.testing import parameterized\nfrom jax import jit\nfrom jax import test_util as jtu\n-from jax.config import config\nfrom jax.experimental import lapax\n+from jax.config import config\nconfig.parse_flags_with_absl()\nclass LapaxTest(jtu.JaxTestCase):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_indexing_test.py",
"new_path": "tests/lax_numpy_indexing_test.py",
"diff": "@@ -29,8 +29,8 @@ from jax import api\nfrom jax import lax\nfrom jax import numpy as lnp\nfrom jax import test_util as jtu\n-from jax.config import config\n+from jax.config import config\nconfig.parse_flags_with_absl()\nFLAGS = config.FLAGS\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -28,8 +28,8 @@ import numpy as onp\nfrom jax import api\nfrom jax import numpy as lnp\nfrom jax import test_util as jtu\n-from jax.config import config\n+from jax.config import config\nconfig.parse_flags_with_absl()\nFLAGS = config.FLAGS\n@@ -189,11 +189,10 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\n@parameterized.named_parameters(itertools.chain.from_iterable(\njtu.cases_from_list(\n- {\"testcase_name\": jtu.format_test_name_suffix(rec.test_name, shapes,\n- dtypes),\n+ {\"testcase_name\": jtu.format_test_name_suffix(\n+ rec.test_name, shapes, dtypes),\n\"rng\": rec.rng, \"shapes\": shapes, \"dtypes\": dtypes,\n\"onp_op\": getattr(onp, rec.name), \"lnp_op\": getattr(lnp, rec.name)}\n- for rec in JAX_BITWISE_OP_RECORDS\nfor shapes in filter(\n_shapes_are_broadcast_compatible,\nCombosWithReplacement(rec.shapes, rec.nargs))\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_scipy_test.py",
"new_path": "tests/lax_scipy_test.py",
"diff": "@@ -30,11 +30,11 @@ import scipy.stats as osp_stats\nfrom jax import api\nfrom jax import test_util as jtu\n-from jax.config import config\nfrom jax.scipy import misc as lsp_misc\nfrom jax.scipy import special as lsp_special\nfrom jax.scipy import stats as lsp_stats\n+from jax.config import config\nconfig.parse_flags_with_absl()\nFLAGS = config.FLAGS\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -32,10 +32,10 @@ from jax import core\nfrom jax import lax\nfrom jax import test_util as jtu\nfrom jax import lax_reference\n-from jax.config import config\nfrom jax.interpreters import xla\nfrom jax.lib import xla_bridge\n+from jax.config import config\nconfig.parse_flags_with_absl()\nFLAGS = config.FLAGS\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/minmax_test.py",
"new_path": "tests/minmax_test.py",
"diff": "@@ -20,13 +20,14 @@ from __future__ import print_function\nimport functools\nfrom absl.testing import absltest\n-from jax.config import config\nimport jax.numpy as np\nimport jax.test_util as jtu\nfrom jax import jit, grad\nfrom jax.experimental import minmax\nfrom jax.lib import xla_bridge as xla\n+from jax.config import config\n+config.parse_flags_with_absl()\nclass OptimizerTests(jtu.JaxTestCase):\n@@ -170,5 +171,4 @@ class OptimizerTests(jtu.JaxTestCase):\nif __name__ == '__main__':\n- config.config_with_absl()\nabsltest.main()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/random_test.py",
"new_path": "tests/random_test.py",
"diff": "@@ -27,8 +27,8 @@ from jax import api\nfrom jax import lax\nfrom jax import random\nfrom jax import test_util as jtu\n-from jax.config import config\n+from jax.config import config\nconfig.parse_flags_with_absl()\nFLAGS = config.FLAGS\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/stax_test.py",
"new_path": "tests/stax_test.py",
"diff": "@@ -24,9 +24,9 @@ import numpy as onp\nfrom jax import test_util as jtu\nfrom jax import random\n-from jax.config import config\nfrom jax.experimental import stax\n+from jax.config import config\nconfig.parse_flags_with_absl()\n"
}
] | Python | Apache License 2.0 | google/jax | fix miscellaneous bugs, incl. complex abs grad |
260,335 | 12.12.2018 13:18:22 | 28,800 | 9ddd30c23c762abb1dd0be90b23a1a99d44605c3 | add onnx2xla compiler example (closes | [
{
"change_type": "ADD",
"old_path": null,
"new_path": "examples/onnx2xla.py",
"diff": "+# Copyright 2018 Google LLC\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# https://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+\"\"\"An ONNX to XLA compiler by JAX-tracing a Numpy-backed ONNX interpreter.\"\"\"\n+from __future__ import absolute_import\n+from __future__ import division\n+from __future__ import print_function\n+\n+from functools import partial\n+from six.moves.urllib.request import urlopen\n+\n+import onnx\n+from onnx import numpy_helper\n+from onnx import onnx_pb2\n+\n+import jax.numpy as np\n+from jax import jit, grad\n+from jax import lax\n+\n+\n+def _asarray(proto):\n+ return numpy_helper.to_array(proto).reshape(tuple(proto.dims))\n+\n+\n+attr_types = dict(onnx_pb2.AttributeProto.AttributeType.items())\n+attribute_handlers = {\n+ attr_types['FLOAT']: lambda a: a.f,\n+ attr_types['INT']: lambda a: a.i,\n+ attr_types['STRING']: lambda a: a.s,\n+ attr_types['TENSOR']: lambda a: _asarray(a.t),\n+ attr_types['FLOATS']: lambda a: a.floats,\n+ attr_types['INTS']: lambda a: a.ints,\n+ attr_types['STRINGS']: lambda a: a.strings,\n+ attr_types['TENSORS']: lambda a: [_asarray(x) for x in a.tensors],\n+}\n+\n+\n+def onnx_maxpool(x, kernel_shape, pads=None, strides=None):\n+ \"\"\"Numpy-backed implementation of ONNX MaxPool op.\"\"\"\n+ prefix = (1,) * (x.ndim - len(kernel_shape))\n+ dims = prefix + tuple(kernel_shape)\n+ pads = tuple(pads) if pads else [0] * len(kernel_shape)\n+ strides = (prefix + tuple(strides)) if strides else [1] * len(kernel_shape)\n+ return [lax.reduce_window(x, -np.inf, lax.max, dims, strides, 'VALID')]\n+\n+\n+def onnx_conv(x, w, b=0, group=1, kernel_shape=None, pads=None, strides=None,\n+ dilations=None, auto_pad=None):\n+ \"\"\"Numpy-backed implementation of ONNX Conv op.\"\"\"\n+ assert group == 1\n+ kernel_shape = kernel_shape or w.shape\n+ strides = strides or [1] * (w.ndim - 2)\n+ if auto_pad:\n+ auto_pad = 'SAME' if auto_pad.startswith('SAME') else 'VALID'\n+ pads = lax.padtype_to_pads(x.shape[2:], w.shape[2:], strides, auto_pad)\n+ else:\n+ pads = pads or [0] * (w.ndim - 2)\n+ lhs_dilation = [1] * (w.ndim - 2)\n+ rhs_dilation = dilations or [1] * (w.ndim - 2)\n+ return [lax.conv_with_general_padding(x, w, strides, pads,\n+ lhs_dilation, rhs_dilation) + b]\n+\n+\n+def onnx_add(a, b, axis=None, broadcast=True):\n+ \"\"\"Numpy-backed implementation of ONNX Add op.\"\"\"\n+ if broadcast:\n+ axis = (a.dim - b.ndim) if axis is None else axis % a.ndim\n+ assert a.shape[axis:][:b.ndim] == b.shape\n+ b_shape = np.ones(a.ndim, dtype='int64').copy()\n+ b_shape[axis:axis + b.ndim] = b.shape\n+ b = np.reshape(b, b_shape)\n+ return [a + b]\n+\n+\n+onnx_ops = {\n+ 'Add': onnx_add,\n+ 'Constant': lambda value: [value],\n+ 'Conv': onnx_conv,\n+ 'MatMul': lambda x, y: [np.matmul(x, y)],\n+ 'MaxPool': onnx_maxpool,\n+ 'Relu': lambda x: [np.maximum(x, 0)],\n+ 'Reshape': lambda x, shape: [np.reshape(x, shape)],\n+}\n+\n+\n+def interpret_onnx(graph, *args):\n+ vals = dict({n.name: a for n, a in zip(graph.input, args)},\n+ **{n.name: _asarray(n) for n in graph.initializer})\n+ for node in graph.node:\n+ args = (vals[name] for name in node.input)\n+ attrs = {a.name: attribute_handlers[a.type](a) for a in node.attribute}\n+ outputs = onnx_ops[node.op_type](*args, **attrs)\n+ for name, output in zip(node.output, outputs):\n+ vals[name] = output\n+ return [vals[n.name] for n in graph.output]\n+\n+\n+if __name__ == \"__main__\":\n+ # It seems that there are several ONNX proto versions (you had one job!) but\n+ # this implementation works with at least this one mnist example file.\n+ url = ('https://github.com/onnx/models/blob/'\n+ '81c4779096d1205edd0b809e191a924c58c38fef/'\n+ 'mnist/model.onnx?raw=true')\n+ model = onnx.load(urlopen(url))\n+\n+ predict = lambda inputs: interpret_onnx(model.graph, inputs)[0]\n+\n+ # Run inference in Numpy-backed interpreter\n+ print(\"interpreted:\")\n+ print(predict(np.ones((1, 1, 28, 28))))\n+\n+ # JIT compile to XLA device, run inference on device\n+ compiled_predict = jit(predict)\n+ print(\"compiled:\")\n+ print(compiled_predict(np.ones((1, 1, 28, 28))))\n+\n+ # The interpreter is differentiable too! Even the compiled one:\n+ fun = lambda inputs: np.sum(compiled_predict(inputs))\n+ print(\"a derivative with respect to inputs:\")\n+ print(grad(fun)(np.ones((1, 1, 28, 28)))[..., :3, :3])\n"
}
] | Python | Apache License 2.0 | google/jax | add onnx2xla compiler example (closes #91) |
260,335 | 12.12.2018 13:51:08 | 28,800 | 88986e4d9e233be5c9558706ba920123302c5684 | add md5 check for downloaded onnx file | [
{
"change_type": "MODIFY",
"old_path": "examples/onnx2xla.py",
"new_path": "examples/onnx2xla.py",
"diff": "@@ -17,12 +17,15 @@ from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n+from cStringIO import StringIO\nfrom functools import partial\n-from six.moves.urllib.request import urlopen\n+import hashlib\n+import sys\nimport onnx\nfrom onnx import numpy_helper\nfrom onnx import onnx_pb2\n+from six.moves.urllib.request import urlopen\nimport jax.numpy as np\nfrom jax import jit, grad\n@@ -112,7 +115,11 @@ if __name__ == \"__main__\":\nurl = ('https://github.com/onnx/models/blob/'\n'81c4779096d1205edd0b809e191a924c58c38fef/'\n'mnist/model.onnx?raw=true')\n- model = onnx.load(urlopen(url))\n+ download = urlopen(url).read()\n+ if hashlib.md5(download).hexdigest() != 'bc8ad9bd19c5a058055dc18d0f089dad':\n+ print(\"onnx file checksum mismatch\")\n+ sys.exit(1)\n+ model = onnx.load(StringIO(download))\npredict = lambda inputs: interpret_onnx(model.graph, inputs)[0]\n"
}
] | Python | Apache License 2.0 | google/jax | add md5 check for downloaded onnx file |
260,335 | 12.12.2018 15:30:41 | 28,800 | 538850e271e43a7edc1d03000ff2ef11ee6c86bb | add misc numpy ops (c.f. | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -561,14 +561,12 @@ def tan(x):\nreturn div(sin(x), cos(x))\ndef asin(x):\n- # asin(x) = 2 * atan(x / (1 + sqrt(1 - x**2)))\n- return mul(_const(x, 2.),\n- atan2(x, add(_const(x, 1.), sqrt(add(_const(x, 1.), square(x))))))\n+ return mul(_const(x, 2),\n+ atan2(x, add(_const(x, 1), sqrt(sub(_const(x, 1), square(x))))))\ndef acos(x):\n- # acos(x) = 2 * atan(sqrt(1 - x**2) / (1 + x))\n- return mul(_const(x, 2.),\n- atan2(sqrt(sub(_const(x, 1.), square(x))), add(_const(x, 1.), x)))\n+ return mul(_const(x, 2),\n+ atan2(sqrt(sub(_const(x, 1), square(x))), add(_const(x, 1), x)))\ndef atan(x):\nreturn atan2(x, _const(x, 1.))\n@@ -588,6 +586,11 @@ def acosh(x):\nreturn log(add(x, mul(sqrt(add(x, _const(x, 1.))),\nsqrt(sub(x, _const(x, 1.))))))\n+def atanh(x):\n+ # atanh(x) = 0.5 * log((1 + x) / (1 - x))\n+ return mul(_const(x, 0.5), log(div(add(_const(x, 1.), x),\n+ sub(_const(x, 1.), x))))\n+\n# Add some methods to ShapedArray that rely on lax primitives\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -195,44 +195,65 @@ def _wraps(fun):\n### implementations of numpy functions in terms of lax\n-def _one_to_one_op(numpy_fn, lax_fn, promote_to_result_dtype=False):\n- if promote_to_result_dtype:\n- promoted_lax_fn = lambda *args: lax_fn(*_promote_args_like(numpy_fn, *args))\n+def _one_to_one_unop(numpy_fn, lax_fn, promote_like=False):\n+ if promote_like:\n+ fn = lambda x: lax_fn(lax.convert_element_type(x, _result_dtype(numpy_fn, x)))\nelse:\n- name = numpy_fn.__name__\n- promoted_lax_fn = lambda *args: lax_fn(*_promote_args(name, *args))\n- return _wraps(numpy_fn)(promoted_lax_fn)\n-\n-absolute = abs = _one_to_one_op(onp.absolute, lax.abs)\n-add = _one_to_one_op(onp.add, lax.add)\n-bitwise_and = _one_to_one_op(onp.bitwise_and, lax.bitwise_and)\n-bitwise_not = _one_to_one_op(onp.bitwise_not, lax.bitwise_not)\n-bitwise_or = _one_to_one_op(onp.bitwise_or, lax.bitwise_or)\n-bitwise_xor = _one_to_one_op(onp.bitwise_xor, lax.bitwise_xor)\n-right_shift = _one_to_one_op(onp.right_shift, lax.shift_right_arithmetic)\n-left_shift = _one_to_one_op(onp.left_shift, lax.shift_left)\n-ceil = _one_to_one_op(onp.ceil, lax.ceil)\n-equal = _one_to_one_op(onp.equal, lax.eq)\n-expm1 = _one_to_one_op(onp.expm1, lax.expm1, True)\n-exp = _one_to_one_op(onp.exp, lax.exp, True)\n-floor = _one_to_one_op(onp.floor, lax.floor)\n-greater_equal = _one_to_one_op(onp.greater_equal, lax.ge)\n-greater = _one_to_one_op(onp.greater, lax.gt)\n-isfinite = _one_to_one_op(onp.isfinite, lax.is_finite)\n-less_equal = _one_to_one_op(onp.less_equal, lax.le)\n-less = _one_to_one_op(onp.less, lax.lt)\n-log1p = _one_to_one_op(onp.log1p, lax.log1p, True)\n-log = _one_to_one_op(onp.log, lax.log, True)\n-maximum = _one_to_one_op(onp.maximum, lax.max)\n-minimum = _one_to_one_op(onp.minimum, lax.min)\n-multiply = _one_to_one_op(onp.multiply, lax.mul)\n-negative = _one_to_one_op(onp.negative, lax.neg)\n-not_equal = _one_to_one_op(onp.not_equal, lax.ne)\n-power = _one_to_one_op(onp.power, lax.pow, True)\n-sign = _one_to_one_op(onp.sign, lax.sign)\n-subtract = _one_to_one_op(onp.subtract, lax.sub)\n-tanh = _one_to_one_op(onp.tanh, lax.tanh, True)\n-sort = _one_to_one_op(onp.sort, lax.sort)\n+ fn = lambda x: lax_fn(x)\n+ return _wraps(numpy_fn)(fn)\n+\n+def _one_to_one_binop(numpy_fn, lax_fn, promote_like=False):\n+ if promote_like:\n+ fn = lambda x, y: lax_fn(*_promote_args_like(numpy_fn, x, y))\n+ else:\n+ fn = lambda x, y: lax_fn(*_promote_args(numpy_fn.__name__, x, y))\n+ return _wraps(numpy_fn)(fn)\n+\n+\n+absolute = abs = _one_to_one_unop(onp.absolute, lax.abs)\n+bitwise_not = _one_to_one_unop(onp.bitwise_not, lax.bitwise_not)\n+negative = _one_to_one_unop(onp.negative, lax.neg)\n+sort = _one_to_one_unop(onp.sort, lax.sort)\n+sign = _one_to_one_unop(onp.sign, lax.sign)\n+\n+floor = _one_to_one_unop(onp.floor, lax.floor, True)\n+ceil = _one_to_one_unop(onp.ceil, lax.ceil, True)\n+exp = _one_to_one_unop(onp.exp, lax.exp, True)\n+log = _one_to_one_unop(onp.log, lax.log, True)\n+expm1 = _one_to_one_unop(onp.expm1, lax.expm1, True)\n+log1p = _one_to_one_unop(onp.log1p, lax.log1p, True)\n+sin = _one_to_one_unop(onp.sin, lax.sin, True)\n+cos = _one_to_one_unop(onp.cos, lax.cos, True)\n+tan = _one_to_one_unop(onp.tan, lax.tan, True)\n+arcsin = _one_to_one_unop(onp.arcsin, lax.asin, True)\n+arccos = _one_to_one_unop(onp.arccos, lax.acos, True)\n+arctan = _one_to_one_unop(onp.arctan, lax.atan, True)\n+sinh = _one_to_one_unop(onp.sinh, lax.sinh, True)\n+cosh = _one_to_one_unop(onp.cosh, lax.cosh, True)\n+tanh = _one_to_one_unop(onp.tanh, lax.tanh, True)\n+arcsinh = _one_to_one_unop(onp.arcsinh, lax.asinh, True)\n+arccosh = _one_to_one_unop(onp.arccosh, lax.acosh, True)\n+arctanh = _one_to_one_unop(onp.arctanh, lax.atanh, True)\n+\n+add = _one_to_one_binop(onp.add, lax.add)\n+bitwise_and = _one_to_one_binop(onp.bitwise_and, lax.bitwise_and)\n+bitwise_or = _one_to_one_binop(onp.bitwise_or, lax.bitwise_or)\n+bitwise_xor = _one_to_one_binop(onp.bitwise_xor, lax.bitwise_xor)\n+right_shift = _one_to_one_binop(onp.right_shift, lax.shift_right_arithmetic)\n+left_shift = _one_to_one_binop(onp.left_shift, lax.shift_left)\n+equal = _one_to_one_binop(onp.equal, lax.eq)\n+greater_equal = _one_to_one_binop(onp.greater_equal, lax.ge)\n+greater = _one_to_one_binop(onp.greater, lax.gt)\n+isfinite = _one_to_one_binop(onp.isfinite, lax.is_finite)\n+less_equal = _one_to_one_binop(onp.less_equal, lax.le)\n+less = _one_to_one_binop(onp.less, lax.lt)\n+maximum = _one_to_one_binop(onp.maximum, lax.max)\n+minimum = _one_to_one_binop(onp.minimum, lax.min)\n+multiply = _one_to_one_binop(onp.multiply, lax.mul)\n+not_equal = _one_to_one_binop(onp.not_equal, lax.ne)\n+subtract = _one_to_one_binop(onp.subtract, lax.sub)\n+power = _one_to_one_binop(onp.power, lax.pow, True)\n+arctan2 = _one_to_one_binop(onp.arctan2, lax.atan2, True)\ndef _logical_op(np_op, bitwise_op):\n@@ -352,30 +373,6 @@ def flip(m, axis):\nreturn lax.rev(m, [axis])\n-@_wraps(onp.sinh)\n-def sinh(x):\n- x, = _promote_to_result_dtype(onp.sinh, x)\n- return lax.div(lax.sub(lax.exp(x), lax.exp(lax.neg(x))), _constant_like(x, 2))\n-\n-\n-@_wraps(onp.cosh)\n-def cosh(x):\n- x, = _promote_to_result_dtype(onp.cosh, x)\n- return lax.div(lax.add(lax.exp(x), lax.exp(lax.neg(x))), _constant_like(x, 2))\n-\n-\n-@_wraps(onp.sin)\n-def sin(x):\n- x, = _promote_to_result_dtype(onp.sin, x)\n- return lax.sin(x)\n-\n-\n-@_wraps(onp.cos)\n-def cos(x):\n- x, = _promote_to_result_dtype(onp.sin, x)\n- return lax.cos(x)\n-\n-\n@_wraps(onp.conjugate)\ndef conjugate(x):\nreturn lax.conj(x) if iscomplexobj(x) else x\n@@ -611,8 +608,8 @@ sum = _make_reduction(onp.sum, lax.add, 0)\nprod = _make_reduction(onp.prod, lax.mul, 1)\nmax = _make_reduction(onp.max, lax.max, -onp.inf)\nmin = _make_reduction(onp.min, lax.min, onp.inf)\n-all = _make_reduction(onp.all, logical_and, True)\n-any = _make_reduction(onp.any, logical_or, False)\n+all = alltrue = _make_reduction(onp.all, logical_and, True)\n+any = sometrue = _make_reduction(onp.any, logical_or, False)\n@_wraps(onp.mean)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -84,13 +84,22 @@ JAX_ONE_TO_ONE_OP_RECORDS = [\nop_record(\"not_equal\", 2, default_dtypes, all_shapes, jtu.rand_some_equal(), [\"rev\"]),\nop_record(\"power\", 2, float_dtypes, all_shapes, jtu.rand_positive(), [\"rev\"]),\nop_record(\"subtract\", 2, default_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\n- op_record(\"tanh\", 1, numeric_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\nop_record(\"sin\", 1, default_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\nop_record(\"cos\", 1, default_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\n+ op_record(\"tan\", 1, default_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\n+ op_record(\"sinh\", 1, numeric_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\n+ op_record(\"cosh\", 1, numeric_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\n+ op_record(\"tanh\", 1, numeric_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\n+ op_record(\"arcsin\", 1, default_dtypes, all_shapes, jtu.rand_small(), [\"rev\"]),\n+ op_record(\"arccos\", 1, default_dtypes, all_shapes, jtu.rand_small(), [\"rev\"]),\n+ op_record(\"arctan\", 1, default_dtypes, all_shapes, jtu.rand_small(), [\"rev\"]),\n+ op_record(\"arctan2\", 2, default_dtypes, all_shapes, jtu.rand_small(), [\"rev\"]),\n+ op_record(\"arcsinh\", 1, default_dtypes, all_shapes, jtu.rand_small(), [\"rev\"]),\n+ op_record(\"arccosh\", 1, default_dtypes, all_shapes, jtu.rand_small(), [\"rev\"]),\n+ op_record(\"arctanh\", 1, default_dtypes, all_shapes, jtu.rand_small(), [\"rev\"]),\n]\nJAX_COMPOUND_OP_RECORDS = [\n- op_record(\"cosh\", 1, default_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\nop_record(\"divide\", 2, default_dtypes, all_shapes, jtu.rand_nonzero(), [\"rev\"]),\nop_record(\"expm1\", 1, numeric_dtypes, all_shapes, jtu.rand_positive(), [],\ntest_name=\"expm1_large\"),\n@@ -103,7 +112,6 @@ JAX_COMPOUND_OP_RECORDS = [\nop_record(\"logaddexp\", 2, float_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\nop_record(\"ravel\", 1, default_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\nop_record(\"remainder\", 2, default_dtypes, all_shapes, jtu.rand_nonzero(), []),\n- op_record(\"sinh\", 1, default_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\nop_record(\"sqrt\", 1, default_dtypes, all_shapes, jtu.rand_positive(), [\"rev\"]),\nop_record(\"transpose\", 1, default_dtypes, all_shapes, jtu.rand_default(), [\"rev\"]),\nop_record(\"true_divide\", 2, default_dtypes, all_shapes, jtu.rand_nonzero(), [\"rev\"]),\n"
}
] | Python | Apache License 2.0 | google/jax | add misc numpy ops (c.f. #70) |
260,335 | 12.12.2018 17:53:37 | 28,800 | 77d6fb4c012a3ea3c6e727b94fa69459561a2525 | transpose shouldn't transpose with identity perm | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -244,7 +244,11 @@ def _index_untake(axes, src, dst, *idxs):\nreturn dst\ndef transpose(operand, permutation):\n- return transpose_p.bind(operand, permutation=tuple(permutation))\n+ permutation = tuple(permutation)\n+ if permutation == tuple(range(len(permutation))):\n+ return operand\n+ else:\n+ return transpose_p.bind(operand, permutation=permutation)\ndef reduce(operand, init_value, computation, dimensions):\nmonoid_reducer = _get_monoid_reducer(computation, init_value)\n"
}
] | Python | Apache License 2.0 | google/jax | transpose shouldn't transpose with identity perm |
260,335 | 12.12.2018 19:05:40 | 28,800 | 1f42d980b86a68a4ac0529e750023ca9b2361e34 | rename ResNet50Test -> ExamplesTest, remove some dots | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -559,7 +559,7 @@ def square(x):\nreturn mul(x, x)\ndef reciprocal(x):\n- return div(_const(x, 1.), x)\n+ return div(_const(x, 1), x)\ndef tan(x):\nreturn div(sin(x), cos(x))\n@@ -573,7 +573,7 @@ def acos(x):\natan2(sqrt(sub(_const(x, 1), square(x))), add(_const(x, 1), x)))\ndef atan(x):\n- return atan2(x, _const(x, 1.))\n+ return atan2(x, _const(x, 1))\ndef sinh(x):\nreturn mul(_const(x, 0.5), sub(exp(x), exp(neg(x))))\n@@ -583,17 +583,17 @@ def cosh(x):\ndef asinh(x):\n# asinh(x) = log(x + sqrt(x**2 + 1))\n- return log(add(x, sqrt(add(mul(x, x), _const(x, 1.)))))\n+ return log(add(x, sqrt(add(mul(x, x), _const(x, 1)))))\ndef acosh(x):\n# acosh(x) = log(x + sqrt((x + 1) * (x - 1)))\n- return log(add(x, mul(sqrt(add(x, _const(x, 1.))),\n- sqrt(sub(x, _const(x, 1.))))))\n+ return log(add(x, mul(sqrt(add(x, _const(x, 1))),\n+ sqrt(sub(x, _const(x, 1))))))\ndef atanh(x):\n# atanh(x) = 0.5 * log((1 + x) / (1 - x))\n- return mul(_const(x, 0.5), log(div(add(_const(x, 1.), x),\n- sub(_const(x, 1.), x))))\n+ return mul(_const(x, 0.5), log(div(add(_const(x, 1), x),\n+ sub(_const(x, 1), x))))\n# Add some methods to ShapedArray that rely on lax primitives\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/examples_test.py",
"new_path": "tests/examples_test.py",
"diff": "@@ -47,7 +47,7 @@ def _CheckShapeAgreement(test_case, init_fun, apply_fun, input_shape):\ntest_case.assertEqual(result.shape, result_shape)\n-class ResNet50Test(jtu.JaxTestCase):\n+class ExamplesTest(jtu.JaxTestCase):\n@parameterized.named_parameters(\n{\"testcase_name\": \"_input_shape={}\".format(input_shape),\n"
}
] | Python | Apache License 2.0 | google/jax | rename ResNet50Test -> ExamplesTest, remove some dots |
260,335 | 12.12.2018 21:32:30 | 28,800 | 51d89a332c48210dc7d832e42358857529336eb2 | tweak readme text | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -27,7 +27,7 @@ composed arbitrarily, so you can express sophisticated algorithms and get\nmaximal performance without leaving Python.\nDig a little deeper, and you'll see that JAX is really an extensible system for\n-[composable transformations of functions](#transformations). Both\n+[composable function transformations](#transformations). Both\n[`grad`](#automatic-differentiation-with-grad) and [`jit`](#compilation-with-jit)\nare instances of such transformations. Another is [`vmap`](#auto-vectorization-with-vmap)\nfor automatic vectorization, with more to come.\n"
}
] | Python | Apache License 2.0 | google/jax | tweak readme text |
260,335 | 13.12.2018 07:28:59 | 28,800 | 579665b1b0ee8caa0f154d6677cd4b13be6adc3f | add set_printoptions (same as onp version) | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -49,12 +49,14 @@ _min = builtins.min\n_sum = builtins.sum\n# We need some numpy scalars\n-# TODO(mattjj): handle constants in an indirected, less explicit way?\npi = onp.pi\ne = onp.e\ninf = onp.inf\nnan = onp.nan\n+# And some numpy utility functions\n+set_printoptions = onp.set_printoptions\n+\n# We want isinstance(x, np.ndarray) checks in user code to work with the our\n# array-like types, including DeviceArray and UnshapedArray (i.e. the abstract\n# array base class). We can override the isinstance behavior directly, without\n@@ -1013,6 +1015,7 @@ def _argminmax(op, a, axis):\ndef _not_implemented(fun):\n+ @_wraps(fun)\ndef wrapped(*args, **kwargs):\nraise Exception(\"Numpy function {} not yet implemented\".format(fun))\nreturn wrapped\n"
}
] | Python | Apache License 2.0 | google/jax | add set_printoptions (same as onp version) |
260,335 | 13.12.2018 08:56:40 | 28,800 | 54bceee9e184039ebdc69f065b0550b5859fb737 | make num_generated_cases also settable by env var | [
{
"change_type": "MODIFY",
"old_path": "jax/test_util.py",
"new_path": "jax/test_util.py",
"diff": "@@ -19,6 +19,7 @@ from __future__ import print_function\nimport functools\nimport re\nimport itertools as it\n+import os\nimport random\nfrom absl.testing import absltest\n@@ -45,7 +46,7 @@ flags.DEFINE_enum(\nflags.DEFINE_integer(\n'num_generated_cases',\n- 100,\n+ os.getenv('JAX_NUM_GENERATED_CASES', 100),\nhelp='Number of generated cases to test')\nEPS = 1e-4\n"
}
] | Python | Apache License 2.0 | google/jax | make num_generated_cases also settable by env var |
260,335 | 13.12.2018 11:52:41 | 28,800 | a28501711018bac59791797ad6f1404c1656e294 | fix failing tests (misc small bugs) | [
{
"change_type": "MODIFY",
"old_path": "jax/config.py",
"new_path": "jax/config.py",
"diff": "@@ -78,9 +78,13 @@ class Config(object):\nself.update(name, getattr(absl_flags.FLAGS, name))\ndef parse_flags_with_absl(self):\n+ global already_configured_with_absl\n+ if not already_configured_with_absl:\nimport absl.flags\nself.config_with_absl()\nabsl.flags.FLAGS(sys.argv)\n+ already_configured_with_absl = True\n+\nclass NameSpace(object):\ndef __init__(self, getter):\n@@ -92,3 +96,4 @@ class NameSpace(object):\nconfig = Config()\nflags = config\n+already_configured_with_absl = False\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/stax.py",
"new_path": "jax/experimental/stax.py",
"diff": "@@ -49,7 +49,7 @@ def logsoftmax(x, axis=-1):\ndef fastvar(x, axis, keepdims):\n\"\"\"A fast but less numerically-stable variance calculation than np.var.\"\"\"\n- return np.mean(x**2, axis, keepdims) - np.mean(x, axis, keepdims)**2\n+ return np.mean(x**2, axis, keepdims=keepdims) - np.mean(x, axis, keepdims=keepdims)**2\n# Initializers\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/fft.py",
"new_path": "jax/numpy/fft.py",
"diff": "@@ -20,9 +20,7 @@ import numpy as onp\nfrom ..util import get_module_functions\nfrom .lax_numpy import _not_implemented\n-from .lax_numpy import IMPLEMENTED_FUNCS\n-UNIMPLEMENTED_FUNCS = get_module_functions(onp.fft) - set(IMPLEMENTED_FUNCS)\n-for func in UNIMPLEMENTED_FUNCS:\n+for func in get_module_functions(onp.fft):\nif func.__name__ not in globals():\nglobals()[func.__name__] = _not_implemented(func)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -180,9 +180,6 @@ def _promote_args_like(op, *args):\ndef _constant_like(x, const):\nreturn onp.array(const, dtype=_dtype(x))\n-# A dictionary mapping implemented funcs to their lax equivalent.\n-IMPLEMENTED_FUNCS = {}\n-\ndef _wraps(fun):\n\"\"\"Like functools.wraps but works with numpy.ufuncs.\"\"\"\ndocstr = \"\"\"\n@@ -195,7 +192,6 @@ def _wraps(fun):\nop.__name__ = fun.__name__\nop.__doc__ = docstr\nfinally:\n- IMPLEMENTED_FUNCS[fun] = op\nreturn op\nreturn wrap\n@@ -1013,19 +1009,6 @@ def _argminmax(op, a, axis):\nmask_idxs = where(lax._eq_meet(a, op(a, axis, keepdims=True)), idxs, maxval)\nreturn min(mask_idxs, axis)\n-\n-def _not_implemented(fun):\n- @_wraps(fun)\n- def wrapped(*args, **kwargs):\n- raise Exception(\"Numpy function {} not yet implemented\".format(fun))\n- return wrapped\n-\n-# Build a set of all unimplemented NumPy functions.\n-UNIMPLEMENTED_FUNCS = get_module_functions(onp) - set(IMPLEMENTED_FUNCS)\n-for func in UNIMPLEMENTED_FUNCS:\n- if func.__name__ not in globals():\n- globals()[func.__name__] = _not_implemented(func)\n-\n### Indexing\n@@ -1198,6 +1181,20 @@ def _static_idx(idx, size):\nreturn stop_inclusive, end, -step, True\n+### track unimplemented functions\n+\n+def _not_implemented(fun):\n+ @_wraps(fun)\n+ def wrapped(*args, **kwargs):\n+ raise Exception(\"Numpy function {} not yet implemented\".format(fun))\n+ return wrapped\n+\n+# Build a set of all unimplemented NumPy functions.\n+for func in get_module_functions(onp):\n+ if func.__name__ not in globals():\n+ globals()[func.__name__] = _not_implemented(func)\n+\n+\n### add method and operator overloads to arraylike classes\n# We add operator overloads to DeviceArray and ShapedArray. These method and\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/linalg.py",
"new_path": "jax/numpy/linalg.py",
"diff": "@@ -20,9 +20,7 @@ import numpy as onp\nfrom ..util import get_module_functions\nfrom .lax_numpy import _not_implemented\n-from .lax_numpy import IMPLEMENTED_FUNCS\n-UNIMPLEMENTED_FUNCS = get_module_functions(onp.linalg) - set(IMPLEMENTED_FUNCS)\n-for func in UNIMPLEMENTED_FUNCS:\n+for func in get_module_functions(onp.linalg):\nif func.__name__ not in globals():\nglobals()[func.__name__] = _not_implemented(func)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/random.py",
"new_path": "jax/random.py",
"diff": "@@ -27,9 +27,6 @@ from . import numpy as np\nfrom . import tree_util\nfrom .api import jit\nfrom jax.lib import xla_bridge\n-from .util import get_module_functions\n-from .numpy.lax_numpy import _not_implemented\n-from .numpy.lax_numpy import IMPLEMENTED_FUNCS\nclass PRNGKey(object):\n\"\"\"A pseudo-random number generator (PRNG) key for use with lax.random.\"\"\"\n@@ -361,10 +358,3 @@ def bernoulli(key, mean=onp.float32(0.5), shape=()):\nif onp.shape(mean) != shape:\nmean = lax.broadcast(mean, shape)\nreturn lax.lt(uniform(key, shape), mean)\n-\n-# TODO(alexbw): np.random.random is an alias of random_sample, and\n-# doesn't show up after a call to `dir(module)`.\n-UNIMPLEMENTED_FUNCS = get_module_functions(onp.random) - set(IMPLEMENTED_FUNCS)\n-for func in UNIMPLEMENTED_FUNCS:\n- if func.__name__ not in globals():\n- globals()[func.__name__] = _not_implemented(func)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/scipy/special.py",
"new_path": "jax/scipy/special.py",
"diff": "@@ -22,8 +22,9 @@ from .. import lax\nfrom ..numpy.lax_numpy import _wraps\n-gammaln = _wraps(osp_special.gammaln)(lax.lgamma)\n-digamma = _wraps(osp_special.digamma)(lax.digamma)\n-erf = _wraps(osp_special.erf)(lax.erf)\n-erfc = _wraps(osp_special.erfc)(lax.erfc)\n-erfinv = _wraps(osp_special.erfinv)(lax.erf_inv)\n+# need to create new functions because _wraps sets the __name__ attribute\n+gammaln = _wraps(osp_special.gammaln)(lambda x: lax.lgamma(x))\n+digamma = _wraps(osp_special.digamma)(lambda x: lax.digamma(x))\n+erf = _wraps(osp_special.erf)(lambda x: lax.erf(x))\n+erfc = _wraps(osp_special.erfc)(lambda x: lax.erfc(x))\n+erfinv = _wraps(osp_special.erfinv)(lambda x: lax.erf_inv(x))\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/core_test.py",
"new_path": "tests/core_test.py",
"diff": "@@ -18,6 +18,7 @@ from __future__ import print_function\nimport operator\nfrom collections import namedtuple\n+from unittest import skip\nimport numpy as onp\nfrom absl.testing import absltest\n@@ -152,7 +153,8 @@ def check_trace_eval(f, pvals, vals, expected_out_pval):\nclass CoreTest(jtu.JaxTestCase):\n- def DISABLED_test_pack_unpack(self):\n+ @skip\n+ def test_pack_unpack(self):\n# TODO(dougalm): figure out what jaxpr-tracing api to expose and re-enable\ny = onp.array(1.0)\ndef foo(x):\n@@ -162,7 +164,8 @@ class CoreTest(jtu.JaxTestCase):\npe.trace_to_jaxpr(foo, (_,))\n- def DISABLED_test_tup_add(self):\n+ @skip\n+ def test_tup_add(self):\n# TODO(mattjj,dougalm): put tup_add somewhere (was in array_type.py)\ny = onp.array(1.0)\ndef foo(x):\n@@ -184,7 +187,8 @@ class CoreTest(jtu.JaxTestCase):\nexcept TypeError:\npass\n- def DIABLED_test_print_jaxpr_compound(self):\n+ @skip\n+ def test_print_jaxpr_compound(self):\n# TODO(dougalm): figure out what jaxpr-tracing api to expose and re-enable\npv = pe.PartialVal((ShapedArray((2, 3), onp.float32), core.unit))\nprint(pe.trace_to_jaxpr(fun_with_call_closure, (pv,))[0])\n@@ -226,7 +230,8 @@ class CoreTest(jtu.JaxTestCase):\napi.trace_to_jaxpr(foo, (__,))\n- def DISABLED_test_nested_grad(self):\n+ @skip\n+ def test_nested_grad(self):\ndef foo(x):\nprint(type(x), x)\ndef bar(y):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_indexing_test.py",
"new_path": "tests/lax_numpy_indexing_test.py",
"diff": "@@ -19,6 +19,7 @@ from __future__ import print_function\nimport collections\nfrom functools import partial\nimport itertools\n+from unittest import skip\nfrom absl.testing import absltest\nfrom absl.testing import parameterized\n@@ -322,6 +323,7 @@ class IndexingTest(jtu.JaxTestCase):\nargs_maker = lambda: [rng(shape, dtype), unpacked_indexer]\nself._CompileAndCheck(fun, args_maker, check_dtypes=True)\n+ @skip\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"{}_inshape={}_indexer={}\"\n.format(name, jtu.format_shape_dtype_string(shape, dtype), indexer),\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -19,6 +19,7 @@ from __future__ import print_function\nimport collections\nimport functools\nimport itertools\n+from unittest import skip\nfrom absl.testing import absltest\nfrom absl.testing import parameterized\n@@ -647,7 +648,8 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\nself.assertFalse(a3)\n@jtu.skip_on_devices(\"tpu\") # TODO(mattjj): investigate this failure\n- def DISABLED_testOnesBroadcastingConstantHandler(self):\n+ @skip\n+ def testOnesBroadcastingConstantHandler(self):\n# TODO(mattjj): update this test for jax3\ndef fun(x):\n@@ -727,7 +729,8 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\nself.assertRaises(TypeError, lambda: g(3.))\n- def DISABLED_testTracingPrimitiveWithNoTranslationErrorMessage(self):\n+ @skip\n+ def testTracingPrimitiveWithNoTranslationErrorMessage(self):\n# TODO(mattjj): update this for jax3\nfoo = lnp._not_implemented(lambda x: x)\n@@ -774,7 +777,8 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\n# TODO(mattjj): test infix operator overrides\n- def DISABLED_testRavel(self):\n+ @skip\n+ def testRavel(self):\n# TODO(mattjj): support this method-based syntax?\nrng = onp.random.RandomState(0)\nargs_maker = lambda: [rng.randn(3, 4).astype(\"float32\")]\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -20,6 +20,7 @@ import collections\nimport functools\nfrom functools import partial\nimport itertools\n+from unittest import skip\nfrom absl.testing import absltest\nfrom absl.testing import parameterized\n@@ -365,6 +366,7 @@ class LaxTest(jtu.JaxTestCase):\nself._CompileAndCheck(fun, args_maker, check_dtypes=True)\n+ @skip\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_lhs_shape={}_rhs_shape={}_strides={}_padding={}\"\n\"_lhs_dilation={}_rhs_dilation={}\".format(\n"
}
] | Python | Apache License 2.0 | google/jax | fix failing tests (misc small bugs) |
260,335 | 13.12.2018 13:39:34 | 28,800 | 32f89e6c5a0830f7ad5a7ac7213344826a72e883 | add initial travis ci file | [
{
"change_type": "ADD",
"old_path": null,
"new_path": ".travis.yml",
"diff": "+sudo: false\n+notifications:\n+ email: false\n+language: python\n+python:\n+ - \"2.7\"\n+ - \"3.6\"\n+env:\n+ - DEPS=\"pip nose six protobuf>=3.6.0 absl-py opt_einsum numpy scipy\"\n+ - DEPS=\"pip nose six protobuf>=3.6.0 absl-py opt_einsum numpy\"\n+before_install:\n+ - if [[ \"$TRAVIS_PYTHON_VERSION\" == \"2.7\" ]]; then\n+ wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;\n+ else\n+ wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;\n+ fi\n+ - bash miniconda.sh -b -p $HOME/miniconda\n+ - export PATH=\"$HOME/miniconda/bin:$PATH\"\n+ - conda update --yes conda\n+ - conda config --add channels conda-forge\n+install:\n+ - conda install --yes python=$TRAVIS_PYTHON_VERSION $DEPS\n+ - pip install -v .\n+script:\n+ - cd tests\n+ - PYTHONPATH=. JAX_NUM_GENERATED_CASES=10 nosetests\n"
}
] | Python | Apache License 2.0 | google/jax | add initial travis ci file |
260,335 | 13.12.2018 16:24:32 | 28,800 | b64df0b759aa4e328bc211532686cd19fb368524 | comment out tests (temp), remove examples imports
examples imports add a new test dependency on matplotlib | [
{
"change_type": "MODIFY",
"old_path": "tests/examples_test.py",
"new_path": "tests/examples_test.py",
"diff": "@@ -29,9 +29,6 @@ import jax.numpy as np\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\nfrom examples import kernel_lsq\n-from examples import mnist_classifier\n-from examples import mnist_classifier_fromscratch\n-from examples import mnist_vae\nfrom examples import resnet50\nsys.path.pop()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/linalg_test.py",
"new_path": "tests/linalg_test.py",
"diff": "@@ -42,114 +42,117 @@ def float_types():\nclass NumpyLinalgTest(jtu.JaxTestCase):\n- @parameterized.named_parameters(jtu.cases_from_list(\n- {\"testcase_name\":\n- \"_shape={}\".format(jtu.format_shape_dtype_string(shape, dtype)),\n- \"shape\": shape, \"dtype\": dtype, \"rng\": rng}\n- for shape in [(1, 1), (4, 4), (2, 5, 5), (200, 200), (1000, 0, 0)]\n- for dtype in float_types()\n- for rng in [jtu.rand_default()]))\n- def testCholesky(self, shape, dtype, rng):\n- def args_maker():\n- a = rng(shape, dtype)\n- return [onp.matmul(a, T(a))]\n-\n- self._CheckAgainstNumpy(onp.linalg.cholesky, np.linalg.cholesky, args_maker,\n- check_dtypes=True, tol=1e-3)\n- self._CompileAndCheck(np.linalg.cholesky, args_maker, check_dtypes=True)\n-\n- @parameterized.named_parameters(jtu.cases_from_list(\n- {\"testcase_name\": \"_shape={}_fullmatrices={}\".format(\n- jtu.format_shape_dtype_string(shape, dtype), full_matrices),\n- \"shape\": shape, \"dtype\": dtype, \"full_matrices\": full_matrices,\n- \"rng\": rng}\n- for shape in [(1, 1), (3, 4), (2, 10, 5), (2, 200, 200)]\n- for dtype in float_types()\n- for full_matrices in [False, True]\n- for rng in [jtu.rand_default()]))\n- def testQr(self, shape, dtype, full_matrices, rng):\n- m, n = shape[-2:]\n-\n- if full_matrices:\n- mode, k = \"complete\", m\n- else:\n- mode, k = \"reduced\", min(m, n)\n-\n- a = rng(shape, dtype)\n- lq, lr = np.linalg.qr(a, mode=mode)\n-\n- # onp.linalg.qr doesn't support broadcasting. But it seems like an\n- # inevitable extension so we support it in our version.\n- nq = onp.zeros(shape[:-2] + (m, k), dtype)\n- nr = onp.zeros(shape[:-2] + (k, n), dtype)\n- for index in onp.ndindex(*shape[:-2]):\n- nq[index], nr[index] = onp.linalg.qr(a[index], mode=mode)\n-\n- max_rank = max(m, n)\n-\n- # Norm, adjusted for dimension and type.\n- def norm(x):\n- n = onp.linalg.norm(x, axis=(-2, -1))\n- return n / (max_rank * onp.finfo(dtype).eps)\n-\n- def compare_orthogonal(q1, q2):\n- # Q is unique up to sign, so normalize the sign first.\n- sum_of_ratios = onp.sum(onp.divide(q1, q2), axis=-2, keepdims=True)\n- phases = onp.divide(sum_of_ratios, onp.abs(sum_of_ratios))\n- q1 *= phases\n- self.assertTrue(onp.all(norm(q1 - q2) < 30))\n-\n- # Check a ~= qr\n- self.assertTrue(onp.all(norm(a - onp.matmul(lq, lr)) < 30))\n-\n- # Compare the first 'k' vectors of Q; the remainder form an arbitrary\n- # orthonormal basis for the null space.\n- compare_orthogonal(nq[..., :k], lq[..., :k])\n-\n- # Check that q is close to unitary.\n- self.assertTrue(onp.all(norm(onp.eye(k) - onp.matmul(T(lq), lq)) < 5))\n-\n-\n-class ScipyLinalgTest(jtu.JaxTestCase):\n-\n- @parameterized.named_parameters(jtu.cases_from_list(\n- {\"testcase_name\":\n- \"_lhs={}_rhs={}_lower={}_transposea={}\".format(\n- jtu.format_shape_dtype_string(lhs_shape, dtype),\n- jtu.format_shape_dtype_string(rhs_shape, dtype),\n- lower, transpose_a),\n- \"lower\": lower, \"transpose_a\": transpose_a,\n- \"lhs_shape\": lhs_shape, \"rhs_shape\": rhs_shape, \"dtype\": dtype,\n- \"rng\": rng}\n- for lower, transpose_a in itertools.product([False, True], repeat=2)\n- for lhs_shape, rhs_shape in [\n- ((4, 4), (4,)),\n- ((4, 4), (4, 3)),\n- ((2, 8, 8), (2, 8, 10)),\n- ]\n- for dtype in float_types()\n- for rng in [jtu.rand_default()]))\n- def testSolveTriangularBlocked(self, lower, transpose_a, lhs_shape,\n- rhs_shape, dtype, rng):\n- k = rng(lhs_shape, dtype)\n- l = onp.linalg.cholesky(onp.matmul(k, T(k))\n- + lhs_shape[-1] * onp.eye(lhs_shape[-1]))\n- l = l.astype(k.dtype)\n- b = rng(rhs_shape, dtype)\n-\n- a = l if lower else T(l)\n- inv = onp.linalg.inv(T(a) if transpose_a else a).astype(a.dtype)\n- if len(lhs_shape) == len(rhs_shape):\n- onp_ans = onp.matmul(inv, b)\n- else:\n- onp_ans = onp.einsum(\"...ij,...j->...i\", inv, b)\n-\n- # The standard scipy.linalg.solve_triangular doesn't support broadcasting.\n- # But it seems like an inevitable extension so we support it.\n- ans = scipy.linalg.solve_triangular(\n- l if lower else T(l), b, trans=1 if transpose_a else 0, lower=lower)\n-\n- self.assertAllClose(onp_ans, ans, check_dtypes=True)\n+ # TODO(mattjj): put these tests back when we update jaxlib\n+ pass\n+\n+ # @parameterized.named_parameters(jtu.cases_from_list(\n+ # {\"testcase_name\":\n+ # \"_shape={}\".format(jtu.format_shape_dtype_string(shape, dtype)),\n+ # \"shape\": shape, \"dtype\": dtype, \"rng\": rng}\n+ # for shape in [(1, 1), (4, 4), (2, 5, 5), (200, 200), (1000, 0, 0)]\n+ # for dtype in float_types()\n+ # for rng in [jtu.rand_default()]))\n+ # def testCholesky(self, shape, dtype, rng):\n+ # def args_maker():\n+ # a = rng(shape, dtype)\n+ # return [onp.matmul(a, T(a))]\n+\n+ # self._CheckAgainstNumpy(onp.linalg.cholesky, np.linalg.cholesky, args_maker,\n+ # check_dtypes=True, tol=1e-3)\n+ # self._CompileAndCheck(np.linalg.cholesky, args_maker, check_dtypes=True)\n+\n+ # @parameterized.named_parameters(jtu.cases_from_list(\n+ # {\"testcase_name\": \"_shape={}_fullmatrices={}\".format(\n+ # jtu.format_shape_dtype_string(shape, dtype), full_matrices),\n+ # \"shape\": shape, \"dtype\": dtype, \"full_matrices\": full_matrices,\n+ # \"rng\": rng}\n+ # for shape in [(1, 1), (3, 4), (2, 10, 5), (2, 200, 200)]\n+ # for dtype in float_types()\n+ # for full_matrices in [False, True]\n+ # for rng in [jtu.rand_default()]))\n+ # def testQr(self, shape, dtype, full_matrices, rng):\n+ # m, n = shape[-2:]\n+\n+ # if full_matrices:\n+ # mode, k = \"complete\", m\n+ # else:\n+ # mode, k = \"reduced\", min(m, n)\n+\n+ # a = rng(shape, dtype)\n+ # lq, lr = np.linalg.qr(a, mode=mode)\n+\n+ # # onp.linalg.qr doesn't support broadcasting. But it seems like an\n+ # # inevitable extension so we support it in our version.\n+ # nq = onp.zeros(shape[:-2] + (m, k), dtype)\n+ # nr = onp.zeros(shape[:-2] + (k, n), dtype)\n+ # for index in onp.ndindex(*shape[:-2]):\n+ # nq[index], nr[index] = onp.linalg.qr(a[index], mode=mode)\n+\n+ # max_rank = max(m, n)\n+\n+ # # Norm, adjusted for dimension and type.\n+ # def norm(x):\n+ # n = onp.linalg.norm(x, axis=(-2, -1))\n+ # return n / (max_rank * onp.finfo(dtype).eps)\n+\n+ # def compare_orthogonal(q1, q2):\n+ # # Q is unique up to sign, so normalize the sign first.\n+ # sum_of_ratios = onp.sum(onp.divide(q1, q2), axis=-2, keepdims=True)\n+ # phases = onp.divide(sum_of_ratios, onp.abs(sum_of_ratios))\n+ # q1 *= phases\n+ # self.assertTrue(onp.all(norm(q1 - q2) < 30))\n+\n+ # # Check a ~= qr\n+ # self.assertTrue(onp.all(norm(a - onp.matmul(lq, lr)) < 30))\n+\n+ # # Compare the first 'k' vectors of Q; the remainder form an arbitrary\n+ # # orthonormal basis for the null space.\n+ # compare_orthogonal(nq[..., :k], lq[..., :k])\n+\n+ # # Check that q is close to unitary.\n+ # self.assertTrue(onp.all(norm(onp.eye(k) - onp.matmul(T(lq), lq)) < 5))\n+\n+\n+# class ScipyLinalgTest(jtu.JaxTestCase):\n+\n+ # @parameterized.named_parameters(jtu.cases_from_list(\n+ # {\"testcase_name\":\n+ # \"_lhs={}_rhs={}_lower={}_transposea={}\".format(\n+ # jtu.format_shape_dtype_string(lhs_shape, dtype),\n+ # jtu.format_shape_dtype_string(rhs_shape, dtype),\n+ # lower, transpose_a),\n+ # \"lower\": lower, \"transpose_a\": transpose_a,\n+ # \"lhs_shape\": lhs_shape, \"rhs_shape\": rhs_shape, \"dtype\": dtype,\n+ # \"rng\": rng}\n+ # for lower, transpose_a in itertools.product([False, True], repeat=2)\n+ # for lhs_shape, rhs_shape in [\n+ # ((4, 4), (4,)),\n+ # ((4, 4), (4, 3)),\n+ # ((2, 8, 8), (2, 8, 10)),\n+ # ]\n+ # for dtype in float_types()\n+ # for rng in [jtu.rand_default()]))\n+ # def testSolveTriangularBlocked(self, lower, transpose_a, lhs_shape,\n+ # rhs_shape, dtype, rng):\n+ # k = rng(lhs_shape, dtype)\n+ # l = onp.linalg.cholesky(onp.matmul(k, T(k))\n+ # + lhs_shape[-1] * onp.eye(lhs_shape[-1]))\n+ # l = l.astype(k.dtype)\n+ # b = rng(rhs_shape, dtype)\n+\n+ # a = l if lower else T(l)\n+ # inv = onp.linalg.inv(T(a) if transpose_a else a).astype(a.dtype)\n+ # if len(lhs_shape) == len(rhs_shape):\n+ # onp_ans = onp.matmul(inv, b)\n+ # else:\n+ # onp_ans = onp.einsum(\"...ij,...j->...i\", inv, b)\n+\n+ # # The standard scipy.linalg.solve_triangular doesn't support broadcasting.\n+ # # But it seems like an inevitable extension so we support it.\n+ # ans = scipy.linalg.solve_triangular(\n+ # l if lower else T(l), b, trans=1 if transpose_a else 0, lower=lower)\n+\n+ # self.assertAllClose(onp_ans, ans, check_dtypes=True)\nif __name__ == \"__main__\":\n"
}
] | Python | Apache License 2.0 | google/jax | comment out tests (temp), remove examples imports
examples imports add a new test dependency on matplotlib |
260,335 | 13.12.2018 16:32:12 | 28,800 | 9b645364c92f5f127026f875bc645301591b0bc6 | tests depend on scipy, tweaks | [
{
"change_type": "MODIFY",
"old_path": ".travis.yml",
"new_path": ".travis.yml",
"diff": "@@ -8,7 +8,6 @@ python:\n- \"3.6\"\nenv:\n- DEPS=\"pip nose six protobuf>=3.6.0 absl-py opt_einsum numpy scipy\"\n- - DEPS=\"pip nose six protobuf>=3.6.0 absl-py opt_einsum numpy\"\nbefore_install:\n- if [[ \"$TRAVIS_PYTHON_VERSION\" == \"2.7\" ]]; then\nwget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;\n@@ -25,4 +24,4 @@ install:\n- pip install -v .\nscript:\n- cd tests\n- - PYTHONPATH=. JAX_NUM_GENERATED_CASES=10 nosetests\n+ - JAX_NUM_GENERATED_CASES=10 nosetests\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -508,7 +508,7 @@ def where(condition, x=None, y=None):\nif not onp.issubdtype(_dtype(condition), onp.bool_):\ncondition = lax.ne(condition, zeros_like(condition))\ncondition, x, y = broadcast_arrays(condition, x, y)\n- if not x.size:\n+ if not onp.size(x):\nempty, _ = _promote_dtypes(x, y)\nreturn empty\nelse:\n"
}
] | Python | Apache License 2.0 | google/jax | tests depend on scipy, tweaks |
260,335 | 13.12.2018 17:02:37 | 28,800 | 228a5d5e3efe95030d0b43fb402afdceabfaba17 | enable linalg tests | [
{
"change_type": "MODIFY",
"old_path": "tests/linalg_test.py",
"new_path": "tests/linalg_test.py",
"diff": "@@ -42,117 +42,114 @@ def float_types():\nclass NumpyLinalgTest(jtu.JaxTestCase):\n- # TODO(mattjj): put these tests back when we update jaxlib\n- pass\n-\n- # @parameterized.named_parameters(jtu.cases_from_list(\n- # {\"testcase_name\":\n- # \"_shape={}\".format(jtu.format_shape_dtype_string(shape, dtype)),\n- # \"shape\": shape, \"dtype\": dtype, \"rng\": rng}\n- # for shape in [(1, 1), (4, 4), (2, 5, 5), (200, 200), (1000, 0, 0)]\n- # for dtype in float_types()\n- # for rng in [jtu.rand_default()]))\n- # def testCholesky(self, shape, dtype, rng):\n- # def args_maker():\n- # a = rng(shape, dtype)\n- # return [onp.matmul(a, T(a))]\n-\n- # self._CheckAgainstNumpy(onp.linalg.cholesky, np.linalg.cholesky, args_maker,\n- # check_dtypes=True, tol=1e-3)\n- # self._CompileAndCheck(np.linalg.cholesky, args_maker, check_dtypes=True)\n-\n- # @parameterized.named_parameters(jtu.cases_from_list(\n- # {\"testcase_name\": \"_shape={}_fullmatrices={}\".format(\n- # jtu.format_shape_dtype_string(shape, dtype), full_matrices),\n- # \"shape\": shape, \"dtype\": dtype, \"full_matrices\": full_matrices,\n- # \"rng\": rng}\n- # for shape in [(1, 1), (3, 4), (2, 10, 5), (2, 200, 200)]\n- # for dtype in float_types()\n- # for full_matrices in [False, True]\n- # for rng in [jtu.rand_default()]))\n- # def testQr(self, shape, dtype, full_matrices, rng):\n- # m, n = shape[-2:]\n-\n- # if full_matrices:\n- # mode, k = \"complete\", m\n- # else:\n- # mode, k = \"reduced\", min(m, n)\n-\n- # a = rng(shape, dtype)\n- # lq, lr = np.linalg.qr(a, mode=mode)\n-\n- # # onp.linalg.qr doesn't support broadcasting. But it seems like an\n- # # inevitable extension so we support it in our version.\n- # nq = onp.zeros(shape[:-2] + (m, k), dtype)\n- # nr = onp.zeros(shape[:-2] + (k, n), dtype)\n- # for index in onp.ndindex(*shape[:-2]):\n- # nq[index], nr[index] = onp.linalg.qr(a[index], mode=mode)\n-\n- # max_rank = max(m, n)\n-\n- # # Norm, adjusted for dimension and type.\n- # def norm(x):\n- # n = onp.linalg.norm(x, axis=(-2, -1))\n- # return n / (max_rank * onp.finfo(dtype).eps)\n-\n- # def compare_orthogonal(q1, q2):\n- # # Q is unique up to sign, so normalize the sign first.\n- # sum_of_ratios = onp.sum(onp.divide(q1, q2), axis=-2, keepdims=True)\n- # phases = onp.divide(sum_of_ratios, onp.abs(sum_of_ratios))\n- # q1 *= phases\n- # self.assertTrue(onp.all(norm(q1 - q2) < 30))\n-\n- # # Check a ~= qr\n- # self.assertTrue(onp.all(norm(a - onp.matmul(lq, lr)) < 30))\n-\n- # # Compare the first 'k' vectors of Q; the remainder form an arbitrary\n- # # orthonormal basis for the null space.\n- # compare_orthogonal(nq[..., :k], lq[..., :k])\n-\n- # # Check that q is close to unitary.\n- # self.assertTrue(onp.all(norm(onp.eye(k) - onp.matmul(T(lq), lq)) < 5))\n-\n-\n-# class ScipyLinalgTest(jtu.JaxTestCase):\n-\n- # @parameterized.named_parameters(jtu.cases_from_list(\n- # {\"testcase_name\":\n- # \"_lhs={}_rhs={}_lower={}_transposea={}\".format(\n- # jtu.format_shape_dtype_string(lhs_shape, dtype),\n- # jtu.format_shape_dtype_string(rhs_shape, dtype),\n- # lower, transpose_a),\n- # \"lower\": lower, \"transpose_a\": transpose_a,\n- # \"lhs_shape\": lhs_shape, \"rhs_shape\": rhs_shape, \"dtype\": dtype,\n- # \"rng\": rng}\n- # for lower, transpose_a in itertools.product([False, True], repeat=2)\n- # for lhs_shape, rhs_shape in [\n- # ((4, 4), (4,)),\n- # ((4, 4), (4, 3)),\n- # ((2, 8, 8), (2, 8, 10)),\n- # ]\n- # for dtype in float_types()\n- # for rng in [jtu.rand_default()]))\n- # def testSolveTriangularBlocked(self, lower, transpose_a, lhs_shape,\n- # rhs_shape, dtype, rng):\n- # k = rng(lhs_shape, dtype)\n- # l = onp.linalg.cholesky(onp.matmul(k, T(k))\n- # + lhs_shape[-1] * onp.eye(lhs_shape[-1]))\n- # l = l.astype(k.dtype)\n- # b = rng(rhs_shape, dtype)\n-\n- # a = l if lower else T(l)\n- # inv = onp.linalg.inv(T(a) if transpose_a else a).astype(a.dtype)\n- # if len(lhs_shape) == len(rhs_shape):\n- # onp_ans = onp.matmul(inv, b)\n- # else:\n- # onp_ans = onp.einsum(\"...ij,...j->...i\", inv, b)\n-\n- # # The standard scipy.linalg.solve_triangular doesn't support broadcasting.\n- # # But it seems like an inevitable extension so we support it.\n- # ans = scipy.linalg.solve_triangular(\n- # l if lower else T(l), b, trans=1 if transpose_a else 0, lower=lower)\n-\n- # self.assertAllClose(onp_ans, ans, check_dtypes=True)\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\":\n+ \"_shape={}\".format(jtu.format_shape_dtype_string(shape, dtype)),\n+ \"shape\": shape, \"dtype\": dtype, \"rng\": rng}\n+ for shape in [(1, 1), (4, 4), (2, 5, 5), (200, 200), (1000, 0, 0)]\n+ for dtype in float_types()\n+ for rng in [jtu.rand_default()]))\n+ def testCholesky(self, shape, dtype, rng):\n+ def args_maker():\n+ a = rng(shape, dtype)\n+ return [onp.matmul(a, T(a))]\n+\n+ self._CheckAgainstNumpy(onp.linalg.cholesky, np.linalg.cholesky, args_maker,\n+ check_dtypes=True, tol=1e-3)\n+ self._CompileAndCheck(np.linalg.cholesky, args_maker, check_dtypes=True)\n+\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\": \"_shape={}_fullmatrices={}\".format(\n+ jtu.format_shape_dtype_string(shape, dtype), full_matrices),\n+ \"shape\": shape, \"dtype\": dtype, \"full_matrices\": full_matrices,\n+ \"rng\": rng}\n+ for shape in [(1, 1), (3, 4), (2, 10, 5), (2, 200, 200)]\n+ for dtype in float_types()\n+ for full_matrices in [False, True]\n+ for rng in [jtu.rand_default()]))\n+ def testQr(self, shape, dtype, full_matrices, rng):\n+ m, n = shape[-2:]\n+\n+ if full_matrices:\n+ mode, k = \"complete\", m\n+ else:\n+ mode, k = \"reduced\", min(m, n)\n+\n+ a = rng(shape, dtype)\n+ lq, lr = np.linalg.qr(a, mode=mode)\n+\n+ # onp.linalg.qr doesn't support broadcasting. But it seems like an\n+ # inevitable extension so we support it in our version.\n+ nq = onp.zeros(shape[:-2] + (m, k), dtype)\n+ nr = onp.zeros(shape[:-2] + (k, n), dtype)\n+ for index in onp.ndindex(*shape[:-2]):\n+ nq[index], nr[index] = onp.linalg.qr(a[index], mode=mode)\n+\n+ max_rank = max(m, n)\n+\n+ # Norm, adjusted for dimension and type.\n+ def norm(x):\n+ n = onp.linalg.norm(x, axis=(-2, -1))\n+ return n / (max_rank * onp.finfo(dtype).eps)\n+\n+ def compare_orthogonal(q1, q2):\n+ # Q is unique up to sign, so normalize the sign first.\n+ sum_of_ratios = onp.sum(onp.divide(q1, q2), axis=-2, keepdims=True)\n+ phases = onp.divide(sum_of_ratios, onp.abs(sum_of_ratios))\n+ q1 *= phases\n+ self.assertTrue(onp.all(norm(q1 - q2) < 30))\n+\n+ # Check a ~= qr\n+ self.assertTrue(onp.all(norm(a - onp.matmul(lq, lr)) < 30))\n+\n+ # Compare the first 'k' vectors of Q; the remainder form an arbitrary\n+ # orthonormal basis for the null space.\n+ compare_orthogonal(nq[..., :k], lq[..., :k])\n+\n+ # Check that q is close to unitary.\n+ self.assertTrue(onp.all(norm(onp.eye(k) - onp.matmul(T(lq), lq)) < 5))\n+\n+\n+class ScipyLinalgTest(jtu.JaxTestCase):\n+\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\":\n+ \"_lhs={}_rhs={}_lower={}_transposea={}\".format(\n+ jtu.format_shape_dtype_string(lhs_shape, dtype),\n+ jtu.format_shape_dtype_string(rhs_shape, dtype),\n+ lower, transpose_a),\n+ \"lower\": lower, \"transpose_a\": transpose_a,\n+ \"lhs_shape\": lhs_shape, \"rhs_shape\": rhs_shape, \"dtype\": dtype,\n+ \"rng\": rng}\n+ for lower, transpose_a in itertools.product([False, True], repeat=2)\n+ for lhs_shape, rhs_shape in [\n+ ((4, 4), (4,)),\n+ ((4, 4), (4, 3)),\n+ ((2, 8, 8), (2, 8, 10)),\n+ ]\n+ for dtype in float_types()\n+ for rng in [jtu.rand_default()]))\n+ def testSolveTriangularBlocked(self, lower, transpose_a, lhs_shape,\n+ rhs_shape, dtype, rng):\n+ k = rng(lhs_shape, dtype)\n+ l = onp.linalg.cholesky(onp.matmul(k, T(k))\n+ + lhs_shape[-1] * onp.eye(lhs_shape[-1]))\n+ l = l.astype(k.dtype)\n+ b = rng(rhs_shape, dtype)\n+\n+ a = l if lower else T(l)\n+ inv = onp.linalg.inv(T(a) if transpose_a else a).astype(a.dtype)\n+ if len(lhs_shape) == len(rhs_shape):\n+ onp_ans = onp.matmul(inv, b)\n+ else:\n+ onp_ans = onp.einsum(\"...ij,...j->...i\", inv, b)\n+\n+ # The standard scipy.linalg.solve_triangular doesn't support broadcasting.\n+ # But it seems like an inevitable extension so we support it.\n+ ans = scipy.linalg.solve_triangular(\n+ l if lower else T(l), b, trans=1 if transpose_a else 0, lower=lower)\n+\n+ self.assertAllClose(onp_ans, ans, check_dtypes=True)\nif __name__ == \"__main__\":\n"
}
] | Python | Apache License 2.0 | google/jax | enable linalg tests |
260,335 | 13.12.2018 17:35:19 | 28,800 | 2e40e63527ab4dc1ad32b742bf89070353f5a28c | update jaxlib wheel urls to 0.1.1 | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -142,7 +142,7 @@ cloud VM), you can run\nPYTHON_VERSION=py2 # alternatives: py2, py3\nCUDA_VERSION=cuda92 # alternatives: cuda90, cuda92, cuda100\nPLATFORM=linux_x86_64 # alternatives: linux_x86_64\n-pip install https://storage.googleapis.com/jax-wheels/$CUDA_VERSION/jaxlib-0.1-$PYTHON_VERSION-none-$PLATFORM.whl\n+pip install https://storage.googleapis.com/jax-wheels/$CUDA_VERSION/jaxlib-0.1.1-$PYTHON_VERSION-none-$PLATFORM.whl\npip install jax # install jax\n```\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/gufuncs.ipynb",
"new_path": "notebooks/gufuncs.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install -q https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1-py3-none-any.whl\\n\",\n+ \"!pip install -q https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n\"!pip install --upgrade -q jax\"\n],\n\"execution_count\": 0,\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/neural_network_and_data_loading.ipynb",
"new_path": "notebooks/neural_network_and_data_loading.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1-py3-none-any.whl\\n\",\n+ \"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n\"!pip install --upgrade jax\"\n],\n\"execution_count\": 0,\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/quickstart.ipynb",
"new_path": "notebooks/quickstart.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1-py3-none-any.whl\\n\",\n+ \"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n\"!pip install --upgrade jax\"\n],\n\"execution_count\": 0,\n"
}
] | Python | Apache License 2.0 | google/jax | update jaxlib wheel urls to 0.1.1 |
260,335 | 13.12.2018 17:35:36 | 28,800 | b986513e97b93698dfb3b275c601cae015b76770 | bump jaxlib version to 0.1.1 | [
{
"change_type": "MODIFY",
"old_path": "build/setup.py",
"new_path": "build/setup.py",
"diff": "@@ -20,7 +20,7 @@ binary_libs = [os.path.basename(f) for f in glob('jaxlib/*.so*')]\nsetup(\nname='jaxlib',\n- version='0.1',\n+ version='0.1.1',\ndescription='XLA library for JAX',\nauthor='JAX team',\nauthor_email='jax-dev@google.com',\n"
}
] | Python | Apache License 2.0 | google/jax | bump jaxlib version to 0.1.1 |
260,335 | 13.12.2018 17:44:08 | 28,800 | 2048874d43094bc952715ed4ac6c61dd73affe83 | add '--upgrade' to pip install examples | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -130,7 +130,7 @@ To install a CPU-only version, which might be useful for doing local\ndevelopment on a laptop, you can run\n```bash\n-pip install jax jaxlib # CPU-only version\n+pip install --upgrade jax jaxlib # CPU-only version\n```\nIf you want to install JAX with both CPU and GPU support, using existing CUDA\n@@ -142,9 +142,10 @@ cloud VM), you can run\nPYTHON_VERSION=py2 # alternatives: py2, py3\nCUDA_VERSION=cuda92 # alternatives: cuda90, cuda92, cuda100\nPLATFORM=linux_x86_64 # alternatives: linux_x86_64\n-pip install https://storage.googleapis.com/jax-wheels/$CUDA_VERSION/jaxlib-0.1.1-$PYTHON_VERSION-none-$PLATFORM.whl\n+BASE_URL='https://storage.googleapis.com/jax-wheels'\n+pip install --upgrade $BASE_URL/$CUDA_VERSION/jaxlib-0.1.1-$PYTHON_VERSION-none-$PLATFORM.whl\n-pip install jax # install jax\n+pip install --upgrade jax # install jax\n```\nThe library package name must correspond to the version of the existing CUDA\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/gufuncs.ipynb",
"new_path": "notebooks/gufuncs.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install -q https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n+ \"!pip install --upgrade -q https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n\"!pip install --upgrade -q jax\"\n],\n\"execution_count\": 0,\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/neural_network_and_data_loading.ipynb",
"new_path": "notebooks/neural_network_and_data_loading.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n+ \"!pip install --upgrade https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n\"!pip install --upgrade jax\"\n],\n\"execution_count\": 0,\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/quickstart.ipynb",
"new_path": "notebooks/quickstart.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n+ \"!pip install --upgrade https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n\"!pip install --upgrade jax\"\n],\n\"execution_count\": 0,\n"
}
] | Python | Apache License 2.0 | google/jax | add '--upgrade' to pip install examples |
260,335 | 13.12.2018 18:24:35 | 28,800 | e682af7ea50687b490be12188a090f919b41f9ae | increase mnist example batch size to 128 | [
{
"change_type": "MODIFY",
"old_path": "examples/mnist_classifier.py",
"new_path": "examples/mnist_classifier.py",
"diff": "@@ -53,7 +53,7 @@ init_random_params, predict = stax.serial(\nif __name__ == \"__main__\":\nstep_size = 0.001\nnum_epochs = 10\n- batch_size = 32\n+ batch_size = 128\nmomentum_mass = 0.9\ntrain_images, train_labels, test_images, test_labels = datasets.mnist()\n"
},
{
"change_type": "MODIFY",
"old_path": "examples/mnist_classifier_fromscratch.py",
"new_path": "examples/mnist_classifier_fromscratch.py",
"diff": "@@ -59,7 +59,7 @@ if __name__ == \"__main__\":\nparam_scale = 0.1\nstep_size = 0.001\nnum_epochs = 10\n- batch_size = 32\n+ batch_size = 128\ntrain_images, train_labels, test_images, test_labels = datasets.mnist()\nnum_train = train_images.shape[0]\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/neural_network_and_data_loading.ipynb",
"new_path": "notebooks/neural_network_and_data_loading.ipynb",
"diff": "\"param_scale = 0.1\\n\",\n\"step_size = 0.001\\n\",\n\"num_epochs = 10\\n\",\n- \"batch_size = 32\\n\",\n+ \"batch_size = 128\\n\",\n\"n_targets = 10\\n\",\n\"params = init_network_params(layer_sizes, random.PRNGKey(0))\"\n],\n\"source\": [\n\"# Define our dataset, using torch datasets\\n\",\n\"mnist_dataset = MNIST('/tmp/mnist/', download=True, transform=FlattenAndCast())\\n\",\n- \"training_generator = NumpyLoader(mnist_dataset, batch_size=32, num_workers=0)\"\n+ \"training_generator = NumpyLoader(mnist_dataset, batch_size=128, num_workers=0)\"\n],\n\"execution_count\": 0,\n\"outputs\": []\n"
}
] | Python | Apache License 2.0 | google/jax | increase mnist example batch size to 128 |
260,335 | 13.12.2018 18:37:31 | 28,800 | 97d747cd083de78bc06de476138283120ccf4cc2 | bump jaxlib version for pypi reasons | [
{
"change_type": "MODIFY",
"old_path": "build/setup.py",
"new_path": "build/setup.py",
"diff": "@@ -20,7 +20,7 @@ binary_libs = [os.path.basename(f) for f in glob('jaxlib/*.so*')]\nsetup(\nname='jaxlib',\n- version='0.1.1',\n+ version='0.1.2',\ndescription='XLA library for JAX',\nauthor='JAX team',\nauthor_email='jax-dev@google.com',\n"
}
] | Python | Apache License 2.0 | google/jax | bump jaxlib version for pypi reasons |
260,335 | 13.12.2018 19:59:08 | 28,800 | 87ee4b7c5642ad31b90f3ef3c51b9aef83c80377 | update jaxlib references to 0.1.2 | [
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -143,7 +143,7 @@ PYTHON_VERSION=py2 # alternatives: py2, py3\nCUDA_VERSION=cuda92 # alternatives: cuda90, cuda92, cuda100\nPLATFORM=linux_x86_64 # alternatives: linux_x86_64\nBASE_URL='https://storage.googleapis.com/jax-wheels'\n-pip install --upgrade $BASE_URL/$CUDA_VERSION/jaxlib-0.1.1-$PYTHON_VERSION-none-$PLATFORM.whl\n+pip install --upgrade $BASE_URL/$CUDA_VERSION/jaxlib-0.1.2-$PYTHON_VERSION-none-$PLATFORM.whl\npip install --upgrade jax # install jax\n```\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/gufuncs.ipynb",
"new_path": "notebooks/gufuncs.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install --upgrade -q https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n+ \"!pip install --upgrade -q https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.2-py3-none-linux_x86_64.whl\\n\",\n\"!pip install --upgrade -q jax\"\n],\n\"execution_count\": 0,\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/neural_network_and_data_loading.ipynb",
"new_path": "notebooks/neural_network_and_data_loading.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install --upgrade https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n+ \"!pip install --upgrade https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.2-py3-none-linux_x86_64.whl\\n\",\n\"!pip install --upgrade jax\"\n],\n\"execution_count\": 0,\n"
},
{
"change_type": "MODIFY",
"old_path": "notebooks/quickstart.ipynb",
"new_path": "notebooks/quickstart.ipynb",
"diff": "},\n\"cell_type\": \"code\",\n\"source\": [\n- \"!pip install --upgrade https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.1-py3-none-any.whl\\n\",\n+ \"!pip install --upgrade https://storage.googleapis.com/jax-wheels/cuda92/jaxlib-0.1.2-py3-none-linux_x86_64.whl\\n\",\n\"!pip install --upgrade jax\"\n],\n\"execution_count\": 0,\n"
}
] | Python | Apache License 2.0 | google/jax | update jaxlib references to 0.1.2 |
260,335 | 14.12.2018 08:07:12 | 28,800 | 693365c239adac1130956b4f3d0882551b31fae6 | np.all and np.any should lead to monoid reducers
fixes | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -111,7 +111,12 @@ def convert_element_type(operand, new_dtype):\nreturn operand\ndef bitcast_convert_type(operand, new_dtype):\n+ new_dtype = xla_bridge.canonicalize_dtype(new_dtype)\n+ old_dtype = _dtype(operand)\n+ if old_dtype != new_dtype:\nreturn bitcast_convert_type_p.bind(operand, new_dtype=new_dtype)\n+ else:\n+ return operand\ndef clamp(min, operand, max):\nreturn clamp_p.bind(min, operand, max)\n@@ -269,9 +274,9 @@ def _get_monoid_reducer(monoid_op, x):\nif (type(aval) is ConcreteArray) and aval.shape == ():\nif monoid_op is add:\nreturn aval.val == 0 and _reduce_sum\n- elif monoid_op is max:\n+ elif monoid_op is max or monoid_op is bitwise_or and aval.dtype == onp.bool_:\nreturn aval.val == _get_max_identity(aval.dtype) and _reduce_max\n- elif monoid_op is min:\n+ elif monoid_op is min or monoid_op is bitwise_and and aval.dtype == onp.bool_:\nreturn aval.val == _get_min_identity(aval.dtype) and _reduce_min\ndef _get_max_identity(dtype):\n@@ -279,12 +284,16 @@ def _get_max_identity(dtype):\nreturn onp.array(-onp.inf, dtype)\nelif onp.issubdtype(dtype, onp.integer):\nreturn onp.array(onp.iinfo(dtype).min, dtype)\n+ elif onp.issubdtype(dtype, onp.bool_):\n+ return onp.array(False, onp.bool_)\ndef _get_min_identity(dtype):\nif onp.issubdtype(dtype, onp.floating):\nreturn onp.array(onp.inf, dtype)\nelif onp.issubdtype(dtype, onp.integer):\nreturn onp.array(onp.iinfo(dtype).max, dtype)\n+ elif onp.issubdtype(dtype, onp.bool_):\n+ return onp.array(True, onp.bool_)\ndef _reduce_sum(operand, axes):\nreturn reduce_sum_p.bind(operand, axes=tuple(axes), input_shape=operand.shape)\n@@ -1828,7 +1837,7 @@ def _reduction_computation(c, jaxpr, consts, init_value):\nreduce_p = standard_primitive(reduce_shape_rule, _input_dtype, 'reduce',\nreduce_translation_rule)\n-batching.defreducer(reduce_p)\n+# batching.defreducer(reduce_p) # TODO batching rule for general reduce\ndef reduce_sum_shape_rule(operand, axes, input_shape):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -25,8 +25,7 @@ from .. import core\nfrom ..abstract_arrays import UnshapedArray, ShapedArray, ConcreteArray\nfrom ..interpreters.xla import DeviceArray\nfrom .. import lax\n-from ..util import memoize\n-from ..util import get_module_functions\n+from ..util import memoize, partial, get_module_functions\nfrom ..lib import xla_bridge\n# To provide the same module-level names as Numpy, we need to redefine builtins\n@@ -591,15 +590,16 @@ around = round\n### Reducers\n-def _make_reduction(np_fun, op, init_val):\n+def _make_reduction(np_fun, op, init_val, preproc=None):\n\"\"\"Creates reduction function given a binary operation and monoid identity.\"\"\"\n- @_wraps(op)\n+ @_wraps(np_fun)\ndef reduction(a, axis=None, dtype=None, out=None, keepdims=False):\nif out is not None:\nraise ValueError(\"reduction does not support `out` argument.\")\na = a if isinstance(a, ndarray) else asarray(a)\n+ a = preproc(a) if preproc else a\ndims = _reduction_dims(a, axis)\nresult_dtype = _dtype(np_fun(onp.ones((), dtype=dtype or _dtype(a))))\nif _dtype(a) != result_dtype:\n@@ -614,7 +614,6 @@ def _make_reduction(np_fun, op, init_val):\nreturn reduction\n-\ndef _reduction_dims(a, axis):\nif axis is None:\nreturn onp.arange(ndim(a))\n@@ -625,7 +624,6 @@ def _reduction_dims(a, axis):\nelse:\nraise TypeError(\"Unexpected type of axis argument: {}\".format(type(axis)))\n-\ndef _reduction_init_val(a, init_val):\na_dtype = xla_bridge.canonicalize_dtype(_dtype(a))\ntry:\n@@ -635,13 +633,14 @@ def _reduction_init_val(a, init_val):\nsign, iinfo = onp.sign(init_val), onp.iinfo(a_dtype)\nreturn onp.array(iinfo.min if sign < 0 else iinfo.max, dtype=a_dtype)\n+_cast_to_bool = partial(lax.convert_element_type, new_dtype=onp.bool_)\nsum = _make_reduction(onp.sum, lax.add, 0)\nprod = _make_reduction(onp.prod, lax.mul, 1)\nmax = _make_reduction(onp.max, lax.max, -onp.inf)\nmin = _make_reduction(onp.min, lax.min, onp.inf)\n-all = alltrue = _make_reduction(onp.all, logical_and, True)\n-any = sometrue = _make_reduction(onp.any, logical_or, False)\n+all = alltrue = _make_reduction(onp.all, lax.bitwise_and, True, _cast_to_bool)\n+any = sometrue = _make_reduction(onp.any, lax.bitwise_or, False, _cast_to_bool)\n@_wraps(onp.mean)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -284,6 +284,13 @@ class BatchingTest(jtu.JaxTestCase):\njacrev(func)(xs) # don't crash\njacfwd(func)(xs) # don't crash\n+ def testAny(self):\n+ # test modeling the code in https://github.com/google/jax/issues/108\n+\n+ ans = vmap(np.any)(np.array([[True, False], [False, False]]))\n+ expected = np.array([True, False])\n+ self.assertAllClose(ans, expected, check_dtypes=True)\n+\nif __name__ == '__main__':\nabsltest.main()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -142,8 +142,8 @@ JAX_REDUCER_RECORDS = [\n]\nJAX_REDUCER_NO_DTYPE_RECORDS = [\n- op_record(\"all\", 1, bool_dtypes, all_shapes, jtu.rand_default(), []),\n- op_record(\"any\", 1, bool_dtypes, all_shapes, jtu.rand_default(), []),\n+ op_record(\"all\", 1, default_dtypes + bool_dtypes, all_shapes, jtu.rand_some_zero(), []),\n+ op_record(\"any\", 1, default_dtypes + bool_dtypes, all_shapes, jtu.rand_some_zero(), []),\nop_record(\"max\", 1, default_dtypes, nonempty_shapes, jtu.rand_default(), []),\nop_record(\"min\", 1, default_dtypes, nonempty_shapes, jtu.rand_default(), []),\n]\n"
}
] | Python | Apache License 2.0 | google/jax | np.all and np.any should lead to monoid reducers
fixes #108 |
260,335 | 14.12.2018 08:42:02 | 28,800 | b164d318fbc4b00f3cdebbe082cf3f07826f43f7 | reduce_and / reduce_or monoid reducer primitives
The parent commit reused reduce_min / reduce_max on booleans, which is
formally equivalent but preserves less information when lowering to XLA. | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -261,8 +261,8 @@ def reduce(operand, init_value, computation, dimensions):\nreturn monoid_reducer(operand, dimensions)\nelse:\njaxpr, consts = _reduction_jaxpr(computation, init_value)\n- return reduce_p.bind(operand, init_value, jaxpr=jaxpr, consts=consts,\n- dimensions=tuple(dimensions))\n+ return reduce_p.bind(operand, init_value, computation=computation,\n+ jaxpr=jaxpr, consts=consts, dimensions=tuple(dimensions))\ndef _reduction_jaxpr(computation, init_value):\npval = _abstractify(init_value)\n@@ -274,10 +274,14 @@ def _get_monoid_reducer(monoid_op, x):\nif (type(aval) is ConcreteArray) and aval.shape == ():\nif monoid_op is add:\nreturn aval.val == 0 and _reduce_sum\n- elif monoid_op is max or monoid_op is bitwise_or and aval.dtype == onp.bool_:\n+ elif monoid_op is max:\nreturn aval.val == _get_max_identity(aval.dtype) and _reduce_max\n- elif monoid_op is min or monoid_op is bitwise_and and aval.dtype == onp.bool_:\n+ elif monoid_op is min:\nreturn aval.val == _get_min_identity(aval.dtype) and _reduce_min\n+ elif monoid_op is bitwise_or and aval.dtype == onp.bool_:\n+ return aval.val == _get_max_identity(aval.dtype) and _reduce_or\n+ elif monoid_op is bitwise_and and aval.dtype == onp.bool_:\n+ return aval.val == _get_min_identity(aval.dtype) and _reduce_and\ndef _get_max_identity(dtype):\nif onp.issubdtype(dtype, onp.floating):\n@@ -304,6 +308,12 @@ def _reduce_max(operand, axes):\ndef _reduce_min(operand, axes):\nreturn reduce_min_p.bind(operand, axes=tuple(axes))\n+def _reduce_or(operand, axes):\n+ return reduce_or_p.bind(operand, axes=tuple(axes))\n+\n+def _reduce_and(operand, axes):\n+ return reduce_and_p.bind(operand, axes=tuple(axes))\n+\ndef reduce_window(operand, init_value, computation, window_dimensions,\nwindow_strides, padding):\nmonoid_reducer = _get_monoid_window_reducer(computation, init_value)\n@@ -1444,7 +1454,7 @@ def pad_batch_rule(batched_args, batch_dims, padding_config):\npadding_config.insert(operand_bdim, (0, 0, 0))\nreturn pad(operand, padding_value, padding_config), operand_bdim\nelse:\n- raise NotImplementedError\n+ raise NotImplementedError # loop and stack\npad_p = standard_primitive(pad_shape_rule, _input_dtype, 'pad')\nad.deflinear(pad_p, pad_transpose)\n@@ -1824,20 +1834,31 @@ ad.primitive_jvps[index_untake_p] = index_untake_jvp\nad.primitive_transposes[index_untake_p] = index_untake_transpose_rule\n-def reduce_shape_rule(operand, init_value, jaxpr, consts, dimensions):\n+def reduce_shape_rule(operand, init_value, computation, jaxpr, consts, dimensions):\nreturn tuple(onp.delete(operand.shape, dimensions))\n-def reduce_translation_rule(c, operand, init_value, jaxpr, consts, dimensions):\n+def reduce_translation_rule(c, operand, init_value, computation, jaxpr, consts, dimensions):\nxla_computation = _reduction_computation(c, jaxpr, consts, init_value)\nreturn c.Reduce(operand, init_value, xla_computation, dimensions)\n+def reduce_batch_rule(batched_args, batch_dims, computation, jaxpr, consts, dimensions):\n+ operand, init_value = batched_args\n+ operand_bdim, init_value_bdim = batch_dims\n+ if init_value_bdim is None:\n+ assert operand_bdim is not None\n+ new_dimensions = [d + bool(d >= operand_bdim) for d in dimensions]\n+ new_operand_bdim = operand_bdim - onp.sum(onp.less(dimensions, operand_bdim))\n+ return reduce(operand, init_value, computation, new_dimensions), new_operand_bdim\n+ else:\n+ raise NotImplementedError # loop and stack\n+\ndef _reduction_computation(c, jaxpr, consts, init_value):\nshape = c.GetShape(init_value)\nreturn xla.jaxpr_computation(jaxpr, consts, (), shape, shape)\nreduce_p = standard_primitive(reduce_shape_rule, _input_dtype, 'reduce',\nreduce_translation_rule)\n-# batching.defreducer(reduce_p) # TODO batching rule for general reduce\n+# batching.primitive_batchers[reduce_p] = reduce_batch_rule # TODO(mattjj): test\ndef reduce_sum_shape_rule(operand, axes, input_shape):\n@@ -1899,6 +1920,31 @@ ad.defjvp2(reduce_min_p, reduce_chooser_jvp_rule)\nbatching.defreducer(reduce_min_p)\n+def reduce_logical_shape_rule(operand, axes):\n+ if operand.dtype != onp.bool_:\n+ msg = \"logical reduction requires operand dtype bool, got {}.\"\n+ raise TypeError(msg.format(operand.dtype))\n+ return tuple(onp.delete(operand.shape, axes))\n+\n+def reduce_logical_translation_rule(prim, identity, c, operand, axes):\n+ scalar = xla_bridge.Shape.array_shape(onp.bool_, ())\n+ return c.Reduce(operand, c.Constant(identity(onp.bool_)),\n+ xla.primitive_computation(prim, scalar, scalar), axes)\n+\n+reduce_or_translation_rule = partial(reduce_logical_translation_rule,\n+ or_p, _get_max_identity)\n+reduce_or_p = standard_primitive(reduce_logical_shape_rule, _fixed_dtype(onp.bool_),\n+ 'reduce_or', reduce_or_translation_rule)\n+batching.defreducer(reduce_or_p)\n+\n+\n+reduce_and_translation_rule = partial(reduce_logical_translation_rule,\n+ and_p, _get_min_identity)\n+reduce_and_p = standard_primitive(reduce_logical_shape_rule, _fixed_dtype(onp.bool_),\n+ 'reduce_and', reduce_and_translation_rule)\n+batching.defreducer(reduce_and_p)\n+\n+\ndef reduce_window_shape_rule(operand, init_value, jaxpr, consts,\nwindow_dimensions, window_strides, padding):\nif operand.dtype != init_value.dtype:\n"
}
] | Python | Apache License 2.0 | google/jax | reduce_and / reduce_or monoid reducer primitives
The parent commit reused reduce_min / reduce_max on booleans, which is
formally equivalent but preserves less information when lowering to XLA. |
260,554 | 14.12.2018 11:58:03 | 28,800 | 5d6ebba2a0a991eff4fdf80f689f2ca3e7f0b41c | Fixed argument order in call to var from std. | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -678,7 +678,7 @@ def var(a, axis=None, dtype=None, keepdims=False, ddof=0):\n@_wraps(onp.std)\ndef std(a, axis=None, keepdims=False, ddof=0):\n- return sqrt(var(a, axis, keepdims, ddof))\n+ return sqrt(var(a, axis=axis, keepdims=keepdims, ddof=ddof))\n@_wraps(onp.allclose)\n"
}
] | Python | Apache License 2.0 | google/jax | Fixed argument order in call to var from std. |
260,335 | 14.12.2018 16:22:51 | 28,800 | c268929f2d3452dc4c0f93ef57c44459ee477a82 | add 'dtype' arg to np.std, add test coverage | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -677,8 +677,8 @@ def var(a, axis=None, dtype=None, keepdims=False, ddof=0):\n@_wraps(onp.std)\n-def std(a, axis=None, keepdims=False, ddof=0):\n- return sqrt(var(a, axis=axis, keepdims=keepdims, ddof=ddof))\n+def std(a, axis=None, dtype=None, keepdims=False, ddof=0):\n+ return sqrt(var(a, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof))\n@_wraps(onp.allclose)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -139,6 +139,7 @@ JAX_REDUCER_RECORDS = [\nop_record(\"prod\", 1, default_dtypes, all_shapes, jtu.rand_small_positive(), []),\nop_record(\"sum\", 1, default_dtypes, all_shapes, jtu.rand_default(), []),\nop_record(\"var\", 1, default_dtypes, nonempty_shapes, jtu.rand_default(), []),\n+ op_record(\"std\", 1, default_dtypes, nonempty_shapes, jtu.rand_default(), []),\n]\nJAX_REDUCER_NO_DTYPE_RECORDS = [\n"
}
] | Python | Apache License 2.0 | google/jax | add 'dtype' arg to np.std, add test coverage |
260,335 | 14.12.2018 16:48:08 | 28,800 | 6de5c8a69855e403b6dfdc92b1d64177eb9864e8 | add test-running instructions (fixes | [
{
"change_type": "MODIFY",
"old_path": ".travis.yml",
"new_path": ".travis.yml",
"diff": "@@ -23,5 +23,4 @@ install:\n- pip install jaxlib\n- pip install -v .\nscript:\n- - cd tests\n- - JAX_NUM_GENERATED_CASES=10 nosetests\n+ - JAX_NUM_GENERATED_CASES=25 nosetests tests examples\n"
},
{
"change_type": "MODIFY",
"old_path": "README.md",
"new_path": "README.md",
"diff": "@@ -66,6 +66,7 @@ open](https://github.com/google/jax) by a growing number of\n### Contents\n* [Quickstart: Colab in the Cloud](#quickstart-colab-in-the-cloud)\n* [Installation](#installation)\n+* [Running the tests](#running-the-tests)\n* [A brief tour](#a-brief-tour)\n* [What's supported](#whats-supported)\n* [Transformations](#transformations)\n@@ -158,6 +159,30 @@ nvcc --version\ngrep CUDNN_MAJOR -A 2 /usr/local/cuda/include/cudnn.h # might need different path\n```\n+## Running the tests\n+\n+To run all the JAX tests, from the repository root directory run\n+\n+```bash\n+nosetests tests\n+```\n+\n+JAX generates test cases combinatorially, and you can control the number of\n+cases that are generated and checked for each test (default 10):\n+\n+```bash\n+JAX_NUM_GENERATED_CASES=100 nosetests tests\n+```\n+\n+You can run a more specific set of tests using\n+[`nose`](https://nose.readthedocs.io/en/latest/usage.html)'s built-in selection\n+mechanisms, or alternatively you can run a specific test file directly to see\n+more detailed information about the cases being run:\n+\n+```bash\n+python tests/lax_numpy_test.py --num_generated_cases=5\n+```\n+\n## A brief tour\n```python\n"
},
{
"change_type": "RENAME",
"old_path": "tests/examples_test.py",
"new_path": "examples/examples_test.py",
"diff": ""
},
{
"change_type": "MODIFY",
"old_path": "jax/test_util.py",
"new_path": "jax/test_util.py",
"diff": "@@ -46,7 +46,7 @@ flags.DEFINE_enum(\nflags.DEFINE_integer(\n'num_generated_cases',\n- os.getenv('JAX_NUM_GENERATED_CASES', 100),\n+ os.getenv('JAX_NUM_GENERATED_CASES', 10),\nhelp='Number of generated cases to test')\nEPS = 1e-4\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -139,7 +139,7 @@ JAX_REDUCER_RECORDS = [\nop_record(\"prod\", 1, default_dtypes, all_shapes, jtu.rand_small_positive(), []),\nop_record(\"sum\", 1, default_dtypes, all_shapes, jtu.rand_default(), []),\nop_record(\"var\", 1, default_dtypes, nonempty_shapes, jtu.rand_default(), []),\n- op_record(\"std\", 1, default_dtypes, nonempty_shapes, jtu.rand_default(), []),\n+ op_record(\"std\", 1, float_dtypes, nonempty_shapes, jtu.rand_default(), []),\n]\nJAX_REDUCER_NO_DTYPE_RECORDS = [\n"
}
] | Python | Apache License 2.0 | google/jax | add test-running instructions (fixes #67) |
260,335 | 14.12.2018 18:40:50 | 28,800 | 13b8e21a1cfb3a76ae65bf61bb7bf855dcea1c5f | squash conv grad bug introduced in
(loudly errored, didn't produce silently incorrect results!) | [
{
"change_type": "MODIFY",
"old_path": ".travis.yml",
"new_path": ".travis.yml",
"diff": "@@ -23,4 +23,4 @@ install:\n- pip install jaxlib\n- pip install -v .\nscript:\n- - JAX_NUM_GENERATED_CASES=25 nosetests tests examples\n+ - JAX_NUM_GENERATED_CASES=100 nosetests tests examples\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -1027,8 +1027,8 @@ def conv_general_dilated_transpose_rhs(\ndimension_numbers, lhs_shape, rhs_shape):\nassert type(dimension_numbers) is ConvDimensionNumbers\nlhs_sdims, rhs_sdims, out_sdims = map(_conv_sdims, dimension_numbers)\n- transposed = map(_conv_transpose, dimension_numbers)\n- trans_dimension_numbers = ConvDimensionNumbers(*transposed)\n+ lhs_trans, rhs_trans, out_trans = map(_conv_transpose, dimension_numbers)\n+ trans_dimension_numbers = ConvDimensionNumbers(lhs_trans, out_trans, rhs_trans)\npadding = _conv_general_vjp_rhs_padding(\nonp.take(lhs_shape, lhs_sdims), onp.take(rhs_shape, rhs_sdims),\nwindow_strides, onp.take(g.shape, out_sdims), padding, lhs_dilation,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/test_util.py",
"new_path": "jax/test_util.py",
"diff": "@@ -71,6 +71,7 @@ def numpy_close(a, b, atol=ATOL, rtol=RTOL, equal_nan=False):\nif testing_tpu or testing_x32:\natol = max(atol, 1e-1)\nrtol = max(rtol, 1e-1)\n+ assert a.shape == b.shape\nreturn onp.allclose(a, b, atol=atol * a.size, rtol=rtol * b.size,\nequal_nan=equal_nan)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -1580,10 +1580,13 @@ class LaxAutodiffTest(jtu.JaxTestCase):\n\"rhs_dil\": rhs_dil, \"rng\": rng, \"dimension_numbers\": dim_nums,\n\"perms\": perms}\nfor lhs_shape, rhs_shape, all_strides, all_pads, lhs_dils, rhs_dils in [\n- ((b, i, 5, 6), (j, i, 1, 2), [(1, 1), (1, 2), (2, 1)],\n- [((0, 0), (0, 0)), ((1, 0), (0, 1)), ((0, -1), (0, 0))],\n- [(1, 1), (2, 1)], [(1, 1)])\n- for b, i, j in itertools.product([2, 3], repeat=3)]\n+ ((b, i, 5, 6), # lhs_shape\n+ (j, i, 1, 2), # rhs_shape\n+ [(1, 1), (1, 2), (2, 1)], # strides\n+ [((0, 0), (0, 0)), ((1, 0), (0, 1)), ((0, -1), (0, 0))], # pads\n+ [(1, 1), (2, 1)], # lhs_dils\n+ [(1, 1)]) # rhs_dils\n+ for b, i, j in itertools.product([1, 2], repeat=3)]\nfor strides in all_strides\nfor rhs_dil in rhs_dils\nfor lhs_dil in lhs_dils\n@@ -1592,7 +1595,7 @@ class LaxAutodiffTest(jtu.JaxTestCase):\nfor rng in [jtu.rand_default()]\nfor dim_nums, perms in [\n((\"NCHW\", \"OIHW\", \"NCHW\"), ([0, 1, 2, 3], [0, 1, 2, 3])),\n- # ((\"NHWC\", \"HWIO\", \"NHWC\"), ([0, 2, 3, 1], [2, 3, 1, 0]))\n+ ((\"NHWC\", \"HWIO\", \"NHWC\"), ([0, 2, 3, 1], [2, 3, 1, 0]))\n]))\n@jtu.skip_on_devices(\"tpu\")\ndef testConvGeneralDilatedGrad(self, lhs_shape, rhs_shape, dtype, strides,\n"
}
] | Python | Apache License 2.0 | google/jax | squash conv grad bug introduced in 0d64aea
(loudly errored, didn't produce silently incorrect results!) |
260,335 | 14.12.2018 19:23:53 | 28,800 | f437ba371ddfdbd651469f5fb0863486ab847b06 | make conv grad test smaller (better numerics) | [
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -1580,7 +1580,7 @@ class LaxAutodiffTest(jtu.JaxTestCase):\n\"rhs_dil\": rhs_dil, \"rng\": rng, \"dimension_numbers\": dim_nums,\n\"perms\": perms}\nfor lhs_shape, rhs_shape, all_strides, all_pads, lhs_dils, rhs_dils in [\n- ((b, i, 5, 6), # lhs_shape\n+ ((b, i, 4, 5), # lhs_shape\n(j, i, 1, 2), # rhs_shape\n[(1, 1), (1, 2), (2, 1)], # strides\n[((0, 0), (0, 0)), ((1, 0), (0, 1)), ((0, -1), (0, 0))], # pads\n"
}
] | Python | Apache License 2.0 | google/jax | make conv grad test smaller (better numerics) |
260,335 | 14.12.2018 19:35:56 | 28,800 | 6956e8a20c2c93bfb531cae0da18ec20d0abe911 | tweak conv grad test | [
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -1580,7 +1580,7 @@ class LaxAutodiffTest(jtu.JaxTestCase):\n\"rhs_dil\": rhs_dil, \"rng\": rng, \"dimension_numbers\": dim_nums,\n\"perms\": perms}\nfor lhs_shape, rhs_shape, all_strides, all_pads, lhs_dils, rhs_dils in [\n- ((b, i, 4, 5), # lhs_shape\n+ ((b, i, 6, 7), # lhs_shape\n(j, i, 1, 2), # rhs_shape\n[(1, 1), (1, 2), (2, 1)], # strides\n[((0, 0), (0, 0)), ((1, 0), (0, 1)), ((0, -1), (0, 0))], # pads\n"
}
] | Python | Apache License 2.0 | google/jax | tweak conv grad test |
260,335 | 16.12.2018 07:22:25 | 28,800 | c54e82ceff64083ded7f80cbdff21a3f392e0961 | be more explicit about last layer in example
closes | [
{
"change_type": "MODIFY",
"old_path": "examples/mnist_classifier_fromscratch.py",
"new_path": "examples/mnist_classifier_fromscratch.py",
"diff": "@@ -37,10 +37,14 @@ def init_random_params(scale, layer_sizes, rng=npr.RandomState(0)):\nfor m, n, in zip(layer_sizes[:-1], layer_sizes[1:])]\ndef predict(params, inputs):\n- for w, b in params:\n- outputs = np.dot(inputs, w) + b\n- inputs = np.tanh(outputs)\n- return outputs - logsumexp(outputs, axis=1, keepdims=True)\n+ activations = inputs\n+ for w, b in params[:-1]:\n+ outputs = np.dot(activations, w) + b\n+ activations = np.tanh(outputs)\n+\n+ final_w, final_b = params[-1]\n+ logits = np.dot(activations, final_w) + final_b\n+ return logits - logsumexp(logits, axis=1, keepdims=True)\ndef loss(params, batch):\ninputs, targets = batch\n"
}
] | Python | Apache License 2.0 | google/jax | be more explicit about last layer in example
closes #119 |
260,335 | 16.12.2018 11:32:55 | 28,800 | ea08ecd5f0e4b18f3bc7ff4176b19da40c8ded1e | add promote_dtypes logic to tensordot | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -988,6 +988,7 @@ def tensordot(a, b, axes=2):\nraise TypeError(msg.format(ndim(a), ndim(b)))\nif type(axes) is int:\n+ a, b = _promote_dtypes(a, b)\na_reshape = lax.reshape(a, (_prod(a.shape[:-axes]), _prod(a.shape[-axes:])))\nb_reshape = lax.reshape(b, (_prod(b.shape[:axes]), _prod(b.shape[axes:])))\nout_reshape = lax.dot(a_reshape, b_reshape)\n"
}
] | Python | Apache License 2.0 | google/jax | add promote_dtypes logic to tensordot |
260,299 | 17.12.2018 16:02:29 | 0 | 1743a936eb26fdefa5f3b61053776d8aee9b0fcd | Add qr decomposition jvp | [
{
"change_type": "MODIFY",
"old_path": "jax/lax_linalg.py",
"new_path": "jax/lax_linalg.py",
"diff": "@@ -135,7 +135,22 @@ def qr_abstract_eval(operand, full_matrices):\ndef qr_dtype_rule(operand, full_matrices=True):\nreturn operand.dtype\n+def qr_jvp_rule(primals, tangents, full_matrices):\n+ x, = primals\n+ if not full_matrices or np.shape(x)[-2] < np.shape(x)[-1]:\n+ raise NotImplementedError\n+ dx, = tangents\n+ q, r = qr_p.bind(x, full_matrices=False)\n+ dx_rinv = triangular_solve(r, dx) # Right side solve by default\n+ qt_dx_rinv = np.matmul(_T(q), dx_rinv)\n+ qt_dx_rinv_lower = np.tril(qt_dx_rinv, -1)\n+ domega = qt_dx_rinv_lower - _T(qt_dx_rinv_lower) # This is skew-symmetric\n+ dq = np.matmul(q, domega - qt_dx_rinv) + dx_rinv\n+ dr = np.matmul(qt_dx_rinv - domega, r)\n+ return core.pack((q, r)), core.pack((dq, dr))\n+\nqr_p = Primitive('qr')\nqr_p.def_impl(qr_impl)\nqr_p.def_abstract_eval(qr_abstract_eval)\nxla.translations[qr_p] = qr_translation_rule\n+ad.primitive_jvps[qr_p] = qr_jvp_rule\n"
}
] | Python | Apache License 2.0 | google/jax | Add qr decomposition jvp |
260,299 | 17.12.2018 16:04:51 | 0 | f5b8d97c9513bf60ac663f30c8e64c2cbe00187e | Add url for qr jvp notes | [
{
"change_type": "MODIFY",
"old_path": "jax/lax_linalg.py",
"new_path": "jax/lax_linalg.py",
"diff": "@@ -136,6 +136,7 @@ def qr_dtype_rule(operand, full_matrices=True):\nreturn operand.dtype\ndef qr_jvp_rule(primals, tangents, full_matrices):\n+ # See j-towns.github.io/papers/qr-derivative.pdf for a terse derivation.\nx, = primals\nif not full_matrices or np.shape(x)[-2] < np.shape(x)[-1]:\nraise NotImplementedError\n"
}
] | Python | Apache License 2.0 | google/jax | Add url for qr jvp notes |
260,335 | 17.12.2018 14:26:28 | 28,800 | 7524f2c087e68436333c29650170ffc7ef20e8c7 | fix mean/var/std kwargs (closes | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -644,7 +644,10 @@ any = sometrue = _make_reduction(onp.any, lax.bitwise_or, False, _cast_to_bool)\n@_wraps(onp.mean)\n-def mean(a, axis=None, dtype=None, keepdims=False):\n+def mean(a, axis=None, dtype=None, out=None, keepdims=False):\n+ if out is not None:\n+ raise ValueError(\"mean does not support `out` argument.\")\n+\nif axis is None:\nnormalizer = size(a)\nelse:\n@@ -663,7 +666,10 @@ def mean(a, axis=None, dtype=None, keepdims=False):\n@_wraps(onp.var)\n-def var(a, axis=None, dtype=None, keepdims=False, ddof=0):\n+def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):\n+ if out is not None:\n+ raise ValueError(\"mean does not support `out` argument.\")\n+\nif ddof != 0:\nraise NotImplementedError(\"Only implemented for ddof=0.\")\nif dtype is None:\n@@ -677,8 +683,9 @@ def var(a, axis=None, dtype=None, keepdims=False, ddof=0):\n@_wraps(onp.std)\n-def std(a, axis=None, dtype=None, keepdims=False, ddof=0):\n- return sqrt(var(a, axis=axis, dtype=dtype, keepdims=keepdims, ddof=ddof))\n+def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):\n+ return sqrt(var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n+ keepdims=keepdims))\n@_wraps(onp.allclose)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -810,6 +810,12 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\n# TODO(mattjj): test other ndarray-like method overrides\n+ def testOnpMean(self):\n+ # from https://github.com/google/jax/issues/125\n+ x = lnp.eye(3) + 0.\n+ ans = onp.mean(x)\n+ self.assertAllClose(ans, onp.array([1./3, 1./3, 1./3]), check_dtypes=False)\n+\nif __name__ == \"__main__\":\nabsltest.main()\n"
}
] | Python | Apache License 2.0 | google/jax | fix mean/var/std kwargs (closes #125) |
260,335 | 17.12.2018 12:52:30 | 28,800 | 1f2925ea8af5ba838776269b4943eb5e0ba51cd3 | add backend-specific translation table in xla.py | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -16,7 +16,7 @@ from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n-from collections import namedtuple\n+from collections import namedtuple, defaultdict\nimport itertools as it\nimport numpy as onp\nimport operator as op\n@@ -153,14 +153,16 @@ def unit_constant(c, val):\nxb.register_constant_handler(JaxTuple, unit_constant)\ndef translation_rule(p):\n+ backend_specific_rule = backend_specific_translations[xb._platform_name].get(p)\ntry:\n- return translations[p]\n+ return backend_specific_rule or translations[p]\nexcept KeyError:\nraise NotImplementedError(\n\"XLA translation rule for '{}' not implemented\".format(p))\ntranslations = {}\n+backend_specific_translations = defaultdict(dict)\ntranslations[core.pack_p] = lambda c, *xs: c.Tuple(*xs)\ntranslations[core.call_p] = lambda c, subc_a1, *a2: c.Call(subc_a1[0],\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax_linalg.py",
"new_path": "jax/lax_linalg.py",
"diff": "@@ -84,8 +84,7 @@ def cholesky_cpu_translation_rule(c, operand):\n# TODO(phawkins): support LAPACK primitives in batched mode.\nreturn c.Cholesky(operand)\n-# TODO(mattjj): add per-backend translation rule support\n-# xla.translations[cholesky_p] = cholesky_cpu_translation_rule\n+xla.backend_specific_translations['Host'][cholesky_p] = cholesky_cpu_translation_rule\ntriangular_solve_dtype_rule = partial(\n@@ -141,8 +140,7 @@ def triangular_solve_cpu_translation_rule(\n# TODO(phawkins): support BLAS primitives in batched mode.\nreturn c.TriangularSolve(a, b, left_side, lower, transpose_a, conjugate_a)\n-# TODO(mattjj): add per-backend translation rule support\n-# xla.translations[triangular_solve_p] = triangular_solve_cpu_translation_rule\n+xla.backend_specific_translations['Host'][triangular_solve_p] = triangular_solve_cpu_translation_rule\ndef qr_impl(operand, full_matrices):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lib/xla_bridge.py",
"new_path": "jax/lib/xla_bridge.py",
"diff": "@@ -61,6 +61,8 @@ flags.DEFINE_string(\n# visible output files.\n_SPONGE_PREFIX = '/SPONGE/'\n+_platform_name = None # set to the active platform name\n+\ndef _hlo_path(path, name):\npath = path.replace(_SPONGE_PREFIX,\n@@ -127,16 +129,20 @@ def _get_xla_client(backend_name, platform_name, replica_count):\nReturns:\nA client library module, or an object that behaves identically to one.\n\"\"\"\n+ global _platform_name\nxla_client.initialize_replica_count(replica_count)\nif backend_name == 'xla':\nif platform_name:\nxla_client.initialize_platform_name(platform_name)\n+ _platform_name = platform_name\nelse:\ntry:\nxla_client.initialize_platform_name('CUDA')\n+ _platform_name = 'CUDA'\nexcept RuntimeError:\nwarnings.warn('No GPU found, falling back to CPU.')\nxla_client.initialize_platform_name('Host')\n+ _platform_name = 'Host'\nreturn xla_client\n"
}
] | Python | Apache License 2.0 | google/jax | add backend-specific translation table in xla.py |
260,335 | 17.12.2018 14:42:32 | 28,800 | 895c122b90a70a1c6d6fd7ee1e31872c56638af6 | tweak test to better reflect | [
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -27,6 +27,7 @@ from absl.testing import parameterized\nimport numpy as onp\nfrom jax import api\n+from jax import lax\nfrom jax import numpy as lnp\nfrom jax import test_util as jtu\n@@ -812,7 +813,7 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\ndef testOnpMean(self):\n# from https://github.com/google/jax/issues/125\n- x = lnp.eye(3) + 0.\n+ x = lax.add(lnp.eye(3), 0.)\nans = onp.mean(x)\nself.assertAllClose(ans, onp.array([1./3, 1./3, 1./3]), check_dtypes=False)\n"
}
] | Python | Apache License 2.0 | google/jax | tweak test to better reflect #125 |
260,335 | 13.12.2018 07:24:14 | 28,800 | f9714152183a63e2e365aa2485f587b606801aa7 | add tie_in and full primitives (constant creation) | [
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "@@ -249,6 +249,8 @@ class Tracer(object):\ndef __hex__(self): return self.aval._hex(self)\ndef __oct__(self): return self.aval._oct(self)\n+ def __setitem__(self, idx, val):\n+ raise TypeError(\"JAX 'Tracer' objects do not support item assignment\")\ndef __getattr__(self, name):\n# if the aval property raises an AttributeError, gets caught here\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -74,6 +74,8 @@ def execute_compiled_primitive(compiled, result_handler, *args):\ndef device_put(x):\nif type(x) is DeviceArray:\nreturn x.device_buffer\n+ elif isinstance(x, DeviceConstant):\n+ return instantiate_device_constant(x)\nelse:\nreturn xb.device_put(x) # can round-trip elements of tuples here\n@@ -237,6 +239,7 @@ class DeviceArray(DeviceValue):\nself.size = size\nself._npy_value = None\n+ # TODO make device_buffer a property, make the _npy_value writeable, invalidate\n@property\ndef _value(self):\nif self._npy_value is None:\n@@ -319,6 +322,31 @@ xb.register_constant_handler(DeviceArray,\nlambda c, val: c.Constant(onp.asarray(val)))\n+class DeviceConstant(DeviceArray):\n+ @staticmethod\n+ def constant_handler(c, constant_instance):\n+ assert False\n+\n+# TODO(mattjj): tune cutoff\n+def instantiate_device_constant(const, cutoff=1000000):\n+ # dispatch an XLA Computation to build the constant on the device if it's\n+ # large, or alternatively build it on the host and transfer it if it's small\n+ assert isinstance(const, DeviceConstant)\n+ if const.size > cutoff:\n+ c = xb.make_computation_builder(\"constant_instantiating_computation\")\n+ xla_const = const.constant_handler(c, const)\n+ compiled = c.Build(xla_const).Compile((), xb.get_compile_options())\n+ return compiled.Execute(())\n+ else:\n+ return xb.device_put(onp.asarray(const))\n+\n+def register_device_constant(cls):\n+ pytype_aval_mappings[cls] = pytype_aval_mappings[DeviceArray]\n+ canonicalize_dtype_handlers[cls] = identity\n+ core.pytype_aval_mappings[cls] = ConcreteArray\n+ xb.register_constant_handler(cls, cls.constant_handler)\n+\n+\ndef xla_shape(x):\ntry:\nreturn xb.Shape.array_shape(x.dtype, x.shape)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -17,7 +17,7 @@ from __future__ import division\nfrom __future__ import print_function\nimport collections\n-from .util import partial\n+from .util import partial, prod\nimport itertools\nimport operator\nimport six\n@@ -43,6 +43,9 @@ from .lib import xla_bridge\n_max = builtins.max\n_min = builtins.max\n+_reduce = six.moves.reduce\n+\n+def identity(x): return x\n### traceables\n@@ -411,6 +414,25 @@ class OpaqueParam(object):\nopaque_param_ids = itertools.count()\n+def tie_in(x, y):\n+ return tie_in_p.bind(x, y)\n+\n+def full(shape, fill_value, dtype=None):\n+ if onp.shape(fill_value):\n+ msg = \"full must be called with scalar fill_value, got fill_value.shape {}.\"\n+ raise TypeError(msg.format(onp.shape(fill_value)))\n+\n+ dtype = dtype and xla_bridge.canonicalize_dtype(dtype)\n+ if dtype is not None and _dtype(fill_value) != dtype:\n+ # for Python scalars and raw ndarrays, we keep fill_value as a cpu ndarray\n+ if onp.isscalar(fill_value) or type(fill_value) is onp.ndarray:\n+ fill_value = onp.array(fill_value, dtype)\n+ else:\n+ fill_value = convert_element_type(fill_value, dtype)\n+\n+ return full_p.bind(fill_value, shape=shape)\n+\n+\n### convenience wrappers around traceables\n@@ -439,7 +461,8 @@ def full_like(x, fill_value, dtype=None, shape=None):\n`fill_value`, similar to the output of np.full.\n\"\"\"\nshape = onp.shape(x) if shape is None else shape\n- return broadcast(onp.array(fill_value, dtype or _dtype(x)), shape)\n+ out = full(shape, fill_value, dtype or _dtype(x))\n+ return tie_in(x, out)\ndef collapse(operand, start_dimension, stop_dimension):\n@@ -631,8 +654,7 @@ ShapedArray._iter = staticmethod(_iter)\n# Add some ad handlers that use (or could use) lax primitives\ndef zeros_like_array(x):\n- dtype = xla_bridge.canonicalize_dtype(_dtype(x))\n- return onp.broadcast_to(onp.zeros((), dtype), onp.shape(x))\n+ return full_like(x, 0)\nfor t in itertools.chain(array_types, [xla.DeviceArray]):\nad_util.jaxval_adders[t] = add\n@@ -648,8 +670,6 @@ _input_dtype = lambda *args, **_: xla_bridge.canonicalize_dtype(args[0].dtype)\n_fixed_dtype = lambda dtype: lambda *args, **kwargs: xla_bridge.canonicalize_dtype(dtype)\n_complex_basetype = lambda dtype: onp.abs(onp.zeros((), dtype)).dtype\n-def identity(x): return x\n-\ndef standard_primitive(shape_rule, dtype_rule, name, translation_rule=None):\nprim = Primitive(name)\n@@ -1561,9 +1581,11 @@ def select_dtype_rule(pred, on_true, on_false):\nreturn on_true.dtype\ndef select_transpose_rule(t, pred, on_true, on_false):\n+ assert pred is not None\n+ zeros = full_like(t, 0)\nreturn [None,\n- select(pred, t, _zeros(on_false)) if on_true is None else None,\n- select(pred, _zeros(on_true), t) if on_false is None else None]\n+ select(pred, t, zeros) if on_true is None else None,\n+ select(pred, zeros, t) if on_false is None else None]\ndef select_batch_rule(batched_args, batch_dims, **unused_kwargs):\noprand, on_true, on_false, = batched_args\n@@ -2218,6 +2240,71 @@ while_p.def_abstract_eval(while_loop_abstract_eval)\nxla.translations[while_p] = while_loop_translation_rule\n+### primitives for handling constants\n+\n+\n+def tie_in_transpose_rule(t):\n+ return [ad_util.zero, t]\n+\n+def tie_in_batch_rule(batched_args, batch_dims):\n+ y = tie_in(*batched_args)\n+ _, bdim_y = batch_dims\n+ return y, bdim_y\n+\n+tie_in_p = Primitive('tie_in')\n+tie_in_p.def_impl(lambda x, y: y)\n+tie_in_p.def_abstract_eval(lambda x, y: y)\n+xla.translations[tie_in_p] = lambda c, x, y: y\n+ad.deflinear(tie_in_p, tie_in_transpose_rule)\n+batching.primitive_batchers[tie_in_p] = tie_in_batch_rule\n+\n+\n+class FilledConstant(xla.DeviceConstant):\n+ __slots__ = [\"fill_value\"]\n+\n+ def __init__(self, fill_value, shape):\n+ assert type(fill_value) is onp.ndarray\n+ self.shape = shape\n+ self.dtype = _dtype(fill_value)\n+ self.ndim = len(shape)\n+ self.size = prod(shape)\n+ self._npy_value = None\n+\n+ self.fill_value = fill_value\n+\n+ @property\n+ def _value(self):\n+ return onp.full(self.shape, self.fill_value)\n+\n+ @staticmethod\n+ def constant_handler(c, filled_const):\n+ return c.Broadcast(c.NumpyArrayConstant(filled_const.fill_value),\n+ filled_const.shape)\n+xla.register_device_constant(FilledConstant)\n+\n+# TODO(mattjj): if we used isinstance rather than handlers here, these would all\n+# be covered as subclasses of DeviceArray. alternatively, just set up these in a\n+# loop after we've defined all the constant DeviceConstant subclasses.\n+batching.pytype_aval_mappings[FilledConstant] = make_shaped_array\n+ad_util.jaxval_adders[FilledConstant] = add\n+ad_util.jaxval_zeros_likers[FilledConstant] = zeros_like_array\n+\n+def full_batch_rule(batched_args, batch_dims, shape):\n+ fill_value, = batched_args\n+ bdim, = batch_dims\n+ assert bdim == 0\n+ return broadcast_in_dim(fill_value, fill_value.shape + shape, [bdim])\n+\n+full_p = Primitive('full_p')\n+full_p.def_impl(FilledConstant)\n+full_p.def_abstract_eval(\n+ lambda fill_value, shape: ShapedArray(shape, _dtype(fill_value)))\n+xla.translations[full_p] = \\\n+ lambda c, fill_value, shape: c.Broadcast(fill_value, shape)\n+ad.deflinear(full_p, lambda t, shape: [_reduce_sum(t, tuple(range(len(shape))))])\n+batching.primitive_batchers[full_p] = full_batch_rule\n+\n+\n### util\ndef _ndim(x):\n@@ -2350,13 +2437,19 @@ def _dynamic_slice_indices(operand, start_indices):\nreturn rem(start_indices, onp.array(operand.shape, start_indices.dtype))\n-_const = lambda example, val: onp.array(val, _dtype(example))\n-_zeros = partial(full_like, fill_value=0)\n-_zero = partial(full_like, shape=(), fill_value=0)\n-_ones = partial(full_like, fill_value=1)\n-_one = partial(full_like, shape=(), fill_value=1)\n-_twos = partial(full_like, fill_value=2)\n-_two = partial(full_like, shape=(), fill_value=2)\n+def _ndarray_full_like(x, fill_value, dtype=None, shape=None):\n+ return onp.broadcast_to(onp.array(fill_value, dtype or _dtype(x)),\n+ onp.shape(x) if shape is None else shape)\n+\n+def _const(example, val):\n+ return onp.array(val, _dtype(example))\n+\n+_zeros = partial(_ndarray_full_like, fill_value=0)\n+_zero = partial(_ndarray_full_like, shape=(), fill_value=0)\n+_ones = partial(_ndarray_full_like, fill_value=1)\n+_one = partial(_ndarray_full_like, shape=(), fill_value=1)\n+_twos = partial(_ndarray_full_like, fill_value=2)\n+_two = partial(_ndarray_full_like, shape=(), fill_value=2)\n_dtype = onp.result_type\n_iscomplex = lambda x: onp.issubdtype(_dtype(x), onp.complexfloating)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lib/xla_bridge.py",
"new_path": "jax/lib/xla_bridge.py",
"diff": "@@ -355,6 +355,7 @@ def _ndarray_constant_handler(c, val):\nAn XLA ComputationDataHandle / XlaOp representing the constant ndarray\nstaged into the XLA Computation.\n\"\"\"\n+ # TODO(mattjj): revise this to use c.BroadcastInDim rather than Transpose\nif onp.any(onp.equal(0, val.strides)) and val.size > 0:\nzero_stride_axes, = onp.where(onp.equal(0, val.strides))\nother_axes, = onp.where(onp.not_equal(0, val.strides))\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -794,33 +794,27 @@ asarray = array\n@_wraps(onp.zeros_like)\ndef zeros_like(x, dtype=None):\n- return zeros(_shape(x), dtype or _dtype(x))\n+ return lax.full_like(x, 0, dtype)\n@_wraps(onp.ones_like)\ndef ones_like(x, dtype=None):\n- return ones(_shape(x), dtype or _dtype(x))\n+ return lax.full_like(x, 1, dtype)\n-@_wraps(onp.full)\n-def full(shape, fill_value, dtype=None):\n- if dtype:\n- fill_value = lax.convert_element_type(fill_value, dtype)\n- return lax.broadcast(fill_value, tuple(shape))\n+full = _wraps(onp.full)(lax.full)\n@_wraps(onp.zeros)\ndef zeros(shape, dtype=onp.dtype(\"float64\")):\nshape = (shape,) if onp.isscalar(shape) else shape\n- dtype = xla_bridge.canonicalize_dtype(dtype)\n- return onp.broadcast_to(onp.zeros((), dtype), tuple(shape))\n+ return lax.full(shape, 0, dtype)\n@_wraps(onp.ones)\ndef ones(shape, dtype=onp.dtype(\"float64\")):\nshape = (shape,) if onp.isscalar(shape) else shape\n- dtype = xla_bridge.canonicalize_dtype(dtype)\n- return onp.broadcast_to(onp.ones((), dtype), tuple(shape))\n+ return lax.full(shape, 1, dtype)\n@_wraps(onp.repeat)\n"
}
] | Python | Apache License 2.0 | google/jax | add tie_in and full primitives (constant creation) |
260,335 | 13.12.2018 11:12:11 | 28,800 | dfc25a06d996501c0d18457ad1fe83453aac6e38 | add IotaConstant (untested) | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -432,6 +432,12 @@ def full(shape, fill_value, dtype=None):\nreturn full_p.bind(fill_value, shape=shape)\n+def iota(dtype, shape, dimension):\n+ dtype = xla_bridge.canonicalize_dtype(dtype)\n+ shape = tuple(map(int(shape)))\n+ dimension = int(dimension)\n+ return IotaConstant(dtype, shape, dimension)\n+\n### convenience wrappers around traceables\n@@ -2282,13 +2288,6 @@ class FilledConstant(xla.DeviceConstant):\nfilled_const.shape)\nxla.register_device_constant(FilledConstant)\n-# TODO(mattjj): if we used isinstance rather than handlers here, these would all\n-# be covered as subclasses of DeviceArray. alternatively, just set up these in a\n-# loop after we've defined all the constant DeviceConstant subclasses.\n-batching.pytype_aval_mappings[FilledConstant] = make_shaped_array\n-ad_util.jaxval_adders[FilledConstant] = add\n-ad_util.jaxval_zeros_likers[FilledConstant] = zeros_like_array\n-\ndef full_batch_rule(batched_args, batch_dims, shape):\nfill_value, = batched_args\nbdim, = batch_dims\n@@ -2305,6 +2304,40 @@ ad.deflinear(full_p, lambda t, shape: [_reduce_sum(t, tuple(range(len(shape))))]\nbatching.primitive_batchers[full_p] = full_batch_rule\n+class IotaConstant(xla.DeviceConstant):\n+ __slots__ = [\"axis\"]\n+\n+ def __init__(self, dtype, shape, axis):\n+ self.shape = shape\n+ self.dtype = onp.dtype(dtype)\n+ self.ndim = len(shape)\n+ self.size = prod(shape)\n+ self._npy_value = None\n+\n+ self.axis = axis\n+\n+ @property\n+ def _value(self):\n+ if self._npy_value is None:\n+ iota = onp.arange(self.shape[self.axis], dtype=self.dtype)\n+ iota = iota.reshape([self.shape[self.axis] if i == self.axis else 1\n+ for i in range(self.ndim)])\n+ self._npy_value = onp.broadcast_to(iota, self.shape)\n+ return self._npy_value\n+\n+ @staticmethod\n+ def constant_handler(c, iota_constant):\n+ return c.BroadcastedIota(iota_constant.dtype, iota_constant.shape,\n+ iota_constant.axis)\n+xla.register_device_constant(IotaConstant)\n+\n+\n+for t in [FilledConstant, IotaConstant]:\n+ batching.pytype_aval_mappings[t] = make_shaped_array\n+ ad_util.jaxval_adders[t] = add\n+ ad_util.jaxval_zeros_likers[t] = zeros_like_array\n+\n+\n### util\ndef _ndim(x):\n@@ -2437,19 +2470,15 @@ def _dynamic_slice_indices(operand, start_indices):\nreturn rem(start_indices, onp.array(operand.shape, start_indices.dtype))\n-def _ndarray_full_like(x, fill_value, dtype=None, shape=None):\n- return onp.broadcast_to(onp.array(fill_value, dtype or _dtype(x)),\n- onp.shape(x) if shape is None else shape)\n-\ndef _const(example, val):\nreturn onp.array(val, _dtype(example))\n-_zeros = partial(_ndarray_full_like, fill_value=0)\n-_zero = partial(_ndarray_full_like, shape=(), fill_value=0)\n-_ones = partial(_ndarray_full_like, fill_value=1)\n-_one = partial(_ndarray_full_like, shape=(), fill_value=1)\n-_twos = partial(_ndarray_full_like, fill_value=2)\n-_two = partial(_ndarray_full_like, shape=(), fill_value=2)\n+_zeros = partial(full_like, fill_value=0)\n+_zero = partial(full_like, shape=(), fill_value=0)\n+_ones = partial(full_like, fill_value=1)\n+_one = partial(full_like, shape=(), fill_value=1)\n+_twos = partial(full_like, fill_value=2)\n+_two = partial(full_like, shape=(), fill_value=2)\n_dtype = onp.result_type\n_iscomplex = lambda x: onp.issubdtype(_dtype(x), onp.complexfloating)\n"
}
] | Python | Apache License 2.0 | google/jax | add IotaConstant (untested) |
260,335 | 15.12.2018 17:49:00 | 28,800 | 1ae1ae17a2ca4769051fb01a64b6e09391728fb3 | add EyeConstant, new np.eye and np.array code | [
{
"change_type": "MODIFY",
"old_path": "jax/abstract_arrays.py",
"new_path": "jax/abstract_arrays.py",
"diff": "@@ -27,9 +27,11 @@ from .lib import xla_bridge\ndef concretization_err_msg(fun):\nfname = getattr(fun, \"__name__\", fun)\n- return (\"Abstract value passed to function {} that requires a concrete value. \"\n- \"Possibly tracing Python control flow using abstract values. \"\n- \"If so, try using lax.cond or lax.while instead.\").format(fname)\n+ msg = (\"Abstract value passed to `{}`, which requires a concrete value. \"\n+ \"The function to be transformed can't be traced at the required level \"\n+ \"of abstraction. If using `jit`, try using `static_argnums` or \"\n+ \"applying `jit` to smaller subfunctions instead.\")\n+ return msg.format(fname)\ndef concretization_function_error(fun):\ndef error(self, *args):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -263,7 +263,7 @@ class DeviceArray(DeviceValue):\ndef __repr__(self):\nshape_str = \",\".join(map(str, self.shape))\n- return \"DeviceArray{{{}[{}]}}\".format(self.dtype.name, shape_str)\n+ return \"DeviceArray{{{}[{}]}}\".format(onp.dtype(self.dtype).name, shape_str)\ndef __len__(self):\ntry:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -432,12 +432,26 @@ def full(shape, fill_value, dtype=None):\nreturn full_p.bind(fill_value, shape=shape)\n-def iota(dtype, shape, dimension):\n+def iota(dtype, size):\n+ return broadcasted_iota(dtype, (size,), 0)\n+\n+def broadcasted_iota(dtype, shape, dimension):\ndtype = xla_bridge.canonicalize_dtype(dtype)\n- shape = tuple(map(int(shape)))\n+ shape = tuple(map(int, shape))\ndimension = int(dimension)\nreturn IotaConstant(dtype, shape, dimension)\n+def eye(dtype, size):\n+ return broadcasted_eye(dtype, (size, size), (0, 1))\n+\n+def broadcasted_eye(dtype, shape, axes):\n+ if not isinstance(axes, (list, tuple)) or not len(axes) >= 2:\n+ raise TypeError(\"make_diagonal `axes` must be a tuple with len at least 2.\")\n+ dtype = xla_bridge.canonicalize_dtype(dtype)\n+ shape = tuple(map(int, shape))\n+ axes = tuple(map(int, axes))\n+ return EyeConstant(shape, axes, dtype)\n+\n### convenience wrappers around traceables\n@@ -2309,7 +2323,7 @@ class IotaConstant(xla.DeviceConstant):\ndef __init__(self, dtype, shape, axis):\nself.shape = shape\n- self.dtype = onp.dtype(dtype)\n+ self.dtype = dtype\nself.ndim = len(shape)\nself.size = prod(shape)\nself._npy_value = None\n@@ -2332,7 +2346,38 @@ class IotaConstant(xla.DeviceConstant):\nxla.register_device_constant(IotaConstant)\n-for t in [FilledConstant, IotaConstant]:\n+class EyeConstant(xla.DeviceConstant):\n+ __slots__ = [\"axes\"]\n+\n+ def __init__(self, shape, axes, dtype):\n+ self.shape = shape\n+ self.dtype = dtype\n+ self.ndim = len(shape)\n+ self.size = prod(shape)\n+ self._npy_value = None\n+\n+ self.axes = axes\n+\n+ @property\n+ def _value(self):\n+ if self._npy_value is None:\n+ ones = [1] * self.ndim\n+ iotas = [onp.arange(self.shape[axis]).reshape(subvals(ones, [(axis, -1)]))\n+ for axis in self.axes]\n+ result = onp.asarray(_reduce(operator.eq, iotas), self.dtype)\n+ self._npy_value = onp.broadcast_to(result, self.shape)\n+ return self._npy_value\n+\n+ @staticmethod\n+ def constant_handler(c, diag_const):\n+ etype = xla_bridge.dtype_to_etype(diag_const.dtype)\n+ iotas = [c.BroadcastedIota(diag_const.dtype, diag_const.shape, axis)\n+ for axis in diag_const.axes]\n+ return c.ConvertElementType(_reduce(c.Eq, iotas), etype)\n+xla.register_device_constant(EyeConstant)\n+\n+\n+for t in [FilledConstant, IotaConstant, EyeConstant]:\nbatching.pytype_aval_mappings[t] = make_shaped_array\nad_util.jaxval_adders[t] = add\nad_util.jaxval_zeros_likers[t] = zeros_like_array\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -697,10 +697,6 @@ def allclose(a, b, rtol=1e-05, atol=1e-08):\n### Array-creation functions\n-arange = onp.arange\n-eye = onp.eye\n-\n-\n@_wraps(onp.stack)\ndef stack(arrays):\nif not arrays:\n@@ -817,6 +813,48 @@ def ones(shape, dtype=onp.dtype(\"float64\")):\nreturn lax.full(shape, 1, dtype)\n+@_wraps(onp.eye)\n+def eye(N, M=None, k=None, dtype=onp.dtype(\"float64\")):\n+ M = N if M is None else M\n+ if k is None:\n+ return lax.broadcasted_eye(dtype, (N, M), (0, 1))\n+ else:\n+ k_dtype = _dtype(k)\n+ if not onp.issubdtype(k_dtype, onp.integer):\n+ msg = \"eye argument `k` must be of integer dtype, got {}\"\n+ raise TypeError(msg.format(k_dtype))\n+ rows = k + lax.broadcasted_iota(k_dtype, (N, M), 0)\n+ cols = lax.broadcasted_iota(k_dtype, (N, M), 1)\n+ return lax.convert_element_type(lax.eq(rows, cols), dtype)\n+\n+\n+@_wraps(onp.arange)\n+def arange(*args, **kwargs):\n+ nargs = len(args)\n+ start, step = 0, 1\n+ dtype = kwargs.pop(\"dtype\", None)\n+ if kwargs:\n+ raise TypeError(\"arange only accepts 'dtype' kwarg, got {}\".format(kwargs))\n+ if nargs == 0:\n+ raise TypeError(\"Required argument 'start' (pos 1) not found\") # same as numpy error\n+ elif nargs == 1:\n+ stop, = args\n+ dtype = dtype or _dtype(stop)\n+ return lax.iota(dtype, stop) # avoids materializing\n+ elif nargs == 2:\n+ start, stop = args\n+ dtype = dtype or onp.result_type(start, stop)\n+ elif nargs == 3:\n+ start, stop, step = args\n+ dtype = dtype or onp.result_type(start, stop, step)\n+ elif nargs == 4:\n+ start, stop, step, dtype = args\n+ dtype = dtype or onp.result_type(start, stop, step)\n+\n+ size = (stop - start - 1) // step + 1\n+ return start + step * lax.iota(dtype, size)\n+\n+\n@_wraps(onp.repeat)\ndef repeat(a, repeats, axis=None):\nif not isscalar(repeats):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/random.py",
"new_path": "jax/random.py",
"diff": "@@ -168,7 +168,7 @@ def split(key, num=2):\nReturns:\nA tuple of length `num` of new PRNGKey instances.\n\"\"\"\n- counts = onp.arange(num * 2, dtype=onp.uint32)\n+ counts = lax.iota(onp.uint32, num * 2)\nbits = lax.reshape(threefry_2x32(key.keypair, counts), (num, 2))\nkeypairs = (lax.index_in_dim(bits, i, keepdims=False) for i in range(num))\nreturn tuple(PRNGKey.from_keypair((kp[0], kp[1])) for kp in keypairs)\n@@ -183,7 +183,7 @@ def _random_bits(key, bit_width, shape):\n# TODO(mattjj): just split the key here\nraise TypeError(\"requesting more random bits than a single call provides.\")\n- bits = threefry_2x32(key.keypair, onp.arange(max_count, dtype=onp.uint32))\n+ bits = threefry_2x32(key.keypair, lax.iota(onp.uint32, max_count))\nif bit_width == 64:\nbits = [lax.convert_element_type(x, onp.uint64) for x in np.split(bits, 2)]\nbits = (bits[0] << onp.uint64(32)) | bits[1]\n"
}
] | Python | Apache License 2.0 | google/jax | add EyeConstant, new np.eye and np.array code |
260,335 | 15.12.2018 18:42:26 | 28,800 | bdc9e92f9491307ba1bfa0be74ae9be1cd742f2f | remove full_p to improve power:weight | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -340,12 +340,6 @@ def instantiate_device_constant(const, cutoff=1000000):\nelse:\nreturn xb.device_put(onp.asarray(const))\n-def register_device_constant(cls):\n- pytype_aval_mappings[cls] = pytype_aval_mappings[DeviceArray]\n- canonicalize_dtype_handlers[cls] = identity\n- core.pytype_aval_mappings[cls] = ConcreteArray\n- xb.register_constant_handler(cls, cls.constant_handler)\n-\ndef xla_shape(x):\ntry:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -422,15 +422,15 @@ def full(shape, fill_value, dtype=None):\nmsg = \"full must be called with scalar fill_value, got fill_value.shape {}.\"\nraise TypeError(msg.format(onp.shape(fill_value)))\n+ # For constants (defined as Python scalars, raw ndarrays, or DeviceValues),\n+ # create a FilledConstant value, otherwise just call broadcast.\ndtype = dtype and xla_bridge.canonicalize_dtype(dtype)\n- if dtype is not None and _dtype(fill_value) != dtype:\n- # for Python scalars and raw ndarrays, we keep fill_value as a cpu ndarray\nif onp.isscalar(fill_value) or type(fill_value) is onp.ndarray:\n- fill_value = onp.array(fill_value, dtype)\n+ return FilledConstant(onp.asarray(fill_value, dtype), shape)\n+ elif isinstance(fill_value, xla.DeviceValue):\n+ return FilledConstant(convert_element_type(fill_value, dtype), shape)\nelse:\n- fill_value = convert_element_type(fill_value, dtype)\n-\n- return full_p.bind(fill_value, shape=shape)\n+ return broadcast(convert_element_type(fill_value, dtype), shape)\ndef iota(dtype, size):\nreturn broadcasted_iota(dtype, (size,), 0)\n@@ -2260,7 +2260,7 @@ while_p.def_abstract_eval(while_loop_abstract_eval)\nxla.translations[while_p] = while_loop_translation_rule\n-### primitives for handling constants\n+### constants\ndef tie_in_transpose_rule(t):\n@@ -2300,22 +2300,6 @@ class FilledConstant(xla.DeviceConstant):\ndef constant_handler(c, filled_const):\nreturn c.Broadcast(c.NumpyArrayConstant(filled_const.fill_value),\nfilled_const.shape)\n-xla.register_device_constant(FilledConstant)\n-\n-def full_batch_rule(batched_args, batch_dims, shape):\n- fill_value, = batched_args\n- bdim, = batch_dims\n- assert bdim == 0\n- return broadcast_in_dim(fill_value, fill_value.shape + shape, [bdim])\n-\n-full_p = Primitive('full_p')\n-full_p.def_impl(FilledConstant)\n-full_p.def_abstract_eval(\n- lambda fill_value, shape: ShapedArray(shape, _dtype(fill_value)))\n-xla.translations[full_p] = \\\n- lambda c, fill_value, shape: c.Broadcast(fill_value, shape)\n-ad.deflinear(full_p, lambda t, shape: [_reduce_sum(t, tuple(range(len(shape))))])\n-batching.primitive_batchers[full_p] = full_batch_rule\nclass IotaConstant(xla.DeviceConstant):\n@@ -2343,7 +2327,6 @@ class IotaConstant(xla.DeviceConstant):\ndef constant_handler(c, iota_constant):\nreturn c.BroadcastedIota(iota_constant.dtype, iota_constant.shape,\niota_constant.axis)\n-xla.register_device_constant(IotaConstant)\nclass EyeConstant(xla.DeviceConstant):\n@@ -2374,10 +2357,13 @@ class EyeConstant(xla.DeviceConstant):\niotas = [c.BroadcastedIota(diag_const.dtype, diag_const.shape, axis)\nfor axis in diag_const.axes]\nreturn c.ConvertElementType(_reduce(c.Eq, iotas), etype)\n-xla.register_device_constant(EyeConstant)\nfor t in [FilledConstant, IotaConstant, EyeConstant]:\n+ xla_bridge.register_constant_handler(t, t.constant_handler)\n+ core.pytype_aval_mappings[t] = ConcreteArray\n+ xla.pytype_aval_mappings[t] = xla.pytype_aval_mappings[xla.DeviceArray]\n+ xla.canonicalize_dtype_handlers[t] = identity\nbatching.pytype_aval_mappings[t] = make_shaped_array\nad_util.jaxval_adders[t] = add\nad_util.jaxval_zeros_likers[t] = zeros_like_array\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -798,7 +798,9 @@ def ones_like(x, dtype=None):\nreturn lax.full_like(x, 1, dtype)\n-full = _wraps(onp.full)(lax.full)\n+@_wraps(onp.full)\n+def full(shape, fill_value, dtype=None):\n+ return lax.full(shape, fill_value, dtype)\n@_wraps(onp.zeros)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -180,17 +180,13 @@ class APITest(jtu.JaxTestCase):\nassert jit(f, static_argnums=(1,))(0, 5) == 10\njtu.check_raises_regexp(\n- lambda: jit(f)(0, 5), TypeError,\n- \"('JaxprTracer' object cannot be interpreted as an integer\"\n- \"|Abstract value passed to function.*)\")\n+ lambda: jit(f)(0, 5), TypeError, \"Abstract value passed to .*\")\ndef test_casts(self):\nfor castfun in [float, complex, hex, oct] + list(six.integer_types):\nf = lambda x: castfun(x)\njtu.check_raises_regexp(\n- lambda: jit(f)(0), TypeError,\n- \"('JaxprTracer' object cannot be interpreted as an integer\"\n- \"|Abstract value passed to function.*)\")\n+ lambda: jit(f)(0), TypeError, \"Abstract value passed to .*\")\ndef test_unimplemented_interpreter_rules(self):\nfoo_p = Primitive('foo')\n"
}
] | Python | Apache License 2.0 | google/jax | remove full_p to improve power:weight |
260,335 | 15.12.2018 19:14:05 | 28,800 | 52c6eac3de854a025866e498dc1f8cd1c2ca0afe | use lax.tie_in in jax.random for better consts | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -328,7 +328,7 @@ class DeviceConstant(DeviceArray):\nassert False\n# TODO(mattjj): tune cutoff\n-def instantiate_device_constant(const, cutoff=1000000):\n+def instantiate_device_constant(const, cutoff=0):\n# dispatch an XLA Computation to build the constant on the device if it's\n# large, or alternatively build it on the host and transfer it if it's small\nassert isinstance(const, DeviceConstant)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/random.py",
"new_path": "jax/random.py",
"diff": "@@ -27,6 +27,7 @@ from . import numpy as np\nfrom . import tree_util\nfrom .api import jit\nfrom jax.lib import xla_bridge\n+from jax import core\nclass PRNGKey(object):\n\"\"\"A pseudo-random number generator (PRNG) key for use with lax.random.\"\"\"\n@@ -51,13 +52,13 @@ class PRNGKey(object):\nelse:\nk1 = convert(lax.shift_right_logical(seed, 32))\nk2 = convert(lax.bitwise_and(seed, 0xFFFFFFFF))\n- self.keypair = (k1, k2)\n+ self.keypair = core.pack((k1, k2))\n@classmethod\ndef from_keypair(cls, keypair):\n\"\"\"Internal method to create a PRNGKey instance from a raw key pair.\"\"\"\nnew = cls.__new__(cls)\n- new.keypair = tuple(keypair)\n+ new.keypair = core.pack(keypair)\nreturn new\n@@ -100,7 +101,7 @@ def threefry_2x32(keypair, count):\nAn array of dtype uint32 with the same shape as `count`.\n\"\"\"\n# Based on ThreeFry2x32 by phawkins@ in //.../xla/client/lib/prng.cc\n- key1, key2 = keypair[0], keypair[1]\n+ key1, key2 = keypair\nif not lax._dtype(key1) == lax._dtype(key2) == lax._dtype(count) == onp.uint32:\nmsg = \"threefry_2x32 requires uint32 arguments, got {}\"\nraise TypeError(msg.format([lax._dtype(x) for x in [key1, key2, count]]))\n@@ -168,7 +169,7 @@ def split(key, num=2):\nReturns:\nA tuple of length `num` of new PRNGKey instances.\n\"\"\"\n- counts = lax.iota(onp.uint32, num * 2)\n+ counts = lax.tie_in(key.keypair, lax.iota(onp.uint32, num * 2))\nbits = lax.reshape(threefry_2x32(key.keypair, counts), (num, 2))\nkeypairs = (lax.index_in_dim(bits, i, keepdims=False) for i in range(num))\nreturn tuple(PRNGKey.from_keypair((kp[0], kp[1])) for kp in keypairs)\n@@ -183,7 +184,8 @@ def _random_bits(key, bit_width, shape):\n# TODO(mattjj): just split the key here\nraise TypeError(\"requesting more random bits than a single call provides.\")\n- bits = threefry_2x32(key.keypair, lax.iota(onp.uint32, max_count))\n+ counts = lax.tie_in(key.keypair, lax.iota(onp.uint32, max_count))\n+ bits = threefry_2x32(key.keypair, counts)\nif bit_width == 64:\nbits = [lax.convert_element_type(x, onp.uint64) for x in np.split(bits, 2)]\nbits = (bits[0] << onp.uint64(32)) | bits[1]\n"
}
] | Python | Apache License 2.0 | google/jax | use lax.tie_in in jax.random for better consts |
260,335 | 18.12.2018 09:18:14 | 28,800 | 78a4240581a55f9b89b7f8765304bfb3d801f7e5 | add fix from einsum branch | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -2347,16 +2347,18 @@ class EyeConstant(xla.DeviceConstant):\nones = [1] * self.ndim\niotas = [onp.arange(self.shape[axis]).reshape(subvals(ones, [(axis, -1)]))\nfor axis in self.axes]\n- result = onp.asarray(_reduce(operator.eq, iotas), self.dtype)\n+ eyes = [i1 == i2 for i1, i2 in zip(iotas[:-1], iotas[1:])]\n+ result = onp.asarray(_reduce(operator.and_, eyes), self.dtype)\nself._npy_value = onp.broadcast_to(result, self.shape)\nreturn self._npy_value\n@staticmethod\ndef constant_handler(c, diag_const):\netype = xla_bridge.dtype_to_etype(diag_const.dtype)\n- iotas = [c.BroadcastedIota(diag_const.dtype, diag_const.shape, axis)\n+ iotas = [c.BroadcastedIota(onp.bool_, diag_const.shape, axis)\nfor axis in diag_const.axes]\n- return c.ConvertElementType(_reduce(c.Eq, iotas), etype)\n+ eyes = [c.Eq(i1, i2) for i1, i2 in zip(iotas[:-1], iotas[1:])]\n+ return c.ConvertElementType(_reduce(c.And, eyes), etype)\nfor t in [FilledConstant, IotaConstant, EyeConstant]:\n"
}
] | Python | Apache License 2.0 | google/jax | add fix from einsum branch |
260,335 | 18.12.2018 09:58:42 | 28,800 | 88bb264e07e430f5b0851c960509a6b941d3d320 | fix exception check for python3 | [
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -180,13 +180,17 @@ class APITest(jtu.JaxTestCase):\nassert jit(f, static_argnums=(1,))(0, 5) == 10\njtu.check_raises_regexp(\n- lambda: jit(f)(0, 5), TypeError, \"Abstract value passed to .*\")\n+ lambda: jit(f)(0, 5), TypeError,\n+ \"('JaxprTracer' object cannot be interpreted as an integer\"\n+ \"|Abstract value passed to .*)\")\ndef test_casts(self):\nfor castfun in [float, complex, hex, oct] + list(six.integer_types):\nf = lambda x: castfun(x)\njtu.check_raises_regexp(\n- lambda: jit(f)(0), TypeError, \"Abstract value passed to .*\")\n+ lambda: jit(f)(0), TypeError,\n+ \"('JaxprTracer' object cannot be interpreted as an integer\"\n+ \"|Abstract value passed to .*)\")\ndef test_unimplemented_interpreter_rules(self):\nfoo_p = Primitive('foo')\n"
}
] | Python | Apache License 2.0 | google/jax | fix exception check for python3 |
260,335 | 18.12.2018 16:31:51 | 28,800 | 6865783cf9ba8c659c7e0176e3e1a71eec8fec9a | fix some longstanding readme typos | [
{
"change_type": "MODIFY",
"old_path": "examples/mnist_vae.py",
"new_path": "examples/mnist_vae.py",
"diff": "@@ -111,13 +111,14 @@ if __name__ == \"__main__\":\n@jit\ndef run_epoch(rng, opt_state):\n- def body_fun(i, rng__opt_state__images):\n- (rng, opt_state, images) = rng__opt_state__images\n+ def body_fun(i, loop_carry):\n+ (rng, opt_state, images) = loop_carry\nrng, elbo_rng, data_rng = random.split(rng, 3)\nbatch = binarize_batch(data_rng, i, images)\nloss = lambda params: -elbo(elbo_rng, params, batch) / batch_size\ng = grad(loss)(minmax.get_params(opt_state))\n- return rng, opt_update(i, g, opt_state), images\n+ loop_carry = rng, opt_update(i, g, opt_state), images\n+ return loop_carry\ninit_val = rng, opt_state, train_images\n_, opt_state, _ = lax.fori_loop(0, num_batches, body_fun, init_val)\nreturn opt_state\n"
}
] | Python | Apache License 2.0 | google/jax | fix some longstanding readme typos |
260,335 | 18.12.2018 17:19:47 | 28,800 | 20ca0bd7334343695580eeff10eaeeb0bb2f6e4f | add cutoff for materialize-and-xfer vs build-on-device
see | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -327,8 +327,7 @@ class DeviceConstant(DeviceArray):\ndef constant_handler(c, constant_instance):\nassert False\n-# TODO(mattjj): tune cutoff\n-def instantiate_device_constant(const, cutoff=0):\n+def instantiate_device_constant(const, cutoff=1e6):\n# dispatch an XLA Computation to build the constant on the device if it's\n# large, or alternatively build it on the host and transfer it if it's small\nassert isinstance(const, DeviceConstant)\n"
}
] | Python | Apache License 2.0 | google/jax | add cutoff for materialize-and-xfer vs build-on-device
see https://github.com/google/jax/pull/140#issuecomment-448433620 |
260,335 | 18.12.2018 19:04:01 | 28,800 | 6757b758a2f0bf58ea42e58ed7fb8469f34b5c15 | bump jaxlib version for wheel building | [
{
"change_type": "MODIFY",
"old_path": "build/setup.py",
"new_path": "build/setup.py",
"diff": "@@ -20,7 +20,7 @@ binary_libs = [os.path.basename(f) for f in glob('jaxlib/*.so*')]\nsetup(\nname='jaxlib',\n- version='0.1.2',\n+ version='0.1.3',\ndescription='XLA library for JAX',\nauthor='JAX team',\nauthor_email='jax-dev@google.com',\n"
}
] | Python | Apache License 2.0 | google/jax | bump jaxlib version for wheel building |
260,335 | 18.12.2018 19:09:15 | 28,800 | f277609708eb39839fc9a539ef860c768f92c091 | add scipy and cython to wheel dockerfile | [
{
"change_type": "MODIFY",
"old_path": "build/Dockerfile",
"new_path": "build/Dockerfile",
"diff": "@@ -6,7 +6,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \\\ndh-autoreconf git curl \\\npython python-pip python-dev \\\npython3 python3-pip python3-dev\n-RUN pip install numpy setuptools wheel && pip3 install numpy setuptools wheel\n+RUN pip install numpy scipy cython setuptools wheel && pip3 install numpy scipy cython setuptools wheel\nRUN git clone https://github.com/nixos/patchelf /tmp/patchelf\nWORKDIR /tmp/patchelf\n"
}
] | Python | Apache License 2.0 | google/jax | add scipy and cython to wheel dockerfile |
260,335 | 18.12.2018 22:45:34 | 28,800 | a18e3f27ace89b8b1bfde2d0e6b563030989c40e | add tests for device constants | [
{
"change_type": "MODIFY",
"old_path": "jax/lax.py",
"new_path": "jax/lax.py",
"diff": "@@ -417,14 +417,14 @@ opaque_param_ids = itertools.count()\ndef tie_in(x, y):\nreturn tie_in_p.bind(x, y)\n-def full(shape, fill_value, dtype=None):\n+def full(shape, fill_value, dtype):\nif onp.shape(fill_value):\nmsg = \"full must be called with scalar fill_value, got fill_value.shape {}.\"\nraise TypeError(msg.format(onp.shape(fill_value)))\n+ dtype = xla_bridge.canonicalize_dtype(dtype)\n# For constants (defined as Python scalars, raw ndarrays, or DeviceValues),\n# create a FilledConstant value, otherwise just call broadcast.\n- dtype = dtype and xla_bridge.canonicalize_dtype(dtype)\nif onp.isscalar(fill_value) or type(fill_value) is onp.ndarray:\nreturn FilledConstant(onp.asarray(fill_value, dtype), shape)\nelif isinstance(fill_value, xla.DeviceValue):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -1346,6 +1346,76 @@ class LaxTest(jtu.JaxTestCase):\nself._CompileAndCheck(fun, args_maker, check_dtypes=True)\n+class DeviceConstantTest(jtu.JaxTestCase):\n+ def _CheckDeviceConstant(self, make_const, expected):\n+ # check casting to ndarray works\n+ asarray_result = onp.asarray(make_const())\n+\n+ # check passing as an argument works (should hit constant handler)\n+ zero = onp.array(0, expected.dtype)\n+ argument_result = lax.add(zero, make_const())\n+\n+ # check looping into a compiled computation works\n+ jit_result = api.jit(lambda x: lax.add(x, make_const()))(zero)\n+\n+ # ensure they're all the same\n+ self.assertAllClose(asarray_result, expected, check_dtypes=True)\n+ self.assertAllClose(argument_result, expected, check_dtypes=True)\n+ self.assertAllClose(jit_result, expected, check_dtypes=True)\n+\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\": \"_{}_fill={}\".format(\n+ jtu.format_shape_dtype_string(shape, dtype), fill_value),\n+ \"shape\": shape, \"dtype\": dtype, \"fill_value\": fill_value}\n+ # for dtype in itertools.chain(all_dtypes, [None])\n+ for dtype in [None]\n+ for shape in [(), (3,), (2, 3), (2, 3, 4)]\n+ for fill_value in [0, 1, onp.pi]))\n+ def testFilledConstant(self, shape, fill_value, dtype):\n+ make_const = lambda: lax.full(shape, fill_value, dtype)\n+ expected = onp.full(shape, fill_value, xla_bridge.canonicalize_dtype(dtype))\n+ self._CheckDeviceConstant(make_const, expected)\n+\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\": \"_{}_dim={}\".format(\n+ jtu.format_shape_dtype_string(shape, dtype), dimension),\n+ \"shape\": shape, \"dtype\": dtype, \"dimension\": dimension}\n+ for dtype in default_dtypes\n+ for shape in [(), (3,), (2, 3), (2, 3, 4)]\n+ for dimension in range(len(shape))))\n+ def testIotaConstant(self, dtype, shape, dimension):\n+ make_const = lambda: lax.broadcasted_iota(dtype, shape, dimension)\n+\n+ arr = onp.arange(shape[dimension], dtype=xla_bridge.canonicalize_dtype(dtype))\n+ singleton_shape = [1] * len(shape)\n+ singleton_shape[dimension] = shape[dimension]\n+ expected = onp.broadcast_to(arr.reshape(singleton_shape), shape)\n+\n+ self._CheckDeviceConstant(make_const, expected)\n+\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\": \"_{}_axes={}\".format(\n+ jtu.format_shape_dtype_string(shape, dtype), axes),\n+ \"shape\": shape, \"dtype\": dtype, \"axes\": axes}\n+ for dtype in default_dtypes\n+ for shape, axes in [\n+ [(2, 3), (0, 1)],\n+ [(2, 3, 4), (0, 1)],\n+ [(2, 3, 4), (0, 2)],\n+ [(2, 3, 4), (1, 2)],\n+ [(2, 3, 4), (0, 1, 2)],\n+ [(2, 3, 4, 2), (0, 1, 2)],\n+ [(2, 3, 4, 2), (0, 2, 3)],\n+ ]))\n+ def testEyeConstant(self, dtype, shape, axes):\n+ make_const = lambda: lax.broadcasted_eye(dtype, shape, axes)\n+\n+ # don't check the asarray case, just assume it's right\n+ expected = onp.asarray(make_const())\n+\n+ self._CheckDeviceConstant(make_const, expected)\n+\n+\nGradTestSpec = collections.namedtuple(\n\"GradTestSpec\", [\"op\", \"nargs\", \"order\", \"rng\", \"dtypes\"])\n"
}
] | Python | Apache License 2.0 | google/jax | add tests for device constants |
260,335 | 18.12.2018 23:06:33 | 28,800 | 910672809f47da636a3f902f1bc51efff8dbfab3 | tweak scipy stats test prngs | [
{
"change_type": "MODIFY",
"old_path": "tests/scipy_stats_test.py",
"new_path": "tests/scipy_stats_test.py",
"diff": "@@ -17,7 +17,6 @@ from __future__ import division\nfrom __future__ import print_function\nimport collections\n-import functools\nimport itertools\nfrom absl.testing import absltest, parameterized\n@@ -31,26 +30,18 @@ from lax_scipy_test import CombosWithReplacement, float_dtypes\nall_shapes = [(), (4,), (3, 4), (3, 1), (1, 4), (2, 1, 4)]\n-def genNamedParametersNArgs(n):\n+def genNamedParametersNArgs(n, rng):\nreturn parameterized.named_parameters(\njtu.cases_from_list(\n{\"testcase_name\": jtu.format_test_name_suffix(\"\", shapes, dtypes),\n\"rng\": rng, \"shapes\": shapes, \"dtypes\": dtypes}\nfor shapes in CombosWithReplacement(all_shapes, n)\n- for dtypes in CombosWithReplacement(float_dtypes, n)\n- for rng in [jtu.rand_default()]\n- ))\n+ for dtypes in CombosWithReplacement(float_dtypes, n)))\nclass LaxBackedScipyStatsTests(jtu.JaxTestCase):\n\"\"\"Tests for LAX-backed scipy.stats implementations\"\"\"\n- beta_decorator = genNamedParametersNArgs(5)\n- expon_decorator = genNamedParametersNArgs(3)\n- laplace_decorator = genNamedParametersNArgs(3)\n- norm_decorator = genNamedParametersNArgs(3)\n- uniform_decorator = genNamedParametersNArgs(3)\n-\n- @beta_decorator\n+ @genNamedParametersNArgs(5, jtu.rand_positive())\ndef testBetaLogPdf(self, rng, shapes, dtypes):\nscipy_fun = osp_stats.beta.logpdf\nlax_fun = lsp_stats.beta.logpdf\n@@ -62,20 +53,21 @@ class LaxBackedScipyStatsTests(jtu.JaxTestCase):\nself._CheckAgainstNumpy(scipy_fun, lax_fun, args_maker, check_dtypes=True)\nself._CompileAndCheck(lax_fun, args_maker, check_dtypes=True)\n- @norm_decorator\n+ @genNamedParametersNArgs(3, jtu.rand_default())\ndef testNormLogPdf(self, rng, shapes, dtypes):\nscipy_fun = osp_stats.norm.logpdf\nlax_fun = lsp_stats.norm.logpdf\ndef args_maker():\nx, loc, scale = map(rng, shapes, dtypes)\n- scale = onp.clip(onp.abs(scale), a_min=0.1, a_max=None) # clipping to ensure that scale is not too low\n+ # clipping to ensure that scale is not too low\n+ scale = onp.clip(onp.abs(scale), a_min=0.1, a_max=None)\nreturn [x, loc, scale]\nself._CheckAgainstNumpy(scipy_fun, lax_fun, args_maker, check_dtypes=True)\nself._CompileAndCheck(lax_fun, args_maker, check_dtypes=True)\n- @expon_decorator\n+ @genNamedParametersNArgs(3, jtu.rand_positive())\ndef testExponLogPdf(self, rng, shapes, dtypes):\nscipy_fun = osp_stats.expon.logpdf\nlax_fun = lsp_stats.expon.logpdf\n@@ -87,20 +79,21 @@ class LaxBackedScipyStatsTests(jtu.JaxTestCase):\nself._CheckAgainstNumpy(scipy_fun, lax_fun, args_maker, check_dtypes=True)\nself._CompileAndCheck(lax_fun, args_maker, check_dtypes=True)\n- @laplace_decorator\n+ @genNamedParametersNArgs(3, jtu.rand_positive())\ndef testLaplaceLogPdf(self, rng, shapes, dtypes):\nscipy_fun = osp_stats.laplace.logpdf\nlax_fun = lsp_stats.laplace.logpdf\ndef args_maker():\nx, loc, scale = map(rng, shapes, dtypes)\n- scale = onp.clip(onp.abs(scale), a_min=0.1, a_max=None) # clipping to ensure that scale is not too low\n+ # clipping to ensure that scale is not too low\n+ scale = onp.clip(onp.abs(scale), a_min=0.1, a_max=None)\nreturn [x, loc, scale]\nself._CheckAgainstNumpy(scipy_fun, lax_fun, args_maker, check_dtypes=True)\nself._CompileAndCheck(lax_fun, args_maker, check_dtypes=True)\n- @uniform_decorator\n+ @genNamedParametersNArgs(3, jtu.rand_default())\ndef testUniformLogPdf(self, rng, shapes, dtypes):\nscipy_fun = osp_stats.uniform.logpdf\nlax_fun = lsp_stats.uniform.logpdf\n"
}
] | Python | Apache License 2.0 | google/jax | tweak scipy stats test prngs |
260,335 | 16.12.2018 10:33:10 | 28,800 | 6a71e9d6ec80093b75f11c820ed1ebcbc21b4d23 | start drafting an einsum implementation | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -20,12 +20,15 @@ from six.moves import builtins\nimport six\nimport numpy as onp\n+import opt_einsum\n+import collections\n+import itertools\nfrom .. import core\nfrom ..abstract_arrays import UnshapedArray, ShapedArray, ConcreteArray\nfrom ..interpreters.xla import DeviceArray\nfrom .. import lax\n-from ..util import memoize, partial, get_module_functions, prod as _prod\n+from ..util import memoize, partial, get_module_functions, unzip2, prod as _prod\nfrom ..lib import xla_bridge\n# To provide the same module-level names as Numpy, we need to redefine builtins\n@@ -1054,6 +1057,78 @@ def tensordot(a, b, axes=2):\nraise TypeError(msg)\n+def einsum(*operands):\n+ operands, contractions = opt_einsum.contract_path(\n+ *operands, einsum_call=True, use_blas=True)\n+ for operand_indices, contracted_names, einstr, _, _ in contractions:\n+ input_str, result_names = einstr.split('->')\n+ input_names = input_str.split(',')\n+\n+ # switch on the number of operands to be processed in this loop iteration.\n+ # every case here sets 'result' and 'names'.\n+ if len(operand_indices) == 1:\n+ operand = operands.pop(operand_indices[0])\n+ names, = input_names\n+ counts = collections.Counter(names)\n+\n+ # sum out non-repeated indices with a single reduction\n+ uniques = tuple(name for name in contracted_names if counts[name] == 1)\n+ if uniques:\n+ axes = tuple(names.index(name) for name in uniques)\n+ operand = lax.reduce(operand, onp.array(0, _dtype(operand)), lax.add, axes)\n+ names = names.translate(None, ''.join(uniques))\n+ map(counts.pop, uniques)\n+\n+ # for every repeated index, do a contraction against an identity matrix\n+ for name, count in counts.items():\n+ if count > 1:\n+ raise NotImplementedError\n+\n+ result = operand\n+\n+ elif len(operand_indices) == 2:\n+ lhs, rhs = map(operands.pop, operand_indices)\n+ lhs_counts, rhs_counts = map(collections.Counter, input_names)\n+ lhs_names, rhs_names = input_names\n+\n+ all_counts = itertools.chain(lhs_counts.values(), rhs_counts.values())\n+ if _any(count > 1 for count in all_counts):\n+ # TODO handle repeated indices (trace out first)\n+ # TODO handle unique axes (sum out first)\n+ raise NotImplementedError\n+\n+ batch_names = set(lhs_names) & set(rhs_names) - contracted_names\n+ lhs_cont, rhs_cont = unzip2((lhs_names.index(name), rhs_names.index(name))\n+ for name in contracted_names)\n+ lhs_batch, rhs_batch = unzip2((lhs_names.find(name), rhs_names.find(name))\n+ for name in batch_names)\n+ batch_dims = tuple(range(len(batch_names)))\n+ if lhs_batch != rhs_batch or set(lhs_batch) != set(batch_dims):\n+ lhs = moveaxis(lhs, lhs_batch, batch_dims)\n+ rhs = moveaxis(rhs, rhs_batch, batch_dims)\n+ batch_names = ''.join(batch_names)\n+ else:\n+ batch_dims = tuple(lhs_batch)\n+ batch_names = ''.join(lhs_names[i] for i in batch_dims)\n+\n+ dimension_numbers = [(lhs_cont, rhs_cont), (batch_dims, batch_dims)]\n+ result = lax.dot_general(lhs, rhs, dimension_numbers)\n+ deleted_names = batch_names + ''.join(contracted_names)\n+ names = (batch_names\n+ + lhs_names.translate(None, deleted_names)\n+ + rhs_names.translate(None, deleted_names))\n+\n+ else:\n+ raise NotImplementedError\n+\n+ if names != result_names:\n+ perm = tuple([names.index(name) for name in result_names])\n+ result = lax.transpose(result, perm)\n+ operands.append(result) # used in next iteration\n+\n+ return operands[0]\n+\n+\n### Misc\n"
},
{
"change_type": "ADD",
"old_path": null,
"new_path": "tests/lax_numpy_einsum_test.py",
"diff": "+# Copyright 2018 Google LLC\n+#\n+# Licensed under the Apache License, Version 2.0 (the \"License\");\n+# you may not use this file except in compliance with the License.\n+# You may obtain a copy of the License at\n+#\n+# https://www.apache.org/licenses/LICENSE-2.0\n+#\n+# Unless required by applicable law or agreed to in writing, software\n+# distributed under the License is distributed on an \"AS IS\" BASIS,\n+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+# See the License for the specific language governing permissions and\n+# limitations under the License.\n+\n+from __future__ import absolute_import\n+from __future__ import division\n+from __future__ import print_function\n+\n+import numpy as onp\n+from absl.testing import absltest\n+\n+import jax.numpy as np\n+import jax.test_util as jtu\n+\n+from jax.config import config\n+config.parse_flags_with_absl()\n+\n+\n+def rng():\n+ return onp.random.RandomState(0)\n+\n+def check(s, *ops):\n+ a = onp.einsum(s, *ops)\n+ b = np.einsum(s, *ops)\n+ assert onp.allclose(a, b, atol=1e-4, rtol=1e-4)\n+\n+\n+class EinsumTest(jtu.JaxTestCase):\n+\n+ def test_two_operands_1(self):\n+ x = rng().randn(3, 4)\n+ y = rng().randn(4)\n+ s = 'ij,j->i'\n+ check(s, x, y)\n+\n+ def test_one_operand_1(self):\n+ x = rng().randn(3, 4, 5)\n+ s = 'ijk->j'\n+ check(s, x)\n+\n+ def test_one_operand_2(self):\n+ x = rng().randn(3, 4, 5)\n+ s = 'ijk->kij'\n+ check(s, x)\n+\n+ def test_one_operand_3(self):\n+ x = rng().randn(3, 4, 5)\n+ s = 'ijk->ki'\n+ check(s, x)\n+\n+ def test_one_operand_4(self):\n+ x = rng().randn(3, 4, 5)\n+ s = 'ijk->ki'\n+ check(s, x)\n+\n+ def test_one_operand_5(self):\n+ x = rng().randn(2, 3, 4, 5)\n+ s = '...ijk->...ki'\n+ check(s, x)\n+\n+ def test_one_operand_6(self):\n+ x = rng().randn(3, 4, 5)\n+ s = '...ijk->ki'\n+ check(s, x)\n+\n+ # def test_one_operand_7(self):\n+ # x = rng().randn(3, 3, 3)\n+ # s = 'iii->'\n+ # check(s, x)\n+\n+ # def test_one_operand_8(self):\n+ # x = rng().randn(3, 3)\n+ # s = 'ii->i'\n+ # check(s, x)\n+\n+ # def test_one_operand_9(self):\n+ # x = rng().randn(3, 3, 4)\n+ # s = 'iij->i'\n+ # check(s, x)\n+\n+ # def test_one_operand_10(self):\n+ # x = rng().randn(3, 3, 3)\n+ # s = 'iii->i'\n+ # check(s, x)\n+\n+if __name__ == '__main__':\n+ absltest.main()\n"
}
] | Python | Apache License 2.0 | google/jax | start drafting an einsum implementation |
260,335 | 17.12.2018 18:16:20 | 28,800 | 13a0e1168e092c773aebf784c115eeef70c81db8 | fix broadcasted eye bug, enable more einsum | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -1060,6 +1060,8 @@ def tensordot(a, b, axes=2):\ndef einsum(*operands):\noperands, contractions = opt_einsum.contract_path(\n*operands, einsum_call=True, use_blas=True)\n+ sum = lambda x, axes: lax.reduce(x, onp.array(0, x.dtype), lax.add, axes)\n+\nfor operand_indices, contracted_names, einstr, _, _ in contractions:\ninput_str, result_names = einstr.split('->')\ninput_names = input_str.split(',')\n@@ -1075,14 +1077,21 @@ def einsum(*operands):\nuniques = tuple(name for name in contracted_names if counts[name] == 1)\nif uniques:\naxes = tuple(names.index(name) for name in uniques)\n- operand = lax.reduce(operand, onp.array(0, _dtype(operand)), lax.add, axes)\n+ operand = sum(operand, axes)\nnames = names.translate(None, ''.join(uniques))\nmap(counts.pop, uniques)\n# for every repeated index, do a contraction against an identity matrix\nfor name, count in counts.items():\nif count > 1:\n- raise NotImplementedError\n+ axes = [i for i, n in enumerate(names) if n == name]\n+ eye = lax.broadcasted_eye(operand.dtype, operand.shape, axes)\n+ if name not in result_names:\n+ operand = sum(operand * eye, axes)\n+ names = names.replace(name, '')\n+ else:\n+ operand = sum(operand * eye, axes[:-1])\n+ names = names.replace(name, '', count - 1)\nresult = operand\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_einsum_test.py",
"new_path": "tests/lax_numpy_einsum_test.py",
"diff": "@@ -73,25 +73,46 @@ class EinsumTest(jtu.JaxTestCase):\ns = '...ijk->ki'\ncheck(s, x)\n- # def test_one_operand_7(self):\n- # x = rng().randn(3, 3, 3)\n- # s = 'iii->'\n- # check(s, x)\n-\n- # def test_one_operand_8(self):\n- # x = rng().randn(3, 3)\n- # s = 'ii->i'\n- # check(s, x)\n-\n- # def test_one_operand_9(self):\n- # x = rng().randn(3, 3, 4)\n- # s = 'iij->i'\n- # check(s, x)\n-\n- # def test_one_operand_10(self):\n- # x = rng().randn(3, 3, 3)\n- # s = 'iii->i'\n- # check(s, x)\n+ def test_one_operand_7(self):\n+ x = rng().randn(3, 3)\n+ s = 'ii->'\n+ check(s, x)\n+\n+ def test_one_operand_8(self):\n+ x = rng().randn(3, 3, 3)\n+ s = 'iii->'\n+ check(s, x)\n+\n+ def test_one_operand_9(self):\n+ x = rng().randn(3, 3)\n+ s = 'ii->i'\n+ check(s, x)\n+\n+ def test_one_operand_10(self):\n+ x = rng().randn(3, 3, 4)\n+ s = 'iij->i'\n+ check(s, x)\n+\n+ def test_one_operand_11(self):\n+ x = rng().randn(3, 3, 3)\n+ s = 'iii->i'\n+ check(s, x)\n+\n+ def test_one_operand_12(self):\n+ x = rng().randn(3, 3, 5, 4, 4)\n+ s = 'iijkk->i'\n+ check(s, x)\n+\n+ def test_one_operand_13(self):\n+ x = rng().randn(3, 3, 5, 4, 4)\n+ s = 'iijkk->ik'\n+ check(s, x)\n+\n+ def test_one_operand_14(self):\n+ x = rng().randn(3, 3, 5, 4, 4)\n+ s = 'iijkl->il'\n+ check(s, x)\n+\nif __name__ == '__main__':\nabsltest.main()\n"
}
] | Python | Apache License 2.0 | google/jax | fix broadcasted eye bug, enable more einsum |
260,335 | 17.12.2018 21:29:04 | 28,800 | fdde6841e6f7f4e62817a709a49f08aee6c880cb | add support for two-operrand cases | [
{
"change_type": "MODIFY",
"old_path": "jax/numpy/lax_numpy.py",
"new_path": "jax/numpy/lax_numpy.py",
"diff": "@@ -1062,50 +1062,66 @@ def einsum(*operands):\n*operands, einsum_call=True, use_blas=True)\nsum = lambda x, axes: lax.reduce(x, onp.array(0, x.dtype), lax.add, axes)\n- for operand_indices, contracted_names, einstr, _, _ in contractions:\n- input_str, result_names = einstr.split('->')\n- input_names = input_str.split(',')\n-\n- # switch on the number of operands to be processed in this loop iteration.\n- # every case here sets 'result' and 'names'.\n- if len(operand_indices) == 1:\n- operand = operands.pop(operand_indices[0])\n- names, = input_names\n- counts = collections.Counter(names)\n-\n- # sum out non-repeated indices with a single reduction\n- uniques = tuple(name for name in contracted_names if counts[name] == 1)\n+ def sum_uniques(operand, names, uniques):\nif uniques:\n- axes = tuple(names.index(name) for name in uniques)\n+ axes = [names.index(name) for name in uniques]\noperand = sum(operand, axes)\nnames = names.translate(None, ''.join(uniques))\n- map(counts.pop, uniques)\n+ return operand, names\n- # for every repeated index, do a contraction against an identity matrix\n+ def sum_repeats(operand, names, counts, keep_names):\nfor name, count in counts.items():\nif count > 1:\naxes = [i for i, n in enumerate(names) if n == name]\neye = lax.broadcasted_eye(operand.dtype, operand.shape, axes)\n- if name not in result_names:\n+ if name not in keep_names:\noperand = sum(operand * eye, axes)\nnames = names.replace(name, '')\nelse:\noperand = sum(operand * eye, axes[:-1])\nnames = names.replace(name, '', count - 1)\n+ return operand, names\n+\n+ for operand_indices, contracted_names, einstr, _, _ in contractions:\n+ input_str, result_names = einstr.split('->')\n+ input_names = input_str.split(',')\n+\n+ # switch on the number of operands to be processed in this loop iteration.\n+ # every case here sets 'result' and 'names'.\n+ if len(operand_indices) == 1:\n+ operand = operands.pop(operand_indices[0])\n+ names, = input_names\n+ counts = collections.Counter(names)\n- result = operand\n+ # sum out unique contracted indices with a single reduce-sum\n+ uniques = [name for name in contracted_names if counts[name] == 1]\n+ operand, names = sum_uniques(operand, names, uniques)\n+\n+ # for every repeated index, do a contraction against an identity matrix\n+ operand, names = sum_repeats(operand, names, counts, result_names)\nelif len(operand_indices) == 2:\nlhs, rhs = map(operands.pop, operand_indices)\nlhs_counts, rhs_counts = map(collections.Counter, input_names)\nlhs_names, rhs_names = input_names\n- all_counts = itertools.chain(lhs_counts.values(), rhs_counts.values())\n- if _any(count > 1 for count in all_counts):\n- # TODO handle repeated indices (trace out first)\n- # TODO handle unique axes (sum out first)\n- raise NotImplementedError\n+ # sum out unique contracted indices in lhs and rhs\n+ lhs_uniques = [name for name in contracted_names\n+ if lhs_counts[name] == 1 and rhs_counts[name] == 0]\n+ lhs, lhs_names = sum_uniques(lhs, lhs_names, lhs_uniques)\n+\n+ rhs_uniques = [name for name in contracted_names\n+ if rhs_counts[name] == 1 and lhs_counts[name] == 0]\n+ rhs, rhs_names = sum_uniques(rhs, rhs_names, rhs_uniques)\n+\n+ # for every repeated index, contract against an identity matrix\n+ lhs, lhs_names = sum_repeats(lhs, lhs_names, lhs_counts,\n+ result_names + rhs_names)\n+ rhs, rhs_names = sum_repeats(rhs, rhs_names, rhs_counts,\n+ result_names + lhs_names)\n+ # contract lhs against rhs\n+ contracted_names = contracted_names & (set(lhs_names) | set(rhs_names))\nbatch_names = set(lhs_names) & set(rhs_names) - contracted_names\nlhs_cont, rhs_cont = unzip2((lhs_names.index(name), rhs_names.index(name))\nfor name in contracted_names)\n@@ -1120,8 +1136,9 @@ def einsum(*operands):\nbatch_dims = tuple(lhs_batch)\nbatch_names = ''.join(lhs_names[i] for i in batch_dims)\n+ # TODO need to flatten lhs_cont / rhs_cont if more than 1d\ndimension_numbers = [(lhs_cont, rhs_cont), (batch_dims, batch_dims)]\n- result = lax.dot_general(lhs, rhs, dimension_numbers)\n+ operand = lax.dot_general(lhs, rhs, dimension_numbers)\ndeleted_names = batch_names + ''.join(contracted_names)\nnames = (batch_names\n+ lhs_names.translate(None, deleted_names)\n@@ -1130,10 +1147,14 @@ def einsum(*operands):\nelse:\nraise NotImplementedError\n+ # the resulting 'operand' with axis labels 'names' should be a permutation\n+ # of the desired result\n+ assert len(names) == len(result_names) == len(set(names))\n+ assert set(names) == set(result_names)\nif names != result_names:\nperm = tuple([names.index(name) for name in result_names])\n- result = lax.transpose(result, perm)\n- operands.append(result) # used in next iteration\n+ operand = lax.transpose(operand, perm)\n+ operands.append(operand) # used in next iteration\nreturn operands[0]\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_einsum_test.py",
"new_path": "tests/lax_numpy_einsum_test.py",
"diff": "@@ -43,6 +43,18 @@ class EinsumTest(jtu.JaxTestCase):\ns = 'ij,j->i'\ncheck(s, x, y)\n+ def test_two_operands_2(self):\n+ x = rng().randn(3, 4, 5)\n+ y = rng().randn(4)\n+ s = 'ijk,j->i'\n+ check(s, x, y)\n+\n+ def test_two_operands_3(self):\n+ x = rng().randn(3, 4, 3)\n+ y = rng().randn(3)\n+ s = 'iji,i->j'\n+ check(s, x, y)\n+\ndef test_one_operand_1(self):\nx = rng().randn(3, 4, 5)\ns = 'ijk->j'\n@@ -79,40 +91,50 @@ class EinsumTest(jtu.JaxTestCase):\ncheck(s, x)\ndef test_one_operand_8(self):\n+ x = rng().randn(3, 3)\n+ s = 'ij->'\n+ check(s, x)\n+\n+ def test_one_operand_9(self):\nx = rng().randn(3, 3, 3)\ns = 'iii->'\ncheck(s, x)\n- def test_one_operand_9(self):\n+ def test_one_operand_10(self):\nx = rng().randn(3, 3)\ns = 'ii->i'\ncheck(s, x)\n- def test_one_operand_10(self):\n+ def test_one_operand_11(self):\nx = rng().randn(3, 3, 4)\ns = 'iij->i'\ncheck(s, x)\n- def test_one_operand_11(self):\n+ def test_one_operand_12(self):\nx = rng().randn(3, 3, 3)\ns = 'iii->i'\ncheck(s, x)\n- def test_one_operand_12(self):\n+ def test_one_operand_13(self):\nx = rng().randn(3, 3, 5, 4, 4)\ns = 'iijkk->i'\ncheck(s, x)\n- def test_one_operand_13(self):\n+ def test_one_operand_14(self):\nx = rng().randn(3, 3, 5, 4, 4)\ns = 'iijkk->ik'\ncheck(s, x)\n- def test_one_operand_14(self):\n+ def test_one_operand_15(self):\nx = rng().randn(3, 3, 5, 4, 4)\ns = 'iijkl->il'\ncheck(s, x)\n+ def test_one_operand_16(self):\n+ x = rng().randn(3, 3)\n+ s = 'ij->ij'\n+ check(s, x)\n+\nif __name__ == '__main__':\nabsltest.main()\n"
}
] | Python | Apache License 2.0 | google/jax | add support for two-operrand cases |
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