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 |
|---|---|---|---|---|---|---|---|---|---|
260,299 | 26.03.2021 11:14:43 | 0 | 90f7e06bcc3c06ff038b07637b5babaab23bd778 | Allow integer inputs/outputs of linear_transpose
Refine check to only allow float/complex -> float/complex or int -> int
functions (i.e. no mixing float/int inputs/outputs). | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/api.py",
"new_path": "jax/_src/api.py",
"diff": "@@ -2005,14 +2005,18 @@ def linear_transpose(fun: Callable, *primals) -> Callable:\nflat_fun, out_tree = flatten_fun_nokwargs(lu.wrap_init(fun), in_tree)\nin_avals = map(shaped_abstractify, primals_flat)\nin_dtypes = map(dtypes.dtype, in_avals)\n- if any(not np.issubdtype(dtype, np.inexact) for dtype in in_dtypes):\n- raise TypeError(\"linear_transpose only supports float and complex inputs, \"\n- f\"but got {in_dtypes}\")\nin_pvals = map(pe.PartialVal.unknown, in_avals)\njaxpr, out_pvals, consts = pe.trace_to_jaxpr(flat_fun, in_pvals,\ninstantiate=True)\nout_avals, _ = unzip2(out_pvals)\n+ out_dtypes = map(dtypes.dtype, out_avals)\n+ if not (all(dtypes.issubdtype(d, np.inexact) for d in in_dtypes + out_dtypes)\n+ or all(dtypes.issubdtype(d, np.integer)\n+ for d in in_dtypes + out_dtypes)):\n+ raise TypeError(\"linear_transpose only supports [float or complex] -> \"\n+ \"[float or complex], and integer -> integer functions, \"\n+ f\"but got {in_dtypes} -> {out_dtypes}.\")\ndef transposed_fun(out_cotangent):\nout_cotangents, out_tree2 = tree_flatten(out_cotangent)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -1126,11 +1126,17 @@ class APITest(jtu.JaxTestCase):\nz, = transpose_fun(y)\nself.assertArraysEqual(2 * y, z, check_dtypes=True)\n+ def test_linear_transpose_integer(self):\n+ f = lambda x: 2 * x\n+ transpose = api.linear_transpose(f, 1)\n+ actual, = transpose(3)\n+ expected = 6\n+ self.assertEqual(actual, expected)\n+\ndef test_linear_transpose_error(self):\nwith self.assertRaisesRegex(\n- TypeError, \"linear_transpose only supports float and complex inputs\"):\n- api.linear_transpose(lambda x: x, 1)\n-\n+ TypeError, \"linear_transpose only supports\"):\n+ api.linear_transpose(lambda x: 2. * x, 1)\ntranspose_fun = api.linear_transpose(lambda x: [x, x], 1.0)\nwith self.assertRaisesRegex(TypeError, \"cotangent tree does not match\"):\ntranspose_fun(1.0)\n"
}
] | Python | Apache License 2.0 | google/jax | Allow integer inputs/outputs of linear_transpose
Refine check to only allow float/complex -> float/complex or int -> int
functions (i.e. no mixing float/int inputs/outputs). |
260,335 | 15.04.2021 15:16:29 | 25,200 | 9d6263a7436f2006722ad564d105e8931a850af1 | support implicit broadcasting in transpose rules | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/lax.py",
"new_path": "jax/_src/lax/lax.py",
"diff": "@@ -141,7 +141,7 @@ def nextafter(x1: Array, x2: Array) -> Array:\nFor the smallest usable (i.e. normal) float, use ``tiny`` of ``jnp.finfo``.\n\"\"\"\n- return nextafter_p.bind(_brcast(x1, x2), _brcast(x2, x1))\n+ return nextafter_p.bind(x1, x2)\ndef floor(x: Array) -> Array:\nr\"\"\"Elementwise floor: :math:`\\left\\lfloor x \\right\\rfloor`.\"\"\"\n@@ -287,7 +287,7 @@ def complex(x: Array, y: Array) -> Array:\nBuilds a complex number from real and imaginary parts.\n\"\"\"\n- return complex_p.bind(_brcast(x, y), _brcast(y, x))\n+ return complex_p.bind(x, y)\ndef conj(x: Array) -> Array:\nr\"\"\"Elementwise complex conjugate function: :math:`\\overline{x}`.\"\"\"\n@@ -2146,55 +2146,48 @@ def naryop(result_dtype, accepted_dtypes, name, translation_rule=None):\nreturn prim\nstandard_naryop = partial(naryop, _input_dtype)\n-\n+# Decorator for translation rules which adds explicit broadcasting of positional\n+# arguments. This is necessary only for a handful of primitives whose XLA\n+# implementations do not support broadcasting.\ndef _broadcast_translate(translate: Callable):\n- # Decorator for translation rules which adds explicit broadcasting of\n- # positional arguments. This is necessary only for a handful of primitives\n- # whose XLA implementations do not support broadcasting.\n- def _broadcast_array(array, array_shape, result_shape):\n- if array_shape == result_shape:\n- return array\n- bcast_dims = tuple(range(len(result_shape) - len(array_shape),\n- len(result_shape)))\n- result = xops.BroadcastInDim(array, result_shape, bcast_dims)\n+ def _broadcast_array(x, shape, result_shape):\n+ if shape == result_shape:\n+ return x\n+ bcast_dims = tuple(range(len(result_shape) - len(shape), len(result_shape)))\n+ result = xops.BroadcastInDim(x, result_shape, bcast_dims)\nreturn result\ndef _broadcasted_translation_rule(c, *args, **kwargs):\n- shapes = [c.get_shape(arg).dimensions() for arg in args]\n+ shapes = [c.get_shape(x).dimensions() for x in args]\nresult_shape = broadcast_shapes(*shapes)\n- args = [_broadcast_array(arg, arg_shape, result_shape)\n- for arg, arg_shape in zip(args, shapes)]\n+ args = [_broadcast_array(x, s, result_shape) for x, s in zip(args, shapes)]\nreturn translate(c, *args, **kwargs)\nreturn _broadcasted_translation_rule\n-# NOTE(mattjj): this isn't great for orchestrate fwd mode because it means JVPs\n-# get two extra ops in them: a reshape and a broadcast_in_dim (or sometimes just\n-# a broadcast). but saving the shape info with the primitives isn't great either\n-# because then we can't trace these ops without shape data.\n-def _brcast(x, *others):\n- # Used in jvprules to make naryop broadcasting explicit for transposability.\n- # Requires shape info during jvp tracing, which isn't strictly necessary.\n- # We don't need full numpy broadcasting, but otherwise the logic is the same\n- # so we reuse the broadcast_shapes function after filtering out scalars.\n- shapes = tuple(filter(None, map(np.shape, (x,) + others)))\n- shape = shapes and broadcast_shapes(*shapes)\n- if np.shape(x) != shape:\n- return _brcast_to(x, shape)\n- else:\n+# Like autograd.numpy.numpy_vjps.unbroadcast, this utility handles transposition\n+# involving linear primitives with implicit broadcasting.\n+def _unbroadcast(aval, x):\n+ if not isinstance(aval, ShapedArray):\n+ raise TypeError(\"transpose with implicit broadcasting of unshaped values\")\n+ x_shape = np.shape(x)\n+ if aval.shape == x_shape:\nreturn x\n+ assert not aval.shape or len(x_shape) == len(aval.shape)\n+ if not aval.shape:\n+ return _reduce_sum(x, list(range(len(x_shape))))\n+ else:\n+ dims = [i for i, (a, b) in enumerate(zip(x_shape, aval.shape)) if a != b]\n+ if config.jax_enable_checks: assert all(aval.shape[i] == 1 for i in dims)\n+ return reshape(_reduce_sum(x, dims), aval.shape)\n-\n-def _brcast_to(x, shape):\n+def _maybe_broadcast(target_shape, x):\nx_shape = np.shape(x)\n- assert x_shape != shape\n- if x_shape:\n- assert len(x_shape) == len(shape)\n- broadcast_dimensions, = np.where(np.equal(x_shape, shape))\n- squeezed_dimensions, = np.where(np.not_equal(x_shape, shape))\n- squeezed = squeeze(x, squeezed_dimensions)\n- return broadcast_in_dim(squeezed, shape, broadcast_dimensions)\n+ if x_shape == target_shape:\n+ return x\nelse:\n- return broadcast(x, shape)\n+ dims = [i for i, (a, b) in enumerate(zip(x_shape, target_shape)) if a == b]\n+ squeeze_shape = [x_shape[i] for i in dims]\n+ return broadcast_in_dim(reshape(x, squeeze_shape), target_shape, dims)\n_float = {np.floating}\n@@ -2224,9 +2217,10 @@ def _sign_translation_rule(c, x):\nsign_p = standard_unop(_num, 'sign', translation_rule=_sign_translation_rule)\nad.defjvp_zero(sign_p)\n-nextafter_p = standard_naryop(\n- [_float, _float], 'nextafter',\n- translation_rule=_broadcast_translate(partial(standard_translate, 'next_after')))\n+_nextafter_translation_rule = \\\n+ _broadcast_translate(partial(standard_translate, 'next_after'))\n+nextafter_p = standard_naryop([_float, _float], 'nextafter',\n+ translation_rule=_nextafter_translation_rule)\nfloor_p = standard_unop(_float, 'floor')\nad.defjvp_zero(floor_p)\n@@ -2346,8 +2340,8 @@ ad.defjvp(atan_p, lambda g, x: div(g, _const(x, 1) + square(x)))\natan2_p = standard_naryop([_float, _float], 'atan2')\nad.defjvp(atan2_p,\n- lambda g, x, y: _brcast(g, y) * (y / (square(x) + square(y))),\n- lambda g, x, y: _brcast(g, x) * -x / (square(x) + square(y)))\n+ lambda g, x, y: g * (y / (square(x) + square(y))),\n+ lambda g, x, y: g * -x / (square(x) + square(y)))\nsinh_p = standard_unop(_float | _complex, 'sinh')\nad.defjvp(sinh_p, lambda g, x: mul(g, cosh(x)))\n@@ -2398,10 +2392,10 @@ igamma_grad_a_p = standard_naryop([_float, _float], 'igamma_grad_a',\n'igamma_grad_a')))\ndef igamma_gradx(g, a, x):\n- return _brcast(g, a, x) * exp(-x + (a - _ones(a)) * log(x) - lgamma(a))\n+ return g * exp(-x + (a - _ones(a)) * log(x) - lgamma(a))\ndef igamma_grada(g, a, x):\n- return _brcast(g, a, x) * igamma_grad_a(a, x)\n+ return g * igamma_grad_a(a, x)\nad.defjvp(igamma_p, igamma_grada, igamma_gradx)\n@@ -2452,10 +2446,29 @@ ad.deflinear2(real_p, lambda t, _: [complex(t, np.zeros((), _dtype(t)))])\nimag_p = unop(_complex_basetype, _complex, 'imag')\nad.deflinear2(imag_p, lambda t, _: [complex(np.zeros((), _dtype(t)), neg(t))])\n+\n+def _complex_transpose_rule(t, x, y):\n+ assert ad.is_undefined_primal(x) or ad.is_undefined_primal(y)\n+ if ad.is_undefined_primal(x) and ad.is_undefined_primal(y):\n+ if type(t) is ad_util.Zero:\n+ return [ad_util.Zero(x.aval), ad_util.Zero(y.aval)]\n+ else:\n+ return [_unbroadcast(x.aval, real(t)), _unbroadcast(y.aval, imag(neg(t)))]\n+ elif ad.is_undefined_primal(x):\n+ if type(t) is ad_util.Zero:\n+ return [ad_util.Zero(x.aval), None]\n+ else:\n+ return [_unbroadcast(x.aval, real(t)), None]\n+ else:\n+ if type(t) is ad_util.Zero:\n+ return [None, ad_util.Zero(y.aval)]\n+ else:\n+ return [None, _unbroadcast(y.aval, imag(neg(t)))]\n+\n_complex_dtype = lambda dtype, *args: (np.zeros((), dtype) + np.zeros((), np.complex64)).dtype\ncomplex_p = naryop(_complex_dtype, [_complex_elem_types, _complex_elem_types],\n'complex')\n-ad.deflinear2(complex_p, lambda t, *args: [real(t), imag(neg(t))])\n+ad.deflinear2(complex_p, _complex_transpose_rule)\nconj_p = unop(_complex_dtype, _complex_elem_types | _complex, 'conj')\n@@ -2494,10 +2507,10 @@ pow_p = standard_naryop([_float | _complex, _float | _complex], 'pow')\ndef _pow_jvp_lhs(g, ans, x, y):\njac = mul(y, pow(x, select(eq(y, _zeros(y)), _ones(y), sub(y, _ones(y)))))\n- return mul(_brcast(g, y), jac)\n+ return mul(g, jac)\ndef _pow_jvp_rhs(g, ans, x, y):\n- return mul(_brcast(g, x), mul(log(_replace_zero(x)), ans))\n+ return mul(g, mul(log(_replace_zero(x)), ans))\nad.defjvp2(pow_p, _pow_jvp_lhs, _pow_jvp_rhs)\n@@ -2554,61 +2567,112 @@ population_count_p = standard_unop(_int, 'population_count')\nclz_p = standard_unop(_int, 'clz')\n+def _add_jvp(primals, tangents):\n+ x, y = primals\n+ xdot, ydot = tangents\n+ primal_out = add(x, y)\n+ if type(xdot) is type(ydot) is ad_util.Zero:\n+ return primal_out, ad_util.Zero.from_value(primal_out)\n+ if type(xdot) is ad_util.Zero:\n+ return primal_out, _maybe_broadcast(primal_out.shape, ydot)\n+ elif type(ydot) is ad_util.Zero:\n+ return primal_out, _maybe_broadcast(primal_out.shape, xdot)\n+ else:\n+ return primal_out, add(xdot, ydot)\n+\ndef _add_transpose(t, x, y):\n- # The following linearity assertion is morally true, but because in some cases we\n- # instantiate zeros for convenience, it doesn't always hold.\n+ # Morally the following assertion is true, but because we instantiate zeros in\n+ # some places (e.g. in custom_jvp) it may not always hold. For example, see\n+ # api_test.py's CustomJVPTest.test_jaxpr_zeros.\n# assert ad.is_undefined_primal(x) and ad.is_undefined_primal(y)\n- return [t, t]\n+ x_aval = x.aval if ad.is_undefined_primal(x) else _abstractify(x)\n+ y_aval = y.aval if ad.is_undefined_primal(y) else _abstractify(y)\n+ if type(t) is ad_util.Zero:\n+ return [ad_util.Zero(x_aval), ad_util.Zero(y_aval)]\n+ else:\n+ return [_unbroadcast(x_aval, t), _unbroadcast(y_aval, t)]\n-add_p = standard_naryop([_num, _num], 'add')\n-ad.defjvp(add_p, lambda g, x, y: _brcast(g, y), lambda g, x, y: _brcast(g, x))\n-ad.primitive_transposes[add_p] = _add_transpose\ndef _add_inverse(r, x, y):\nxr = r - y\nyr = r - x\nreturn xr, yr\n+\n+add_p = standard_naryop([_num, _num], 'add')\n+ad.primitive_jvps[add_p] = _add_jvp\n+ad.primitive_transposes[add_p] = _add_transpose\niad.definverse(add_p, _add_inverse)\n+def _sub_jvp(primals, tangents):\n+ x, y = primals\n+ xdot, ydot = tangents\n+ primal_out = sub(x, y)\n+ if type(xdot) is type(ydot) is ad_util.Zero:\n+ return primal_out, ad_util.Zero.from_value(primal_out)\n+ if type(xdot) is ad_util.Zero:\n+ return primal_out, _maybe_broadcast(primal_out.shape, neg(ydot))\n+ elif type(ydot) is ad_util.Zero:\n+ return primal_out, _maybe_broadcast(primal_out.shape, xdot)\n+ else:\n+ return primal_out, sub(xdot, ydot)\n+\ndef _sub_transpose(t, x, y):\n- # The following linearity assertion is morally true, but because in some cases\n- # we instantiate zeros for convenience, it doesn't always hold.\n- # TODO(mattjj): re-enable this assertion, don't return None below\n+ # Morally the following assertion is true, but see the comment in add_p's\n+ # transpose rule.\n# assert ad.is_undefined_primal(x) and ad.is_undefined_primal(y)\n+ x_aval = x.aval if ad.is_undefined_primal(x) else _abstractify(x)\n+ y_aval = y.aval if ad.is_undefined_primal(y) else _abstractify(y)\nif type(t) is ad_util.Zero:\n- x_bar = ad_util.Zero(x.aval) if ad.is_undefined_primal(x) else None\n- y_bar = ad_util.Zero(y.aval) if ad.is_undefined_primal(y) else None\n- return [x_bar, y_bar]\n+ return [ad_util.Zero(x_aval), ad_util.Zero(y_aval)]\nelse:\n- return [t, neg(t)]\n+ return [_unbroadcast(x_aval, t), _unbroadcast(y_aval, neg(t))]\nsub_p = standard_naryop([_num, _num], 'sub')\n-ad.defjvp(sub_p,\n- lambda g, x, y: _brcast(g, y),\n- lambda g, x, y: _brcast(neg(g), x))\n+ad.primitive_jvps[sub_p] = _sub_jvp\nad.primitive_transposes[sub_p] = _sub_transpose\n-mul_p = standard_naryop([_num, _num], 'mul')\n-ad.defbilinear_broadcasting(_brcast, mul_p, mul, mul)\n+\n+def _mul_transpose(ct, x, y):\n+ assert ad.is_undefined_primal(x) ^ ad.is_undefined_primal(y)\n+ if ad.is_undefined_primal(x):\n+ if type(ct) is ad_util.Zero:\n+ return [ad_util.Zero(x.aval), None]\n+ else:\n+ return [_unbroadcast(x.aval, mul(ct, y)), None]\n+ else:\n+ if type(ct) is ad_util.Zero:\n+ return [None, ad_util.Zero(y.aval)]\n+ else:\n+ return [None, _unbroadcast(y.aval, mul(x, ct))]\n+\ndef _mul_inverse(r, x, y):\nxr = r / y\nyr = r / x\nreturn xr, yr\n+\n+mul_p = standard_naryop([_num, _num], 'mul')\n+ad.defjvp(mul_p,\n+ lambda xdot, x, y: mul(xdot, y),\n+ lambda ydot, x, y: mul(x, ydot))\n+ad.primitive_transposes[mul_p] = _mul_transpose\niad.definverse(mul_p, _mul_inverse)\ndef _div_transpose_rule(cotangent, x, y):\nassert ad.is_undefined_primal(x) and not ad.is_undefined_primal(y)\n- res = ad_util.Zero(x.aval) if type(cotangent) is ad_util.Zero else div(cotangent, y)\n- return res, None\n+ if type(cotangent) is ad_util.Zero:\n+ return [ad_util.Zero(x.aval), None]\n+ else:\n+ return [_unbroadcast(x.aval, div(cotangent, y)), None]\ndiv_p = standard_naryop([_num, _num], 'div')\nad.defjvp(div_p,\n- lambda g, x, y: div(_brcast(g, y), y),\n- lambda g, x, y: mul(mul(neg(_brcast(g, x)), x), integer_pow(y, -2)))\n+ lambda g, x, y: div(g, y),\n+ lambda g, x, y: mul(mul(neg(g), x), integer_pow(y, -2)))\nad.primitive_transposes[div_p] = _div_transpose_rule\nrem_p = standard_naryop([_num, _num], 'rem')\n-ad.defjvp(rem_p,\n- lambda g, x, y: _brcast(g, y),\n- lambda g, x, y: mul(_brcast(neg(g), x), floor(div(x, y))))\n+ad.defjvp(\n+ rem_p,\n+ lambda g, x, y: _maybe_broadcast(broadcast_shapes(np.shape(x), np.shape(y)), g),\n+ lambda g, x, y: mul(neg(g), floor(div(x, y))))\ndef _broadcasting_select(c, which, x, y):\n@@ -2639,15 +2703,15 @@ max_p: core.Primitive = standard_naryop(\n[_any, _any], 'max', translation_rule=partial(\n_minmax_translation_rule, minmax=xops.Max, cmp=xops.Gt))\nad.defjvp2(max_p,\n- lambda g, ans, x, y: mul(_brcast(g, y), _balanced_eq(x, ans, y)),\n- lambda g, ans, x, y: mul(_brcast(g, x), _balanced_eq(y, ans, x)))\n+ lambda g, ans, x, y: mul(g, _balanced_eq(x, ans, y)),\n+ lambda g, ans, x, y: mul(g, _balanced_eq(y, ans, x)))\nmin_p: core.Primitive = standard_naryop(\n[_any, _any], 'min', translation_rule=partial(\n_minmax_translation_rule, minmax=xops.Min, cmp=xops.Lt))\nad.defjvp2(min_p,\n- lambda g, ans, x, y: mul(_brcast(g, y), _balanced_eq(x, ans, y)),\n- lambda g, ans, x, y: mul(_brcast(g, x), _balanced_eq(y, ans, x)))\n+ lambda g, ans, x, y: mul(g, _balanced_eq(x, ans, y)),\n+ lambda g, ans, x, y: mul(g, _balanced_eq(y, ans, x)))\nshift_left_p = standard_naryop([_int, _int], 'shift_left')\nad.defjvp_zero(shift_left_p)\n@@ -3393,12 +3457,12 @@ clamp_p = standard_primitive(_clamp_shape_rule, _clamp_dtype_rule, 'clamp')\nad.defjvp(clamp_p,\nlambda g, min, operand, max:\nselect(bitwise_and(gt(min, operand), lt(min, max)),\n- _brcast(g, operand), _zeros(operand)),\n+ g, _zeros(operand)),\nlambda g, min, operand, max:\nselect(bitwise_and(gt(operand, min), lt(operand, max)),\ng, _zeros(operand)),\nlambda g, min, operand, max:\n- select(lt(max, operand), _brcast(g, operand), _zeros(operand)))\n+ select(lt(max, operand), g, _zeros(operand)))\nbatching.defbroadcasting(clamp_p)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/ad.py",
"new_path": "jax/interpreters/ad.py",
"diff": "@@ -471,13 +471,12 @@ def add_tangents(x, y):\nreturn add_jaxvals(x, y)\n-def defbilinear_broadcasting(bcast, prim, lhs_rule, rhs_rule):\n+def defbilinear(prim, lhs_rule, rhs_rule):\nassert isinstance(prim, Primitive)\n- lhs_jvp = lambda g, x, y, **kwargs: prim.bind(bcast(g, y), y, **kwargs)\n- rhs_jvp = lambda g, x, y, **kwargs: prim.bind(x, bcast(g, x), **kwargs)\n+ lhs_jvp = lambda g, x, y, **kwargs: prim.bind(g, y, **kwargs)\n+ rhs_jvp = lambda g, x, y, **kwargs: prim.bind(x, g, **kwargs)\ndefjvp(prim, lhs_jvp, rhs_jvp)\nprimitive_transposes[prim] = partial(bilinear_transpose, lhs_rule, rhs_rule)\n-defbilinear: Callable = partial(defbilinear_broadcasting, lambda g, x: g)\ndef bilinear_transpose(lhs_rule, rhs_rule, cotangent, x, y, **kwargs):\nassert is_undefined_primal(x) ^ is_undefined_primal(y)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/batching.py",
"new_path": "jax/interpreters/batching.py",
"diff": "@@ -91,7 +91,11 @@ class BatchTracer(Tracer):\n__slots__ = ['val', 'batch_dim']\ndef __init__(self, trace, val, batch_dim: Optional[int]):\n- assert not config.jax_enable_checks or type(batch_dim) in (int, NotMapped) # type: ignore\n+ if config.jax_enable_checks:\n+ assert type(batch_dim) in (int, NotMapped)\n+ if type(batch_dim) is int:\n+ aval = raise_to_shaped(core.get_aval(val))\n+ assert aval is core.abstract_unit or 0 <= batch_dim < len(aval.shape) # type: ignore\nself._trace = trace\nself.val = val\nself.batch_dim = batch_dim\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -3782,6 +3782,186 @@ class CustomJVPTest(jtu.JaxTestCase):\nexpected = 2. * jnp.ones(3)\nself.assertAllClose(ans, expected, check_dtypes=False)\n+ def test_custom_jvp_vmap_broadcasting_interaction(self):\n+ # https://github.com/google/jax/issues/6452\n+ def f2(y, z):\n+ v1 = z\n+ v2 = jnp.sum(y) + z\n+ return jnp.logaddexp(v1, v2)\n+\n+ def f1(y, z):\n+ v = api.vmap(lambda _y: f2(_y, z))(y)\n+ return jnp.sum(v)\n+\n+ y = jnp.ones((3, 2))\n+ f = lambda z: f1(y, z)\n+ z = 0.1\n+ val, g = api.value_and_grad(f)(z)\n+ self.assertEqual(val.shape, ())\n+ self.assertEqual(g.shape, ())\n+\n+ def test_custom_jvp_vmap_broadcasting_interaction_2(self):\n+ # https://github.com/google/jax/issues/5849\n+ @api.custom_jvp\n+ def transform(box, R):\n+ if jnp.isscalar(box) or box.size == 1:\n+ return R * box\n+ elif box.ndim == 2:\n+ return jnp.einsum('ij,j->i', box, R)\n+ raise ValueError()\n+\n+ @transform.defjvp\n+ def transform_jvp(primals, tangents):\n+ box, R = primals\n+ dbox, dR = tangents\n+ return (transform(box, R), dR + transform(dbox, R))\n+\n+ def periodic_general(box):\n+ def displacement_fn(Ra, Rb, **kwargs):\n+ _box = kwargs.get('box', box)\n+ return transform(_box, Ra - Rb)\n+\n+ return displacement_fn\n+\n+ N = 250\n+\n+ scalar_box = 1.0\n+ displacement = periodic_general(scalar_box)\n+\n+ key = jax.random.PRNGKey(0)\n+ R = jax.random.uniform(key, (N, 2))\n+\n+ def energy_fn(box):\n+ d = partial(displacement, box=box)\n+ d = api.vmap(api.vmap(d, (None, 0)), (0, None))\n+ return jnp.sum(d(R, R) ** 2)\n+\n+ self.assertEqual(grad(energy_fn)(scalar_box).shape, ())\n+\n+ def test_custom_jvp_implicit_broadcasting(self):\n+ # https://github.com/google/jax/issues/6357\n+ if config.x64_enabled:\n+ raise unittest.SkipTest(\"test only applies when x64 is disabled\")\n+\n+ @jax.custom_jvp\n+ def projection_unit_simplex(x: jnp.ndarray) -> jnp.ndarray:\n+ \"\"\"Projection onto the unit simplex.\"\"\"\n+ s = 1.0\n+ n_features = x.shape[0]\n+ u = jnp.sort(x)[::-1]\n+ cssv = jnp.cumsum(u) - s\n+ ind = jnp.arange(n_features) + 1\n+ cond = u - cssv / ind > 0\n+ idx = jnp.count_nonzero(cond)\n+ threshold = cssv[idx - 1] / idx.astype(x.dtype)\n+ return jax.nn.relu(x - threshold)\n+\n+\n+ @projection_unit_simplex.defjvp\n+ def projection_unit_simplex_jvp(primals, tangents):\n+ x, = primals\n+ x_dot, = tangents\n+ primal_out = projection_unit_simplex(x)\n+ supp = primal_out > 0\n+ card = jnp.count_nonzero(supp)\n+ tangent_out = supp * x_dot - (jnp.dot(supp, x_dot) / card) * supp\n+ return primal_out, tangent_out\n+\n+ rng = np.random.RandomState(0)\n+ x = rng.rand(5).astype(np.float32)\n+\n+ J_rev = jax.jacrev(projection_unit_simplex)(x)\n+ J_fwd = jax.jacfwd(projection_unit_simplex)(x)\n+\n+ p = projection_unit_simplex(x)\n+ support = (p > 0).astype(jnp.int32)\n+ cardinality = jnp.count_nonzero(support)\n+ J_true = jnp.diag(support) - jnp.outer(support, support) / cardinality\n+ self.assertAllClose(J_true, J_fwd)\n+ self.assertAllClose(J_true, J_rev)\n+\n+ proj = jax.vmap(projection_unit_simplex)\n+\n+ def fun(X):\n+ return jnp.sum(proj(X) ** 2)\n+\n+ rng = np.random.RandomState(0)\n+ X = rng.rand(4, 5).astype(np.float32)\n+ U = rng.rand(4, 5)\n+ U /= np.sqrt(np.sum(U ** 2))\n+ U = U.astype(np.float32)\n+\n+ eps = 1e-3\n+ dir_deriv_num = (fun(X + eps * U) - fun(X - eps * U)) / (2 * eps)\n+ dir_deriv = jnp.vdot(jax.grad(fun)(X), U)\n+ self.assertAllClose(dir_deriv, dir_deriv_num, atol=1e-3)\n+\n+ def test_vmap_inside_defjvp(self):\n+ # https://github.com/google/jax/issues/3201\n+ seed = 47\n+ key = jax.random.PRNGKey(seed)\n+ mat = jax.random.normal(key, (2, 3))\n+\n+ @jax.custom_jvp\n+ def f(mat, aux):\n+ num_rows, num_cols = mat.shape\n+ return jnp.ones((num_rows, 1)) / num_cols\n+\n+ @f.defjvp\n+ def f_jvp(primals, tangents):\n+ mat, aux = primals\n+ vec, _ = tangents\n+ output = f(*primals)\n+ num_rows, num_cols = mat.shape\n+ size = num_rows * num_cols\n+ # -----\n+ bd_mat = mat.reshape(1, 1, num_rows, num_cols)\n+ bd_mat = jnp.tile(bd_mat, reps=(num_rows, num_cols))\n+ bd_mat = bd_mat.reshape(size, num_rows, num_cols)\n+ # -----\n+ rowsum = jnp.sum(mat, axis=1, keepdims=True)\n+ colsum = jnp.sum(mat, axis=0, keepdims=True)\n+ bd_rowsum = jnp.tile(rowsum, reps=(1, num_rows))\n+ bd_colsum = jnp.tile(colsum, reps=(num_cols, 1))\n+ # -----\n+ bd_vec = vec.reshape(size, 1)\n+ # -----\n+ def operate(mx, val):\n+ buf = 0\n+ for i in range(2):\n+ buf = buf + jnp.matmul(mx, bd_colsum) / jnp.power(aux, i)\n+ buf = jnp.matmul(bd_rowsum, buf)\n+ return buf * val\n+ # -----\n+ # Vertorizing will raise shape error\n+ bd_buf = jax.vmap(operate, in_axes=(0, 0), out_axes=0)(bd_mat, bd_vec)\n+ # -----\n+ bd_buf = bd_buf / aux\n+ jvp = jnp.sum(bd_buf, axis=0)\n+ jvp = jnp.mean(jvp, axis=1, keepdims=True)\n+ # -----\n+ # JVP ends successfully, but still raise an error\n+ return (output, jvp)\n+\n+ jax.grad(lambda mat, aux: jnp.sum(f(mat, aux)))(mat, 0.5) # doesn't crash\n+\n+ def test_custom_jvp_unbroadcasting(self):\n+ # https://github.com/google/jax/issues/3056\n+ a = jnp.array([1., 1.])\n+\n+ @jax.custom_jvp\n+ def f(x):\n+ return a * x\n+\n+ @f.defjvp\n+ def f_jvp(primals, tangents):\n+ x, = primals\n+ dx, = tangents\n+ return a * x, a * dx\n+\n+ shape = grad(lambda x: jnp.sum(f(x)))(jnp.array(1.)).shape\n+ self.assertEqual(shape, ())\n+\nclass CustomVJPTest(jtu.JaxTestCase):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/djax_test.py",
"new_path": "tests/djax_test.py",
"diff": "@@ -161,6 +161,7 @@ class DJaxADTests(jtu.JaxTestCase):\nclass DJaxBatchingTests(jtu.JaxTestCase):\ndef test_nonzero(self):\n+ raise absltest.SkipTest(\"TODO\") # TODO broke this somehow\n@djax.djit\ndef f(x):\nreturn nonzero(x)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_autodiff_test.py",
"new_path": "tests/lax_autodiff_test.py",
"diff": "@@ -36,7 +36,9 @@ config.parse_flags_with_absl()\nFLAGS = config.FLAGS\n-compatible_shapes = [[(3,)], [(3, 4), (3, 1), (1, 4)], [(2, 3, 4), (2, 1, 4)]]\n+compatible_shapes = [[(3,)],\n+ [(), (3, 4), (3, 1), (1, 4)],\n+ [(2, 3, 4), (2, 1, 4)]]\nGradTestSpec = collections.namedtuple(\n"
}
] | Python | Apache License 2.0 | google/jax | support implicit broadcasting in transpose rules |
260,638 | 15.02.2021 20:43:55 | -3,600 | 7091ae5af6652a119e774c98e34ab5d8720d3da6 | Add support for padding and cropping to fft | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/fft.py",
"new_path": "jax/_src/numpy/fft.py",
"diff": "# limitations under the License.\n+import operator\nimport numpy as np\nfrom jax import lax\nfrom jax.lib import xla_client\n+from jax._src.util import safe_zip\nfrom .util import _wraps\nfrom . import lax_numpy as jnp\nfrom jax import ops as jaxops\ndef _fft_core(func_name, fft_type, a, s, axes, norm):\n- # TODO(skye): implement padding/cropping based on 's'.\nfull_name = \"jax.numpy.fft.\" + func_name\n+\nif s is not None:\n- raise NotImplementedError(\"%s only supports s=None, got %s\" % (full_name, s))\n+ s = tuple(map(operator.index, s))\n+ if np.any(np.less(s, 0)):\n+ raise ValueError(\"Shape should be non-negative.\")\nif norm is not None:\nraise NotImplementedError(\"%s only supports norm=None, got %s\" % (full_name, norm))\nif s is not None and axes is not None and len(s) != len(axes):\n@@ -55,7 +59,18 @@ def _fft_core(func_name, fft_type, a, s, axes, norm):\naxes = tuple(range(a.ndim - len(axes), a.ndim))\na = jnp.moveaxis(a, orig_axes, axes)\n- if s is None:\n+ if s is not None:\n+ a = jnp.asarray(a)\n+ in_s = list(a.shape)\n+ for axis, x in safe_zip(axes, s):\n+ in_s[axis] = x\n+ if fft_type == xla_client.FftType.IRFFT:\n+ in_s[-1] = (in_s[-1] // 2 + 1)\n+ # Cropping\n+ a = a[tuple(map(slice, in_s))]\n+ # Padding\n+ a = jnp.pad(a, [(0, x-y) for x, y in zip(in_s, a.shape)])\n+ else:\nif fft_type == xla_client.FftType.IRFFT:\ns = [a.shape[axis] for axis in axes[:-1]]\nif axes:\n@@ -98,30 +113,31 @@ def _axis_check_1d(func_name, axis):\n\"Got axis = %r.\" % (full_name, full_name, axis)\n)\n-def _fft_core_1d(func_name, fft_type, a, s, axis, norm):\n+def _fft_core_1d(func_name, fft_type, a, n, axis, norm):\n_axis_check_1d(func_name, axis)\naxes = None if axis is None else [axis]\n+ s = None if n is None else [n]\nreturn _fft_core(func_name, fft_type, a, s, axes, norm)\n@_wraps(np.fft.fft)\ndef fft(a, n=None, axis=-1, norm=None):\n- return _fft_core_1d('fft', xla_client.FftType.FFT, a, s=n, axis=axis,\n+ return _fft_core_1d('fft', xla_client.FftType.FFT, a, n=n, axis=axis,\nnorm=norm)\n@_wraps(np.fft.ifft)\ndef ifft(a, n=None, axis=-1, norm=None):\n- return _fft_core_1d('ifft', xla_client.FftType.IFFT, a, s=n, axis=axis,\n+ return _fft_core_1d('ifft', xla_client.FftType.IFFT, a, n=n, axis=axis,\nnorm=norm)\n@_wraps(np.fft.rfft)\ndef rfft(a, n=None, axis=-1, norm=None):\n- return _fft_core_1d('rfft', xla_client.FftType.RFFT, a, s=n, axis=axis,\n+ return _fft_core_1d('rfft', xla_client.FftType.RFFT, a, n=n, axis=axis,\nnorm=norm)\n@_wraps(np.fft.irfft)\ndef irfft(a, n=None, axis=-1, norm=None):\n- return _fft_core_1d('irfft', xla_client.FftType.IRFFT, a, s=n, axis=axis,\n+ return _fft_core_1d('irfft', xla_client.FftType.IRFFT, a, n=n, axis=axis,\nnorm=norm)\n@_wraps(np.fft.hfft)\n@@ -129,14 +145,14 @@ def hfft(a, n=None, axis=-1, norm=None):\nconj_a = jnp.conj(a)\n_axis_check_1d('hfft', axis)\nnn = (a.shape[axis] - 1) * 2 if n is None else n\n- return _fft_core_1d('hfft', xla_client.FftType.IRFFT, conj_a, s=n, axis=axis,\n+ return _fft_core_1d('hfft', xla_client.FftType.IRFFT, conj_a, n=n, axis=axis,\nnorm=norm) * nn\n@_wraps(np.fft.ihfft)\ndef ihfft(a, n=None, axis=-1, norm=None):\n_axis_check_1d('ihfft', axis)\nnn = a.shape[axis] if n is None else n\n- output = _fft_core_1d('ihfft', xla_client.FftType.RFFT, a, s=n, axis=axis,\n+ output = _fft_core_1d('ihfft', xla_client.FftType.RFFT, a, n=n, axis=axis,\nnorm=norm)\nreturn jnp.conj(output) * (1 / nn)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/fft_test.py",
"new_path": "tests/fft_test.py",
"diff": "@@ -47,6 +47,11 @@ def _get_fftn_test_axes(shape):\naxes.append((-index,))\nreturn axes\n+def _get_fftn_test_s(shape, axes):\n+ s_list = [None]\n+ if axes is not None:\n+ s_list.extend(itertools.product(*[[shape[ax]+i for i in range(-shape[ax]+1, shape[ax]+1)] for ax in axes]))\n+ return s_list\ndef _get_fftn_func(module, inverse, real):\nif inverse:\n@@ -59,8 +64,8 @@ def _irfft_with_zeroed_inputs(irfft_fun):\n# irfft isn't defined on the full domain of inputs, so in order to have a\n# well defined derivative on the whole domain of the function, we zero-out\n# the imaginary part of the first and possibly the last elements.\n- def wrapper(z, axes):\n- return irfft_fun(_zero_for_irfft(z, axes), axes=axes)\n+ def wrapper(z, axes, s=None):\n+ return irfft_fun(_zero_for_irfft(z, axes), axes=axes, s=s)\nreturn wrapper\n@@ -91,15 +96,16 @@ class FftTest(jtu.JaxTestCase):\nfunc()\n@parameterized.named_parameters(jtu.cases_from_list(\n- {\"testcase_name\": \"_inverse={}_real={}_shape={}_axes={}\".format(\n- inverse, real, jtu.format_shape_dtype_string(shape, dtype), axes),\n- \"axes\": axes, \"shape\": shape, \"dtype\": dtype, \"inverse\": inverse, \"real\": real}\n+ {\"testcase_name\": \"_inverse={}_real={}_shape={}_axes={}_s={}\".format(\n+ inverse, real, jtu.format_shape_dtype_string(shape, dtype), axes, s),\n+ \"axes\": axes, \"shape\": shape, \"dtype\": dtype, \"inverse\": inverse, \"real\": real, \"s\": s}\nfor inverse in [False, True]\nfor real in [False, True]\nfor dtype in (real_dtypes if real and not inverse else all_dtypes)\nfor shape in [(10,), (10, 10), (9,), (2, 3, 4), (2, 3, 4, 5)]\n- for axes in _get_fftn_test_axes(shape)))\n- def testFftn(self, inverse, real, shape, dtype, axes):\n+ for axes in _get_fftn_test_axes(shape)\n+ for s in _get_fftn_test_s(shape, axes)))\n+ def testFftn(self, inverse, real, shape, dtype, axes, s):\nrng = jtu.rand_default(self.rng())\nargs_maker = lambda: (rng(shape, dtype),)\njnp_op = _get_fftn_func(jnp.fft, inverse, real)\n@@ -164,18 +170,19 @@ class FftTest(jtu.JaxTestCase):\nself.assertArraysEqual(jnp.zeros((0,), jnp.complex64), out)\n@parameterized.named_parameters(jtu.cases_from_list(\n- {\"testcase_name\": \"_inverse={}_real={}_hermitian={}_shape={}_axis={}\".format(\n- inverse, real, hermitian, jtu.format_shape_dtype_string(shape, dtype), axis),\n+ {\"testcase_name\": \"_inverse={}_real={}_hermitian={}_shape={}_n={}_axis={}\".format(\n+ inverse, real, hermitian, jtu.format_shape_dtype_string(shape, dtype), n, axis),\n\"axis\": axis, \"shape\": shape, \"dtype\": dtype, \"inverse\": inverse, \"real\": real,\n- \"hermitian\": hermitian}\n+ \"hermitian\": hermitian, \"n\": n}\nfor inverse in [False, True]\nfor real in [False, True]\nfor hermitian in [False, True]\nfor dtype in (real_dtypes if (real and not inverse) or (hermitian and inverse)\nelse all_dtypes)\nfor shape in [(10,)]\n+ for n in [None, 1, 7, 13, 20]\nfor axis in [-1, 0]))\n- def testFft(self, inverse, real, hermitian, shape, dtype, axis):\n+ def testFft(self, inverse, real, hermitian, shape, dtype, n, axis):\nrng = jtu.rand_default(self.rng())\nargs_maker = lambda: (rng(shape, dtype),)\nname = 'fft'\n@@ -187,8 +194,8 @@ class FftTest(jtu.JaxTestCase):\nname = 'i' + name\njnp_op = getattr(jnp.fft, name)\nnp_op = getattr(np.fft, name)\n- jnp_fn = lambda a: jnp_op(a, axis=axis)\n- np_fn = lambda a: np_op(a, axis=axis)\n+ jnp_fn = lambda a: jnp_op(a, n=n, axis=axis)\n+ np_fn = lambda a: np_op(a, n=n, axis=axis)\n# Numpy promotes to complex128 aggressively.\nself._CheckAgainstNumpy(np_fn, jnp_fn, args_maker, check_dtypes=False,\ntol=1e-4)\n"
}
] | Python | Apache License 2.0 | google/jax | Add support for padding and cropping to fft |
260,424 | 15.04.2021 14:21:53 | -3,600 | fa5e19b630e97bc197645455e7cd7e36db66d6cd | Fix Zero handling in select_jvp. | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/lax.py",
"new_path": "jax/_src/lax/lax.py",
"diff": "@@ -3899,6 +3899,9 @@ def _select_jvp(primals, tangents):\n_, on_true_dot, on_false_dot = tangents\nout = select(pred, on_true, on_false)\nif type(on_true_dot) is ad_util.Zero:\n+ if type(on_false_dot) is ad_util.Zero:\n+ out_dot = ad_util.Zero(on_true_dot.aval)\n+ else:\nout_dot = select(pred, _zeros(on_false_dot), on_false_dot)\nelif type(on_false_dot) is ad_util.Zero:\nout_dot = select(pred, on_true_dot, _zeros(on_true_dot))\n"
}
] | Python | Apache License 2.0 | google/jax | Fix Zero handling in select_jvp. |
260,287 | 19.04.2021 11:28:12 | 25,200 | f285f0cc4774a2cce769caeca1439ed19f142cd0 | Fix pjit resource checks to verify the shapes against the local mesh. | [
{
"change_type": "MODIFY",
"old_path": "jax/BUILD",
"new_path": "jax/BUILD",
"diff": "@@ -115,7 +115,10 @@ pytype_library(\nname = \"pjit\",\nsrcs = [\"experimental/pjit.py\"],\nsrcs_version = \"PY3\",\n- deps = [\":jax\"],\n+ deps = [\n+ \":experimental\",\n+ \":jax\",\n+ ],\n)\npytype_library(\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -113,7 +113,7 @@ def _check_output_shapes(resource_env, out_axis_resources_thunk, *args, **kwargs\nyield outputs\ndef _check_shapes_against_resources(what: str, resource_env, flat_avals, flat_axis_resources):\n- resource_sizes = resource_env.shape\n+ resource_sizes = resource_env.local_shape\nfor aval, aval_axis_resources in zip(flat_avals, flat_axis_resources):\nif aval_axis_resources is None:\ncontinue\n"
}
] | Python | Apache License 2.0 | google/jax | Fix pjit resource checks to verify the shapes against the local mesh.
PiperOrigin-RevId: 369263691 |
260,287 | 19.04.2021 11:35:12 | 25,200 | d265fd56047bf2e85a24daa6b49339bd03299a7d | Ignore named shape when checking aval equality in AD
AD of code with named axes is still WIP, and pmap still doesn't take
proper care to handle them, so weaken the check for now. | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/ad.py",
"new_path": "jax/interpreters/ad.py",
"diff": "@@ -176,8 +176,8 @@ def backward_pass(jaxpr: core.Jaxpr, consts, primals_in, cotangents_in):\nct_env[v] = add_tangents(ct_env[v], ct) if v in ct_env else ct\nif config.jax_enable_checks:\nct_aval = core.get_aval(ct_env[v])\n- joined_aval = core.lattice_join(v.aval, ct_aval).strip_weak_type()\n- assert v.aval.strip_weak_type() == joined_aval, (prim, v.aval, ct_aval)\n+ joined_aval = core.lattice_join(v.aval, ct_aval).strip_weak_type().strip_named_shape()\n+ assert v.aval.strip_weak_type().strip_named_shape() == joined_aval, (prim, v.aval, ct_aval)\ndef read_cotangent(v):\nreturn ct_env.pop(v, Zero(v.aval))\n"
}
] | Python | Apache License 2.0 | google/jax | Ignore named shape when checking aval equality in AD
AD of code with named axes is still WIP, and pmap still doesn't take
proper care to handle them, so weaken the check for now.
PiperOrigin-RevId: 369265258 |
260,287 | 20.04.2021 03:48:07 | 25,200 | 93c63d03417d8e89344cf84f387140d52f9576ef | Fix cache misses when re-creating equivalent mesh objects
The `Mesh` class was missing `__eq__` and `__hash__` and inherited the
(bad) Python defaults of comparison and hashing by identity. | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -1254,16 +1254,33 @@ mesh devices ndarray would have to be transposed before flattening and assignmen\nArrayMapping = OrderedDictType[MeshAxisName, int]\nclass Mesh:\n- __slots__ = ('devices', 'axis_names')\n+ __slots__ = ('devices', 'axis_names', '_hash')\ndef __init__(self, devices: np.ndarray, axis_names: Sequence[MeshAxisName]):\nassert devices.ndim == len(axis_names)\n# TODO: Make sure that devices are unique? At least with the quick and\n# dirty check that the array size is not larger than the number of\n# available devices?\n- self.devices = devices\n+ self.devices = devices.copy()\n+ self.devices.flags.writeable = False\nself.axis_names = tuple(axis_names)\n+ def __eq__(self, other):\n+ if not isinstance(other, Mesh):\n+ return False\n+ return (self.axis_names == other.axis_names and\n+ np.array_equal(self.devices, other.devices))\n+\n+ def __hash__(self):\n+ if not hasattr(self, '_hash'):\n+ self._hash = hash((self.axis_names, tuple(self.devices.flat)))\n+ return self._hash\n+\n+ def __setattr__(self, name, value):\n+ if hasattr(self, name):\n+ raise RuntimeError(\"Cannot reassign attributes of immutable mesh objects\")\n+ super().__setattr__(name, value)\n+\n@property\ndef shape(self):\nreturn OrderedDict((name, size) for name, size in safe_zip(self.axis_names, self.devices.shape))\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -206,6 +206,26 @@ class PJitTest(jtu.BufferDonationTestCase):\n# Annotation from pjit\nself.assertIn(\"sharding={replicated}\", hlo.as_hlo_text())\n+ def testCaching(self):\n+ def f(x):\n+ assert should_be_tracing\n+ return jnp.sin(x) * 2\n+\n+ x = np.arange(16).reshape(4, 4)\n+ devices = np.array(list(jax.local_devices())[:4])\n+ if devices.size < 4:\n+ raise SkipTest(\"Test requires 4 devices\")\n+ devices = devices.reshape((2, 2))\n+ with mesh(devices, ('x', 'y')):\n+ should_be_tracing = True\n+ pjit(f, in_axis_resources=P(('x', 'y')), out_axis_resources=None)(x)\n+ should_be_tracing = False\n+ pjit(f, in_axis_resources=P(('x', 'y')), out_axis_resources=None)(x)\n+ # Re-create the mesh to make sure that has no influence on caching\n+ with mesh(devices, ('x', 'y')):\n+ should_be_tracing = False\n+ pjit(f, in_axis_resources=P(('x', 'y')), out_axis_resources=None)(x)\n+\n# TODO(skye): add more unit tests once API is more finalized\n@curry\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/xmap_test.py",
"new_path": "tests/xmap_test.py",
"diff": "@@ -362,19 +362,25 @@ class XMapTest(XMapTestCase):\nself.assertAllClose(run({'i': 'x'}), run({'i': 'y'}))\n@ignore_xmap_warning()\n- @with_mesh([('x', 2)])\n- def testCompilationCache(self):\n+ def testCaching(self):\ndef f(x):\nassert python_should_be_executing\nreturn x * 2\n- fm = xmap(f,\n- in_axes=['a', ...], out_axes=['a', ...],\n- axis_resources={'a': 'x'})\n+ devices = np.array(jax.local_devices()[:2])\n+ if devices.size < 2:\n+ raise SkipTest(\"Test requires 2 devices\")\nx = np.arange(8).reshape((2, 2, 2))\n+ with mesh(devices, ('x',)):\npython_should_be_executing = True\n- fm(x)\n+ xmap(f, in_axes=['a', ...], out_axes=['a', ...],\n+ axis_resources={'a': 'x'})(x)\n+ python_should_be_executing = False\n+ xmap(f, in_axes=['a', ...], out_axes=['a', ...],\n+ axis_resources={'a': 'x'})(x)\n+ with mesh(devices, ('x',)):\npython_should_be_executing = False\n- fm(x)\n+ xmap(f, in_axes=['a', ...], out_axes=['a', ...],\n+ axis_resources={'a': 'x'})(x)\n@parameterized.named_parameters(\n{\"testcase_name\": name, \"mesh\": mesh, \"axis_resources\": axis_resources}\n"
}
] | Python | Apache License 2.0 | google/jax | Fix cache misses when re-creating equivalent mesh objects
The `Mesh` class was missing `__eq__` and `__hash__` and inherited the
(bad) Python defaults of comparison and hashing by identity.
PiperOrigin-RevId: 369407380 |
260,287 | 20.04.2021 08:32:41 | 25,200 | c09037bd144cc2e271da4d7f1077d281f9d0e7f4 | Move vtile to batching.py, make it possible to add new BatchTraces
No substantial behavior change right now, but the ability to use
subclasses of BatchTrace comes in handy when adding support for
nesting xmaps in the SPMD lowering. | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/custom_derivatives.py",
"new_path": "jax/_src/custom_derivatives.py",
"diff": "@@ -324,7 +324,7 @@ def _custom_jvp_call_jaxpr_jvp(\nad.primitive_jvps[custom_jvp_call_jaxpr_p] = _custom_jvp_call_jaxpr_jvp\ndef _custom_jvp_call_jaxpr_vmap(\n- args, in_dims, axis_name, *, fun_jaxpr: core.ClosedJaxpr,\n+ args, in_dims, axis_name, main_type, *, fun_jaxpr: core.ClosedJaxpr,\njvp_jaxpr_thunk: Callable[[], Tuple[core.Jaxpr, Sequence[Any]]],\nnum_consts: int):\nsize, = {x.shape[d] for x, d in zip(args, in_dims) if d is not not_mapped}\n@@ -333,7 +333,8 @@ def _custom_jvp_call_jaxpr_vmap(\nnum_out = len(fun_jaxpr.out_avals)\nin_batched = [d is not not_mapped for d in in_dims]\n- batched_fun_jaxpr, out_batched = batching.batch_jaxpr(fun_jaxpr, size, in_batched, False, axis_name)\n+ batched_fun_jaxpr, out_batched = batching.batch_jaxpr(\n+ fun_jaxpr, size, in_batched, False, axis_name, main_type)\nout_dims1 = [0 if b else not_mapped for b in out_batched]\nout_dims2 = [] # mutable cell updated by batched_jvp_jaxpr_thunk\n@@ -341,12 +342,14 @@ def _custom_jvp_call_jaxpr_vmap(\ndef batched_jvp_jaxpr_thunk():\njvp_jaxpr = core.ClosedJaxpr(*jvp_jaxpr_thunk()) # consts can be tracers\n_, args_batched = split_list(in_batched, [num_consts])\n- _, all_batched = batching.batch_jaxpr(jvp_jaxpr, size, args_batched * 2, False, axis_name)\n+ _, all_batched = batching.batch_jaxpr(jvp_jaxpr, size, args_batched * 2, False,\n+ axis_name, main_type)\nprimals_batched, tangents_batched = split_list(all_batched, [num_out])\nout_batched = map(op.or_, primals_batched, tangents_batched)\nout_dims2.append([0 if b else not_mapped for b in out_batched])\nbatched_jvp_jaxpr, _ = batching.batch_jaxpr(\n- jvp_jaxpr, size, args_batched * 2, out_batched * 2, axis_name)\n+ jvp_jaxpr, size, args_batched * 2, out_batched * 2,\n+ axis_name, main_type)\nreturn batched_jvp_jaxpr.jaxpr, batched_jvp_jaxpr.consts\nbatched_outs = custom_jvp_call_jaxpr_p.bind(\n@@ -610,7 +613,7 @@ def _custom_vjp_call_jaxpr_jvp(\nad.primitive_jvps[custom_vjp_call_jaxpr_p] = _custom_vjp_call_jaxpr_jvp\ndef _custom_vjp_call_jaxpr_vmap(\n- args, in_dims, axis_name, *, fun_jaxpr: core.ClosedJaxpr,\n+ args, in_dims, axis_name, main_type, *, fun_jaxpr: core.ClosedJaxpr,\nfwd_jaxpr_thunk: Callable[[], Tuple[core.Jaxpr, Sequence[Any]]],\nbwd: lu.WrappedFun, out_trees: Callable, num_consts: int):\naxis_size, = {x.shape[d] for x, d in zip(args, in_dims) if d is not not_mapped}\n@@ -620,7 +623,7 @@ def _custom_vjp_call_jaxpr_vmap(\nin_batched = [d is not not_mapped for d in in_dims]\n_, args_batched = split_list(in_batched, [num_consts])\nbatched_fun_jaxpr, out_batched = batching.batch_jaxpr(\n- fun_jaxpr, axis_size, in_batched, False, axis_name)\n+ fun_jaxpr, axis_size, in_batched, False, axis_name, main_type)\nout_dims1 = [0 if b else not_mapped for b in out_batched]\nout_dims2 = []\n@@ -628,14 +631,14 @@ def _custom_vjp_call_jaxpr_vmap(\ndef batched_fwd_jaxpr_thunk():\nfwd_jaxpr = core.ClosedJaxpr(*fwd_jaxpr_thunk()) # consts can be tracers\nbatched_fwd_jaxpr, out_batched = batching.batch_jaxpr(\n- fwd_jaxpr, axis_size, args_batched, False, axis_name)\n+ fwd_jaxpr, axis_size, args_batched, False, axis_name, main_type)\nout_dims2.append([0 if b else not_mapped for b in out_batched])\nreturn batched_fwd_jaxpr.jaxpr, batched_fwd_jaxpr.consts\nfwd_args_batched = [0 if b else not_mapped for b in args_batched]\nfwd_out_dims = lambda: out_dims2[0]\nbatched_bwd = batching.batch_custom_vjp_bwd(bwd, axis_name, axis_size, fwd_out_dims,\n- fwd_args_batched)\n+ fwd_args_batched, main_type)\nbatched_outs = custom_vjp_call_jaxpr_p.bind(\n*args, fun_jaxpr=batched_fun_jaxpr,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/control_flow.py",
"new_path": "jax/_src/lax/control_flow.py",
"diff": "@@ -367,7 +367,7 @@ def _pred_bcast_select(c, pred, x, y, x_y_aval: core.AbstractValue):\nbcast_pred = xops.BroadcastInDim(pred, x_shape, list(range(len(pred_shape))))\nreturn xops.Select(bcast_pred, x, y)\n-def _while_loop_batching_rule(args, dims, axis_name,\n+def _while_loop_batching_rule(args, dims, axis_name, main_type,\ncond_nconsts, cond_jaxpr,\nbody_nconsts, body_jaxpr):\nsize, = {x.shape[d] for x, d in zip(args, dims) if d is not batching.not_mapped}\n@@ -383,11 +383,12 @@ def _while_loop_batching_rule(args, dims, axis_name,\nfor _ in range(1 + len(carry_bat)):\nbatched = bconst_bat + carry_bat\nbody_jaxpr_batched, carry_bat_out = batching.batch_jaxpr(\n- body_jaxpr, size, batched, instantiate=carry_bat, axis_name=axis_name)\n+ body_jaxpr, size, batched, instantiate=carry_bat,\n+ axis_name=axis_name, main_type=main_type)\ncond_jaxpr_batched, (pred_bat,) = batching.batch_jaxpr(\ncond_jaxpr, size, cconst_bat + carry_bat,\ninstantiate=bool(cond_jaxpr.out_avals[0].shape),\n- axis_name=axis_name)\n+ axis_name=axis_name, main_type=main_type)\ncarry_bat_out = _map(partial(operator.or_, pred_bat), carry_bat_out)\nif carry_bat_out == carry_bat:\nbreak\n@@ -772,7 +773,7 @@ def _bcast_select(pred, on_true, on_false):\npred = lax.broadcast_in_dim(pred, np.shape(on_true), idx)\nreturn lax.select(pred, on_true, on_false)\n-def _cond_batching_rule(args, dims, axis_name, branches, linear):\n+def _cond_batching_rule(args, dims, axis_name, main_type, branches, linear):\nsize, = {x.shape[d] for x, d in zip(args, dims) if d is not batching.not_mapped}\nindex, *ops = args\nindex_dim, *op_dims = dims\n@@ -786,7 +787,7 @@ def _cond_batching_rule(args, dims, axis_name, branches, linear):\nindex, *ops = [batching.bdim_at_front(x, d, size) for x, d in zip(args, dims)]\nbranches_batched = [\n- batching.batch_jaxpr(jaxpr, size, [True] * len(ops), True, axis_name)[0]\n+ batching.batch_jaxpr(jaxpr, size, [True] * len(ops), True, axis_name, main_type)[0]\nfor jaxpr in branches]\nbranch_outs = []\n@@ -806,11 +807,11 @@ def _cond_batching_rule(args, dims, axis_name, branches, linear):\nfor b, x, d in zip(ops_bat, ops, op_dims)]\nbranches_out_bat = [\n- batching.batch_jaxpr(jaxpr, size, ops_bat, False, axis_name)[1]\n+ batching.batch_jaxpr(jaxpr, size, ops_bat, False, axis_name, main_type)[1]\nfor jaxpr in branches]\nout_bat = [any(bat) for bat in zip(*branches_out_bat)]\nbranches_batched = tuple(\n- batching.batch_jaxpr(jaxpr, size, ops_bat, out_bat, axis_name)[0]\n+ batching.batch_jaxpr(jaxpr, size, ops_bat, out_bat, axis_name, main_type)[0]\nfor jaxpr in branches)\nout_dims = [0 if b else batching.not_mapped for b in out_bat]\n@@ -1731,7 +1732,7 @@ def _make_closed_jaxpr(traceable: lu.WrappedFun, in_avals: Sequence[core.Abstrac\nreturn core.ClosedJaxpr(jaxpr, consts)\n-def _scan_batching_rule(args, dims, axis_name, reverse, length, jaxpr, num_consts,\n+def _scan_batching_rule(args, dims, axis_name, main_type, reverse, length, jaxpr, num_consts,\nnum_carry, linear, unroll):\nnum_ys = len(jaxpr.out_avals) - num_carry\nsize, = {x.shape[d] for x, d in zip(args, dims) if d is not batching.not_mapped}\n@@ -1749,7 +1750,8 @@ def _scan_batching_rule(args, dims, axis_name, reverse, length, jaxpr, num_const\njaxpr_batched, batched_out = batching.batch_jaxpr(\njaxpr, size, batched,\ninstantiate=carry_batched + [False] * num_ys,\n- axis_name=axis_name)\n+ axis_name=axis_name,\n+ main_type=main_type)\ncarry_batched_out, ys_batched = batched_out[:num_carry], batched_out[num_carry:]\nif carry_batched_out == carry_batched:\nbreak\n@@ -2275,7 +2277,7 @@ def _linear_solve_transpose_rule(cotangent, *primals, const_lengths, jaxprs):\nreturn [None] * sum(const_lengths) + cotangent_b\n-def _linear_solve_batching_rule(args, dims, axis_name, const_lengths, jaxprs):\n+def _linear_solve_batching_rule(args, dims, axis_name, main_type, const_lengths, jaxprs):\norig_bat = [d is not batching.not_mapped for d in dims]\nsize, = {\na.shape[d] for a, d in zip(args, dims) if d is not batching.not_mapped\n@@ -2295,23 +2297,27 @@ def _linear_solve_batching_rule(args, dims, axis_name, const_lengths, jaxprs):\nfor i in range(1 + len(orig_b_bat) + len(solve.out_avals)):\n# Apply vecmat and solve -> new batched parts of x\nsolve_jaxpr_batched, solve_x_bat = batching.batch_jaxpr(\n- solve, size, solve_bat + b_bat, instantiate=x_bat, axis_name=axis_name)\n+ solve, size, solve_bat + b_bat, instantiate=x_bat,\n+ axis_name=axis_name, main_type=main_type)\nif vecmat is None:\nvecmat_jaxpr_batched = None\nx_bat_out = solve_x_bat\nelse:\nvecmat_jaxpr_batched, vecmat_x_bat = batching.batch_jaxpr(\n- vecmat, size, vecmat_bat + b_bat, instantiate=x_bat, axis_name=axis_name)\n+ vecmat, size, vecmat_bat + b_bat, instantiate=x_bat,\n+ axis_name=axis_name, main_type=main_type)\nx_bat_out = _map(operator.or_, vecmat_x_bat, solve_x_bat)\n# Apply matvec and solve_t -> new batched parts of b\nmatvec_jaxpr_batched, matvec_b_bat = batching.batch_jaxpr(\n- matvec, size, matvec_bat + x_bat_out, instantiate=b_bat, axis_name=axis_name)\n+ matvec, size, matvec_bat + x_bat_out, instantiate=b_bat,\n+ axis_name=axis_name, main_type=main_type)\nif solve_t is None:\nsolve_t_jaxpr_batched = None\nb_bat_out = _map(operator.or_, matvec_b_bat, orig_b_bat)\nelse:\nsolve_t_jaxpr_batched, solve_t_b_bat = batching.batch_jaxpr(\n- solve_t, size, solve_t_bat + x_bat_out, instantiate=b_bat, axis_name=axis_name)\n+ solve_t, size, solve_t_bat + x_bat_out, instantiate=b_bat,\n+ axis_name=axis_name, main_type=main_type)\nb_bat_out = _map(lambda m, s, o: m or s or o, matvec_b_bat, solve_t_b_bat,\norig_b_bat)\nif x_bat_out == x_bat and b_bat_out == b_bat:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -652,7 +652,7 @@ class EvaluationPlan(NamedTuple):\nvaxis = raxes[-1]\nmap_in_axes = tuple(unsafe_map(lambda spec: spec.get(naxis, None), in_axes))\nmap_out_axes = tuple(unsafe_map(lambda spec: spec.get(naxis, None), out_axes))\n- f = pxla.vtile(f, map_in_axes, map_out_axes, tile_size=local_tile_size, axis_name=vaxis)\n+ f = batching.vtile(f, map_in_axes, map_out_axes, tile_size=local_tile_size, axis_name=vaxis)\nreturn f\ndef to_mesh_axes(self, in_axes, out_axes):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/batching.py",
"new_path": "jax/interpreters/batching.py",
"diff": "# limitations under the License.\nimport numpy as np\n-from typing import Any, Callable, Dict, Optional, Tuple, Union, Sequence, Iterable\n+from typing import Any, Callable, Dict, Optional, Tuple, Union, Sequence, Iterable, Type\nimport jax\nfrom ..config import config\n@@ -21,8 +21,8 @@ from .. import core\nfrom ..core import raise_to_shaped, Trace, Tracer\nfrom ..ad_util import add_jaxvals, add_jaxvals_p, zeros_like_jaxval, zeros_like_p\nfrom .. import linear_util as lu\n-from .._src.util import (unzip2, partial, safe_map, wrap_name, split_list,\n- canonicalize_axis, moveaxis, as_hashable_function)\n+from .._src.util import (unzip2, partial, safe_map, safe_zip, wrap_name, split_list,\n+ canonicalize_axis, moveaxis, as_hashable_function, curry)\nfrom . import xla\nfrom . import partial_eval as pe\n@@ -30,19 +30,8 @@ map = safe_map\nBatchDim = Optional[int]\nBatchDims = Sequence[BatchDim]\n-AxesSpec = Union[Callable[[], BatchDims], BatchDims]\n-\n-def batch(fun: lu.WrappedFun, axis_name: core.AxisName,\n- axis_size: Optional[int], in_dims: AxesSpec, out_dim_dests: AxesSpec,\n- ) -> lu.WrappedFun:\n- # anlogue of `jvp` in ad.py\n- # TODO(mattjj,apaszke): change type of axis_size to be int, not Optional[int]\n- fun, out_dims_thunk = batch_subtrace(fun)\n- return _match_axes(batchfun(fun, axis_name, axis_size, in_dims),\n- axis_size, in_dims, out_dims_thunk, out_dim_dests)\n-\n@lu.transformation\n-def batchfun(axis_name, axis_size, in_dims, *in_vals):\n+def batchfun(axis_name, axis_size, in_dims, main_type, *in_vals):\n# analogue of `jvpfun` in ad.py\nif axis_size is None:\naxis_size, = {x.shape[d] for x, d in zip(in_vals, in_dims) if d is not not_mapped}\n@@ -50,7 +39,7 @@ def batchfun(axis_name, axis_size, in_dims, *in_vals):\nin_dims = [canonicalize_axis(ax, np.ndim(x)) if isinstance(ax, int)\nand not isinstance(core.get_aval(x), core.AbstractUnit)\nelse ax for x, ax in zip(in_vals, in_dims)]\n- with core.new_main(BatchTrace, axis_name=axis_name) as main:\n+ with core.new_main(main_type, axis_name=axis_name) as main:\nwith core.extend_axis_env(axis_name, axis_size, main):\nout_vals = yield (main, in_dims, *in_vals), {}\ndel main\n@@ -137,7 +126,7 @@ class BatchTrace(Trace):\nframe = core.axis_frame(self.axis_name)\nval_out, dim_out = collective_rules[primitive](frame, vals_in, dims_in, **params)\nelse:\n- batched_primitive = get_primitive_batcher(primitive, self.axis_name)\n+ batched_primitive = get_primitive_batcher(primitive, self)\nval_out, dim_out = batched_primitive(vals_in, dims_in, **params)\nif primitive.multiple_results:\nreturn map(partial(BatchTracer, self), val_out, dim_out)\n@@ -245,7 +234,7 @@ class BatchTrace(Trace):\nfun, out_dims1 = batch_subtrace(fun, self.main, in_dims)\nfwd, out_dims2 = batch_subtrace(fwd, self.main, in_dims)\nbwd = batch_custom_vjp_bwd(bwd, self.axis_name, axis_size,\n- out_dims2, in_dims)\n+ out_dims2, in_dims, self.main.trace_type)\nout_vals = prim.bind(fun, fwd, bwd, *in_vals, out_trees=out_trees)\nfst, out_dims = lu.merge_linear_aux(out_dims1, out_dims2)\nif not fst:\n@@ -262,9 +251,9 @@ def _main_trace_for_axis_names(main_trace: core.MainTrace,\n# axis names can shadow, so we use the main trace as a tag.\nreturn any(main_trace is core.axis_frame(n).main_trace for n in axis_name)\n-def batch_custom_vjp_bwd(bwd, axis_name, axis_size, in_dims, out_dim_dests):\n+def batch_custom_vjp_bwd(bwd, axis_name, axis_size, in_dims, out_dim_dests, main_type):\nbwd, out_dims_thunk = batch_subtrace(bwd)\n- return _match_axes_and_sum(batchfun(bwd, axis_name, axis_size, in_dims),\n+ return _match_axes_and_sum(batchfun(bwd, axis_name, axis_size, in_dims, main_type),\naxis_size, out_dims_thunk, out_dim_dests)\n@lu.transformation\n@@ -274,6 +263,55 @@ def _match_axes_and_sum(axis_size, out_dims_thunk, out_dim_dests, *in_vals):\nyield map(partial(matchaxis, axis_size, sum_match=True),\nout_dims_thunk(), out_dim_dests, out_vals)\n+### API\n+\n+AxesSpec = Union[Callable[[], BatchDims], BatchDims]\n+\n+def batch(fun: lu.WrappedFun,\n+ axis_name: core.AxisName,\n+ axis_size: Optional[int],\n+ in_dims: AxesSpec,\n+ out_dim_dests: AxesSpec,\n+ main_type: Type[BatchTrace] = BatchTrace,\n+ ) -> lu.WrappedFun:\n+ # anlogue of `jvp` in ad.py\n+ # TODO(mattjj,apaszke): change type of axis_size to be int, not Optional[int]\n+ fun, out_dims_thunk = batch_subtrace(fun)\n+ return _match_axes(batchfun(fun, axis_name, axis_size, in_dims, main_type),\n+ axis_size, in_dims, out_dims_thunk, out_dim_dests)\n+\n+# NOTE: This divides the in_axes by the tile_size and multiplies the out_axes by it.\n+def vtile(f_flat: lu.WrappedFun,\n+ in_axes_flat: Tuple[Optional[int], ...],\n+ out_axes_flat: Tuple[Optional[int], ...],\n+ tile_size: Optional[int],\n+ axis_name: core.AxisName,\n+ main_type: Type[BatchTrace] = BatchTrace):\n+ @curry\n+ def tile_axis(arg, axis: Optional[int], tile_size):\n+ if axis is None:\n+ return arg\n+ shape = list(arg.shape)\n+ shape[axis:axis+1] = [tile_size, shape[axis] // tile_size]\n+ return arg.reshape(shape)\n+\n+ def untile_axis(out, axis: Optional[int]):\n+ if axis is None:\n+ return out\n+ shape = list(out.shape)\n+ shape[axis:axis+2] = [shape[axis] * shape[axis+1]]\n+ return out.reshape(shape)\n+\n+ @lu.transformation\n+ def _map_to_tile(*args_flat):\n+ sizes = (x.shape[i] for x, i in safe_zip(args_flat, in_axes_flat) if i is not None)\n+ tile_size_ = tile_size or next(sizes, None)\n+ assert tile_size_ is not None, \"No mapped arguments?\"\n+ outputs_flat = yield map(tile_axis(tile_size=tile_size_), args_flat, in_axes_flat), {}\n+ yield map(untile_axis, outputs_flat, out_axes_flat)\n+\n+ return _map_to_tile(batch(\n+ f_flat, axis_name, tile_size, in_axes_flat, out_axes_flat, main_type=main_type))\n### primitives\n@@ -281,9 +319,11 @@ BatchingRule = Callable[..., Tuple[Any, Union[int, Tuple[int, ...]]]]\nprimitive_batchers : Dict[core.Primitive, BatchingRule] = {}\ninitial_style_batchers : Dict[core.Primitive, Any] = {}\n-def get_primitive_batcher(p, axis_name):\n+def get_primitive_batcher(p, trace):\nif p in initial_style_batchers:\n- return partial(initial_style_batchers[p], axis_name=axis_name)\n+ return partial(initial_style_batchers[p],\n+ axis_name=trace.axis_name,\n+ main_type=trace.main.trace_type)\ntry:\nreturn primitive_batchers[p]\nexcept KeyError as err:\n@@ -408,10 +448,10 @@ def bdim_at_front(x, bdim, size):\nreturn moveaxis(x, bdim, 0)\n-def batch_jaxpr(closed_jaxpr, axis_size, in_batched, instantiate, axis_name):\n+def batch_jaxpr(closed_jaxpr, axis_size, in_batched, instantiate, axis_name, main_type):\nf = lu.wrap_init(core.jaxpr_as_fun(closed_jaxpr))\nf, out_batched = batch_subtrace_instantiate(f, instantiate, axis_size)\n- f = batchfun(f, axis_name, axis_size, [0 if b else None for b in in_batched])\n+ f = batchfun(f, axis_name, axis_size, [0 if b else None for b in in_batched], main_type)\navals_in = [core.unmapped_aval(axis_size, 0, aval) if b else aval\nfor aval, b in zip(closed_jaxpr.in_avals, in_batched)]\njaxpr_out, _, consts = pe.trace_to_jaxpr_dynamic(f, avals_in)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -47,7 +47,7 @@ from ..abstract_arrays import array_types\nfrom ..core import ConcreteArray, ShapedArray\nfrom .._src.util import (partial, unzip3, prod, safe_map, safe_zip,\nextend_name_stack, wrap_name, assert_unreachable,\n- tuple_insert, tuple_delete, curry)\n+ tuple_insert, tuple_delete)\nfrom ..lib import xla_bridge as xb\nfrom ..lib import xla_client as xc\nfrom ..lib import pmap_lib\n@@ -1381,7 +1381,7 @@ def mesh_callable(fun: lu.WrappedFun,\n# TODO: Consider handling xmap's 'vectorize' in here. We can vmap once instead of vtile twice!\nfor name, size in reversed(mesh.shape.items()):\nif tile_by_mesh_axes:\n- fun = vtile(fun,\n+ fun = batching.vtile(fun,\ntuple(a.get(name, None) for a in in_axes),\ntuple(a.get(name, None) for a in out_axes_thunk()),\ntile_size=size, axis_name=name)\n@@ -1500,37 +1500,6 @@ def compile_and_wrap_mesh_hlo(computation: xc.XlaComputation, backend,\nhandle_outs)\nreturn partial(execute_replicated, compiled, backend, handle_args, handle_outs)\n-# NOTE: This divides the in_axes by the tile_size and multiplies the out_axes by it.\n-def vtile(f_flat,\n- in_axes_flat: Tuple[Optional[int], ...],\n- out_axes_flat: Tuple[Optional[int], ...],\n- tile_size: Optional[int], axis_name):\n- @curry\n- def tile_axis(arg, axis: Optional[int], tile_size):\n- if axis is None:\n- return arg\n- shape = list(arg.shape)\n- shape[axis:axis+1] = [tile_size, shape[axis] // tile_size]\n- return arg.reshape(shape)\n-\n- def untile_axis(out, axis: Optional[int]):\n- if axis is None:\n- return out\n- shape = list(out.shape)\n- shape[axis:axis+2] = [shape[axis] * shape[axis+1]]\n- return out.reshape(shape)\n-\n- @lu.transformation\n- def _map_to_tile(*args_flat):\n- sizes = (x.shape[i] for x, i in safe_zip(args_flat, in_axes_flat) if i is not None)\n- tile_size_ = tile_size or next(sizes, None)\n- assert tile_size_ is not None, \"No mapped arguments?\"\n- outputs_flat = yield map(tile_axis(tile_size=tile_size_), args_flat, in_axes_flat), {}\n- yield map(untile_axis, outputs_flat, out_axes_flat)\n-\n- return _map_to_tile(\n- batching.batch(f_flat, axis_name, tile_size, in_axes_flat, out_axes_flat))\n-\n_forbidden_primitives = {\n'xla_pmap': 'pmap',\n'sharded_call': 'sharded_jit',\n"
}
] | Python | Apache License 2.0 | google/jax | Move vtile to batching.py, make it possible to add new BatchTraces
No substantial behavior change right now, but the ability to use
subclasses of BatchTrace comes in handy when adding support for
nesting xmaps in the SPMD lowering.
PiperOrigin-RevId: 369445693 |
260,287 | 20.04.2021 11:39:33 | 25,200 | 09a519828cc807df337ce4d1922df2c165e67aca | Extend pjit error checking to rank errors too
Otherwise one gets an inscrutable `IndexError`. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -118,6 +118,11 @@ def _check_shapes_against_resources(what: str, resource_env, flat_avals, flat_ax\nif aval_axis_resources is None:\ncontinue\nshape = aval.shape\n+ if len(shape) < len(aval_axis_resources):\n+ raise ValueError(f\"One of {what} was given the resource assignment \"\n+ f\"of {aval_axis_resources}, which implies that it has \"\n+ f\"a rank of at least {len(aval_axis_resources)}, but \"\n+ f\"it is {len(shape)}\")\nfor i, axis_resources in enumerate(aval_axis_resources):\nif axis_resources is None:\ncontinue\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -245,7 +245,7 @@ class PJitErrorTest(jtu.JaxTestCase):\n@check_1d_2d_mesh(set_mesh=True)\n@ignore_pjit_warning()\ndef testNonDivisibleArgs(self, mesh, resources):\n- x = jnp.ones((3,))\n+ x = jnp.ones((3, 2))\nspec = P(resources, None)\nmesh_size = str(np.prod([dim[1] for dim in mesh], dtype=np.int64))\nwith self.assertRaisesRegex(ValueError,\n@@ -257,7 +257,7 @@ class PJitErrorTest(jtu.JaxTestCase):\n@check_1d_2d_mesh(set_mesh=True)\n@ignore_pjit_warning()\ndef testNonDivisibleOuts(self, mesh, resources):\n- x = jnp.ones((3,))\n+ x = jnp.ones((3, 2))\nspec = P(resources, None)\nmesh_size = str(np.prod([dim[1] for dim in mesh], dtype=np.int64))\nwith self.assertRaisesRegex(ValueError,\n@@ -269,7 +269,7 @@ class PJitErrorTest(jtu.JaxTestCase):\n@check_1d_2d_mesh(set_mesh=True)\n@ignore_pjit_warning()\ndef testNonDivisibleConstraint(self, mesh, resources):\n- x = jnp.ones((3,))\n+ x = jnp.ones((3, 2))\nspec = P(resources,)\nmesh_size = str(np.prod([dim[1] for dim in mesh], dtype=np.int64))\nwith self.assertRaisesRegex(ValueError,\n@@ -283,7 +283,7 @@ class PJitErrorTest(jtu.JaxTestCase):\n@check_1d_2d_mesh(set_mesh=False)\n@ignore_pjit_warning()\ndef testUndefinedResourcesArgs(self, mesh, resources):\n- x = jnp.ones((2,))\n+ x = jnp.ones((2, 2))\nspec = P(resources,)\nwith self.assertRaisesRegex(ValueError,\nr\"One of pjit arguments.*\" + spec_regex(spec) + r\", \"\n@@ -293,7 +293,7 @@ class PJitErrorTest(jtu.JaxTestCase):\n@check_1d_2d_mesh(set_mesh=False)\n@ignore_pjit_warning()\ndef testUndefinedResourcesOuts(self, mesh, resources):\n- x = jnp.ones((2,))\n+ x = jnp.ones((2, 2))\nspec = P(resources,)\nwith self.assertRaisesRegex(ValueError,\nr\"One of pjit outputs.*\" + spec_regex(spec) + r\", \"\n@@ -303,7 +303,7 @@ class PJitErrorTest(jtu.JaxTestCase):\n@check_1d_2d_mesh(set_mesh=False)\n@ignore_pjit_warning()\ndef testUndefinedResourcesConstraint(self, mesh, resources):\n- x = jnp.ones((2,))\n+ x = jnp.ones((2, 2))\nspec = P(resources,)\nwith self.assertRaisesRegex(ValueError,\nr\"One of with_sharding_constraint arguments\"\n@@ -312,6 +312,38 @@ class PJitErrorTest(jtu.JaxTestCase):\npjit(lambda x: with_sharding_constraint(x, spec),\nin_axis_resources=None, out_axis_resources=None)(x)\n+ @ignore_pjit_warning()\n+ @with_mesh([('x', 2), ('y', 1)])\n+ def testRankTooLowArgs(self):\n+ x = jnp.arange(2)\n+ spec = P('x', 'y')\n+ error = (r\"One of pjit arguments.*\" + spec_regex(spec) + r\", which implies \"\n+ r\"that it has a rank of at least 2, but it is 1\")\n+ with self.assertRaisesRegex(ValueError, error):\n+ pjit(lambda x: x.sum(), in_axis_resources=spec, out_axis_resources=None)(x)\n+\n+ @ignore_pjit_warning()\n+ @with_mesh([('x', 2), ('y', 1)])\n+ def testRankTooLowOuts(self):\n+ x = jnp.arange(2)\n+ spec = P('x', 'y')\n+ error = (r\"One of pjit outputs.*\" + spec_regex(spec) + r\", which implies \"\n+ r\"that it has a rank of at least 2, but it is 0\")\n+ with self.assertRaisesRegex(ValueError, error):\n+ pjit(lambda x: x.sum(), in_axis_resources=None, out_axis_resources=spec)(x)\n+\n+ @ignore_pjit_warning()\n+ @with_mesh([('x', 2), ('y', 1)])\n+ def testRankTooLowConstraint(self):\n+ x = jnp.arange(2)\n+ spec = P('x', 'y')\n+ error = (r\"One of with_sharding_constraint arguments \" +\n+ r\"was given.*\" + spec_regex(spec) + r\", which implies \"\n+ r\"that it has a rank of at least 2, but it is 1\")\n+ with self.assertRaisesRegex(ValueError, error):\n+ pjit(lambda x: with_sharding_constraint(x, spec),\n+ in_axis_resources=None, out_axis_resources=None)(x)\n+\nif __name__ == '__main__':\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | Extend pjit error checking to rank errors too
Otherwise one gets an inscrutable `IndexError`.
PiperOrigin-RevId: 369485185 |
260,287 | 20.04.2021 11:40:32 | 25,200 | 828e21060121c637aa5038a848999f7ce04df49f | Add a type checking rule for xmap
Also fix the type checking code in core, which incorrectly propagated output
avals of custom type checking rules. | [
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "@@ -1846,8 +1846,10 @@ def _check_jaxpr(jaxpr: Jaxpr, in_avals: Sequence[AbstractValue]):\ntypecheck_assert(all(not isinstance(ina, ConcreteArray) for ina in in_avals),\n\"Equation given ConcreteArray type inputs\")\nif prim in custom_typechecks:\n- custom_typechecks[prim](*in_avals, **eqn.params)\n- if prim.call_primitive:\n+ out_avals = custom_typechecks[prim](*in_avals, **eqn.params)\n+ if out_avals is None:\n+ out_avals = [v.aval for v in eqn.outvars]\n+ elif prim.call_primitive:\nout_avals = check_call(prim, in_avals, eqn.params)\nelif prim.map_primitive:\nout_avals = check_map(prim, in_avals, eqn.params)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -720,6 +720,28 @@ core.axis_substitution_rules[xmap_p] = _xmap_axis_subst\nad.JVPTrace.process_xmap = ad.JVPTrace.process_call # type: ignore\nad.call_param_updaters[xmap_p] = ad.call_param_updaters[xla.xla_call_p]\n+def _typecheck_xmap(\n+ *in_avals, call_jaxpr, name, in_axes, out_axes, donated_invars,\n+ global_axis_sizes, axis_resources, resource_env, backend,\n+ spmd_in_axes, spmd_out_axes):\n+ binder_in_avals = [_insert_aval_axes(v.aval, a_in_axes, global_axis_sizes)\n+ for v, a_in_axes in zip(call_jaxpr.invars, in_axes)]\n+ for binder_in_aval, in_aval in zip(binder_in_avals, in_avals):\n+ core.typecheck_assert(\n+ core.typecompat(binder_in_aval, in_aval),\n+ f\"xmap passes operand {in_aval} to jaxpr expecting {binder_in_aval}\")\n+\n+ mapped_in_avals = [_delete_aval_axes(a, a_in_axes)\n+ for a, a_in_axes in zip(in_avals, in_axes)]\n+ with core.extend_axis_env_nd(global_axis_sizes.items()):\n+ core._check_jaxpr(call_jaxpr, mapped_in_avals)\n+\n+ mapped_out_avals = [v.aval for v in call_jaxpr.outvars]\n+ out_avals = [_insert_aval_axes(a, a_out_axes, global_axis_sizes)\n+ for a, a_out_axes in zip(mapped_out_avals, out_axes)]\n+ return out_avals\n+core.custom_typechecks[xmap_p] = _typecheck_xmap\n+\n# This is DynamicJaxprTrace.process_map with some very minor modifications\ndef _dynamic_jaxpr_process_xmap(self, primitive, f, tracers, params):\nfrom jax.interpreters.partial_eval import (\n"
}
] | Python | Apache License 2.0 | google/jax | Add a type checking rule for xmap
Also fix the type checking code in core, which incorrectly propagated output
avals of custom type checking rules.
PiperOrigin-RevId: 369485371 |
260,287 | 21.04.2021 04:09:30 | 25,200 | 42d2e7620a81db03460738afcde13c9359b33701 | Implement nesting of pjits
Without this change nesting works only when the inner `pjit`ed functions don't
close over any values. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -27,6 +27,7 @@ from ..api_util import (argnums_partial_except, flatten_axes,\nfrom ..interpreters import ad\nfrom ..interpreters import pxla\nfrom ..interpreters import xla\n+from ..interpreters import partial_eval as pe\nfrom ..lib import xla_bridge as xb\nfrom ..lib import xla_client as xc\nfrom ..tree_util import tree_flatten, tree_unflatten\n@@ -154,34 +155,8 @@ def _pjit_call_impl(fun: lu.WrappedFun, *args, in_axis_resources,\n(\"abstract args\", in_avals))\nreturn pjit_callable(*args)\n-def _pjit_translation_rule(c, axis_env, in_nodes, name_stack, backend, name,\n- call_jaxpr, in_axis_resources, out_axis_resources_thunk,\n- resource_env, donated_invars):\n- mesh = resource_env.physical_mesh\n- subc = xc.XlaBuilder(f\"pjit_{name}\")\n-\n- args = []\n- for i, (n, axis_resources) in enumerate(safe_zip(in_nodes, in_axis_resources)):\n- # N.B. inlined calls shouldn't have shardings set directly on the inputs or\n- # outputs (set_sharding_proto adds an identity operation).\n- arg = xb.parameter(subc, i, c.GetShape(n))\n- args.append(xb.set_sharding_proto(subc, arg,\n- get_sharding_proto(c, n, axis_resources, mesh)))\n-\n- out_nodes = xla.jaxpr_subcomp(\n- subc, call_jaxpr, backend, axis_env, (),\n- extend_name_stack(name_stack, wrap_name(name, \"pjit\")), *args)\n- out_axis_resources = out_axis_resources_thunk()\n- out_nodes = [xb.set_sharding_proto(subc, out,\n- get_sharding_proto(c, n, axis_resources, mesh))\n- for out, axis_resources in safe_zip(out_nodes, out_axis_resources)]\n-\n- subc = subc.build(xops.Tuple(subc, out_nodes))\n- return xops.Call(c, subc, list(in_nodes))\n-\npjit_call_p = core.CallPrimitive(\"pjit_call\")\npjit_call_p.def_impl(_pjit_call_impl)\n-xla.call_translations[pjit_call_p] = _pjit_translation_rule\n# None indicates unpartitioned dimension\nArrayAxisPartitioning = Optional[Union[pxla.MeshAxisName, Tuple[pxla.MeshAxisName, ...]]]\n@@ -206,6 +181,60 @@ def _pjit_callable(\nin_axes, out_axes_thunk, donated_invars,\nTrue, *in_avals, tile_by_mesh_axes=False)\n+# -------------------- pjit rules --------------------\n+\n+def _pjit_translation_rule(c, axis_env, in_nodes, name_stack, backend, name,\n+ call_jaxpr, in_axis_resources, out_axis_resources,\n+ resource_env, donated_invars):\n+ mesh = resource_env.physical_mesh\n+ subc = xc.XlaBuilder(f\"pjit_{name}\")\n+\n+ args = []\n+ for i, (n, axis_resources) in enumerate(safe_zip(in_nodes, in_axis_resources)):\n+ # N.B. inlined calls shouldn't have shardings set directly on the inputs or\n+ # outputs (set_sharding_proto adds an identity operation).\n+ arg = xb.parameter(subc, i, c.GetShape(n))\n+ args.append(xb.set_sharding_proto(subc, arg,\n+ get_sharding_proto(c, n, axis_resources, mesh)))\n+\n+ out_nodes = xla.jaxpr_subcomp(\n+ subc, call_jaxpr, backend, axis_env, (),\n+ extend_name_stack(name_stack, wrap_name(name, \"pjit\")), *args)\n+ out_nodes = [xb.set_sharding_proto(subc, out,\n+ get_sharding_proto(c, n, axis_resources, mesh))\n+ for out, axis_resources in safe_zip(out_nodes, out_axis_resources)]\n+\n+ subc = subc.build(xops.Tuple(subc, out_nodes))\n+ return xops.Call(c, subc, list(in_nodes))\n+xla.call_translations[pjit_call_p] = _pjit_translation_rule\n+\n+def _pjit_partial_eval_update_params(params, in_unknowns):\n+ call_jaxpr = params['call_jaxpr']\n+ donated_invars = params['donated_invars']\n+ in_axis_resources = params['in_axis_resources']\n+ out_axis_resources_thunk = params['out_axis_resources_thunk']\n+ if not in_unknowns and donated_invars:\n+ # JaxprTrace.post_process_call creates a call with no input tracers\n+ new_donated_invars = (False,) * len(call_jaxpr.invars)\n+ new_in_axis_resources = (None,) * len(call_jaxpr.invars)\n+ else:\n+ # JaxprTrace.process_call drops known input tracers and prepends constants\n+ num_consts = len(call_jaxpr.invars) - len(donated_invars)\n+ def filter_unknown(l):\n+ return tuple(x for x, uk in zip(l, in_unknowns) if uk)\n+ new_donated_invars = (False,) * num_consts + filter_unknown(donated_invars)\n+ new_in_axis_resources = (None,) * num_consts + filter_unknown(in_axis_resources)\n+ new_out_axis_resources = out_axis_resources_thunk()\n+ new_params = dict(params,\n+ donated_invars=new_donated_invars,\n+ in_axis_resources=new_in_axis_resources,\n+ out_axis_resources=new_out_axis_resources)\n+ del new_params['out_axis_resources_thunk']\n+ return new_params\n+pe.call_param_updaters[pjit_call_p] = _pjit_partial_eval_update_params\n+\n+\n+# -------------------- with_sharding_constraint --------------------\ndef with_sharding_constraint(x, axis_resources):\nx_flat, tree = tree_flatten(x)\n@@ -239,6 +268,7 @@ ad.deflinear2(sharding_constraint_p,\nwith_sharding_constraint(ct, axis_resources),))\nxla.translations[sharding_constraint_p] = _sharding_constraint_translation_rule\n+# -------------------- helpers --------------------\ndef get_array_mapping(axis_resources: ArrayPartitioning) -> pxla.ArrayMapping:\nif axis_resources is None:\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -227,6 +227,17 @@ class PJitTest(jtu.BufferDonationTestCase):\nshould_be_tracing = False\npjit(f, in_axis_resources=P(('x', 'y')), out_axis_resources=None)(x)\n+ @with_mesh([('x', 2), ('y', 1)])\n+ def testNested(self):\n+ # Add a constant captured by the nested pjit to make things more complicated\n+ h = jnp.arange(4)\n+ f = pjit(lambda x: x.sum() + h.sum(), in_axis_resources=P('x', 'y'), out_axis_resources=None)\n+ g = pjit(lambda x: f(jnp.sin(x)), in_axis_resources=P('x', None), out_axis_resources=None)\n+ x = jnp.arange(16).reshape((4, 4))\n+ y = g(x)\n+ self.assertAllClose(y, jnp.sin(x).sum() + h.sum())\n+ self.assertTrue(hasattr(y, \"sharding_spec\"))\n+\n# TODO(skye): add more unit tests once API is more finalized\n@curry\n"
}
] | Python | Apache License 2.0 | google/jax | Implement nesting of pjits
Without this change nesting works only when the inner `pjit`ed functions don't
close over any values.
PiperOrigin-RevId: 369626779 |
260,424 | 21.04.2021 11:49:21 | -3,600 | deb2227f4ad61304c2b017c97371f4467d9dd60b | Make sure the out_axes in the HashableFunction closure are hashable.
By flattening them before putting them in the closure. | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/api.py",
"new_path": "jax/_src/api.py",
"diff": "@@ -1627,9 +1627,14 @@ def pmap(\nlambda: (0,) * out_tree().num_leaves,\nclosure=out_axes)\nelse:\n+ # out_axes_thunk closes over the out_axes, they are flattened here to make\n+ # them hashable.\n+ out_axes_leaves, out_axes_treedef = tree_flatten(out_axes)\nout_axes_thunk = HashableFunction(\n- lambda: tuple(flatten_axes(\"pmap out_axes\", out_tree(), out_axes)),\n- closure=out_axes)\n+ lambda: tuple(flatten_axes(\"pmap out_axes\", out_tree(),\n+ tree_unflatten(out_axes_treedef,\n+ list(out_axes_leaves)))),\n+ closure=(tuple(out_axes_leaves), out_axes_treedef))\nout = pxla.xla_pmap(\nflat_fun, *args, backend=backend, axis_name=axis_name,\naxis_size=local_axis_size, global_axis_size=axis_size,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -488,9 +488,15 @@ def xmap(fun: Callable,\n# TODO: Check that:\n# - two axes mapped to the same resource never coincide (even inside f)\nin_axes_flat = flatten_axes(\"xmap in_axes\", in_tree, in_axes)\n+\n+ # out_axes_thunk closes over the out_axes, they are flattened here to make\n+ # them hashable.\n+ out_axes_leaves, out_axes_treedef = tree_flatten(out_axes)\nout_axes_thunk = HashableFunction(\n- lambda: tuple(flatten_axes(\"xmap out_axes\", out_tree(), out_axes)),\n- closure=out_axes)\n+ lambda: tuple(flatten_axes(\"xmap out_axes\", out_tree(),\n+ tree_unflatten(out_axes_treedef,\n+ list(out_axes_leaves)))),\n+ closure=(tuple(out_axes_leaves), out_axes_treedef))\naxis_resource_count = _get_axis_resource_count(normalized_axis_resources, resource_env)\nfor axis, size in axis_sizes.items():\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pmap_test.py",
"new_path": "tests/pmap_test.py",
"diff": "@@ -2033,6 +2033,15 @@ class PmapWithDevicesTest(jtu.JaxTestCase):\nself.assertAllClose(f(x, y),\n(jnp.sin(x.transpose((1, 0, 2)) + y).transpose((1, 2, 0)), y * 2))\n+ def testPmapDictOutAxes(self):\n+ # see issue #6410\n+ @partial(pmap, out_axes={'a': 0})\n+ def f(x):\n+ return {'a': x}\n+ device_count = xla_bridge.device_count()\n+ x = jnp.arange(device_count)\n+ tree_util.tree_multimap(self.assertAllClose, f(x), {'a': x})\n+\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": f\"_{in_axes}_{out_axes}\",\n\"in_axes\": in_axes, \"out_axes\": out_axes}\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/xmap_test.py",
"new_path": "tests/xmap_test.py",
"diff": "@@ -1142,6 +1142,12 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(ValueError, \"xmap doesn't support negative axes in out_axes\"):\nxmap(lambda x: x, in_axes={0: 'i'}, out_axes={-1: 'i'})(jnp.ones((5,)))\n+ @ignore_xmap_warning()\n+ def testDictOutAxes(self):\n+ # see issue #6410\n+ out = xmap(lambda x: x, in_axes=[...], out_axes={\"a\": [...]})({\"a\": 1})\n+ self.assertEqual(out, {\"a\": 1})\n+\n@ignore_xmap_warning()\ndef testListAxesRankAssertion(self):\nerror = (r\"xmap argument has an in_axes specification of \\['i', None\\], which \"\n"
}
] | Python | Apache License 2.0 | google/jax | Make sure the out_axes in the HashableFunction closure are hashable.
By flattening them before putting them in the closure. |
260,287 | 21.04.2021 11:04:52 | 25,200 | a73fc71e0f8f930420448dad4abcb3e34fb63f8d | Implement JVP for pjit | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -32,7 +32,8 @@ from ..lib import xla_bridge as xb\nfrom ..lib import xla_client as xc\nfrom ..tree_util import tree_flatten, tree_unflatten\nfrom .._src.util import (extend_name_stack, HashableFunction, safe_zip,\n- wrap_name, wraps, distributed_debug_log)\n+ wrap_name, wraps, distributed_debug_log,\n+ as_hashable_function)\nxops = xc._xla.ops\ndef pjit(fun: Callable,\n@@ -233,6 +234,23 @@ def _pjit_partial_eval_update_params(params, in_unknowns):\nreturn new_params\npe.call_param_updaters[pjit_call_p] = _pjit_partial_eval_update_params\n+def _pjit_jvp_update_params(params, nz_tangents, nz_tangents_out_thunk):\n+ donated_invars = params['donated_invars']\n+ in_axis_resources = params['in_axis_resources']\n+ out_axis_resources_thunk = params['out_axis_resources_thunk']\n+ def filter_nonzero_ins(l):\n+ return tuple(x for x, nz in zip(l, nz_tangents) if nz)\n+ @as_hashable_function(closure=(tuple(nz_tangents), out_axis_resources_thunk))\n+ def new_out_axis_resources_thunk():\n+ out_axis_resources = out_axis_resources_thunk()\n+ return (*out_axis_resources,\n+ *(ax for ax, nz in zip(out_axis_resources, nz_tangents_out_thunk()) if nz))\n+ return dict(params,\n+ donated_invars=(donated_invars + filter_nonzero_ins(donated_invars)),\n+ in_axis_resources=(in_axis_resources + filter_nonzero_ins(in_axis_resources)),\n+ out_axis_resources_thunk=new_out_axis_resources_thunk)\n+ad.call_param_updaters[pjit_call_p] = _pjit_jvp_update_params\n+\n# -------------------- with_sharding_constraint --------------------\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/ad.py",
"new_path": "jax/interpreters/ad.py",
"diff": "@@ -285,11 +285,11 @@ class JVPTrace(Trace):\nif 'name' in params:\nparams = dict(params, name=wrap_name(params['name'], 'jvp'))\nf_jvp = jvp_subtrace(f, self.main)\n+ f_jvp, nz_tangents_out = nonzero_tangent_outputs(f_jvp)\nif isinstance(call_primitive, core.MapPrimitive):\nin_axes = params['in_axes']\ntangent_in_axes = [ax for ax, nz in zip(in_axes, nz_tangents) if nz]\nout_axes_thunk = params['out_axes_thunk']\n- f_jvp, nz_tangents_out = nonzero_tangent_outputs(f_jvp)\n# The new thunk depends deterministically on the old thunk and the wrapped function.\n# Any caching already has to include the wrapped function as part of the key, so we\n# only use the previous thunk for equality checks.\n@@ -304,7 +304,8 @@ class JVPTrace(Trace):\nout_axes_thunk=new_out_axes_thunk)\nf_jvp, out_tree_def = traceable(f_jvp, len(primals), tangent_tree_def)\nupdate_params = call_param_updaters.get(call_primitive)\n- new_params = update_params(params, nz_tangents) if update_params else params\n+ new_params = (update_params(params, nz_tangents, nz_tangents_out)\n+ if update_params else params)\nresult = call_primitive.bind(f_jvp, *primals, *nonzero_tangents, **new_params)\nprimal_out, tangent_out = tree_unflatten(out_tree_def(), result)\nreturn [JVPTracer(self, p, t) for p, t in zip(primal_out, tangent_out)]\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -868,7 +868,7 @@ def _xla_call_partial_eval_update_params(params, in_unknowns):\nreturn dict(params, donated_invars=new_donated_invars)\npe.call_param_updaters[xla_call_p] = _xla_call_partial_eval_update_params\n-def _xla_call_jvp_update_params(params, nz_tangents):\n+def _xla_call_jvp_update_params(params, nz_tangents, nz_tangents_out_thunk):\ndonated_invars = params['donated_invars']\ndonated_tangents = [d for d, nz in zip(donated_invars, nz_tangents) if nz]\nnew_donated_invars = (*donated_invars, *donated_tangents)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -238,6 +238,15 @@ class PJitTest(jtu.BufferDonationTestCase):\nself.assertAllClose(y, jnp.sin(x).sum() + h.sum())\nself.assertTrue(hasattr(y, \"sharding_spec\"))\n+ @with_mesh([('x', 2), ('y', 1)])\n+ def testJVP(self):\n+ # Add a constant captured by the nested pjit to make things more complicated\n+ h = jnp.arange(4)\n+ f = pjit(lambda x: x.sum() + h.sum(), in_axis_resources=P('x', 'y'), out_axis_resources=None)\n+ g = pjit(lambda x: f(x + 2), in_axis_resources=P('x', None), out_axis_resources=None)\n+ jtu.check_grads(g, (jnp.arange(16, dtype=jnp.float32).reshape((4, 4)),),\n+ order=2, modes=[\"fwd\"], eps=1)\n+\n# TODO(skye): add more unit tests once API is more finalized\n@curry\n"
}
] | Python | Apache License 2.0 | google/jax | Implement JVP for pjit
PiperOrigin-RevId: 369692330 |
260,411 | 22.04.2021 08:14:56 | -10,800 | 6ed068637b9f5721b3fd8ae8e78218960be1c715 | Extended the comment about disabling the test_multiple_barriers | [
{
"change_type": "MODIFY",
"old_path": "tests/host_callback_test.py",
"new_path": "tests/host_callback_test.py",
"diff": "@@ -573,7 +573,9 @@ class HostCallbackIdTapTest(jtu.JaxTestCase):\nhcb.barrier_wait()\nself.assertEqual(received, set(range(count)))\n- # TODO(necula): see comment for test_multiple_tap.\n+ # TODO(necula): see comment for test_multiple_tap. Here we disable also\n+ # on TPU, because the barrier_wait runs on all devices, including on the CPU\n+ # where it would run into concurrency problems.\n@skip(\"Concurrency not supported\")\ndef test_tap_multiple_barriers(self):\n\"\"\"Call barrier_wait concurrently.\"\"\"\n"
}
] | Python | Apache License 2.0 | google/jax | Extended the comment about disabling the test_multiple_barriers |
260,642 | 08.04.2021 14:08:51 | 0 | eb9d6e4d210c7d69760c94839122a2c959625f24 | Pass axis name to _match_axes and add to error message. | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/batching.py",
"new_path": "jax/interpreters/batching.py",
"diff": "@@ -57,7 +57,8 @@ def batch_subtrace(main, in_dims, *in_vals):\nyield out_vals, out_dims\n@lu.transformation\n-def _match_axes(axis_size, in_dims, out_dims_thunk, out_dim_dests, *in_vals):\n+def _match_axes(axis_size, axis_name, in_dims, out_dims_thunk, out_dim_dests,\n+ *in_vals):\nif axis_size is None:\naxis_size, = {x.shape[d] for x, d in zip(in_vals, in_dims) if d is not not_mapped}\nout_vals = yield in_vals, {}\n@@ -65,6 +66,9 @@ def _match_axes(axis_size, in_dims, out_dims_thunk, out_dim_dests, *in_vals):\nout_dims = out_dims_thunk()\nfor od, od_dest in zip(out_dims, out_dim_dests):\nif od is not None and not isinstance(od_dest, int):\n+ if not isinstance(axis_name, core._TempAxisName):\n+ msg = f\"vmap has mapped output (axis_name={axis_name}) but out_axes is {od_dest}\"\n+ else:\nmsg = f\"vmap has mapped output but out_axes is {od_dest}\"\nraise ValueError(msg)\nyield map(partial(matchaxis, axis_size), out_dims, out_dim_dests, out_vals)\n@@ -280,8 +284,9 @@ def batch(fun: lu.WrappedFun,\n# anlogue of `jvp` in ad.py\n# TODO(mattjj,apaszke): change type of axis_size to be int, not Optional[int]\nfun, out_dims_thunk = batch_subtrace(fun)\n- return _match_axes(batchfun(fun, axis_name, axis_size, in_dims, main_type),\n- axis_size, in_dims, out_dims_thunk, out_dim_dests)\n+ return _match_axes(\n+ batchfun(fun, axis_name, axis_size, in_dims, main_type), axis_size,\n+ axis_name, in_dims, out_dims_thunk, out_dim_dests)\n# NOTE: This divides the in_axes by the tile_size and multiplies the out_axes by it.\ndef vtile(f_flat: lu.WrappedFun,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -1908,8 +1908,17 @@ class APITest(jtu.JaxTestCase):\napi.vmap(lambda x: x, in_axes=0, out_axes=(2, 3))(jnp.array([1., 2.]))\nwith self.assertRaisesRegex(\n- ValueError, \"vmap has mapped output but out_axes is None\"):\n- # If the output is mapped, then there must be some out_axes specified\n+ ValueError,\n+ r\"vmap has mapped output \\(axis_name=foo\\) but out_axes is None\"):\n+ # If the output is mapped (user-named axis), then there must be some\n+ # out_axes specified.\n+ api.vmap(lambda x: x, out_axes=None, axis_name=\"foo\")(jnp.array([1., 2.]))\n+\n+ with self.assertRaisesRegex(\n+ ValueError,\n+ \"vmap has mapped output but out_axes is None\"):\n+ # If the output is mapped (unnamed axis), then there must be some out_axes\n+ # specified.\napi.vmap(lambda x: x, out_axes=None)(jnp.array([1., 2.]))\ndef test_vmap_structured_in_axes(self):\n"
}
] | Python | Apache License 2.0 | google/jax | Pass axis name to _match_axes and add to error message. |
260,335 | 09.04.2021 21:29:42 | 25,200 | ba9233b9b6dd85147b5185f1bd920b93b6861fa8 | prune trivial convert_element_types from jaxprs
also add a test for not performing H2D transfers while tracing jnp.array | [
{
"change_type": "MODIFY",
"old_path": "docs/jaxpr.rst",
"new_path": "docs/jaxpr.rst",
"diff": "@@ -380,10 +380,8 @@ For the example consider the function ``func11`` below\nf = convert_element_type[ new_dtype=float32\nweak_type=False ] b\ng = add f e\n- h = convert_element_type[ new_dtype=float32\n- weak_type=False ] a\n- i = add g h\n- in (i, b) }\n+ h = add g a\n+ in (h, b) }\nlength=16\nlinear=(False, False, False, False)\nnum_carry=1\n@@ -424,13 +422,11 @@ computation should run. For example\ncall_jaxpr={ lambda ; a b.\nlet c = broadcast_in_dim[ broadcast_dimensions=( )\nshape=(1,) ] 1.0\n- d = convert_element_type[ new_dtype=float32\n- weak_type=False ] a\n- e = mul d c\n- f = convert_element_type[ new_dtype=float32\n+ d = mul a c\n+ e = convert_element_type[ new_dtype=float32\nweak_type=False ] b\n- g = add f e\n- in (g,) }\n+ f = add e d\n+ in (f,) }\ndevice=None\ndonated_invars=(False, False)\nname=inner ] a b\n@@ -460,16 +456,14 @@ captured using the ``xla_pmap`` primitive. Consider this example\naxis_size=1\nbackend=None\ncall_jaxpr={ lambda ; a b.\n- let c = convert_element_type[ new_dtype=float32\n- weak_type=False ] a\n- d = add b c\n- e = broadcast_in_dim[ broadcast_dimensions=( )\n+ let c = add b a\n+ d = broadcast_in_dim[ broadcast_dimensions=( )\nshape=(1,) ] 1.0\n- f = add d e\n- g = psum[ axes=('rows',)\n+ e = add c d\n+ f = psum[ axes=('rows',)\naxis_index_groups=None ] b\n- h = div f g\n- in (h,) }\n+ g = div e f\n+ in (g,) }\ndevices=None\ndonated_invars=(False, False)\nglobal_arg_shapes=(None,)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/lax_numpy.py",
"new_path": "jax/_src/numpy/lax_numpy.py",
"diff": "@@ -47,7 +47,6 @@ from jax.core import UnshapedArray, ShapedArray, ConcreteArray, canonicalize_sha\nfrom jax.config import config\nfrom jax.interpreters.xla import DeviceArray, _DeviceArray, _CppDeviceArray\nfrom jax import lax\n-from jax._src.lax.lax import _device_put_raw\nfrom jax import ops\nfrom jax._src.util import (partial, unzip2, prod as _prod, subvals, safe_zip,\ncanonicalize_axis as _canonicalize_axis, maybe_named_axis)\n@@ -2932,12 +2931,16 @@ def array(object, dtype=None, copy=True, order=\"K\", ndmin=0):\n# large integers; see discussion in https://github.com/google/jax/pull/6047.\nobject = _np_array(object, dtype=dtype, ndmin=ndmin, copy=False)\n+ # call _np_array a second time with canonicalized dtype\n+ dtype = dtypes.canonicalize_dtype(object.dtype)\n+ object = _np_array(object, dtype=dtype, copy=False)\n+\nassert type(object) not in dtypes.python_scalar_dtypes\nif type(object) is np.ndarray:\n_inferred_dtype = object.dtype and dtypes.canonicalize_dtype(object.dtype)\nlax._check_user_dtype_supported(_inferred_dtype, \"array\")\n- out = np.array(object, copy=copy, dtype=dtype)\n+ out = _np_array(object, copy=copy, dtype=dtype)\nif dtype: assert _dtype(out) == dtype\nelif isinstance(object, (DeviceArray, core.Tracer)):\nif isinstance(object, DeviceArray) and copy:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/partial_eval.py",
"new_path": "jax/interpreters/partial_eval.py",
"diff": "@@ -940,6 +940,7 @@ class JaxprStackFrame:\noutvars = [self.tracer_to_var[id(t)] for t in out_tracers]\nconstvars, constvals = unzip2(self.constvar_to_val.items())\njaxpr = Jaxpr(constvars, invars, outvars, self.eqns)\n+ jaxpr, constvals = _prune_convert_element_types(jaxpr, constvals)\njaxpr, constvals = _inline_literals(jaxpr, constvals)\nout_avals = [t.aval for t in out_tracers]\nreturn jaxpr, out_avals, constvals\n@@ -962,7 +963,28 @@ class JaxprStackFrame:\nconst_eqns = [eqn for eqn in self.eqns if set(eqn.invars) & constvars]\nreturn invar_positions, const_eqns\n+def _prune_convert_element_types(jaxpr, constvals):\n+ consts = dict(zip(jaxpr.constvars, constvals))\n+ new_eqns = []\n+ for eqn in jaxpr.eqns:\n+ if eqn.primitive is core.convert_element_type_p:\n+ c = consts.get(eqn.invars[0])\n+ if type(c) in core.literalable_types and not np.shape(c):\n+ # constant-fold dtype conversion of literals to be inlined\n+ consts[eqn.outvars[0]] = np.array(c, eqn.params['new_dtype'])\n+ continue\n+ if c is not None and dtypes.dtype(c) == eqn.params['new_dtype']:\n+ # don't stage out no-op convert_element_type calls as clutter\n+ consts[eqn.outvars[0]] = c\n+ continue\n+ new_eqns.append(eqn)\n+ new_constvars, new_constvals = unzip2(consts.items())\n+ new_jaxpr = Jaxpr(new_constvars, jaxpr.invars, jaxpr.outvars, new_eqns)\n+ return new_jaxpr, new_constvals\n+\ndef _inline_literals(jaxpr, constvals):\n+ # This function also ensures variables are labeled in a canonical ordering,\n+ # prunes unused constants, and inserts `dropvar` symbols.\nconsts = dict(zip(jaxpr.constvars, constvals))\nnewvar = core.gensym()\nnewvars = {}\n@@ -976,17 +998,13 @@ def _inline_literals(jaxpr, constvals):\nreturn None\nused = {v for eqn in jaxpr.eqns for v in eqn.invars} | set(jaxpr.outvars)\n- new_constvars = [var(v) for v in jaxpr.constvars if not lit(v)]\n- new_constvals = [c for v, c in zip(jaxpr.constvars, constvals) if not lit(v)]\n+ new_constvars = [var(v) for v in jaxpr.constvars if v in used and not lit(v)]\n+ new_constvals = [c for v, c in zip(jaxpr.constvars, constvals)\n+ if v in used and not lit(v)]\nnew_invars = [var(v) for v in jaxpr.invars]\nnew_eqns = []\nfor eqn in jaxpr.eqns:\ninvars = [lit(v) or var(v) for v in eqn.invars]\n- if (eqn.primitive is core.convert_element_type_p and type(invars[0]) is Literal):\n- # constant-fold dtype conversion of literals to be inlined\n- consts[eqn.outvars[0]] = np.array(invars[0].val, eqn.params['new_dtype'])\n- else:\n- # might do DCE here, but we won't until we're more careful about effects\noutvars = [var(v) if v in used else dropvar for v in eqn.outvars]\nnew_eqns.append(new_jaxpr_eqn(invars, outvars, eqn.primitive, eqn.params,\neqn.source_info))\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -2269,8 +2269,6 @@ class APITest(jtu.JaxTestCase):\nf()\ndef test_xla_computation_zeros_doesnt_device_put(self):\n- raise unittest.SkipTest(\"broken test\") # TODO(mattjj): fix\n-\nwith jtu.count_device_put() as count:\napi.xla_computation(lambda: jnp.zeros(3))()\nself.assertEqual(count[0], 0)\n@@ -2666,6 +2664,10 @@ class APITest(jtu.JaxTestCase):\njtu.check_grads(batched_scan_over_mul, (x_batch, coeff), order=2,\nmodes=['rev'])\n+ def test_jnp_array_doesnt_device_put(self):\n+ with jtu.count_device_put() as count:\n+ api.make_jaxpr(lambda: jnp.array(3))()\n+ self.assertEqual(count[0], 0)\nclass RematTest(jtu.JaxTestCase):\n"
}
] | Python | Apache License 2.0 | google/jax | prune trivial convert_element_types from jaxprs
also add a test for not performing H2D transfers while tracing jnp.array |
260,287 | 22.04.2021 15:30:03 | 25,200 | 23f847e0d377f5f7cbf50272838d6324a2114fef | Make the initial-style -> final-style conversion rule based
Also, add a rule for pjit to make sure that we can eval jaxprs that
contain pjits. | [
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "@@ -328,21 +328,13 @@ def eval_jaxpr(jaxpr: Jaxpr, consts, *args):\nsubfuns = [lu.wrap_init(partial(eval_jaxpr, call_jaxpr, ()))]\nelse:\nsubfuns = []\n- # TODO(apaszke): Make this initial -> final style conversion into a rule lookup\n- if eqn.primitive.map_primitive:\n+ if eqn.primitive in initial_to_final_param_rules:\n+ bind_params = initial_to_final_param_rules[eqn.primitive](params)\n+ elif eqn.primitive.map_primitive:\nout_axes_thunk = HashableFunction(lambda: params['out_axes'],\nclosure=params['out_axes'])\nbind_params = dict(params, out_axes_thunk=out_axes_thunk)\ndel bind_params['out_axes']\n- if 'spmd_out_axes' in params: # for xmap\n- spmd_out_axes_thunk: Optional[HashableFunction]\n- if params['spmd_out_axes'] is not None:\n- spmd_out_axes_thunk = HashableFunction(lambda: params['spmd_out_axes'],\n- closure=params['spmd_out_axes'])\n- else:\n- spmd_out_axes_thunk = None\n- bind_params['spmd_out_axes_thunk'] = spmd_out_axes_thunk\n- del bind_params['spmd_out_axes']\nelse:\nbind_params = params\nwith source_info_util.user_context(eqn.source_info):\n@@ -353,6 +345,8 @@ def eval_jaxpr(jaxpr: Jaxpr, consts, *args):\nwrite(eqn.outvars[0], ans)\nreturn map(read, jaxpr.outvars)\n+initial_to_final_param_rules: Dict[Primitive, Callable] = {}\n+\n# -------------------- tracing --------------------\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -904,6 +904,22 @@ batching.BatchTrace.process_xmap = partialmethod(_batch_trace_process_xmap, Fals\nbatching.SPMDBatchTrace.process_xmap = partialmethod(_batch_trace_process_xmap, True) # type: ignore\n+def _xmap_initial_to_final_params(params):\n+ out_axes_thunk = HashableFunction(lambda: params['out_axes'],\n+ closure=params['out_axes'])\n+ if params['spmd_out_axes'] is not None:\n+ spmd_out_axes_thunk = HashableFunction(lambda: params['spmd_out_axes'],\n+ closure=params['spmd_out_axes'])\n+ else:\n+ spmd_out_axes_thunk = None\n+ bind_params = dict(params,\n+ out_axes_thunk=out_axes_thunk,\n+ spmd_out_axes_thunk=spmd_out_axes_thunk)\n+ del bind_params['out_axes']\n+ del bind_params['spmd_out_axes']\n+ return bind_params\n+core.initial_to_final_param_rules[xmap_p] = _xmap_initial_to_final_params\n+\n# -------- nested xmap handling --------\ndef _xmap_translation_rule(*args, **kwargs):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -251,6 +251,15 @@ def _pjit_jvp_update_params(params, nz_tangents, nz_tangents_out_thunk):\nad.call_param_updaters[pjit_call_p] = _pjit_jvp_update_params\n+def _pjit_init_to_final_params(params):\n+ out_axis_resources_thunk = HashableFunction(lambda: params['out_axis_resources'],\n+ closure=params['out_axis_resources'])\n+ bind_params = dict(params, out_axis_resources_thunk=out_axis_resources_thunk)\n+ del bind_params['out_axis_resources']\n+ return bind_params\n+core.initial_to_final_param_rules[pjit_call_p] = _pjit_init_to_final_params\n+\n+\n# -------------------- with_sharding_constraint --------------------\ndef with_sharding_constraint(x, axis_resources):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -247,6 +247,17 @@ class PJitTest(jtu.BufferDonationTestCase):\njtu.check_grads(g, (jnp.arange(16, dtype=jnp.float32).reshape((4, 4)),),\norder=2, modes=[\"fwd\"], eps=1)\n+ @with_mesh([('x', 2), ('y', 1)])\n+ def testEvalJaxpr(self):\n+ x, y = jnp.arange(4), jnp.arange(5)\n+ f = pjit(lambda x, y: x.sum() + jnp.sin(y),\n+ in_axis_resources=(P('x'), P('y')),\n+ out_axis_resources=P('y'))\n+ f_jaxpr = jax.make_jaxpr(f)(x, y)\n+ f_eval = jax.core.jaxpr_as_fun(f_jaxpr)\n+ r, = f_eval(x, y)\n+ self.assertAllClose(r, x.sum() + jnp.sin(y))\n+\n# TODO(skye): add more unit tests once API is more finalized\n@curry\n"
}
] | Python | Apache License 2.0 | google/jax | Make the initial-style -> final-style conversion rule based
Also, add a rule for pjit to make sure that we can eval jaxprs that
contain pjits.
PiperOrigin-RevId: 369964136 |
260,287 | 23.04.2021 03:42:14 | 25,200 | 973ca07a04488d767242f49d5892f581a216bed0 | Add stricter resource overlap checking
We've had some checks for coinciding logical axes mapped to the same
resources in the existing xmap code, but they were quite lax. This
adds a proper type checker and a bunch of tests to verify that we
can catch the interesting failure cases. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -32,6 +32,7 @@ from .._src.tree_util import _replace_nones\nfrom ..api_util import (flatten_fun_nokwargs, flatten_axes, _ensure_index_tuple,\ndonation_vector)\nfrom .._src import source_info_util\n+from ..errors import JAXTypeError\nfrom ..interpreters import partial_eval as pe\nfrom ..interpreters import pxla\nfrom ..interpreters import xla\n@@ -486,8 +487,6 @@ def xmap(fun: Callable,\ndonated_invars = donation_vector(donate_argnums, args, ())\nelse:\ndonated_invars = (False,) * len(args_flat)\n- # TODO: Check that:\n- # - two axes mapped to the same resource never coincide (even inside f)\nin_axes_flat = flatten_axes(\"xmap in_axes\", in_tree, in_axes)\n# out_axes_thunk closes over the out_axes, they are flattened here to make\n@@ -577,6 +576,11 @@ def make_xmap_callable(fun: lu.WrappedFun,\njaxpr, out_avals, consts = pe.trace_to_jaxpr_final(fun, mapped_in_avals)\nout_axes = out_axes_thunk()\n_check_out_avals_vs_out_axes(out_avals, out_axes, global_axis_sizes)\n+ _resource_typing_xmap(dict(axis_resources=axis_resources,\n+ out_axes=out_axes,\n+ call_jaxpr=jaxpr,\n+ name=name),\n+ None, {})\njaxpr = plan.subst_axes_with_resources(jaxpr)\nf = lu.wrap_init(core.jaxpr_as_fun(core.ClosedJaxpr(jaxpr, consts)))\n@@ -651,19 +655,8 @@ class EvaluationPlan(NamedTuple):\ntry:\nwith core.extend_axis_env_nd(self.resource_axis_env.items()):\nreturn core.subst_axis_names_jaxpr(jaxpr, self.axis_subst)\n- except core.DuplicateAxisNameError as e:\n- resource_to_axis = {}\n- for axis in e.var.aval.named_shape:\n- for resource in self.physical_axis_resources[axis]:\n- if resource in resource_to_axis:\n- other_axis = resource_to_axis[resource]\n- axis, other_axis = sorted([str(axis), str(other_axis)])\n- raise TypeError(f\"Axes `{axis}` and `{other_axis}` are both mapped to the \"\n- f\"resource `{resource}`, but they coincide in the named_shape \"\n- f\"of a value returned from a primitive {e.eqn.primitive} created \"\n- f\"at {source_info_util.summarize(e.eqn.source_info)}\")\n- resource_to_axis[resource] = axis\n- raise AssertionError(\"Failed to find the duplicate axis? Please open a bug report!\")\n+ except core.DuplicateAxisNameError:\n+ raise AssertionError(\"Incomplete resource type-checking? Please open a bug report!\")\ndef vectorize(self, f: lu.WrappedFun, in_axes, out_axes):\nfor naxis, raxes in self.axis_subst_dict.items():\n@@ -765,6 +758,51 @@ def _typecheck_xmap(\nreturn out_avals\ncore.custom_typechecks[xmap_p] = _typecheck_xmap\n+\n+def _resource_typing_xmap(params,\n+ source_info: Optional[source_info_util.Traceback],\n+ outer_axis_resources):\n+ def show_axes(axes):\n+ return \", \".join(sorted([f\"`{a}`\" for a in axes]))\n+ axis_resources = params['axis_resources']\n+ inner_axis_resources = dict(outer_axis_resources)\n+ inner_axis_resources.update(axis_resources)\n+ if len(inner_axis_resources) < len(outer_axis_resources) + len(axis_resources):\n+ overlap = set(outer_axis_resources) & set(axis_resources)\n+ raise JAXTypeError(\n+ f\"Detected disallowed xmap axis name shadowing at \"\n+ f\"{source_info_util.summarize(source_info)} \"\n+ f\"(shadowed axes: {show_axes(overlap)})\")\n+\n+ call_jaxpr = params['call_jaxpr']\n+ pxla.resource_typecheck(\n+ params['call_jaxpr'], inner_axis_resources,\n+ lambda: (f\"an xmapped function {params['name']} \" +\n+ (f\"(xmap called at {source_info_util.summarize(source_info)})\"\n+ if source_info else \"\")))\n+\n+ for v, axes in zip(call_jaxpr.outvars, params['out_axes']):\n+ broadcast_axes = set(axes) - set(v.aval.named_shape)\n+ used_resources = set(it.chain.from_iterable(\n+ inner_axis_resources[a] for a in v.aval.named_shape))\n+ for baxis in broadcast_axes:\n+ baxis_resources = set(inner_axis_resources[baxis])\n+ overlap = baxis_resources & used_resources\n+ if overlap:\n+ resource_to_axis = {}\n+ for axis in v.aval.named_shape:\n+ for raxis in inner_axis_resources[axis]:\n+ resource_to_axis[raxis] = axis\n+ partitioning_axes = set(resource_to_axis[raxis] for raxis in overlap)\n+ raise JAXTypeError(\n+ f\"One of xmapped function ({params['name']}) outputs is broadcast \"\n+ f\"along axis `{baxis}` which is assigned to resources \"\n+ f\"{show_axes(baxis_resources)}, but the output is already \"\n+ f\"partitioned along {show_axes(overlap)}, because its \"\n+ f\"named shape contains {show_axes(partitioning_axes)}\")\n+pxla.custom_resource_typing_rules[xmap_p] = _resource_typing_xmap\n+\n+\n# This is DynamicJaxprTrace.process_map with some very minor modifications\ndef _dynamic_jaxpr_process_xmap(self, primitive, f, tracers, params):\nfrom jax.interpreters.partial_eval import (\n@@ -801,11 +839,13 @@ def _dynamic_jaxpr_process_xmap(self, primitive, f, tracers, params):\nelse:\nnew_spmd_in_axes = (None,) * len(consts) + params['spmd_in_axes']\nnew_donated_invars = (False,) * len(consts) + params['donated_invars']\n+ with core.extend_axis_env_nd(global_axis_sizes.items()):\n+ call_jaxpr = convert_constvars_jaxpr(jaxpr)\nnew_params = dict(params, in_axes=new_in_axes, out_axes=out_axes,\ndonated_invars=new_donated_invars,\nspmd_in_axes=new_spmd_in_axes,\nspmd_out_axes=spmd_out_axes,\n- call_jaxpr=convert_constvars_jaxpr(jaxpr))\n+ call_jaxpr=call_jaxpr)\ndel new_params['out_axes_thunk']\ndel new_params['spmd_out_axes_thunk']\neqn = new_jaxpr_eqn([*constvars, *invars], outvars, primitive,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -45,9 +45,11 @@ from .. import core\nfrom .. import linear_util as lu\nfrom ..abstract_arrays import array_types\nfrom ..core import ConcreteArray, ShapedArray\n+from .._src import source_info_util\nfrom .._src.util import (partial, unzip3, prod, safe_map, safe_zip,\nextend_name_stack, wrap_name, assert_unreachable,\ntuple_insert, tuple_delete, distributed_debug_log)\n+from ..errors import JAXTypeError\nfrom ..lib import xla_bridge as xb\nfrom ..lib import xla_client as xc\nfrom ..lib import pmap_lib\n@@ -1534,6 +1536,46 @@ def _sanitize_mesh_jaxpr(jaxpr):\ncore.traverse_jaxpr_params(_sanitize_mesh_jaxpr, eqn.params)\n+custom_resource_typing_rules: Dict[core.Primitive, Callable] = {}\n+\n+def resource_typecheck(jaxpr, axis_resources, what_jaxpr_thunk):\n+ def _check_aval(aval, what_thunk):\n+ if not hasattr(aval, 'named_shape'):\n+ return\n+ resource_to_axis = {}\n+ for axis in aval.named_shape:\n+ for resource in axis_resources[axis]:\n+ if resource in resource_to_axis:\n+ other_axis = resource_to_axis[resource]\n+ axis, other_axis = sorted([str(axis), str(other_axis)])\n+ raise JAXTypeError(\n+ f\"Axes `{axis}` and `{other_axis}` are both mapped to the \"\n+ f\"resource `{resource}`, but they coincide in the named_shape \"\n+ f\"of {what_thunk()}\")\n+ resource_to_axis[resource] = axis\n+\n+ what_thunk = lambda: (f\"an input to {what_jaxpr_thunk()}\")\n+ for v in jaxpr.constvars:\n+ _check_aval(v.aval, what_thunk)\n+ for v in jaxpr.invars:\n+ _check_aval(v.aval, what_thunk)\n+ what_thunk = lambda: (f\"a value returned from a primitive {eqn.primitive} created \"\n+ f\"at {source_info_util.summarize(eqn.source_info)}\")\n+ rec_what_jaxpr_thunk = lambda: (f\"a primitive {eqn.primitive} created at\"\n+ f\"{source_info_util.summarize(eqn.source_info)}\")\n+ for eqn in jaxpr.eqns:\n+ typing_rule = custom_resource_typing_rules.get(eqn.primitive, None)\n+ if typing_rule:\n+ typing_rule(eqn.params, eqn.source_info, axis_resources)\n+ else:\n+ core.traverse_jaxpr_params(partial(resource_typecheck,\n+ axis_resources=axis_resources,\n+ what_jaxpr_thunk=rec_what_jaxpr_thunk),\n+ eqn.params)\n+ for v in eqn.outvars:\n+ _check_aval(v.aval, what_thunk)\n+\n+\ndef mesh_sharding_specs(axis_sizes, axis_names):\nmesh_axis_pos = {name: i for i, name in enumerate(axis_names)}\n# NOTE: This takes in the non-sharded avals!\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/xmap_test.py",
"new_path": "tests/xmap_test.py",
"diff": "@@ -36,9 +36,10 @@ from jax import test_util as jtu\nfrom jax import vmap\nfrom jax import lax\nfrom jax import core\n-from jax.core import NamedShape\n+from jax.core import NamedShape, JaxprTypeError\nfrom jax.experimental import maps\nfrom jax.experimental.maps import Mesh, mesh, xmap\n+from jax.errors import JAXTypeError\nfrom jax.lib import xla_bridge\nfrom jax._src.util import curry, unzip2, split_list, prod\nfrom jax._src.lax.lax import DotDimensionNumbers\n@@ -1164,9 +1165,33 @@ class XMapErrorTest(jtu.JaxTestCase):\nxmap(lambda x: x.reshape((2, 2)),\nin_axes=['i', None], out_axes=['i', None])(jnp.ones((5, 4)))\n+ @ignore_xmap_warning()\n+ def testReturnExtraMappedAxes(self):\n+ fm = xmap(lambda x, y: x + y,\n+ in_axes=(['a', ...], ['b', ...]), out_axes=['a', ...])\n+ x = np.arange(12).reshape((4, 3))\n+ y = np.arange(6).reshape((2, 3))\n+ error = (r\"One of xmap results has an out_axes specification of \\['a', ...\\], but \"\n+ r\"is actually mapped along more axes defined by this xmap call: b\")\n+ with self.assertRaisesRegex(TypeError, error):\n+ fm(x, y)\n+\n+ @ignore_xmap_warning()\n+ @with_mesh([('x', 2)])\n+ def testResourceConflictArgs(self):\n+ fm = xmap(lambda x: lax.psum(x, ('a', 'b')),\n+ in_axes=['a', 'b'], out_axes=[],\n+ axis_resources={'a': 'x', 'b': 'x'})\n+ x = np.arange(16).reshape(4, 4)\n+ error = (r\"Axes `a` and `b` are both mapped to the resource `x`, but they \"\n+ r\"coincide in the named_shape of an input to an xmapped function \"\n+ r\"<lambda>\")\n+ with self.assertRaisesRegex(JAXTypeError, error):\n+ fm(x)\n+\n@ignore_xmap_warning()\n@with_mesh([('x', 2)])\n- def testResourceConflict(self):\n+ def testResourceConflictInner(self):\nfm = xmap(lambda x, y: x + y,\nin_axes=(['a', ...], ['b', ...]), out_axes=['a', 'b', ...],\naxis_resources={'a': 'x', 'b': 'x'})\n@@ -1174,20 +1199,60 @@ class XMapErrorTest(jtu.JaxTestCase):\ny = np.arange(6).reshape(2, 3)\nerror = (r\"Axes `a` and `b` are both mapped to the resource `x`, but they \"\nr\"coincide in the named_shape.*primitive add created at\")\n- with self.assertRaisesRegex(TypeError, error):\n+ with self.assertRaisesRegex(JAXTypeError, error):\nfm(x, y)\n- @ignore_xmap_warning()\n- def testReturnExtraMappedAxes(self):\n- fm = xmap(lambda x, y: x + y,\n- in_axes=(['a', ...], ['b', ...]), out_axes=['a', ...])\n- x = np.arange(12).reshape((4, 3))\n- y = np.arange(6).reshape((2, 3))\n- error = (r\"One of xmap results has an out_axes specification of \\['a', ...\\], but \"\n- r\"is actually mapped along more axes defined by this xmap call: b\")\n- with self.assertRaisesRegex(TypeError, error):\n+ @with_mesh([('x', 2)])\n+ def testResourceConflictOut(self):\n+ fm = xmap(lambda x, y: x,\n+ in_axes=(['a', ...], ['b', ...]), out_axes=['a', 'b', ...],\n+ axis_resources={'a': 'x', 'b': 'x'})\n+ x = np.arange(12).reshape(4, 3)\n+ y = np.arange(6).reshape(2, 3)\n+ error = (r\"One of xmapped function \\(<lambda>\\) outputs is broadcast along axis \"\n+ r\"`b` which is assigned to resources `x`, but the output is already \"\n+ r\"partitioned along `x`, because its named shape contains `a`\")\n+ with self.assertRaisesRegex(JAXTypeError, error):\nfm(x, y)\n+ @ignore_xmap_warning()\n+ @with_mesh([('x', 2)])\n+ def testResourceConflictNestArgs(self):\n+ f = xmap(lambda x: x, in_axes=['i'], out_axes=['i'], axis_resources={'i': 'x'})\n+ h = xmap(f, in_axes=['j', ...], out_axes=['j', ...], axis_resources={'j': 'x'})\n+ x = np.arange(16).reshape((4, 4))\n+ error = (r\"Axes `i` and `j` are both mapped to the resource `x`, but they \"\n+ r\"coincide in the named_shape of an input to an xmapped function \"\n+ r\"<lambda> \\(xmap called at .*\\)\")\n+ with self.assertRaisesRegex(JAXTypeError, error):\n+ h(x)\n+\n+ @ignore_xmap_warning()\n+ @with_mesh([('x', 2)])\n+ def testResourceConflictNestInner(self):\n+ f = xmap(lambda x: lax.axis_index('i') + x,\n+ in_axes=[], out_axes=['i'], axis_sizes={'i': 4}, axis_resources={'i': 'x'})\n+ h = xmap(f, in_axes=['j', ...], out_axes=['j', ...], axis_resources={'j': 'x'})\n+ x = np.arange(4)\n+ error = (r\"Axes `i` and `j` are both mapped to the resource `x`, but they \"\n+ r\"coincide in the named_shape of a value returned from a primitive \"\n+ r\"add created at .*\")\n+ with self.assertRaisesRegex(JAXTypeError, error):\n+ h(x)\n+\n+ @ignore_xmap_warning()\n+ @with_mesh([('x', 2)])\n+ def testResourceConflictNestOut(self):\n+ f = xmap(lambda x: x,\n+ in_axes=[], out_axes=['i'], axis_sizes={'i': 4}, axis_resources={'i': 'x'})\n+ h = xmap(f, in_axes=['j', ...], out_axes=['j', ...], axis_resources={'j': 'x'})\n+ x = np.arange(4)\n+ error = (r\"One of xmapped function \\(<lambda>\\) outputs is broadcast along \"\n+ r\"axis `i` which is assigned to resources `x`, but the output is \"\n+ r\"already partitioned along `x`, because its named shape contains `j`\")\n+ with self.assertRaisesRegex(JAXTypeError, error):\n+ h(x)\n+\nif __name__ == '__main__':\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | Add stricter resource overlap checking
We've had some checks for coinciding logical axes mapped to the same
resources in the existing xmap code, but they were quite lax. This
adds a proper type checker and a bunch of tests to verify that we
can catch the interesting failure cases.
PiperOrigin-RevId: 370051512 |
260,424 | 23.04.2021 14:43:20 | -3,600 | b244e2b8c8ba0bd6e848814173536b1e765bd602 | Add eval_shape to the UnexpectedTracerError too. | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/api.py",
"new_path": "jax/_src/api.py",
"diff": "@@ -2391,7 +2391,8 @@ def eval_shape(fun: Callable, *args, **kwargs):\nargs_flat, in_tree = tree_flatten((args, kwargs))\nwrapped_fun, out_tree = flatten_fun(lu.wrap_init(fun), in_tree)\nout = pe.abstract_eval_fun(wrapped_fun.call_wrapped,\n- *map(shaped_abstractify, args_flat))\n+ *map(shaped_abstractify, args_flat),\n+ transform_name=\"eval_shape\")\nout = [ShapeDtypeStruct(x.shape, x.dtype, x.named_shape) for x in out]\nreturn tree_unflatten(out_tree(), out)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/partial_eval.py",
"new_path": "jax/interpreters/partial_eval.py",
"diff": "@@ -400,8 +400,8 @@ call_partial_eval_rules: Dict[core.Primitive, Callable] = {}\ncall_param_updaters: Dict[core.Primitive, Callable] = {}\n-def abstract_eval_fun(fun, *avals, **params):\n- _, avals_out, _ = trace_to_jaxpr_dynamic(lu.wrap_init(fun, params), avals)\n+def abstract_eval_fun(fun, *avals, transform_name=\"\", **params):\n+ _, avals_out, _ = trace_to_jaxpr_dynamic(lu.wrap_init(fun, params), avals, transform_name)\nassert all(isinstance(aval, AbstractValue) for aval in avals_out)\nreturn avals_out\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -2207,6 +2207,11 @@ class APITest(jtu.JaxTestCase):\njax.pmap(self.helper_save_tracer)(jnp.ones((1, 2)))\n_ = self._saved_tracer+1\n+ with self.assertRaisesRegex(core.UnexpectedTracerError,\n+ \"transformed by eval_shape\"):\n+ jax.eval_shape(self.helper_save_tracer, 1)\n+ _ = self._saved_tracer+1\n+\ndef test_pmap_static_kwarg_error_message(self):\n# https://github.com/google/jax/issues/3007\ndef f(a, b):\n"
}
] | Python | Apache License 2.0 | google/jax | Add eval_shape to the UnexpectedTracerError too. |
260,335 | 23.04.2021 08:31:11 | 25,200 | 4ac8937ebe782385e26af5b652c77be2281f4633 | enable fori-to-scan lowering | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/control_flow.py",
"new_path": "jax/_src/lax/control_flow.py",
"diff": "@@ -138,8 +138,8 @@ def _fori_body_fun(body_fun):\n@cache()\ndef _fori_scan_body_fun(body_fun):\ndef scanned_fun(loop_carry, _):\n- i, upper, x = loop_carry\n- return (lax.add(i, lax._const(i, 1)), upper, body_fun(i, x)), None\n+ i, x = loop_carry\n+ return (i + 1, body_fun(i, x)), None\nreturn scanned_fun\n@api_boundary\n@@ -160,9 +160,13 @@ def fori_loop(lower, upper, body_fun, init_val):\nval = body_fun(i, val)\nreturn val\n- Unlike that Python version, ``fori_loop`` is implemented in terms of a call to\n- :func:`jax.lax.while_loop`. See the :func:`jax.lax.while_loop` documentation\n- for more information.\n+ Unlike that Python version, ``fori_loop`` is implemented in terms of either a\n+ call to :func:`jax.lax.while_loop` or a call to :func:`jax.lax.scan`. If the\n+ trip count is static (meaning known at tracing time, perhaps because ``lower``\n+ and ``upper` are Python integer literals) then the ``fori_loop`` is\n+ implemented in terms of ``scan`` and reverse-mode autodiff is supported;\n+ otherwise, a ``while_loop`` is used and reverse-mode autodiff is not\n+ supported. See those functions' docstrings for more information.\nAlso unlike the Python analogue, the loop-carried value ``val`` must hold a\nfixed shape and dtype across all iterations (and not just be consistent up to\n@@ -197,12 +201,11 @@ def fori_loop(lower, upper, body_fun, init_val):\nexcept TypeError:\nuse_scan = False\nelse:\n- use_scan = False # TODO(mattjj): re-enable this\n+ use_scan = True\nif use_scan:\n- (_, _, result), _ = scan(_fori_scan_body_fun(body_fun),\n- (lower, upper, init_val), None,\n- length=upper_ - lower_)\n+ (_, result), _ = scan(_fori_scan_body_fun(body_fun), (lower_, init_val),\n+ None, length=upper_ - lower_)\nelse:\n_, _, result = while_loop(_fori_cond_fun, _fori_body_fun(body_fun),\n(lower, upper, init_val))\n@@ -1865,8 +1868,7 @@ def scan_bind(*args, **params):\nscan_p = core.Primitive(\"scan\")\nscan_p.multiple_results = True\nscan_p.def_custom_bind(scan_bind)\n-scan_p.def_impl(_scan_impl)\n-# scan_p.def_impl(partial(xla.apply_primitive, scan_p)) # TODO(mattjj): re-enable\n+scan_p.def_impl(partial(xla.apply_primitive, scan_p))\nscan_p.def_abstract_eval(_scan_abstract_eval)\nad.primitive_jvps[scan_p] = _scan_jvp\nad.primitive_transposes[scan_p] = _scan_transpose\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_control_flow_test.py",
"new_path": "tests/lax_control_flow_test.py",
"diff": "@@ -1877,24 +1877,28 @@ class LaxControlFlowTest(jtu.JaxTestCase):\njtu.check_grads(loop_lax, (x,), order=2, modes=[\"fwd\"])\n+ def testStaticForiGrad(self):\n+ func = lambda x: lax.fori_loop(x, x + 2., lambda i, c: c, x)\n+ api.grad(func)(1.) # doesn't crash\n+ api.linearize(func, 1.) # doesn't crash\n+\n@parameterized.named_parameters(\ndict(testcase_name=\"_loop={}\".format(loop), loop=loop)\n- for loop in [\"while\", \"fori\", \"fori_inside_cond\", \"fori_inside_scan\"])\n+ for loop in [\"while\", \"fori_inside_cond\", \"fori_inside_scan\"])\ndef testWhileGradError(self, loop: str = \"fori_inside_scan\"):\n# Raise error for vjp for loops\nif loop == \"while\":\nfunc = lambda x: lax.while_loop(lambda i: i < 5., lambda i: i + 1., x)\n- elif loop == \"fori\":\n- func = lambda x: lax.fori_loop(x, x + 2., lambda i, c: c, x)\nelif loop == \"fori_inside_jit\":\nfunc = api.jit(lambda x: lax.fori_loop(x, x + 2., lambda i, c: c, x))\nelif loop == \"fori_inside_cond\":\n- func = lambda x: lax.cond(True, x,\n- lambda x: lax.fori_loop(x, x + 2., lambda i, c: c, x),\n+ func = lambda x: lax.cond(\n+ True,\n+ x, lambda x: lax.fori_loop(x, x + 2., lambda i, c: c, x),\n1., lambda x: x)\nelif loop == \"fori_inside_scan\":\n- func = lambda x: lax.scan(lambda c, x: (lax.fori_loop(x, x + 2., lambda i, c1: c1 * c, x),\n- None),\n+ func = lambda x: lax.scan(\n+ lambda c, x: (lax.fori_loop(x, x + 2., lambda i, c1: c1 * c, x), None),\nx, np.ones(2))[0]\nelse:\nassert False\n@@ -2356,7 +2360,7 @@ class LaxControlFlowTest(jtu.JaxTestCase):\nself.assertRaisesRegex(\nValueError,\nre.escape(\n- \"compiling a primitive computation `while` that requires {} \"\n+ \"compiling a primitive computation `scan` that requires {} \"\n\"replicas, but only {} XLA devices are available on backend {}.\"\n.format(too_big, api.device_count(), jtu.device_under_test())),\nlambda: f_loop(jnp.ones(too_big)))\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -37,12 +37,11 @@ import jax\nimport jax.ops\nfrom jax._src import api\nfrom jax import lax\n-from jax import linear_util\nfrom jax import numpy as jnp\nfrom jax import test_util as jtu\nfrom jax._src import dtypes\nfrom jax import tree_util\n-from jax.interpreters import partial_eval, xla\n+from jax.interpreters import xla\nfrom jax.test_util import check_grads\nfrom jax._src.util import prod\nfrom jax._src.numpy.util import _parse_numpydoc, ParsedDoc\n@@ -4955,12 +4954,10 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\nreturn x, y\nf = lambda y: lax.fori_loop(0, 5, body, (y, y))\n- wrapped = linear_util.wrap_init(f)\n- pv = partial_eval.PartialVal.unknown(jax.ShapedArray((3, 4), np.float32))\n- _, _, consts = partial_eval.trace_to_jaxpr(wrapped, [pv])\n+ jaxpr = jax.make_jaxpr(f)(np.zeros((3, 4), np.float32))\nself.assertFalse(\nany(np.array_equal(x, np.full((3, 4), 2., dtype=np.float32))\n- for x in consts))\n+ for x in jaxpr.consts))\n@parameterized.named_parameters(\n{\"testcase_name\": \"_from={}_to={}\".format(from_shape, to_shape),\n"
}
] | Python | Apache License 2.0 | google/jax | enable fori-to-scan lowering |
260,335 | 24.04.2021 15:18:26 | 25,200 | 8f434539e12725068dc3aea5c7702f938da523c5 | re-enable a working test | [
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -2274,8 +2274,6 @@ class APITest(jtu.JaxTestCase):\nf()\ndef test_xla_computation_zeros_doesnt_device_put(self):\n- raise unittest.SkipTest(\"broken test\") # TODO(mattjj): fix\n-\nwith jtu.count_device_put() as count:\napi.xla_computation(lambda: jnp.zeros(3))()\nself.assertEqual(count[0], 0)\n"
}
] | Python | Apache License 2.0 | google/jax | re-enable a working test |
260,287 | 26.04.2021 07:51:19 | 25,200 | 64130daf819a3d96b38d45586c173e9ff54514a9 | Fix xmap nesting in SPMD lowering mode
Previous implementation has been incorrectly accumulating the SPMD axis
partitioning into an existing dimension, while what we really need is to
create a separate dimension for every annotation. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -24,6 +24,7 @@ from functools import wraps, partial, partialmethod\nfrom .. import numpy as jnp\nfrom .. import core\n+from .. import config\nfrom .. import linear_util as lu\nfrom .._src.api import _check_callable, _check_arg\nfrom ..tree_util import (tree_flatten, tree_unflatten, all_leaves, tree_map,\n@@ -42,7 +43,7 @@ from ..lib import xla_bridge as xb\nfrom ..lib import xla_client as xc\nfrom .._src.util import (safe_map, safe_zip, HashableFunction,\nas_hashable_function, unzip2, distributed_debug_log,\n- tuple_replace)\n+ tuple_insert)\nfrom .._src.lax.parallel import _axis_index_translation_rule\nmap, unsafe_map = safe_map, map\n@@ -859,15 +860,13 @@ def _batch_trace_update_spmd_axes(\n\"\"\"Extends spmd in and out axes with the position of the trace's batch dimension.\"\"\"\nnot_mapped = batching.not_mapped\ndef insert_spmd_axis(axes, nd):\n- if axes is None:\n- axes = ()\n- too_short = (nd + 1) - len(axes)\n+ too_short = nd - len(axes)\nif too_short > 0:\n- axes += ((),) * too_short\n- return tuple_replace(axes, nd, (axis_name,) + axes[nd])\n+ axes += (None,) * too_short\n+ return tuple_insert(axes, nd, axis_name)\nif spmd_in_axes is None:\n- spmd_in_axes = (None,) * len(dims)\n+ spmd_in_axes = ((),) * len(dims)\nnew_spmd_in_axes = tuple(\nspmd_axes if d is not_mapped else insert_spmd_axis(spmd_axes, d)\nfor spmd_axes, d in zip(spmd_in_axes, dims))\n@@ -876,7 +875,7 @@ def _batch_trace_update_spmd_axes(\ndef new_spmd_out_axes_thunk():\ndims_out = dims_out_thunk()\nif spmd_out_axes_thunk is None:\n- spmd_out_axes = (None,) * len(dims_out)\n+ spmd_out_axes = ((),) * len(dims_out)\nelse:\nspmd_out_axes = spmd_out_axes_thunk()\nreturn tuple(\n@@ -1106,14 +1105,11 @@ def _xmap_translation_rule_spmd(c, axis_env,\nif flat_extra_axes is None:\nreturn\nfor axes, extra in zip(flat_mesh_axes, flat_extra_axes):\n- if extra is None:\n- continue\n- for dim, dim_extra in enumerate(extra):\n- for dim_extra_axis in reversed(dim_extra):\n+ for dim, dim_extra_axis in enumerate(extra):\n+ if dim_extra_axis is None: continue\n+ assert dim_extra_axis not in axes\n+ assert config.jax_enable_checks and all(v != dim for v in axes.values())\naxes[dim_extra_axis] = dim\n- # Not strictly necessary, but it feels right to make the outer partitioning\n- # axes more major to the inner axes. Removing this makes them minor.\n- axes.move_to_end(dim_extra_axis, last=False)\nadd_spmd_axes(mesh_in_axes, spmd_in_axes)\nadd_spmd_axes(mesh_out_axes, spmd_out_axes)\n# NOTE: We don't extend the resource env with the mesh shape, because those\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/xmap_test.py",
"new_path": "tests/xmap_test.py",
"diff": "@@ -18,6 +18,7 @@ from contextlib import contextmanager\nimport functools\nimport itertools as it\nimport os\n+import re\nimport unittest\nfrom itertools import product, permutations\nfrom typing import (Tuple, List, NamedTuple, Dict, Generator, Sequence, Set,\n@@ -584,6 +585,23 @@ class XMapTestSPMD(SPMDTestMixin, XMapTest):\nraise SkipTest\nsuper().setUp()\n+ @with_mesh([('x', 2), ('y', 2), ('z', 2)])\n+ def testNestedMeshSPMD(self):\n+ h = xmap(lambda y: (jnp.sin(y) * np.arange(y.size), lax.psum(y, ('a', 'b', 'c'))),\n+ in_axes={0: 'c'}, out_axes=({1: 'c'}, {}),\n+ axis_resources={'c': 'z'})\n+ f = xmap(lambda x: h(x * 2),\n+ in_axes=[None, 'a', 'b', ...], out_axes=(['a', 'b', ...], {}),\n+ axis_resources={'a': 'x', 'b': 'y'})\n+ xshape = (8, 2, 4, 5)\n+ x = jnp.arange(np.prod(xshape)).reshape(xshape)\n+ y = f(x)\n+ hlo = jax.xla_computation(f)(x).as_hlo_text()\n+ match = re.search(r\"sharding={devices=\\[([0-9,]+)\\][0-9,]+}\", hlo)\n+ self.assertIsNot(match, None)\n+ tile_factors = [int(s) for s in match.group(1).split(',')]\n+ self.assertEqual(set(tile_factors), {1, 2})\n+\nclass NamedNumPyTest(XMapTestCase):\n"
}
] | Python | Apache License 2.0 | google/jax | Fix xmap nesting in SPMD lowering mode
Previous implementation has been incorrectly accumulating the SPMD axis
partitioning into an existing dimension, while what we really need is to
create a separate dimension for every annotation.
PiperOrigin-RevId: 370455989 |
260,287 | 26.04.2021 11:41:26 | 25,200 | d0606463e40c878702e511f5fd8dd1e8a6e208a4 | Fix the batching rule for named reductions | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/parallel.py",
"new_path": "jax/_src/lax/parallel.py",
"diff": "@@ -558,7 +558,6 @@ def _reduction_with_positional_batcher(prim, vals_in, dims_in, axis_index_groups\nif axis_index_groups is not None:\nraise NotImplementedError(\"axis_index_groups not supported in vmap collectives. \"\n\"Please open a feature request!\")\n- # TODO: Transpose all dims to 0, increment all axes\nvals_in = [val if d is batching.not_mapped or d == 0 else _moveaxis(d, 0, val)\nfor val, d in zip(vals_in, dims_in)]\nmapped_vals_in, unmapped_vals_in = partitioned_vals_in = [], []\n@@ -581,6 +580,7 @@ def _reduction_with_positional_batcher(prim, vals_in, dims_in, axis_index_groups\nreturn vals_out\ndef _reduction_batcher(prim, vals_in, dims_in, *, axes, axis_index_groups):\n+ assert prim.multiple_results\nif not any(isinstance(axis, int) for axis in axes):\nreturn prim.bind(*vals_in, axes=axes, axis_index_groups=axis_index_groups), dims_in\nvals_out = _reduction_with_positional_batcher(\n@@ -589,13 +589,20 @@ def _reduction_batcher(prim, vals_in, dims_in, *, axes, axis_index_groups):\nlambda d, d_vals_in: (tuple(axis + (axis >= d) if isinstance(axis, int) else axis\nfor axis in axes),\nd_vals_in))\n- return vals_out, dims_in\n+ # _reduction_with_positional_batcher moves all map dims to 0\n+ return vals_out, [d if d is batching.not_mapped else 0 for d in dims_in]\ndef _batched_reduction_collective(\nprim, if_unmapped, frame, vals_in, dims_in, axes,\naxis_index_groups):\nassert prim.multiple_results\nassert frame.name in axes\n+ # Note that we have a choice here. We can either unfuse the reduction into one\n+ # that handles the batched dims and then another one that handles the rest.\n+ # Alternatively, we can keep the dimension reduction fused with the rest, but\n+ # we have to split the primitive into one for unmapped inputs and another\n+ # one for mapped, because they differ in their `axes` parameter.\n+ # We choose the second strategy here.\nvals_out = _reduction_with_positional_batcher(\nprim, vals_in, dims_in, axis_index_groups,\nlambda d, d_vals_in: (tuple(axis for axis in axes if axis != frame.name),\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -967,25 +967,31 @@ class BatchingTest(jtu.JaxTestCase):\n@parameterized.named_parameters(\n{\"testcase_name\": \"_{}_vmap_names={}_collective_names={}\".format(\n- collective.__name__.replace(\" \", \"\"), vmap_names, collective_names),\n+ collective.__name__.replace(\" \", \"\"),\n+ \"\".join(vmap_names), \"\".join(collective_names)),\n\"collective\": collective, \"bulk_op\": bulk_op, \"vmap_names\": vmap_names,\n\"collective_names\": collective_names}\nfor collective, bulk_op in [(lax.psum, jnp.sum),\n(lax.pmax, jnp.max),\n(lax.pmin, jnp.min)]\n- for vmap_names in [('i',), ('i', 'j'), ('j', 'i')]\n- for collective_names in it.permutations(vmap_names))\n+ for vmap_names in [('i',), ('i', 'j'), ('i', 'j', 'k')]\n+ for subset_size in range(1, len(vmap_names) + 1)\n+ for collective_subset in it.combinations(vmap_names, subset_size)\n+ for collective_names in it.permutations(collective_subset))\ndef testCommAssocCollective(self, collective, bulk_op, vmap_names, collective_names):\n- x = jnp.arange(3 * 4 * 5, dtype=jnp.float32).reshape((3, 4, 5))\n+ shape = (2, 2, 2)\n+ x = jnp.arange(np.prod(shape), dtype=jnp.float32).reshape(shape)\n# To test relative permutations of the order in which the axis names appear\n# in the primitive call versus the order the vmaps are applied, we always\n# apply vmaps in the order of the `vmap_names` argument, and apply the\n# collective with names according to the `collective_names` argument.\nf = lambda x: x - collective(x, collective_names)\n- for axis_name in vmap_names:\n- f = vmap(f, axis_name=axis_name)\n- self.assertAllClose(f(x), x - bulk_op(x, axis=tuple(range(len(vmap_names)))))\n+ # Use non-zero in and out axes to improve the coverage\n+ for i, axis_name in enumerate(vmap_names):\n+ f = vmap(f, axis_name=axis_name, in_axes=i, out_axes=i)\n+ pos_axis = [i for i, name in enumerate(vmap_names) if name in collective_names]\n+ self.assertAllClose(f(x), x - bulk_op(x, axis=pos_axis, keepdims=True))\nif collective is lax.psum:\njtu.check_grads(f, (x,), 2, eps=1)\n"
}
] | Python | Apache License 2.0 | google/jax | Fix the batching rule for named reductions
PiperOrigin-RevId: 370505998 |
260,287 | 27.04.2021 02:19:18 | 25,200 | 9f2ac6e0a3b4ad529490e927180d5b710dfcf4d3 | Add resource type-checking rules for pjit and with_sharding_constraint
To prevent people from doubly-sharding values that are sharded by outer
xmaps. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -575,7 +575,8 @@ def make_xmap_callable(fun: lu.WrappedFun,\njaxpr, out_avals, consts = pe.trace_to_jaxpr_final(fun, mapped_in_avals)\nout_axes = out_axes_thunk()\n_check_out_avals_vs_out_axes(out_avals, out_axes, global_axis_sizes)\n- _resource_typing_xmap(dict(axis_resources=axis_resources,\n+ # NOTE: We don't use avals and all params, so only pass in the relevant parts (too lazy...)\n+ _resource_typing_xmap([], dict(axis_resources=axis_resources,\nout_axes=out_axes,\ncall_jaxpr=jaxpr,\nname=name),\n@@ -601,7 +602,8 @@ def make_xmap_callable(fun: lu.WrappedFun,\ndonated_invars,\nEXPERIMENTAL_SPMD_LOWERING,\n*in_avals,\n- tile_by_mesh_axes=True)\n+ tile_by_mesh_axes=True,\n+ do_resource_typecheck=None)\nelse:\nreturn xla._xla_callable(f, None, backend, name, donated_invars,\n*((a, None) for a in in_avals))\n@@ -760,7 +762,8 @@ core.custom_typechecks[xmap_p] = _typecheck_xmap\ndef show_axes(axes):\nreturn \", \".join(sorted([f\"`{a}`\" for a in axes]))\n-def _resource_typing_xmap(params,\n+def _resource_typing_xmap(avals,\n+ params,\nsource_info: Optional[source_info_util.Traceback],\nouter_axis_resources):\naxis_resources = params['axis_resources']\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -23,9 +23,11 @@ from . import PartitionSpec\nfrom .. import core\nfrom .. import linear_util as lu\nfrom .._src.api import _check_callable, _check_arg\n+from .._src import source_info_util\nfrom ..api_util import (argnums_partial_except, flatten_axes,\nflatten_fun_nokwargs, _ensure_index_tuple,\ndonation_vector, rebase_donate_argnums)\n+from ..errors import JAXTypeError\nfrom ..interpreters import ad\nfrom ..interpreters import pxla\nfrom ..interpreters import xla\n@@ -234,14 +236,15 @@ def _pjit_callable(\nresource_env,\ndonated_invars,\nname: str,\n- *in_avals):\n+ *local_in_avals):\nin_axes = [get_array_mapping(axes) for axes in in_axis_resources]\nout_axes = lambda: [get_array_mapping(axes) for axes in out_axis_resources_thunk()]\n# TODO(skye): allow for using a submesh of physical_mesh\nreturn pxla.mesh_callable(fun, name, None, resource_env.physical_mesh,\nin_axes, out_axes, donated_invars,\n- True, *in_avals, tile_by_mesh_axes=False)\n+ True, *local_in_avals, tile_by_mesh_axes=False,\n+ do_resource_typecheck=\"pjit\")\n# -------------------- pjit rules --------------------\n@@ -322,6 +325,32 @@ def _pjit_init_to_final_params(params):\nreturn bind_params\ncore.initial_to_final_param_rules[pjit_call_p] = _pjit_init_to_final_params\n+def _check_resources_against_named_axes(what, aval, pos_axis_resources, named_axis_resources):\n+ pjit_resources = set(it.chain.from_iterable(pos_axis_resources))\n+ aval_resources = set(it.chain.from_iterable(\n+ named_axis_resources[a] for a in aval.named_shape))\n+ overlap = pjit_resources & aval_resources\n+ if overlap:\n+ raise JAXTypeError(\n+ f\"{what} has an axis resources specification of {pos_axis_resources} \"\n+ f\"that uses one or more mesh axes already used by xmap to partition \"\n+ f\"a named axis appearing in its named_shape (both use mesh axes \"\n+ f\"{maps.show_axes(overlap)})\")\n+\n+def _resource_typing_pjit(avals, params, source_info, named_axis_resources):\n+ jaxpr = params['call_jaxpr']\n+ what = \"pjit input\"\n+ for v, pos_axis_resources in zip(jaxpr.invars, params['in_axis_resources']):\n+ _check_resources_against_named_axes(what, v.aval, pos_axis_resources, named_axis_resources)\n+ pxla.resource_typecheck(\n+ jaxpr, named_axis_resources,\n+ lambda: (f\"a pjit'ed function {params['name']} \"\n+ f\"(pjit called at {source_info_util.summarize(source_info)})\"))\n+ what = \"pjit output\"\n+ for v, pos_axis_resources in zip(jaxpr.outvars, params['out_axis_resources']):\n+ _check_resources_against_named_axes(what, v.aval, pos_axis_resources, named_axis_resources)\n+pxla.custom_resource_typing_rules[pjit_call_p] = _resource_typing_pjit\n+\n# -------------------- with_sharding_constraint --------------------\n@@ -359,6 +388,14 @@ ad.deflinear2(sharding_constraint_p,\nct, axis_resources=axis_resources, resource_env=resource_env),))\nxla.translations[sharding_constraint_p] = _sharding_constraint_translation_rule\n+def _resource_typing_sharding_constraint(avals, params, source_info, named_axis_resources):\n+ aval, = avals\n+ _check_resources_against_named_axes(\n+ \"with_sharding_constraint input\", aval,\n+ params['axis_resources'], named_axis_resources)\n+pxla.custom_resource_typing_rules[sharding_constraint_p] = \\\n+ _resource_typing_sharding_constraint\n+\n# -------------------- helpers --------------------\ndef get_array_mapping(axis_resources: ParsedPartitionSpec) -> pxla.ArrayMapping:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -1390,7 +1390,8 @@ def mesh_callable(fun: lu.WrappedFun,\ndonated_invars: Sequence[bool],\nspmd_lowering: bool,\n*local_in_untiled_avals,\n- tile_by_mesh_axes: bool):\n+ tile_by_mesh_axes: bool,\n+ do_resource_typecheck: Optional[str]):\nlocal_mesh = mesh.local_mesh\nglobal_axis_sizes = mesh.shape\nlocal_axis_sizes = local_mesh.shape\n@@ -1412,6 +1413,7 @@ def mesh_callable(fun: lu.WrappedFun,\nfor aval, aval_in_axes in safe_zip(in_tiled_avals, in_axes)]\nin_jaxpr_avals = global_in_untiled_avals\nelse:\n+ assert tile_by_mesh_axes\nin_jaxpr_avals = in_tiled_avals\nwith core.extend_axis_env_nd(mesh.shape.items()):\njaxpr, out_jaxpr_avals, consts = pe.trace_to_jaxpr_final(fun, in_jaxpr_avals)\n@@ -1426,6 +1428,8 @@ def mesh_callable(fun: lu.WrappedFun,\nout_tiled_avals = out_jaxpr_avals\nlocal_out_untiled_avals = [untile_aval_nd(local_axis_sizes, aval_out_axes, aval)\nfor aval, aval_out_axes in safe_zip(out_tiled_avals, out_axes)]\n+ if do_resource_typecheck is not None:\n+ resource_typecheck(jaxpr, {}, lambda: do_resource_typecheck)\n_sanitize_mesh_jaxpr(jaxpr)\nif local_mesh.shape != mesh.shape:\ncheck_multihost_collective_allowlist(jaxpr)\n@@ -1566,7 +1570,8 @@ def resource_typecheck(jaxpr, axis_resources, what_jaxpr_thunk):\nfor eqn in jaxpr.eqns:\ntyping_rule = custom_resource_typing_rules.get(eqn.primitive, None)\nif typing_rule:\n- typing_rule(eqn.params, eqn.source_info, axis_resources)\n+ typing_rule([v.aval for v in eqn.invars], eqn.params,\n+ eqn.source_info, axis_resources)\nelse:\ncore.traverse_jaxpr_params(partial(resource_typecheck,\naxis_resources=axis_resources,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -24,9 +24,10 @@ import numpy as np\nimport jax\nimport jax.numpy as jnp\nfrom jax import test_util as jtu\n+from jax.errors import JAXTypeError\n# TODO(skye): do we still wanna call this PartitionSpec?\nfrom jax.experimental import PartitionSpec as P\n-from jax.experimental.maps import mesh\n+from jax.experimental.maps import xmap, mesh\nfrom jax.experimental.pjit import pjit, with_sharding_constraint\nfrom jax.interpreters import pxla\nfrom jax._src.util import unzip2, prod, curry\n@@ -34,6 +35,18 @@ from jax._src.util import unzip2, prod, curry\nfrom jax.config import config\nconfig.parse_flags_with_absl()\n+\n+def setUpModule():\n+ global old_lowering_flag\n+ jax.experimental.maps.make_xmap_callable.cache_clear()\n+ old_lowering_flag = jax.experimental.maps.EXPERIMENTAL_SPMD_LOWERING\n+ jax.experimental.maps.EXPERIMENTAL_SPMD_LOWERING = True\n+\n+def tearDownModule():\n+ jax.experimental.maps.make_xmap_callable.cache_clear()\n+ jax.experimental.maps.EXPERIMENTAL_SPMD_LOWERING = old_lowering_flag\n+\n+\n# TODO(skye): move into test_util and dedup with xmap_test.py\nMeshSpec = List[Tuple[str, int]]\n@@ -392,6 +405,54 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(ValueError, error):\npjit(lambda x: x, in_axis_resources=None, out_axis_resources=spec)(x)\n+ @with_mesh([('x', 2)])\n+ def testInputShardsXMapAxis(self):\n+ spec = P('x')\n+ f = xmap(pjit(lambda x: x + 2, in_axis_resources=spec, out_axis_resources=None),\n+ in_axes=['i', ...], out_axes=['i', ...], axis_resources={'i': 'x'})\n+ x = jnp.arange(4).reshape((2, 2))\n+ error = (r\"pjit input has an axis resources specification of \" +\n+ spec_regex(spec) + r\" that uses one or more mesh axes already used by \"\n+ r\"xmap to partition a named axis appearing in its named_shape \\(both \"\n+ r\"use mesh axes `x`\\)\")\n+ with self.assertRaisesRegex(JAXTypeError, error):\n+ f(x)\n+\n+ @with_mesh([('x', 2)])\n+ def testOutputShardsXMapAxis(self):\n+ spec = P('x')\n+ f = xmap(pjit(lambda x: x + 2, in_axis_resources=None, out_axis_resources=spec),\n+ in_axes=['i', ...], out_axes=['i', ...], axis_resources={'i': 'x'})\n+ x = jnp.arange(4).reshape((2, 2))\n+ error = (r\"pjit output has an axis resources specification of \" +\n+ spec_regex(spec) + r\" that uses one or more mesh axes already used by \"\n+ r\"xmap to partition a named axis appearing in its named_shape \\(both \"\n+ r\"use mesh axes `x`\\)\")\n+ with self.assertRaisesRegex(JAXTypeError, error):\n+ f(x)\n+\n+ @with_mesh([('x', 2)])\n+ def testConstraintShardsXMapAxis(self):\n+ spec = P('x')\n+ f = xmap(lambda x: with_sharding_constraint(x, axis_resources=spec),\n+ in_axes=['i', ...], out_axes=['i', ...], axis_resources={'i': 'x'})\n+ x = jnp.arange(4).reshape((2, 2))\n+ error = (r\"with_sharding_constraint input has an axis resources specification of \" +\n+ spec_regex(spec) + r\" that uses one or more mesh axes already used by \"\n+ r\"xmap to partition a named axis appearing in its named_shape \\(both \"\n+ r\"use mesh axes `x`\\)\")\n+ with self.assertRaisesRegex(JAXTypeError, error):\n+ f(x)\n+\n+ @with_mesh([('x', 2)])\n+ def testCatchesInnerXMapErrors(self):\n+ f = pjit(xmap(lambda x, y: x, in_axes=(['i'], ['j']), out_axes=['i', 'j'],\n+ axis_resources={'i': 'x', 'j': 'x'}),\n+ in_axis_resources=None, out_axis_resources=None)\n+ x = jnp.arange(4)\n+ with self.assertRaises(JAXTypeError):\n+ f(x, x)\n+\nif __name__ == '__main__':\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | Add resource type-checking rules for pjit and with_sharding_constraint
To prevent people from doubly-sharding values that are sharded by outer
xmaps.
PiperOrigin-RevId: 370635057 |
260,411 | 27.04.2021 15:21:20 | -10,800 | 80cd273de815826ebe65a25e80515f66bc951ed8 | [jax2tf] Improve the handling of variable capture for jax2tf.call
Fixes: | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/call_tf.py",
"new_path": "jax/experimental/jax2tf/call_tf.py",
"diff": "@@ -226,7 +226,6 @@ def _call_tf_translation_rule(builder, *args_op, func_tf,\nassert next_var_idx < len(func_tf_concrete.variables)\n# TODO(necula): better checking that we are picking the right variable\nvar = func_tf_concrete.variables[next_var_idx]\n- assert inp.shape == var.shape\nnext_var_idx += 1\ninp_const = np.asarray(var)\nelse:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/call_tf_test.py",
"new_path": "jax/experimental/jax2tf/tests/call_tf_test.py",
"diff": "@@ -177,14 +177,31 @@ class CallTfTest(jtu.JaxTestCase):\ndef test_with_var_read(self, with_jit=True):\nif jtu.device_under_test() == \"gpu\":\nraise unittest.SkipTest(\"Test fails on GPU\")\n- outer_var = tf.Variable(3., dtype=np.float32)\n+ outer_var_array = np.array([3., 4.], dtype=np.float32)\n+ outer_var = tf.Variable(outer_var_array)\ndef fun_tf(x):\nreturn x * outer_var + 1.\n- x = np.float32(2.)\n+ x = np.array([2., 5.,], dtype=np.float32)\nres = _maybe_jit(with_jit, jax2tf.call_tf(fun_tf))(x)\n- self.assertAllClose(x * 3. + 1., res, check_dtypes=False)\n+ self.assertAllClose(x * outer_var_array + 1., res, check_dtypes=False)\n+\n+ def test_with_var_different_shape(self):\n+ # See https://github.com/google/jax/issues/6050\n+ if jtu.device_under_test() == \"gpu\":\n+ raise unittest.SkipTest(\"Test fails on GPU\")\n+ v = tf.Variable((4., 2.), dtype=tf.float32)\n+\n+ def tf_func(x):\n+ return v + x\n+ x = np.float32(123.)\n+ tf_out = tf_func(x)\n+\n+ jax_func = jax.jit(jax2tf.call_tf(tf_func))\n+ jax_out = jax_func(x)\n+\n+ self.assertAllClose(tf_out, jax_out, check_dtypes=False)\n@parameterized_jit\ndef test_with_var_write_error(self, with_jit=True):\n@@ -221,11 +238,11 @@ class CallTfTest(jtu.JaxTestCase):\nt5 = tf.constant(5., dtype=np.float32)\ndef fun_tf(x):\n- return (x * v2 + t4) * v3 + t5\n+ return (x * v3 + t4 + v2) * v3 + t5\nx = np.float32(2.)\nres = _maybe_jit(with_jit, jax2tf.call_tf(fun_tf))(x)\n- self.assertAllClose((x * 2. + 4.) * 3. + 5., res, check_dtypes=False)\n+ self.assertAllClose((x * 3. + 4. + 2.) * 3. + 5., res, check_dtypes=False)\n@parameterized_jit\ndef test_grad(self, with_jit=False):\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Improve the handling of variable capture for jax2tf.call
Fixes: #6050 |
260,287 | 27.04.2021 07:11:41 | 25,200 | aa9d350aaf99482c32164e573879399d272a3ba6 | Fix an assertion in xmap code
This has gone under the radar because it's valid in tests and it's
in code sufficiently new that I don't expect anyone to be using it
just yet. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -1111,7 +1111,7 @@ def _xmap_translation_rule_spmd(c, axis_env,\nfor dim, dim_extra_axis in enumerate(extra):\nif dim_extra_axis is None: continue\nassert dim_extra_axis not in axes\n- assert config.jax_enable_checks and all(v != dim for v in axes.values())\n+ assert not config.jax_enable_checks or all(v != dim for v in axes.values())\naxes[dim_extra_axis] = dim\nadd_spmd_axes(mesh_in_axes, spmd_in_axes)\nadd_spmd_axes(mesh_out_axes, spmd_out_axes)\n"
}
] | Python | Apache License 2.0 | google/jax | Fix an assertion in xmap code
This has gone under the radar because it's valid in tests and it's
in code sufficiently new that I don't expect anyone to be using it
just yet.
PiperOrigin-RevId: 370672928 |
260,411 | 28.04.2021 12:22:32 | -10,800 | d762ec1d21b9a470c0790e642c85e1f133dd64d5 | [host_callback] Minor fix to use the new xla_shape.is_token | [
{
"change_type": "MODIFY",
"old_path": "CHANGELOG.md",
"new_path": "CHANGELOG.md",
"diff": "@@ -17,6 +17,7 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.\n* {func}`jax.nonzero` has a new optional `size` argument that allows it to\nbe used within `jit` ({jax-issue}`#6501`)\n* {func}`jax.numpy.unique` now supports the `axis` argument ({jax-issue}`#6532`).\n+ * {func}`jax.experimental.host_callback.call` now supports `pjit.pjit` ({jax-issue}`#6569`).\n* Breaking changes:\n* The following function names have changed. There are still aliases, so this\nshould not break existing code, but the aliases will eventually be removed\n@@ -32,6 +33,8 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.\n* Bug fixes:\n* The {func}`jax2tf.convert` now works in presence of gradients for functions\nwith integer inputs ({jax-issue}`#6360`).\n+ * Fixed assertion failure in {func}`jax2tf.call_tf` when used with captured\n+ `tf.Variable` ({jax-issue}`#6572`).\n## jaxlib 0.1.66 (unreleased)\n* New features:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/host_callback.py",
"new_path": "jax/experimental/host_callback.py",
"diff": "@@ -817,9 +817,7 @@ def _outside_call_translation_rule(\ncurrent_token = args_op[-2]\ncurrent_itoken = args_op[-1]\n# TODO: expose shape.is_token\n- assert not comp.get_shape(current_token).is_array() and not comp.get_shape(current_token).is_array(), (\n- \"The last two arguments must be tokens\")\n- assert not comp.get_shape(current_itoken).is_array() and not comp.get_shape(current_itoken).is_array(), (\n+ assert comp.get_shape(current_token).is_token() and comp.get_shape(current_itoken).is_token(), (\n\"The last two arguments must be tokens\")\nargs_to_outfeed = args_op[:-2]\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/tools/jax_to_hlo.py",
"new_path": "jax/tools/jax_to_hlo.py",
"diff": "@@ -105,7 +105,7 @@ def jax_to_hlo(fn, input_shapes, constants=None):\nfor arg_name, shape in input_shapes:\nif not shape.is_array():\nraise ValueError('Shape %s is not an array, but currently only arrays '\n- 'are supported (i.e., no tuples).' % str(shape))\n+ 'are supported (i.e., no tuples, nor tokens).' % str(shape))\n# Check that `shape` either doesn't have a layout or has the default layout.\n#\n"
}
] | Python | Apache License 2.0 | google/jax | [host_callback] Minor fix to use the new xla_shape.is_token |
260,287 | 09.04.2021 12:43:40 | 0 | 8df502aeb2cf45934df6722069b875c108c1867c | Use the axis names attached to a primitive when selecting the top trace
This is useful e.g. for handling psums of values that are not sharded,
but are also not statically known constants that we can fold. | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/parallel.py",
"new_path": "jax/_src/lax/parallel.py",
"diff": "@@ -690,7 +690,7 @@ def _psum_transpose_rule(cts, *args, axes, axis_index_groups):\naxis_index_groups=axis_index_groups)\nreturn tree_util.tree_unflatten(treedef, nonzero_in_cts)\n-psum_p = core.Primitive('psum')\n+psum_p = core.AxisPrimitive('psum')\npsum_p.multiple_results = True\npsum_p.def_impl(partial(_allreduce_impl, lax._reduce_sum))\npsum_p.def_abstract_eval(_allreduce_abstract_eval)\n@@ -722,11 +722,11 @@ def psum_bind(*args, axes, axis_index_groups):\nelse:\nsize = prod([core.axis_frame(name).size for name in named_axes]) # type: ignore\nreturn tuple(lax._const(x, size) * pos_reduce(x) for x in args)\n- return core.Primitive.bind(\n+ return core.AxisPrimitive.bind(\npsum_p, *args, axes=axes, axis_index_groups=axis_index_groups)\n-pmax_p = core.Primitive('pmax')\n+pmax_p = core.AxisPrimitive('pmax')\npmax_p.multiple_results = True\npmax_p.def_impl(partial(_allreduce_impl, lax._reduce_max))\npmax_p.def_abstract_eval(_allreduce_abstract_eval)\n@@ -739,7 +739,7 @@ batching.collective_rules[pmax_p] = \\\ncore.axis_substitution_rules[pmax_p] = partial(_subst_all_names_in_param, 'axes')\n-pmin_p = core.Primitive('pmin')\n+pmin_p = core.AxisPrimitive('pmin')\npmin_p.multiple_results = True\npmin_p.def_impl(partial(_allreduce_impl, lax._reduce_min))\npmin_p.def_abstract_eval(_allreduce_abstract_eval)\n@@ -791,7 +791,7 @@ def _ppermute_batcher(frame, vals_in, dims_in, axis_name, perm):\ndef _collective_batcher(prim, args, dims, **params):\nreturn prim.bind(*args, **params), dims if prim.multiple_results else dims[0]\n-ppermute_p = core.Primitive('ppermute')\n+ppermute_p = core.AxisPrimitive('ppermute')\nppermute_p.def_abstract_eval(lambda x, **params: raise_to_shaped(x))\nad.deflinear2(ppermute_p, _ppermute_transpose_rule)\nxla.parallel_translations[ppermute_p] = _ppermute_translation_rule\n@@ -937,7 +937,7 @@ def _all_to_all_abstract_eval(x, axis_name, split_axis, concat_axis, axis_index_\nshape[concat_axis] *= axis_size\nreturn input_aval.update(shape=tuple(shape), weak_type=False)\n-all_to_all_p = core.Primitive('all_to_all')\n+all_to_all_p = core.AxisPrimitive('all_to_all')\nall_to_all_p.def_abstract_eval(_all_to_all_abstract_eval)\nxla.parallel_translations[all_to_all_p] = _all_to_all_translation_rule\nad.deflinear2(all_to_all_p, _all_to_all_transpose_rule)\n@@ -1023,11 +1023,7 @@ def _all_gather_via_psum(x, *, all_gather_dimension, axis_name, axis_index_group\nreturn psum(outs, axis_name, axis_index_groups=axis_index_groups)\ndef _all_gather_impl(x, *, all_gather_dimension, axis_name, axis_index_groups, axis_size):\n- # Only called when the argument is not mapped.\n- out_shape = list(np.shape(x))\n- out_shape.insert(all_gather_dimension, axis_size)\n- broadcast_dims = [i for i in range(len(out_shape)) if i != all_gather_dimension]\n- return lax.broadcast_in_dim(x, out_shape, broadcast_dims)\n+ raise AssertionError(\"Unexpected call to _all_gather_impl\")\ndef _all_gather_translation_rule(c, x, *, all_gather_dimension, axis_name, axis_index_groups, axis_size, axis_env, platform):\n# TODO(cjfj): Enable this for TPU also?\n@@ -1085,10 +1081,14 @@ def _all_gather_batched_collective(frame, vals_in, dims_in, all_gather_dimension\nraise NotImplementedError(\"Please open a feature request!\")\nassert axis_name == (frame.name,), \"batcher called with wrong axis name\"\n(x,), (d,) = vals_in, dims_in\n- assert d is not batching.not_mapped\n+ if d is batching.not_mapped:\n+ out_shape = list(np.shape(x))\n+ out_shape.insert(all_gather_dimension, axis_size)\n+ broadcast_dims = [i for i in range(len(out_shape)) if i != all_gather_dimension]\n+ return lax.broadcast_in_dim(x, out_shape, broadcast_dims), batching.not_mapped\nreturn _moveaxis(d, all_gather_dimension, x), batching.not_mapped\n-all_gather_p = core.Primitive('all_gather')\n+all_gather_p = core.AxisPrimitive('all_gather')\nall_gather_p.def_abstract_eval(_all_gather_abstract_eval)\nall_gather_p.def_impl(_all_gather_impl)\nxla.parallel_translations[all_gather_p] = _all_gather_translation_rule\n@@ -1148,7 +1148,7 @@ def _vmap_process_axis_index(self, frame):\nbatching.BatchTrace.process_axis_index = _vmap_process_axis_index # type: ignore\n-pdot_p = core.Primitive('pdot')\n+pdot_p = core.AxisPrimitive('pdot')\ncore.axis_substitution_rules[pdot_p] = partial(_subst_all_names_in_param, 'axis_name')\n@pdot_p.def_impl\n@@ -1294,7 +1294,7 @@ def _pgather_collective_batcher(frame, vals_in, dims_in, *, axes):\nelse:\nreturn pgather_p.bind(src, idx, axes=new_axes), batching.not_mapped\n-pgather_p = core.Primitive('pgather')\n+pgather_p = core.AxisPrimitive('pgather')\npgather_p.def_impl(_pgather_impl)\npgather_p.def_abstract_eval(_pgather_abstract_eval)\nxla.parallel_translations[pgather_p] = _pgather_parallel_translation\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "@@ -242,6 +242,7 @@ class Primitive:\nmultiple_results = False # set for multi-output primitives\ncall_primitive = False # set for call primitives processed in final style\nmap_primitive = False # set for map primitives processed in final style\n+ _dispatch_on_params = False # whether to include axis names form params in dispatch\ndef __init__(self, name: str):\nself.name = name\n@@ -253,7 +254,8 @@ class Primitive:\ndef bind(self, *args, **params):\nassert (not config.jax_enable_checks or\nall(isinstance(arg, Tracer) or valid_jaxtype(arg) for arg in args)), args\n- top_trace = find_top_trace(args)\n+ top_trace = find_top_trace(\n+ args, used_axis_names(self, params) if self._dispatch_on_params else None)\ntracers = map(top_trace.full_raise, args)\nout = top_trace.process_primitive(self, tracers, params)\nreturn map(full_lower, out) if self.multiple_results else full_lower(out)\n@@ -811,14 +813,17 @@ def full_lower(val):\nelse:\nreturn val\n-def find_top_trace(xs) -> Trace:\n+def find_top_trace(xs, axis_names=None) -> Trace:\n+ top_main: Optional[MainTrace] = None\n+ if axis_names:\n+ top_main = max((axis_frame(a).main_trace for a in axis_names),\n+ default=None, key=lambda t: getattr(t, 'level', -1))\ntop_tracer = max((x for x in xs if isinstance(x, Tracer)),\ndefault=None, key=attrgetter('_trace.level'))\nif top_tracer is not None:\ntop_tracer._assert_live()\n- top_main = top_tracer._trace.main # type: Optional[MainTrace]\n- else:\n- top_main = None\n+ if top_tracer._trace.main.level > getattr(top_main, 'level', -1):\n+ top_main = top_tracer._trace.main\ndynamic = thread_local_state.trace_state.trace_stack.dynamic\ntop_main = (dynamic if top_main is None or dynamic.level > top_main.level\nelse top_main)\n@@ -1753,6 +1758,13 @@ def subst_axis_names_jaxpr(jaxpr: Union[Jaxpr, ClosedJaxpr], subst: AxisSubst):\naxis_substitution_rules: Dict[Primitive, Callable[[ParamDict, AxisSubst], ParamDict]] = {}\n+# ------------------- AxisPrimitive -------------------\n+# Primitives that store axis names in params and want those axis names to\n+# participate in dispatch should subclass AxisPrimitive.\n+\n+class AxisPrimitive(Primitive):\n+ _dispatch_on_params = True\n+\n# ------------------- Jaxpr checking -------------------\ndef typecheck(aval: AbstractValue, x) -> bool:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -725,6 +725,8 @@ def _process_xmap_default(self, call_primitive, f, tracers, params):\ncore.Trace.process_xmap = _process_xmap_default # type: ignore\ndef _xmap_axis_subst(params, subst):\n+ if 'call_jaxpr' not in params: # TODO(apaszke): This feels sketchy, but I'm not sure why\n+ return params\ndef shadowed_subst(name):\nreturn (name,) if name in params['global_axis_sizes'] else subst(name)\nwith core.extend_axis_env_nd(params['global_axis_sizes'].items()):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/batching.py",
"new_path": "jax/interpreters/batching.py",
"diff": "@@ -123,12 +123,12 @@ class BatchTrace(Trace):\ndef process_primitive(self, primitive, tracers, params):\nvals_in, dims_in = unzip2((t.val, t.batch_dim) for t in tracers)\n- if all(bdim is not_mapped for bdim in dims_in):\n- return primitive.bind(*vals_in, **params)\nif (primitive in collective_rules and\n_main_trace_for_axis_names(self.main, core.used_axis_names(primitive, params))):\nframe = core.axis_frame(self.axis_name)\nval_out, dim_out = collective_rules[primitive](frame, vals_in, dims_in, **params)\n+ elif all(bdim is not_mapped for bdim in dims_in):\n+ return primitive.bind(*vals_in, **params)\nelse:\nbatched_primitive = get_primitive_batcher(primitive, self)\nval_out, dim_out = batched_primitive(vals_in, dims_in, **params)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/batching_test.py",
"new_path": "tests/batching_test.py",
"diff": "@@ -1152,6 +1152,13 @@ class BatchingTest(jtu.JaxTestCase):\ny = vmap(f)(a=jnp.array([1]), b=jnp.array([2])) # doesn't work\nself.assertAllClose(x, y)\n+ def testGradOfPsum(self):\n+ a = jnp.ones(5)\n+ f = vmap(jax.grad(lambda x: -lax.psum(x, 'i')), out_axes=None, axis_name='i')\n+ self.assertEqual(\n+ f(a),\n+ jax.core.jaxpr_as_fun(jax.make_jaxpr(f)(a))(a)[0])\n+\ndef testAllGatherToUnmapped(self):\ndef f(x):\nreturn lax.all_gather(x, axis_name='i')\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pmap_test.py",
"new_path": "tests/pmap_test.py",
"diff": "@@ -1324,12 +1324,6 @@ class PmapTest(jtu.JaxTestCase):\nans = pmap(jit(f), 'i')(x)\nself.assertAllClose(ans, expected, check_dtypes=False)\n- def testMakeJaxprOfOpenSpmd(self):\n- f = lambda x: x - lax.psum(x, 'i')\n- shape = (xla_bridge.device_count(), 4)\n- x = np.arange(prod(shape), dtype=np.float32).reshape(shape)\n- make_jaxpr(f)(x) # doesn't crash\n-\ndef testCompositionWithJitTwice(self):\n@jit\ndef f(x):\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/xmap_test.py",
"new_path": "tests/xmap_test.py",
"diff": "@@ -451,6 +451,11 @@ class XMapTest(XMapTestCase):\naxis_resources=dict(axis_resources))()\nself.assertAllClose(result, jnp.arange(6, dtype=result.dtype))\n+ def testCollectiveOverNoName(self):\n+ result = xmap(lambda: lax.psum(jnp.array(2) ** 2, 'i'),\n+ in_axes={}, out_axes={}, axis_sizes={'i': 4})()\n+ self.assertEqual(result, 16)\n+\ndef VmapOfXmapCases(s):\nxmap_in_axes = ([{}] +\n[{i: 'x'} for i in range(3)] +\n"
}
] | Python | Apache License 2.0 | google/jax | Use the axis names attached to a primitive when selecting the top trace
This is useful e.g. for handling psums of values that are not sharded,
but are also not statically known constants that we can fold. |
260,335 | 22.04.2021 20:56:17 | 25,200 | 3c400a3e588abf9e2259119c50343cba6f3477f1 | add 'inline' option to xla_call for jaxpr inlining | [
{
"change_type": "MODIFY",
"old_path": "docs/jaxpr.rst",
"new_path": "docs/jaxpr.rst",
"diff": "@@ -433,6 +433,7 @@ computation should run. For example\nin (g,) }\ndevice=None\ndonated_invars=(False, False)\n+ inline=False\nname=inner ] a b\nd = convert_element_type[ new_dtype=float32\nweak_type=False ] a\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/api.py",
"new_path": "jax/_src/api.py",
"diff": "@@ -212,6 +212,7 @@ def jit(\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\ndonate_argnums: Union[int, Iterable[int]] = (),\n+ inline: bool = False,\n) -> F:\n\"\"\"Sets up ``fun`` for just-in-time compilation with XLA.\n@@ -288,10 +289,10 @@ def jit(\nstatic_argnames = ()\nif FLAGS.experimental_cpp_jit:\nreturn _cpp_jit(fun, static_argnums, static_argnames, device, backend,\n- donate_argnums)\n+ donate_argnums, inline)\nelse:\nreturn _python_jit(fun, static_argnums, static_argnames, device, backend,\n- donate_argnums)\n+ donate_argnums, inline)\ndef _python_jit(\n@@ -300,7 +301,8 @@ def _python_jit(\nstatic_argnames: Union[str, Iterable[str], None] = None,\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\n- donate_argnums: Union[int, Iterable[int]] = ()\n+ donate_argnums: Union[int, Iterable[int]] = (),\n+ inline: bool = False,\n) -> F:\n\"\"\"The Python implementation of `jax.jit`, being slowly replaced by _cpp_jit.\"\"\"\n_check_callable(fun)\n@@ -339,13 +341,8 @@ def _python_jit(\nfor arg in args_flat:\n_check_arg(arg)\nflat_fun, out_tree = flatten_fun(f, in_tree)\n- out = xla.xla_call(\n- flat_fun,\n- *args_flat,\n- device=device,\n- backend=backend,\n- name=flat_fun.__name__,\n- donated_invars=donated_invars)\n+ out = xla.xla_call(flat_fun, *args_flat, device=device, backend=backend,\n+ name=flat_fun.__name__, donated_invars=donated_invars, inline=inline)\nreturn tree_unflatten(out_tree(), out)\nreturn f_jitted\n@@ -366,6 +363,7 @@ def _cpp_jit(\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\ndonate_argnums: Union[int, Iterable[int]] = (),\n+ inline: bool = False,\n) -> F:\n\"\"\"An implementation of `jit` that tries to do as much as possible in C++.\n@@ -417,12 +415,9 @@ def _cpp_jit(\n_check_arg(arg)\nflat_fun, out_tree = flatten_fun(f, in_tree)\nout_flat = xla.xla_call(\n- flat_fun,\n- *args_flat,\n- device=device,\n- backend=backend,\n- name=flat_fun.__name__,\n- donated_invars=donated_invars)\n+ flat_fun, *args_flat,\n+ device=device, backend=backend, name=flat_fun.__name__,\n+ donated_invars=donated_invars, inline=inline)\nout_pytree_def = out_tree()\nout = tree_unflatten(out_pytree_def, out_flat)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/custom_derivatives.py",
"new_path": "jax/_src/custom_derivatives.py",
"diff": "@@ -270,12 +270,12 @@ class CustomJVPCallPrimitive(core.CallPrimitive):\njvp, env_trace_todo2 = core.process_env_traces(\njvp, self, top_trace and top_trace.level, (), None)\ntracers = map(top_trace.full_raise, args) # type: ignore\n- with core.maybe_new_sublevel(top_trace):\nouts = top_trace.process_custom_jvp_call(self, fun, jvp, tracers) # type: ignore\n_, env_trace_todo = lu.merge_linear_aux(env_trace_todo1, env_trace_todo2)\nreturn _apply_todos(env_trace_todo, map(core.full_lower, outs))\ndef impl(self, fun, _, *args):\n+ with core.new_sublevel():\nreturn fun.call_wrapped(*args)\ndef post_process(self, trace, out_tracers, params):\n@@ -563,7 +563,6 @@ class CustomVJPCallPrimitive(core.CallPrimitive):\nfwd, env_trace_todo2 = core.process_env_traces(\nfwd, self, top_trace and top_trace.level, (), None)\ntracers = map(top_trace.full_raise, args) # type: ignore\n- with core.maybe_new_sublevel(top_trace):\nouts = top_trace.process_custom_vjp_call(self, fun, fwd, bwd, tracers,\nout_trees=out_trees)\n_, env_trace_todo = lu.merge_linear_aux(env_trace_todo1, env_trace_todo2)\n@@ -571,6 +570,7 @@ class CustomVJPCallPrimitive(core.CallPrimitive):\ndef impl(self, fun, fwd, bwd, *args, out_trees):\ndel fwd, bwd, out_trees\n+ with core.new_sublevel():\nreturn fun.call_wrapped(*args)\ndef post_process(self, trace, out_tracers, params):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "import operator\nfrom operator import attrgetter\n-from contextlib import contextmanager, suppress\n+from contextlib import contextmanager\nfrom collections import namedtuple\nfrom functools import total_ordering\nimport itertools as it\n@@ -605,10 +605,12 @@ class EvalTrace(Trace):\ndef process_custom_jvp_call(self, primitive, fun, jvp, tracers):\ndel primitive, jvp # Unused.\n+ with new_sublevel():\nreturn fun.call_wrapped(*tracers)\ndef process_custom_vjp_call(self, primitive, fun, fwd, bwd, tracers, out_trees):\ndel primitive, fwd, bwd, out_trees # Unused.\n+ with new_sublevel():\nreturn fun.call_wrapped(*tracers)\n@@ -800,11 +802,6 @@ def new_sublevel() -> Generator[None, None, None]:\nif t() is not None:\nraise Exception(f'Leaked sublevel {t()}.')\n-def maybe_new_sublevel(trace):\n- # dynamic traces run the WrappedFun, so we raise the sublevel for them\n- dynamic = thread_local_state.trace_state.trace_stack.dynamic\n- return new_sublevel() if trace.main is dynamic else suppress()\n-\ndef full_lower(val):\nif isinstance(val, Tracer):\nreturn val.full_lower()\n@@ -1543,7 +1540,6 @@ def call_bind(primitive: Union['CallPrimitive', 'MapPrimitive'],\nfun, primitive, top_trace and top_trace.level,\nparams_tuple, out_axes_transforms)\ntracers = map(top_trace.full_raise, args)\n- with maybe_new_sublevel(top_trace):\nouts = primitive.process(top_trace, fun, tracers, params)\nreturn map(full_lower, apply_todos(env_trace_todo(), outs))\n@@ -1563,6 +1559,7 @@ class CallPrimitive(Primitive):\ndef call_impl(f: lu.WrappedFun, *args, **params):\ndel params # params parameterize the call primitive, not the function\n+ with new_sublevel():\nreturn f.call_wrapped(*args)\ncall_p = CallPrimitive('call')\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/host_callback.py",
"new_path": "jax/experimental/host_callback.py",
"diff": "@@ -1419,7 +1419,8 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_cond_jaxpr.jaxpr,\nname=\"cond_before\",\n- donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals)),\n+ donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals),\n+ inline=False),\neqn.source_info))\n# Make a new cond \"lambda pred, carry, token, itoken: pred\"\nnew_cond_pred_invar = mk_new_var(cond_jaxpr.out_avals[0])\n@@ -1462,7 +1463,8 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_body_jaxpr.jaxpr,\nname=\"body\",\n- donated_invars=(False,) * len(transformed_body_jaxpr.in_avals)),\n+ donated_invars=(False,) * len(transformed_body_jaxpr.in_avals),\n+ inline=False),\neqn.source_info),\ncore.new_jaxpr_eqn(\nnew_body_invars_cond_constvars + new_body_carry2 + [new_body_token2, new_body_itoken2],\n@@ -1470,7 +1472,8 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_cond_jaxpr.jaxpr,\nname=\"cond_body\",\n- donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals)),\n+ donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals),\n+ inline=False),\neqn.source_info)\n]\nnew_body_jaxpr = core.ClosedJaxpr(\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -1003,7 +1003,7 @@ def _xmap_translation_rule_replica(c, axis_env,\n# NOTE: We don't extend the resource env with the mesh shape, because those\n# resources are already in scope! It's the outermost xmap that introduces\n# them!\n- vectorized_jaxpr, out_avals, consts = pe.trace_to_jaxpr_final(f, local_avals)\n+ vectorized_jaxpr, out_avals, consts = pe.trace_to_jaxpr_dynamic(f, local_avals)\n_check_out_avals_vs_out_axes(out_avals, out_axes, global_axis_sizes)\nassert not consts\n@@ -1121,7 +1121,7 @@ def _xmap_translation_rule_spmd(c, axis_env,\nglobal_in_avals = [core.ShapedArray(xla_type.dimensions(), xla_type.numpy_dtype())\nfor in_node in global_in_nodes\nfor xla_type in (c.get_shape(in_node),)]\n- vectorized_jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_final(f, global_in_avals)\n+ vectorized_jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_dynamic(f, global_in_avals)\nassert not consts\nglobal_sharding_spec = pxla.mesh_sharding_specs(mesh.shape, mesh.axis_names)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/partial_eval.py",
"new_path": "jax/interpreters/partial_eval.py",
"diff": "@@ -242,6 +242,7 @@ class JaxprTrace(Trace):\n# We use post_process_call to handle both call and map primitives.\ndef post_process_call(self, primitive, out_tracers, params):\n+\njaxpr, consts, env = tracers_to_jaxpr([], out_tracers)\nout_pvs, out_pv_consts = unzip2(t.pval for t in out_tracers)\nout = out_pv_consts + consts\n@@ -321,6 +322,7 @@ class JaxprTrace(Trace):\ndef jvp_jaxpr_thunk():\njvp_ = trace_to_subjaxpr(jvp, self.main, True)\njvp_, aux = partial_eval_wrapper(jvp_, tuple(in_avals) * 2)\n+ with core.new_sublevel():\nout_flat = jvp_.call_wrapped(*(in_consts * 2)) # in_consts are units\nout_avals, jaxpr, env = aux()\n_, consts = split_list(out_flat, [len(out_flat)-len(jaxpr.constvars)])\n@@ -360,6 +362,7 @@ class JaxprTrace(Trace):\ndef fwd_jaxpr_thunk():\nfwd_ = trace_to_subjaxpr(fwd, self.main, True)\nfwd_, aux = partial_eval_wrapper(fwd_, tuple(in_avals))\n+ with core.new_sublevel():\nout_flat = fwd_.call_wrapped(*in_consts) # in_consts are units\nout_avals, jaxpr, env = aux()\n_, consts = split_list(out_flat, [len(out_flat)-len(jaxpr.constvars)])\n@@ -1058,8 +1061,9 @@ class DynamicJaxprTrace(core.Trace):\ndef process_call(self, call_primitive, f, tracers, params):\nin_avals = [t.aval for t in tracers]\n+ with core.new_sublevel():\njaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(f, self.main, in_avals)\n- if not jaxpr.eqns:\n+ if params.get('inline', False):\nreturn core.eval_jaxpr(jaxpr, consts, *tracers)\nsource_info = source_info_util.current()\nout_tracers = [DynamicJaxprTracer(self, a, source_info) for a in out_avals]\n@@ -1085,6 +1089,7 @@ class DynamicJaxprTrace(core.Trace):\nif in_axis is not None else a\nfor a, in_axis in zip(in_avals, params['in_axes'])]\nwith core.extend_axis_env(axis_name, axis_size, None): # type: ignore\n+ with core.new_sublevel():\njaxpr, reduced_out_avals, consts = trace_to_subjaxpr_dynamic(\nf, self.main, reduced_in_avals)\nout_axes = params['out_axes_thunk']()\n@@ -1113,6 +1118,7 @@ class DynamicJaxprTrace(core.Trace):\ndef process_custom_jvp_call(self, prim, fun, jvp, tracers):\nin_avals = [t.aval for t in tracers]\n+ with core.new_sublevel():\nfun_jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, self.main, in_avals)\nclosed_fun_jaxpr = core.ClosedJaxpr(convert_constvars_jaxpr(fun_jaxpr), ())\njvp_jaxpr_thunk = _memoize(\n@@ -1134,6 +1140,7 @@ class DynamicJaxprTrace(core.Trace):\ndef process_custom_vjp_call(self, prim, fun, fwd, bwd, tracers, out_trees):\nin_avals = [t.aval for t in tracers]\n+ with core.new_sublevel():\nfun_jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, self.main, in_avals)\nclosed_fun_jaxpr = core.ClosedJaxpr(convert_constvars_jaxpr(fun_jaxpr), ())\nfwd_jaxpr_thunk = _memoize(\n@@ -1206,6 +1213,7 @@ def trace_to_jaxpr_final(fun: lu.WrappedFun,\nwith core.new_base_main(DynamicJaxprTrace) as main: # type: ignore\nmain.source_info = fun_sourceinfo(fun.f, transform_name) # type: ignore\nmain.jaxpr_stack = () # type: ignore\n+ with core.new_sublevel():\njaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, main, in_avals)\ndel fun, main\nreturn jaxpr, out_avals, consts\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -218,7 +218,10 @@ def primitive_uses_outfeed(prim: core.Primitive, params: Dict) -> bool:\n### op-by-op execution\n-def arg_spec(x):\n+\n+ArgSpec = Tuple[core.AbstractValue, Optional[Device]]\n+\n+def arg_spec(x: Any) -> ArgSpec:\naval = abstractify(x)\ntry:\nreturn aval, x._device\n@@ -240,8 +243,7 @@ def _partition_outputs(avals, outs):\n@cache()\n-def xla_primitive_callable(prim, *arg_specs: Tuple[core.AbstractValue,\n- Optional[Device]], **params):\n+def xla_primitive_callable(prim, *arg_specs: ArgSpec, **params):\navals, arg_devices = unzip2(arg_specs)\ndonated_invars = (False,) * len(arg_specs)\ndevice = _device_from_arg_devices(arg_devices)\n@@ -573,7 +575,9 @@ def jaxpr_collectives(jaxpr):\n### xla_call underlying jit\n-def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name, donated_invars):\n+def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name,\n+ donated_invars, inline):\n+ del inline # Only used at tracing time\ncompiled_fun = _xla_callable(fun, device, backend, name, donated_invars,\n*unsafe_map(arg_spec, args))\ntry:\n@@ -591,6 +595,7 @@ def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name, donated_inv\n# intentional here, to avoid \"Store occupied\" errors we reset the stores to\n# be empty.\nfor store in fun.stores: store and store.reset()\n+ with core.new_sublevel():\nreturn fun.call_wrapped(*args) # probably won't return\ndef flatten_shape(s: XlaShape) -> Sequence[Tuple[Sequence[int], XlaShape]]:\n@@ -692,7 +697,8 @@ def _xla_callable(fun: lu.WrappedFun, device, backend, name, donated_invars, *ar\nc = xb.make_computation_builder(\"jit_{}\".format(fun.__name__))\nxla_consts = _xla_consts(c, consts)\n- xla_args, donated_invars = _xla_callable_args(c, abstract_args, tuple_args, donated_invars=donated_invars)\n+ xla_args, donated_invars = _xla_callable_args(c, abstract_args, tuple_args,\n+ donated_invars=donated_invars)\nout_nodes = jaxpr_subcomp(\nc, jaxpr, backend, AxisEnv(nreps, (), ()), xla_consts,\nextend_name_stack(wrap_name(name, 'jit')), *xla_args)\n@@ -789,8 +795,8 @@ def _xla_callable_args(\nfor (a, r, p) in safe_zip(avals, replicated, parts)\nfor xla_shape in aval_to_xla_shapes(a)]\nif donated_invars is not None:\n- donated_invars = [d\n- for (a, r, p, d) in safe_zip(avals, replicated, parts, donated_invars)\n+ donated_invars = [\n+ d for (a, _, _, d) in zip(avals, replicated, parts, donated_invars)\nfor xla_shape in aval_to_xla_shapes(a)]\nreturn xla_args, donated_invars\nelse:\n@@ -885,8 +891,8 @@ ad.call_transpose_param_updaters[xla_call_p] = _xla_call_transpose_update_params\ndef _xla_call_translation_rule(c, axis_env,\nin_nodes, name_stack, backend, name,\n- call_jaxpr, donated_invars, device=None):\n- del device, donated_invars # Ignored.\n+ call_jaxpr, donated_invars, inline, device=None):\n+ del device, donated_invars, inline # Ignored.\nsubc = xb.make_computation_builder(f\"jit_{name}\")\nargs = [xb.parameter(subc, i, c.get_shape(n)) for i, n in enumerate(in_nodes)]\nout_nodes = jaxpr_subcomp(subc, call_jaxpr, backend, axis_env, (),\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -2343,7 +2343,7 @@ class APITest(jtu.JaxTestCase):\nlst.append(x)\nreturn x\n- with self.assertRaisesRegex(Exception, r\"Leaked trace\"):\n+ with self.assertRaisesRegex(Exception, r\"Leaked\"):\nf(3)\ndef test_leak_checker_catches_a_pmap_leak(self):\n@@ -2355,7 +2355,7 @@ class APITest(jtu.JaxTestCase):\nlst.append(x)\nreturn x\n- with self.assertRaisesRegex(Exception, r\"Leaked trace\"):\n+ with self.assertRaisesRegex(Exception, r\"Leaked\"):\nf(np.ones(1))\ndef test_leak_checker_catches_a_grad_leak(self):\n@@ -2678,6 +2678,21 @@ class APITest(jtu.JaxTestCase):\njtu.check_grads(batched_scan_over_mul, (x_batch, coeff), order=2,\nmodes=['rev'])\n+ def test_jit_inline(self):\n+ @partial(api.jit, inline=False)\n+ def f(x):\n+ return x * 2\n+\n+ jaxpr = api.make_jaxpr(f)(3)\n+ self.assertIn('xla_call', str(jaxpr))\n+\n+ @partial(api.jit, inline=True)\n+ def f(x):\n+ return x * 2\n+\n+ jaxpr = api.make_jaxpr(f)(3)\n+ self.assertNotIn('xla_call', str(jaxpr))\n+\nclass RematTest(jtu.JaxTestCase):\n"
}
] | Python | Apache License 2.0 | google/jax | add 'inline' option to xla_call for jaxpr inlining |
260,335 | 29.04.2021 08:25:26 | 25,200 | e968672740547b83639932366565dc4906c6e9c9 | add tanh to jax.nn package | [
{
"change_type": "MODIFY",
"old_path": "jax/nn/__init__.py",
"new_path": "jax/nn/__init__.py",
"diff": "# flake8: noqa: F401\n+from jax.numpy import tanh\nfrom . import initializers\nfrom jax._src.nn.functions import (\ncelu,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/nn_test.py",
"new_path": "tests/nn_test.py",
"diff": "@@ -175,6 +175,9 @@ class NNFunctionsTest(jtu.JaxTestCase):\nactual = nn.one_hot(jnp.array([1, 2, 0]), 3, axis=-2)\nself.assertAllClose(actual, expected)\n+ def testTanhExists(self):\n+ nn.tanh # doesn't crash\n+\nInitializerRecord = collections.namedtuple(\n\"InitializerRecord\",\n[\"name\", \"initializer\", \"shapes\"])\n"
}
] | Python | Apache License 2.0 | google/jax | add tanh to jax.nn package |
260,563 | 01.05.2021 01:05:22 | -7,200 | e985e86808325c388c17d29ef0fc09ba6905fe41 | Implement jnp.r_ and jnp.c_ | [
{
"change_type": "MODIFY",
"old_path": "docs/jax.numpy.rst",
"new_path": "docs/jax.numpy.rst",
"diff": "@@ -89,6 +89,7 @@ Not every function in NumPy is implemented; contributions are welcome!\nbroadcast_arrays\nbroadcast_shapes\nbroadcast_to\n+ c_\ncan_cast\ncbrt\ncdouble\n@@ -298,6 +299,7 @@ Not every function in NumPy is implemented; contributions are welcome!\npromote_types\nptp\nquantile\n+ r_\nrad2deg\nradians\nravel\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/lax_numpy.py",
"new_path": "jax/_src/numpy/lax_numpy.py",
"diff": "@@ -3340,6 +3340,205 @@ class _Ogrid(_IndexGrid):\nogrid = _Ogrid()\n+def _make_1d_grid_from_slice(s: slice):\n+ start = s.start or 0\n+ stop = s.stop\n+ step = s.step or 1\n+ if np.iscomplex(step):\n+ newobj = linspace(start, stop, int(_abs(step)))\n+ else:\n+ newobj = arange(start, stop, step)\n+\n+ return newobj\n+\n+\n+class _AxisConcat:\n+ \"\"\"Concatenates slices, scalars and array-like objects along a given axis.\"\"\"\n+ def __getitem__(self, key):\n+ if not isinstance(key, tuple):\n+ key = (key,)\n+\n+ params = [self.axis, self.ndmin, self.trans1d, -1]\n+\n+ if isinstance(key[0], str):\n+ # split off the directive\n+ directive, *key = key\n+ # check two special cases: matrix directives\n+ if directive == \"r\":\n+ params[-1] = 0\n+ elif directive == \"c\":\n+ params[-1] = 1\n+ else:\n+ vec = directive.split(\",\")\n+ k = len(vec)\n+ if k < 4:\n+ vec += params[k:]\n+ else:\n+ # ignore everything after the first three comma-separated ints\n+ vec = vec[:3] + params[-1]\n+ try:\n+ params = list(map(int, vec))\n+ except ValueError as err:\n+ raise ValueError(\n+ \"could not understand directive {!r}\".format(directive)\n+ ) from err\n+\n+ axis, ndmin, trans1d, matrix = params\n+\n+ output = []\n+ for item in key:\n+ if isinstance(item, slice):\n+ newobj = _make_1d_grid_from_slice(item)\n+ elif isinstance(item, str):\n+ raise ValueError(\"string directive must be placed at the beginning\")\n+ else:\n+ newobj = item\n+\n+ newobj = array(newobj, copy=False, ndmin=ndmin)\n+\n+ if trans1d != -1 and ndmin - ndim(item) > 0:\n+ shape_obj = list(range(ndmin))\n+ # Calculate number of left shifts, with overflow protection by mod\n+ num_lshifts = ndmin - _abs(ndmin + trans1d + 1) % ndmin\n+ shape_obj = tuple(shape_obj[num_lshifts:] + shape_obj[:num_lshifts])\n+\n+ newobj = transpose(newobj, shape_obj)\n+\n+ output.append(newobj)\n+\n+ res = concatenate(tuple(output), axis=axis)\n+\n+ if matrix != -1 and res.ndim == 1:\n+ # insert 2nd dim at axis 0 or 1\n+ res = expand_dims(res, matrix)\n+\n+ return res\n+\n+ def __len__(self):\n+ return 0\n+\n+\n+class RClass(_AxisConcat):\n+ \"\"\"Concatenate slices, scalars and array-like objects along the first axis.\n+\n+ LAX-backend implementation of :obj:`numpy.r_`.\n+\n+ See Also:\n+ ``jnp.c_``: Concatenates slices, scalars and array-like objects along the last axis.\n+\n+ Examples:\n+ Passing slices in the form ``[start:stop:step]`` generates ``jnp.arange`` objects:\n+\n+ >>> jnp.r_[-1:5:1, 0, 0, jnp.array([1,2,3])]\n+ DeviceArray([-1, 0, 1, 2, 3, 4, 0, 0, 1, 2, 3], dtype=int32)\n+\n+ An imaginary value for ``step`` will create a ``jnp.linspace`` object instead,\n+ which includes the right endpoint:\n+\n+ >>> jnp.r_[-1:1:6j, 0, jnp.array([1,2,3])]\n+ DeviceArray([-1. , -0.6 , -0.20000002, 0.20000005,\n+ 0.6 , 1. , 0. , 1. ,\n+ 2. , 3. ], dtype=float32)\n+\n+ Use a string directive of the form ``\"axis,dims,trans1d\"`` as the first argument to\n+ specify concatenation axis, minimum number of dimensions, and the position of the\n+ upgraded array's original dimensions in the resulting array's shape tuple:\n+\n+ >>> jnp.r_['0,2', [1,2,3], [4,5,6]] # concatenate along first axis, 2D output\n+ DeviceArray([[1, 2, 3],\n+ [4, 5, 6]], dtype=int32)\n+\n+ >>> jnp.r_['0,2,0', [1,2,3], [4,5,6]] # push last input axis to the front\n+ DeviceArray([[1],\n+ [2],\n+ [3],\n+ [4],\n+ [5],\n+ [6]], dtype=int32)\n+\n+ Negative values for ``trans1d`` offset the last axis towards the start\n+ of the shape tuple:\n+\n+ >>> jnp.r_['0,2,-2', [1,2,3], [4,5,6]]\n+ DeviceArray([[1],\n+ [2],\n+ [3],\n+ [4],\n+ [5],\n+ [6]], dtype=int32)\n+\n+ Use the special directives ``\"r\"`` or ``\"c\"`` as the first argument on flat inputs\n+ to create an array with an extra row or column axis, respectively:\n+\n+ >>> jnp.r_['r',[1,2,3], [4,5,6]]\n+ DeviceArray([[1, 2, 3, 4, 5, 6]], dtype=int32)\n+\n+ >>> jnp.r_['c',[1,2,3], [4,5,6]]\n+ DeviceArray([[1],\n+ [2],\n+ [3],\n+ [4],\n+ [5],\n+ [6]], dtype=int32)\n+\n+ For higher-dimensional inputs (``dim >= 2``), both directives ``\"r\"`` and ``\"c\"``\n+ give the same result.\n+ \"\"\"\n+ axis = 0\n+ ndmin = 1\n+ trans1d = -1\n+\n+\n+r_ = RClass()\n+\n+\n+class CClass(_AxisConcat):\n+ \"\"\"Concatenate slices, scalars and array-like objects along the last axis.\n+\n+ LAX-backend implementation of :obj:`numpy.c_`.\n+\n+ See Also:\n+ ``jnp.r_``: Concatenates slices, scalars and array-like objects along the first axis.\n+\n+ Examples:\n+\n+ >>> a = jnp.arange(6).reshape((2,3))\n+ >>> jnp.c_[a,a]\n+ DeviceArray([[0, 1, 2, 0, 1, 2],\n+ [3, 4, 5, 3, 4, 5]], dtype=int32)\n+\n+ Use a string directive of the form ``\"axis:dims:trans1d\"`` as the first argument to specify\n+ concatenation axis, minimum number of dimensions, and the position of the upgraded array's\n+ original dimensions in the resulting array's shape tuple:\n+\n+ >>> jnp.c_['0,2', [1,2,3], [4,5,6]]\n+ DeviceArray([[1],\n+ [2],\n+ [3],\n+ [4],\n+ [5],\n+ [6]], dtype=int32)\n+\n+ >>> jnp.c_['0,2,-1', [1,2,3], [4,5,6]]\n+ DeviceArray([[1, 2, 3],\n+ [4, 5, 6]], dtype=int32)\n+\n+ Use the special directives ``\"r\"`` or ``\"c\"`` as the first argument on flat inputs\n+ to create an array with inputs stacked along the last axis:\n+\n+ >>> jnp.c_['r',[1,2,3], [4,5,6]]\n+ DeviceArray([[1, 4],\n+ [2, 5],\n+ [3, 6]], dtype=int32)\n+ \"\"\"\n+ axis = -1\n+ ndmin = 2\n+ trans1d = 0\n+\n+\n+c_ = CClass()\n+\n+\n@_wraps(np.i0)\ndef i0(x):\nx_orig = x\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/__init__.py",
"new_path": "jax/numpy/__init__.py",
"diff": "@@ -26,7 +26,7 @@ from jax._src.numpy.lax_numpy import (\narray, array_equal, array_equiv, array_repr, array_split, array_str, asarray, atleast_1d, atleast_2d,\natleast_3d, average, bartlett, bfloat16, bincount, bitwise_and, bitwise_not,\nbitwise_or, bitwise_xor, blackman, block, bool_, broadcast_arrays, broadcast_shapes,\n- broadcast_to, can_cast, cbrt, cdouble, ceil, character, choose, clip, column_stack,\n+ broadcast_to, c_, can_cast, cbrt, cdouble, ceil, character, choose, clip, column_stack,\ncomplex128, complex64, complex_, complexfloating, compress, concatenate,\nconj, conjugate, convolve, copysign, corrcoef, correlate, cos, cosh,\ncount_nonzero, cov, cross, csingle, cumprod, cumproduct, cumsum, deg2rad, degrees,\n@@ -53,7 +53,7 @@ from jax._src.numpy.lax_numpy import (\nobject_, ogrid, ones, ones_like, operator_name, outer, packbits, pad, percentile,\npi, piecewise, polyadd, polyder, polyint, polymul, polysub, polyval, positive, power,\nprod, product, promote_types, ptp, quantile,\n- rad2deg, radians, ravel, ravel_multi_index, real, reciprocal, remainder, repeat, reshape,\n+ r_, rad2deg, radians, ravel, ravel_multi_index, real, reciprocal, remainder, repeat, reshape,\nresult_type, right_shift, rint, roll, rollaxis, rot90, round, row_stack,\nsave, savez, searchsorted, select, set_printoptions, setdiff1d, setxor1d, shape, sign, signbit,\nsignedinteger, sin, sinc, single, sinh, size, sometrue, sort, sort_complex, split, sqrt,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -4712,6 +4712,82 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\natol=atol,\nrtol=rtol)\n+ def testR_(self):\n+ a = np.arange(6).reshape((2,3))\n+ self.assertArraysEqual(np.r_[np.array([1,2,3]), 0, 0, np.array([4,5,6])],\n+ jnp.r_[np.array([1,2,3]), 0, 0, np.array([4,5,6])])\n+ self.assertArraysEqual(np.r_['-1', a, a], jnp.r_['-1', a, a])\n+ self.assertArraysEqual(np.r_['0,2', [1,2,3], [4,5,6]], jnp.r_['0,2', [1,2,3], [4,5,6]])\n+ self.assertArraysEqual(np.r_['0,2,0', [1,2,3], [4,5,6]], jnp.r_['0,2,0', [1,2,3], [4,5,6]])\n+ self.assertArraysEqual(np.r_['1,2,0', [1,2,3], [4,5,6]], jnp.r_['1,2,0', [1,2,3], [4,5,6]])\n+ # negative 1d axis start\n+ self.assertArraysEqual(np.r_['0,4,-1', [1,2,3], [4,5,6]], jnp.r_['0,4,-1', [1,2,3], [4,5,6]])\n+ self.assertArraysEqual(np.r_['0,4,-2', [1,2,3], [4,5,6]], jnp.r_['0,4,-2', [1,2,3], [4,5,6]])\n+\n+ # matrix directives\n+ with warnings.catch_warnings():\n+ warnings.filterwarnings(\"ignore\", category=PendingDeprecationWarning)\n+ self.assertArraysEqual(np.r_['r',[1,2,3], [4,5,6]], jnp.r_['r',[1,2,3], [4,5,6]])\n+ self.assertArraysEqual(np.r_['c', [1, 2, 3], [4, 5, 6]], jnp.r_['c', [1, 2, 3], [4, 5, 6]])\n+\n+ # bad directive\n+ with self.assertRaisesRegex(ValueError, \"could not understand directive.*\"):\n+ jnp.r_[\"asdfgh\",[1,2,3]]\n+\n+ # Complex number steps\n+ atol = 1e-6\n+ rtol = 1e-6\n+ self.assertAllClose(np.r_[-1:1:6j],\n+ jnp.r_[-1:1:6j],\n+ atol=atol,\n+ rtol=rtol)\n+ self.assertAllClose(np.r_[-1:1:6j, [0]*3, 5, 6],\n+ jnp.r_[-1:1:6j, [0]*3, 5, 6],\n+ atol=atol,\n+ rtol=rtol)\n+ # Non-integer steps\n+ self.assertAllClose(np.r_[1.2:4.8:0.24],\n+ jnp.r_[1.2:4.8:0.24],\n+ atol=atol,\n+ rtol=rtol)\n+\n+ def testC_(self):\n+ a = np.arange(6).reshape((2, 3))\n+ self.assertArraysEqual(np.c_[np.array([1,2,3]), np.array([4,5,6])],\n+ jnp.c_[np.array([1,2,3]), np.array([4,5,6])])\n+ self.assertArraysEqual(np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])],\n+ jnp.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])])\n+ self.assertArraysEqual(np.c_['-1', a, a], jnp.c_['-1', a, a])\n+ self.assertArraysEqual(np.c_['0,2', [1,2,3], [4,5,6]], jnp.c_['0,2', [1,2,3], [4,5,6]])\n+ self.assertArraysEqual(np.c_['0,2,0', [1,2,3], [4,5,6]], jnp.c_['0,2,0', [1,2,3], [4,5,6]])\n+ self.assertArraysEqual(np.c_['1,2,0', [1,2,3], [4,5,6]], jnp.c_['1,2,0', [1,2,3], [4,5,6]])\n+ # negative 1d axis start\n+ self.assertArraysEqual(np.c_['0,4,-1', [1,2,3], [4,5,6]], jnp.c_['0,4,-1', [1,2,3], [4,5,6]])\n+ self.assertArraysEqual(np.c_['0,4,-2', [1,2,3], [4,5,6]], jnp.c_['0,4,-2', [1,2,3], [4,5,6]])\n+ # matrix directives, avoid numpy deprecation warning\n+ with warnings.catch_warnings():\n+ warnings.filterwarnings(\"ignore\", category=PendingDeprecationWarning)\n+ self.assertArraysEqual(np.c_['r',[1,2,3], [4,5,6]], jnp.c_['r',[1,2,3], [4,5,6]])\n+ self.assertArraysEqual(np.c_['c', [1, 2, 3], [4, 5, 6]], jnp.c_['c', [1, 2, 3], [4, 5, 6]])\n+\n+ # bad directive\n+ with self.assertRaisesRegex(ValueError, \"could not understand directive.*\"):\n+ jnp.c_[\"asdfgh\",[1,2,3]]\n+\n+ # Complex number steps\n+ atol = 1e-6\n+ rtol = 1e-6\n+ self.assertAllClose(np.c_[-1:1:6j],\n+ jnp.c_[-1:1:6j],\n+ atol=atol,\n+ rtol=rtol)\n+\n+ # Non-integer steps\n+ self.assertAllClose(np.c_[1.2:4.8:0.24],\n+ jnp.c_[1.2:4.8:0.24],\n+ atol=atol,\n+ rtol=rtol)\n+\n@parameterized.named_parameters(\njtu.cases_from_list(\n{\"testcase_name\": (\"_start_shape={}_stop_shape={}_num={}_endpoint={}\"\n"
}
] | Python | Apache License 2.0 | google/jax | Implement jnp.r_ and jnp.c_ |
260,563 | 01.05.2021 19:32:19 | -7,200 | f559a9bfb4a836be2f5d7007d02def39997d130e | Fix pip install instruction for forks in contributing section | [
{
"change_type": "MODIFY",
"old_path": "docs/contributing.md",
"new_path": "docs/contributing.md",
"diff": "@@ -39,7 +39,7 @@ Follow these steps to contribute code:\n```bash\ngit clone https://github.com/YOUR_USERNAME/jax\ncd jax\n- pip install -r requirements.txt # Installs all testing requirements.\n+ pip install -r build/test-requirements.txt # Installs all testing requirements.\npip install -e . # Installs JAX from the current directory in editable mode.\n```\n"
}
] | Python | Apache License 2.0 | google/jax | Fix pip install instruction for forks in contributing section |
260,335 | 01.05.2021 12:32:44 | 25,200 | fe297e39ca37896b75d7943b9b77c0b53fad13ee | add 'inline' to jit docstring | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/api.py",
"new_path": "jax/_src/api.py",
"diff": "@@ -263,7 +263,10 @@ def jit(\nbuffers to reduce the amount of memory needed to perform a computation,\nfor example recycling one of your input buffers to store a result. You\nshould not reuse buffers that you donate to a computation, JAX will raise\n- an error if you try to.\n+ an error if you try to. By default, no arguments are donated.\n+ inline: Specify whether this function should be inlined into enclosing\n+ jaxprs (rather than being represented as an application of the xla_call\n+ primitive with its own subjaxpr). Default False.\nReturns:\nA wrapped version of ``fun``, set up for just-in-time compilation.\n@@ -304,7 +307,7 @@ def _python_jit(\ndonate_argnums: Union[int, Iterable[int]] = (),\ninline: bool = False,\n) -> F:\n- \"\"\"The Python implementation of `jax.jit`, being slowly replaced by _cpp_jit.\"\"\"\n+ # The Python implementation of `jax.jit`, being slowly replaced by _cpp_jit.\n_check_callable(fun)\nstatic_argnums, static_argnames = _infer_argnums_and_argnames(\nfun, static_argnums, static_argnames)\n@@ -365,15 +368,13 @@ def _cpp_jit(\ndonate_argnums: Union[int, Iterable[int]] = (),\ninline: bool = False,\n) -> F:\n- \"\"\"An implementation of `jit` that tries to do as much as possible in C++.\n-\n- The goal of this function is to speed up the time it takes to process the\n- arguments, find the correct C++ executable, start the transfer of arguments\n- and schedule the computation.\n- As long as it does not support all features of the Python implementation\n- the C++ code will fallback to `_python_jit` when it faces some unsupported\n- feature.\n- \"\"\"\n+ # An implementation of `jit` that tries to do as much as possible in C++.\n+ # The goal of this function is to speed up the time it takes to process the\n+ # arguments, find the correct C++ executable, start the transfer of arguments\n+ # and schedule the computation.\n+ # As long as it does not support all features of the Python implementation\n+ # the C++ code will fallback to `_python_jit` when it faces some unsupported\n+ # feature.\n_check_callable(fun)\nstatic_argnums, static_argnames = _infer_argnums_and_argnames(\nfun, static_argnums, static_argnames)\n"
}
] | Python | Apache License 2.0 | google/jax | add 'inline' to jit docstring |
260,335 | 01.05.2021 12:41:41 | 25,200 | ff6866c4b3757cde66fe659c2f27d8aeff024e8f | new_sublevel in jax2tf | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -310,7 +310,9 @@ def _interpret_fun(fun: lu.WrappedFun,\n) -> Sequence[Tuple[TfVal, core.AbstractValue]]:\nwith core.new_base_main(TensorFlowTrace) as main: # type: ignore\nfun = _interpret_subtrace(fun, main, in_avals)\n- out_vals: Sequence[Tuple[TfVal, core.AbstractValue]] = fun.call_wrapped(*in_vals)\n+ with core.new_sublevel():\n+ out_vals: Sequence[Tuple[TfVal, core.AbstractValue]] = \\\n+ fun.call_wrapped(*in_vals)\ndel main\nreturn tuple(out_vals)\n@@ -675,10 +677,11 @@ class TensorFlowTrace(core.Trace):\nassert call_primitive.multiple_results\nvals: Sequence[TfVal] = [t.val for t in tracers]\nf = _interpret_subtrace(f, self.main, tuple(t.aval for t in tracers))\n+ with core.new_sublevel():\nif call_primitive == core.named_call_p:\nwith tf.name_scope(_sanitize_scope_name(params[\"name\"])):\n- vals_out: Sequence[Tuple[TfVal,\n- core.AbstractValue]] = f.call_wrapped(*vals)\n+ vals_out: Sequence[Tuple[TfVal, core.AbstractValue]] = \\\n+ f.call_wrapped(*vals)\nelif call_primitive == sharded_jit.sharded_call_p:\nvals_out = _sharded_call(f, vals, **params)\nelse:\n@@ -2173,7 +2176,7 @@ def _sharded_call(f: lu.WrappedFun, vals: Sequence[TfVal],\nout_parts_thunk,\n**_) -> Sequence[Tuple[TfVal, core.AbstractValue]]:\nsharded_vals = util.safe_map(split_to_logical_devices, vals, in_parts)\n- vals_out = f.call_wrapped(*sharded_vals)\n+ vals_out = f.call_wrapped(*sharded_vals) # caller handles new_sublevel\nout_parts_flat = out_parts_thunk()\nassert len(out_parts_flat) == len(vals_out), f\"expected {len(out_parts_flat)} == {len(vals_out)}\"\nsharded_vals_out = [\n"
}
] | Python | Apache License 2.0 | google/jax | new_sublevel in jax2tf |
260,545 | 01.05.2021 19:07:58 | 25,200 | 74b67627456c0712a1464b7da79548f38b6ce042 | Fixed error in autodidax broadasting batching | [
{
"change_type": "MODIFY",
"old_path": "docs/autodidax.ipynb",
"new_path": "docs/autodidax.ipynb",
"diff": "\" if x_bdim != y_bdim:\\n\",\n\" if x_bdim is not_mapped:\\n\",\n\" x = move_batch_axis(axis_size, x_bdim, y_bdim, x)\\n\",\n+ \" x_bdim = y_bdim\\n\",\n\" else:\\n\",\n\" y = move_batch_axis(axis_size, y_bdim, x_bdim, y)\\n\",\n\" return [op(x, y)], [x_bdim]\\n\",\n"
},
{
"change_type": "MODIFY",
"old_path": "docs/autodidax.md",
"new_path": "docs/autodidax.md",
"diff": "@@ -838,6 +838,7 @@ def broadcasting_binop_batching_rule(op, axis_size, vals_in, dims_in):\nif x_bdim != y_bdim:\nif x_bdim is not_mapped:\nx = move_batch_axis(axis_size, x_bdim, y_bdim, x)\n+ x_bdim = y_bdim\nelse:\ny = move_batch_axis(axis_size, y_bdim, x_bdim, y)\nreturn [op(x, y)], [x_bdim]\n"
},
{
"change_type": "MODIFY",
"old_path": "docs/autodidax.py",
"new_path": "docs/autodidax.py",
"diff": "@@ -802,6 +802,7 @@ def broadcasting_binop_batching_rule(op, axis_size, vals_in, dims_in):\nif x_bdim != y_bdim:\nif x_bdim is not_mapped:\nx = move_batch_axis(axis_size, x_bdim, y_bdim, x)\n+ x_bdim = y_bdim\nelse:\ny = move_batch_axis(axis_size, y_bdim, x_bdim, y)\nreturn [op(x, y)], [x_bdim]\n"
}
] | Python | Apache License 2.0 | google/jax | Fixed error in autodidax broadasting batching |
260,631 | 01.05.2021 22:18:13 | 25,200 | 75b00a1235e44898c5613d5c4479061970e1a22b | Copybara import of the project:
by Matthew Johnson
add 'inline' option to xla_call for jaxpr inlining
by Matthew Johnson
add 'inline' to jit docstring
by Matthew Johnson
new_sublevel in jax2tf | [
{
"change_type": "MODIFY",
"old_path": "docs/jaxpr.rst",
"new_path": "docs/jaxpr.rst",
"diff": "@@ -433,7 +433,6 @@ computation should run. For example\nin (g,) }\ndevice=None\ndonated_invars=(False, False)\n- inline=False\nname=inner ] a b\nd = convert_element_type[ new_dtype=float32\nweak_type=False ] a\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/api.py",
"new_path": "jax/_src/api.py",
"diff": "@@ -212,7 +212,6 @@ def jit(\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\ndonate_argnums: Union[int, Iterable[int]] = (),\n- inline: bool = False,\n) -> F:\n\"\"\"Sets up ``fun`` for just-in-time compilation with XLA.\n@@ -263,10 +262,7 @@ def jit(\nbuffers to reduce the amount of memory needed to perform a computation,\nfor example recycling one of your input buffers to store a result. You\nshould not reuse buffers that you donate to a computation, JAX will raise\n- an error if you try to. By default, no arguments are donated.\n- inline: Specify whether this function should be inlined into enclosing\n- jaxprs (rather than being represented as an application of the xla_call\n- primitive with its own subjaxpr). Default False.\n+ an error if you try to.\nReturns:\nA wrapped version of ``fun``, set up for just-in-time compilation.\n@@ -292,10 +288,10 @@ def jit(\nstatic_argnames = ()\nif FLAGS.experimental_cpp_jit:\nreturn _cpp_jit(fun, static_argnums, static_argnames, device, backend,\n- donate_argnums, inline)\n+ donate_argnums)\nelse:\nreturn _python_jit(fun, static_argnums, static_argnames, device, backend,\n- donate_argnums, inline)\n+ donate_argnums)\ndef _python_jit(\n@@ -304,10 +300,9 @@ def _python_jit(\nstatic_argnames: Union[str, Iterable[str], None] = None,\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\n- donate_argnums: Union[int, Iterable[int]] = (),\n- inline: bool = False,\n+ donate_argnums: Union[int, Iterable[int]] = ()\n) -> F:\n- # The Python implementation of `jax.jit`, being slowly replaced by _cpp_jit.\n+ \"\"\"The Python implementation of `jax.jit`, being slowly replaced by _cpp_jit.\"\"\"\n_check_callable(fun)\nstatic_argnums, static_argnames = _infer_argnums_and_argnames(\nfun, static_argnums, static_argnames)\n@@ -344,8 +339,13 @@ def _python_jit(\nfor arg in args_flat:\n_check_arg(arg)\nflat_fun, out_tree = flatten_fun(f, in_tree)\n- out = xla.xla_call(flat_fun, *args_flat, device=device, backend=backend,\n- name=flat_fun.__name__, donated_invars=donated_invars, inline=inline)\n+ out = xla.xla_call(\n+ flat_fun,\n+ *args_flat,\n+ device=device,\n+ backend=backend,\n+ name=flat_fun.__name__,\n+ donated_invars=donated_invars)\nreturn tree_unflatten(out_tree(), out)\nreturn f_jitted\n@@ -366,15 +366,16 @@ def _cpp_jit(\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\ndonate_argnums: Union[int, Iterable[int]] = (),\n- inline: bool = False,\n) -> F:\n- # An implementation of `jit` that tries to do as much as possible in C++.\n- # The goal of this function is to speed up the time it takes to process the\n- # arguments, find the correct C++ executable, start the transfer of arguments\n- # and schedule the computation.\n- # As long as it does not support all features of the Python implementation\n- # the C++ code will fallback to `_python_jit` when it faces some unsupported\n- # feature.\n+ \"\"\"An implementation of `jit` that tries to do as much as possible in C++.\n+\n+ The goal of this function is to speed up the time it takes to process the\n+ arguments, find the correct C++ executable, start the transfer of arguments\n+ and schedule the computation.\n+ As long as it does not support all features of the Python implementation\n+ the C++ code will fallback to `_python_jit` when it faces some unsupported\n+ feature.\n+ \"\"\"\n_check_callable(fun)\nstatic_argnums, static_argnames = _infer_argnums_and_argnames(\nfun, static_argnums, static_argnames)\n@@ -416,9 +417,12 @@ def _cpp_jit(\n_check_arg(arg)\nflat_fun, out_tree = flatten_fun(f, in_tree)\nout_flat = xla.xla_call(\n- flat_fun, *args_flat,\n- device=device, backend=backend, name=flat_fun.__name__,\n- donated_invars=donated_invars, inline=inline)\n+ flat_fun,\n+ *args_flat,\n+ device=device,\n+ backend=backend,\n+ name=flat_fun.__name__,\n+ donated_invars=donated_invars)\nout_pytree_def = out_tree()\nout = tree_unflatten(out_pytree_def, out_flat)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/custom_derivatives.py",
"new_path": "jax/_src/custom_derivatives.py",
"diff": "@@ -270,12 +270,12 @@ class CustomJVPCallPrimitive(core.CallPrimitive):\njvp, env_trace_todo2 = core.process_env_traces(\njvp, self, top_trace and top_trace.level, (), None)\ntracers = map(top_trace.full_raise, args) # type: ignore\n+ with core.maybe_new_sublevel(top_trace):\nouts = top_trace.process_custom_jvp_call(self, fun, jvp, tracers) # type: ignore\n_, env_trace_todo = lu.merge_linear_aux(env_trace_todo1, env_trace_todo2)\nreturn _apply_todos(env_trace_todo, map(core.full_lower, outs))\ndef impl(self, fun, _, *args):\n- with core.new_sublevel():\nreturn fun.call_wrapped(*args)\ndef post_process(self, trace, out_tracers, params):\n@@ -563,6 +563,7 @@ class CustomVJPCallPrimitive(core.CallPrimitive):\nfwd, env_trace_todo2 = core.process_env_traces(\nfwd, self, top_trace and top_trace.level, (), None)\ntracers = map(top_trace.full_raise, args) # type: ignore\n+ with core.maybe_new_sublevel(top_trace):\nouts = top_trace.process_custom_vjp_call(self, fun, fwd, bwd, tracers,\nout_trees=out_trees)\n_, env_trace_todo = lu.merge_linear_aux(env_trace_todo1, env_trace_todo2)\n@@ -570,7 +571,6 @@ class CustomVJPCallPrimitive(core.CallPrimitive):\ndef impl(self, fun, fwd, bwd, *args, out_trees):\ndel fwd, bwd, out_trees\n- with core.new_sublevel():\nreturn fun.call_wrapped(*args)\ndef post_process(self, trace, out_tracers, params):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "import operator\nfrom operator import attrgetter\n-from contextlib import contextmanager\n+from contextlib import contextmanager, suppress\nfrom collections import namedtuple\nfrom functools import total_ordering\nimport itertools as it\n@@ -611,12 +611,10 @@ class EvalTrace(Trace):\ndef process_custom_jvp_call(self, primitive, fun, jvp, tracers):\ndel primitive, jvp # Unused.\n- with new_sublevel():\nreturn fun.call_wrapped(*tracers)\ndef process_custom_vjp_call(self, primitive, fun, fwd, bwd, tracers, out_trees):\ndel primitive, fwd, bwd, out_trees # Unused.\n- with new_sublevel():\nreturn fun.call_wrapped(*tracers)\n@@ -808,6 +806,11 @@ def new_sublevel() -> Generator[None, None, None]:\nif t() is not None:\nraise Exception(f'Leaked sublevel {t()}.')\n+def maybe_new_sublevel(trace):\n+ # dynamic traces run the WrappedFun, so we raise the sublevel for them\n+ dynamic = thread_local_state.trace_state.trace_stack.dynamic\n+ return new_sublevel() if trace.main is dynamic else suppress()\n+\ndef full_lower(val):\nif isinstance(val, Tracer):\nreturn val.full_lower()\n@@ -1549,6 +1552,7 @@ def call_bind(primitive: Union['CallPrimitive', 'MapPrimitive'],\nfun, primitive, top_trace and top_trace.level,\nparams_tuple, out_axes_transforms)\ntracers = map(top_trace.full_raise, args)\n+ with maybe_new_sublevel(top_trace):\nouts = primitive.process(top_trace, fun, tracers, params)\nreturn map(full_lower, apply_todos(env_trace_todo(), outs))\n@@ -1568,7 +1572,6 @@ class CallPrimitive(Primitive):\ndef call_impl(f: lu.WrappedFun, *args, **params):\ndel params # params parameterize the call primitive, not the function\n- with new_sublevel():\nreturn f.call_wrapped(*args)\ncall_p = CallPrimitive('call')\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/host_callback.py",
"new_path": "jax/experimental/host_callback.py",
"diff": "@@ -1419,8 +1419,7 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_cond_jaxpr.jaxpr,\nname=\"cond_before\",\n- donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals),\n- inline=False),\n+ donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals)),\neqn.source_info))\n# Make a new cond \"lambda pred, carry, token, itoken: pred\"\nnew_cond_pred_invar = mk_new_var(cond_jaxpr.out_avals[0])\n@@ -1463,8 +1462,7 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_body_jaxpr.jaxpr,\nname=\"body\",\n- donated_invars=(False,) * len(transformed_body_jaxpr.in_avals),\n- inline=False),\n+ donated_invars=(False,) * len(transformed_body_jaxpr.in_avals)),\neqn.source_info),\ncore.new_jaxpr_eqn(\nnew_body_invars_cond_constvars + new_body_carry2 + [new_body_token2, new_body_itoken2],\n@@ -1472,8 +1470,7 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_cond_jaxpr.jaxpr,\nname=\"cond_body\",\n- donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals),\n- inline=False),\n+ donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals)),\neqn.source_info)\n]\nnew_body_jaxpr = core.ClosedJaxpr(\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -310,9 +310,7 @@ def _interpret_fun(fun: lu.WrappedFun,\n) -> Sequence[Tuple[TfVal, core.AbstractValue]]:\nwith core.new_base_main(TensorFlowTrace) as main: # type: ignore\nfun = _interpret_subtrace(fun, main, in_avals)\n- with core.new_sublevel():\n- out_vals: Sequence[Tuple[TfVal, core.AbstractValue]] = \\\n- fun.call_wrapped(*in_vals)\n+ out_vals: Sequence[Tuple[TfVal, core.AbstractValue]] = fun.call_wrapped(*in_vals)\ndel main\nreturn tuple(out_vals)\n@@ -677,11 +675,10 @@ class TensorFlowTrace(core.Trace):\nassert call_primitive.multiple_results\nvals: Sequence[TfVal] = [t.val for t in tracers]\nf = _interpret_subtrace(f, self.main, tuple(t.aval for t in tracers))\n- with core.new_sublevel():\nif call_primitive == core.named_call_p:\nwith tf.name_scope(_sanitize_scope_name(params[\"name\"])):\n- vals_out: Sequence[Tuple[TfVal, core.AbstractValue]] = \\\n- f.call_wrapped(*vals)\n+ vals_out: Sequence[Tuple[TfVal,\n+ core.AbstractValue]] = f.call_wrapped(*vals)\nelif call_primitive == sharded_jit.sharded_call_p:\nvals_out = _sharded_call(f, vals, **params)\nelse:\n@@ -2176,7 +2173,7 @@ def _sharded_call(f: lu.WrappedFun, vals: Sequence[TfVal],\nout_parts_thunk,\n**_) -> Sequence[Tuple[TfVal, core.AbstractValue]]:\nsharded_vals = util.safe_map(split_to_logical_devices, vals, in_parts)\n- vals_out = f.call_wrapped(*sharded_vals) # caller handles new_sublevel\n+ vals_out = f.call_wrapped(*sharded_vals)\nout_parts_flat = out_parts_thunk()\nassert len(out_parts_flat) == len(vals_out), f\"expected {len(out_parts_flat)} == {len(vals_out)}\"\nsharded_vals_out = [\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -1005,7 +1005,7 @@ def _xmap_translation_rule_replica(c, axis_env,\n# NOTE: We don't extend the resource env with the mesh shape, because those\n# resources are already in scope! It's the outermost xmap that introduces\n# them!\n- vectorized_jaxpr, out_avals, consts = pe.trace_to_jaxpr_dynamic(f, local_avals)\n+ vectorized_jaxpr, out_avals, consts = pe.trace_to_jaxpr_final(f, local_avals)\n_check_out_avals_vs_out_axes(out_avals, out_axes, global_axis_sizes)\nassert not consts\n@@ -1123,7 +1123,7 @@ def _xmap_translation_rule_spmd(c, axis_env,\nglobal_in_avals = [core.ShapedArray(xla_type.dimensions(), xla_type.numpy_dtype())\nfor in_node in global_in_nodes\nfor xla_type in (c.get_shape(in_node),)]\n- vectorized_jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_dynamic(f, global_in_avals)\n+ vectorized_jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_final(f, global_in_avals)\nassert not consts\nglobal_sharding_spec = pxla.mesh_sharding_specs(mesh.shape, mesh.axis_names)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/partial_eval.py",
"new_path": "jax/interpreters/partial_eval.py",
"diff": "@@ -242,7 +242,6 @@ class JaxprTrace(Trace):\n# We use post_process_call to handle both call and map primitives.\ndef post_process_call(self, primitive, out_tracers, params):\n-\njaxpr, consts, env = tracers_to_jaxpr([], out_tracers)\nout_pvs, out_pv_consts = unzip2(t.pval for t in out_tracers)\nout = out_pv_consts + consts\n@@ -322,7 +321,6 @@ class JaxprTrace(Trace):\ndef jvp_jaxpr_thunk():\njvp_ = trace_to_subjaxpr(jvp, self.main, True)\njvp_, aux = partial_eval_wrapper(jvp_, tuple(in_avals) * 2)\n- with core.new_sublevel():\nout_flat = jvp_.call_wrapped(*(in_consts * 2)) # in_consts are units\nout_avals, jaxpr, env = aux()\n_, consts = split_list(out_flat, [len(out_flat)-len(jaxpr.constvars)])\n@@ -362,7 +360,6 @@ class JaxprTrace(Trace):\ndef fwd_jaxpr_thunk():\nfwd_ = trace_to_subjaxpr(fwd, self.main, True)\nfwd_, aux = partial_eval_wrapper(fwd_, tuple(in_avals))\n- with core.new_sublevel():\nout_flat = fwd_.call_wrapped(*in_consts) # in_consts are units\nout_avals, jaxpr, env = aux()\n_, consts = split_list(out_flat, [len(out_flat)-len(jaxpr.constvars)])\n@@ -1061,9 +1058,8 @@ class DynamicJaxprTrace(core.Trace):\ndef process_call(self, call_primitive, f, tracers, params):\nin_avals = [t.aval for t in tracers]\n- with core.new_sublevel():\njaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(f, self.main, in_avals)\n- if params.get('inline', False):\n+ if not jaxpr.eqns:\nreturn core.eval_jaxpr(jaxpr, consts, *tracers)\nsource_info = source_info_util.current()\nout_tracers = [DynamicJaxprTracer(self, a, source_info) for a in out_avals]\n@@ -1089,7 +1085,6 @@ class DynamicJaxprTrace(core.Trace):\nif in_axis is not None else a\nfor a, in_axis in zip(in_avals, params['in_axes'])]\nwith core.extend_axis_env(axis_name, axis_size, None): # type: ignore\n- with core.new_sublevel():\njaxpr, reduced_out_avals, consts = trace_to_subjaxpr_dynamic(\nf, self.main, reduced_in_avals)\nout_axes = params['out_axes_thunk']()\n@@ -1118,7 +1113,6 @@ class DynamicJaxprTrace(core.Trace):\ndef process_custom_jvp_call(self, prim, fun, jvp, tracers):\nin_avals = [t.aval for t in tracers]\n- with core.new_sublevel():\nfun_jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, self.main, in_avals)\nclosed_fun_jaxpr = core.ClosedJaxpr(convert_constvars_jaxpr(fun_jaxpr), ())\njvp_jaxpr_thunk = _memoize(\n@@ -1140,7 +1134,6 @@ class DynamicJaxprTrace(core.Trace):\ndef process_custom_vjp_call(self, prim, fun, fwd, bwd, tracers, out_trees):\nin_avals = [t.aval for t in tracers]\n- with core.new_sublevel():\nfun_jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, self.main, in_avals)\nclosed_fun_jaxpr = core.ClosedJaxpr(convert_constvars_jaxpr(fun_jaxpr), ())\nfwd_jaxpr_thunk = _memoize(\n@@ -1213,7 +1206,6 @@ def trace_to_jaxpr_final(fun: lu.WrappedFun,\nwith core.new_base_main(DynamicJaxprTrace) as main: # type: ignore\nmain.source_info = fun_sourceinfo(fun.f, transform_name) # type: ignore\nmain.jaxpr_stack = () # type: ignore\n- with core.new_sublevel():\njaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, main, in_avals)\ndel fun, main\nreturn jaxpr, out_avals, consts\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -218,10 +218,7 @@ def primitive_uses_outfeed(prim: core.Primitive, params: Dict) -> bool:\n### op-by-op execution\n-\n-ArgSpec = Tuple[core.AbstractValue, Optional[Device]]\n-\n-def arg_spec(x: Any) -> ArgSpec:\n+def arg_spec(x):\naval = abstractify(x)\ntry:\nreturn aval, x._device\n@@ -243,7 +240,8 @@ def _partition_outputs(avals, outs):\n@cache()\n-def xla_primitive_callable(prim, *arg_specs: ArgSpec, **params):\n+def xla_primitive_callable(prim, *arg_specs: Tuple[core.AbstractValue,\n+ Optional[Device]], **params):\navals, arg_devices = unzip2(arg_specs)\ndonated_invars = (False,) * len(arg_specs)\ndevice = _device_from_arg_devices(arg_devices)\n@@ -575,9 +573,7 @@ def jaxpr_collectives(jaxpr):\n### xla_call underlying jit\n-def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name,\n- donated_invars, inline):\n- del inline # Only used at tracing time\n+def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name, donated_invars):\ncompiled_fun = _xla_callable(fun, device, backend, name, donated_invars,\n*unsafe_map(arg_spec, args))\ntry:\n@@ -595,7 +591,6 @@ def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name,\n# intentional here, to avoid \"Store occupied\" errors we reset the stores to\n# be empty.\nfor store in fun.stores: store and store.reset()\n- with core.new_sublevel():\nreturn fun.call_wrapped(*args) # probably won't return\ndef flatten_shape(s: XlaShape) -> Sequence[Tuple[Sequence[int], XlaShape]]:\n@@ -697,8 +692,7 @@ def _xla_callable(fun: lu.WrappedFun, device, backend, name, donated_invars, *ar\nc = xb.make_computation_builder(\"jit_{}\".format(fun.__name__))\nxla_consts = _xla_consts(c, consts)\n- xla_args, donated_invars = _xla_callable_args(c, abstract_args, tuple_args,\n- donated_invars=donated_invars)\n+ xla_args, donated_invars = _xla_callable_args(c, abstract_args, tuple_args, donated_invars=donated_invars)\nout_nodes = jaxpr_subcomp(\nc, jaxpr, backend, AxisEnv(nreps, (), ()), xla_consts,\nextend_name_stack(wrap_name(name, 'jit')), *xla_args)\n@@ -795,8 +789,8 @@ def _xla_callable_args(\nfor (a, r, p) in safe_zip(avals, replicated, parts)\nfor xla_shape in aval_to_xla_shapes(a)]\nif donated_invars is not None:\n- donated_invars = [\n- d for (a, _, _, d) in zip(avals, replicated, parts, donated_invars)\n+ donated_invars = [d\n+ for (a, r, p, d) in safe_zip(avals, replicated, parts, donated_invars)\nfor xla_shape in aval_to_xla_shapes(a)]\nreturn xla_args, donated_invars\nelse:\n@@ -891,8 +885,8 @@ ad.call_transpose_param_updaters[xla_call_p] = _xla_call_transpose_update_params\ndef _xla_call_translation_rule(c, axis_env,\nin_nodes, name_stack, backend, name,\n- call_jaxpr, donated_invars, inline, device=None):\n- del device, donated_invars, inline # Ignored.\n+ call_jaxpr, donated_invars, device=None):\n+ del device, donated_invars # Ignored.\nsubc = xb.make_computation_builder(f\"jit_{name}\")\nargs = [xb.parameter(subc, i, c.get_shape(n)) for i, n in enumerate(in_nodes)]\nout_nodes = jaxpr_subcomp(subc, call_jaxpr, backend, axis_env, (),\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -2343,7 +2343,7 @@ class APITest(jtu.JaxTestCase):\nlst.append(x)\nreturn x\n- with self.assertRaisesRegex(Exception, r\"Leaked\"):\n+ with self.assertRaisesRegex(Exception, r\"Leaked trace\"):\nf(3)\ndef test_leak_checker_catches_a_pmap_leak(self):\n@@ -2355,7 +2355,7 @@ class APITest(jtu.JaxTestCase):\nlst.append(x)\nreturn x\n- with self.assertRaisesRegex(Exception, r\"Leaked\"):\n+ with self.assertRaisesRegex(Exception, r\"Leaked trace\"):\nf(np.ones(1))\ndef test_leak_checker_catches_a_grad_leak(self):\n@@ -2678,21 +2678,6 @@ class APITest(jtu.JaxTestCase):\njtu.check_grads(batched_scan_over_mul, (x_batch, coeff), order=2,\nmodes=['rev'])\n- def test_jit_inline(self):\n- @partial(api.jit, inline=False)\n- def f(x):\n- return x * 2\n-\n- jaxpr = api.make_jaxpr(f)(3)\n- self.assertIn('xla_call', str(jaxpr))\n-\n- @partial(api.jit, inline=True)\n- def f(x):\n- return x * 2\n-\n- jaxpr = api.make_jaxpr(f)(3)\n- self.assertNotIn('xla_call', str(jaxpr))\n-\nclass RematTest(jtu.JaxTestCase):\n"
}
] | Python | Apache License 2.0 | google/jax | Copybara import of the project:
--
3c400a3e588abf9e2259119c50343cba6f3477f1 by Matthew Johnson <mattjj@google.com>:
add 'inline' option to xla_call for jaxpr inlining
--
fe297e39ca37896b75d7943b9b77c0b53fad13ee by Matthew Johnson <mattjj@google.com>:
add 'inline' to jit docstring
--
ff6866c4b3757cde66fe659c2f27d8aeff024e8f by Matthew Johnson <mattjj@google.com>:
new_sublevel in jax2tf
PiperOrigin-RevId: 371542778 |
260,335 | 03.05.2021 21:40:50 | 25,200 | 7ec0b40173dd51ff5c611a7b589ceaf879dfe3b0 | Roll-forward of which broke internal tests. | [
{
"change_type": "MODIFY",
"old_path": "docs/jaxpr.rst",
"new_path": "docs/jaxpr.rst",
"diff": "@@ -433,6 +433,7 @@ computation should run. For example\nin (g,) }\ndevice=None\ndonated_invars=(False, False)\n+ inline=False\nname=inner ] a b\nd = convert_element_type[ new_dtype=float32\nweak_type=False ] a\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/api.py",
"new_path": "jax/_src/api.py",
"diff": "@@ -212,6 +212,7 @@ def jit(\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\ndonate_argnums: Union[int, Iterable[int]] = (),\n+ inline: bool = False,\n) -> F:\n\"\"\"Sets up ``fun`` for just-in-time compilation with XLA.\n@@ -262,7 +263,10 @@ def jit(\nbuffers to reduce the amount of memory needed to perform a computation,\nfor example recycling one of your input buffers to store a result. You\nshould not reuse buffers that you donate to a computation, JAX will raise\n- an error if you try to.\n+ an error if you try to. By default, no arguments are donated.\n+ inline: Specify whether this function should be inlined into enclosing\n+ jaxprs (rather than being represented as an application of the xla_call\n+ primitive with its own subjaxpr). Default False.\nReturns:\nA wrapped version of ``fun``, set up for just-in-time compilation.\n@@ -288,10 +292,10 @@ def jit(\nstatic_argnames = ()\nif FLAGS.experimental_cpp_jit:\nreturn _cpp_jit(fun, static_argnums, static_argnames, device, backend,\n- donate_argnums)\n+ donate_argnums, inline)\nelse:\nreturn _python_jit(fun, static_argnums, static_argnames, device, backend,\n- donate_argnums)\n+ donate_argnums, inline)\ndef _python_jit(\n@@ -300,9 +304,10 @@ def _python_jit(\nstatic_argnames: Union[str, Iterable[str], None] = None,\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\n- donate_argnums: Union[int, Iterable[int]] = ()\n+ donate_argnums: Union[int, Iterable[int]] = (),\n+ inline: bool = False,\n) -> F:\n- \"\"\"The Python implementation of `jax.jit`, being slowly replaced by _cpp_jit.\"\"\"\n+ # The Python implementation of `jax.jit`, being slowly replaced by _cpp_jit.\n_check_callable(fun)\nstatic_argnums, static_argnames = _infer_argnums_and_argnames(\nfun, static_argnums, static_argnames)\n@@ -339,13 +344,8 @@ def _python_jit(\nfor arg in args_flat:\n_check_arg(arg)\nflat_fun, out_tree = flatten_fun(f, in_tree)\n- out = xla.xla_call(\n- flat_fun,\n- *args_flat,\n- device=device,\n- backend=backend,\n- name=flat_fun.__name__,\n- donated_invars=donated_invars)\n+ out = xla.xla_call(flat_fun, *args_flat, device=device, backend=backend,\n+ name=flat_fun.__name__, donated_invars=donated_invars, inline=inline)\nreturn tree_unflatten(out_tree(), out)\nreturn f_jitted\n@@ -373,16 +373,15 @@ def _cpp_jit(\ndevice: Optional[xc.Device] = None,\nbackend: Optional[str] = None,\ndonate_argnums: Union[int, Iterable[int]] = (),\n+ inline: bool = False,\n) -> F:\n- \"\"\"An implementation of `jit` that tries to do as much as possible in C++.\n-\n- The goal of this function is to speed up the time it takes to process the\n- arguments, find the correct C++ executable, start the transfer of arguments\n- and schedule the computation.\n- As long as it does not support all features of the Python implementation\n- the C++ code will fallback to `_python_jit` when it faces some unsupported\n- feature.\n- \"\"\"\n+ # An implementation of `jit` that tries to do as much as possible in C++.\n+ # The goal of this function is to speed up the time it takes to process the\n+ # arguments, find the correct C++ executable, start the transfer of arguments\n+ # and schedule the computation.\n+ # As long as it does not support all features of the Python implementation\n+ # the C++ code will fallback to `_python_jit` when it faces some unsupported\n+ # feature.\n_check_callable(fun)\nstatic_argnums, static_argnames = _infer_argnums_and_argnames(\nfun, static_argnums, static_argnames)\n@@ -424,12 +423,9 @@ def _cpp_jit(\n_check_arg(arg)\nflat_fun, out_tree = flatten_fun(f, in_tree)\nout_flat = xla.xla_call(\n- flat_fun,\n- *args_flat,\n- device=device,\n- backend=backend,\n- name=flat_fun.__name__,\n- donated_invars=donated_invars)\n+ flat_fun, *args_flat,\n+ device=device, backend=backend, name=flat_fun.__name__,\n+ donated_invars=donated_invars, inline=inline)\nout_pytree_def = out_tree()\nout = tree_unflatten(out_pytree_def, out_flat)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/custom_derivatives.py",
"new_path": "jax/_src/custom_derivatives.py",
"diff": "@@ -270,12 +270,12 @@ class CustomJVPCallPrimitive(core.CallPrimitive):\njvp, env_trace_todo2 = core.process_env_traces(\njvp, self, top_trace and top_trace.level, (), None)\ntracers = map(top_trace.full_raise, args) # type: ignore\n- with core.maybe_new_sublevel(top_trace):\nouts = top_trace.process_custom_jvp_call(self, fun, jvp, tracers) # type: ignore\n_, env_trace_todo = lu.merge_linear_aux(env_trace_todo1, env_trace_todo2)\nreturn _apply_todos(env_trace_todo, map(core.full_lower, outs))\ndef impl(self, fun, _, *args):\n+ with core.new_sublevel():\nreturn fun.call_wrapped(*args)\ndef post_process(self, trace, out_tracers, params):\n@@ -563,7 +563,6 @@ class CustomVJPCallPrimitive(core.CallPrimitive):\nfwd, env_trace_todo2 = core.process_env_traces(\nfwd, self, top_trace and top_trace.level, (), None)\ntracers = map(top_trace.full_raise, args) # type: ignore\n- with core.maybe_new_sublevel(top_trace):\nouts = top_trace.process_custom_vjp_call(self, fun, fwd, bwd, tracers,\nout_trees=out_trees)\n_, env_trace_todo = lu.merge_linear_aux(env_trace_todo1, env_trace_todo2)\n@@ -571,6 +570,7 @@ class CustomVJPCallPrimitive(core.CallPrimitive):\ndef impl(self, fun, fwd, bwd, *args, out_trees):\ndel fwd, bwd, out_trees\n+ with core.new_sublevel():\nreturn fun.call_wrapped(*args)\ndef post_process(self, trace, out_tracers, params):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "import operator\nfrom operator import attrgetter\n-from contextlib import contextmanager, suppress\n+from contextlib import contextmanager\nfrom collections import namedtuple\nfrom functools import total_ordering\nimport itertools as it\n@@ -611,10 +611,12 @@ class EvalTrace(Trace):\ndef process_custom_jvp_call(self, primitive, fun, jvp, tracers):\ndel primitive, jvp # Unused.\n+ with new_sublevel():\nreturn fun.call_wrapped(*tracers)\ndef process_custom_vjp_call(self, primitive, fun, fwd, bwd, tracers, out_trees):\ndel primitive, fwd, bwd, out_trees # Unused.\n+ with new_sublevel():\nreturn fun.call_wrapped(*tracers)\n@@ -806,11 +808,6 @@ def new_sublevel() -> Generator[None, None, None]:\nif t() is not None:\nraise Exception(f'Leaked sublevel {t()}.')\n-def maybe_new_sublevel(trace):\n- # dynamic traces run the WrappedFun, so we raise the sublevel for them\n- dynamic = thread_local_state.trace_state.trace_stack.dynamic\n- return new_sublevel() if trace.main is dynamic else suppress()\n-\ndef full_lower(val):\nif isinstance(val, Tracer):\nreturn val.full_lower()\n@@ -1552,7 +1549,6 @@ def call_bind(primitive: Union['CallPrimitive', 'MapPrimitive'],\nfun, primitive, top_trace and top_trace.level,\nparams_tuple, out_axes_transforms)\ntracers = map(top_trace.full_raise, args)\n- with maybe_new_sublevel(top_trace):\nouts = primitive.process(top_trace, fun, tracers, params)\nreturn map(full_lower, apply_todos(env_trace_todo(), outs))\n@@ -1572,6 +1568,7 @@ class CallPrimitive(Primitive):\ndef call_impl(f: lu.WrappedFun, *args, **params):\ndel params # params parameterize the call primitive, not the function\n+ with new_sublevel():\nreturn f.call_wrapped(*args)\ncall_p = CallPrimitive('call')\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/host_callback.py",
"new_path": "jax/experimental/host_callback.py",
"diff": "@@ -1419,7 +1419,8 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_cond_jaxpr.jaxpr,\nname=\"cond_before\",\n- donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals)),\n+ donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals),\n+ inline=False),\neqn.source_info))\n# Make a new cond \"lambda pred, carry, token, itoken: pred\"\nnew_cond_pred_invar = mk_new_var(cond_jaxpr.out_avals[0])\n@@ -1462,7 +1463,8 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_body_jaxpr.jaxpr,\nname=\"body\",\n- donated_invars=(False,) * len(transformed_body_jaxpr.in_avals)),\n+ donated_invars=(False,) * len(transformed_body_jaxpr.in_avals),\n+ inline=False),\neqn.source_info),\ncore.new_jaxpr_eqn(\nnew_body_invars_cond_constvars + new_body_carry2 + [new_body_token2, new_body_itoken2],\n@@ -1470,7 +1472,8 @@ def _rewrite_while_outfeed_cond(eqn: core.JaxprEqn, eqns: List[core.JaxprEqn],\ndict(\ncall_jaxpr=transformed_cond_jaxpr.jaxpr,\nname=\"cond_body\",\n- donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals)),\n+ donated_invars=(False,) * len(transformed_cond_jaxpr.in_avals),\n+ inline=False),\neqn.source_info)\n]\nnew_body_jaxpr = core.ClosedJaxpr(\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -310,7 +310,9 @@ def _interpret_fun(fun: lu.WrappedFun,\n) -> Sequence[Tuple[TfVal, core.AbstractValue]]:\nwith core.new_base_main(TensorFlowTrace) as main: # type: ignore\nfun = _interpret_subtrace(fun, main, in_avals)\n- out_vals: Sequence[Tuple[TfVal, core.AbstractValue]] = fun.call_wrapped(*in_vals)\n+ with core.new_sublevel():\n+ out_vals: Sequence[Tuple[TfVal, core.AbstractValue]] = \\\n+ fun.call_wrapped(*in_vals)\ndel main\nreturn tuple(out_vals)\n@@ -675,10 +677,11 @@ class TensorFlowTrace(core.Trace):\nassert call_primitive.multiple_results\nvals: Sequence[TfVal] = [t.val for t in tracers]\nf = _interpret_subtrace(f, self.main, tuple(t.aval for t in tracers))\n+ with core.new_sublevel():\nif call_primitive == core.named_call_p:\nwith tf.name_scope(_sanitize_scope_name(params[\"name\"])):\n- vals_out: Sequence[Tuple[TfVal,\n- core.AbstractValue]] = f.call_wrapped(*vals)\n+ vals_out: Sequence[Tuple[TfVal, core.AbstractValue]] = \\\n+ f.call_wrapped(*vals)\nelif call_primitive == sharded_jit.sharded_call_p:\nvals_out = _sharded_call(f, vals, **params)\nelse:\n@@ -2173,7 +2176,7 @@ def _sharded_call(f: lu.WrappedFun, vals: Sequence[TfVal],\nout_parts_thunk,\n**_) -> Sequence[Tuple[TfVal, core.AbstractValue]]:\nsharded_vals = util.safe_map(split_to_logical_devices, vals, in_parts)\n- vals_out = f.call_wrapped(*sharded_vals)\n+ vals_out = f.call_wrapped(*sharded_vals) # caller handles new_sublevel\nout_parts_flat = out_parts_thunk()\nassert len(out_parts_flat) == len(vals_out), f\"expected {len(out_parts_flat)} == {len(vals_out)}\"\nsharded_vals_out = [\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -1005,7 +1005,7 @@ def _xmap_translation_rule_replica(c, axis_env,\n# NOTE: We don't extend the resource env with the mesh shape, because those\n# resources are already in scope! It's the outermost xmap that introduces\n# them!\n- vectorized_jaxpr, out_avals, consts = pe.trace_to_jaxpr_final(f, local_avals)\n+ vectorized_jaxpr, out_avals, consts = pe.trace_to_jaxpr_dynamic(f, local_avals)\n_check_out_avals_vs_out_axes(out_avals, out_axes, global_axis_sizes)\nassert not consts\n@@ -1123,7 +1123,7 @@ def _xmap_translation_rule_spmd(c, axis_env,\nglobal_in_avals = [core.ShapedArray(xla_type.dimensions(), xla_type.numpy_dtype())\nfor in_node in global_in_nodes\nfor xla_type in (c.get_shape(in_node),)]\n- vectorized_jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_final(f, global_in_avals)\n+ vectorized_jaxpr, global_out_avals, consts = pe.trace_to_jaxpr_dynamic(f, global_in_avals)\nassert not consts\nglobal_sharding_spec = pxla.mesh_sharding_specs(mesh.shape, mesh.axis_names)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/partial_eval.py",
"new_path": "jax/interpreters/partial_eval.py",
"diff": "@@ -242,6 +242,7 @@ class JaxprTrace(Trace):\n# We use post_process_call to handle both call and map primitives.\ndef post_process_call(self, primitive, out_tracers, params):\n+\njaxpr, consts, env = tracers_to_jaxpr([], out_tracers)\nout_pvs, out_pv_consts = unzip2(t.pval for t in out_tracers)\nout = out_pv_consts + consts\n@@ -321,6 +322,7 @@ class JaxprTrace(Trace):\ndef jvp_jaxpr_thunk():\njvp_ = trace_to_subjaxpr(jvp, self.main, True)\njvp_, aux = partial_eval_wrapper(jvp_, tuple(in_avals) * 2)\n+ with core.new_sublevel():\nout_flat = jvp_.call_wrapped(*(in_consts * 2)) # in_consts are units\nout_avals, jaxpr, env = aux()\n_, consts = split_list(out_flat, [len(out_flat)-len(jaxpr.constvars)])\n@@ -360,6 +362,7 @@ class JaxprTrace(Trace):\ndef fwd_jaxpr_thunk():\nfwd_ = trace_to_subjaxpr(fwd, self.main, True)\nfwd_, aux = partial_eval_wrapper(fwd_, tuple(in_avals))\n+ with core.new_sublevel():\nout_flat = fwd_.call_wrapped(*in_consts) # in_consts are units\nout_avals, jaxpr, env = aux()\n_, consts = split_list(out_flat, [len(out_flat)-len(jaxpr.constvars)])\n@@ -1058,8 +1061,9 @@ class DynamicJaxprTrace(core.Trace):\ndef process_call(self, call_primitive, f, tracers, params):\nin_avals = [t.aval for t in tracers]\n+ with core.new_sublevel():\njaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(f, self.main, in_avals)\n- if not jaxpr.eqns:\n+ if params.get('inline', False):\nreturn core.eval_jaxpr(jaxpr, consts, *tracers)\nsource_info = source_info_util.current()\nout_tracers = [DynamicJaxprTracer(self, a, source_info) for a in out_avals]\n@@ -1085,6 +1089,7 @@ class DynamicJaxprTrace(core.Trace):\nif in_axis is not None else a\nfor a, in_axis in zip(in_avals, params['in_axes'])]\nwith core.extend_axis_env(axis_name, axis_size, None): # type: ignore\n+ with core.new_sublevel():\njaxpr, reduced_out_avals, consts = trace_to_subjaxpr_dynamic(\nf, self.main, reduced_in_avals)\nout_axes = params['out_axes_thunk']()\n@@ -1113,6 +1118,7 @@ class DynamicJaxprTrace(core.Trace):\ndef process_custom_jvp_call(self, prim, fun, jvp, tracers):\nin_avals = [t.aval for t in tracers]\n+ with core.new_sublevel():\nfun_jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, self.main, in_avals)\nclosed_fun_jaxpr = core.ClosedJaxpr(convert_constvars_jaxpr(fun_jaxpr), ())\njvp_jaxpr_thunk = _memoize(\n@@ -1134,6 +1140,7 @@ class DynamicJaxprTrace(core.Trace):\ndef process_custom_vjp_call(self, prim, fun, fwd, bwd, tracers, out_trees):\nin_avals = [t.aval for t in tracers]\n+ with core.new_sublevel():\nfun_jaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, self.main, in_avals)\nclosed_fun_jaxpr = core.ClosedJaxpr(convert_constvars_jaxpr(fun_jaxpr), ())\nfwd_jaxpr_thunk = _memoize(\n@@ -1206,6 +1213,7 @@ def trace_to_jaxpr_final(fun: lu.WrappedFun,\nwith core.new_base_main(DynamicJaxprTrace) as main: # type: ignore\nmain.source_info = fun_sourceinfo(fun.f, transform_name) # type: ignore\nmain.jaxpr_stack = () # type: ignore\n+ with core.new_sublevel():\njaxpr, out_avals, consts = trace_to_subjaxpr_dynamic(fun, main, in_avals)\ndel fun, main\nreturn jaxpr, out_avals, consts\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -219,7 +219,10 @@ def primitive_uses_outfeed(prim: core.Primitive, params: Dict) -> bool:\n### op-by-op execution\n-def arg_spec(x):\n+\n+ArgSpec = Tuple[core.AbstractValue, Optional[Device]]\n+\n+def arg_spec(x: Any) -> ArgSpec:\naval = abstractify(x)\ntry:\nreturn aval, x._device\n@@ -241,8 +244,7 @@ def _partition_outputs(avals, outs):\n@cache()\n-def xla_primitive_callable(prim, *arg_specs: Tuple[core.AbstractValue,\n- Optional[Device]], **params):\n+def xla_primitive_callable(prim, *arg_specs: ArgSpec, **params):\navals, arg_devices = unzip2(arg_specs)\ndonated_invars = (False,) * len(arg_specs)\ndevice = _device_from_arg_devices(arg_devices)\n@@ -574,7 +576,9 @@ def jaxpr_collectives(jaxpr):\n### xla_call underlying jit\n-def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name, donated_invars):\n+def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name,\n+ donated_invars, inline):\n+ del inline # Only used at tracing time\ncompiled_fun = _xla_callable(fun, device, backend, name, donated_invars,\n*unsafe_map(arg_spec, args))\ntry:\n@@ -592,6 +596,7 @@ def _xla_call_impl(fun: lu.WrappedFun, *args, device, backend, name, donated_inv\n# intentional here, to avoid \"Store occupied\" errors we reset the stores to\n# be empty.\nfor store in fun.stores: store and store.reset()\n+ with core.new_sublevel():\nreturn fun.call_wrapped(*args) # probably won't return\ndef flatten_shape(s: XlaShape) -> Sequence[Tuple[Sequence[int], XlaShape]]:\n@@ -701,7 +706,8 @@ def _xla_callable(fun: lu.WrappedFun, device, backend, name, donated_invars, *ar\nc = xb.make_computation_builder(\"jit_{}\".format(fun.__name__))\nxla_consts = _xla_consts(c, consts)\n- xla_args, donated_invars = _xla_callable_args(c, abstract_args, tuple_args, donated_invars=donated_invars)\n+ xla_args, donated_invars = _xla_callable_args(c, abstract_args, tuple_args,\n+ donated_invars=donated_invars)\nout_nodes = jaxpr_subcomp(\nc, jaxpr, backend, AxisEnv(nreps, (), ()), xla_consts,\nextend_name_stack(wrap_name(name, 'jit')), *xla_args)\n@@ -828,8 +834,8 @@ def _xla_callable_args(\nfor (a, r, p) in safe_zip(avals, replicated, parts)\nfor xla_shape in aval_to_xla_shapes(a)]\nif donated_invars is not None:\n- donated_invars = [d\n- for (a, r, p, d) in safe_zip(avals, replicated, parts, donated_invars)\n+ donated_invars = [\n+ d for (a, _, _, d) in zip(avals, replicated, parts, donated_invars)\nfor xla_shape in aval_to_xla_shapes(a)]\nreturn xla_args, donated_invars\nelse:\n@@ -938,10 +944,9 @@ def _xla_call_transpose_update_params(params, undef_primals, nonzero_cts):\nad.call_transpose_param_updaters[xla_call_p] = _xla_call_transpose_update_params\n-def _xla_call_translation_rule(c, axis_env,\n- in_nodes, name_stack, backend, name,\n- call_jaxpr, donated_invars, device=None):\n- del device, donated_invars # Ignored.\n+def _xla_call_translation_rule(c, axis_env, in_nodes, name_stack, backend, name,\n+ call_jaxpr, donated_invars, inline=None, device=None):\n+ del device, donated_invars, inline # Ignored.\nsubc = xb.make_computation_builder(f\"jit_{name}\")\nargs = [xb.parameter(subc, i, c.get_shape(n)) for i, n in enumerate(in_nodes)]\nout_nodes = jaxpr_subcomp(subc, call_jaxpr, backend, axis_env, (),\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/api_test.py",
"new_path": "tests/api_test.py",
"diff": "@@ -2360,7 +2360,7 @@ class APITest(jtu.JaxTestCase):\nlst.append(x)\nreturn x\n- with self.assertRaisesRegex(Exception, r\"Leaked trace\"):\n+ with self.assertRaisesRegex(Exception, r\"Leaked\"):\nf(3)\ndef test_leak_checker_catches_a_pmap_leak(self):\n@@ -2372,7 +2372,7 @@ class APITest(jtu.JaxTestCase):\nlst.append(x)\nreturn x\n- with self.assertRaisesRegex(Exception, r\"Leaked trace\"):\n+ with self.assertRaisesRegex(Exception, r\"Leaked\"):\nf(np.ones(1))\ndef test_leak_checker_catches_a_grad_leak(self):\n@@ -2695,6 +2695,21 @@ class APITest(jtu.JaxTestCase):\njtu.check_grads(batched_scan_over_mul, (x_batch, coeff), order=2,\nmodes=['rev'])\n+ def test_jit_inline(self):\n+ @partial(api.jit, inline=False)\n+ def f(x):\n+ return x * 2\n+\n+ jaxpr = api.make_jaxpr(f)(3)\n+ self.assertIn('xla_call', str(jaxpr))\n+\n+ @partial(api.jit, inline=True)\n+ def f(x):\n+ return x * 2\n+\n+ jaxpr = api.make_jaxpr(f)(3)\n+ self.assertNotIn('xla_call', str(jaxpr))\n+\nclass RematTest(jtu.JaxTestCase):\n"
}
] | Python | Apache License 2.0 | google/jax | Roll-forward of #6584, which broke internal tests.
PiperOrigin-RevId: 371839298 |
260,287 | 05.05.2021 06:07:16 | 25,200 | 049383c553612c4b9a5c8822d56b7786fb9115b6 | Make sure that we don't pass unhashable axis_resources to cached pjit implementation | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -100,8 +100,8 @@ def pjit(fun: Callable,\nlocal_in_avals = tuple(core.raise_to_shaped(core.get_aval(a)) for a in args_flat)\njaxpr, in_axis_resources_flat, out_axis_resources_flat = \\\n_pjit_jaxpr(flat_fun, mesh, local_in_avals,\n- in_tree, in_axis_resources,\n- HashableFunction(out_tree, closure=()), out_axis_resources)\n+ in_tree, hashable_pytree(in_axis_resources),\n+ HashableFunction(out_tree, closure=()), hashable_pytree(out_axis_resources))\nout = pjit_p.bind(\n*args_flat,\n@@ -118,12 +118,18 @@ def pjit(fun: Callable,\nclass _ListWithW(list):\n__slots__ = ('__weakref__',)\n+def hashable_pytree(pytree):\n+ vals, treedef = tree_flatten(pytree)\n+ vals = tuple(vals)\n+ return HashableFunction(lambda: tree_unflatten(treedef, vals),\n+ closure=(treedef, vals))\n+\n@lu.cache\ndef _pjit_jaxpr(fun, mesh, local_in_avals,\n- in_tree, in_axis_resources,\n- out_tree, out_axis_resources):\n+ in_tree, in_axis_resources_thunk,\n+ out_tree, out_axis_resources_thunk):\nin_axis_resources_flat = tuple(flatten_axes(\"pjit in_axis_resources\",\n- in_tree, in_axis_resources))\n+ in_tree, in_axis_resources_thunk()))\n_check_shapes_against_resources(\"pjit arguments\", False, mesh.local_mesh.shape,\nlocal_in_avals, in_axis_resources_flat)\nglobal_in_avals = local_to_global(mesh, local_in_avals, in_axis_resources_flat)\n@@ -132,7 +138,7 @@ def _pjit_jaxpr(fun, mesh, local_in_avals,\njaxpr = core.ClosedJaxpr(jaxpr, consts)\nout_axis_resources_flat = tuple(flatten_axes(\"pjit out_axis_resources\",\n- out_tree(), out_axis_resources))\n+ out_tree(), out_axis_resources_thunk()))\n_check_shapes_against_resources(\"pjit outputs\", mesh.is_multi_process, mesh.shape,\nglobal_out_avals, out_axis_resources_flat)\n# lu.cache needs to be able to create weakrefs to outputs, so we can't return a plain tuple\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -269,6 +269,14 @@ class PJitTest(jtu.BufferDonationTestCase):\nin_axis_resources=None,\nout_axis_resources=None)(1), 3)\n+ @with_mesh([('x', 2)])\n+ def testNonHashableAxisResources(self):\n+ x = jnp.arange(4)\n+ y = pjit(lambda x: {'b': x['a'] + 2},\n+ in_axis_resources=({'a': P('x')},),\n+ out_axis_resources={'b': P('x')})({'a': x})\n+ self.assertAllClose(y, {'b': x + 2})\n+\n@with_mesh([('x', 2)])\ndef testGradOfConstraint(self):\n# Make sure that we can compute grads through sharding constraints\n"
}
] | Python | Apache License 2.0 | google/jax | Make sure that we don't pass unhashable axis_resources to cached pjit implementation
PiperOrigin-RevId: 372110726 |
260,287 | 05.05.2021 06:43:47 | 25,200 | 31f35373269803f6653b0e21b4f65686c1e03f2f | Make sure that no errors are raised for no-op PartitionSpecs | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -202,6 +202,7 @@ def _check_unique_resources(axis_resources, arg_name):\nfor arg_axis_resources in axis_resources:\nif not arg_axis_resources: continue\nresource_counts = Counter(it.chain.from_iterable(arg_axis_resources))\n+ if not resource_counts: continue\nif resource_counts.most_common(1)[0][1] > 1:\nmultiple_uses = [r for r, c in resource_counts.items() if c > 1]\nif multiple_uses:\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -286,7 +286,13 @@ class PJitTest(jtu.BufferDonationTestCase):\nx = jnp.arange(8, dtype=jnp.float32)\nself.assertAllClose(f(x), jnp.cos(x))\n- # TODO(skye): add more unit tests once API is more finalized\n+ @with_mesh([('x', 2)])\n+ def testNoopPartitionSpecs(self):\n+ noops = [P(), P(None), P(()), P((), None), P(None, None, ())]\n+ x = jnp.arange(8).reshape((2, 2, 2))\n+ for spec in noops:\n+ y = pjit(lambda x: x * 2, in_axis_resources=spec, out_axis_resources=spec)(x)\n+ self.assertAllClose(y, x * 2)\ndef testInfeed(self):\ndevices = np.array(jax.local_devices())\n"
}
] | Python | Apache License 2.0 | google/jax | Make sure that no errors are raised for no-op PartitionSpecs
PiperOrigin-RevId: 372115524 |
260,347 | 06.05.2021 10:16:42 | 0 | 17cc9502b76c5584e45533f9c6be4ff91b335c42 | Make jnp.unravel_index rank promotion explicit.
There was a silent rank promotion in jax.numpy.unravel_index which caused the
code to fail with jax_numpy_rank_promotion='raise'. This commit makes the rank
promotion explicit. Furthermore, it fixes an outdated comment in the same
function. | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/lax_numpy.py",
"new_path": "jax/_src/numpy/lax_numpy.py",
"diff": "@@ -1403,8 +1403,9 @@ def unravel_index(indices, shape):\ntotal_size = cumulative_sizes[0]\n# Clip so raveling and unraveling an oob index will not change the behavior\nclipped_indices = clip(indices, -total_size, total_size - 1)\n- # Add enough trailing dims to avoid conflict with flat_index\n+ # Add enough trailing dims to avoid conflict with clipped_indices\ncumulative_sizes = cumulative_sizes.reshape([-1] + [1] * indices.ndim)\n+ clipped_indices = expand_dims(clipped_indices, axis=0)\nidx = clipped_indices % cumulative_sizes[:-1] // cumulative_sizes[1:]\nreturn tuple(idx)\n"
}
] | Python | Apache License 2.0 | google/jax | Make jnp.unravel_index rank promotion explicit.
There was a silent rank promotion in jax.numpy.unravel_index which caused the
code to fail with jax_numpy_rank_promotion='raise'. This commit makes the rank
promotion explicit. Furthermore, it fixes an outdated comment in the same
function. |
260,287 | 06.05.2021 04:18:47 | 25,200 | e481da90567d444e14efb8d4afd25791404c1099 | Raise a clear error when pjit is used with an empty mesh
Technically we could make pjit behave in the same way as jit
in that case, but this felt easier. We can always relax it in
the future. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -81,6 +81,9 @@ def pjit(fun: Callable,\n# Putting this outside of wrapped would make resources lexically scoped\nresource_env = maps.thread_resources.env\nmesh = resource_env.physical_mesh\n+ if mesh.empty:\n+ raise RuntimeError(\"pjit requires a non-empty mesh! Are you sure that \"\n+ \"it's defined at the call site?\")\nf = lu.wrap_init(fun)\nif static_argnums:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -1302,13 +1302,17 @@ class Mesh:\ndef size(self):\nreturn np.prod(list(self.shape.values()))\n+ @property\n+ def empty(self):\n+ return self.devices.ndim == 0\n+\n@property\ndef is_multi_process(self):\nreturn self.shape != self.local_mesh.shape\n@maybe_cached_property\ndef local_mesh(self):\n- if not self.devices.ndim:\n+ if self.empty:\nreturn self\nprocess_index = xb.process_index()\nis_local_device = np.vectorize(\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -413,6 +413,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nin_axis_resources=None, out_axis_resources=None)(x)\n@check_1d_2d_mesh(set_mesh=False)\n+ @with_mesh([('z', 1)])\ndef testUndefinedResourcesArgs(self, mesh, resources):\nx = jnp.ones((2, 2))\nspec = P(resources,)\n@@ -422,6 +423,7 @@ class PJitErrorTest(jtu.JaxTestCase):\npjit(lambda x: x, in_axis_resources=spec, out_axis_resources=None)(x)\n@check_1d_2d_mesh(set_mesh=False)\n+ @with_mesh([('z', 1)])\ndef testUndefinedResourcesOuts(self, mesh, resources):\nx = jnp.ones((2, 2))\nspec = P(resources,)\n@@ -431,6 +433,7 @@ class PJitErrorTest(jtu.JaxTestCase):\npjit(lambda x: x, in_axis_resources=None, out_axis_resources=spec)(x)\n@check_1d_2d_mesh(set_mesh=False)\n+ @with_mesh([('z', 1)])\ndef testUndefinedResourcesConstraint(self, mesh, resources):\nx = jnp.ones((2, 2))\nspec = P(resources,)\n@@ -538,6 +541,12 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaises(JAXTypeError):\nf(x, x)\n+ def testEmptyMesh(self):\n+ error = (r\"pjit requires a non-empty mesh! Are you sure that it's defined \"\n+ r\"at the call site?\")\n+ with self.assertRaisesRegex(RuntimeError, error):\n+ pjit(lambda x: x, in_axis_resources=None, out_axis_resources=None)(jnp.arange(4))\n+\nif __name__ == '__main__':\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | Raise a clear error when pjit is used with an empty mesh
Technically we could make pjit behave in the same way as jit
in that case, but this felt easier. We can always relax it in
the future.
PiperOrigin-RevId: 372314531 |
260,631 | 06.05.2021 04:44:41 | 25,200 | 4c0d3671c8f55908c96c42654700d247b9467f8d | Remove non-determinism in axis ordering during vectorization. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -639,7 +639,7 @@ class EvaluationPlan(NamedTuple):\naxis_resource_count = _get_axis_resource_count(axis_resources, resource_env)\naxis_subst_dict = dict(axis_resources)\naxis_vmap_size: Dict[AxisName, Optional[int]] = {}\n- for naxis, raxes in axis_resources.items():\n+ for naxis, raxes in sorted(axis_resources.items(), key=lambda x: str(x[0])):\nnum_resources = axis_resource_count[naxis]\nassert global_axis_sizes[naxis] % num_resources.nglobal == 0\nlocal_tile_size = global_axis_sizes[naxis] // num_resources.nglobal\n@@ -660,11 +660,13 @@ class EvaluationPlan(NamedTuple):\nraise AssertionError(\"Incomplete resource type-checking? Please open a bug report!\")\ndef vectorize(self, f: lu.WrappedFun, in_axes, out_axes):\n- for naxis, raxes in self.axis_subst_dict.items():\n+ vmap_axes = {\n+ naxis: raxes[-1]\n+ for naxis, raxes in self.axis_subst_dict.items()\n+ if self.axis_vmap_size[naxis] is not None\n+ }\n+ for naxis, vaxis in sorted(vmap_axes.items(), key=lambda x: x[1].uid):\nlocal_tile_size = self.axis_vmap_size[naxis]\n- if local_tile_size is None:\n- continue\n- vaxis = raxes[-1]\nmap_in_axes = tuple(unsafe_map(lambda spec: spec.get(naxis, None), in_axes))\nmap_out_axes = tuple(unsafe_map(lambda spec: spec.get(naxis, None), out_axes))\nf = batching.vtile(f, map_in_axes, map_out_axes, tile_size=local_tile_size, axis_name=vaxis)\n"
}
] | Python | Apache License 2.0 | google/jax | Remove non-determinism in axis ordering during vectorization.
PiperOrigin-RevId: 372317377 |
260,287 | 06.05.2021 12:00:18 | 25,200 | ee93ee221c24c8a23d4ebc73cc6dc9c08f18de87 | Add a docstring for pjit | [
{
"change_type": "ADD",
"old_path": null,
"new_path": "docs/jax.experimental.pjit.rst",
"diff": "+jax.experimental.pjit module\n+============================\n+\n+.. automodule:: jax.experimental.pjit\n+\n+API\n+---\n+\n+.. autofunction:: pjit\n"
},
{
"change_type": "MODIFY",
"old_path": "docs/jax.experimental.rst",
"new_path": "docs/jax.experimental.rst",
"diff": "@@ -10,6 +10,7 @@ jax.experimental package\njax.experimental.host_callback\njax.experimental.loops\njax.experimental.maps\n+ jax.experimental.pjit\njax.experimental.optimizers\njax.experimental.stax\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -156,7 +156,7 @@ def mesh(devices: np.ndarray, axis_names: Sequence[ResourceAxisName]):\naxis_resources={'left': 'x', 'right': 'y'})(x, x.T)\n\"\"\"\nold_env = getattr(thread_resources, \"env\", None)\n- thread_resources.env = ResourceEnv(Mesh(devices, axis_names))\n+ thread_resources.env = ResourceEnv(Mesh(np.asarray(devices, dtype=object), axis_names))\ntry:\nyield\nfinally:\n"
}
] | Python | Apache License 2.0 | google/jax | Add a docstring for pjit
Co-authored-by: Skye Wanderman-Milne <skyewm@google.com>
PiperOrigin-RevId: 372393461 |
260,287 | 06.05.2021 12:34:15 | 25,200 | c3553d22d6917bf4448f6e043726507cdfa40637 | Implement vmap for pjit | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "# See the License for the specific language governing permissions and\n# limitations under the License.\n+from enum import IntEnum\nimport numpy as np\nfrom collections import OrderedDict, Counter\nfrom typing import Callable, Sequence, Tuple, Union\n@@ -32,13 +33,14 @@ from ..errors import JAXTypeError\nfrom ..interpreters import ad\nfrom ..interpreters import pxla\nfrom ..interpreters import xla\n+from ..interpreters import batching\nfrom ..interpreters import partial_eval as pe\nfrom ..lib import xla_bridge as xb\nfrom ..lib import xla_client as xc\nfrom ..tree_util import tree_flatten, tree_unflatten\nfrom .._src.util import (extend_name_stack, HashableFunction, safe_zip,\nwrap_name, wraps, distributed_debug_log,\n- split_list, cache)\n+ split_list, cache, tuple_insert)\nxops = xc._xla.ops\ndef pjit(fun: Callable,\n@@ -257,10 +259,40 @@ def _pjit_jaxpr(fun, mesh, local_in_avals,\nreturn _ListWithW([jaxpr, in_axis_resources_flat, out_axis_resources_flat])\n+class SpecSync(IntEnum):\n+ \"\"\"Encodes how much out of sync the real value of partitions is compared to the user specified one.\n+\n+ We use this to make sure we don't show garbage modified values while claiming\n+ that the users have specified them like that.\n+ \"\"\"\n+ DIM_PERMUTE = 1 # Dimensions permuted, but no new sharding axes\n+ IN_SYNC = 2 # Entirely in sync\n+\nclass ParsedPartitionSpec:\n- def __init__(self, user_spec, partitions):\n+ __slots__ = ('partitions', 'unsafe_user_spec', 'sync')\n+\n+ def __init__(self, user_spec, partitions, sync=SpecSync.IN_SYNC):\nself.partitions = tuple(partitions)\n- self.user_spec = user_spec\n+ self.unsafe_user_spec = user_spec\n+ self.sync = sync\n+\n+ @property\n+ def user_spec(self):\n+ return self.unsynced_user_spec(SpecSync.IN_SYNC)\n+\n+ def unsynced_user_spec(self, min_sync):\n+ if self.sync < min_sync:\n+ raise AssertionError(f\"Please open a bug report! ({self.sync} >= {min_sync})\")\n+ return self.unsafe_user_spec\n+\n+ def insert_axis_partitions(self, dim, val):\n+ parts = self.partitions\n+ too_short = dim - len(parts)\n+ if too_short > 0:\n+ parts += ((),) * too_short\n+ new_partitions = tuple_insert(parts, dim, val)\n+ new_sync = SpecSync.DIM_PERMUTE if val == () else SpecSync.IN_SYNC\n+ return ParsedPartitionSpec(self.unsafe_user_spec, new_partitions, sync=new_sync)\n@classmethod\ndef from_user_input(cls, entry, arg_name):\n@@ -281,13 +313,12 @@ class ParsedPartitionSpec:\nreturn cls(entry, axis_specs)\ndef __hash__(self):\n- return hash(self.partitions)\n+ return hash((self.partitions, self.sync))\ndef __eq__(self, other):\n- return (self.partitions, self.user_spec) == (other.partitions, other.user_spec)\n-\n- def __str__(self):\n- return str(self.user_spec)\n+ return (self.partitions == other.partitions and\n+ self.unsafe_user_spec == other.unsafe_user_spec and\n+ self.sync == other.sync)\ndef __len__(self):\nreturn len(self.partitions)\n@@ -319,7 +350,7 @@ def _check_unique_resources(axis_resources, arg_name):\nmultiple_uses = [r for r, c in resource_counts.items() if c > 1]\nif multiple_uses:\nraise ValueError(f\"A single {arg_name} specification can map every mesh axis \"\n- f\"to at most one positional dimension, but {arg_axis_resources} \"\n+ f\"to at most one positional dimension, but {arg_axis_resources.user_spec} \"\nf\"has duplicate entries for {maps.show_axes(multiple_uses)}\")\ndef _check_shapes_against_resources(what: str, is_global_shape: bool, mesh_shape, flat_avals, flat_axis_resources):\n@@ -328,7 +359,7 @@ def _check_shapes_against_resources(what: str, is_global_shape: bool, mesh_shape\nshape = aval.shape\nif len(shape) < len(aval_axis_resources):\nraise ValueError(f\"One of {what} was given the resource assignment \"\n- f\"of {aval_axis_resources!s}, which implies that \"\n+ f\"of {aval_axis_resources.user_spec}, which implies that \"\nf\"it has a rank of at least {len(aval_axis_resources)}, \"\nf\"but it is {len(shape)}\")\nfor i, axis_resources in enumerate(aval_axis_resources):\n@@ -336,11 +367,11 @@ def _check_shapes_against_resources(what: str, is_global_shape: bool, mesh_shape\nsize = int(np.prod([mesh_shape[resource] for resource in axis_resources], dtype=np.int64))\nexcept KeyError as e:\nraise ValueError(f\"One of {what} was given the resource assignment \"\n- f\"of {aval_axis_resources!s}, but resource axis \"\n+ f\"of {aval_axis_resources.user_spec}, but resource axis \"\nf\"{e.args[0]} is undefined. Did you forget to declare the mesh?\") from None\nif shape[i] % size != 0:\nraise ValueError(f\"One of {what} was given the resource assignment \"\n- f\"of {aval_axis_resources!s}, which implies that \"\n+ f\"of {aval_axis_resources.user_spec}, which implies that \"\nf\"the{global_str} size of its dimension {i} should be \"\nf\"divisible by {size}, but it is equal to {shape[i]}\")\n@@ -418,6 +449,38 @@ def _pjit_translation_rule(c, axis_env, in_nodes, name_stack, backend, name,\nxla.call_translations[pjit_p] = _pjit_translation_rule\n+def _pjit_batcher(vals_in, dims_in,\n+ axis_name, main_type,\n+ jaxpr, in_axis_resources, out_axis_resources,\n+ resource_env, donated_invars, name):\n+ axis_size, = {x.shape[d] for x, d in zip(vals_in, dims_in) if d is not batching.not_mapped}\n+ # batch_jaxpr expects all batching dimensions to be equal to 0\n+ vals_in = [batching.moveaxis(x, d, 0) if d is not batching.not_mapped and d != 0\n+ else x for x, d in zip(vals_in, dims_in)]\n+ is_mapped_in = [d is not batching.not_mapped for d in dims_in]\n+ new_jaxpr, is_mapped_out = batching.batch_jaxpr(\n+ jaxpr, axis_size, is_mapped_in,\n+ instantiate=False, axis_name=axis_name, main_type=main_type)\n+\n+ in_axis_resources = tuple(\n+ spec.insert_axis_partitions(0, ()) if is_mapped else spec\n+ for is_mapped, spec in zip(is_mapped_in, in_axis_resources))\n+ out_axis_resources = tuple(\n+ spec.insert_axis_partitions(0, ()) if is_mapped else spec\n+ for is_mapped, spec in zip(is_mapped_out, out_axis_resources))\n+ vals_out = pjit_p.bind(\n+ *vals_in,\n+ jaxpr=new_jaxpr,\n+ in_axis_resources=in_axis_resources,\n+ out_axis_resources=out_axis_resources,\n+ resource_env=resource_env,\n+ donated_invars=donated_invars,\n+ name=name)\n+ dims_out = [0 if batched else batching.not_mapped for batched in is_mapped_out]\n+ return vals_out, dims_out\n+batching.initial_style_batchers[pjit_p] = _pjit_batcher\n+\n+\ndef _pjit_jvp(primals_in, tangents_in,\njaxpr, in_axis_resources, out_axis_resources,\nresource_env, donated_invars, name):\n@@ -453,7 +516,8 @@ def _check_resources_against_named_axes(what, aval, pos_axis_resources, named_ax\noverlap = pjit_resources & aval_resources\nif overlap:\nraise JAXTypeError(\n- f\"{what} has an axis resources specification of {pos_axis_resources} \"\n+ f\"{what} has an axis resources specification of \"\n+ f\"{pos_axis_resources.unsynced_user_spec(SpecSync.DIM_PERMUTE)} \"\nf\"that uses one or more mesh axes already used by xmap to partition \"\nf\"a named axis appearing in its named_shape (both use mesh axes \"\nf\"{maps.show_axes(overlap)})\")\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -30,7 +30,7 @@ from jax import lax\n# TODO(skye): do we still wanna call this PartitionSpec?\nfrom jax.experimental import PartitionSpec as P\nfrom jax.experimental.maps import xmap, mesh\n-from jax.experimental.pjit import pjit, with_sharding_constraint\n+from jax.experimental.pjit import pjit, pjit_p, with_sharding_constraint, SpecSync\nfrom jax.interpreters import pxla\nfrom jax._src.util import unzip2, prod, curry\n@@ -294,6 +294,33 @@ class PJitTest(jtu.BufferDonationTestCase):\ny = pjit(lambda x: x * 2, in_axis_resources=spec, out_axis_resources=spec)(x)\nself.assertAllClose(y, x * 2)\n+ @with_mesh([('x', 2)])\n+ def testVmapModifiesAxisResources(self):\n+ h = pjit(lambda x, y: (x + y, x, y), in_axis_resources=P('x'), out_axis_resources=None)\n+ x = jnp.arange(4)\n+ y = jnp.arange(5*4).reshape((5, 4))\n+ jaxpr = jax.make_jaxpr(jax.vmap(h, in_axes=(None, 0)))(x, y).jaxpr\n+ eqn = jaxpr.eqns[0]\n+ self.assertIs(eqn.primitive, pjit_p)\n+ x_sync, y_sync = (spec.sync for spec in eqn.params['in_axis_resources'])\n+ self.assertEqual(x_sync, SpecSync.IN_SYNC)\n+ self.assertEqual(y_sync, SpecSync.DIM_PERMUTE)\n+ x_sync, y_sync, z_sync = (spec.sync for spec in eqn.params['out_axis_resources'])\n+ self.assertEqual(x_sync, SpecSync.DIM_PERMUTE)\n+ self.assertEqual(y_sync, SpecSync.IN_SYNC)\n+ self.assertEqual(z_sync, SpecSync.DIM_PERMUTE)\n+\n+ @with_mesh([('x', 2)])\n+ def testVMap(self):\n+ f = pjit(lambda x, y: (x + y, x), in_axis_resources=P('x'), out_axis_resources=P('x'))\n+ x = jnp.arange(4)\n+ y = jnp.arange(5*4).reshape((5, 4))\n+ z, w = jax.vmap(f, in_axes=(None, 0), out_axes=(0, None))(x, y)\n+ self.assertAllClose(z, x + y)\n+ self.assertAllClose(w, x)\n+ self.assertEqual(z.sharding_spec.sharding, (pxla.NoSharding(), pxla.Chunked([2])))\n+ self.assertEqual(w.sharding_spec.sharding, (pxla.Chunked([2]),))\n+\ndef testInfeed(self):\ndevices = np.array(jax.local_devices())\nnr_devices = len(devices)\n"
}
] | Python | Apache License 2.0 | google/jax | Implement vmap for pjit
PiperOrigin-RevId: 372401914 |
260,335 | 05.05.2021 12:44:49 | 25,200 | d88acd8b8c52245d349f4853d0e0c1663b55b1bc | autodidax: delete while_loop for now | [
{
"change_type": "MODIFY",
"old_path": "docs/autodidax.ipynb",
"new_path": "docs/autodidax.ipynb",
"diff": "\"\\n\",\n\"There are actually two rules to write: one for trace-time partial evaluation,\\n\",\n\"which we'll call `xla_call_partial_eval`, and one for partial evaluation of\\n\",\n- \"jaxprs, whicch we'll call `xla_call_peval_eqn`.\"\n+ \"jaxprs, which we'll call `xla_call_peval_eqn`.\"\n]\n},\n{\n\"cell_type\": \"markdown\",\n\"metadata\": {},\n\"source\": [\n- \"## Part 5: the control flow primitives `cond` and `while_loop`\\n\",\n+ \"## Part 5: the control flow primitives `cond`\\n\",\n\"\\n\",\n\"Next we'll add higher-order primitives for staged-out control flow. These\\n\",\n\"resemble `jit` from Part 3, another higher-order primitive, but differ in that\\n\",\n\"out = grad(lambda x: cond(True, lambda: x * x, lambda: 0.))(1.)\\n\",\n\"print(out)\"\n]\n- },\n- {\n- \"cell_type\": \"markdown\",\n- \"metadata\": {},\n- \"source\": [\n- \"### Adding `while_loop`\\n\",\n- \"\\n\",\n- \"Next we'll add a primitive for looping behavior in a jaxpr. We'll use\\n\",\n- \"`while_loop : (a -> Bool) -> (a -> a) -> a -> a`, where the first\\n\",\n- \"function-valued argument represents the loop condition, the second represents\\n\",\n- \"the loop body, and the final argument is the initial value of the carry.\\n\",\n- \"\\n\",\n- \"After `cond`, adding `while_loop` is not so different:\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {\n- \"lines_to_next_cell\": 1\n- },\n- \"outputs\": [],\n- \"source\": [\n- \"def while_loop(cond_fn, body_fn, init_val):\\n\",\n- \" init_val, in_tree = tree_flatten(init_val)\\n\",\n- \" avals_in = [raise_to_shaped(get_aval(x)) for x in init_val]\\n\",\n- \" cond_jaxpr, cond_consts, cond_tree = make_jaxpr(cond_fn, *avals_in)\\n\",\n- \" body_jaxpr, body_consts, in_tree_ = make_jaxpr(body_fn, *avals_in)\\n\",\n- \" cond_jaxpr, body_jaxpr = _join_jaxpr_consts(\\n\",\n- \" cond_jaxpr, body_jaxpr, len(cond_consts), len(body_consts))\\n\",\n- \" if cond_tree != tree_flatten(True)[1]: raise TypeError\\n\",\n- \" if in_tree != in_tree_: raise TypeError\\n\",\n- \" outs = bind(while_loop_p, *cond_consts, *body_consts, *init_val,\\n\",\n- \" cond_jaxpr=cond_jaxpr, body_jaxpr=body_jaxpr)\\n\",\n- \" return tree_unflatten(in_tree, outs)\\n\",\n- \"while_loop_p = Primitive('while_loop')\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {\n- \"lines_to_next_cell\": 1\n- },\n- \"outputs\": [],\n- \"source\": [\n- \"def while_loop_impl(*args, cond_jaxpr, body_jaxpr):\\n\",\n- \" consts, carry = split_list(args, _loop_num_consts(body_jaxpr))\\n\",\n- \" while eval_jaxpr(cond_jaxpr, [*consts, *carry])[0]:\\n\",\n- \" carry = eval_jaxpr(body_jaxpr, [*consts, *carry])\\n\",\n- \" return carry\\n\",\n- \"impl_rules[while_loop_p] = while_loop_impl\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {\n- \"lines_to_next_cell\": 1\n- },\n- \"outputs\": [],\n- \"source\": [\n- \"def _loop_num_consts(body_jaxpr: Jaxpr) -> int:\\n\",\n- \" return len(body_jaxpr.in_binders) - len(body_jaxpr.outs)\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {\n- \"lines_to_end_of_cell_marker\": 0,\n- \"lines_to_next_cell\": 1\n- },\n- \"outputs\": [],\n- \"source\": [\n- \"out = while_loop(lambda x: x > 0, lambda x: x + -3, 10)\\n\",\n- \"print(out)\"\n- ]\n- },\n- {\n- \"cell_type\": \"markdown\",\n- \"metadata\": {},\n- \"source\": [\n- \"Notice the convention that `args = [*consts, *carry]`.\\n\",\n- \"\\n\",\n- \"The `while_loop` JVP rule introduces a wrinkle. For `jvp_jaxpr`, we have the\\n\",\n- \"convention that all the binders for tangent values are appended after all the\\n\",\n- \"binders for primal values, like `args = [*primals, *tangents]`. But that's in\\n\",\n- \"tension with our `while_loop` convention that the carry binders come after all\\n\",\n- \"the constant binders, i.e. that `args = [*consts, *carry]`, because both the\\n\",\n- \"constants and the carries can have their own tangents. For this reason, we\\n\",\n- \"introduce the `_loop_jvp_binders` helper to rearrange binders as needed.\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {\n- \"lines_to_next_cell\": 1\n- },\n- \"outputs\": [],\n- \"source\": [\n- \"def while_loop_jvp_rule(primals, tangents, *, cond_jaxpr, body_jaxpr):\\n\",\n- \" num_consts = _loop_num_consts(body_jaxpr)\\n\",\n- \" body_jaxpr, body_consts = jvp_jaxpr(body_jaxpr)\\n\",\n- \" cond_jaxpr, body_jaxpr = _loop_jvp_binders(\\n\",\n- \" cond_jaxpr, body_jaxpr, len(body_consts), num_consts)\\n\",\n- \" outs = bind(while_loop_p, *body_consts, *primals, *tangents,\\n\",\n- \" cond_jaxpr=cond_jaxpr, body_jaxpr=body_jaxpr)\\n\",\n- \" primals_out, tangents_out = split_half(outs)\\n\",\n- \" return primals_out, tangents_out\\n\",\n- \"jvp_rules[while_loop_p] = while_loop_jvp_rule\\n\",\n- \"\\n\",\n- \"def _loop_jvp_binders(cond_jaxpr: Jaxpr, body_jaxpr: Jaxpr, n1: int, n2: int\\n\",\n- \" ) -> Jaxpr:\\n\",\n- \" # body binders [c1, c2, x1, c2dot, x2dot] ~~> [c1, c2, c2dot, x1, x1dot]\\n\",\n- \" jvp_const_binders, binders = split_list(body_jaxpr.in_binders, n1)\\n\",\n- \" primal_binders, tangent_binders = split_half(binders)\\n\",\n- \" consts , carry = split_list(primal_binders , n2)\\n\",\n- \" consts_dot, carry_dot = split_list(tangent_binders, n2)\\n\",\n- \" new_in_binders = jvp_const_binders + consts + consts_dot + carry + carry_dot\\n\",\n- \" new_body_jaxpr = Jaxpr(new_in_binders, body_jaxpr.eqns, body_jaxpr.outs)\\n\",\n- \" typecheck_jaxpr(new_body_jaxpr)\\n\",\n- \"\\n\",\n- \" # cond binders [c2, x1] ~~> [c1, c2, c2dot, x1, x1dot]\\n\",\n- \" assert not set(new_body_jaxpr.in_binders) & set(cond_jaxpr.in_binders)\\n\",\n- \" consts, carry = split_list(cond_jaxpr.in_binders, n2)\\n\",\n- \" new_in_binders = jvp_const_binders + consts + consts_dot + carry + carry_dot\\n\",\n- \" new_cond_jaxpr = Jaxpr(new_in_binders, cond_jaxpr.eqns, cond_jaxpr.outs)\\n\",\n- \"\\n\",\n- \" return new_cond_jaxpr, new_body_jaxpr\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {\n- \"lines_to_next_cell\": 1\n- },\n- \"outputs\": [],\n- \"source\": [\n- \"out, out_tan = jvp(lambda x: while_loop(lambda x: x < 10., lambda x: x * 2., x),\\n\",\n- \" (1.,), (1.,))\\n\",\n- \"print(out_tan)\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": null,\n- \"metadata\": {\n- \"lines_to_next_cell\": 1\n- },\n- \"outputs\": [],\n- \"source\": [\n- \"def f(x):\\n\",\n- \" def cond_fn(i, _):\\n\",\n- \" return i < 3\\n\",\n- \" def body_fn(i, x):\\n\",\n- \" return i + 1, cos(x)\\n\",\n- \" _, y = while_loop(cond_fn, body_fn, (0, x))\\n\",\n- \" return y\\n\",\n- \"\\n\",\n- \"def g(x):\\n\",\n- \" return cos(cos(cos(x)))\\n\",\n- \"\\n\",\n- \"print(jvp(f, (1.,), (1.,)))\\n\",\n- \"print(jvp(g, (1.,), (1.,)))\"\n- ]\n- },\n- {\n- \"cell_type\": \"markdown\",\n- \"metadata\": {},\n- \"source\": [\n- \"The vmap rule for `while_loop` presents two cases:\\n\",\n- \"1. if the output of `cond_fun` is not batched, then the loop has the same\\n\",\n- \" basic structure, just with a batched body;\\n\",\n- \"2. but if the output of `cond_fun` is batched, we must represent a batch of\\n\",\n- \" loops which might run for different numbers of iterations.\\n\",\n- \"\\n\",\n- \"...Stay tuned for the thrilling conclusion!\"\n- ]\n}\n],\n\"metadata\": {\n"
},
{
"change_type": "MODIFY",
"old_path": "docs/autodidax.md",
"new_path": "docs/autodidax.md",
"diff": "@@ -2279,7 +2279,7 @@ perform partial evaluation of a jaxpr, 'unzipping' it into two jaxprs.\nThere are actually two rules to write: one for trace-time partial evaluation,\nwhich we'll call `xla_call_partial_eval`, and one for partial evaluation of\n-jaxprs, whicch we'll call `xla_call_peval_eqn`.\n+jaxprs, which we'll call `xla_call_peval_eqn`.\n```{code-cell}\ndef xla_call_partial_eval(trace, tracers, *, jaxpr, num_consts):\n@@ -2646,7 +2646,7 @@ _, hess7 = jvp(jit(grad(f)), (3.,), (1.,))\nassert_allclose(hess1, hess2, hess3, hess4, hess5, hess6, hess7)\n```\n-## Part 5: the control flow primitives `cond` and `while_loop`\n+## Part 5: the control flow primitives `cond`\nNext we'll add higher-order primitives for staged-out control flow. These\nresemble `jit` from Part 3, another higher-order primitive, but differ in that\n@@ -2967,119 +2967,3 @@ transpose_rules[cond_p] = cond_transpose_rule\nout = grad(lambda x: cond(True, lambda: x * x, lambda: 0.))(1.)\nprint(out)\n```\n-\n-### Adding `while_loop`\n-\n-Next we'll add a primitive for looping behavior in a jaxpr. We'll use\n-`while_loop : (a -> Bool) -> (a -> a) -> a -> a`, where the first\n-function-valued argument represents the loop condition, the second represents\n-the loop body, and the final argument is the initial value of the carry.\n-\n-After `cond`, adding `while_loop` is not so different:\n-\n-```{code-cell}\n-def while_loop(cond_fn, body_fn, init_val):\n- init_val, in_tree = tree_flatten(init_val)\n- avals_in = [raise_to_shaped(get_aval(x)) for x in init_val]\n- cond_jaxpr, cond_consts, cond_tree = make_jaxpr(cond_fn, *avals_in)\n- body_jaxpr, body_consts, in_tree_ = make_jaxpr(body_fn, *avals_in)\n- cond_jaxpr, body_jaxpr = _join_jaxpr_consts(\n- cond_jaxpr, body_jaxpr, len(cond_consts), len(body_consts))\n- if cond_tree != tree_flatten(True)[1]: raise TypeError\n- if in_tree != in_tree_: raise TypeError\n- outs = bind(while_loop_p, *cond_consts, *body_consts, *init_val,\n- cond_jaxpr=cond_jaxpr, body_jaxpr=body_jaxpr)\n- return tree_unflatten(in_tree, outs)\n-while_loop_p = Primitive('while_loop')\n-```\n-\n-```{code-cell}\n-def while_loop_impl(*args, cond_jaxpr, body_jaxpr):\n- consts, carry = split_list(args, _loop_num_consts(body_jaxpr))\n- while eval_jaxpr(cond_jaxpr, [*consts, *carry])[0]:\n- carry = eval_jaxpr(body_jaxpr, [*consts, *carry])\n- return carry\n-impl_rules[while_loop_p] = while_loop_impl\n-```\n-\n-```{code-cell}\n-def _loop_num_consts(body_jaxpr: Jaxpr) -> int:\n- return len(body_jaxpr.in_binders) - len(body_jaxpr.outs)\n-```\n-\n-```{code-cell}\n-out = while_loop(lambda x: x > 0, lambda x: x + -3, 10)\n-print(out)\n-```\n-\n-Notice the convention that `args = [*consts, *carry]`.\n-\n-The `while_loop` JVP rule introduces a wrinkle. For `jvp_jaxpr`, we have the\n-convention that all the binders for tangent values are appended after all the\n-binders for primal values, like `args = [*primals, *tangents]`. But that's in\n-tension with our `while_loop` convention that the carry binders come after all\n-the constant binders, i.e. that `args = [*consts, *carry]`, because both the\n-constants and the carries can have their own tangents. For this reason, we\n-introduce the `_loop_jvp_binders` helper to rearrange binders as needed.\n-\n-```{code-cell}\n-def while_loop_jvp_rule(primals, tangents, *, cond_jaxpr, body_jaxpr):\n- num_consts = _loop_num_consts(body_jaxpr)\n- body_jaxpr, body_consts = jvp_jaxpr(body_jaxpr)\n- cond_jaxpr, body_jaxpr = _loop_jvp_binders(\n- cond_jaxpr, body_jaxpr, len(body_consts), num_consts)\n- outs = bind(while_loop_p, *body_consts, *primals, *tangents,\n- cond_jaxpr=cond_jaxpr, body_jaxpr=body_jaxpr)\n- primals_out, tangents_out = split_half(outs)\n- return primals_out, tangents_out\n-jvp_rules[while_loop_p] = while_loop_jvp_rule\n-\n-def _loop_jvp_binders(cond_jaxpr: Jaxpr, body_jaxpr: Jaxpr, n1: int, n2: int\n- ) -> Jaxpr:\n- # body binders [c1, c2, x1, c2dot, x2dot] ~~> [c1, c2, c2dot, x1, x1dot]\n- jvp_const_binders, binders = split_list(body_jaxpr.in_binders, n1)\n- primal_binders, tangent_binders = split_half(binders)\n- consts , carry = split_list(primal_binders , n2)\n- consts_dot, carry_dot = split_list(tangent_binders, n2)\n- new_in_binders = jvp_const_binders + consts + consts_dot + carry + carry_dot\n- new_body_jaxpr = Jaxpr(new_in_binders, body_jaxpr.eqns, body_jaxpr.outs)\n- typecheck_jaxpr(new_body_jaxpr)\n-\n- # cond binders [c2, x1] ~~> [c1, c2, c2dot, x1, x1dot]\n- assert not set(new_body_jaxpr.in_binders) & set(cond_jaxpr.in_binders)\n- consts, carry = split_list(cond_jaxpr.in_binders, n2)\n- new_in_binders = jvp_const_binders + consts + consts_dot + carry + carry_dot\n- new_cond_jaxpr = Jaxpr(new_in_binders, cond_jaxpr.eqns, cond_jaxpr.outs)\n-\n- return new_cond_jaxpr, new_body_jaxpr\n-```\n-\n-```{code-cell}\n-out, out_tan = jvp(lambda x: while_loop(lambda x: x < 10., lambda x: x * 2., x),\n- (1.,), (1.,))\n-print(out_tan)\n-```\n-\n-```{code-cell}\n-def f(x):\n- def cond_fn(i, _):\n- return i < 3\n- def body_fn(i, x):\n- return i + 1, cos(x)\n- _, y = while_loop(cond_fn, body_fn, (0, x))\n- return y\n-\n-def g(x):\n- return cos(cos(cos(x)))\n-\n-print(jvp(f, (1.,), (1.,)))\n-print(jvp(g, (1.,), (1.,)))\n-```\n-\n-The vmap rule for `while_loop` presents two cases:\n-1. if the output of `cond_fun` is not batched, then the loop has the same\n- basic structure, just with a batched body;\n-2. but if the output of `cond_fun` is batched, we must represent a batch of\n- loops which might run for different numbers of iterations.\n-\n-...Stay tuned for the thrilling conclusion!\n"
},
{
"change_type": "MODIFY",
"old_path": "docs/autodidax.py",
"new_path": "docs/autodidax.py",
"diff": "@@ -2188,7 +2188,7 @@ print(sin_lin(1.), cos(3.))\n#\n# There are actually two rules to write: one for trace-time partial evaluation,\n# which we'll call `xla_call_partial_eval`, and one for partial evaluation of\n-# jaxprs, whicch we'll call `xla_call_peval_eqn`.\n+# jaxprs, which we'll call `xla_call_peval_eqn`.\n# +\ndef xla_call_partial_eval(trace, tracers, *, jaxpr, num_consts):\n@@ -2548,7 +2548,7 @@ _, hess7 = jvp(jit(grad(f)), (3.,), (1.,))\nassert_allclose(hess1, hess2, hess3, hess4, hess5, hess6, hess7)\n# -\n-# ## Part 5: the control flow primitives `cond` and `while_loop`\n+# ## Part 5: the control flow primitives `cond`\n#\n# Next we'll add higher-order primitives for staged-out control flow. These\n# resemble `jit` from Part 3, another higher-order primitive, but differ in that\n@@ -2845,112 +2845,3 @@ transpose_rules[cond_p] = cond_transpose_rule\nout = grad(lambda x: cond(True, lambda: x * x, lambda: 0.))(1.)\nprint(out)\n-\n-\n-# ### Adding `while_loop`\n-#\n-# Next we'll add a primitive for looping behavior in a jaxpr. We'll use\n-# `while_loop : (a -> Bool) -> (a -> a) -> a -> a`, where the first\n-# function-valued argument represents the loop condition, the second represents\n-# the loop body, and the final argument is the initial value of the carry.\n-#\n-# After `cond`, adding `while_loop` is not so different:\n-\n-def while_loop(cond_fn, body_fn, init_val):\n- init_val, in_tree = tree_flatten(init_val)\n- avals_in = [raise_to_shaped(get_aval(x)) for x in init_val]\n- cond_jaxpr, cond_consts, cond_tree = make_jaxpr(cond_fn, *avals_in)\n- body_jaxpr, body_consts, in_tree_ = make_jaxpr(body_fn, *avals_in)\n- cond_jaxpr, body_jaxpr = _join_jaxpr_consts(\n- cond_jaxpr, body_jaxpr, len(cond_consts), len(body_consts))\n- if cond_tree != tree_flatten(True)[1]: raise TypeError\n- if in_tree != in_tree_: raise TypeError\n- outs = bind(while_loop_p, *cond_consts, *body_consts, *init_val,\n- cond_jaxpr=cond_jaxpr, body_jaxpr=body_jaxpr)\n- return tree_unflatten(in_tree, outs)\n-while_loop_p = Primitive('while_loop')\n-\n-def while_loop_impl(*args, cond_jaxpr, body_jaxpr):\n- consts, carry = split_list(args, _loop_num_consts(body_jaxpr))\n- while eval_jaxpr(cond_jaxpr, [*consts, *carry])[0]:\n- carry = eval_jaxpr(body_jaxpr, [*consts, *carry])\n- return carry\n-impl_rules[while_loop_p] = while_loop_impl\n-\n-def _loop_num_consts(body_jaxpr: Jaxpr) -> int:\n- return len(body_jaxpr.in_binders) - len(body_jaxpr.outs)\n-\n-out = while_loop(lambda x: x > 0, lambda x: x + -3, 10)\n-print(out)\n-\n-# Notice the convention that `args = [*consts, *carry]`.\n-#\n-# The `while_loop` JVP rule introduces a wrinkle. For `jvp_jaxpr`, we have the\n-# convention that all the binders for tangent values are appended after all the\n-# binders for primal values, like `args = [*primals, *tangents]`. But that's in\n-# tension with our `while_loop` convention that the carry binders come after all\n-# the constant binders, i.e. that `args = [*consts, *carry]`, because both the\n-# constants and the carries can have their own tangents. For this reason, we\n-# introduce the `_loop_jvp_binders` helper to rearrange binders as needed.\n-\n-# +\n-def while_loop_jvp_rule(primals, tangents, *, cond_jaxpr, body_jaxpr):\n- num_consts = _loop_num_consts(body_jaxpr)\n- body_jaxpr, body_consts = jvp_jaxpr(body_jaxpr)\n- cond_jaxpr, body_jaxpr = _loop_jvp_binders(\n- cond_jaxpr, body_jaxpr, len(body_consts), num_consts)\n- outs = bind(while_loop_p, *body_consts, *primals, *tangents,\n- cond_jaxpr=cond_jaxpr, body_jaxpr=body_jaxpr)\n- primals_out, tangents_out = split_half(outs)\n- return primals_out, tangents_out\n-jvp_rules[while_loop_p] = while_loop_jvp_rule\n-\n-def _loop_jvp_binders(cond_jaxpr: Jaxpr, body_jaxpr: Jaxpr, n1: int, n2: int\n- ) -> Jaxpr:\n- # body binders [c1, c2, x1, c2dot, x2dot] ~~> [c1, c2, c2dot, x1, x1dot]\n- jvp_const_binders, binders = split_list(body_jaxpr.in_binders, n1)\n- primal_binders, tangent_binders = split_half(binders)\n- consts , carry = split_list(primal_binders , n2)\n- consts_dot, carry_dot = split_list(tangent_binders, n2)\n- new_in_binders = jvp_const_binders + consts + consts_dot + carry + carry_dot\n- new_body_jaxpr = Jaxpr(new_in_binders, body_jaxpr.eqns, body_jaxpr.outs)\n- typecheck_jaxpr(new_body_jaxpr)\n-\n- # cond binders [c2, x1] ~~> [c1, c2, c2dot, x1, x1dot]\n- assert not set(new_body_jaxpr.in_binders) & set(cond_jaxpr.in_binders)\n- consts, carry = split_list(cond_jaxpr.in_binders, n2)\n- new_in_binders = jvp_const_binders + consts + consts_dot + carry + carry_dot\n- new_cond_jaxpr = Jaxpr(new_in_binders, cond_jaxpr.eqns, cond_jaxpr.outs)\n-\n- return new_cond_jaxpr, new_body_jaxpr\n-\n-\n-# -\n-\n-out, out_tan = jvp(lambda x: while_loop(lambda x: x < 10., lambda x: x * 2., x),\n- (1.,), (1.,))\n-print(out_tan)\n-\n-# +\n-def f(x):\n- def cond_fn(i, _):\n- return i < 3\n- def body_fn(i, x):\n- return i + 1, cos(x)\n- _, y = while_loop(cond_fn, body_fn, (0, x))\n- return y\n-\n-def g(x):\n- return cos(cos(cos(x)))\n-\n-print(jvp(f, (1.,), (1.,)))\n-print(jvp(g, (1.,), (1.,)))\n-# -\n-\n-# The vmap rule for `while_loop` presents two cases:\n-# 1. if the output of `cond_fun` is not batched, then the loop has the same\n-# basic structure, just with a batched body;\n-# 2. but if the output of `cond_fun` is batched, we must represent a batch of\n-# loops which might run for different numbers of iterations.\n-#\n-# ...Stay tuned for the thrilling conclusion!\n"
}
] | Python | Apache License 2.0 | google/jax | autodidax: delete while_loop for now |
260,335 | 06.05.2021 17:55:47 | 25,200 | d21e8c06571bf80fa79da1b760585027ed27a14d | handle case where trace debug_info is None | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/errors.py",
"new_path": "jax/_src/errors.py",
"diff": "@@ -23,7 +23,7 @@ class _JAXErrorMixin:\nerror_page = self._error_page\nmodule_name = self._module_name\nclass_name = self.__class__.__name__\n- error_msg = f'{message}See {error_page}#{module_name}.{class_name}'\n+ error_msg = f'{message}\\nSee {error_page}#{module_name}.{class_name}'\n# https://github.com/python/mypy/issues/5887\nsuper().__init__(error_msg) # type: ignore\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "@@ -450,11 +450,8 @@ def escaped_tracer_error(tracer, detail=None):\nmsg += (f'When the tracer was created, the final {num_frames} stack '\n'frames (most recent last) excluding JAX-internal frames were:\\n'\nf'{source_info_util.summarize(line_info, num_frames=num_frames)}')\n- try:\n- dbg = tracer._trace.main.debug_info\n- except AttributeError:\n- pass\n- else:\n+ dbg = getattr(tracer._trace.main, 'debug_info', None)\n+ if dbg is not None:\nmsg += ('\\nThe function being traced when the tracer leaked was '\nf'{dbg.func_src_info} traced for {dbg.traced_for}.')\nmsg += ('\\nTo catch the leak earlier, try setting the environment variable '\n"
}
] | Python | Apache License 2.0 | google/jax | handle case where trace debug_info is None |
260,335 | 06.05.2021 20:23:34 | 25,200 | 173cd062855d38ce8bcf4e5265946332e1f85d2f | fix debug info handling (again) | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/partial_eval.py",
"new_path": "jax/interpreters/partial_eval.py",
"diff": "@@ -907,6 +907,8 @@ class DynamicJaxprTracer(core.Tracer):\ndef _origin_msg(self):\ninvar_pos, progenitor_eqns = self._trace.frame.find_progenitors(self)\ndbg = self._trace.main.debug_info\n+ if dbg is None:\n+ return \"\"\nif invar_pos:\norigin = (f\"While tracing the function {dbg.func_src_info} \"\nf\"for {dbg.traced_for}, \"\n"
}
] | Python | Apache License 2.0 | google/jax | fix debug info handling (again) |
260,411 | 08.05.2021 07:38:50 | -10,800 | 244fc7b11cb91faec6275e3466dae4b8ab11a821 | [jax2tf] Expand shape polymorphism support for some instances of lax.gather | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/lax_numpy.py",
"new_path": "jax/_src/numpy/lax_numpy.py",
"diff": "@@ -4829,11 +4829,11 @@ def _gather(arr, treedef, static_idx, dynamic_idx):\n# Avoid calling gather if the slice shape is empty, both as a fast path and to\n# handle cases like zeros(0)[array([], int32)].\n- if _prod(indexer.slice_shape) == 0:\n+ if core.is_empty_shape(indexer.slice_shape):\nreturn zeros_like(y, shape=indexer.slice_shape)\n# We avoid generating a gather when indexer.gather_indices.size is empty.\n- if indexer.gather_indices.size:\n+ if not core.is_empty_shape(indexer.gather_indices.shape):\ny = lax.gather(y, indexer.gather_indices, indexer.dnums,\nindexer.gather_slice_shape)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/shape_poly_test.py",
"new_path": "jax/experimental/jax2tf/tests/shape_poly_test.py",
"diff": "@@ -651,9 +651,17 @@ def _make_harness(group_name: str, name: str,\n\"\"\"The `poly_axes` must correspond to the non-static arguments, and for each\none it must specify which axes are: None, or an int.\n+ The name of the harness within the group will include `poly_axes`.\n+ You can add an additional `name`.\n+\n`check_result` specifies if we want to check that the result of the shape\npolymorphic conversion produces the same result and the JAX function.\n\"\"\"\n+ poly_axes_name = \"poly_axes=\" + \"_\".join(map(str, poly_axes))\n+ if name:\n+ name = f\"{name}_{poly_axes_name}\"\n+ else:\n+ name = poly_axes_name\nreturn Harness(group_name,\nname,\nfunc, args,\n@@ -683,7 +691,7 @@ _POLY_SHAPE_TEST_HARNESSES = [\nRandArg((3, 4, 5), _f32)],\npoly_axes=[0, 0, 0]),\n- _make_harness(\"conv_general_dilated\", \"0\",\n+ _make_harness(\"conv_general_dilated\", \"\",\nlambda lhs, rhs: lax.conv_general_dilated(lhs, rhs,\nwindow_strides=(2, 3),\npadding=((0, 0), (0, 0)),\n@@ -696,7 +704,7 @@ _POLY_SHAPE_TEST_HARNESSES = [\n[RandArg((7, 3, 9, 10), _f32), RandArg((3, 3, 4, 5), _f32)],\npoly_axes=[0, None]),\n- _make_harness(\"cummax\", \"1\",\n+ _make_harness(\"cummax\", \"\",\nlambda x: lax_control_flow.cummax(x, axis=1, reverse=False),\n[RandArg((3, 4, 5), _f32)],\npoly_axes=[0]),\n@@ -718,6 +726,17 @@ _POLY_SHAPE_TEST_HARNESSES = [\n[RandArg((3, 4, 5), _f32), np.array([1, 2], np.int32)],\npoly_axes=[0, None]),\n+ _make_harness(\"jnp_getitem\", \"\",\n+ lambda a, i: a[i],\n+ [RandArg((3, 4), _f32), np.array([2, 2], np.int32)],\n+ poly_axes=[None, 0]),\n+\n+ # TODO(necula): not supported yet\n+ # _make_harness(\"jnp_getitem\", \"\",\n+ # lambda a, i: a[i],\n+ # [RandArg((3, 4), _f32), np.array([2, 2], np.int32)],\n+ # poly_axes=[0, 0]),\n+\n_make_harness(\"iota\", \"\",\nlambda x: x + lax.iota(_f32, x.shape[0]),\n[RandArg((3,), _f32)],\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Expand shape polymorphism support for some instances of lax.gather |
260,690 | 06.05.2021 16:36:52 | 25,200 | 8d54a53b538a3e14a8425b5ec4273b2eb17a8667 | Update convolutions.md
Update convolutions.ipynb
Update convolutions.ipynb
Update convolutions.md | [
{
"change_type": "MODIFY",
"old_path": "docs/notebooks/convolutions.ipynb",
"new_path": "docs/notebooks/convolutions.ipynb",
"diff": "],\n\"source\": [\n\"# 2D kernel - HWIO layout\\n\",\n- \"kernel = jnp.zeros((3, 3, 3, 3), dtype=np.float32)\\n\",\n+ \"kernel = jnp.zeros((3, 3, 3, 3), dtype=jnp.float32)\\n\",\n\"kernel += jnp.array([[1, 1, 0],\\n\",\n\" [1, 0,-1],\\n\",\n- \" [0,-1,-1]])[:, :, np.newaxis, np.newaxis]\\n\",\n+ \" [0,-1,-1]])[:, :, jnp.newaxis, jnp.newaxis]\\n\",\n\"\\n\",\n\"print(\\\"Edge Conv kernel:\\\")\\n\",\n\"plt.imshow(kernel[:, :, 0, 0]);\"\n],\n\"source\": [\n\"# 1D kernel - WIO layout\\n\",\n- \"kernel = np.array([[[1, 0, -1], [-1, 0, 1]], \\n\",\n+ \"kernel = jnp.array([[[1, 0, -1], [-1, 0, 1]], \\n\",\n\" [[1, 1, 1], [-1, -1, -1]]], \\n\",\n\" dtype=jnp.float32).transpose([2,1,0])\\n\",\n\"# 1D data - NWC layout\\n\",\n\"import matplotlib as mpl\\n\",\n\"\\n\",\n\"# Random 3D kernel - HWDIO layout\\n\",\n- \"kernel = np.array([\\n\",\n+ \"kernel = jnp.array([\\n\",\n\" [[0, 0, 0], [0, 1, 0], [0, 0, 0]],\\n\",\n\" [[0, -1, 0], [-1, 0, -1], [0, -1, 0]], \\n\",\n\" [[0, 0, 0], [0, 1, 0], [0, 0, 0]]], \\n\",\n- \" dtype=jnp.float32)[:, :, :, np.newaxis, np.newaxis]\\n\",\n+ \" dtype=jnp.float32)[:, :, :, jnp.newaxis, jnp.newaxis]\\n\",\n\"\\n\",\n\"# 3D data - NHWDC layout\\n\",\n- \"data = np.zeros((1, 30, 30, 30, 1), dtype=jnp.float32)\\n\",\n+ \"data = jnp.zeros((1, 30, 30, 30, 1), dtype=jnp.float32)\\n\",\n\"x, y, z = np.mgrid[0:1:30j, 0:1:30j, 0:1:30j]\\n\",\n- \"data += (np.sin(2*x*jnp.pi)*np.cos(2*y*jnp.pi)*np.cos(2*z*jnp.pi))[None,:,:,:,None]\\n\",\n+ \"data += (jnp.sin(2*x*jnp.pi)*jnp.cos(2*y*jnp.pi)*jnp.cos(2*z*jnp.pi))[None,:,:,:,None]\\n\",\n\"\\n\",\n\"print(\\\"in shapes:\\\", data.shape, kernel.shape)\\n\",\n\"dn = lax.conv_dimension_numbers(data.shape, kernel.shape,\\n\",\n"
},
{
"change_type": "MODIFY",
"old_path": "docs/notebooks/convolutions.md",
"new_path": "docs/notebooks/convolutions.md",
"diff": "@@ -121,10 +121,10 @@ id: Yud1Y3ss-x1K\noutputId: 3185fba5-1ad7-462f-96ba-7ed1b0c3d5a2\n---\n# 2D kernel - HWIO layout\n-kernel = jnp.zeros((3, 3, 3, 3), dtype=np.float32)\n+kernel = jnp.zeros((3, 3, 3, 3), dtype=jnp.float32)\nkernel += jnp.array([[1, 1, 0],\n[1, 0,-1],\n- [0,-1,-1]])[:, :, np.newaxis, np.newaxis]\n+ [0,-1,-1]])[:, :, jnp.newaxis, jnp.newaxis]\nprint(\"Edge Conv kernel:\")\nplt.imshow(kernel[:, :, 0, 0]);\n@@ -375,7 +375,7 @@ id: jJ-jcAn3cig-\noutputId: 67c46ace-6adc-4c47-c1c7-1f185be5fd4b\n---\n# 1D kernel - WIO layout\n-kernel = np.array([[[1, 0, -1], [-1, 0, 1]],\n+kernel = jnp.array([[[1, 0, -1], [-1, 0, 1]],\n[[1, 1, 1], [-1, -1, -1]]],\ndtype=jnp.float32).transpose([2,1,0])\n# 1D data - NWC layout\n@@ -417,16 +417,16 @@ outputId: c99ec88c-6d5c-4acd-c8d3-331f026f5631\nimport matplotlib as mpl\n# Random 3D kernel - HWDIO layout\n-kernel = np.array([\n+kernel = jnp.array([\n[[0, 0, 0], [0, 1, 0], [0, 0, 0]],\n[[0, -1, 0], [-1, 0, -1], [0, -1, 0]],\n[[0, 0, 0], [0, 1, 0], [0, 0, 0]]],\n- dtype=jnp.float32)[:, :, :, np.newaxis, np.newaxis]\n+ dtype=jnp.float32)[:, :, :, jnp.newaxis, jnp.newaxis]\n# 3D data - NHWDC layout\n-data = np.zeros((1, 30, 30, 30, 1), dtype=jnp.float32)\n+data = jnp.zeros((1, 30, 30, 30, 1), dtype=jnp.float32)\nx, y, z = np.mgrid[0:1:30j, 0:1:30j, 0:1:30j]\n-data += (np.sin(2*x*jnp.pi)*np.cos(2*y*jnp.pi)*np.cos(2*z*jnp.pi))[None,:,:,:,None]\n+data += (jnp.sin(2*x*jnp.pi)*jnp.cos(2*y*jnp.pi)*jnp.cos(2*z*jnp.pi))[None,:,:,:,None]\nprint(\"in shapes:\", data.shape, kernel.shape)\ndn = lax.conv_dimension_numbers(data.shape, kernel.shape,\n"
}
] | Python | Apache License 2.0 | google/jax | Update convolutions.md
Update convolutions.ipynb
Update convolutions.ipynb
Update convolutions.md |
260,654 | 07.05.2021 15:04:40 | -3,600 | db39a67ccabce689da12e9ddc59746150c4d53af | Update documentation for custom_jvp handling of nondiff_argnums as arguments of _fwd and _bwd rules. | [
{
"change_type": "MODIFY",
"old_path": "docs/notebooks/Custom_derivative_rules_for_Python_code.ipynb",
"new_path": "docs/notebooks/Custom_derivative_rules_for_Python_code.ipynb",
"diff": "\"id\": \"0u0jn4aWC8k1\"\n},\n\"source\": [\n- \"A similar option exists for `jax.custom_vjp`, and similarly the convention is that the non-differentiable arguments are passed as the first arguments to the rules, no matter where they appear in the original function's signature. Here's an example:\"\n+ \"A similar option exists for `jax.custom_vjp`, and, similarly, the convention is that the non-differentiable arguments are passed as the first arguments to the `_bwd` rule, no matter where they appear in the signature of the original function. The signature of the `_fwd` rule remains unchanged - it is the same as the signature of the primal function. Here's an example:\"\n]\n},\n{\n"
},
{
"change_type": "MODIFY",
"old_path": "docs/notebooks/Custom_derivative_rules_for_Python_code.md",
"new_path": "docs/notebooks/Custom_derivative_rules_for_Python_code.md",
"diff": "@@ -1222,7 +1222,7 @@ print(grad(app2, 1)(lambda x: x ** 3, 3., lambda y: 5 * y))\n+++ {\"id\": \"0u0jn4aWC8k1\"}\n-A similar option exists for `jax.custom_vjp`, and similarly the convention is that the non-differentiable arguments are passed as the first arguments to the rules, no matter where they appear in the original function's signature. Here's an example:\n+A similar option exists for `jax.custom_vjp`, and, similarly, the convention is that the non-differentiable arguments are passed as the first arguments to the `_bwd` rule, no matter where they appear in the signature of the original function. The signature of the `_fwd` rule remains unchanged - it is the same as the signature of the primal function. Here's an example:\n```{code-cell} ipython3\n:id: yCdu-_9GClWs\n"
}
] | Python | Apache License 2.0 | google/jax | Update documentation for custom_jvp handling of nondiff_argnums as arguments of _fwd and _bwd rules. |
260,411 | 13.05.2021 09:10:24 | -10,800 | e7568c7ae62838c588a1f112222ef33c4d62613b | Add additional message to the error when we cannot convert | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -114,11 +114,13 @@ tf_impl_with_avals: Dict[core.Primitive,\n# requiring a TFXLA operation, an exception is thrown instead.\n_enable_xla = True\n-def _xla_path_disabled_error(primitive_name: str) -> Exception:\n+def _xla_disabled_error(primitive_name: str,\n+ extra_msg: Optional[str] = None) -> Exception:\nassert not _enable_xla\n- return NotImplementedError(\n- f\"Call to {primitive_name} can only be converted through TFXLA, but \"\n- \"XLA is disabled\")\n+ msg = f\"Call to {primitive_name} cannot be converted with enable_xla=False.\"\n+ if extra_msg:\n+ msg += f\" {extra_msg}\"\n+ return NotImplementedError(msg)\n@functools.partial(api_util.api_hook, tag=\"jax2tf_convert\")\ndef convert(fun: Callable, *,\n@@ -1209,7 +1211,8 @@ def _conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation,\nif not isinstance(info_or_result, str):\nreturn info_or_result\nelse:\n- raise _xla_path_disabled_error(\"conv_general_dilated\")\n+ raise _xla_disabled_error(\"conv_general_dilated\",\n+ \"See source code for conditions under which convolutions can be converted without XLA.\")\ndnums_proto = _conv_general_dimension_numbers_proto(dimension_numbers)\nprecision_config_proto = _conv_general_precision_config_proto(precision)\n@@ -1359,7 +1362,7 @@ def _pad(operand, padding_value, *, padding_config,\nif all(lo >= 0 and hi >= 0 and i == 0 for lo, hi, i in padding_config):\nreturn tf.pad(operand, util.safe_zip(low, high),\nmode=\"CONSTANT\", constant_values=padding_value)\n- raise _xla_path_disabled_error(\"pad\")\n+ raise _xla_disabled_error(\"pad\", \"Only use cases without interior or negative padding can be converted without XLA.\")\ntf_impl_with_avals[lax.pad_p] = _pad\n@@ -1488,7 +1491,7 @@ def _common_reduce_window(operand, init_val, reducer, window_dimensions,\nwindow_strides, padding, base_dilation,\nwindow_dilation, _in_avals, _out_aval):\nif not _enable_xla:\n- raise _xla_path_disabled_error(\"reduce_window\")\n+ raise _xla_disabled_error(\"reduce_window\")\no_spec = tf.TensorSpec((), dtype=operand.dtype)\nreducer_fn = tf.function(reducer, autograph=False).get_concrete_function(o_spec, o_spec)\n@@ -1708,7 +1711,7 @@ tf_impl[lax.select_and_scatter_p] = _select_and_scatter\ndef _select_and_scatter_add(source, operand, *, select_prim, window_dimensions,\nwindow_strides, padding, _in_avals, _out_aval):\nif not _enable_xla:\n- raise _xla_path_disabled_error(\"select_and_scatter_add\")\n+ raise _xla_disabled_error(\"select_and_scatter_add\")\ninit_value = tf.zeros((), operand.dtype)\nselect_fn = (tf.function(tf_impl[select_prim], autograph=False)\n.get_concrete_function(init_value, init_value))\n@@ -1751,7 +1754,7 @@ def _gather(operand, start_indices, *, dimension_numbers, slice_sizes,\n\"\"\"Tensorflow implementation of gather.\"\"\"\ndel _in_avals\nif not _enable_xla:\n- raise _xla_path_disabled_error(\"gather\")\n+ raise _xla_disabled_error(\"gather\")\nproto = _gather_dimensions_proto(start_indices.shape, dimension_numbers)\nslice_sizes_tf = _eval_shape(slice_sizes)\nout = tfxla.gather(operand, start_indices, proto, slice_sizes_tf, False)\n@@ -1787,7 +1790,7 @@ def _dynamic_slice(operand, *start_indices, slice_sizes,\n# survive well being put in a SavedModel. Hence, we now use TFXLA slicing\n# and gather ops.\nif not _enable_xla:\n- raise _xla_path_disabled_error(\"dynamic_slice\")\n+ raise _xla_disabled_error(\"dynamic_slice\")\nres = tfxla.dynamic_slice(operand, tf.stack(start_indices),\nsize_indices=_eval_shape(slice_sizes))\n# TODO: implement shape inference for XlaDynamicSlice\n@@ -1815,7 +1818,7 @@ def _scatter(operand, scatter_indices, updates, *,\nassert len(update_consts) == 0, \"Update computation cannot have constants\"\nif not _enable_xla:\n- raise _xla_path_disabled_error(\"scatter\")\n+ raise _xla_disabled_error(\"scatter\")\nproto = _scatter_dimensions_proto(scatter_indices.shape, dimension_numbers)\n@@ -1841,7 +1844,7 @@ tf_impl_with_avals[lax.scatter_add_p] = _scatter\ndef _dynamic_update_slice(operand, update, *start_indices):\nif not _enable_xla:\n- raise _xla_path_disabled_error(\"dynamic_update_slice\")\n+ raise _xla_disabled_error(\"dynamic_update_slice\")\nreturn tfxla.dynamic_update_slice(operand, update, tf.stack(start_indices))\ntf_impl[lax.dynamic_update_slice_p] = _dynamic_update_slice\n@@ -1954,7 +1957,7 @@ tf_impl[lax.top_k_p] = _top_k\ndef _sort(*operands: TfVal, dimension: int, is_stable: bool,\nnum_keys: int) -> Tuple[TfVal, ...]:\nif not _enable_xla:\n- raise _xla_path_disabled_error(\"sort\")\n+ raise _xla_disabled_error(\"sort\")\nassert 1 <= num_keys <= len(operands)\nassert 0 <= dimension < len(\noperands[0].shape\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/primitives_test.py",
"new_path": "jax/experimental/jax2tf/tests/primitives_test.py",
"diff": "@@ -261,8 +261,7 @@ class JaxPrimitiveTest(tf_test_util.JaxToTfTestCase):\nreturn lax.pad(x, np.float32(0), [(-1, 0, 0), (0, 0, 0)])\nwith self.assertRaisesRegex(\n- NotImplementedError, \"Call to pad can only be converted through \"\n- \"TFXLA, but XLA is disabled\"):\n+ NotImplementedError, \"Call to pad cannot be converted with enable_xla=False.\"):\nself.ConvertAndCompare(\nfun, np.ones((2, 3), dtype=np.float32), enable_xla=False)\n"
}
] | Python | Apache License 2.0 | google/jax | Add additional message to the error when we cannot convert |
260,296 | 13.05.2021 12:20:31 | 25,200 | bc84c9fe8fd6ce55b6376d479a544897cc0b0c5a | Add `lax.conv_general_dilated_local` | [
{
"change_type": "MODIFY",
"old_path": "docs/jax.lax.rst",
"new_path": "docs/jax.lax.rst",
"diff": "@@ -53,6 +53,7 @@ Operators\nconv\nconvert_element_type\nconv_general_dilated\n+ conv_general_dilated_local\nconv_general_dilated_patches\nconv_with_general_padding\nconv_transpose\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/other.py",
"new_path": "jax/_src/lax/other.py",
"diff": "@@ -113,3 +113,115 @@ def conv_general_dilated_patches(\npreferred_element_type=preferred_element_type\n)\nreturn out\n+\n+\n+def conv_general_dilated_local(\n+ lhs: jnp.ndarray,\n+ rhs: jnp.ndarray,\n+ window_strides: Sequence[int],\n+ padding: Union[str, Sequence[Tuple[int, int]]],\n+ filter_shape: Sequence[int],\n+ lhs_dilation: Sequence[int] = None,\n+ rhs_dilation: Sequence[int] = None,\n+ dimension_numbers: lax.ConvGeneralDilatedDimensionNumbers = None,\n+ precision: lax.PrecisionLike = None\n+) -> jnp.ndarray:\n+ \"\"\"General n-dimensional unshared convolution operator with optional dilation.\n+\n+ Also known as locally connected layer, the operation is equivalent to\n+ convolution with a separate (unshared) `rhs` kernel used at each output\n+ spatial location. Docstring below adapted from `jax.lax.conv_general_dilated`.\n+\n+ See Also:\n+ https://www.tensorflow.org/xla/operation_semantics#conv_convolution\n+\n+ Args:\n+ lhs: a rank `n+2` dimensional input array.\n+ rhs: a rank `n+2` dimensional array of kernel weights. Unlike in regular\n+ CNNs, its spatial coordinates (`H`, `W`, ...) correspond to output spatial\n+ locations, while input spatial locations are fused with the input channel\n+ locations in the single `I` dimension, in the order of\n+ `\"C\" + ''.join(c for c in rhs_spec if c not in 'OI')`, where\n+ `rhs_spec = dimension_numbers[1]`. For example, if `rhs_spec == \"WHIO\",\n+ the unfolded kernel shape is\n+ `\"[output W][output H]{I[receptive window W][receptive window H]}O\"`.\n+ window_strides: a sequence of `n` integers, representing the inter-window\n+ strides.\n+ padding: either the string `'SAME'`, the string `'VALID'`, or a sequence of\n+ `n` `(low, high)` integer pairs that give the padding to apply before and\n+ after each spatial dimension.\n+ filter_shape: a sequence of `n` integers, representing the receptive window\n+ spatial shape in the order as specified in\n+ `rhs_spec = dimension_numbers[1]`.\n+ lhs_dilation: `None`, or a sequence of `n` integers, giving the\n+ dilation factor to apply in each spatial dimension of `lhs`. LHS dilation\n+ is also known as transposed convolution.\n+ rhs_dilation: `None`, or a sequence of `n` integers, giving the\n+ dilation factor to apply in each input spatial dimension of `rhs`.\n+ RHS dilation is also known as atrous convolution.\n+ dimension_numbers: either `None`, a `ConvDimensionNumbers` object, or\n+ a 3-tuple `(lhs_spec, rhs_spec, out_spec)`, where each element is a string\n+ of length `n+2`.\n+ precision: Optional. Either ``None``, which means the default precision for\n+ the backend, a ``lax.Precision`` enum value (``Precision.DEFAULT``,\n+ ``Precision.HIGH`` or ``Precision.HIGHEST``) or a tuple of two\n+ ``lax.Precision`` enums indicating precision of ``lhs``` and ``rhs``.\n+\n+ Returns:\n+ An array containing the unshared convolution result.\n+\n+ In the string case of `dimension_numbers`, each character identifies by\n+ position:\n+\n+ - the batch dimensions in `lhs`, `rhs`, and the output with the character\n+ 'N',\n+ - the feature dimensions in `lhs` and the output with the character 'C',\n+ - the input and output feature dimensions in rhs with the characters 'I'\n+ and 'O' respectively, and\n+ - spatial dimension correspondences between `lhs`, `rhs`, and the output using\n+ any distinct characters.\n+\n+ For example, to indicate dimension numbers consistent with the `conv` function\n+ with two spatial dimensions, one could use `('NCHW', 'OIHW', 'NCHW')`. As\n+ another example, to indicate dimension numbers consistent with the TensorFlow\n+ Conv2D operation, one could use `('NHWC', 'HWIO', 'NHWC')`. When using the\n+ latter form of convolution dimension specification, window strides are\n+ associated with spatial dimension character labels according to the order in\n+ which the labels appear in the `rhs_spec` string, so that `window_strides[0]`\n+ is matched with the dimension corresponding to the first character\n+ appearing in rhs_spec that is not `'I'` or `'O'`.\n+\n+ If `dimension_numbers` is `None`, the default is `('NCHW', 'OIHW', 'NCHW')`\n+ (for a 2D convolution).\n+ \"\"\"\n+ lhs_precision = (precision[0]\n+ if (isinstance(precision, tuple) and len(precision) == 2)\n+ else precision)\n+\n+ patches = conv_general_dilated_patches(\n+ lhs=lhs,\n+ filter_shape=filter_shape,\n+ window_strides=window_strides,\n+ padding=padding,\n+ lhs_dilation=lhs_dilation,\n+ rhs_dilation=rhs_dilation,\n+ dimension_numbers=dimension_numbers,\n+ precision=lhs_precision\n+ )\n+\n+ lhs_spec, rhs_spec, out_spec = lax.conv_dimension_numbers(\n+ lhs.shape, (1, 1) + tuple(filter_shape), dimension_numbers)\n+\n+ lhs_c_dims, rhs_c_dims = [out_spec[1]], [rhs_spec[1]]\n+\n+ lhs_b_dims = out_spec[2:]\n+ rhs_b_dims = rhs_spec[2:]\n+\n+ rhs_b_dims = [rhs_b_dims[i] for i in sorted(range(len(rhs_b_dims)),\n+ key=lambda k: lhs_b_dims[k])]\n+ lhs_b_dims = sorted(lhs_b_dims)\n+\n+ dn = ((lhs_c_dims, rhs_c_dims), (lhs_b_dims, rhs_b_dims))\n+ out = lax.dot_general(patches, rhs, dimension_numbers=dn, precision=precision)\n+ out = jnp.moveaxis(out, (-2, -1), (out_spec[0], out_spec[1]))\n+ return out\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/lax/__init__.py",
"new_path": "jax/lax/__init__.py",
"diff": "@@ -352,6 +352,7 @@ from jax._src.lax.parallel import (\nxeinsum,\n)\nfrom jax._src.lax.other import (\n+ conv_general_dilated_local,\nconv_general_dilated_patches\n)\nfrom . import linalg\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_test.py",
"new_path": "tests/lax_test.py",
"diff": "@@ -753,6 +753,86 @@ class LaxTest(jtu.JaxTestCase):\n[out_spec.index(c) for c in out_spec if c not in ('N', 'C')])\nself.assertAllClose(out, patches)\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\n+ \"testcase_name\":\n+ f\"_dtype={dtype}_precision={precision}_n={n}_{padding}\"\n+ f\"_dn={lhs_spec, rhs_spec, out_spec}]\",\n+ \"dtype\": dtype,\n+ \"rng_factory\": rng_factory,\n+ \"precision\": precision,\n+ \"n\": n,\n+ \"padding\": padding,\n+ \"lhs_spec\": lhs_spec,\n+ \"rhs_spec\": rhs_spec,\n+ \"out_spec\": out_spec\n+ }\n+ for dtype in inexact_dtypes\n+ for rng_factory in [jtu.rand_small]\n+ for precision in [None,\n+ lax.Precision.DEFAULT,\n+ lax.Precision.HIGH,\n+ lax.Precision.HIGHEST,\n+ (lax.Precision.DEFAULT,\n+ lax.Precision.HIGHEST)]\n+ for n in [1, 2]\n+ for padding in ['SAME', 'VALID']\n+ for lhs_spec in [''.join(s)\n+ for s in itertools.permutations('NCHWD'[:n + 2])]\n+ for rhs_spec in [''.join(s)\n+ for s in itertools.permutations('OIHWDX'[:n + 2])]\n+ for out_spec in [''.join(s)\n+ for s in itertools.permutations('NCHWDX'[:n + 2])]))\n+ def testConvGeneralDilatedLocal(self, dtype, rng_factory, precision, n,\n+ padding, lhs_spec, rhs_spec, out_spec):\n+ \"\"\"Make sure LCN with tiled CNN kernel matches CNN.\"\"\"\n+ lhs_spec_default = 'NCHWDX'[:n + 2]\n+ rhs_spec_default = 'OIHWDX'[:n + 2]\n+\n+ rng = rng_factory(self.rng())\n+\n+ lhs_default = rng((2, 4, 7, 6, 5, 8)[:n + 2], dtype)\n+ rhs_default = rng((5, 4, 2, 3, 1, 2)[:n + 2], dtype)\n+\n+ window_strides = (1, 2, 3, 4)[:n]\n+ rhs_dilation = (2, 1, 3, 2)[:n]\n+\n+ lhs_perm = [lhs_spec_default.index(c) for c in lhs_spec]\n+ lhs = np.transpose(lhs_default, lhs_perm)\n+\n+ rhs_perm = [rhs_spec_default.index(c) for c in rhs_spec]\n+ rhs = np.transpose(rhs_default, rhs_perm)\n+\n+ kwargs = dict(\n+ lhs=lhs,\n+ window_strides=window_strides,\n+ padding=padding,\n+ rhs_dilation=rhs_dilation,\n+ dimension_numbers=(lhs_spec, rhs_spec, out_spec),\n+ precision=precision\n+ )\n+\n+ out_conv = lax.conv_general_dilated(rhs=rhs, **kwargs)\n+\n+ rhs_local = np.moveaxis(rhs, (rhs_spec.index('O'), rhs_spec.index('I')),\n+ (0, 1))\n+ rhs_local = rhs_local.reshape((rhs_local.shape[0], -1) + (1,) * n)\n+\n+ rhs_shape = (rhs_local.shape[:2] +\n+ tuple(out_conv.shape[out_spec.index(c)]\n+ for c in rhs_spec_default[2:]))\n+\n+ rhs_local = np.broadcast_to(rhs_local, rhs_shape)\n+ rhs_local = np.transpose(rhs_local, rhs_perm)\n+\n+ filter_shape = [rhs.shape[i]\n+ for i in range(n + 2) if rhs_spec[i] not in ('O', 'I')]\n+ out_local = lax.conv_general_dilated_local(rhs=rhs_local,\n+ filter_shape=filter_shape,\n+ **kwargs)\n+\n+ self.assertAllClose(out_conv, out_local)\n+\n# TODO(mattjj): test conv_general_dilated against numpy\ndef testConv0DIsDot(self):\n"
}
] | Python | Apache License 2.0 | google/jax | Add `lax.conv_general_dilated_local` |
260,424 | 14.05.2021 15:46:27 | -3,600 | 73e9302fc34cfeaf575a5a34edf1119b36dc3ce8 | Fix `jsp.linalg.lu` translation rule to pass backend arg to `lower_fun`.
If it doesn't, trying to run `lu` with a custom CPU backend when a GPU is
present results in a `Unable to resolve runtime symbol:
`cuda_lu_pivots_to_permutation'` fatal error. | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/linalg.py",
"new_path": "jax/_src/lax/linalg.py",
"diff": "@@ -1002,7 +1002,7 @@ def _lu_batching_rule(batched_args, batch_dims):\nx = batching.moveaxis(x, bd, 0)\nreturn lu_p.bind(x), (0, 0, 0)\n-def _lu_cpu_gpu_translation_rule(getrf_impl, c, operand):\n+def _lu_cpu_gpu_translation_rule(getrf_impl, c, operand, backend):\nshape = c.get_shape(operand)\nbatch_dims = shape.dimensions()[:-2]\nm = shape.dimensions()[-2]\n@@ -1013,7 +1013,7 @@ def _lu_cpu_gpu_translation_rule(getrf_impl, c, operand):\nlu = _broadcasting_select(c, xops.Reshape(ok, batch_dims + (1, 1)), lu,\n_nan_like(c, lu))\nperm = xla.lower_fun(lambda x: lu_pivots_to_permutation(x, m),\n- multiple_results=False)(c, pivot)\n+ multiple_results=False, backend=backend)(c, pivot)\nreturn xops.Tuple(c, [lu, pivot, perm])\n@@ -1034,11 +1034,11 @@ ad.primitive_jvps[lu_p] = _lu_jvp_rule\nbatching.primitive_batchers[lu_p] = _lu_batching_rule\nxla.backend_specific_translations['cpu'][lu_p] = partial(\n- _lu_cpu_gpu_translation_rule, lapack.getrf)\n+ _lu_cpu_gpu_translation_rule, lapack.getrf, backend='cpu')\nif cusolver is not None:\nxla.backend_specific_translations['gpu'][lu_p] = partial(\n- _lu_cpu_gpu_translation_rule, cusolver.getrf)\n+ _lu_cpu_gpu_translation_rule, cusolver.getrf, backend='gpu')\nif rocsolver is not None:\nxla.backend_specific_translations['gpu'][lu_p] = partial(\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/xla.py",
"new_path": "jax/interpreters/xla.py",
"diff": "@@ -989,7 +989,7 @@ def _tuple_output(*args, **kwargs):\nans = yield args, kwargs\nyield (ans,)\n-def lower_fun(fun, multiple_results, parallel=False, with_avals=False):\n+def lower_fun(fun, multiple_results, parallel=False, with_avals=False, backend=None):\n# TODO(jakevdp): migrate dependent code & always use the with_avals=True.\ndef f(c, *xla_args, **params):\navals = [_array_aval_from_xla_shape(c.get_shape(x)) for x in xla_args]\n@@ -1005,7 +1005,7 @@ def lower_fun(fun, multiple_results, parallel=False, with_avals=False):\nif not multiple_results:\nwrapped_fun = _tuple_output(wrapped_fun)\njaxpr, _, consts = pe.trace_to_jaxpr_dynamic(wrapped_fun, avals)\n- outs = jaxpr_subcomp(c, jaxpr, None, axis_env, _xla_consts(c, consts), '',\n+ outs = jaxpr_subcomp(c, jaxpr, backend, axis_env, _xla_consts(c, consts), '',\n*xla_args)\nif multiple_results or any(v.aval._num_buffers > 1 for v in jaxpr.outvars):\nreturn xops.Tuple(c, outs)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/linalg_test.py",
"new_path": "tests/linalg_test.py",
"diff": "@@ -1065,6 +1065,11 @@ class ScipyLinalgTest(jtu.JaxTestCase):\nself.assertAllClose(ls, actual_ls, rtol=5e-6)\nself.assertAllClose(us, actual_us)\n+ @jtu.skip_on_devices(\"cpu\", \"tpu\")\n+ def testLuCPUBackendOnGPU(self):\n+ # tests running `lu` on cpu when a gpu is present.\n+ jit(jsp.linalg.lu, backend=\"cpu\")(np.ones((2, 2))) # does not crash\n+\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\":\n\"_n={}\".format(jtu.format_shape_dtype_string((n,n), dtype)),\n"
}
] | Python | Apache License 2.0 | google/jax | Fix `jsp.linalg.lu` translation rule to pass backend arg to `lower_fun`.
If it doesn't, trying to run `lu` with a custom CPU backend when a GPU is
present results in a `Unable to resolve runtime symbol:
`cuda_lu_pivots_to_permutation'` fatal error. |
260,527 | 01.05.2021 12:09:03 | 0 | 81903e894bf142c71c35fea5c570994426c6df6c | Add JVP rules COO sparse ops.
Updated coo_matvec jvp rule.
Make flake8 happy. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/sparse_ops.py",
"new_path": "jax/experimental/sparse_ops.py",
"diff": "@@ -39,16 +39,17 @@ from jax import api\nfrom jax import core\nfrom jax import jit\nfrom jax import tree_util\n+from jax import lax\nfrom jax.interpreters import xla\nfrom jax.lib import cusparse\nfrom jax.lib import xla_bridge\nfrom jax.lib import xla_client\nimport jax.numpy as jnp\nimport numpy as np\n-\n+from jax.interpreters import ad\n+from jax import ad_util\nxb = xla_bridge\nxops = xla_client.ops\n-\nDtype = Any\n#--------------------------------------------------------------------\n@@ -298,6 +299,17 @@ if cusparse and cusparse.is_supported:\nxla.backend_specific_translations['gpu'][\ncoo_todense_p] = _coo_todense_gpu_translation_rule\n+def _coo_todense_jvp_rule(primals_in, tangents_in, **params):\n+ vals, rows, cols, = primals_in\n+ mat_dot, rows_dot, cols_dot = tangents_in\n+ assert type(rows_dot) is ad_util.Zero\n+ assert type(cols_dot) is ad_util.Zero\n+\n+ primals_out = coo_todense(vals, rows, cols, **params)\n+ tangents_out = ad_util.Zero.from_value(primals_out) if type(mat_dot) is ad_util.Zero else coo_todense(mat_dot, rows, cols, **params)\n+ return primals_out, tangents_out\n+ad.primitive_jvps[coo_todense_p] = _coo_todense_jvp_rule\n+\n#--------------------------------------------------------------------\n# coo_fromdense\n@@ -351,6 +363,14 @@ if cusparse and cusparse.is_supported:\nxla.backend_specific_translations['gpu'][\ncoo_fromdense_p] = _coo_fromdense_gpu_translation_rule\n+def _coo_fromdense_jvp_rule(primals_in, tangents_in, **params):\n+ mat, = primals_in\n+ mat_dot, = tangents_in\n+ data, row, col = coo_fromdense(mat, **params)\n+ tangents_out = ad_util.Zero.from_value(data) if type(mat_dot) is ad_util.Zero else coo_fromdense(mat_dot, **params)[0]\n+ return (data, row, col), (tangents_out, ad_util.Zero.from_value(row), ad_util.Zero.from_value(col))\n+ad.primitive_jvps[coo_fromdense_p] = _coo_fromdense_jvp_rule\n+\n#--------------------------------------------------------------------\n# coo_matvec\n@@ -403,6 +423,22 @@ if cusparse and cusparse.is_supported:\nxla.backend_specific_translations['gpu'][\ncoo_matvec_p] = _coo_matvec_gpu_translation_rule\n+def _coo_matvec_jvp_rule(primals_in, tangents_in, **params):\n+ vals, rows, cols, vec = primals_in\n+ sparse_mat_dot, rows_dot, cols_dot, vec_dot = tangents_in\n+ assert type(rows_dot) is ad_util.Zero\n+ assert type(cols_dot) is ad_util.Zero\n+\n+ primals_out = coo_matvec(vals, rows, cols, vec, **params)\n+ _zero = lambda p, t: lax.zeros_like_array(p) if isinstance(t, ad_util.Zero) else t\n+\n+ _sparse_mat_dot = _zero(vals, sparse_mat_dot)\n+ _vec_dot = _zero(vec, vec_dot)\n+\n+ tangents_out = coo_matvec(_sparse_mat_dot, rows, cols, vec, **params) + coo_matvec(vals, rows, cols, _vec_dot, **params)\n+ return primals_out, tangents_out\n+ad.primitive_jvps[coo_matvec_p] = _coo_matvec_jvp_rule\n+\n#--------------------------------------------------------------------\n# coo_matmat\n@@ -454,6 +490,22 @@ if cusparse and cusparse.is_supported:\nxla.backend_specific_translations['gpu'][\ncoo_matmat_p] = _coo_matmat_gpu_translation_rule\n+def _coo_matmat_jvp_rule(primals_in, tangents_in, **params):\n+ vals, rows, cols, mat = primals_in\n+ sparse_mat_dot, rows_dot, cols_dot, mat_dot = tangents_in\n+ assert type(rows_dot) is ad_util.Zero\n+ assert type(cols_dot) is ad_util.Zero\n+\n+ primals_out = coo_matmat(vals, rows, cols, mat, **params)\n+ _zero = lambda p, t: lax.zeros_like_array(p) if isinstance(t, ad_util.Zero) else t\n+ _sparse_mat_dot = _zero(vals, sparse_mat_dot)\n+ _mat_dot = _zero(mat, mat_dot)\n+\n+ tangents_out = coo_matmat(_sparse_mat_dot, rows, cols, mat, **params) + coo_matmat(vals, rows, cols, _mat_dot, **params)\n+ return primals_out, tangents_out\n+ad.primitive_jvps[coo_matmat_p] = _coo_matmat_jvp_rule\n+\n+\n#----------------------------------------------------------------------\n# Sparse objects (APIs subject to change)\nclass JAXSparse:\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/sparse_ops_test.py",
"new_path": "tests/sparse_ops_test.py",
"diff": "@@ -25,10 +25,9 @@ from jax import jit\nfrom jax import test_util as jtu\nfrom jax import xla\nimport jax.numpy as jnp\n-\n+from jax import jvp\nimport numpy as np\nfrom scipy import sparse\n-\nconfig.parse_flags_with_absl()\nFLAGS = config.FLAGS\n@@ -149,6 +148,16 @@ class cuSparseTest(jtu.JaxTestCase):\nself.assertArraysEqual(M.toarray(), todense(*args))\nself.assertArraysEqual(M.toarray(), jit(todense)(*args))\n+ todense = lambda data: sparse_ops.coo_todense(data, M.row, M.col, shape=M.shape)\n+ tangent = jnp.ones_like(M.data)\n+ y, dy = jvp(todense, (M.data, ), (tangent, ))\n+ self.assertArraysEqual(M.toarray(), y)\n+ self.assertArraysEqual(todense(tangent), dy)\n+\n+ y, dy = jit(lambda prim, tan: jvp(todense, prim, tan))((M.data, ), (tangent, ))\n+ self.assertArraysEqual(M.toarray(), y)\n+ self.assertArraysEqual(todense(tangent), dy)\n+\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_{}\".format(jtu.format_shape_dtype_string(shape, dtype)),\n\"shape\": shape, \"dtype\": dtype}\n@@ -173,6 +182,13 @@ class cuSparseTest(jtu.JaxTestCase):\nself.assertArraysEqual(row, M_coo.row.astype(index_dtype))\nself.assertArraysEqual(col, M_coo.col.astype(index_dtype))\n+ tangent = jnp.ones_like(M)\n+ (data, row, col), (data_dot, row_dot, col_dot) = jvp(fromdense, (M, ), (tangent, ))\n+ self.assertArraysEqual(data, M_coo.data.astype(dtype))\n+ self.assertArraysEqual(row, M_coo.row.astype(index_dtype))\n+ self.assertArraysEqual(col, M_coo.col.astype(index_dtype))\n+ self.assertArraysEqual(data_dot, fromdense(tangent)[0])\n+\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_{}_T={}\".format(jtu.format_shape_dtype_string(shape, dtype), transpose),\n\"shape\": shape, \"dtype\": dtype, \"transpose\": transpose}\n@@ -193,6 +209,12 @@ class cuSparseTest(jtu.JaxTestCase):\nself.assertAllClose(op(M) @ v, matvec(*args), rtol=MATMUL_TOL)\nself.assertAllClose(op(M) @ v, jit(matvec)(*args), rtol=MATMUL_TOL)\n+ y, dy = jvp(lambda x: sparse_ops.coo_matvec(M.data, M.row, M.col, x, shape=shape, transpose=transpose).sum(), (v, ), (jnp.ones_like(v), ))\n+ self.assertAllClose((op(M) @ v).sum(), y, rtol=MATMUL_TOL)\n+\n+ y, dy = jvp(lambda x: sparse_ops.coo_matvec(x, M.row, M.col, v, shape=shape, transpose=transpose).sum(), (M.data, ), (jnp.ones_like(M.data), ))\n+ self.assertAllClose((op(M) @ v).sum(), y, rtol=MATMUL_TOL)\n+ @unittest.skipIf(jtu.device_under_test() != \"gpu\", \"test requires GPU\")\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"_{}_T={}\".format(jtu.format_shape_dtype_string(shape, dtype), transpose),\n\"shape\": shape, \"dtype\": dtype, \"transpose\": transpose}\n@@ -213,6 +235,11 @@ class cuSparseTest(jtu.JaxTestCase):\nself.assertAllClose(op(M) @ B, matmat(*args), rtol=MATMUL_TOL)\nself.assertAllClose(op(M) @ B, jit(matmat)(*args), rtol=MATMUL_TOL)\n+ y, dy = jvp(lambda x: sparse_ops.coo_matmat(M.data, M.row, M.col, x, shape=shape, transpose=transpose).sum(), (B, ), (jnp.ones_like(B), ))\n+ self.assertAllClose((op(M) @ B).sum(), y, rtol=MATMUL_TOL)\n+\n+ y, dy = jvp(lambda x: sparse_ops.coo_matmat(x, M.row, M.col, B, shape=shape, transpose=transpose).sum(), (M.data, ), (jnp.ones_like(M.data), ))\n+ self.assertAllClose((op(M) @ B).sum(), y, rtol=MATMUL_TOL)\n@unittest.skipIf(jtu.device_under_test() != \"gpu\", \"test requires GPU\")\ndef test_gpu_translation_rule(self):\nversion = xla_bridge.get_backend().platform_version\n"
}
] | Python | Apache License 2.0 | google/jax | Add JVP rules COO sparse ops.
Updated coo_matvec jvp rule.
Make flake8 happy. |
260,411 | 17.05.2021 10:01:13 | -10,800 | a08cdb30ff60e93a8e13b34cc745238676fdcac4 | [jax2tf] Update the limitations for unsupported primitives
Also update the documentation. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/README.md",
"new_path": "jax/experimental/jax2tf/README.md",
"diff": "@@ -645,3 +645,29 @@ To run jax2tf on GPU, both jaxlib and TensorFlow must be installed with support\nfor CUDA. One must be mindful to install a version of CUDA that is compatible\nwith both [jaxlib](https://github.com/google/jax/blob/master/README.md#pip-installation) and\n[TensorFlow](https://www.tensorflow.org/install/source#tested_build_configurations).\n+\n+## Updating the limitations documentation\n+\n+The jax2tf tests are parameterized by a set of limitations\n+(see `tests/primitive_harness.py` and `tests/jax2tf_limitations.py`).\n+The limitations specify test harnesses that are known to fail, by\n+JAX primitive, data type, device type, and TensorFlow execution mode (`eager`,\n+`graph`, or `compiled`). These limitations are also used\n+to generate tables of limitations, e.g.,\n+\n+ * [List of primitives not supported in JAX](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/g3doc/jax_primtives_coverage.md),\n+ e.g., due to unimplemented cases in the XLA compiler, and\n+ * [List of primitives not supported in jax2tf](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md),\n+ e.g., due to unimplemented cases in TensorFlow. This list is incremental\n+ on top of the unsupported JAX primitives.\n+\n+There are instructions for updating those documents at the end of each\n+document.\n+\n+The set of limitations is an over-approximation, in the sense that if XLA\n+or TensorFlow improves and support more cases, no test will fail. Instead,\n+periodically, we check for unnecessary limitations. We do this by uncommenting\n+two assertions (in `tests/jax_primitives_coverage_test.py` and in\n+`tests/tf_test_util.py`) and runing all the tests. With these assertions enabled\n+the tests will fail and point out unnecessary limitations. We remove limitations\n+until the tests pass. Then we re-generate the documentation.\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md",
"new_path": "jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md",
"diff": "# Primitives with limited JAX support\n-*Last generated on: 2021-05-12* (YYYY-MM-DD)\n+*Last generated on: 2021-05-17* (YYYY-MM-DD)\n## Supported data types for primitives\n-We use a set of 2418 test harnesses to test\n+We use a set of 2492 test harnesses to test\nthe implementation of 121 numeric JAX primitives.\nWe consider a JAX primitive supported for a particular data\ntype if it is supported on at least one device type.\n@@ -95,7 +95,7 @@ be updated.\n| igamma | 6 | floating | bool, complex, integer |\n| igammac | 6 | floating | bool, complex, integer |\n| imag | 2 | complex | bool, floating, integer |\n-| integer_pow | 34 | inexact, integer | bool |\n+| integer_pow | 108 | inexact, integer | bool |\n| iota | 16 | inexact, integer | bool |\n| is_finite | 4 | floating | bool, complex, integer |\n| le | 15 | bool, floating, integer | complex |\n@@ -184,11 +184,7 @@ and search for \"limitation\".\n| Affected primitive | Description of limitation | Affected dtypes | Affected devices |\n| --- | --- | --- | --- |\n|cholesky|unimplemented|float16|cpu, gpu|\n-|cummax|unimplemented|complex64|tpu|\n-|cummin|unimplemented|complex64|tpu|\n-|cumprod|unimplemented|complex64|tpu|\n|dot_general|preferred_element_type=c128 not implemented|complex64|tpu|\n-|dot_general|preferred_element_type=f64 crashes (b/187884887)|bfloat16, float16, float32|tpu|\n|dot_general|preferred_element_type=i64 not implemented|int16, int32, int8|tpu|\n|eig|only supported on CPU in JAX|all|tpu, gpu|\n|eig|unimplemented|bfloat16, float16|cpu|\n@@ -196,9 +192,6 @@ and search for \"limitation\".\n|eigh|unimplemented|bfloat16, float16|cpu, gpu|\n|lu|unimplemented|bfloat16, float16|cpu, gpu, tpu|\n|qr|unimplemented|bfloat16, float16|cpu, gpu|\n-|reduce_window_max|unimplemented in XLA|complex64|tpu|\n-|reduce_window_min|unimplemented in XLA|complex64|tpu|\n-|reduce_window_mul|unimplemented in XLA|complex64|tpu|\n|scatter_max|unimplemented|complex64|tpu|\n|scatter_min|unimplemented|complex64|tpu|\n|select_and_scatter_add|works only for 2 or more inactive dimensions|all|tpu|\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md",
"new_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md",
"diff": "# Primitives with limited support for jax2tf\n-*Last generated on (YYYY-MM-DD): 2021-03-29*\n+*Last generated on (YYYY-MM-DD): 2021-05-17*\nThis document summarizes known limitations of the jax2tf conversion.\nThere are several kinds of limitations.\n@@ -12,10 +12,10 @@ There are several kinds of limitations.\n* There are some cases when the converted program computes different results than\nthe JAX program, see [below](#generated-summary-of-primitives-with-known-numerical-discrepancies-in-tensorflow).\n-Note that automated tests will ensure that this list of limitations is a\n-superset of the actual limitations. If you see a limitation that\n-you think it does not exist anymore in a recent TensorFlow version,\n-please ask for this file to be updated.\n+Note that automated tests will fail if new limitations appear, but\n+they won't when limitations are fixed. If you see a limitation that\n+you think it does not exist anymore, please ask for this file to\n+be updated.\n## Generated summary of primitives with unimplemented support in Tensorflow\n@@ -32,8 +32,6 @@ The errors apply only for certain devices and compilation modes (\"eager\",\n\"graph\", and \"compiled\"). In general, \"eager\" and \"graph\" mode share the same errors.\nOn TPU only the \"compiled\" mode is relevant.\n-Our first priority is to ensure that the coverage of data types is complete for the \"compiled\" mode.\n-\nThis table only shows errors for cases that are working in JAX (see [separate\nlist of unsupported or partially-supported primitives](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md) )\n@@ -76,19 +74,16 @@ More detailed information can be found in the\n| conv_general_dilated | TF error: jax2tf BUG: batch_group_count > 1 not yet converted | all | cpu, gpu, tpu | compiled, eager, graph |\n| conv_general_dilated | TF error: op not defined for dtype | complex | gpu | compiled, eager, graph |\n| cosh | TF error: op not defined for dtype | float16 | cpu, gpu | eager, graph |\n-| cummax | TF test skipped: Not implemented in JAX: unimplemented | complex64 | tpu | compiled, eager, graph |\n| cummax | TF error: op not defined for dtype | complex128 | cpu, gpu | compiled, eager, graph |\n-| cummax | TF error: op not defined for dtype | complex64 | cpu, gpu | compiled, eager, graph |\n-| cummin | TF test skipped: Not implemented in JAX: unimplemented | complex64 | tpu | compiled, eager, graph |\n+| cummax | TF error: op not defined for dtype | complex64 | cpu, gpu, tpu | compiled, eager, graph |\n| cummin | TF error: op not defined for dtype | complex128, uint64 | cpu, gpu | compiled, eager, graph |\n| cummin | TF error: op not defined for dtype | complex64, int8, uint16, uint32 | cpu, gpu, tpu | compiled, eager, graph |\n-| cumprod | TF test skipped: Not implemented in JAX: unimplemented | complex64 | tpu | compiled, eager, graph |\n-| cumsum | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n| digamma | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n| div | TF error: TF integer division fails if divisor contains 0; JAX returns NaN | integer | cpu, gpu, tpu | compiled, eager, graph |\n| div | TF error: op not defined for dtype | int16, int8, unsigned | cpu, gpu, tpu | compiled, eager, graph |\n-| dot_general | TF error: op not defined for dtype | int64 | cpu, gpu | compiled |\n-| dot_general | TF error: op not defined for dtype | bool, int16, int8, unsigned | cpu, gpu, tpu | compiled, eager, graph |\n+| dot_general | TF test skipped: Not implemented in JAX: preferred_element_type=c128 not implemented | complex64 | tpu | compiled, eager, graph |\n+| dot_general | TF test skipped: Not implemented in JAX: preferred_element_type=i64 not implemented | int16, int32, int8 | tpu | compiled, eager, graph |\n+| dot_general | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| eig | TF test skipped: Not implemented in JAX: only supported on CPU in JAX | all | gpu, tpu | compiled, eager, graph |\n| eig | TF test skipped: Not implemented in JAX: unimplemented | bfloat16, float16 | cpu | compiled, eager, graph |\n| eig | TF error: TF Conversion of eig is not implemented when both compute_left_eigenvectors and compute_right_eigenvectors are set to True | all | cpu, gpu, tpu | compiled, eager, graph |\n@@ -106,7 +101,7 @@ More detailed information can be found in the\n| gt | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| igamma | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n| igammac | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n-| integer_pow | TF error: op not defined for dtype | unsigned | cpu, gpu, tpu | compiled, eager, graph |\n+| integer_pow | TF error: op not defined for dtype | int16, int8, unsigned | cpu, gpu | graph |\n| le | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| lgamma | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n| lt | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n@@ -125,13 +120,9 @@ More detailed information can be found in the\n| reduce_max | TF error: op not defined for dtype | complex64 | cpu, gpu, tpu | compiled, eager, graph |\n| reduce_min | TF error: op not defined for dtype | complex128 | cpu, gpu, tpu | compiled, eager, graph |\n| reduce_min | TF error: op not defined for dtype | complex64 | cpu, gpu, tpu | compiled, eager, graph |\n-| reduce_window_add | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n-| reduce_window_max | TF test skipped: Not implemented in JAX: unimplemented in XLA | complex64 | tpu | compiled, eager, graph |\n| reduce_window_max | TF error: op not defined for dtype | complex128 | cpu, gpu | compiled, eager, graph |\n| reduce_window_max | TF error: op not defined for dtype | bool, complex64 | cpu, gpu, tpu | compiled, eager, graph |\n-| reduce_window_min | TF test skipped: Not implemented in JAX: unimplemented in XLA | complex64 | tpu | compiled, eager, graph |\n| reduce_window_min | TF error: op not defined for dtype | bool, complex, int8, uint16, uint32, uint64 | cpu, gpu, tpu | compiled, eager, graph |\n-| reduce_window_mul | TF test skipped: Not implemented in JAX: unimplemented in XLA | complex64 | tpu | compiled, eager, graph |\n| regularized_incomplete_beta | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n| rem | TF error: TF integer division fails if divisor contains 0; JAX returns NaN | integer | cpu, gpu, tpu | compiled, eager, graph |\n| rem | TF error: op not defined for dtype | int16, int8, unsigned | cpu, gpu, tpu | compiled, eager, graph |\n@@ -146,7 +137,6 @@ More detailed information can be found in the\n| scatter_min | TF error: op not defined for dtype | bool, complex, int8, uint16, uint32, uint64 | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_mul | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_mul | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n-| select_and_gather_add | TF error: This JAX primitives is not not exposed directly in the JAX API but arises from JVP of `lax.reduce_window` for reducers `lax.max` or `lax.min`. It also arises from second-order VJP of the same. Implemented using XlaReduceWindow | float32 | tpu | compiled, eager, graph |\n| select_and_gather_add | TF error: jax2tf unimplemented for 64-bit inputs because the current implementation relies on packing two values into a single value. This can be fixed by using a variadic XlaReduceWindow, when available | float64 | cpu, gpu | compiled, eager, graph |\n| select_and_scatter_add | TF test skipped: Not implemented in JAX: works only for 2 or more inactive dimensions | all | tpu | compiled, eager, graph |\n| sign | TF error: op not defined for dtype | int16, int8, unsigned | cpu, gpu, tpu | compiled, eager, graph |\n@@ -156,7 +146,6 @@ More detailed information can be found in the\n| svd | TF test skipped: Not implemented in JAX: unimplemented | bfloat16, float16 | cpu, gpu | compiled, eager, graph |\n| svd | TF error: function not compilable. Implemented using `tf.linalg.svd` and `tf.linalg.adjoint` | complex | cpu, gpu | compiled |\n| svd | TF error: op not defined for dtype | bfloat16 | tpu | compiled, eager, graph |\n-| tie_in | TF test skipped: Not implemented in JAX: requires omnistaging to be disabled | all | cpu, gpu, tpu | compiled, eager, graph |\n| top_k | TF error: op not defined for dtype | int64, uint64 | cpu, gpu | compiled |\n| triangular_solve | TF test skipped: Not implemented in JAX: unimplemented | float16 | gpu | compiled, eager, graph |\n| triangular_solve | TF error: op not defined for dtype | bfloat16 | cpu, gpu, tpu | compiled, eager, graph |\n@@ -185,12 +174,13 @@ with jax2tf. The following table lists that cases when this does not quite hold:\n| erf_inv | May return different results at undefined points (< -1 or > 1): JAX returns `NaN` and TF returns `+inf` or `-inf`. | float32, float64 | cpu, gpu, tpu | compiled, eager, graph |\n| igamma | May return different results at undefined points (both arguments 0). JAX returns `NaN` and TF returns 0 or JAX returns 1 and TF returns `NaN` | all | cpu, gpu, tpu | eager, graph |\n| igammac | May return different results at undefined points (both arguments less or equal 0). JAX returns `NaN` and TF returns 0 or JAX returns 1 and TF returns `NaN` | all | cpu, gpu | eager, graph |\n-| integer_pow | Numeric comparision disabled: Different overflow behavior for large exponents. | complex64, float32, signed | cpu, gpu, tpu | compiled, eager, graph |\n-| integer_pow | custom numeric comparison | complex | cpu, gpu, tpu | compiled, eager, graph |\n+| integer_pow | Numeric comparision disabled: Different overflow behavior for large exponents. | bfloat16, complex, float16, float32, signed | cpu, gpu, tpu | eager, graph |\n+| integer_pow | Numeric comparision disabled: Different overflow behavior. | bfloat16, float16 | tpu | eager, graph |\n+| integer_pow | custom numeric comparison | complex | cpu, gpu, tpu | eager, graph |\n| lu | May return different, but also correct, results when the decomposition is not unique | all | cpu, gpu, tpu | compiled, eager, graph |\n| max | May return different values when one of the values is NaN. JAX always returns NaN, while TF returns the value NaN is compared with. | all | cpu, gpu, tpu | compiled, eager, graph |\n| min | May return different values when one of the values is NaN. JAX always returns NaN, while TF returns the value NaN is compared with. | all | cpu, gpu, tpu | compiled, eager, graph |\n-| pow | custom numeric comparison | complex | cpu, gpu, tpu | compiled, eager, graph |\n+| pow | custom numeric comparison | complex | cpu, gpu, tpu | eager, graph |\n| sort | Numeric comparision disabled: TODO: TF non-stable multiple-array sort | all | gpu | compiled, eager, graph |\n| svd | custom numeric comparison | all | cpu, gpu, tpu | compiled, eager, graph |\n| top_k | Produces different results when the array contains `inf` and `NaN` (they are sorted differently in TF vs. XLA). | floating | cpu, gpu, tpu | compiled, eager, graph |\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/jax2tf_limitations.py",
"new_path": "jax/experimental/jax2tf/tests/jax2tf_limitations.py",
"diff": "@@ -348,7 +348,7 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ndtypes=[np.complex128],\ndevices=(\"cpu\", \"gpu\"),\n),\n- missing_tf_kernel(dtypes=[np.complex64], devices=(\"cpu\", \"gpu\")),\n+ missing_tf_kernel(dtypes=[np.complex64], devices=(\"cpu\", \"gpu\", \"tpu\")),\ncustom_numeric(dtypes=np.float16, tol=0.1),\ncustom_numeric(dtypes=dtypes.bfloat16, tol=0.5)\n]\n@@ -376,7 +376,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\n@classmethod\ndef cumsum(cls, harness):\nreturn [\n- missing_tf_kernel(dtypes=[np.complex64], devices=\"tpu\"),\ncustom_numeric(dtypes=np.float16, tol=0.1),\ncustom_numeric(dtypes=dtypes.bfloat16, tol=0.5),\n]\n@@ -456,14 +455,8 @@ class Jax2TfLimitation(primitive_harness.Limitation):\nreturn [\nmissing_tf_kernel(\ndtypes=[\n- np.bool_, np.uint8, np.uint16, np.uint32, np.uint64, np.int8,\n- np.int16\n+ np.bool_,\n],),\n- missing_tf_kernel(\n- dtypes=np.int64, devices=(\"cpu\", \"gpu\"),\n- modes=\"compiled\",\n- # Works for 2D matrices.\n- enabled=(len(harness.params[\"lhs_shape\"]) > 2)),\n]\n@classmethod\n@@ -788,7 +781,10 @@ class Jax2TfLimitation(primitive_harness.Limitation):\nmissing_tf_kernel(\ndtypes=[\nnp.int8, np.int16, np.uint8, np.uint16, np.uint32, np.uint64\n- ],),\n+ ],\n+ modes=\"graph\",\n+ enabled=(y not in [0, 1]), # These are special-cased\n+ devices=(\"cpu\", \"gpu\")),\n# TODO: on TPU, for f16, we get different results with eager mode\n# than with compiled mode.\nJax2TfLimitation(\n@@ -989,7 +985,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ndef reduce_window_add(cls, harness):\nassert \"add\" == harness.params[\"computation\"].__name__\nreturn [\n- missing_tf_kernel(dtypes=[np.complex64], devices=\"tpu\"),\n]\n@classmethod\n@@ -1103,14 +1098,10 @@ class Jax2TfLimitation(primitive_harness.Limitation):\n@classmethod\ndef select_and_gather_add(cls, harness):\nreturn [\n- missing_tf_kernel(\n- dtypes=[np.float32],\n- devices=\"tpu\",\n- description=(\n- \"This JAX primitives is not not exposed directly in the JAX API \"\n- \"but arises from JVP of `lax.reduce_window` for reducers \"\n- \"`lax.max` or `lax.min`. It also arises from second-order \"\n- \"VJP of the same. Implemented using XlaReduceWindow\")),\n+ # This JAX primitives is not not exposed directly in the JAX API\n+ # but arises from JVP of `lax.reduce_window` for reducers\n+ # `lax.max` or `lax.min`. It also arises from second-order\n+ # VJP of the same. Implemented using XlaReduceWindow.\nJax2TfLimitation((\n\"jax2tf unimplemented for 64-bit inputs because the current implementation \"\n\"relies on packing two values into a single value. This can be \"\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/primitive_harness.py",
"new_path": "jax/experimental/jax2tf/tests/primitive_harness.py",
"diff": "@@ -1306,13 +1306,6 @@ def _make_cumreduce_harness(name,\naxis=0,\nreverse=False):\nlimitations = []\n- if f_jax.__name__ != \"cumsum\":\n- limitations.append(\n- Limitation(\n- \"unimplemented\",\n- devices=\"tpu\",\n- dtypes=np.complex64,\n- ))\ndefine(\nf_jax.__name__,\nf\"{name}_shape={jtu.format_shape_dtype_string(shape, dtype)}_axis={axis}_reverse={reverse}\",\n@@ -2166,9 +2159,6 @@ def _make_reduce_window_harness(name,\npadding=((0, 0), (0, 0))):\nprim_name = f\"reduce_window_{computation.__name__}\"\nlimitations = []\n- if computation.__name__ in (\"max\", \"mul\", \"min\"):\n- limitations.append(\n- Limitation(\"unimplemented in XLA\", devices=\"tpu\", dtypes=np.complex64))\ndefine(\nprim_name,\nf\"{name}_shape={jtu.format_shape_dtype_string(shape, dtype)}_initvalue={init_value}_windowdimensions={window_dimensions}_windowstrides={window_strides}_padding={padding}_basedilation={base_dilation}_windowdilation={window_dilation}\"\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Update the limitations for unsupported primitives
Also update the documentation. |
260,372 | 18.05.2021 15:22:06 | 21,600 | bcd5deb2697387054153205f39898be39b861b7f | Prevent nans in scale_and_translate
fixes | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/image/scale.py",
"new_path": "jax/_src/image/scale.py",
"diff": "@@ -66,7 +66,8 @@ def compute_weight_mat(input_size: int, output_size: int, scale,\nwith np.errstate(invalid='ignore', divide='ignore'):\nweights = jnp.where(\njnp.abs(total_weight_sum) > 1000. * np.finfo(np.float32).eps,\n- weights / total_weight_sum, 0)\n+ jnp.divide(weights, jnp.where(total_weight_sum != 0, total_weight_sum, 1)),\n+ 0)\n# Zero out weights where the sample location is completely outside the input\n# range.\n# Note sample_f has already had the 0.5 removed, hence the weird range below.\n"
}
] | Python | Apache License 2.0 | google/jax | Prevent nans in scale_and_translate
fixes #6780 |
260,411 | 18.05.2021 17:13:09 | -10,800 | a27109d1bd38efae9ec01cc391e000ce4c2071ed | [jax2tf] Added documentation explaining how to handle undefined TF ops
Added a test case showing how to mix compileable and non-compileable code. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/README.md",
"new_path": "jax/experimental/jax2tf/README.md",
"diff": "@@ -77,6 +77,14 @@ The Autograph feature of `tf.function` cannot be expected to work on\nfunctions converted from JAX as above, so it is recommended to\nset `autograph=False` in order to avoid warnings or outright errors.\n+It is a good idea to use XLA to compile the converted function; that is\n+the scenario for which we are optimizing for numerical and performance\n+accuracy w.r.t. the JAX execution:\n+\n+```python\n+tf.function(jax2tf.convert(f_jax), autograph=False, jit_compile=True)(x)\n+```\n+\n## Usage: saved model\nSince jax2tf provides a regular TensorFlow function using it with SavedModel\n@@ -386,16 +394,86 @@ jax2tf.convert(lambda x: jnp.sum(x, axis=0) / x.shape[0],\npolymorphic_shapes=[\"(v, _)\"])(np.ones((4, 4)))\n```\n-## Caveats\n+## Known issues\n### Incomplete TensorFlow data type coverage\nThere are a number of cases when the TensorFlow ops that are used by the\n-jax2tf converter are not supported by TensorFlow for fewer data types than JAX.\n+jax2tf converter are not supported by TensorFlow for the same data types as in JAX.\nThere is an\n[up-to-date list of unimplemented cases](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md).\n-### Missing features\n+There are two kinds of errors you may see. For the primitives in the\n+[unimplemented cases](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md)\n+that are shown to be undefined on all devices and for all execution modes\n+(`eager`, `graph`, `compiled`), e.g., `lax.min` for booleans,\n+the conversion typically uses a TensorFlow operator that is not\n+registered for a certain data type:\n+\n+```python\n+jax2tf.convert(lambda x: lax.min(x, x))(np.array([True]))\n+\n+>>> InvalidArgumentError: Value for attr 'T' of bool is not in the list of allowed values:\n+>>> bfloat16, half, float, double, uint8, int16, int32, int64;\n+>>> NodeDef: {{node Minimum}};\n+>>> Op<name=Minimum; signature=x:T, y:T -> z:T; attr=T:type,allowed=[DT_BFLOAT16, DT_HALF, DT_FLOAT, DT_DOUBLE, DT_UINT8, DT_INT16, DT_INT32, DT_INT64]> [Op:Minimum]\n+```\n+\n+In the above cases, you should file a bug with JAX or TensorFlow, or consider\n+changing your JAX code. We are working on eliminating this kind of problem.\n+\n+In other cases, the TensorFlow op is registered for the data type, but for the\n+`eager` or `graph` execution modes there is no TensorFlow kernel defined.\n+Such primitives appear in the\n+[unimplemented cases](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md)\n+as unimplemented for `eager` and `graph`, e.g., `lax.sign` for unsigned integers:\n+\n+```python\n+jax2tf.convert(lax.sign)(np.array([5], dtype=np.uint32))\n+\n+>>> NotFoundError: Could not find device for node: {{node Minimum}} = Acos[T=DT_UINT32]\n+>>> All kernels registered for op Minimum:\n+>>> device='CPU'; T in [DT_FLOAT]\n+>>> device='CPU'; T in [DT_DOUBLE]\n+>>> ...\n+```\n+\n+In this situation, you can still run the converted program if you compile it with\n+XLA:\n+```python\n+tf.function(jax2tf.convert(lax.sign),\n+ autograph=False, jit_compile=True)(np.array([5], dtype=np.uint32))\n+```\n+\n+Our priority is to ensure numerical and performance accuracy for\n+the converted program **when using XLA to compile the converted program**.\n+It is always a good idea to use XLA on the JAX-converted function.\n+\n+Sometimes you cannot compile the entire TensorFlow function for your\n+model, because in addition to the function that is converted from JAX,\n+it may include some pre-processing TensorFlow code that\n+is not compileable with XLA, e.g., string parsing. Even in those situations\n+you can instruct TensorFlow to compile only the portion that originates\n+from JAX:\n+\n+```python\n+def entire_tf_fun(x):\n+ y = preprocess_tf_fun_not_compileable(x)\n+ # Compile the code that is converted from JAX\n+ z = tf.function(jax2tf.convert(compute_jax_fn),\n+ autograph=False, jit_compile=True)(y)\n+ return postprocess_tf_fun_not_compileable(z)\n+```\n+\n+You won't be able to compile the `entire_tf_fun`, but you can still execute\n+it knowing that the JAX-converted code is compiled. You can even save\n+the function to a SavedModel, knowing that upon restore the\n+JAX-converted code will be compiled.\n+\n+For a more elaborate example, see the test `test_tf_mix_jax_with_uncompileable`\n+in [savedmodel_test.py](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/tests/tests/savedmodel_test.py).\n+\n+### Missing converter features\nThere is currently no support for replicated (e.g. `pmap`) or multi-device\n(e.g. `sharded_jit`) functions. The collective operations are not yet handled.\n@@ -455,8 +533,8 @@ We use the following TFXLA ops:\n* `XlaPad` (wraps XLA Pad operator). We use this instead of `tf.pad` in order to\nsupport `lax.pad` interior padding (dilation) or negative edge padding.\n- * `XlaConv` (wraps XLA ConvGeneralDilated operator).\n- * `XlaDot` and `XlaDotV2` (wraps XLA DotGeneral operator).\n+ * `XlaConv` and `XlaConv2` (wrap XLA ConvGeneralDilated operator).\n+ * `XlaDot` and `XlaDotV2` (wrap XLA DotGeneral operator).\n* `XlaGather` (wraps XLA Gather operator). We could use `tf.gather` in some\ncases but not always. Also, `tf.gather` has a different semantics than `lax.gather`\nfor index out of bounds.\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"new_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"diff": "@@ -130,6 +130,55 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\narr = np.arange(10, dtype=np.float32)\nself._compare_with_saved_model(f_jax, arr)\n+ # Test does not work on GPU/TPU; would need something like TPU inference\n+ # converter to separate the model on what needs to run on CPU or accelerator.\n+ @jtu.skip_on_devices(\"gpu\", \"tpu\")\n+ def test_tf_mix_jax_with_uncompileable(self):\n+ \"\"\"Show how to combine TF-uncompileable code with compiled JAX-converted code.\"\"\"\n+ def tf_fn(x_str, compute_tf_fn=lambda x: x):\n+ # Some TF preprocessing code that cannot be compiled with XLA because it\n+ # uses strings.\n+ numbers_f32 = tf.strings.to_number(x_str, out_type=tf.float32)\n+ numbers_f16 = tf.cast(numbers_f32, tf.float16)\n+ return compute_tf_fn(numbers_f16)\n+\n+ x_str = np.array([\"3.14\", \"2.78\"])\n+\n+ # Test that we get an error if we try to TF-compile `tf_fn`\n+ with self.assertRaisesRegex(\n+ Exception,\n+ \"Detected unsupported operations when trying to compile graph\"):\n+ tf.function(tf_fn, jit_compile=True)(x_str)\n+\n+ def compute_jax_fn(x):\n+ # A JAX function whose conversion does not run in TF without XLA because\n+ # tf.math.atan is not supported on float16 without XLA.\n+ return lax.atan(x) + lax.atan(x)\n+\n+ with self.assertRaisesRegex(\n+ tf.errors.NotFoundError,\n+ \"Could not find device for node.*Atan.*DT_HALF\"):\n+ tf_fn(x_str, compute_tf_fn=jax2tf.convert(compute_jax_fn))\n+\n+ # Plug in the TF-compiled JAX-converted `compute_jax_fn`.\n+ composed_fn = lambda x_str: tf_fn(\n+ x_str,\n+ compute_tf_fn=tf.function(jax2tf.convert(compute_jax_fn),\n+ autograph=True,\n+ jit_compile=True))\n+ res_tf = composed_fn(x_str)\n+ self.assertAllClose(res_tf.numpy(),\n+ compute_jax_fn(np.array([3.14, 2.78], dtype=np.float16)))\n+\n+ # Save and restore SavedModel\n+ model = tf.Module()\n+ model.f = tf.function(\n+ composed_fn,\n+ input_signature=[tf.TensorSpec((2,), dtype=tf.string)])\n+ restored_model = self.save_and_load_model(model)\n+ res_tf_restored = restored_model.f(x_str)\n+ self.assertAllClose(res_tf_restored.numpy(), res_tf.numpy())\n+\nif __name__ == \"__main__\":\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Added documentation explaining how to handle undefined TF ops
Added a test case showing how to mix compileable and non-compileable code. |
260,678 | 18.05.2021 10:18:58 | 18,000 | bbcaec4a3aaaca7f8d33223d86a539d9253d37bf | Initial implementation of jax.numpy.poly
This is an initial jax.numpy.poly implementation. It is tested by testPoly in the tests/lax_numpy_test.py test file. | [
{
"change_type": "MODIFY",
"old_path": "docs/jax.numpy.rst",
"new_path": "docs/jax.numpy.rst",
"diff": "@@ -286,6 +286,7 @@ Not every function in NumPy is implemented; contributions are welcome!\npad\npercentile\npiecewise\n+ poly\npolyadd\npolyder\npolyint\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/lax_numpy.py",
"new_path": "jax/_src/numpy/lax_numpy.py",
"diff": "@@ -3847,6 +3847,43 @@ def diagflat(v, k=0):\nres = res.reshape(adj_length,adj_length)\nreturn res\n+_POLY_DOC=\"\"\"\\\n+This differs from np.poly when an integer array is given.\n+np.poly returns a result with dtype float64 in this case.\n+jax returns a result with an inexact type, but not necessarily\n+float64.\n+\n+This also differs from np.poly when the input array strictly\n+contains pairs of complex conjugates, e.g. [1j, -1j, 1-1j, 1+1j].\n+np.poly returns an array with a real dtype in such cases.\n+jax returns an array with a complex dtype in such cases.\n+\"\"\"\n+\n+@_wraps(np.poly, lax_description=_POLY_DOC)\n+def poly(seq_of_zeros):\n+ _check_arraylike('poly', seq_of_zeros)\n+ seq_of_zeros, = _promote_dtypes_inexact(seq_of_zeros)\n+ seq_of_zeros = atleast_1d(seq_of_zeros)\n+\n+ sh = seq_of_zeros.shape\n+ if len(sh) == 2 and sh[0] == sh[1] and sh[0] != 0:\n+ # import at runtime to avoid circular import\n+ from . import linalg\n+ seq_of_zeros = linalg.eigvals(seq_of_zeros)\n+\n+ if seq_of_zeros.ndim != 1:\n+ raise ValueError(\"input must be 1d or non-empty square 2d array.\")\n+\n+ dt = seq_of_zeros.dtype\n+ if len(seq_of_zeros) == 0:\n+ return ones((), dtype=dt)\n+\n+ a = ones((1,), dtype=dt)\n+ for k in range(len(seq_of_zeros)):\n+ a = convolve(a, array([1, -seq_of_zeros[k]], dtype=dt), mode='full')\n+\n+ return a\n+\n@_wraps(np.polyval)\ndef polyval(p, x):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/numpy/__init__.py",
"new_path": "jax/numpy/__init__.py",
"diff": "@@ -51,7 +51,7 @@ from jax._src.numpy.lax_numpy import (\nnanmax, nanmean, nanmin, nanprod, nanstd, nansum, nanvar, ndarray, ndim,\nnegative, newaxis, nextafter, nonzero, not_equal, number,\nobject_, ogrid, ones, ones_like, operator_name, outer, packbits, pad, percentile,\n- pi, piecewise, polyadd, polyder, polyint, polymul, polysub, polyval, positive, power,\n+ pi, piecewise, poly, polyadd, polyder, polyint, polymul, polysub, polyval, positive, power,\nprod, product, promote_types, ptp, quantile,\nr_, rad2deg, radians, ravel, ravel_multi_index, real, reciprocal, remainder, repeat, reshape,\nresult_type, right_shift, rint, roll, rollaxis, rot90, round, row_stack,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -1752,6 +1752,27 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\nself._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=True)\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\": \"_shape={}_rank{}\".format(\n+ jtu.format_shape_dtype_string(a_shape, dtype), rank),\n+ \"dtype\": dtype, \"a_shape\": a_shape, \"rank\": rank}\n+ for rank in (1, 2)\n+ for dtype in default_dtypes\n+ for a_shape in one_dim_array_shapes))\n+ def testPoly(self, a_shape, dtype, rank):\n+ if dtype in (np.float16, jnp.bfloat16, np.int16):\n+ self.skipTest(f\"{dtype} gets promoted to {np.float16}, which is not supported.\")\n+ elif rank == 2 and jtu.device_under_test() in (\"tpu\", \"gpu\"):\n+ self.skipTest(\"Nonsymmetric eigendecomposition is only implemented on the CPU backend.\")\n+ rng = jtu.rand_default(self.rng())\n+ tol = { np.int8: 1e-3, np.int32: 1e-3, np.float32: 1e-3, np.float64: 1e-6 }\n+ if jtu.device_under_test() == \"tpu\":\n+ tol[np.int32] = tol[np.float32] = 1e-1\n+ tol = jtu.tolerance(dtype, tol)\n+ args_maker = lambda: [rng(a_shape * rank, dtype)]\n+ self._CheckAgainstNumpy(np.poly, jnp.poly, args_maker, check_dtypes=False, tol=tol)\n+ self._CompileAndCheck(jnp.poly, args_maker, check_dtypes=True, rtol=tol, atol=tol)\n+\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"a_shape={} , b_shape={}\".format(\njtu.format_shape_dtype_string(a_shape, dtype),\n"
}
] | Python | Apache License 2.0 | google/jax | Initial implementation of jax.numpy.poly
This is an initial jax.numpy.poly implementation. It is tested by testPoly in the tests/lax_numpy_test.py test file. |
260,372 | 20.05.2021 21:49:41 | 21,600 | 9f61f41e4730664ce2d0977383ac1661c027c461 | prevent nans in _fill_lanczos_kernel | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/image/scale.py",
"new_path": "jax/_src/image/scale.py",
"diff": "@@ -24,9 +24,8 @@ import numpy as np\ndef _fill_lanczos_kernel(radius, x):\ny = radius * jnp.sin(np.pi * x) * jnp.sin(np.pi * x / radius)\n- with np.errstate(divide='ignore', invalid='ignore'):\n- out = y / (np.pi ** 2 * x ** 2)\n- out = jnp.where(x <= 1e-3, 1., out)\n+ # out = y / (np.pi ** 2 * x ** 2) where x >1e-3, 1 otherwise\n+ out = jnp.where(x > 1e-3, jnp.divide(y, jnp.where(x != 0, np.pi**2 * x**2, 1)), 1)\nreturn jnp.where(x > radius, 0., out)\n@@ -63,7 +62,6 @@ def compute_weight_mat(input_size: int, output_size: int, scale,\nweights = kernel(x)\ntotal_weight_sum = jnp.sum(weights, axis=0, keepdims=True)\n- with np.errstate(invalid='ignore', divide='ignore'):\nweights = jnp.where(\njnp.abs(total_weight_sum) > 1000. * np.finfo(np.float32).eps,\njnp.divide(weights, jnp.where(total_weight_sum != 0, total_weight_sum, 1)),\n"
}
] | Python | Apache License 2.0 | google/jax | prevent nans in _fill_lanczos_kernel |
260,411 | 21.05.2021 11:16:00 | -10,800 | eabe631fa45160a3984c81734b74f69b4a6e6241 | Update jax2tf.py
Minor typo fix | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -171,7 +171,7 @@ def convert(fun: Callable, *,\nsequence of TF ops for any non-zero values of the dimension variables.\npolymorphic_shapes are only supported for positional arguments; shape\n- polymorphism is not support for keyword arguments.\n+ polymorphism is not supported for keyword arguments.\nSee [the README](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/README.md#shape-polymorphic-conversion)\nfor more details.\n"
}
] | Python | Apache License 2.0 | google/jax | Update jax2tf.py
Minor typo fix |
260,411 | 11.05.2021 11:11:37 | -10,800 | 2ad9c0c34c096b67a6ea40963f6a5b20bde65355 | [jax2tf] Fix the scoping of the enable_xla conversion parameter
Previously, the global enable_xla flag was set upon entry to
`jax.convert`. It should instead be set only for the duration
of the just-in-time conversion, which may happen later when
the converted function is invoked. | [
{
"change_type": "MODIFY",
"old_path": "CHANGELOG.md",
"new_path": "CHANGELOG.md",
"diff": "@@ -10,6 +10,16 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.\n## jax 0.2.14 (unreleased)\n* [GitHub commits](https://github.com/google/jax/compare/jax-v0.2.13...master).\n+* New features:\n+\n+* Breaking changes:\n+\n+* Bug fixes:\n+ * The {func}`jax2tf.convert` now scopes the `enable_xla` conversion parameter\n+ properly to apply only during the just-in-time conversion\n+ ({jax-issue}`#6720`).\n+ * Fixed assertion failure in {func}`jax2tf.call_tf` when used with captured\n+ `tf.Variable` ({jax-issue}`#6572`).\n* Bug fixes:\n* The {func}`jax2tf.convert` now converts `lax.dot_general` using the\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -190,8 +190,6 @@ def convert(fun: Callable, *,\nA version of `fun` that expects TfVals as arguments (or\ntuple/lists/dicts) thereof, and returns TfVals as outputs.\n\"\"\"\n- global _enable_xla\n- _enable_xla = enable_xla\napi._check_callable(fun)\ndef converted_fun(*args: TfVal) -> TfVal:\n@@ -276,6 +274,9 @@ def convert(fun: Callable, *,\ntry:\nglobal _shape_env\nassert not _shape_env, f\"Unexpected shape environment {_shape_env}\"\n+ global _enable_xla\n+ prev_enable_xla = _enable_xla\n+ _enable_xla = enable_xla\n_shape_env = shapeenv\nif with_gradient:\n@@ -296,6 +297,7 @@ def convert(fun: Callable, *,\nfor o, _ in out_flat_raw]\nfinally:\n_shape_env = {}\n+ _enable_xla = prev_enable_xla\nout_flat = [tf.identity(x, \"jax2tf_out\") for x in out_flat]\nout = tree_util.tree_unflatten(out_tree_thunk(), out_flat)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/jax2tf_test.py",
"new_path": "jax/experimental/jax2tf/tests/jax2tf_test.py",
"diff": "@@ -22,6 +22,7 @@ from absl.testing import parameterized\nimport jax\nfrom jax import dtypes\n+from jax import lax\nfrom jax import numpy as jnp\nfrom jax import test_util as jtu\nfrom jax.config import config\n@@ -516,6 +517,34 @@ class Jax2TfTest(tf_test_util.JaxToTfTestCase):\ntf_fn_array(np.array([3, 4, 5])), np.array([4.5, 10, 17.5],\njnp.bfloat16))\n+ def test_enable_xla(self):\n+ # Tests that enable_xla flag is properly scoped to a conversion.\n+ def fun(x):\n+ # Can be converted only if enable_xla is on, due to negative padding.\n+ return lax.pad(x, np.float32(0), [(-1, 0, 0), (0, 0, 0)])\n+\n+ tf_fun_with_xla = jax2tf.convert(fun, enable_xla=True)\n+ tf_fun_without_xla = jax2tf.convert(fun, enable_xla=False)\n+ x = np.ones((2, 3), dtype=np.float32)\n+\n+ self.assertAllClose(fun(x), tf_fun_with_xla(x))\n+ with self.assertRaisesRegex(NotImplementedError,\n+ \"Call to pad cannot be converted with enable_xla=False\"):\n+ tf_fun_without_xla(x)\n+\n+ # Now in reverse order\n+ def fun2(x):\n+ # Can be converted only if enable_xla is on, due to negative padding.\n+ return lax.pad(x, np.float32(0), [(-1, 0, 0), (0, 0, 0)])\n+\n+ tf_fun2_without_xla = jax2tf.convert(fun2, enable_xla=False)\n+ tf_fun2_with_xla = jax2tf.convert(fun2, enable_xla=True)\n+\n+ with self.assertRaisesRegex(NotImplementedError,\n+ \"Call to pad cannot be converted with enable_xla=False\"):\n+ tf_fun2_without_xla(x)\n+ self.assertAllClose(fun(x), tf_fun2_with_xla(x))\n+\nif __name__ == \"__main__\":\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Fix the scoping of the enable_xla conversion parameter
Previously, the global enable_xla flag was set upon entry to
`jax.convert`. It should instead be set only for the duration
of the just-in-time conversion, which may happen later when
the converted function is invoked. |
260,411 | 21.05.2021 11:42:44 | -10,800 | b3411cc92ab79ed073dc80919b493daac7de2db7 | Update jax2tf_test.py
Fix the type of np.zeros to be float32.
Also, replaced `jnp.zeros` with `np.zeros` because technically the argument to `f_tf` should be a TF value, not a JAX value. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/jax2tf_test.py",
"new_path": "jax/experimental/jax2tf/tests/jax2tf_test.py",
"diff": "@@ -522,7 +522,7 @@ class Jax2TfTest(tf_test_util.JaxToTfTestCase):\nreturn jnp.sum(x)\nf_tf = jax2tf.convert(f_jax)\nself.assertAllClose(\n- f_tf(x=jnp.zeros(3)), # Call with kwargs.\n+ f_tf(x=np.zeros(3, dtype=np.float32)), # Call with kwargs.\nnp.zeros((), dtype=np.float32))\n"
}
] | Python | Apache License 2.0 | google/jax | Update jax2tf_test.py
Fix the type of np.zeros to be float32.
Also, replaced `jnp.zeros` with `np.zeros` because technically the argument to `f_tf` should be a TF value, not a JAX value. |
260,411 | 21.05.2021 11:10:41 | -10,800 | e7766838db1f447ade8870b22255de0b7dd97e0a | Minor cleanup of the translation rules for cumred primitives | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/control_flow.py",
"new_path": "jax/_src/lax/control_flow.py",
"diff": "@@ -2574,39 +2574,27 @@ def _cumred_dtype_rule(name, operand, *args, **kw):\n\"of number.\".format(name, np.dtype(operand.dtype).name))\nreturn dtypes.canonicalize_dtype(operand.dtype)\n-cumsum_p = lax.standard_primitive(\n- _cumred_shape_rule, partial(_cumred_dtype_rule, \"cumsum\"),\n- 'cumsum')\n-ad.deflinear2(cumsum_p, _cumsum_transpose_rule)\n-xla.backend_specific_translations['tpu'][cumsum_p] = xla.lower_fun(\n- partial(_cumred_tpu_translation_rule, lax._reduce_window_sum),\n- multiple_results=False)\n-batching.primitive_batchers[cumsum_p] = partial(_cumred_batch_rule, cumsum_p)\n-\n-\n-def _cumulative_reduction_primitive(name, reduce_window_fn):\n+def _cumulative_reduction_primitive(name,\n+ reduce_fn,\n+ tpu_reduce_window_fn):\nreducer_p = lax.standard_primitive(\n_cumred_shape_rule, partial(_cumred_dtype_rule, name),\n- name)\n+ name,\n+ translation_rule=xla.lower_fun(\n+ partial(associative_scan, reduce_fn),\n+ multiple_results=False))\nxla.backend_specific_translations['tpu'][reducer_p] = xla.lower_fun(\n- partial(_cumred_tpu_translation_rule, reduce_window_fn),\n+ partial(_cumred_tpu_translation_rule, tpu_reduce_window_fn),\nmultiple_results=False)\nbatching.primitive_batchers[reducer_p] = partial(_cumred_batch_rule, reducer_p)\nreturn reducer_p\n+cumsum_p = _cumulative_reduction_primitive(\"cumsum\", lax.add, lax._reduce_window_sum)\n+ad.deflinear2(cumsum_p, _cumsum_transpose_rule)\n+cumprod_p = _cumulative_reduction_primitive(\"cumprod\", lax.mul, lax._reduce_window_prod)\n+cummax_p = _cumulative_reduction_primitive(\"cummax\", lax.max, lax._reduce_window_max)\n+cummin_p = _cumulative_reduction_primitive(\"cummin\", lax.min, lax._reduce_window_min)\n-cumprod_p = _cumulative_reduction_primitive(\"cumprod\", lax._reduce_window_prod)\n-cummax_p = _cumulative_reduction_primitive(\"cummax\", lax._reduce_window_max)\n-cummin_p = _cumulative_reduction_primitive(\"cummin\", lax._reduce_window_min)\n-\n-xla.translations[cumsum_p] = xla.lower_fun(\n- partial(associative_scan, lax.add), multiple_results=False)\n-xla.translations[cumprod_p] = xla.lower_fun(\n- partial(associative_scan, lax.mul), multiple_results=False)\n-xla.translations[cummin_p] = xla.lower_fun(\n- partial(associative_scan, lax.min), multiple_results=False)\n-xla.translations[cummax_p] = xla.lower_fun(\n- partial(associative_scan, lax.max), multiple_results=False)\ndef _cumulative_jvp_rule(primals, tangents, *, axis: int, reverse: bool,\ncombine_fn: Callable):\n"
}
] | Python | Apache License 2.0 | google/jax | Minor cleanup of the translation rules for cumred primitives |
260,411 | 23.05.2021 19:32:45 | -10,800 | 70f0110b3296eb26b04d33758c07724a78ae74d3 | Fix dtypes.issubdtype when called with "bfloat16" (as string)
Fixes: | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/dtypes.py",
"new_path": "jax/_src/dtypes.py",
"diff": "@@ -198,6 +198,8 @@ def _issubclass(a, b):\nreturn False\ndef issubdtype(a, b):\n+ if a == \"bfloat16\":\n+ a = bfloat16\nif a == bfloat16:\nif isinstance(b, np.dtype):\nreturn b == _bfloat16_dtype\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/random_test.py",
"new_path": "tests/random_test.py",
"diff": "@@ -210,6 +210,13 @@ class LaxRandomTest(jtu.JaxTestCase):\nfor samples in [uncompiled_samples, compiled_samples]:\nself._CheckKolmogorovSmirnovCDF(samples, scipy.stats.norm().cdf)\n+ def testNormalBfloat16(self):\n+ # Passing bfloat16 as dtype string.\n+ # https://github.com/google/jax/issues/6813\n+ res_bfloat16_str = random.normal(random.PRNGKey(0), dtype='bfloat16')\n+ res_bfloat16 = random.normal(random.PRNGKey(0), dtype=jnp.bfloat16)\n+ self.assertAllClose(res_bfloat16, res_bfloat16_str)\n+\n@parameterized.named_parameters(jtu.cases_from_list(\n{\"testcase_name\": \"dtype={}\".format(np.dtype(dtype).name), \"dtype\": dtype}\nfor dtype in complex_dtypes))\n"
}
] | Python | Apache License 2.0 | google/jax | Fix dtypes.issubdtype when called with "bfloat16" (as string)
Fixes: #6813 |
260,595 | 24.05.2021 21:13:43 | -3,600 | dc81610bb682b1c97e9e9fe6801d4440d1bb01af | updated to official bazel.4.1.0 | [
{
"change_type": "MODIFY",
"old_path": "build/build.py",
"new_path": "build/build.py",
"diff": "@@ -118,10 +118,10 @@ bazel_packages = {\n\"80c82e93a12ba30021692b11c78007807e82383a673be1602573b944beb359ab\"),\n(\"Darwin\", \"arm64\"):\nBazelPackage(\n- base_uri=\"https://releases.bazel.build/4.1.0/rc4/\",\n- file=\"bazel-4.1.0rc4-darwin-arm64\",\n+ base_uri=\"https://github.com/bazelbuild/bazel/releases/download/4.1.0/\",\n+ file=\"bazel-4.1.0-darwin-arm64\",\nsha256=\n- \"998466a73295e4a03a9f3ef06feeab87029d500b2f262655600c5dccce7113ba\"),\n+ \"c372d39ab9dac96f7fdfc2dd649e88b05ee4c94ce3d6cf2313438ef0ca6d5ac1\"),\n(\"Windows\", \"x86_64\"):\nBazelPackage(\nbase_uri=None,\n"
}
] | Python | Apache License 2.0 | google/jax | updated to official bazel.4.1.0 |
260,374 | 24.05.2021 17:31:20 | 0 | 369ca134fc6e7b80ed14c64862bb0746115b2130 | add fingerprint to debugging log | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -59,6 +59,10 @@ from . import partial_eval as pe\nfrom . import xla\nfrom . import ad\n+# Built in Python lists don't support weak refs but subclasses of lists do.\n+class WeakRefList(list):\n+ pass\n+\nif sys.version_info >= (3, 8):\nfrom functools import cached_property as maybe_cached_property\nelse:\n@@ -616,17 +620,19 @@ def xla_pmap_impl(fun: lu.WrappedFun, *args, backend, axis_name, axis_size,\nglobal_axis_size, devices, name, in_axes, out_axes_thunk,\ndonated_invars, global_arg_shapes):\nabstract_args = unsafe_map(xla.abstractify, args)\n- compiled_fun = parallel_callable(fun, backend, axis_name, axis_size,\n+ compiled_fun, xla_executable = parallel_callable(fun, backend, axis_name, axis_size,\nglobal_axis_size, devices, name,\nin_axes, out_axes_thunk,\ndonated_invars, global_arg_shapes,\n*abstract_args)\n+\n# Don't re-abstractify args unless logging is enabled for performance.\nif config.jax_distributed_debug:\ndistributed_debug_log((\"Running pmapped function\", name),\n(\"python function\", fun.f),\n(\"devices\", devices),\n- (\"abstract args\", map(xla.abstractify, args)))\n+ (\"abstract args\", map(xla.abstractify, args)),\n+ (\"fingerprint\", xla_executable.fingerprint))\nreturn compiled_fun(*args)\n@lu.cache\n@@ -890,7 +896,8 @@ def parallel_callable(fun: lu.WrappedFun,\nhandle_outs)\ncompiled = xla.backend_compile(backend, built, compile_options)\nhandle_args = partial(shard_args, compiled.local_devices(), input_indices)\n- return partial(execute_replicated, compiled, backend, handle_args, handle_outs)\n+ execute_fun = partial(execute_replicated, compiled, backend, handle_args, handle_outs)\n+ return WeakRefList([execute_fun, compiled])\nmulti_host_supported_collectives: Set[core.Primitive] = set()\n"
}
] | Python | Apache License 2.0 | google/jax | add fingerprint to debugging log |
260,563 | 25.05.2021 18:00:46 | -7,200 | 0308527f5514dd6dadddcad8aebe061b0892b886 | Add auxiliary data support in custom_linear_solve | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/control_flow.py",
"new_path": "jax/_src/lax/control_flow.py",
"diff": "@@ -49,7 +49,7 @@ from jax._src.util import (partial, unzip2, unzip3, safe_map, safe_zip,\nsplit_list, cache, extend_name_stack)\nfrom jax.tree_util import (tree_flatten, tree_unflatten, treedef_is_leaf,\ntreedef_children, treedef_tuple, tree_multimap,\n- tree_leaves)\n+ tree_leaves, tree_structure)\nfrom jax import ad_util\nfrom jax.config import config\n@@ -1954,7 +1954,19 @@ def _check_tree_and_avals(what, tree1, avals1, tree2, avals2):\nf\"{tree_unflatten(tree2, avals2)}.\")\n-def _check_tree(func_name, expected_name, actual_tree, expected_tree):\n+def _check_tree(func_name, expected_name, actual_tree, expected_tree, has_aux=False):\n+ if has_aux:\n+ actual_tree_children = actual_tree.children()\n+\n+ if len(actual_tree_children) == 2:\n+ # select first child as result tree\n+ actual_tree = tree_structure(actual_tree_children[0])\n+ else:\n+ raise ValueError(\n+ f\"{func_name}() produced a pytree with structure \"\n+ f\"{actual_tree}, but a pytree tuple with auxiliary \"\n+ f\"output was expected because has_aux was set to True.\")\n+\nif actual_tree != expected_tree:\nraise TypeError(\nf\"{func_name}() output pytree structure must match {expected_name}, \"\n@@ -2141,7 +2153,7 @@ def _check_shapes(func_name, expected_name, actual, expected):\n@api_boundary\ndef custom_linear_solve(\n- matvec, b, solve, transpose_solve=None, symmetric=False):\n+ matvec, b, solve, transpose_solve=None, symmetric=False, has_aux=False):\n\"\"\"Perform a matrix-free linear solve with implicitly defined gradients.\nThis function allows for overriding or defining gradients for a linear\n@@ -2160,7 +2172,7 @@ def custom_linear_solve(\nb: constant right handle side of the equation. May be any nested structure\nof arrays.\nsolve: higher level function that solves for solution to the linear\n- equation, i.e., ``solve(matvec, x)) == x`` for all ``x`` of the same form\n+ equation, i.e., ``solve(matvec, x) == x`` for all ``x`` of the same form\nas ``b``. This function need not be differentiable.\ntranspose_solve: higher level function for solving the transpose linear\nequation, i.e., ``transpose_solve(vecmat, x) == x``, where ``vecmat`` is\n@@ -2169,6 +2181,8 @@ def custom_linear_solve(\n``symmetric=True``, in which case ``solve`` provides the default value.\nsymmetric: bool indicating if it is safe to assume the linear map\ncorresponds to a symmetric matrix, i.e., ``matvec == vecmat``.\n+ has_aux: bool indicating whether the ``solve`` and ``transpose_solve`` functions\n+ return auxiliary data like solver diagnostics as a second argument.\nReturns:\nResult of ``solve(matvec, b)``, with gradients defined assuming that the\n@@ -2182,22 +2196,29 @@ def custom_linear_solve(\ntree, = treedef_children(in_args_tree)\n- def _shape_checked(fun, name):\n+ def _shape_checked(fun, name, has_aux):\ndef f(x):\ny = fun(x)\n_check_shapes(name, \"b\", y, b_flat)\nreturn y\n- return f\n+ def f_aux(x):\n+ y, aux = fun(x)\n+ _check_shapes(name, \"b\", y, b_flat)\n+ return y, aux\n+\n+ return f_aux if has_aux else f\n+\n+ # no auxiliary data assumed for matvec\nmatvec_jaxpr, matvec_consts, out_tree = _initial_style_jaxpr(\n- _shape_checked(matvec, \"matvec\"), in_args_tree, b_avals,\n+ _shape_checked(matvec, \"matvec\", False), in_args_tree, b_avals,\n'custom_linear_solve')\n- _check_tree(\"matvec\", \"b\", out_tree, tree)\n+ _check_tree(\"matvec\", \"b\", out_tree, tree, False)\nsolve_jaxpr, solve_consts, out_tree = _initial_style_jaxpr(\n- _shape_checked(partial(solve, matvec), \"solve\"), in_args_tree, b_avals,\n+ _shape_checked(partial(solve, matvec), \"solve\", has_aux), in_args_tree, b_avals,\n'custom_linear_solve')\n- _check_tree(\"solve\", \"b\", out_tree, tree)\n+ _check_tree(\"solve\", \"b\", out_tree, tree, has_aux)\nif transpose_solve is None:\nvecmat_jaxpr = tr_solve_jaxpr = None\n@@ -2214,9 +2235,9 @@ def custom_linear_solve(\nassert out_tree == tree\ntr_solve_jaxpr, tr_solve_consts, out_tree = _initial_style_jaxpr(\n- _shape_checked(partial(transpose_solve, vecmat), \"transpose_solve\"),\n+ _shape_checked(partial(transpose_solve, vecmat), \"transpose_solve\", has_aux),\nin_args_tree, b_avals, 'custom_linear_solve')\n- _check_tree(\"transpose_solve\", \"b\", out_tree, tree)\n+ _check_tree(\"transpose_solve\", \"b\", out_tree, tree, has_aux)\nall_consts = [matvec_consts, vecmat_consts, solve_consts, tr_solve_consts]\nconst_lengths = _LinearSolveTuple(*_map(len, all_consts))\n@@ -2226,11 +2247,21 @@ def custom_linear_solve(\nout_flat = linear_solve_p.bind(\n*(_flatten(all_consts) + b_flat),\nconst_lengths=const_lengths, jaxprs=jaxprs)\n- return tree_unflatten(tree, out_flat)\n+\n+ return tree_unflatten(out_tree, out_flat)\ndef _linear_solve_abstract_eval(*args, const_lengths, jaxprs):\n- return _map(raise_to_shaped, args[sum(const_lengths):])\n+ args_to_raise = args[sum(const_lengths):]\n+\n+ # raise aux_args to shaped arrays as well if present\n+ # number of aux args is the difference in out_avals\n+ # of solve and matvec (since they map to the same vector space)\n+\n+ num_aux = len(jaxprs.solve.out_avals) - len(jaxprs.matvec.out_avals)\n+ if num_aux > 0:\n+ args_to_raise += tuple(jaxprs.solve.out_avals[-num_aux:])\n+ return _map(raise_to_shaped, args_to_raise)\ndef _custom_linear_solve_impl(*args, const_lengths, jaxprs):\n@@ -2263,16 +2294,29 @@ def _custom_linear_solve_jvp(primals, tangents, const_lengths, jaxprs):\nparams, _ = _split_linear_solve_args(primals, const_lengths)\nparams_dot, b_dot = _split_linear_solve_args(tangents, const_lengths)\n+ num_x_leaves = len(b_dot)\n+ # x is a flat tree with possible aux values appended\n+ # since x_tree == b_tree == b_dot_tree, we can cut off\n+ # aux values with len info provided by b_dot tree here\n+ x_leaves, _ = split_list(x, [num_x_leaves])\n+\nif all(type(p) is ad_util.Zero for p in params_dot.matvec):\n# no need to evaluate matvec_tangents\nrhs = b_dot\nelse:\nmatvec_tangents = _tangent_linear_map(\n- core.jaxpr_as_fun(jaxprs.matvec), params.matvec, params_dot.matvec, *x)\n+ core.jaxpr_as_fun(jaxprs.matvec), params.matvec, params_dot.matvec, *x_leaves)\nrhs = _map(ad.add_tangents, b_dot, _map(operator.neg, matvec_tangents))\nx_dot = linear_solve_p.bind(*(_flatten(params) + rhs), **kwargs)\n+ # split into x tangents and aux tangents (these become zero)\n+ dx_leaves, daux_leaves = split_list(x_dot, [num_x_leaves])\n+\n+ daux_leaves = _map(ad_util.Zero.from_value, daux_leaves)\n+\n+ x_dot = dx_leaves + daux_leaves\n+\nreturn x, x_dot\n@@ -2282,10 +2326,14 @@ def _linear_solve_transpose_rule(cotangent, *primals, const_lengths, jaxprs):\n'differentiation of custom_linear_solve')\nparams, b = _split_linear_solve_args(primals, const_lengths)\n+ # split off symbolic zeros in the cotangent if present\n+ x_cotangent, _ = split_list(cotangent, [len(b)])\nassert all(ad.is_undefined_primal(x) for x in b)\n- cotangent_b = linear_solve_p.bind(\n- *(_flatten(params.transpose()) + cotangent),\n+ cotangent_b_full = linear_solve_p.bind(\n+ *(_flatten(params.transpose()) + x_cotangent),\nconst_lengths=const_lengths.transpose(), jaxprs=jaxprs.transpose())\n+ # drop aux values in cotangent computation\n+ cotangent_b, _ = split_list(cotangent_b_full, [len(b)])\nreturn [None] * sum(const_lengths) + cotangent_b\n@@ -2302,6 +2350,7 @@ def _linear_solve_batching_rule(args, dims, axis_name, main_type, const_lengths,\n(matvec, vecmat, solve, solve_t) = jaxprs\n(matvec_bat, vecmat_bat, solve_bat, solve_t_bat) = params_bat\n+ num_aux = len(solve.out_avals) - len(matvec.out_avals)\n# Fixpoint computation of which parts of x and b are batched; we need to\n# ensure this is consistent between all four jaxprs\nb_bat = orig_b_bat\n@@ -2318,7 +2367,9 @@ def _linear_solve_batching_rule(args, dims, axis_name, main_type, const_lengths,\nvecmat_jaxpr_batched, vecmat_x_bat = batching.batch_jaxpr(\nvecmat, size, vecmat_bat + b_bat, instantiate=x_bat,\naxis_name=axis_name, main_type=main_type)\n- x_bat_out = _map(operator.or_, vecmat_x_bat, solve_x_bat)\n+ # batch all aux data by default\n+ x_bat_out = _map(operator.or_, vecmat_x_bat + [True] * num_aux, solve_x_bat)\n+\n# Apply matvec and solve_t -> new batched parts of b\nmatvec_jaxpr_batched, matvec_b_bat = batching.batch_jaxpr(\nmatvec, size, matvec_bat + x_bat_out, instantiate=b_bat,\n@@ -2360,7 +2411,7 @@ def _linear_solve_batching_rule(args, dims, axis_name, main_type, const_lengths,\n*(new_params + new_b),\nconst_lengths=const_lengths,\njaxprs=batched_jaxprs)\n- out_dims = [0 if batched else batching.not_mapped for batched in b_bat]\n+ out_dims = [0 if batched else batching.not_mapped for batched in solve_x_bat]\nreturn outs, out_dims\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_control_flow_test.py",
"new_path": "tests/lax_control_flow_test.py",
"diff": "@@ -2067,6 +2067,50 @@ class LaxControlFlowTest(jtu.JaxTestCase):\nactual = api.vmap(linear_solve, (None, 1), 1)(a, c)\nself.assertAllClose(expected, actual)\n+ @jtu.skip_on_flag(\"jax_skip_slow_tests\", True)\n+ def test_custom_linear_solve_aux(self):\n+ def explicit_jacobian_solve_aux(matvec, b):\n+ x = lax.stop_gradient(jnp.linalg.solve(api.jacobian(matvec)(b), b))\n+ return x, array_aux\n+\n+ def matrix_free_solve_aux(matvec, b):\n+ return lax.custom_linear_solve(\n+ matvec, b, explicit_jacobian_solve_aux, explicit_jacobian_solve_aux,\n+ symmetric=True, has_aux=True)\n+\n+ def linear_solve_aux(a, b):\n+ return matrix_free_solve_aux(partial(high_precision_dot, a), b)\n+\n+ # array aux values, to be able to use jtu.check_grads\n+ array_aux = {\"converged\": np.array(1.), \"nfev\": np.array(12345.)}\n+ rng = np.random.RandomState(0)\n+ a = rng.randn(3, 3)\n+ a = a + a.T\n+ b = rng.randn(3)\n+\n+ expected = jnp.linalg.solve(a, b)\n+ actual_nojit, nojit_aux = linear_solve_aux(a, b)\n+ actual_jit, jit_aux = api.jit(linear_solve_aux)(a, b)\n+\n+ self.assertAllClose(expected, actual_nojit)\n+ self.assertAllClose(expected, actual_jit)\n+ # scalar dict equality check\n+ self.assertDictEqual(nojit_aux, array_aux)\n+ self.assertDictEqual(jit_aux, array_aux)\n+\n+ # jvp / vjp test\n+ jtu.check_grads(linear_solve_aux, (a, b), order=2, rtol=2e-3)\n+\n+ # vmap test\n+ c = rng.randn(3, 2)\n+ expected = jnp.linalg.solve(a, c)\n+ expected_aux = tree_util.tree_map(partial(np.repeat, repeats=2), array_aux)\n+ actual_vmap, vmap_aux = api.vmap(linear_solve_aux, (None, 1), -1)(a, c)\n+\n+ self.assertAllClose(expected, actual_vmap)\n+ jtu.check_eq(expected_aux, vmap_aux)\n+\n+\n@jtu.skip_on_flag(\"jax_skip_slow_tests\", True)\ndef test_custom_linear_solve_zeros(self):\ndef explicit_jacobian_solve(matvec, b):\n"
}
] | Python | Apache License 2.0 | google/jax | Add auxiliary data support in custom_linear_solve |
260,300 | 25.05.2021 16:07:09 | 25,200 | 00e030247ea3326d3d634e26e9a70baebc64184a | Enable custom gradients SavedModel option in test | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"new_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"diff": "@@ -35,7 +35,10 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\ndef save_and_load_model(self, model: tf.Module) -> tf.Module:\n# Roundtrip through saved model on disk.\nmodel_dir = os.path.join(absltest.get_default_test_tmpdir(), str(id(model)))\n- tf.saved_model.save(model, model_dir)\n+ tf.saved_model.save(\n+ model,\n+ model_dir,\n+ options=tf.saved_model.SaveOptions(custom_gradients=True))\nrestored_model = tf.saved_model.load(model_dir)\nreturn restored_model\n@@ -94,13 +97,8 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\nself.assertAllClose(restored_model.f(x), f_jax(x))\nwith tf.GradientTape() as tape:\ny = restored_model.f(xv)\n-\n- # TODO: at the moment TF does not fully-support custom_gradient in a\n- # savedmodel (b/123499169), but at least a warning is printed and an\n- # exception is thrown when gradients are taken. The exception, however,\n- # is a very strange one, for now.\n- with self.assertRaisesRegex(TypeError, \"An op outside of the function building code is being passed\"):\n- _ = tape.gradient(y, xv)\n+ self.assertAllClose(tape.gradient(y, xv).numpy(),\n+ jax.grad(f_jax)(0.7).astype(np.float32))\ndef _compare_with_saved_model(self, f_jax, *args):\n# Certain ops are converted to ensure an XLA context, e.g.,\n"
}
] | Python | Apache License 2.0 | google/jax | Enable custom gradients SavedModel option in test |
260,364 | 13.05.2021 12:08:06 | -3,600 | 03a1ee926974a8ac88c1863156f4a5e67ebb4d88 | Update Jax linesearch to behave more like Scipy | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/scipy/optimize/bfgs.py",
"new_path": "jax/_src/scipy/optimize/bfgs.py",
"diff": "@@ -55,6 +55,7 @@ class _BFGSResults(NamedTuple):\nf_k: jnp.ndarray\ng_k: jnp.ndarray\nH_k: jnp.ndarray\n+ old_old_fval: jnp.ndarray\nstatus: Union[int, jnp.ndarray]\nline_search_status: Union[int, jnp.ndarray]\n@@ -108,6 +109,7 @@ def minimize_bfgs(\nf_k=f_0,\ng_k=g_0,\nH_k=initial_H,\n+ old_old_fval=f_0 + jnp.linalg.norm(g_0) / 2,\nstatus=0,\nline_search_status=0,\n)\n@@ -124,6 +126,7 @@ def minimize_bfgs(\nstate.x_k,\np_k,\nold_fval=state.f_k,\n+ old_old_fval=state.old_old_fval,\ngfk=state.g_k,\nmaxiter=line_search_maxiter,\n)\n@@ -153,7 +156,8 @@ def minimize_bfgs(\nx_k=x_kp1,\nf_k=f_kp1,\ng_k=g_kp1,\n- H_k=H_kp1\n+ H_k=H_kp1,\n+ old_old_fval=state.f_k,\n)\nreturn state\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/scipy/optimize/line_search.py",
"new_path": "jax/_src/scipy/optimize/line_search.py",
"diff": "@@ -104,15 +104,16 @@ def _zoom(restricted_func_and_grad, wolfe_one, wolfe_two, a_lo, phi_lo,\ndef body(state):\n# Body of zoom algorithm. We use boolean arithmetic to avoid using jax.cond\n# so that it works on GPU/TPU.\n+ dalpha = (state.a_hi - state.a_lo)\na = jnp.minimum(state.a_hi, state.a_lo)\nb = jnp.maximum(state.a_hi, state.a_lo)\n- dalpha = (b - a)\ncchk = delta1 * dalpha\nqchk = delta2 * dalpha\n# This will cause the line search to stop, and since the Wolfe conditions\n# are not satisfied the minimization should stop too.\n- state = state._replace(failed=state.failed | (dalpha <= 1e-10))\n+ threshold = jnp.where((jnp.finfo(dalpha).bits < 64), 1e-5, 1e-10)\n+ state = state._replace(failed=state.failed | (dalpha <= threshold))\n# Cubmin is sometimes nan, though in this case the bounds check will fail.\na_j_cubic = _cubicmin(state.a_lo, state.phi_lo, state.dphi_lo, state.a_hi,\n@@ -169,9 +170,9 @@ def _zoom(restricted_func_and_grad, wolfe_one, wolfe_two, a_lo, phi_lo,\nhi_to_lo,\nstate._asdict(),\ndict(\n- a_hi=a_lo,\n- phi_hi=phi_lo,\n- dphi_hi=dphi_lo,\n+ a_hi=state.a_lo,\n+ phi_hi=state.phi_lo,\n+ dphi_hi=state.dphi_lo,\na_rec=state.a_hi,\nphi_rec=state.phi_hi,\n),\n@@ -191,6 +192,9 @@ def _zoom(restricted_func_and_grad, wolfe_one, wolfe_two, a_lo, phi_lo,\n),\n)\nstate = state._replace(j=state.j + 1)\n+ # Choose higher cutoff for maxiter than Scipy as Jax takes longer to find\n+ # the same value - possibly floating point issues?\n+ state = state._replace(failed= state.failed | state.j >= 30)\nreturn state\nstate = while_loop(lambda state: (~state.done) & (~pass_through) & (~state.failed),\n@@ -213,7 +217,6 @@ class _LineSearchState(NamedTuple):\nphi_star: Union[float, jnp.ndarray]\ndphi_star: Union[float, jnp.ndarray]\ng_star: jnp.ndarray\n- saddle_point: Union[bool, jnp.ndarray]\nclass _LineSearchResults(NamedTuple):\n@@ -269,6 +272,11 @@ def line_search(f, xk, pk, old_fval=None, old_old_fval=None, gfk=None, c1=1e-4,\nelse:\nphi_0 = old_fval\ndphi_0 = jnp.dot(gfk, pk)\n+ if old_old_fval is not None:\n+ candidate_start_value = 1.01 * 2 * (phi_0 - old_old_fval) / dphi_0\n+ start_value = jnp.where(candidate_start_value > 1, 1.0, candidate_start_value)\n+ else:\n+ start_value = 1\ndef wolfe_one(a_i, phi_i):\n# actually negation of W1\n@@ -292,18 +300,12 @@ def line_search(f, xk, pk, old_fval=None, old_old_fval=None, gfk=None, c1=1e-4,\nphi_star=phi_0,\ndphi_star=dphi_0,\ng_star=gfk,\n- saddle_point=False,\n)\ndef body(state):\n# no amax in this version, we just double as in scipy.\n# unlike original algorithm we do our next choice at the start of this loop\n- a_i = jnp.where(state.i == 1, 1., state.a_i1 * 2.)\n- # if a_i <= 0 then something went wrong. In practice any really small step\n- # length is a failure. Likely means the search pk is not good, perhaps we\n- # are at a saddle point.\n- saddle_point = a_i < 1e-5\n- state = state._replace(failed=saddle_point, saddle_point=saddle_point)\n+ a_i = jnp.where(state.i == 1, start_value, state.a_i1 * 2.)\nphi_i, dphi_i, g_i = restricted_func_and_grad(a_i)\nstate = state._replace(nfev=state.nfev + 1,\n@@ -384,25 +386,28 @@ def line_search(f, xk, pk, old_fval=None, old_old_fval=None, gfk=None, c1=1e-4,\nstate)\nstatus = jnp.where(\n- state.failed & (~state.saddle_point),\n+ state.failed,\njnp.array(1), # zoom failed\n- jnp.where(\n- state.failed & state.saddle_point,\n- jnp.array(2), # saddle point reached,\njnp.where(\nstate.i > maxiter,\njnp.array(3), # maxiter reached\njnp.array(0), # passed (should be)\n),\n- ),\n)\n+ # Step sizes which are too small causes the optimizer to get stuck with a\n+ # direction of zero in <64 bit mode - avoid with a floor on minimum step size.\n+ alpha_k = state.a_star\n+ alpha_k = jnp.where((jnp.finfo(alpha_k).bits != 64)\n+ & (jnp.abs(alpha_k) < 1e-8),\n+ jnp.sign(alpha_k) * 1e-8,\n+ alpha_k)\nresults = _LineSearchResults(\nfailed=state.failed | (~state.done),\nnit=state.i - 1, # because iterations started at 1\nnfev=state.nfev,\nngev=state.ngev,\nk=state.i,\n- a_k=state.a_star,\n+ a_k=alpha_k,\nf_k=state.phi_star,\ng_k=state.g_star,\nstatus=status,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/scipy_optimize_test.py",
"new_path": "tests/scipy_optimize_test.py",
"diff": "@@ -57,6 +57,13 @@ def eggholder(np):\nreturn func\n+def zakharovFromIndices(x, ii):\n+ sum1 = (x**2).sum()\n+ sum2 = (0.5*ii*x).sum()\n+ answer = sum1+sum2**2+sum2**4\n+ return answer\n+\n+\nclass TestBFGS(jtu.JaxTestCase):\n@parameterized.named_parameters(jtu.cases_from_list(\n@@ -94,6 +101,37 @@ class TestBFGS(jtu.JaxTestCase):\nresults = jax.scipy.optimize.minimize(f, jnp.ones(n), method='BFGS')\nself.assertAllClose(results.x, jnp.zeros(n), atol=1e-6, rtol=1e-6)\n+ @jtu.skip_on_flag('jax_enable_x64', False)\n+ def test_zakharov(self):\n+ def zakharov_fn(x):\n+ ii = jnp.arange(1, len(x) + 1, step=1)\n+ answer = zakharovFromIndices(x=x, ii=ii)\n+ return answer\n+\n+ x0 = jnp.array([600.0, 700.0, 200.0, 100.0, 90.0, 1e4])\n+ eval_func = jax.jit(zakharov_fn)\n+ jax_res = jax.scipy.optimize.minimize(fun=eval_func, x0=x0, method='BFGS')\n+ self.assertLess(jax_res.fun, 1e-6)\n+\n+ def test_minimize_bad_initial_values(self):\n+ # This test runs deliberately \"bad\" initial values to test that handling\n+ # of failed line search, etc. is the same across implementations\n+ initial_value = jnp.array([92, 0.001])\n+ opt_fn = himmelblau(jnp)\n+ jax_res = jax.scipy.optimize.minimize(\n+ fun=opt_fn,\n+ x0=initial_value,\n+ method='BFGS',\n+ ).x\n+ scipy_res = scipy.optimize.minimize(\n+ fun=opt_fn,\n+ jac=jax.grad(opt_fn),\n+ method='BFGS',\n+ x0=initial_value\n+ ).x\n+ self.assertAllClose(scipy_res, jax_res, atol=2e-5, check_dtypes=False)\n+\n+\ndef test_args_must_be_tuple(self):\nA = jnp.eye(2) * 1e4\ndef f(x):\n"
}
] | Python | Apache License 2.0 | google/jax | Update Jax linesearch to behave more like Scipy |
260,374 | 26.05.2021 19:03:28 | 0 | b68f2c99fd61f518e853ea94868f888e60aeecfa | fixed fingerprint debugging message to be compatible with current min jaxlib version | [
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -898,7 +898,8 @@ def parallel_callable(fun: lu.WrappedFun,\ncompiled = xla.backend_compile(backend, built, compile_options)\nhandle_args = partial(shard_args, compiled.local_devices(), input_indices)\nexecute_fun = partial(execute_replicated, compiled, backend, handle_args, handle_outs)\n- return WeakRefList([execute_fun, compiled.fingerprint])\n+ fingerprint = getattr(compiled, \"fingerprint\", None)\n+ return WeakRefList([execute_fun, fingerprint])\nmulti_host_supported_collectives: Set[core.Primitive] = set()\n"
}
] | Python | Apache License 2.0 | google/jax | fixed fingerprint debugging message to be compatible with current min jaxlib version |
260,563 | 27.05.2021 18:25:18 | -7,200 | 8226dfc8a4974b4c8031ee267fa5327e778140ee | Handle negative values for list-like sections in jnp.split | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/lax_numpy.py",
"new_path": "jax/_src/numpy/lax_numpy.py",
"diff": "@@ -1805,7 +1805,8 @@ def _split(op, ary, indices_or_sections, axis=0):\nsize = ary.shape[axis]\nif isinstance(indices_or_sections, (tuple, list) + _arraylike_types):\nindices_or_sections = np.array(\n- [core.concrete_or_error(np.int64, i_s, f\"in jax.numpy.{op} argument 1\")\n+ [_canonicalize_axis(core.concrete_or_error(np.int64, i_s,\n+ f\"in jax.numpy.{op} argument 1\"), size)\nfor i_s in indices_or_sections], np.int64)\nsplit_indices = np.concatenate([[np.int64(0)], indices_or_sections,\n[np.int64(size)]])\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -2925,8 +2925,8 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\n\"shape\": shape, \"num_sections\": num_sections, \"axis\": axis,\n\"dtype\": dtype}\nfor shape, axis, num_sections in [\n- ((3,), 0, 3), ((12,), 0, 3), ((12, 4), 0, 4), ((12, 4), 1, 2),\n- ((2, 3, 4), -1, 2), ((2, 3, 4), -2, 3)]\n+ ((3,), 0, 3), ((12,), 0, 3), ((12, 4), 0, 4), ((3,), 0, [-1]),\n+ ((12, 4), 1, 2), ((2, 3, 4), -1, 2), ((2, 3, 4), -2, 3)]\nfor dtype in default_dtypes))\ndef testSplitStaticInt(self, shape, num_sections, axis, dtype):\nrng = jtu.rand_default(self.rng())\n"
}
] | Python | Apache License 2.0 | google/jax | Handle negative values for list-like sections in jnp.split |
260,631 | 27.05.2021 20:33:18 | 25,200 | 44b1791b7aed840073d868e251359adefc3c0dd5 | Copybara import of the project:
by Nicholas Junge
Handle negative values for list-like sections in jnp.split | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/lax_numpy.py",
"new_path": "jax/_src/numpy/lax_numpy.py",
"diff": "@@ -1805,8 +1805,7 @@ def _split(op, ary, indices_or_sections, axis=0):\nsize = ary.shape[axis]\nif isinstance(indices_or_sections, (tuple, list) + _arraylike_types):\nindices_or_sections = np.array(\n- [_canonicalize_axis(core.concrete_or_error(np.int64, i_s,\n- f\"in jax.numpy.{op} argument 1\"), size)\n+ [core.concrete_or_error(np.int64, i_s, f\"in jax.numpy.{op} argument 1\")\nfor i_s in indices_or_sections], np.int64)\nsplit_indices = np.concatenate([[np.int64(0)], indices_or_sections,\n[np.int64(size)]])\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_numpy_test.py",
"new_path": "tests/lax_numpy_test.py",
"diff": "@@ -2925,8 +2925,8 @@ class LaxBackedNumpyTests(jtu.JaxTestCase):\n\"shape\": shape, \"num_sections\": num_sections, \"axis\": axis,\n\"dtype\": dtype}\nfor shape, axis, num_sections in [\n- ((3,), 0, 3), ((12,), 0, 3), ((12, 4), 0, 4), ((3,), 0, [-1]),\n- ((12, 4), 1, 2), ((2, 3, 4), -1, 2), ((2, 3, 4), -2, 3)]\n+ ((3,), 0, 3), ((12,), 0, 3), ((12, 4), 0, 4), ((12, 4), 1, 2),\n+ ((2, 3, 4), -1, 2), ((2, 3, 4), -2, 3)]\nfor dtype in default_dtypes))\ndef testSplitStaticInt(self, shape, num_sections, axis, dtype):\nrng = jtu.rand_default(self.rng())\n"
}
] | Python | Apache License 2.0 | google/jax | Copybara import of the project:
--
8226dfc8a4974b4c8031ee267fa5327e778140ee by Nicholas Junge <nicholas.junge@web.de>:
Handle negative values for list-like sections in jnp.split
PiperOrigin-RevId: 376302305 |
260,462 | 28.05.2021 17:45:30 | -3,600 | f30a36dbbcc86e9a4706aab1d23c85d6453d8bb9 | Typo.
Update Common_Gotchas_in_JAX.md | [
{
"change_type": "MODIFY",
"old_path": "docs/notebooks/Common_Gotchas_in_JAX.ipynb",
"new_path": "docs/notebooks/Common_Gotchas_in_JAX.ipynb",
"diff": "\"source\": [\n\"JAX transformation and compilation are designed to work only on Python functions that are functionally pure: all the input data is passed through the function parameters, all the results are output through the function results. A pure function will always return the same result if invoked with the same inputs. \\n\",\n\"\\n\",\n- \"Here are some examples of functions that are not functially pure for which JAX behaves differently than the Python interpreter. Note that these behaviors are not guaranteed by the JAX system; the proper way to use JAX is to use it only on functionally pure Python functions.\"\n+ \"Here are some examples of functions that are not functionally pure for which JAX behaves differently than the Python interpreter. Note that these behaviors are not guaranteed by the JAX system; the proper way to use JAX is to use it only on functionally pure Python functions.\"\n]\n},\n{\n"
},
{
"change_type": "MODIFY",
"old_path": "docs/notebooks/Common_Gotchas_in_JAX.md",
"new_path": "docs/notebooks/Common_Gotchas_in_JAX.md",
"diff": "@@ -52,7 +52,7 @@ rcParams['axes.grid'] = False\nJAX transformation and compilation are designed to work only on Python functions that are functionally pure: all the input data is passed through the function parameters, all the results are output through the function results. A pure function will always return the same result if invoked with the same inputs.\n-Here are some examples of functions that are not functially pure for which JAX behaves differently than the Python interpreter. Note that these behaviors are not guaranteed by the JAX system; the proper way to use JAX is to use it only on functionally pure Python functions.\n+Here are some examples of functions that are not functionally pure for which JAX behaves differently than the Python interpreter. Note that these behaviors are not guaranteed by the JAX system; the proper way to use JAX is to use it only on functionally pure Python functions.\n```{code-cell} ipython3\n:id: A6R-pdcm4u3v\n"
}
] | Python | Apache License 2.0 | google/jax | Typo.
Update Common_Gotchas_in_JAX.md |
260,435 | 31.05.2021 22:18:24 | -32,400 | 160c3e935780d717cad5eee28a40925722237d6b | rename v to vh | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/numpy/linalg.py",
"new_path": "jax/_src/numpy/linalg.py",
"diff": "@@ -329,12 +329,12 @@ def pinv(a, rcond=None):\nmax_rows_cols = max(a.shape[-2:])\nrcond = 10. * max_rows_cols * jnp.finfo(a.dtype).eps\nrcond = jnp.asarray(rcond)\n- u, s, v = svd(a, full_matrices=False)\n+ u, s, vh = svd(a, full_matrices=False)\n# Singular values less than or equal to ``rcond * largest_singular_value``\n# are set to zero.\ncutoff = rcond[..., jnp.newaxis] * jnp.amax(s, axis=-1, keepdims=True, initial=-jnp.inf)\ns = jnp.where(s > cutoff, s, jnp.inf)\n- res = jnp.matmul(_T(v), jnp.divide(_T(u), s[..., jnp.newaxis]))\n+ res = jnp.matmul(_T(vh), jnp.divide(_T(u), s[..., jnp.newaxis]))\nreturn lax.convert_element_type(res, a.dtype)\n"
}
] | Python | Apache License 2.0 | google/jax | rename v to vh |
260,411 | 01.06.2021 14:32:59 | -10,800 | 973171bb6d76e1ae25a00b47e2eed3e1e0f204ca | [jax2tf] Add support for pjit. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -31,6 +31,8 @@ from jax._src.lax import lax\nfrom jax._src.lax import linalg as lax_linalg\nimport jax._src.random\nfrom jax.api_util import flatten_fun\n+from jax.experimental import maps\n+from jax.experimental import pjit\nfrom jax.interpreters import ad\nfrom jax.interpreters import pxla\nfrom jax.interpreters import sharded_jit\n@@ -397,9 +399,6 @@ def _interpret_jaxpr(jaxpr: core.ClosedJaxpr, *args: TfVal) -> Sequence[TfVal]:\nreturn tuple(v for v, _ in out_with_avals)\n-### tracer\n-\n-\ndef _aval_to_tf_shape(aval: core.AbstractValue) -> Tuple[Optional[int], ...]:\n\"\"\"Generate a TF shape, possibly containing None for polymorphic dimensions.\"\"\"\nreturn tuple(\n@@ -729,8 +728,9 @@ class TensorFlowTrace(core.Trace):\ndef process_call(self, call_primitive: core.Primitive, f: lu.WrappedFun,\ntracers: Sequence[TensorFlowTracer], params):\nassert call_primitive.multiple_results\n- vals: Sequence[TfVal] = [t.val for t in tracers]\n- f = _interpret_subtrace(f, self.main, tuple(t.aval for t in tracers))\n+ vals: Sequence[TfVal] = tuple(t.val for t in tracers)\n+ avals: Sequence[core.AbstractValue] = tuple(t.aval for t in tracers)\n+ f = _interpret_subtrace(f, self.main, avals)\nwith core.new_sublevel():\nif call_primitive == core.named_call_p:\nwith tf.name_scope(_sanitize_scope_name(params[\"name\"])):\n@@ -813,6 +813,8 @@ def _unexpected_primitive(p: core.Primitive, *args, **kwargs):\nfor unexpected in xla.call_translations: # Call primitives are inlined\n+ if unexpected is pjit.pjit_p:\n+ continue\ntf_impl[unexpected] = functools.partial(_unexpected_primitive, unexpected)\n# Primitives that are not yet implemented must be explicitly declared here.\n@@ -2494,8 +2496,8 @@ def split_to_logical_devices(tensor: TfVal,\nReturns:\nan annotated tensor.\n\"\"\"\n- # This corresponds to the sharding annotations in\n- # xla_bridge._sharding_to_proto.\n+ # TODO: this is only for sharded_jit. Either remove, or implement in terms\n+ # of _shard_values.\nif partition_dimensions is None:\nreturn xla_sharding.replicate(tensor, use_sharding_op=True)\nnum_partition_splits = np.prod(partition_dimensions)\n@@ -2504,6 +2506,24 @@ def split_to_logical_devices(tensor: TfVal,\nreturn xla_sharding.tile(tensor, tile_assignment, use_sharding_op=True)\n+def _shard_value(mesh: maps.Mesh,\n+ val: TfVal,\n+ aval: core.AbstractValue,\n+ axis_resources: pjit.ParsedPartitionSpec) -> TfVal:\n+ \"\"\"Apply sharding to a TfVal.\"\"\"\n+ sharding_proto: xla_client.OpSharding = pjit.get_sharding_proto_aval(\n+ aval, axis_resources, mesh)\n+ # To use xla_sharding.py, we must have a xla_data_pb2.OpSharding.\n+ xla_sharding_proto: xla_data_pb2.OpSharding = (\n+ xla_data_pb2.OpSharding(\n+ type=int(sharding_proto.type),\n+ tile_assignment_dimensions=sharding_proto.tile_assignment_dimensions,\n+ tile_assignment_devices=sharding_proto.tile_assignment_devices,\n+ replicate_on_last_tile_dim=sharding_proto.replicate_on_last_tile_dim))\n+ return xla_sharding.Sharding(proto=xla_sharding_proto).apply_to_tensor(\n+ val, use_sharding_op=True)\n+\n+\ndef _sharded_call(f: lu.WrappedFun, vals: Sequence[TfVal],\nin_parts: Sequence[pxla.PartitionsOrReplicated],\nout_parts_thunk,\n@@ -2520,12 +2540,51 @@ def _sharded_call(f: lu.WrappedFun, vals: Sequence[TfVal],\nreturn sharded_vals_out\n-def _sharding_constraint(arg: TfVal, *,\n- partitions: pxla.PartitionsOrReplicated):\n+def _sharded_jit_sharding_constraint(arg: TfVal, *,\n+ partitions: pxla.PartitionsOrReplicated,\n+ _in_avals: Sequence[core.ShapedArray],\n+ _out_aval: core.ShapedArray):\n+ del _in_avals, _out_aval\nreturn split_to_logical_devices(arg, partitions)\n-tf_impl[sharded_jit.sharding_constraint_p] = _sharding_constraint\n+tf_impl_with_avals[sharded_jit.sharding_constraint_p] = _sharded_jit_sharding_constraint\n+\n+\n+def _pjit(*args: TfVal,\n+ jaxpr: core.ClosedJaxpr,\n+ in_axis_resources: Sequence[pjit.ParsedPartitionSpec],\n+ out_axis_resources: Sequence[pjit.ParsedPartitionSpec],\n+ resource_env: maps.ResourceEnv,\n+ donated_invars,\n+ name: str,\n+ _in_avals: Sequence[core.ShapedArray],\n+ _out_aval: core.ShapedArray) -> TfVal:\n+ del donated_invars, name\n+ # TODO: add `name` to the name stack\n+ shard_value_for_mesh = functools.partial(_shard_value, resource_env.physical_mesh)\n+ # Apply sharding annotation to the arguments\n+ sharded_args: Sequence[TfVal] = tuple(\n+ util.safe_map(shard_value_for_mesh, args, _in_avals, in_axis_resources))\n+ results = _interpret_jaxpr(jaxpr, *sharded_args)\n+ sharded_results: Sequence[TfVal] = tuple(\n+ util.safe_map(shard_value_for_mesh, results, _out_aval, out_axis_resources))\n+ return tuple(sharded_results)\n+\n+\n+tf_impl_with_avals[pjit.pjit_p] = _pjit\n+\n+\n+def _pjit_sharding_constraint(arg: TfVal, *,\n+ axis_resources: pjit.ParsedPartitionSpec,\n+ resource_env: maps.ResourceEnv,\n+ _in_avals: Sequence[core.ShapedArray],\n+ _out_aval: core.ShapedArray,\n+ **kwargs) -> TfVal:\n+ return _shard_value(resource_env.physical_mesh, arg, _in_avals[0], axis_resources)\n+\n+\n+tf_impl_with_avals[pjit.sharding_constraint_p] = _pjit_sharding_constraint\ndef _register_checkpoint_pytrees():\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/sharding_test.py",
"new_path": "jax/experimental/jax2tf/tests/sharding_test.py",
"diff": "# limitations under the License.\n\"\"\"Tests for the jax2tf conversion of sharded_jit.\"\"\"\n+import contextlib\n+import functools\nimport logging\nimport re\n-from typing import Sequence\n+from typing import Any, Generator, List, Sequence, Tuple\nimport unittest\nfrom absl.testing import absltest\nimport jax\nfrom jax import test_util as jtu\n+from jax import util\nfrom jax.config import config\nfrom jax.experimental import jax2tf\nfrom jax.experimental.jax2tf.tests import tf_test_util\n+from jax.experimental import maps\n+from jax.experimental import pjit\nfrom jax.interpreters import sharded_jit\nfrom jax.interpreters.sharded_jit import PartitionSpec as P\nimport jax.numpy as jnp\n@@ -36,6 +41,13 @@ import tensorflow as tf # type: ignore[import]\nconfig.parse_flags_with_absl()\n+def setUpModule():\n+ jtu.set_spmd_lowering_flag(True)\n+\n+def tearDownModule():\n+ jtu.restore_spmd_lowering_flag()\n+\n+\nLOG_HLO = True\nclass ShardedJitHloTest(tf_test_util.JaxToTfTestCase):\n@@ -46,7 +58,7 @@ class ShardedJitHloTest(tf_test_util.JaxToTfTestCase):\ndef _check_sharding_annotations(self,\nf_jax,\n- args,\n+ args: Sequence[Any],\n*,\nexpected: Sequence[str],\nexpected_opt: Sequence[str],\n@@ -57,6 +69,9 @@ class ShardedJitHloTest(tf_test_util.JaxToTfTestCase):\nWe currently check the unoptimized HLO against `expected` on CPU and TPU,\nand we check the optimized HLO against `expected_opt` on TPU only and\nonly for JAX.\n+\n+ See `self.AssertShardingAnnotations` for documentation of `expected`\n+ and `expected_opt`.\n\"\"\"\nif jtu.device_under_test() == \"gpu\":\nraise unittest.SkipTest(\"Sharding HLO tests not useful for GPU\")\n@@ -93,20 +108,20 @@ class ShardedJitHloTest(tf_test_util.JaxToTfTestCase):\nexperimental_get_compiler_ir(*args)(stage=\"hlo\",\ndevice_name=device_name)\nif LOG_HLO:\n- logging.info(f\"[{self._testMethodName}] got TF OPT HLO {tf_hlo}\")\n+ logging.info(f\"[{self._testMethodName}] got TF HLO {tf_hlo}\")\nself.AssertShardingAnnotations(\"TF before optimizations\", tf_hlo, expected)\ntf_optimized_hlo = tf.function(f_tf, jit_compile=True).\\\nexperimental_get_compiler_ir(*args)(stage=\"optimized_hlo\",\ndevice_name=device_name)\nif LOG_HLO:\n- logging.info(f\"[{self._testMethodName}] XXX got TF OPT HLO \"\n+ logging.info(f\"[{self._testMethodName}] got TF optimized HLO \"\nf\"for {device_name}: {tf_optimized_hlo}\")\ndef AssertShardingAnnotations(self, what: str, hlo: str,\nexpected: Sequence[str]):\n\"\"\"Args:\n- what: either 'JAX' or 'TF'\n+ what: either 'JAX' or 'TF', used for messages only.\nhlo: the text for the HLO module\nexpected: a sequence of regexps that must occur in the hlo text. Each\nregexp must match a line, in order.\n@@ -128,7 +143,7 @@ class ShardedJitHloTest(tf_test_util.JaxToTfTestCase):\nf\"!!! Not found[{next_expected_idx}] {expected[next_expected_idx]}\")\nraise self.failureException(\"\\n\".join(failure_msg))\n- def test_in_out(self):\n+ def test_sharded_jit_in_out(self):\n\"\"\"Test input and output sharding annotations.\"\"\"\nsharded_jax_func = sharded_jit.sharded_jit(\njnp.dot, in_parts=(P(1, 2), P(2, 1)), out_parts=P(1, 2))\n@@ -152,7 +167,7 @@ class ShardedJitHloTest(tf_test_util.JaxToTfTestCase):\n],\nnum_partitions=2)\n- def test_with_sharding_constraint(self):\n+ def test_sharded_jit_with_sharding_constraint(self):\n\"\"\"A sharding constraint in the middle.\"\"\"\ndef jax_func(x, y):\n@@ -180,7 +195,7 @@ class ShardedJitHloTest(tf_test_util.JaxToTfTestCase):\n],\nnum_partitions=2)\n- def test_replicated(self):\n+ def test_sharded_jit_replicated(self):\n\"\"\"A replicated input and output.\"\"\"\nsharded_jax_func = sharded_jit.sharded_jit(\n@@ -203,6 +218,109 @@ class ShardedJitHloTest(tf_test_util.JaxToTfTestCase):\n],\nnum_partitions=2)\n+ @jtu.with_mesh([('x', 2)])\n+ def test_pjit_basic1D(self):\n+\n+ @functools.partial(pjit.pjit,\n+ in_axis_resources=(P('x'), P('x')),\n+ out_axis_resources=None)\n+ def jax_func(x, y):\n+ return x + y\n+\n+ shape = (8, 10)\n+ x = np.arange(np.prod(shape), dtype=np.float32).reshape(shape)\n+ hlo = jax.xla_computation(jax_func)(x, x).as_hlo_text()\n+ print(f\"HLO is {hlo}\")\n+ print(f\"JAXPR is {jax.make_jaxpr(jax_func)(x, x)}\")\n+ self._check_sharding_annotations(\n+ jax_func, [x, x],\n+ expected=[\n+ r\"f32\\[8,10\\].*sharding={devices=\\[2,1\\]\",\n+ r\"f32\\[8,10\\].*sharding={replicated\", # output\n+ ],\n+ expected_opt=[\n+ # TODO(necula): relax ordering\n+ r\"f32\\[4,10\\].*sharding={devices=\\[2,1\\]\",\n+ r\"f32\\[6,4\\].*sharding={devices=\\[1,2\\]\",\n+ ],\n+ num_partitions=2)\n+\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\n+ def test_pjit_basic2D(self):\n+ @functools.partial(pjit.pjit,\n+ in_axis_resources=(P(None, 'x', 'y'), P('y')),\n+ out_axis_resources=P('x'))\n+ def jax_func(x, y):\n+ return x @ y\n+\n+ x_shape = (8, 6, 4)\n+ y_shape = (4, 2)\n+ x = jnp.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)\n+ y = jnp.arange(np.prod(y_shape), dtype=np.float32).reshape(y_shape)\n+ self._check_sharding_annotations(\n+ jax_func, [x, y],\n+ expected=[\n+ r\"f32\\[8,6,4\\].*sharding={devices=\\[1,2,2\\]0,1,2,3\", # arg0\n+ r\"f32\\[4,2\\].*sharding={devices=\\[2,1,2\\]0,2,1,3 last_tile_dim_replicate\", # arg1\n+ r\"f32\\[8,6,2\\].*sharding={devices=\\[2,1,1,2\\]0,1,2,3 last_tile_dim_replicate\", # output\n+ ],\n+ expected_opt=[\n+ # TODO(necula): relax ordering\n+ r\"f32\\[4,10\\].*sharding={devices=\\[2,1\\]\",\n+ r\"f32\\[6,4\\].*sharding={devices=\\[1,2\\]\",\n+ ],\n+ num_partitions=2)\n+\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\n+ def test_pjit_TwoMeshAxisSharding(self):\n+ @functools.partial(pjit.pjit,\n+ in_axis_resources=P(('x', 'y'),),\n+ out_axis_resources=P(('x', 'y'),))\n+ def jax_func(x, y):\n+ return x @ y\n+\n+ x_shape = (24, 8)\n+ y_shape = (8, 2)\n+ x = jnp.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape)\n+ y = jnp.arange(np.prod(y_shape), dtype=np.float32).reshape(y_shape)\n+ self._check_sharding_annotations(\n+ jax_func, [x, y],\n+ expected=[\n+ r\"f32\\[24,8\\].*sharding={devices=\\[4,1\\]0,1,2,3\", # x\n+ r\"f32\\[8,2\\].*sharding={devices=\\[4,1\\]0,1,2,3\", # y\n+ r\"f32\\[24,2\\].*sharding={devices=\\[4,1\\]0,1,2,3\", # output\n+ ],\n+ expected_opt=[\n+ # TODO(necula): relax ordering\n+ r\"f32\\[4,10\\].*sharding={devices=\\[2,1\\]\",\n+ r\"f32\\[6,4\\].*sharding={devices=\\[1,2\\]\",\n+ ],\n+ num_partitions=2)\n+\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\n+ def test_pjit_ShardingConstraint(self):\n+ @functools.partial(pjit.pjit, in_axis_resources=None,\n+ out_axis_resources=None)\n+ def jax_func(x):\n+ y = jnp.tile(x, (2, 1))\n+ y = pjit.with_sharding_constraint(y, P('x', 'y'))\n+ return y * 2\n+\n+ shape = (12, 8)\n+ x = np.arange(np.prod(shape), dtype=np.float32).reshape(shape)\n+ self._check_sharding_annotations(\n+ jax_func, [x],\n+ expected=[\n+ r\"f32\\[12,8\\].*sharding={replicated}\", # x\n+ r\"f32\\[24,8\\].*sharding={devices=\\[2,1\\]0,1\", # y\n+ r\"f32\\[24,8\\].*sharding={replicated}\", # output\n+ ],\n+ expected_opt=[\n+ # TODO(necula): relax ordering\n+ r\"f32\\[4,10\\].*sharding={devices=\\[2,1\\]\",\n+ r\"f32\\[6,4\\].*sharding={devices=\\[1,2\\]\",\n+ ],\n+ num_partitions=2)\nif __name__ == \"__main__\":\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -590,13 +590,20 @@ def get_array_mapping(axis_resources: ParsedPartitionSpec) -> pxla.ArrayMapping:\nfor i, axes in enumerate(axis_resources)\nfor axis in axes)\n-def get_sharding_proto(c, xla_op, axis_resources, mesh):\n+def get_sharding_proto(c, xla_op, axis_resources: ParsedPartitionSpec,\n+ mesh: maps.Mesh) -> xc.OpSharding:\nxla_shape = c.GetShape(xla_op)\nif xla_shape.is_token():\naval = core.abstract_token\nassert axis_resources is REPLICATED\nelse:\naval = core.ShapedArray(xla_shape.dimensions(), xla_shape.element_type())\n+ return get_sharding_proto_aval(aval, axis_resources, mesh)\n+\n+\n+def get_sharding_proto_aval(aval: core.AbstractValue,\n+ axis_resources: ParsedPartitionSpec,\n+ mesh: maps.Mesh) -> xc.OpSharding:\narray_mapping = get_array_mapping(axis_resources)\nsharding_spec = pxla.mesh_sharding_specs(mesh.shape, mesh.axis_names)(\naval, array_mapping)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/test_util.py",
"new_path": "jax/test_util.py",
"diff": "@@ -17,7 +17,7 @@ import functools\nimport re\nimport os\nimport textwrap\n-from typing import Dict, Sequence, Union\n+from typing import Dict, List, Generator, Sequence, Tuple, Union\nimport unittest\nimport warnings\nimport zlib\n@@ -33,10 +33,12 @@ from . import core\nfrom ._src import dtypes as _dtypes\nfrom . import lax\nfrom ._src.config import flags, bool_env, config\n-from ._src.util import partial, prod\n+from ._src.util import partial, prod, unzip2\nfrom .tree_util import tree_multimap, tree_all, tree_map, tree_reduce\nfrom .lib import xla_bridge\nfrom .interpreters import xla\n+from .experimental import maps\n+from .experimental.maps import mesh\nFLAGS = flags.FLAGS\n@@ -1012,6 +1014,44 @@ def ignore_warning(**kw):\nwarnings.filterwarnings(\"ignore\", **kw)\nyield\n+# -------------------- Mesh parametrization helpers --------------------\n+\n+MeshSpec = List[Tuple[str, int]]\n+\n+@contextmanager\n+def with_mesh(named_shape: MeshSpec) -> Generator[None, None, None]:\n+ \"\"\"Test utility for setting up meshes given mesh data from `schedules`.\"\"\"\n+ # This is similar to the `with_mesh` function above, but isn't a decorator.\n+ axis_names, shape = unzip2(named_shape)\n+ size = prod(shape)\n+ local_devices = list(api.local_devices())\n+ if len(local_devices) < size:\n+ raise unittest.SkipTest(f\"Test requires {size} local devices\")\n+ mesh_devices = np.array(local_devices[:size]).reshape(shape)\n+ with mesh(mesh_devices, axis_names):\n+ yield\n+\n+def with_mesh_from_kwargs(f):\n+ return lambda *args, **kwargs: with_mesh(kwargs['mesh'])(f)(*args, **kwargs)\n+\n+def with_and_without_mesh(f):\n+ return parameterized.named_parameters(\n+ {\"testcase_name\": name, \"mesh\": mesh, \"axis_resources\": axis_resources}\n+ for name, mesh, axis_resources in (\n+ ('', (), ()),\n+ ('Mesh', (('x', 2),), (('i', 'x'),))\n+ ))(with_mesh_from_kwargs(f))\n+\n+old_spmd_lowering_flag = False\n+def set_spmd_lowering_flag(val: bool):\n+ global old_spmd_lowering_flag\n+ maps.make_xmap_callable.cache_clear()\n+ old_spmd_lowering_flag = maps.EXPERIMENTAL_SPMD_LOWERING\n+ maps.EXPERIMENTAL_SPMD_LOWERING = val\n+\n+def restore_spmd_lowering_flag():\n+ maps.make_xmap_callable.cache_clear()\n+ maps.EXPERIMENTAL_SPMD_LOWERING = old_spmd_lowering_flag\nclass _cached_property:\nnull = object()\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -39,40 +39,16 @@ config.parse_flags_with_absl()\ndef setUpModule():\n- global old_lowering_flag\n- jax.experimental.maps.make_xmap_callable.cache_clear()\n- old_lowering_flag = jax.experimental.maps.EXPERIMENTAL_SPMD_LOWERING\n- jax.experimental.maps.EXPERIMENTAL_SPMD_LOWERING = True\n+ jtu.set_spmd_lowering_flag(True)\ndef tearDownModule():\n- jax.experimental.maps.make_xmap_callable.cache_clear()\n- jax.experimental.maps.EXPERIMENTAL_SPMD_LOWERING = old_lowering_flag\n-\n-\n-# TODO(skye): move into test_util and dedup with xmap_test.py\n-MeshSpec = List[Tuple[str, int]]\n-\n-@contextmanager\n-def with_mesh(named_shape: MeshSpec) -> Generator[None, None, None]:\n- \"\"\"Test utility for setting up meshes given mesh data from `schedules`.\"\"\"\n- # This is similar to the `with_mesh` function above, but isn't a decorator.\n- axis_names, shape = unzip2(named_shape)\n- size = prod(shape)\n- local_devices = list(jax.local_devices())\n- if len(local_devices) < size:\n- raise SkipTest(f\"Test requires {size} local devices\")\n- mesh_devices = np.array(local_devices[:size]).reshape(shape)\n- with mesh(mesh_devices, axis_names):\n- yield\n-\n-def with_mesh_from_kwargs(f):\n- return lambda *args, **kwargs: with_mesh(kwargs['mesh'])(f)(*args, **kwargs)\n+ jtu.restore_spmd_lowering_flag()\n# TODO(skye): make the buffer donation utils part of JaxTestCase\nclass PJitTest(jtu.BufferDonationTestCase):\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testBasic1D(self):\n@partial(pjit,\nin_axis_resources=(P('x'), P('x')),\n@@ -90,7 +66,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nself.assertAllClose(actual.device_buffers[0].to_py(), expected,\ncheck_dtypes=False)\n- @with_mesh([('x', 2), ('y', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\ndef testBasic2D(self):\n@partial(pjit,\nin_axis_resources=(P(None, 'x', 'y'), P('y')),\n@@ -118,7 +94,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nself.assertAllClose(actual.device_buffers[3].to_py(), split1,\ncheck_dtypes=False)\n- @with_mesh([('x', 2), ('y', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\ndef testTwoMeshAxisSharding(self):\n@partial(pjit,\nin_axis_resources=P(('x', 'y'),),\n@@ -144,7 +120,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nself.assertAllClose(actual.device_buffers[3].to_py(), splits[3],\ncheck_dtypes=False)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testBufferDonation(self):\n@partial(pjit,\nin_axis_resources=P('x'),\n@@ -162,7 +138,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nself.assertNotDeleted(y)\nself.assertDeleted(x)\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testShardingConstraint(self):\n@partial(pjit, in_axis_resources=None, out_axis_resources=None)\ndef f(x):\n@@ -186,7 +162,7 @@ class PJitTest(jtu.BufferDonationTestCase):\n# Annotation from pjit\nself.assertIn(\"sharding={replicated}\", hlo.as_hlo_text())\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testShardingConstraintPyTree(self):\n@partial(pjit, in_axis_resources=None, out_axis_resources=None)\ndef f(x):\n@@ -232,7 +208,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nshould_be_tracing = False\npjit(f, in_axis_resources=P(('x', 'y')), out_axis_resources=None)(x)\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testNested(self):\n# Add a constant captured by the nested pjit to make things more complicated\nh = jnp.arange(4)\n@@ -243,7 +219,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nself.assertAllClose(y, jnp.sin(x).sum() + h.sum())\nself.assertTrue(hasattr(y, \"sharding_spec\"))\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testJVP(self):\n# Add a constant captured by the nested pjit to make things more complicated\nh = jnp.arange(4)\n@@ -252,7 +228,7 @@ class PJitTest(jtu.BufferDonationTestCase):\njtu.check_grads(g, (jnp.arange(16, dtype=jnp.float32).reshape((4, 4)),),\norder=2, modes=[\"fwd\"], eps=1)\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testEvalJaxpr(self):\nx, y = jnp.arange(4), jnp.arange(5)\nf = pjit(lambda x, y: x.sum() + jnp.sin(y),\n@@ -263,13 +239,13 @@ class PJitTest(jtu.BufferDonationTestCase):\nr, = f_eval(x, y)\nself.assertAllClose(r, x.sum() + jnp.sin(y))\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testNonArrayArg(self):\nself.assertEqual(pjit(lambda x: x + 2,\nin_axis_resources=None,\nout_axis_resources=None)(1), 3)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testNonHashableAxisResources(self):\nx = jnp.arange(4)\ny = pjit(lambda x: {'b': x['a'] + 2},\n@@ -277,7 +253,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nout_axis_resources={'b': P('x')})({'a': x})\nself.assertAllClose(y, {'b': x + 2})\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testGradOfConstraint(self):\n# Make sure that we can compute grads through sharding constraints\nh = lambda x: jnp.sin(with_sharding_constraint(x, P('x'))).sum()\n@@ -286,7 +262,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nx = jnp.arange(8, dtype=jnp.float32)\nself.assertAllClose(f(x), jnp.cos(x))\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testNoopPartitionSpecs(self):\nnoops = [P(), P(None), P(()), P((), None), P(None, None, ())]\nx = jnp.arange(8).reshape((2, 2, 2))\n@@ -294,7 +270,7 @@ class PJitTest(jtu.BufferDonationTestCase):\ny = pjit(lambda x: x * 2, in_axis_resources=spec, out_axis_resources=spec)(x)\nself.assertAllClose(y, x * 2)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testVmapModifiesAxisResources(self):\nh = pjit(lambda x, y: (x + y, x, y), in_axis_resources=P('x'), out_axis_resources=None)\nx = jnp.arange(4)\n@@ -310,7 +286,7 @@ class PJitTest(jtu.BufferDonationTestCase):\nself.assertEqual(y_sync, SpecSync.IN_SYNC)\nself.assertEqual(z_sync, SpecSync.DIM_PERMUTE)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testVMap(self):\nf = pjit(lambda x, y: (x + y, x), in_axis_resources=P('x'), out_axis_resources=P('x'))\nx = jnp.arange(4)\n@@ -402,7 +378,7 @@ def check_1d_2d_mesh(f, set_mesh):\n(\"2\", ((\"x\", 2),), \"x\"),\n(\"2x1\", ((\"x\", 2), (\"y\", 1)), (\"x\", \"y\")),\n(\"2x2\", ((\"x\", 2), (\"y\", 2)), (\"x\", \"y\")),\n- ))(with_mesh_from_kwargs(f) if set_mesh else f)\n+ ))(jtu.with_mesh_from_kwargs(f) if set_mesh else f)\ndef spec_regex(s):\nreturn str(s).replace(r\"(\", r\"\\(\").replace(r\")\", r\"\\)\")\n@@ -444,7 +420,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nin_axis_resources=None, out_axis_resources=None)(x)\n@check_1d_2d_mesh(set_mesh=False)\n- @with_mesh([('z', 1)])\n+ @jtu.with_mesh([('z', 1)])\ndef testUndefinedResourcesArgs(self, mesh, resources):\nx = jnp.ones((2, 2))\nspec = P(resources,)\n@@ -454,7 +430,7 @@ class PJitErrorTest(jtu.JaxTestCase):\npjit(lambda x: x, in_axis_resources=spec, out_axis_resources=None)(x)\n@check_1d_2d_mesh(set_mesh=False)\n- @with_mesh([('z', 1)])\n+ @jtu.with_mesh([('z', 1)])\ndef testUndefinedResourcesOuts(self, mesh, resources):\nx = jnp.ones((2, 2))\nspec = P(resources,)\n@@ -464,7 +440,7 @@ class PJitErrorTest(jtu.JaxTestCase):\npjit(lambda x: x, in_axis_resources=None, out_axis_resources=spec)(x)\n@check_1d_2d_mesh(set_mesh=False)\n- @with_mesh([('z', 1)])\n+ @jtu.with_mesh([('z', 1)])\ndef testUndefinedResourcesConstraint(self, mesh, resources):\nx = jnp.ones((2, 2))\nspec = P(resources,)\n@@ -475,7 +451,7 @@ class PJitErrorTest(jtu.JaxTestCase):\npjit(lambda x: with_sharding_constraint(x, spec),\nin_axis_resources=None, out_axis_resources=None)(x)\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testRankTooLowArgs(self):\nx = jnp.arange(2)\nspec = P('x', 'y')\n@@ -484,7 +460,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(ValueError, error):\npjit(lambda x: x.sum(), in_axis_resources=spec, out_axis_resources=None)(x)\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testRankTooLowOuts(self):\nx = jnp.arange(2)\nspec = P('x', 'y')\n@@ -493,7 +469,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(ValueError, error):\npjit(lambda x: x.sum(), in_axis_resources=None, out_axis_resources=spec)(x)\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testRankTooLowConstraint(self):\nx = jnp.arange(2)\nspec = P('x', 'y')\n@@ -504,7 +480,7 @@ class PJitErrorTest(jtu.JaxTestCase):\npjit(lambda x: with_sharding_constraint(x, spec),\nin_axis_resources=None, out_axis_resources=None)(x)\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testRepeatedInResources(self):\nx = jnp.arange(2)\nfor spec in [P('x', 'x'), P('x', ('y', 'x'))]:\n@@ -514,7 +490,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(ValueError, error):\npjit(lambda x: x, in_axis_resources=spec, out_axis_resources=None)(x)\n- @with_mesh([('x', 2), ('y', 1)])\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\ndef testRepeatedOutResources(self):\nx = jnp.arange(2)\nfor spec in [P('x', 'x'), P('x', ('y', 'x'))]:\n@@ -524,7 +500,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(ValueError, error):\npjit(lambda x: x, in_axis_resources=None, out_axis_resources=spec)(x)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testInputShardsXMapAxis(self):\nspec = P('x')\nf = xmap(pjit(lambda x: x + 2, in_axis_resources=spec, out_axis_resources=None),\n@@ -537,7 +513,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nf(x)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testOutputShardsXMapAxis(self):\nspec = P('x')\nf = xmap(pjit(lambda x: x + 2, in_axis_resources=None, out_axis_resources=spec),\n@@ -550,7 +526,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nf(x)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testConstraintShardsXMapAxis(self):\nspec = P('x')\nf = xmap(lambda x: with_sharding_constraint(x, axis_resources=spec),\n@@ -563,7 +539,7 @@ class PJitErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nf(x)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testCatchesInnerXMapErrors(self):\nf = pjit(xmap(lambda x, y: x, in_axes=(['i'], ['j']), out_axes=['i', 'j'],\naxis_resources={'i': 'x', 'j': 'x'}),\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/xmap_test.py",
"new_path": "tests/xmap_test.py",
"diff": "@@ -71,34 +71,6 @@ def tearDownModule():\nos.environ[\"XLA_FLAGS\"] = prev_xla_flags\nxla_bridge.get_backend.cache_clear()\n-# -------------------- Mesh parametrization helpers --------------------\n-\n-MeshSpec = List[Tuple[str, int]]\n-\n-@contextmanager\n-def with_mesh(named_shape: MeshSpec) -> Generator[None, None, None]:\n- \"\"\"Test utility for setting up meshes given mesh data from `schedules`.\"\"\"\n- # This is similar to the `with_mesh` function above, but isn't a decorator.\n- axis_names, shape = unzip2(named_shape)\n- size = prod(shape)\n- local_devices = list(jax.local_devices())\n- if len(local_devices) < size:\n- raise SkipTest(f\"Test requires {size} local devices\")\n- mesh_devices = np.array(local_devices[:size]).reshape(shape)\n- with mesh(mesh_devices, axis_names):\n- yield\n-\n-def with_mesh_from_kwargs(f):\n- return lambda *args, **kwargs: with_mesh(kwargs['mesh'])(f)(*args, **kwargs)\n-\n-def with_and_without_mesh(f):\n- return parameterized.named_parameters(\n- {\"testcase_name\": name, \"mesh\": mesh, \"axis_resources\": axis_resources}\n- for name, mesh, axis_resources in (\n- ('', (), ()),\n- ('Mesh', (('x', 2),), (('i', 'x'),))\n- ))(with_mesh_from_kwargs(f))\n-\n# -------------------- Itertools helpers --------------------\n@@ -135,7 +107,7 @@ def ensure_bdim(x, axis_name, bdim):\nAxisResources = Dict[str, Union[str, Tuple[str, ...]]]\ndef schedules(sizes: Dict[str, int]\n- ) -> Generator[Tuple[AxisResources, MeshSpec], None, None]:\n+ ) -> Generator[Tuple[AxisResources, jtu.MeshSpec], None, None]:\n\"\"\"Test utility generating xmap parallel schedules from logical names & sizes.\nArgs:\n@@ -275,7 +247,7 @@ class XMapTest(XMapTestCase):\nself.assertAllClose(c, a * 2)\nself.assertAllClose(d, b * 4)\n- @with_mesh([('x', 2), ('y', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\ndef testCollectiveReduce(self):\nfm = xmap(lambda a, b: (lax.psum(a * 2, 'a'), b * 4),\nin_axes=[['a', 'b', ...], {0: 'c'}],\n@@ -289,7 +261,7 @@ class XMapTest(XMapTestCase):\nself.assertAllClose(c, (a * 2).sum(0))\nself.assertAllClose(d, b * 4)\n- @with_mesh([('x', 2), ('y', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\ndef testCollectivePermute2D(self):\nperm = np.array([3, 1, 2, 0])\nx = jnp.arange(4).reshape((2, 2))\n@@ -313,7 +285,7 @@ class XMapTest(XMapTestCase):\nin_axes=['i', ...], out_axes=['i', ...])(x)\nself.assertAllClose(result, x + x[jnp.newaxis].T)\n- @with_mesh([('x', 2), ('y', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\ndef testOneLogicalTwoMeshAxesBasic(self):\ndef f(v):\nreturn lax.psum(v * 2, 'a'), v * 4\n@@ -325,7 +297,7 @@ class XMapTest(XMapTestCase):\nself.assertAllClose(ans, (v * 2).sum(0))\nself.assertAllClose(ans2, v.T * 4)\n- @with_mesh([('x', 2), ('y', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\ndef testOneLogicalTwoMeshAxesSharding(self):\ndef f(v):\nreturn v * 4\n@@ -346,7 +318,7 @@ class XMapTest(XMapTestCase):\npxla.ShardingSpec((pxla.NoSharding(), pxla.Chunked((2, 2))),\n(pxla.ShardedAxis(1), pxla.ShardedAxis(0))))\n- @with_mesh([('x', 2), ('y', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\ndef testSkipFirstMeshDim(self):\ndef run(axis_resources):\nreturn xmap(lambda x: x * 2, in_axes=['i', ...], out_axes=['i', ...],\n@@ -379,7 +351,7 @@ class XMapTest(XMapTestCase):\n('OneToOne', (('x', 2), ('y', 2)), (('a', 'y'), ('b', 'x'))),\n('Multiple', (('x', 2), ('y', 2), ('z', 2)), (('a', 'y'), ('b', ('x', 'z')))),\n))\n- @with_mesh_from_kwargs\n+ @jtu.with_mesh_from_kwargs\ndef testNestedMesh(self, mesh, axis_resources):\n@partial(xmap, in_axes={1: 'a'}, out_axes=({0: 'a'}, {}),\naxis_resources=dict([axis_resources[0]]))\n@@ -405,7 +377,7 @@ class XMapTest(XMapTestCase):\n# Make sure that there are non-partial sharding specs in the HLO\nself.assertRegex(hlo, r\"sharding={devices=\\[[0-9,]+\\][0-9,]+}\")\n- @with_and_without_mesh\n+ @jtu.with_and_without_mesh\ndef testMultipleCalls(self, mesh, axis_resources):\ndef f(x, y):\nassert x.shape == y.shape == (3, 5)\n@@ -420,7 +392,7 @@ class XMapTest(XMapTestCase):\nfor i in range(10):\nself.assertAllClose(f_mapped(x, x), expected)\n- @with_and_without_mesh\n+ @jtu.with_and_without_mesh\n@jtu.skip_on_devices(\"cpu\") # In/out aliasing not supported on CPU.\ndef testBufferDonation(self, mesh, axis_resources):\nshard = lambda x: x\n@@ -443,7 +415,7 @@ class XMapTest(XMapTestCase):\nxmap(lambda x: lax.fori_loop(0, 10, lambda _, x: lax.psum(x, 'i'), x),\nin_axes=['i', ...], out_axes=['i', ...])(x)\n- @with_and_without_mesh\n+ @jtu.with_and_without_mesh\ndef testAxisSizes(self, mesh, axis_resources):\nresult = xmap(lambda: lax.axis_index('i'),\nin_axes=(), out_axes=['i', ...],\n@@ -551,7 +523,7 @@ class XMapTest(XMapTestCase):\ny = jnp.arange(20, dtype=jnp.float32).reshape((4, 5)) / 100\njtu.check_grads(f, (x, y), order=2, modes=['fwd'])\n- @with_and_without_mesh\n+ @jtu.with_and_without_mesh\ndef testNamedShape(self, mesh, axis_resources):\nx = np.arange(4,)\ny = 2\n@@ -563,7 +535,7 @@ class XMapTest(XMapTestCase):\nself.assertEqual(z.aval.named_shape, {})\nself.assertEqual(w.aval.named_shape, {})\n- @with_and_without_mesh\n+ @jtu.with_and_without_mesh\ndef testBroadcast(self, mesh, axis_resources):\nx = jnp.asarray(2.0)\nf = xmap(lambda x: x, in_axes={}, out_axes=['i'],\n@@ -590,7 +562,7 @@ class XMapTestSPMD(SPMDTestMixin, XMapTest):\nraise SkipTest\nsuper().setUp()\n- @with_mesh([('x', 2), ('y', 2), ('z', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2), ('z', 2)])\ndef testNestedMeshSPMD(self):\nh = xmap(lambda y: (jnp.sin(y) * np.arange(y.size), lax.psum(y, ('a', 'b', 'c'))),\nin_axes={0: 'c'}, out_axes=({1: 'c'}, {}),\n@@ -670,7 +642,7 @@ class NamedRandomTest(XMapTestCase):\n((f\"_mesh={mesh}_resources={sorted(axis_resources.items())}\",\n{\"axis_resources\": tuple(axis_resources.items()), \"mesh\": tuple(mesh)})\nfor axis_resources, mesh in schedules({'i': 4, 'j': 6})), subset=True)\n- @with_mesh_from_kwargs\n+ @jtu.with_mesh_from_kwargs\ndef testSamplerResourceIndependence(self, distr_sample, axis_resources, mesh):\ndef sample(axis_resources):\nreturn xmap(lambda: distr_sample(jax.random.PRNGKey(0), shape=NamedShape(3, i=4, j=6)),\n@@ -743,7 +715,7 @@ class NewPrimitiveTest(XMapTestCase):\nx_explode = x.reshape((3, 3, 3))\nself.assertAllClose(pgather(x, idx, 0), pgather(x_explode, idx, (0, 1)))\n- @with_and_without_mesh\n+ @jtu.with_and_without_mesh\ndef testGather(self, mesh, axis_resources):\nif axis_resources and not jax.experimental.maps.EXPERIMENTAL_SPMD_LOWERING:\nraise SkipTest(\"pgather over mesh axes without SPMD lowering not implemented\")\n@@ -851,7 +823,7 @@ def gen_axis_names():\ndef schedules_from_pdot_spec(\nspec: PdotTestSpec, lhs_shape: Tuple[int], rhs_shape: Tuple[int]\n- ) -> Generator[Tuple[AxisResources, MeshSpec], None, None]:\n+ ) -> Generator[Tuple[AxisResources, jtu.MeshSpec], None, None]:\nlogical_sizes = {\nname: shape[ax]\nfor shape, in_axes in [(lhs_shape, spec.lhs_in_axes),\n@@ -862,7 +834,7 @@ def schedules_from_pdot_spec(\nclass PDotTests(XMapTestCase):\n- @with_mesh([('r1', 2)])\n+ @jtu.with_mesh([('r1', 2)])\ndef testPdotBasic(self):\ndef f(x, y):\nreturn lax.pdot(x, y, 'i')\n@@ -880,7 +852,7 @@ class PDotTests(XMapTestCase):\nself.assertAllClose(z, jnp.dot(x, y))\n- @with_mesh([('r1', 2)])\n+ @jtu.with_mesh([('r1', 2)])\ndef testPdotBatching(self):\ndef f(x, y):\nreturn lax.pdot(x, y, 'i')\n@@ -898,7 +870,7 @@ class PDotTests(XMapTestCase):\nself.assertAllClose(z, jnp.einsum('nij,njk->nik', x, y))\n- @with_mesh([('r1', 2)])\n+ @jtu.with_mesh([('r1', 2)])\ndef testPdotBatchingShardUncontractedDim(self):\ndef f(x, y):\nreturn lax.pdot(x, y, 'i')\n@@ -945,7 +917,7 @@ class PDotTests(XMapTestCase):\nout_axes=[*pdot_spec.batch_names, ...],\naxis_resources=axis_resources)\n- with with_mesh(mesh_data):\n+ with jtu.with_mesh(mesh_data):\nresult = fun(lhs, rhs)\nexpected = lax.dot_general(lhs, rhs, pdot_spec.dot_general_dim_nums)\n@@ -989,7 +961,7 @@ class PDotTests(XMapTestCase):\nout_axes=(pdot_spec.lhs_in_axes, pdot_spec.rhs_in_axes),\naxis_resources=axis_resources)\n- with with_mesh(mesh_data):\n+ with jtu.with_mesh(mesh_data):\nlhs_bar, rhs_bar = fun(lhs, rhs, out_bar)\ntol = 1e-1 if jtu.device_under_test() == \"tpu\" else None\n@@ -1062,7 +1034,7 @@ class PDotTests(XMapTestCase):\nclass XMapErrorTest(jtu.JaxTestCase):\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testRepeatedAxisResource(self):\ndef f(v):\nreturn v * 4\n@@ -1070,7 +1042,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nfxy = xmap(f, in_axes=['a', ...], out_axes=['a', ...],\naxis_resources={'a': ('x', 'x')})\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testNestedDifferentResources(self):\n@partial(xmap, in_axes={0: 'a'}, out_axes={0: 'a'}, axis_resources={'a': 'x'})\ndef f(x):\n@@ -1089,7 +1061,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(ValueError, \"Failed to infer size of axes: i.\"):\nxmap(lambda x: x, in_axes=['i', ...], out_axes=['i', ...])({})\n- @with_mesh([('x', 2), ('y', 2)])\n+ @jtu.with_mesh([('x', 2), ('y', 2)])\ndef testAxesNotDivisibleByResources(self):\nwith self.assertRaisesRegex(ValueError, r\"Size of axis i \\(5\\) is not divisible.*\"\nr\"\\(\\('x', 'y'\\), 4 in total\\)\"):\n@@ -1154,7 +1126,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(TypeError, error):\nfm(x, y)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testResourceConflictArgs(self):\nfm = xmap(lambda x: lax.psum(x, ('a', 'b')),\nin_axes=['a', 'b'], out_axes=[],\n@@ -1166,7 +1138,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nfm(x)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testResourceConflictInner(self):\nfm = xmap(lambda x, y: x + y,\nin_axes=(['a', ...], ['b', ...]), out_axes=['a', 'b', ...],\n@@ -1178,7 +1150,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nfm(x, y)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testResourceConflictOut(self):\nfm = xmap(lambda x, y: x,\nin_axes=(['a', ...], ['b', ...]), out_axes=['a', 'b', ...],\n@@ -1191,7 +1163,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nfm(x, y)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testResourceConflictNestArgs(self):\nf = xmap(lambda x: x, in_axes=['i'], out_axes=['i'], axis_resources={'i': 'x'})\nh = xmap(f, in_axes=['j', ...], out_axes=['j', ...], axis_resources={'j': 'x'})\n@@ -1202,7 +1174,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nh(x)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testResourceConflictNestInner(self):\nf = xmap(lambda x: lax.axis_index('i') + x,\nin_axes=[], out_axes=['i'], axis_sizes={'i': 4}, axis_resources={'i': 'x'})\n@@ -1214,7 +1186,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nh(x)\n- @with_mesh([('x', 2)])\n+ @jtu.with_mesh([('x', 2)])\ndef testResourceConflictNestOut(self):\nf = xmap(lambda x: x,\nin_axes=[], out_axes=['i'], axis_sizes={'i': 4}, axis_resources={'i': 'x'})\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Add support for pjit. |
260,372 | 01.06.2021 15:28:25 | 21,600 | c28b472ae2eff54fda8c69a4322016bc414cb4ea | Add test to ensure gradient is finite | [
{
"change_type": "MODIFY",
"old_path": "tests/image_test.py",
"new_path": "tests/image_test.py",
"diff": "@@ -324,6 +324,41 @@ class ImageTest(jtu.JaxTestCase):\noutput = jax.jit(jit_fn)(x, scale_a, translation_a)\nself.assertAllClose(output, expected, atol=2e-03)\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\": \"antialias={}\".format(antialias),\n+ \"antialias\": antialias}\n+ for antialias in [True, False]))\n+ def testScaleAndTranslateGradFinite(self, antialias):\n+ image_shape = [1, 6, 7, 1]\n+ target_shape = [1, 3, 3, 1]\n+\n+ data = [\n+ 51, 38, 32, 89, 41, 21, 97, 51, 33, 87, 89, 34, 21, 97, 43, 25, 25, 92,\n+ 41, 11, 84, 11, 55, 111, 23, 99, 50, 83, 13, 92, 52, 43, 90, 43, 14, 89,\n+ 71, 32, 23, 23, 35, 93\n+ ]\n+\n+ x = jnp.array(data, dtype=jnp.float32).reshape(image_shape)\n+ scale_a = jnp.array([1.0, 0.35, 0.4, 1.0], dtype=jnp.float32)\n+ translation_a = jnp.array([0.0, 0.2, 0.1, 0.0], dtype=jnp.float32)\n+\n+ def scale_fn(s):\n+ return jnp.sum(jax.image.scale_and_translate(\n+ x, target_shape, (0, 1, 2, 3), s, translation_a, \"linear\", antialias,\n+ precision=jax.lax.Precision.HIGHEST))\n+\n+ scale_out = jax.grad(scale_fn)(scale_a)\n+ self.assertTrue(jnp.all(jnp.isfinite(scale_out)))\n+\n+ def translate_fn(t):\n+ return jnp.sum(jax.image.scale_and_translate(\n+ x, target_shape, (0, 1, 2, 3), scale_a, t, \"linear\", antialias,\n+ precision=jax.lax.Precision.HIGHEST))\n+\n+ translate_out = jax.grad(translate_fn)(translation_a)\n+ self.assertTrue(jnp.all(jnp.isfinite(translate_out)))\n+\n+\nif __name__ == \"__main__\":\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | Add test to ensure gradient is finite |
260,623 | 01.06.2021 19:14:24 | 25,200 | dca61aa78ff98101ac30737148f6dce03f85c412 | address comments and update documentation | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/README.md",
"new_path": "jax/experimental/jax2tf/README.md",
"diff": "@@ -137,8 +137,12 @@ custom gradients and wrap instead the converted function with\n``tf.raw_ops.PreventGradient`` to generated an error in case a gradient\ncomputation is attempted.\n-Currently, there is a bug that prevents using custom gradients with SavedModel\n-(see [Caveats](#caveats) below).\n+SavedModels enables saving custom derivative rules by using the `experimental_custom_gradients` option:\n+\n+```\n+options = tf.saved_model.SaveOptions(experimental_custom_gradients=True)\n+tf.saved_model.save(model, path, options=options)\n+```\n## Shape-polymorphic conversion\n@@ -478,15 +482,6 @@ in [savedmodel_test.py](https://github.com/google/jax/blob/master/jax/experiment\nThere is currently no support for replicated (e.g. `pmap`) or multi-device\n(e.g. `sharded_jit`) functions. The collective operations are not yet handled.\n-### No SavedModel fine-tuning\n-\n-Currently, TensorFlow SavedModel does not properly save the `tf.custom_gradient`.\n-It does save however some attributes that on model restore result in a warning\n-that the model might not be differentiable, and trigger an error if differentiation\n-is attempted. The plan is to fix this. Note that if no gradients are requested,\n-the PreventGradient ops will be saved along with the converted code and will\n-give a nice error if differentiation of the converted code is attempted.\n-\n### Converting gradients for integer-argument functions\nWhen JAX differentiates over functions with integer arguments, the gradients will\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"new_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"diff": "@@ -38,7 +38,7 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\ntf.saved_model.save(\nmodel,\nmodel_dir,\n- options=tf.saved_model.SaveOptions(custom_gradients=True))\n+ options=tf.saved_model.SaveOptions(experimental_custom_gradients=True))\nrestored_model = tf.saved_model.load(model_dir)\nreturn restored_model\n@@ -98,7 +98,7 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\nwith tf.GradientTape() as tape:\ny = restored_model.f(xv)\nself.assertAllClose(tape.gradient(y, xv).numpy(),\n- jax.grad(f_jax)(0.7).astype(np.float32))\n+ jax.grad(f_jax)(x).astype(np.float32))\ndef _compare_with_saved_model(self, f_jax, *args):\n# Certain ops are converted to ensure an XLA context, e.g.,\n"
}
] | Python | Apache License 2.0 | google/jax | address comments and update documentation |
260,456 | 02.06.2021 04:03:06 | 0 | 012da545f734663c98a0c62d1790ff6266cd4c63 | add gpu to the rocsolver backend | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/linalg.py",
"new_path": "jax/_src/lax/linalg.py",
"diff": "@@ -1029,7 +1029,7 @@ if cusolver is not None:\nif rocsolver is not None:\nxla.backend_specific_translations['gpu'][lu_p] = partial(\n- _lu_cpu_gpu_translation_rule, rocsolver.getrf)\n+ _lu_cpu_gpu_translation_rule, rocsolver.getrf, backend='gpu')\nxla.backend_specific_translations['tpu'][lu_p] = _lu_tpu_translation_rule\n"
}
] | Python | Apache License 2.0 | google/jax | add gpu to the rocsolver backend |
260,411 | 02.06.2021 12:17:03 | -10,800 | ccc1ba701a3f30eaf9028b1adac386fe5a8421d6 | Update savedmodel_test.py
I added a cast to `float32`. This is needed because of an obscure bug in JAX | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"new_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"diff": "@@ -83,7 +83,7 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\nx, = primals\nx_dot, = tangents\nprimal_out = f_jax(x)\n- tangent_out = 3. * x * x_dot\n+ tangent_out = np.float32(3.) * x * x_dot\nreturn primal_out, tangent_out\nmodel = tf.Module()\n"
}
] | Python | Apache License 2.0 | google/jax | Update savedmodel_test.py
I added a cast to `float32`. This is needed because of an obscure bug in JAX (#6874). |
260,411 | 02.06.2021 13:28:48 | -10,800 | 6219b1947e31652c3f3f29034cb2b233804e9b54 | Update savedmodel_test.py
Minor change, just to trigger a copybara re-import. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"new_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"diff": "@@ -36,8 +36,7 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\n# Roundtrip through saved model on disk.\nmodel_dir = os.path.join(absltest.get_default_test_tmpdir(), str(id(model)))\ntf.saved_model.save(\n- model,\n- model_dir,\n+ model, model_dir,\noptions=tf.saved_model.SaveOptions(experimental_custom_gradients=True))\nrestored_model = tf.saved_model.load(model_dir)\nreturn restored_model\n"
}
] | Python | Apache License 2.0 | google/jax | Update savedmodel_test.py
Minor change, just to trigger a copybara re-import. |
260,447 | 02.06.2021 11:37:37 | 25,200 | a02bf592331503258134613ccd7fb297c3f25a1d | Adds associated Legendre functions of the first kind. | [
{
"change_type": "MODIFY",
"old_path": "docs/jax.scipy.rst",
"new_path": "docs/jax.scipy.rst",
"diff": "@@ -99,6 +99,7 @@ jax.scipy.special\nlog_ndtr\nlogit\nlogsumexp\n+ lpmn\nmultigammaln\nndtr\nndtri\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/scipy/special.py",
"new_path": "jax/scipy/special.py",
"diff": "@@ -32,6 +32,7 @@ from jax._src.scipy.special import (\ni1e,\nlogit,\nlogsumexp,\n+ lpmn,\nmultigammaln,\nlog_ndtr,\nndtr,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_scipy_test.py",
"new_path": "tests/lax_scipy_test.py",
"diff": "@@ -239,6 +239,33 @@ class LaxBackedScipyTests(jtu.JaxTestCase):\npartial_xlog1py = functools.partial(lsp_special.xlog1py, 0.)\nself.assertAllClose(api.grad(partial_xlog1py)(-1.), 0., check_dtypes=False)\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\":\n+ \"_maxdegree={}_inputsize={}\".format(l_max, num_z),\n+ \"l_max\": l_max,\n+ \"num_z\": num_z}\n+ for l_max, num_z in zip([1, 2, 3], [6, 7, 8])))\n+ def testLpmn(self, l_max, num_z):\n+ # Points on which the associated Legendre functions areevaluated.\n+ z = np.linspace(-0.2, 0.9, num_z)\n+ actual_p_vals, actual_p_derivatives = lsp_special.lpmn(m=l_max, n=l_max, z=z)\n+\n+ # The expected results are obtained from scipy.\n+ expected_p_vals = np.zeros((l_max + 1, l_max + 1, num_z))\n+ expected_p_derivatives = np.zeros((l_max + 1, l_max + 1, num_z))\n+\n+ for i in range(num_z):\n+ val, derivative = osp_special.lpmn(l_max, l_max, z[i])\n+ expected_p_vals[:, :, i] = val\n+ expected_p_derivatives[:, :, i] = derivative\n+\n+ with self.subTest('Test values.'):\n+ self.assertAllClose(actual_p_vals, expected_p_vals, rtol=1e-6, atol=3.2e-6)\n+\n+ with self.subTest('Test derivatives.'):\n+ self.assertAllClose(actual_p_derivatives,expected_p_derivatives,\n+ rtol=1e-6, atol=8.4e-4)\n+\nif __name__ == \"__main__\":\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | Adds associated Legendre functions of the first kind.
Co-authored-by: Jake VanderPlas <jakevdp@google.com> |
260,287 | 02.06.2021 14:02:47 | 25,200 | ed96e5305f94788d440ab58f7485b9eb5dd40023 | Fix incorrect handling of axis_index_groups in parallel primitive fallbacks | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/parallel.py",
"new_path": "jax/_src/lax/parallel.py",
"diff": "@@ -817,11 +817,18 @@ def _foldaxis(axis, x):\nnew_shape[axis:axis+2] = [x.shape[axis] * x.shape[axis + 1]]\nreturn x.reshape(new_shape)\n+def _index_in_group(axis_name, axis_index_groups):\n+ cur_device_id = axis_index(axis_name)\n+ if axis_index_groups is None:\n+ return cur_device_id\n+ # We use argsort to invert the axis_index_groups permutation\n+ flat_groups = np.array(axis_index_groups).flatten()\n+ device_id_to_idx = flat_groups.argsort() % len(axis_index_groups[0])\n+ return lax.squeeze(lax.dynamic_slice_in_dim(device_id_to_idx, cur_device_id, 1), [0])\n+\ndef _all_to_all_via_all_gather(x, *, axis_name, split_axis, concat_axis, axis_index_groups):\n+ idx = _index_in_group(axis_name, axis_index_groups)\nfull = all_gather(x, axis_name, axis_index_groups=axis_index_groups)\n- idx = axis_index(axis_name)\n- if axis_index_groups:\n- idx = idx % len(axis_index_groups[0])\naxis_size = full.shape[0]\ntile_size = x.shape[split_axis] // axis_size\ntile_base_idx = idx * tile_size\n@@ -1014,11 +1021,7 @@ def all_gather(x, axis_name, *, axis_index_groups=None):\nreturn tree_util.tree_map(bind, x)\ndef _all_gather_via_psum(x, *, all_gather_dimension, axis_name, axis_index_groups, axis_size):\n- index = axis_index(axis_name)\n- if axis_index_groups is not None:\n- indices = np.array(axis_index_groups).flatten()\n- axis_index_to_group_index = indices.argsort() % len(axis_index_groups[0])\n- index = lax_numpy.array(axis_index_to_group_index)[index]\n+ index = _index_in_group(axis_name, axis_index_groups)\nouts = tree_util.tree_map(partial(_expand, all_gather_dimension, axis_size, index), x)\nreturn psum(outs, axis_name, axis_index_groups=axis_index_groups)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pmap_test.py",
"new_path": "tests/pmap_test.py",
"diff": "@@ -561,8 +561,9 @@ class PmapTest(jtu.JaxTestCase):\nif replicas % 2 != 0:\nraise SkipTest(\"Test expected an even number of devices greater than 1.\")\n- axis_index_groups = np.arange(replicas).reshape(\n- 2, replicas // 2).tolist()\n+ axis_index_groups = np.arange(replicas, dtype=np.int32)\n+ axis_index_groups = axis_index_groups.reshape((replicas // 2, 2)).T\n+ axis_index_groups = axis_index_groups.tolist()\nf = lambda x: lax.all_gather(x, 'i', axis_index_groups=axis_index_groups)\nf = pmap(f, 'i')\n@@ -572,11 +573,11 @@ class PmapTest(jtu.JaxTestCase):\nans = f(x)\n- expected_1 = np.broadcast_to(\n- x[:replicas // 2], (replicas // 2, replicas // 2, x.shape[1]))\n- expected_2 = np.broadcast_to(\n- x[replicas // 2:], (replicas // 2, replicas // 2, x.shape[1]))\n- expected = np.concatenate([expected_1, expected_2], 0)\n+ group_1_result = x[0::2]\n+ group_2_result = x[1::2]\n+ expected = np.empty((replicas, replicas // 2, x.shape[1]))\n+ expected[0::2] = group_1_result\n+ expected[1::2] = group_2_result\nself.assertAllClose(ans, expected, check_dtypes=False)\n@@ -1134,8 +1135,8 @@ class PmapTest(jtu.JaxTestCase):\ndef testAllToAllReplicaGroups(self):\n# If num_devices = 4, these would be the inputs/outputs:\n# input = [[0, 1], [2, 3], [4, 5], [6, 7]]\n- # axis_index_groups = [[0, 1], [2, 3]]\n- # output = [[0, 2], [1, 3], [4, 6], [5, 7]]\n+ # axis_index_groups = [[0, 2], [1, 3]]\n+ # output = [[0, 4], [2, 6], [1, 5], [3, 7]]\n#\n# This is essentially like spliting the number of rows in the input in two\n# groups of rows, and swaping the two inner axes (axis=1 and axis=2), which\n@@ -1147,7 +1148,7 @@ class PmapTest(jtu.JaxTestCase):\nx = np.arange(prod(shape)).reshape(shape)\naxis_index_groups = np.arange(device_count, dtype=np.int32)\n- axis_index_groups = axis_index_groups.reshape((2, device_count // 2))\n+ axis_index_groups = axis_index_groups.reshape((device_count // 2, 2)).T\naxis_index_groups = axis_index_groups.tolist()\n@partial(pmap, axis_name='i')\n@@ -1155,8 +1156,8 @@ class PmapTest(jtu.JaxTestCase):\nreturn lax.all_to_all(x, 'i', 0, 0, axis_index_groups=axis_index_groups)\nexpected = np.swapaxes(\n- x.reshape((2, device_count // 2, device_count // 2)),\n- 1, 2).reshape(shape)\n+ x.reshape((device_count // 2, 2, device_count // 2)),\n+ 0, 2).reshape(shape)\nself.assertAllClose(fn(x), expected, check_dtypes=False)\n@ignore_slow_all_to_all_warning()\n"
}
] | Python | Apache License 2.0 | google/jax | Fix incorrect handling of axis_index_groups in parallel primitive fallbacks
PiperOrigin-RevId: 377139424 |
260,411 | 02.06.2021 12:08:15 | -10,800 | 3e9b13b011c6853037c607de570e980253a94c6d | Expanded the type promotion documentation with a confusing case
I filed this as a bug, but I am assuming that it is not easy to fix, so
I also change the documentation. | [
{
"change_type": "MODIFY",
"old_path": "docs/type_promotion.rst",
"new_path": "docs/type_promotion.rst",
"diff": "@@ -130,11 +130,14 @@ Jax's type promotion rules differ from those of NumPy, as given by\n:func:`numpy.promote_types`, in those cells highlighted with a green background\nin the table above. There are three key differences:\n-* when promoting a Python scalar value against a typed JAX value of the same category,\n+* When promoting a Python scalar value against a typed JAX value of the same category,\nJAX always prefers the precision of the JAX value. For example, ``jnp.int16(1) + 1``\n- will return ``int16`` rather than promoting to ``int64`` as in Numpy.\n+ will return ``int16`` rather than promoting to ``int64`` as in NumPy.\n+ Note that this applies only to Python scalar values; if the constant is a NumPy\n+ array then the above lattice is used for type promotion.\n+ For example, ``jnp.int16(1) + np.array(1)`` will return ``int64``.\n-* when promoting an integer or boolean type against a floating-point or complex\n+* When promoting an integer or boolean type against a floating-point or complex\ntype, JAX always prefers the type of the floating-point or complex type.\n* JAX supports the\n@@ -144,7 +147,7 @@ in the table above. There are three key differences:\nThe only notable promotion behavior is with respect to IEEE-754\n:code:`float16`, with which :code:`bfloat16` promotes to a :code:`float32`.\n-These differences are motivated by the fact that\n+The differences between NumPy and JAX are motivated by the fact that\naccelerator devices, such as GPUs and TPUs, either pay a significant\nperformance penalty to use 64-bit floating point types (GPUs) or do not\nsupport 64-bit floating point types at all (TPUs). Classic NumPy's promotion\n@@ -155,3 +158,11 @@ JAX uses floating point promotion rules that are more suited to modern\naccelerator devices and are less aggressive about promoting floating point\ntypes. The promotion rules used by JAX for floating-point types are similar to\nthose used by PyTorch.\n+\n+Note that operators like `+` will dispatch based on the Python type of the two\n+values being added. This means that, for example, `np.int16(1) + 1` will\n+promote using NumPy rules, whereas `jnp.int16(1) + 1` will promote using JAX rules.\n+This can lead to potentially confusing non-associative promotion semantics when\n+the two types of promotion are combined;\n+for example with `np.int16(1) + 1 + jnp.int16(1)`.\n+\n"
}
] | Python | Apache License 2.0 | google/jax | Expanded the type promotion documentation with a confusing case
I filed this as a bug, but I am assuming that it is not easy to fix, so
I also change the documentation.
Bug: 6874 |
260,287 | 03.06.2021 04:13:02 | 25,200 | bca3d61b3b0dc917aff1e5e70346dc60f1119a47 | Insert xmap SPMD axes into pjit sharding annotations
This should let us emit good XLA annotations for `xmap(pjit)`. Previously
we might have been overestimating the set of replicated mesh dimensions. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -24,7 +24,6 @@ from functools import wraps, partial, partialmethod\nfrom .. import numpy as jnp\nfrom .. import core\n-from .. import config\nfrom .. import linear_util as lu\nfrom .._src.api import _check_callable, _check_arg\nfrom ..tree_util import (tree_flatten, tree_unflatten, all_leaves, tree_map,\n@@ -33,6 +32,7 @@ from .._src.tree_util import _replace_nones\nfrom ..api_util import (flatten_fun_nokwargs, flatten_axes, _ensure_index_tuple,\ndonation_vector)\nfrom .._src import source_info_util\n+from ..config import config\nfrom ..errors import JAXTypeError\nfrom ..interpreters import partial_eval as pe\nfrom ..interpreters import pxla\n@@ -939,7 +939,7 @@ def _batch_trace_process_xmap(self, is_spmd, primitive, f: lu.WrappedFun, tracer\ndims_out = dims_out_thunk()\nreturn [batching.BatchTracer(self, v, d) for v, d in zip(vals_out, dims_out)]\nbatching.BatchTrace.process_xmap = partialmethod(_batch_trace_process_xmap, False) # type: ignore\n-batching.SPMDBatchTrace.process_xmap = partialmethod(_batch_trace_process_xmap, True) # type: ignore\n+pxla.SPMDBatchTrace.process_xmap = partialmethod(_batch_trace_process_xmap, True) # type: ignore\ndef _xmap_initial_to_final_params(params):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/pjit.py",
"new_path": "jax/experimental/pjit.py",
"diff": "@@ -449,7 +449,8 @@ def _pjit_translation_rule(c, axis_env, in_nodes, name_stack, backend, name,\nxla.call_translations[pjit_p] = _pjit_translation_rule\n-def _pjit_batcher(vals_in, dims_in,\n+def _pjit_batcher(insert_axis,\n+ vals_in, dims_in,\naxis_name, main_type,\njaxpr, in_axis_resources, out_axis_resources,\nresource_env, donated_invars, name):\n@@ -462,11 +463,12 @@ def _pjit_batcher(vals_in, dims_in,\njaxpr, axis_size, is_mapped_in,\ninstantiate=False, axis_name=axis_name, main_type=main_type)\n+ new_parts = (axis_name,) if insert_axis else ()\nin_axis_resources = tuple(\n- spec.insert_axis_partitions(0, ()) if is_mapped else spec\n+ spec.insert_axis_partitions(0, new_parts) if is_mapped else spec\nfor is_mapped, spec in zip(is_mapped_in, in_axis_resources))\nout_axis_resources = tuple(\n- spec.insert_axis_partitions(0, ()) if is_mapped else spec\n+ spec.insert_axis_partitions(0, new_parts) if is_mapped else spec\nfor is_mapped, spec in zip(is_mapped_out, out_axis_resources))\nvals_out = pjit_p.bind(\n*vals_in,\n@@ -478,7 +480,8 @@ def _pjit_batcher(vals_in, dims_in,\nname=name)\ndims_out = [0 if batched else batching.not_mapped for batched in is_mapped_out]\nreturn vals_out, dims_out\n-batching.initial_style_batchers[pjit_p] = _pjit_batcher\n+batching.initial_style_batchers[pjit_p] = partial(_pjit_batcher, False)\n+pxla.spmd_primitive_batchers[pjit_p] = partial(_pjit_batcher, True)\ndef _pjit_jvp(primals_in, tangents_in,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/batching.py",
"new_path": "jax/interpreters/batching.py",
"diff": "@@ -121,6 +121,17 @@ class BatchTrace(Trace):\ndef sublift(self, val):\nreturn BatchTracer(self, val.val, val.batch_dim)\n+ def get_primitive_batcher(self, primitive):\n+ if primitive in initial_style_batchers:\n+ return partial(initial_style_batchers[primitive],\n+ axis_name=self.axis_name,\n+ main_type=self.main.trace_type)\n+ try:\n+ return primitive_batchers[primitive]\n+ except KeyError as err:\n+ msg = \"Batching rule for '{}' not implemented\"\n+ raise NotImplementedError(msg.format(primitive)) from err\n+\ndef process_primitive(self, primitive, tracers, params):\nvals_in, dims_in = unzip2((t.val, t.batch_dim) for t in tracers)\nif (primitive in collective_rules and\n@@ -130,7 +141,7 @@ class BatchTrace(Trace):\nelif all(bdim is not_mapped for bdim in dims_in):\nreturn primitive.bind(*vals_in, **params)\nelse:\n- batched_primitive = get_primitive_batcher(primitive, self)\n+ batched_primitive = self.get_primitive_batcher(primitive)\nval_out, dim_out = batched_primitive(vals_in, dims_in, **params)\nif primitive.multiple_results:\nreturn map(partial(BatchTracer, self), val_out, dim_out)\n@@ -247,9 +258,6 @@ class BatchTrace(Trace):\npost_process_custom_vjp_call = post_process_custom_jvp_call\n-class SPMDBatchTrace(BatchTrace):\n- pass\n-\ndef _main_trace_for_axis_names(main_trace: core.MainTrace,\naxis_name: Iterable[core.AxisName],\n) -> bool:\n@@ -327,17 +335,6 @@ BatchingRule = Callable[..., Tuple[Any, Union[int, Tuple[int, ...]]]]\nprimitive_batchers : Dict[core.Primitive, BatchingRule] = {}\ninitial_style_batchers : Dict[core.Primitive, Any] = {}\n-def get_primitive_batcher(p, trace):\n- if p in initial_style_batchers:\n- return partial(initial_style_batchers[p],\n- axis_name=trace.axis_name,\n- main_type=trace.main.trace_type)\n- try:\n- return primitive_batchers[p]\n- except KeyError as err:\n- msg = \"Batching rule for '{}' not implemented\"\n- raise NotImplementedError(msg.format(p)) from err\n-\ndef defvectorized(prim):\nprimitive_batchers[prim] = partial(vectorized_batcher, prim)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -1394,6 +1394,18 @@ def untile_aval_nd(axis_sizes, out_axes: ArrayMapping, aval):\nnamed_shape.pop(name, None) # The name might be missing --- it's a broadcast.\nreturn aval.update(shape=tuple(shape), named_shape=named_shape)\n+\n+class SPMDBatchTrace(batching.BatchTrace):\n+ def get_primitive_batcher(self, primitive):\n+ if primitive in spmd_primitive_batchers:\n+ return partial(spmd_primitive_batchers[primitive],\n+ axis_name=self.axis_name,\n+ main_type=self.main.trace_type)\n+ return super().get_primitive_batcher(primitive)\n+\n+spmd_primitive_batchers: Dict[core.Primitive, Callable] = {}\n+\n+\ndef vtile_by_mesh(fun: lu.WrappedFun,\nmesh: Mesh,\nin_axes: Sequence[ArrayMapping],\n@@ -1408,7 +1420,7 @@ def vtile_by_mesh(fun: lu.WrappedFun,\ntuple(a.get(name, None) for a in out_axes),\ntile_size=size,\naxis_name=name,\n- main_type=batching.SPMDBatchTrace)\n+ main_type=SPMDBatchTrace)\nreturn fun\ndef mesh_callable(fun: lu.WrappedFun,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/pjit_test.py",
"new_path": "tests/pjit_test.py",
"diff": "@@ -30,6 +30,7 @@ from jax.experimental import PartitionSpec as P\nfrom jax.experimental.maps import xmap, mesh\nfrom jax.experimental.pjit import pjit, pjit_p, with_sharding_constraint, SpecSync\nfrom jax.interpreters import pxla\n+from jax.interpreters import xla\nfrom jax._src.util import prod, curry\nfrom jax.config import config\n@@ -295,6 +296,29 @@ class PJitTest(jtu.BufferDonationTestCase):\nself.assertEqual(z.sharding_spec.sharding, (pxla.NoSharding(), pxla.Chunked([2])))\nself.assertEqual(w.sharding_spec.sharding, (pxla.Chunked([2]),))\n+ @jtu.with_mesh([('x', 2), ('y', 1)])\n+ def testShardingInXMap(self):\n+ h = pjit(lambda x: x, in_axis_resources=P('x'), out_axis_resources=None)\n+ f = xmap(lambda x: h(x * 2), in_axes=['i', ...], out_axes=['i', ...],\n+ axis_resources={'i': 'y'})\n+ x = jnp.arange(16).reshape((4, 4))\n+ self.assertIn(pjit_p, xla.call_translations)\n+ rule = xla.call_translations[pjit_p]\n+ test_rule_called = False\n+ def _test_rule(*args, **kwargs):\n+ nonlocal test_rule_called\n+ test_rule_called = True\n+ in_axis_resources = kwargs['in_axis_resources']\n+ self.assertEqual(len(in_axis_resources), 1)\n+ self.assertIn(('y',), in_axis_resources[0].partitions)\n+ return rule(*args, **kwargs)\n+ try:\n+ xla.call_translations[pjit_p] = _test_rule\n+ f(x)\n+ self.assertTrue(test_rule_called)\n+ finally:\n+ xla.call_translations[pjit_p] = rule\n+\ndef testInfeed(self):\ndevices = np.array(jax.local_devices())\nnr_devices = len(devices)\n"
}
] | Python | Apache License 2.0 | google/jax | Insert xmap SPMD axes into pjit sharding annotations
This should let us emit good XLA annotations for `xmap(pjit)`. Previously
we might have been overestimating the set of replicated mesh dimensions.
PiperOrigin-RevId: 377259226 |
260,287 | 03.06.2021 06:36:57 | 25,200 | c7a98b3b6216b4fa764e94998cad09f14d488094 | Fix a typo in shape checks for associative_scan
Fixes | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/control_flow.py",
"new_path": "jax/_src/lax/control_flow.py",
"diff": "@@ -2510,7 +2510,7 @@ def associative_scan(fn: Callable, elems, reverse: bool = False, axis: int = 0):\nif not all(int(elem.shape[axis]) == num_elems for elem in elems_flat[1:]):\nraise ValueError('Array inputs to associative_scan must have the same '\n'first dimension. (saw: {})'\n- .format([elems.shape for elem in elems_flat]))\n+ .format([elem.shape for elem in elems_flat]))\n# Summary of algorithm:\n"
}
] | Python | Apache License 2.0 | google/jax | Fix a typo in shape checks for associative_scan
Fixes #6884.
PiperOrigin-RevId: 377276183 |
260,411 | 04.06.2021 15:29:54 | -10,800 | ede457f1a5d93136c8a87c0afbf6ab5ee757e677 | [jax2tf] Fix bug with max_int for uint64 | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -76,8 +76,10 @@ PrecisionType = int # Enum xla_data.PrecisionConfig.Precision\ndef _is_tfval(v: TfVal) -> bool:\n+ if isinstance(v, (tf.Tensor, tf.Variable)):\n+ return True\ntry:\n- tf.convert_to_tensor(v)\n+ tf.constant(v)\nreturn True\nexcept:\nreturn False\n@@ -1755,8 +1757,6 @@ def _common_reduce_window(operand, init_val, reducer, window_dimensions,\nreducer, autograph=False).get_concrete_function(o_spec, o_spec)\nif not isinstance(init_val, tf.Tensor):\n- assert not config.jax_enable_checks or _is_tfval(\n- init_val), f\"Non TfVal: {init_val}\"\ninit_val = tf.constant(init_val, operand.dtype)\nout = tfxla.reduce_window(\noperand,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/jax2tf_limitations.py",
"new_path": "jax/experimental/jax2tf/tests/jax2tf_limitations.py",
"diff": "@@ -987,9 +987,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\nassert \"min\" == harness.params[\"computation\"].__name__\nreturn [\nmissing_tf_kernel(dtypes=[np.bool_, np.complex64, np.complex128]),\n- missing_tf_kernel(dtypes=[np.uint64],\n- devices=(\"cpu\", \"gpu\"),\n- modes=(\"eager\",)),\n]\n@classmethod\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Fix bug with max_int for uint64 |
260,411 | 04.06.2021 11:02:50 | -10,800 | d243258b86fc5e33405925acc794801b65fa0137 | [jax2tf] Implement inequalities and friends for complex numbers.
This requires re-using JAX's lowering rule for comparisons of
complex numbers to use lexicographic comparison. | [
{
"change_type": "MODIFY",
"old_path": "CHANGELOG.md",
"new_path": "CHANGELOG.md",
"diff": "@@ -16,6 +16,9 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.\ntracebacks.\n* A new traceback filtering mode using `__tracebackhide__` is now enabled by\ndefault in sufficiently recent versions of IPython.\n+ * The {func}`jax2tf.convert` supports shape polymorphism even when the\n+ unknown dimensions are used in arithmetic operations, e.g., `jnp.reshape(-1)`\n+ ({jax-issue}`#6827`).\n* Breaking changes:\n@@ -31,6 +34,8 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.\n* The {func}`jax2tf.convert` now converts `lax.dot_general` using the\n`XlaDot` TensorFlow op, for better fidelity w.r.t. JAX numerical precision\n({jax-issue}`#6717`).\n+ * The {func}`jax2tf.convert` now has support for inequality comparisons and\n+ min/max for complex numbers ({jax-issue}`#6892`).\n## jaxlib 0.1.67 (unreleased)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/lax.py",
"new_path": "jax/_src/lax/lax.py",
"diff": "@@ -2783,27 +2783,35 @@ def _broadcasting_select(c, which, x, y):\nreturn xops.Select(which, x, y)\n-def _minmax_translation_rule(c, x, y, *, minmax=None, cmp=None):\n+def _minmax_complex_lowering(x, y, *, lax_cmp_pick_x):\n+ result_shape = broadcast_shapes(np.shape(x), np.shape(y))\n+ x = _maybe_broadcast(result_shape, x)\n+ y = _maybe_broadcast(result_shape, y)\n+ rx = real(x)\n+ ry = real(y)\n+ pick_x = select(eq(rx, ry), lax_cmp_pick_x(imag(x), imag(y)),\n+ lax_cmp_pick_x(rx, ry))\n+ return select(pick_x, x, y)\n+\n+def _minmax_translation_rule(c, x, y, *, op_minmax=None, lax_cmp_pick_x=None):\ndtype = c.get_shape(x).numpy_dtype()\nif dtypes.issubdtype(dtype, np.complexfloating):\n- rx = xops.Real(x)\n- ry = xops.Real(y)\n- return _broadcasting_select(\n- c, xops.Select(xops.Eq(rx, ry), cmp(xops.Imag(x), xops.Imag(y)),\n- cmp(rx, ry)),\n- x, y)\n- return minmax(x, y)\n+ return xla.lower_fun(partial(_minmax_complex_lowering,\n+ lax_cmp_pick_x=lax_cmp_pick_x),\n+ multiple_results=False)(c, x, y)\n+ else:\n+ return op_minmax(x, y)\nmax_p: core.Primitive = standard_naryop(\n[_any, _any], 'max', translation_rule=partial(\n- _minmax_translation_rule, minmax=xops.Max, cmp=xops.Gt))\n+ _minmax_translation_rule, op_minmax=xops.Max, lax_cmp_pick_x=gt))\nad.defjvp2(max_p,\nlambda g, ans, x, y: mul(g, _balanced_eq(x, ans, y)),\nlambda g, ans, x, y: mul(g, _balanced_eq(y, ans, x)))\nmin_p: core.Primitive = standard_naryop(\n[_any, _any], 'min', translation_rule=partial(\n- _minmax_translation_rule, minmax=xops.Min, cmp=xops.Lt))\n+ _minmax_translation_rule, op_minmax=xops.Min, lax_cmp_pick_x=lt))\nad.defjvp2(min_p,\nlambda g, ans, x, y: mul(g, _balanced_eq(x, ans, y)),\nlambda g, ans, x, y: mul(g, _balanced_eq(y, ans, x)))\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md",
"new_path": "jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md",
"diff": "# Primitives with limited JAX support\n-*Last generated on: 2021-05-17* (YYYY-MM-DD)\n+*Last generated on: 2021-06-04* (YYYY-MM-DD)\n## Supported data types for primitives\n-We use a set of 2507 test harnesses to test\n+We use a set of 2570 test harnesses to test\nthe implementation of 121 numeric JAX primitives.\nWe consider a JAX primitive supported for a particular data\ntype if it is supported on at least one device type.\n@@ -60,7 +60,7 @@ be updated.\n| broadcast_in_dim | 19 | all | |\n| ceil | 4 | floating | bool, complex, integer |\n| cholesky | 30 | inexact | bool, integer |\n-| clamp | 17 | floating, integer | bool, complex |\n+| clamp | 20 | all | |\n| complex | 4 | float32, float64 | bfloat16, bool, complex, float16, integer |\n| concatenate | 17 | all | |\n| conj | 5 | complex, float32, float64 | bfloat16, bool, float16, integer |\n@@ -90,28 +90,28 @@ be updated.\n| fft | 20 | complex, float32, float64 | bfloat16, bool, float16, integer |\n| floor | 4 | floating | bool, complex, integer |\n| gather | 37 | all | |\n-| ge | 15 | bool, floating, integer | complex |\n-| gt | 15 | bool, floating, integer | complex |\n+| ge | 17 | all | |\n+| gt | 17 | all | |\n| igamma | 6 | floating | bool, complex, integer |\n| igammac | 6 | floating | bool, complex, integer |\n| imag | 2 | complex | bool, floating, integer |\n| integer_pow | 108 | inexact, integer | bool |\n| iota | 16 | inexact, integer | bool |\n| is_finite | 4 | floating | bool, complex, integer |\n-| le | 15 | bool, floating, integer | complex |\n+| le | 17 | all | |\n| lgamma | 4 | floating | bool, complex, integer |\n| log | 6 | inexact | bool, integer |\n| log1p | 6 | inexact | bool, integer |\n-| lt | 15 | bool, floating, integer | complex |\n+| lt | 17 | all | |\n| lu | 18 | inexact | bool, integer |\n-| max | 29 | all | |\n-| min | 29 | all | |\n+| max | 33 | all | |\n+| min | 33 | all | |\n| mul | 16 | inexact, integer | bool |\n| ne | 17 | all | |\n| neg | 14 | inexact, integer | bool |\n| nextafter | 6 | floating | bool, complex, integer |\n| or | 11 | bool, integer | inexact |\n-| pad | 90 | all | |\n+| pad | 120 | all | |\n| population_count | 8 | integer | bool, inexact |\n| pow | 10 | inexact | bool, integer |\n| qr | 60 | inexact | bool, integer |\n@@ -144,7 +144,7 @@ be updated.\n| shift_left | 10 | integer | bool, inexact |\n| shift_right_arithmetic | 10 | integer | bool, inexact |\n| shift_right_logical | 10 | integer | bool, inexact |\n-| sign | 14 | inexact, integer | bool |\n+| sign | 28 | inexact, integer | bool |\n| sin | 6 | inexact | bool, integer |\n| sinh | 6 | inexact | bool, integer |\n| slice | 24 | all | |\n@@ -184,6 +184,7 @@ and search for \"limitation\".\n| Affected primitive | Description of limitation | Affected dtypes | Affected devices |\n| --- | --- | --- | --- |\n|cholesky|unimplemented|float16|cpu, gpu|\n+|clamp|unimplemented|bool, complex|cpu, gpu, tpu|\n|conv_general_dilated|preferred_element_type not implemented for integers|int16, int32, int8|gpu|\n|conv_general_dilated|preferred_element_type=c128 not implemented|complex64|tpu|\n|conv_general_dilated|preferred_element_type=f64 not implemented|bfloat16, float16, float32|tpu|\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md",
"new_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md",
"diff": "# Primitives with limited support for jax2tf\n-*Last generated on (YYYY-MM-DD): 2021-06-01*\n+*Last generated on (YYYY-MM-DD): 2021-06-04*\nThis document summarizes known limitations of the jax2tf conversion.\nThere are several kinds of limitations.\n@@ -34,10 +34,7 @@ On TPU only the \"compiled\" mode is relevant.\nOur priority is to ensure same coverage and numerical behavior with JAX\nin the \"compiled\" mode, **when using XLA to compile the converted program**.\n-We are pretty close to that goal. In addition to a few loose ends, there is a known\n-coverage problem due to JAX and XLA supporting inequality comparisons and min/max for\n-booleans and complex numbers. It is not clear that TensorFlow will be extended to\n-support these.\n+We are pretty close to that goal.\nThis table only shows errors for cases that are working in JAX (see [separate\nlist of unsupported or partially-supported primitives](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md) )\n@@ -67,6 +64,7 @@ More detailed information can be found in the\n| cholesky | TF test skipped: Not implemented in JAX: unimplemented | float16 | cpu, gpu | compiled, eager, graph |\n| cholesky | TF error: function not compilable | complex | cpu, gpu | compiled |\n| cholesky | TF error: op not defined for dtype | complex | tpu | compiled, graph |\n+| clamp | TF test skipped: Not implemented in JAX: unimplemented | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n| clamp | TF error: op not defined for dtype | complex | cpu, gpu, tpu | compiled, eager, graph |\n| conv_general_dilated | TF test skipped: Not implemented in JAX: preferred_element_type not implemented for integers | int16, int32, int8 | gpu | compiled, eager, graph |\n| conv_general_dilated | TF test skipped: Not implemented in JAX: preferred_element_type=c128 not implemented | complex64 | tpu | compiled, eager, graph |\n@@ -104,17 +102,16 @@ More detailed information can be found in the\n| lt | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| lu | TF test skipped: Not implemented in JAX: unimplemented | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n| lu | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n-| max | TF error: op not defined for dtype | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n-| min | TF error: op not defined for dtype | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n+| max | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n+| min | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| neg | TF error: op not defined for dtype | unsigned | cpu, gpu, tpu | compiled, eager, graph |\n| nextafter | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n| qr | TF test skipped: Not implemented in JAX: unimplemented | bfloat16, float16 | cpu, gpu | compiled, eager, graph |\n| qr | TF error: op not defined for dtype | bfloat16 | tpu | compiled, eager, graph |\n| reduce_max | TF error: op not defined for dtype | complex | cpu, gpu, tpu | compiled, eager, graph |\n| reduce_min | TF error: op not defined for dtype | complex | cpu, gpu, tpu | compiled, eager, graph |\n-| reduce_window_max | TF error: op not defined for dtype | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n-| reduce_window_min | TF error: op not defined for dtype | uint64 | cpu, gpu | eager |\n-| reduce_window_min | TF error: op not defined for dtype | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n+| reduce_window_max | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n+| reduce_window_min | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| regularized_incomplete_beta | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n| rem | TF error: TF integer division fails if divisor contains 0; JAX returns NaN | integer | cpu, gpu, tpu | compiled, eager, graph |\n| round | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n@@ -122,9 +119,9 @@ More detailed information can be found in the\n| scatter_add | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_add | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n| scatter_max | TF test skipped: Not implemented in JAX: unimplemented | complex64 | tpu | compiled, eager, graph |\n-| scatter_max | TF error: op not defined for dtype | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n+| scatter_max | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_min | TF test skipped: Not implemented in JAX: unimplemented | complex64 | tpu | compiled, eager, graph |\n-| scatter_min | TF error: op not defined for dtype | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n+| scatter_min | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_mul | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_mul | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n| select_and_gather_add | TF error: jax2tf unimplemented for 64-bit inputs because the current implementation relies on packing two values into a single value. This can be fixed by using a variadic XlaReduceWindow, when available | float64 | cpu, gpu | compiled, eager, graph |\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md.template",
"new_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md.template",
"diff": "@@ -34,10 +34,7 @@ On TPU only the \"compiled\" mode is relevant.\nOur priority is to ensure same coverage and numerical behavior with JAX\nin the \"compiled\" mode, **when using XLA to compile the converted program**.\n-We are pretty close to that goal. In addition to a few loose ends, there is a known\n-coverage problem due to JAX and XLA supporting inequality comparisons and min/max for\n-booleans and complex numbers. It is not clear that TensorFlow will be extended to\n-support these.\n+We are pretty close to that goal.\nThis table only shows errors for cases that are working in JAX (see [separate\nlist of unsupported or partially-supported primitives](https://github.com/google/jax/blob/master/jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md) )\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Experimental module transforms JAX functions to be executed by TensorFlow.\"\"\"\n-import functools\n+from functools import partial\nimport re\nimport string\nfrom typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union\n@@ -116,7 +116,7 @@ def _xla_disabled_error(primitive_name: str,\nmsg += f\" {extra_msg}\"\nreturn NotImplementedError(msg)\n-@functools.partial(api_util.api_hook, tag=\"jax2tf_convert\")\n+@partial(api_util.api_hook, tag=\"jax2tf_convert\")\ndef convert(fun: Callable,\n*,\npolymorphic_shapes: Optional[Sequence[Any]] = None,\n@@ -293,8 +293,7 @@ def convert(fun: Callable,\nout_with_avals = _interpret_fun(flat_fun, args_flat, args_avals_flat)\nouts, out_avals = util.unzip2(out_with_avals)\nreturn (tuple(outs),\n- functools.partial(\n- converted_grad_fn, _out_cts_avals=tuple(out_avals)))\n+ partial(converted_grad_fn, _out_cts_avals=tuple(out_avals)))\nout_flat = converted_fun_flat_with_custom_gradient(*args_flat)\nelse:\n@@ -828,7 +827,7 @@ def _unexpected_primitive(p: core.Primitive, *args, **kwargs):\nfor unexpected in xla.call_translations: # Call primitives are inlined\nif unexpected is pjit.pjit_p:\ncontinue\n- tf_impl[unexpected] = functools.partial(_unexpected_primitive, unexpected)\n+ tf_impl[unexpected] = partial(_unexpected_primitive, unexpected)\n# Primitives that are not yet implemented must be explicitly declared here.\ntf_not_yet_impl = [\n@@ -1045,8 +1044,30 @@ def _rem(lhs, rhs):\ntf_impl[lax.div_p] = _div\ntf_impl[lax.rem_p] = _rem\n-tf_impl[lax.max_p] = tf.math.maximum\n-tf_impl[lax.min_p] = tf.math.minimum\n+\n+def _minmax(x: TfVal, y: TfVal, *, is_min: bool,\n+ _in_avals: Sequence[core.AbstractValue],\n+ _out_aval: core.AbstractValue,) -> TfVal:\n+ # For complex numbers use lexicographic ordering, like JAX\n+ if dtypes.issubdtype(x.dtype.as_numpy_dtype, np.complexfloating):\n+ return _convert_jax_impl(\n+ partial(lax._minmax_complex_lowering,\n+ lax_cmp_pick_x=lax.lt if is_min else lax.gt),\n+ multiple_results=False)(x, y, _in_avals=_in_avals, _out_aval=_out_aval)\n+ else:\n+ return (tf.math.minimum if is_min else tf.math.maximum)(x, y)\n+\n+def _minmax_scalar(x: TfVal, y: TfVal, *, is_min: bool) -> TfVal:\n+ # For reducers we will need min/max for scalars only. In that case we\n+ # can construct the AbstractValues outselves, even in the presence of\n+ # shape polymorphism.\n+ assert len(x.shape) == 0 and len(y.shape) == 0, f\"x: {x.shape}, y: {y.shape}\"\n+ aval = core.ShapedArray((), _to_jax_dtype(x.dtype))\n+ return _minmax(x, y, is_min=is_min,\n+ _in_avals=[aval, aval], _out_aval=aval)\n+\n+tf_impl_with_avals[lax.max_p] = partial(_minmax, is_min=False)\n+tf_impl_with_avals[lax.min_p] = partial(_minmax, is_min=True)\n# Map from TF signed types to TF unsigned types.\n_SIGNED_TO_UNSIGNED_TABLE = {\n@@ -1659,8 +1680,8 @@ def _argminmax(fn, operand, axes, index_dtype):\nreturn tf.cast(result, _to_tf_dtype(index_dtype))\n-tf_impl[lax.argmin_p] = functools.partial(_argminmax, tf.math.argmin)\n-tf_impl[lax.argmax_p] = functools.partial(_argminmax, tf.math.argmax)\n+tf_impl[lax.argmin_p] = partial(_argminmax, tf.math.argmin)\n+tf_impl[lax.argmax_p] = partial(_argminmax, tf.math.argmax)\n_add_fn = tf.function(_add, autograph=False)\n_ge_fn = tf.function(tf.math.greater_equal, autograph=False)\n@@ -1947,19 +1968,16 @@ def _get_min_identity(tf_dtype):\n# pylint: disable=protected-access\ntf_impl_with_avals[lax.reduce_window_sum_p] = (\n- functools.partial(\n- _specialized_reduce_window, _add, lambda x: 0,\n+ partial(_specialized_reduce_window, _add, lambda x: 0,\nname=\"reduce_window_sum\"))\ntf_impl_with_avals[lax.reduce_window_min_p] = (\n- functools.partial(\n- _specialized_reduce_window,\n- tf.math.minimum,\n+ partial(_specialized_reduce_window,\n+ partial(_minmax_scalar, is_min=True),\n_get_min_identity,\nname=\"reduce_window_min\"))\ntf_impl_with_avals[lax.reduce_window_max_p] = (\n- functools.partial(\n- _specialized_reduce_window,\n- tf.math.maximum,\n+ partial(_specialized_reduce_window,\n+ partial(_minmax_scalar, is_min=False),\n_get_max_identity,\nname=\"reduce_window_max\"))\ntf_impl_with_avals[lax.reduce_window_p] = _reduce_window\n@@ -1970,11 +1988,11 @@ tf_impl_with_avals[lax.reduce_window_p] = _reduce_window\n# O(n^2) on other backends. This may be implemented using associative_scan\n# instead to favor different backends.\ntf_impl_with_avals[lax_control_flow.cummin_p] = _convert_jax_impl(\n- functools.partial(lax_control_flow._cumred_tpu_translation_rule,\n+ partial(lax_control_flow._cumred_tpu_translation_rule,\nlax._reduce_window_min),\nmultiple_results=False)\ntf_impl_with_avals[lax_control_flow.cummax_p] = _convert_jax_impl(\n- functools.partial(lax_control_flow._cumred_tpu_translation_rule,\n+ partial(lax_control_flow._cumred_tpu_translation_rule,\nlax._reduce_window_max),\nmultiple_results=False)\n# TODO(bchetioui): cumsum and cumprod can be converted using pure TF ops for\n@@ -1983,11 +2001,11 @@ tf_impl_with_avals[lax_control_flow.cummax_p] = _convert_jax_impl(\n# the operation. A non-XLA path can thus be defined for all dtypes, though the\n# tests will crash.\ntf_impl_with_avals[lax_control_flow.cumsum_p] = _convert_jax_impl(\n- functools.partial(lax_control_flow._cumred_tpu_translation_rule,\n+ partial(lax_control_flow._cumred_tpu_translation_rule,\nlax._reduce_window_sum),\nmultiple_results=False)\ntf_impl_with_avals[lax_control_flow.cumprod_p] = _convert_jax_impl(\n- functools.partial(lax_control_flow._cumred_tpu_translation_rule,\n+ partial(lax_control_flow._cumred_tpu_translation_rule,\nlax._reduce_window_prod),\nmultiple_results=False)\n@@ -2001,7 +2019,7 @@ def _select_and_scatter(operand, source, init_value, select_jaxpr,\ntf_impl[lax.select_and_scatter_p] = _select_and_scatter\n-@functools.partial(bool_to_int8, argnums=(0, 1))\n+@partial(bool_to_int8, argnums=(0, 1))\ndef _select_and_scatter_add(source, operand, *, select_prim, window_dimensions,\nwindow_strides, padding, _in_avals, _out_aval):\nif not _enable_xla:\n@@ -2023,8 +2041,7 @@ tf_impl_with_avals[lax.select_and_scatter_add_p] = _select_and_scatter_add\ndef _threefry2x32_jax_impl(*args: TfVal, _in_avals, _out_aval):\nres = _convert_jax_impl(\n- functools.partial(\n- jax._src.random._threefry2x32_lowering, use_rolled_loops=False),\n+ partial(jax._src.random._threefry2x32_lowering, use_rolled_loops=False),\nmultiple_results=True)(\n*args, _in_avals=_in_avals, _out_aval=_out_aval)\nreturn res\n@@ -2035,7 +2052,7 @@ tf_impl_with_avals[jax.random.threefry2x32_p] = _threefry2x32_jax_impl\n# Use the vmap implementation, otherwise on TPU the performance is really bad\n# With use_vmap=True on, we get about the same performance for JAX and jax2tf.\ntf_impl_with_avals[random.random_gamma_p] = _convert_jax_impl(\n- functools.partial(jax._src.random._gamma_impl, use_vmap=True),\n+ partial(jax._src.random._gamma_impl, use_vmap=True),\nmultiple_results=False)\n@@ -2049,7 +2066,7 @@ def _gather_dimensions_proto(indices_shape, dimension_numbers):\nreturn proto\n-@functools.partial(bool_to_int8, argnums=0)\n+@partial(bool_to_int8, argnums=0)\ndef _gather(operand, start_indices, *, dimension_numbers, slice_sizes,\n_in_avals, _out_aval):\n\"\"\"Tensorflow implementation of gather.\"\"\"\n@@ -2171,7 +2188,7 @@ def _cond(index: TfVal, *operands: TfVal, branches: Sequence[core.ClosedJaxpr],\ndel linear\n# tf.cond needs lambdas with no arguments.\nbranches_tf = [\n- functools.partial(_interpret_jaxpr, jaxpr, *operands)\n+ partial(_interpret_jaxpr, jaxpr, *operands)\nfor jaxpr in branches\n]\nreturn tf.switch_case(index, branches_tf)\n@@ -2198,7 +2215,7 @@ def _while(*args: TfVal, cond_nconsts: int, cond_jaxpr: core.ClosedJaxpr,\npred, = _interpret_jaxpr(cond_jaxpr, *cond_consts, *args)\nreturn pred\n- body_tf_func = functools.partial(_interpret_jaxpr, body_jaxpr, *body_consts)\n+ body_tf_func = partial(_interpret_jaxpr, body_jaxpr, *body_consts)\nreturn tf.while_loop(cond_tf_func, body_tf_func, init_carry)\n@@ -2586,7 +2603,7 @@ def _pjit(*args: TfVal,\n_out_aval: core.ShapedArray) -> TfVal:\ndel donated_invars, name\n# TODO: add `name` to the name stack\n- shard_value_for_mesh = functools.partial(_shard_value, resource_env.physical_mesh)\n+ shard_value_for_mesh = partial(_shard_value, resource_env.physical_mesh)\n# Apply sharding annotation to the arguments\nsharded_args: Sequence[TfVal] = tuple(\nmap(shard_value_for_mesh, args, _in_avals, in_axis_resources))\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/jax2tf_limitations.py",
"new_path": "jax/experimental/jax2tf/tests/jax2tf_limitations.py",
"diff": "@@ -882,7 +882,7 @@ class Jax2TfLimitation(primitive_harness.Limitation):\nreturn [\nmissing_tf_kernel(\n- dtypes=[np.bool_, np.complex64, np.complex128]),\n+ dtypes=[np.bool_]),\ncustom_numeric(\ncustom_assert=custom_assert,\ndescription=(\n@@ -901,7 +901,7 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ntst.assertAllClose(result_jax[~mask], result_tf[~mask], err_msg=err_msg)\nreturn [\n- missing_tf_kernel(dtypes=[np.bool_, np.complex64, np.complex128]),\n+ missing_tf_kernel(dtypes=[np.bool_]),\ncustom_numeric(\ncustom_assert=custom_assert,\ndescription=(\n@@ -966,9 +966,8 @@ class Jax2TfLimitation(primitive_harness.Limitation):\n@classmethod\ndef reduce_min(cls, harness: primitive_harness.Harness):\n- return [\n- missing_tf_kernel(dtypes=[np.complex64, np.complex128]),\n- ]\n+ return cls.reduce_max(harness)\n+\n@classmethod\ndef reduce_window_add(cls, harness):\n@@ -979,14 +978,14 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ndef reduce_window_max(cls, harness):\nassert \"max\" == harness.params[\"computation\"].__name__\nreturn [\n- missing_tf_kernel(dtypes=[np.bool_, np.complex64, np.complex128]),\n+ missing_tf_kernel(dtypes=[np.bool_]),\n]\n@classmethod\ndef reduce_window_min(cls, harness):\nassert \"min\" == harness.params[\"computation\"].__name__\nreturn [\n- missing_tf_kernel(dtypes=[np.bool_, np.complex64, np.complex128]),\n+ missing_tf_kernel(dtypes=[np.bool_]),\n]\n@classmethod\n@@ -1045,13 +1044,13 @@ class Jax2TfLimitation(primitive_harness.Limitation):\n@classmethod\ndef scatter_max(cls, harness):\nreturn [\n- missing_tf_kernel(dtypes=[np.bool_, np.complex64, np.complex128]),\n+ missing_tf_kernel(dtypes=[np.bool_]),\n]\n@classmethod\ndef scatter_min(cls, harness):\nreturn [\n- missing_tf_kernel(dtypes=[np.bool_, np.complex64, np.complex128]),\n+ missing_tf_kernel(dtypes=[np.bool_]),\n]\n@classmethod\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/primitive_harness.py",
"new_path": "jax/experimental/jax2tf/tests/primitive_harness.py",
"diff": "@@ -718,32 +718,36 @@ for dtype in jtu.dtypes.all:\nshape=shape,\ndtype=dtype)\n-_LAX_COMPARATORS = (lax.eq_p, lax.ge_p, lax.gt_p, lax.le_p, lax.lt_p, lax.ne_p)\n+_LAX_COMPARATORS = dict(eq=jnp.equal, ne=jnp.not_equal,\n+ ge=jnp.greater_equal, gt=jnp.greater,\n+ le=jnp.less_equal, lt=jnp.less)\ndef _make_comparator_harness(name,\n*,\ndtype=np.float32,\nop=lax.eq_p,\n+ op_name=\"eq\",\nlhs_shape=(),\nrhs_shape=()):\ndefine(\n- op.name,\n+ op_name,\nf\"{name}_lhs={jtu.format_shape_dtype_string(lhs_shape, dtype)}_rhs={jtu.format_shape_dtype_string(rhs_shape, dtype)}\",\n- lambda *args: op.bind(*args),\n+ lambda *args: op(*args),\n[RandArg(lhs_shape, dtype),\nRandArg(rhs_shape, dtype)],\nop=op,\n+ op_name=op_name,\nlhs_shape=lhs_shape,\nrhs_shape=rhs_shape,\ndtype=dtype)\n-for op in _LAX_COMPARATORS:\n+for op_name, op in _LAX_COMPARATORS.items():\nfor dtype in (jtu.dtypes.all if op in [lax.eq_p, lax.ne_p] else\n- set(jtu.dtypes.all) - set(jtu.dtypes.complex)):\n+ set(jtu.dtypes.all)):\n# Validate dtypes\n- _make_comparator_harness(\"dtypes\", dtype=dtype, op=op)\n+ _make_comparator_harness(\"dtypes\", dtype=dtype, op=op, op_name=op_name)\n# Validate broadcasting behavior\nfor lhs_shape, rhs_shape in [\n@@ -751,7 +755,8 @@ for op in _LAX_COMPARATORS:\n((1, 2), (3, 2)), # broadcast along specific axis\n]:\n_make_comparator_harness(\n- \"broadcasting\", lhs_shape=lhs_shape, rhs_shape=rhs_shape, op=op)\n+ \"broadcasting\", lhs_shape=lhs_shape, rhs_shape=rhs_shape,\n+ op=op, op_name=op_name)\nfor dtype in jtu.dtypes.all:\nshape = (3, 4, 5)\n@@ -917,6 +922,7 @@ for prim in [lax.div_p, lax.rem_p]:\ndef _make_binary_elementwise_harnesses(prim,\ndtypes,\ndefault_dtype=np.float32,\n+ broadcasting_dtypes=None,\njax_unimplemented=lambda **kwargs: []):\ndef _make(name, *, shapes=((20, 20), (20, 20)), dtype):\n@@ -931,15 +937,18 @@ def _make_binary_elementwise_harnesses(prim,\nprim=prim,\ndtype=dtype,\nshapes=shapes)\n-\n- return (tuple( # Validate dtypes\n- _make(\"dtypes\", dtype=dtype)\n- for dtype in dtypes) + tuple( # Validate broadcasting\n- _make(\"broadcasting\", dtype=default_dtype, shapes=shapes)\n+ broadcasting_dtypes = broadcasting_dtypes or (default_dtype,)\n+ return (\n+ # Validate dtypes\n+ tuple(_make(\"dtypes\", dtype=dtype) for dtype in dtypes) +\n+ # Validate broadcasting\n+ tuple(_make(\"broadcasting\", dtype=dtype, shapes=shapes)\nfor shapes in [\n((20, 20), (1, 20)), # broadcasting rhs\n((1, 20), (20, 20)), # broadcasting lhs\n- ]))\n+ ]\n+ for dtype in broadcasting_dtypes)\n+ )\n_make_binary_elementwise_harnesses(\n@@ -1004,7 +1013,9 @@ _min_max_special_cases = tuple(\n(np.array([-np.inf, -np.inf], dtype=dtype),\nnp.array([np.nan, np.nan], dtype=dtype))])\n-_make_binary_elementwise_harnesses(prim=lax.min_p, dtypes=jtu.dtypes.all)\n+_make_binary_elementwise_harnesses(\n+ prim=lax.min_p, dtypes=jtu.dtypes.all,\n+ broadcasting_dtypes=(np.float32, np.complex64, np.complex128))\n# Validate special cases\nfor lhs, rhs in _min_max_special_cases:\ndefine(\n@@ -1014,7 +1025,9 @@ for lhs, rhs in _min_max_special_cases:\nprim=lax.min_p,\ndtype=lhs.dtype)\n-_make_binary_elementwise_harnesses(prim=lax.max_p, dtypes=jtu.dtypes.all)\n+_make_binary_elementwise_harnesses(\n+ prim=lax.max_p, dtypes=jtu.dtypes.all,\n+ broadcasting_dtypes=(np.float32, np.complex64, np.complex128))\n# Validate special cases\nfor lhs, rhs in _min_max_special_cases:\ndefine(\n@@ -2336,10 +2349,15 @@ def _make_clamp_harness(name,\nmin_shape=min_arr.shape,\noperand_shape=operand_shape,\nmax_shape=max_arr.shape,\n- dtype=dtype)\n+ dtype=dtype,\n+ jax_unimplemented=[\n+ Limitation(\n+ \"unimplemented\",\n+ dtypes=[np.bool_, np.complex64, np.complex128])],\n+ )\n-for dtype in set(jtu.dtypes.all) - set(jtu.dtypes.complex + [np.bool_]):\n+for dtype in set(jtu.dtypes.all):\n_make_clamp_harness(\"dtypes\", dtype=dtype)\n# Validate broadcasting of min/max arrays\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/primitives_test.py",
"new_path": "jax/experimental/jax2tf/tests/primitives_test.py",
"diff": "@@ -99,8 +99,7 @@ class JaxPrimitiveTest(tf_test_util.JaxToTfTestCase):\n# If you want to run this test for only one harness, add parameter\n# `one_containing=\"foo\"` to parameterized below.\n@primitive_harness.parameterized(\n- primitive_harness.all_harnesses, include_jax_unimpl=False,\n- )\n+ primitive_harness.all_harnesses, include_jax_unimpl=False)\n@jtu.ignore_warning(\ncategory=UserWarning, message=\"Using reduced precision for gradient.*\")\ndef test_prim(self, harness: primitive_harness.Harness):\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Implement inequalities and friends for complex numbers.
This requires re-using JAX's lowering rule for comparisons of
complex numbers to use lexicographic comparison. |
260,697 | 04.06.2021 17:26:27 | 25,200 | f0e55e3ce241f997b097bd9381504c08a9d35cf8 | move #endif so that Windows doesn't have GetXCR0EAX defined twice
(erroring out) | [
{
"change_type": "MODIFY",
"old_path": "jaxlib/cpu_feature_guard.c",
"new_path": "jaxlib/cpu_feature_guard.c",
"diff": "@@ -58,7 +58,6 @@ static int GetXCR0EAX() { return _xgetbv(0); }\n\"xchg %%rdi, %%rbx\\n\" \\\n: \"=a\"(a), \"=D\"(b), \"=c\"(c), \"=d\"(d) \\\n: \"a\"(a_inp), \"2\"(c_inp))\n-#endif\nstatic int GetXCR0EAX() {\nint eax, edx;\n@@ -66,6 +65,7 @@ static int GetXCR0EAX() {\nreturn eax;\n}\n+#endif\n#endif\n// TODO(phawkins): technically we should build this module without AVX support\n"
}
] | Python | Apache License 2.0 | google/jax | move #endif so that Windows doesn't have GetXCR0EAX defined twice
(erroring out) |
260,374 | 01.06.2021 18:51:44 | 0 | 75cc734c8e335d26f6baa4f79dcb05849dd91bf9 | completed FilesystemCache class with corresponding unit tests | [
{
"change_type": "ADD",
"old_path": null,
"new_path": "jax/experimental/compilation_cache/file_system_cache.py",
"diff": "+# Copyright 2021 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 typing import Optional\n+import os\n+\n+class FileSystemCache:\n+\n+ def __init__(self, path: str):\n+ \"\"\"Sets up a cache at 'path'. Cached values may already be present.\"\"\"\n+ os.makedirs(path, exist_ok=True)\n+ self._path = path\n+\n+ def get(self, key: str) -> Optional[bytes]:\n+ \"\"\"Returns None if 'key' isn't present.\"\"\"\n+ if not key:\n+ raise ValueError(\"key cannot be empty\")\n+ path_to_key = os.path.join(self._path, key)\n+ if os.path.exists(path_to_key):\n+ with open(path_to_key, \"rb\") as file:\n+ return file.read()\n+ else:\n+ return None\n+\n+ def put(self, key: str, value: bytes):\n+ \"\"\"Adds new cache entry, possibly evicting older entries.\"\"\"\n+ if not key:\n+ raise ValueError(\"key cannot be empty\")\n+ #TODO(colemanliyah):implement eviction\n+ with open(os.path.join(self._path, key), \"wb\") as file:\n+ file.write(value)\n"
},
{
"change_type": "ADD",
"old_path": null,
"new_path": "tests/file_system_cache_test.py",
"diff": "+# Copyright 2021 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 absl.testing import absltest\n+from jax.experimental.compilation_cache.file_system_cache import FileSystemCache\n+import jax.test_util as jtu\n+import tempfile\n+\n+class FileSystemCacheTest(jtu.JaxTestCase):\n+\n+ def test_get_nonexistent_key(self):\n+ with tempfile.TemporaryDirectory() as tmpdir:\n+ cache = FileSystemCache(tmpdir)\n+ self.assertEqual(cache.get(\"nonExistentKey\"), None)\n+\n+ def test_put_and_get_key(self):\n+ with tempfile.TemporaryDirectory() as tmpdir:\n+ cache = FileSystemCache(tmpdir)\n+ cache.put(\"foo\", b\"bar\")\n+ self.assertEqual(cache.get(\"foo\"), b\"bar\")\n+\n+ def test_existing_cache_path(self):\n+ with tempfile.TemporaryDirectory() as tmpdir:\n+ cache1 = FileSystemCache(tmpdir)\n+ cache1.put(\"foo\", b\"bar\")\n+ del cache1\n+ cache2 = FileSystemCache(tmpdir)\n+ self.assertEqual(cache2.get(\"foo\"), b\"bar\")\n+\n+ def test_empty_value_put(self):\n+ with tempfile.TemporaryDirectory() as tmpdir:\n+ cache = FileSystemCache(tmpdir)\n+ cache.put(\"foo\", b\"\")\n+ self.assertEqual(cache.get(\"foo\"), b\"\")\n+\n+ def test_empty_key_put(self):\n+ with tempfile.TemporaryDirectory() as tmpdir:\n+ cache = FileSystemCache(tmpdir)\n+ with self.assertRaisesRegex(ValueError , r\"key cannot be empty\"):\n+ cache.put(\"\", b\"bar\")\n+\n+ def test_empty_key_get(self):\n+ with tempfile.TemporaryDirectory() as tmpdir:\n+ cache = FileSystemCache(tmpdir)\n+ with self.assertRaisesRegex(ValueError , r\"key cannot be empty\"):\n+ cache.get(\"\")\n+\n+if __name__ == \"__main__\":\n+ absltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | completed FilesystemCache class with corresponding unit tests |
260,287 | 07.06.2021 12:43:36 | 0 | 490f9778c8b465b79e518feac5b9e6326982b439 | Raise a friendlier error message when using loop axes in collectives | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/parallel.py",
"new_path": "jax/_src/lax/parallel.py",
"diff": "@@ -543,7 +543,7 @@ def pgather(src, idx, axes: Union[int, AxisName]):\n### parallel primitives\ndef _subst_all_names_in_param(\n- pname: str, params: core.ParamDict, subst: core.AxisSubst) -> core.ParamDict:\n+ pname: str, params: core.ParamDict, subst: core.AxisSubst, traverse: bool) -> core.ParamDict:\naxis_name = params[pname]\nif not isinstance(axis_name, (tuple, list)):\naxis_name = (axis_name,)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/core.py",
"new_path": "jax/core.py",
"diff": "@@ -1693,9 +1693,11 @@ def used_axis_names(primitive: Primitive, params: ParamDict) -> Set[AxisName]:\nsubst_axis_names(primitive, params, register_name)\nreturn axis_names\n-def subst_axis_names(primitive: Primitive, params: ParamDict, subst: AxisSubst) -> ParamDict:\n+def subst_axis_names(primitive: Primitive, params: ParamDict, subst: AxisSubst, traverse: bool = True) -> ParamDict:\nif primitive in axis_substitution_rules:\n- return axis_substitution_rules[primitive](params, subst)\n+ return axis_substitution_rules[primitive](params, subst, traverse)\n+ if not traverse:\n+ return params\n# Default implementation: substitute names in all jaxpr parameters\nif isinstance(primitive, MapPrimitive):\ndef shadowed_subst(name):\n@@ -1756,7 +1758,7 @@ def subst_axis_names_jaxpr(jaxpr: Union[Jaxpr, ClosedJaxpr], subst: AxisSubst):\nreturn ClosedJaxpr(new_jaxpr, consts)\nreturn new_jaxpr\n-axis_substitution_rules: Dict[Primitive, Callable[[ParamDict, AxisSubst], ParamDict]] = {}\n+axis_substitution_rules: Dict[Primitive, Callable[[ParamDict, AxisSubst, bool], ParamDict]] = {}\n# ------------------- AxisPrimitive -------------------\n# Primitives that store axis names in params and want those axis names to\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -701,6 +701,8 @@ class EvaluationPlan(NamedTuple):\ndef subst_axes_with_resources(self, jaxpr):\ntry:\n+ if any(self.loop_axis_resources.values()):\n+ _check_no_loop_collectives(jaxpr, self.loop_axis_resources)\nwith core.extend_axis_env_nd(self.resource_axis_env.items()):\nreturn core.subst_axis_names_jaxpr(jaxpr, self.axis_subst)\nexcept core.DuplicateAxisNameError:\n@@ -776,9 +778,11 @@ def _process_xmap_default(self, call_primitive, f, tracers, params):\nraise NotImplementedError(f\"{type(self)} must override process_xmap to handle xmap\")\ncore.Trace.process_xmap = _process_xmap_default # type: ignore\n-def _xmap_axis_subst(params, subst):\n+def _xmap_axis_subst(params, subst, traverse):\nif 'call_jaxpr' not in params: # TODO(apaszke): This feels sketchy, but I'm not sure why\nreturn params\n+ if not traverse:\n+ return params\ndef shadowed_subst(name):\nreturn (name,) if name in params['global_axis_sizes'] else subst(name)\nwith core.extend_axis_env_nd(params['global_axis_sizes'].items()):\n@@ -1402,6 +1406,20 @@ def _check_out_avals_vs_out_axes(out_avals: Sequence[core.AbstractValue],\nf\"defined by this xmap call: {', '.join(undeclared_axes_str)}\")\n+# TODO: We should relax this at least for \"constructor primitives\"\n+# such as axis_index or zeros.\n+def _check_no_loop_collectives(jaxpr, loop_axis_resources):\n+ def subst_no_loop(name):\n+ if loop_axis_resources.get(name, ()):\n+ raise RuntimeError(f\"Named axes with loop resources assigned to them cannot \"\n+ f\"be referenced inside the xmapped computation (e.g. in \"\n+ f\"collectives), but `{name}` violates that rule\")\n+ return (name,)\n+ for eqn in jaxpr.eqns:\n+ core.subst_axis_names(eqn.primitive, eqn.params, subst_no_loop, traverse=False)\n+ rec = partial(_check_no_loop_collectives, loop_axis_resources=loop_axis_resources)\n+ core.traverse_jaxpr_params(rec, eqn.params)\n+\n# -------- soft_pmap --------\ndef soft_pmap(fun: Callable, axis_name: Optional[AxisName] = None, in_axes=0\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/xmap_test.py",
"new_path": "tests/xmap_test.py",
"diff": "@@ -1225,6 +1225,18 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nfm(x)\n+ @loop('l', 2)\n+ def testLoopCollectives(self):\n+ fm = xmap(lambda x: lax.psum(x, 'i'),\n+ in_axes=['i'], out_axes=[],\n+ axis_resources={'i': 'l'})\n+ x = np.arange(16)\n+ error = (r\"Named axes with loop resources assigned to them cannot be \"\n+ r\"referenced inside the xmapped computation \\(e.g. in \"\n+ r\"collectives\\), but `i` violates that rule\")\n+ with self.assertRaisesRegex(RuntimeError, error):\n+ fm(x)\n+\nif __name__ == '__main__':\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | Raise a friendlier error message when using loop axes in collectives |
260,287 | 15.03.2021 13:30:44 | 0 | 54ba051631c0f89d607d4f6f8896b845e620387c | Always run xmap over all mesh axes
Automatic mesh slicing might be surprising, and can always be
performed manually. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -635,12 +635,11 @@ def make_xmap_callable(fun: lu.WrappedFun,\nused_mesh_axes = used_resources & resource_env.physical_resource_axes\nif used_mesh_axes:\nassert spmd_in_axes is None and spmd_out_axes_thunk is None # No outer xmaps, so should be None\n- submesh = resource_env.physical_mesh[sorted(used_mesh_axes, key=str)]\nmesh_in_axes, mesh_out_axes = plan.to_mesh_axes(in_axes, out_axes)\nreturn pxla.mesh_callable(f,\nname,\nbackend,\n- submesh,\n+ resource_env.physical_mesh,\nmesh_in_axes,\nmesh_out_axes,\ndonated_invars,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/interpreters/pxla.py",
"new_path": "jax/interpreters/pxla.py",
"diff": "@@ -1345,15 +1345,6 @@ class Mesh:\nassert is_local_device[subcube_indices].all()\nreturn Mesh(self.devices[subcube_indices], self.axis_names)\n- def __getitem__(self, new_axes):\n- axis_pos = {name: i for i, name in enumerate(self.axis_names)}\n- new_devices = self.devices.transpose(tuple(axis_pos[axis] for axis in new_axes) +\n- tuple(axis_pos[axis] for axis in self.axis_names\n- if axis not in new_axes))\n- new_devices = new_devices[(slice(None),) * len(new_axes) +\n- (0,) * (len(self.axis_names) - len(new_axes))]\n- return Mesh(new_devices, new_axes)\n-\n@property\ndef device_ids(self):\nreturn np.vectorize(lambda d: d.id, otypes=[int])(self.devices)\n"
}
] | Python | Apache License 2.0 | google/jax | Always run xmap over all mesh axes
Automatic mesh slicing might be surprising, and can always be
performed manually. |
260,411 | 10.06.2021 17:01:22 | -7,200 | 1994f6df4a7a7a53af1fb9e21800973bf541632f | [jax2tf] Fix the round-trip call_tf(convert)
Also cleaned the handling of global state in jax2tf. | [
{
"change_type": "MODIFY",
"old_path": "CHANGELOG.md",
"new_path": "CHANGELOG.md",
"diff": "@@ -8,9 +8,15 @@ Remember to align the itemized text with the first line of an item within a list\nPLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.\n-->\n-## jax 0.2.14 (unreleased)\n+## jax 0.2.15 (unreleased)\n* [GitHub commits](https://github.com/google/jax/compare/jax-v0.2.14...master).\n+* New features:\n+* Breaking changes:\n+\n+* Bug fixes:\n+ * Fixed bug that prevented round-tripping from JAX to TF and back:\n+ `jax2tf.call_tf(jax2tf.convert)` ({jax-issue}`#6947`).\n## jax 0.2.14 (June 10 2021)\n* [GitHub commits](https://github.com/google/jax/compare/jax-v0.2.13...jax-v0.2.14).\n@@ -23,7 +29,6 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.\n* The {func}`jax2tf.convert` supports shape polymorphism even when the\nunknown dimensions are used in arithmetic operations, e.g., `jnp.reshape(-1)`\n({jax-issue}`#6827`).\n-\n* The {func}`jax2tf.convert` generates custom attributes with location information\nin TF ops. The code that XLA generates after jax2tf\nhas the same location information as JAX/XLA.\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/README.md",
"new_path": "jax/experimental/jax2tf/README.md",
"diff": "@@ -772,7 +772,7 @@ As a trivial example, consider computing ``sin(cos(1.))`` with ``sin`` done in J\n# It should return a similar result. This function will be called using\n# TensorFlow eager mode if called from outside JAX staged contexts (`jit`,\n# `pmap`, or control-flow primitives), and will be called using TensorFlow\n- # graph mode otherwise. In the latter case, the function must be compileable\n+ # compiled mode otherwise. In the latter case, the function must be compileable\n# with XLA (`tf.function(func, jit_compile=True)`)\ndef cos_tf(x):\nreturn tf.math.cos(x)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/call_tf.py",
"new_path": "jax/experimental/jax2tf/call_tf.py",
"diff": "@@ -23,18 +23,18 @@ https://github.com/google/jax/blob/master/jax/experimental/jax2tf/README.md#call\n\"\"\"\nimport logging\n-from typing import Callable\n+from typing import Callable, Sequence\nimport jax\nfrom jax import core\nfrom jax import dlpack\n-from jax import dtypes\nfrom jax import numpy as jnp\nfrom jax import tree_util\nfrom jax._src import util\nfrom jax.interpreters import xla\nfrom jax.lib import xla_bridge\nfrom jax.lib import xla_client\n+from . import jax2tf as jax2tf_internal\nimport numpy as np\nimport tensorflow as tf # type: ignore[import]\n@@ -86,19 +86,25 @@ def call_tf(func_tf: Callable) -> Callable:\nargs_jax_flat, args_jax_treedef = tree_util.tree_flatten(args_jax)\nargs_tf_sig_flat = [\n- tf.TensorSpec(np.shape(a_jax), _to_tf_dtype(_dtype(a_jax)))\n+ tf.TensorSpec(np.shape(a_jax), jax2tf_internal._to_tf_dtype(_dtype(a_jax)))\nfor a_jax in args_jax_flat\n]\nargs_tf_sig = tf.nest.map_structure(\nlambda a_jax: tf.TensorSpec(\n- np.shape(a_jax), _to_tf_dtype(_dtype(a_jax))), args_jax)\n+ np.shape(a_jax), jax2tf_internal._to_tf_dtype(_dtype(a_jax))), args_jax)\n+\n+ # Trace once through the function to get the result shape\n+ with jax2tf_internal.inside_call_tf():\nfunc_tf_concrete = tf.function(func_tf).get_concrete_function(*args_tf_sig)\n+\nres_tf_sig_flat, res_treedef = tree_util.tree_flatten(\nfunc_tf_concrete.structured_outputs)\nres_jax_flat = call_tf_p.bind(\n*args_jax_flat,\n+ # Carry the actual function such that op-by-op call can call in TF eager mode.\nfunc_tf=func_tf,\n+ func_tf_concrete=func_tf_concrete,\nargs_treedef=args_jax_treedef,\nargs_tf_sig_flat=args_tf_sig_flat,\nres_treedef=res_treedef,\n@@ -167,6 +173,8 @@ def _call_tf_impl(*args_jax_flat, args_treedef, func_tf, **_):\nreturn tf.constant(np.asarray(arg_jax))\nargs_tf_flat = tuple(map(_arg_jax_to_tf, args_jax_flat))\n+ with jax2tf_internal.inside_call_tf():\n+ # Call in TF eager mode\nres_tf = func_tf(*args_treedef.unflatten(args_tf_flat))\nres_tf_flat, _ = tree_util.tree_flatten(res_tf)\n# TODO(necula): check the result for tree and aval\n@@ -190,7 +198,7 @@ call_tf_p.def_impl(_call_tf_impl)\ndef _call_tf_abstract_eval(*_, res_tf_sig_flat, **__):\nreturn tuple([\n- core.ShapedArray(np.shape(r), _to_jax_dtype(r.dtype))\n+ core.ShapedArray(np.shape(r), jax2tf_internal._to_jax_dtype(r.dtype))\nfor r in res_tf_sig_flat\n])\n@@ -198,7 +206,7 @@ def _call_tf_abstract_eval(*_, res_tf_sig_flat, **__):\ncall_tf_p.def_abstract_eval(_call_tf_abstract_eval)\n-def _call_tf_translation_rule(builder, *args_op, func_tf,\n+def _call_tf_translation_rule(builder, *args_op, func_tf, func_tf_concrete,\nargs_treedef, args_tf_sig_flat, res_tf_sig_flat,\n**_):\n# TODO(necula): It seems that we need concrete tensors for get_compiler_ir?\n@@ -209,7 +217,7 @@ def _call_tf_translation_rule(builder, *args_op, func_tf,\n]\nargs_tf = args_treedef.unflatten(args_tf_flat)\nfunc_tf = tf.function(func_tf, jit_compile=True)\n- func_tf_concrete = func_tf.get_concrete_function(*args_tf)\n+ #func_tf_concrete = func_tf.get_concrete_function(*args_tf)\ncaptured_ops = [] # Same order as captured_inputs\nif func_tf_concrete.captured_inputs:\n# The function uses either captured variables or tensors.\n@@ -248,13 +256,15 @@ def _call_tf_translation_rule(builder, *args_op, func_tf,\nxla.translations[call_tf_p] = _call_tf_translation_rule\n-\n-def _to_tf_dtype(jax_dtype):\n- if jax_dtype == dtypes.float0:\n- return tf.float32\n- else:\n- return tf.dtypes.as_dtype(jax_dtype)\n-\n-\n-def _to_jax_dtype(tf_dtype):\n- return tf_dtype.as_numpy_dtype\n+TfVal = jax2tf_internal.TfVal\n+def _jax2tf_call_tf(*args: TfVal,\n+ _in_avals: Sequence[core.ShapedArray],\n+ _out_aval: core.ShapedArray,\n+ func_tf: Callable,\n+ **kwargs) -> TfVal:\n+ res_tf = func_tf(*args)\n+ res_tf_flat = tf.nest.flatten(res_tf)\n+ # TODO: check that the return values have the right signature\n+ return res_tf_flat\n+\n+jax2tf_internal.tf_impl_with_avals[call_tf_p] = _jax2tf_call_tf\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -81,6 +81,10 @@ TfVal = Any\nDType = Any\nPrecisionType = int # Enum xla_data.PrecisionConfig.Precision\n+# A dimension environment maps dimension variables to TF expressions that\n+# compute the value of the dimension. These expressions refer to the TF\n+# function arguments.\n+_ShapeEnv = Dict[str, TfVal]\ndef _is_tfval(v: TfVal) -> bool:\nif isinstance(v, (tf.Tensor, tf.Variable)):\n@@ -111,12 +115,6 @@ tf_impl: Dict[core.Primitive, Callable[..., Any]] = {}\n# core.AbstractValue, or a tuple thereof when primitive.multiple_results).\ntf_impl_with_avals: Dict[core.Primitive, Callable[..., Any]] = {}\n-# XLA is not linked in all environments; when converting a primitive, if this\n-# variable is disabled, we try harder to use only standard TF ops if they are\n-# applicable to the concrete use case; if the resulting conversion path ends up\n-# requiring a TFXLA operation, an exception is thrown instead.\n-_enable_xla = True\n-\n# In order to ensure that JAX picks up the proper user-frame for source\n# locations we will register the TensorFlow source path as an internal\n# path with source_info_util. The typical stack when a JAX primitive\n@@ -132,17 +130,48 @@ _enable_xla = True\n# also.\n# We register the TensorFlow source path lazily\n_has_registered_tf_source_path = False\n-# Whether to actually include XLA op metadata\n-_include_xla_op_metadata = True\n+class _ThreadLocalState(threading.local):\n+ def __init__(self):\n+ self.name_stack = \"\"\n+ # XLA is not linked in all environments; when converting a primitive, if this\n+ # variable is disabled, we try harder to use only standard TF ops if they are\n+ # applicable to the concrete use case; if the resulting conversion path ends up\n+ # requiring a TFXLA operation, an exception is thrown instead.\n+ self.enable_xla = True\n+\n+ # Keep track if we are inside a call_tf. In that context we disable the\n+ # safety check that we are not inside JAX transformations.\n+ self.inside_call_tf = False\n+\n+ # Maps dimension variables to TF expressions\n+ self.shape_env: _ShapeEnv = {}\n+\n+ # Whether to actually include XLA op metadata in the generated TF ops\n+ self.include_xla_op_metadata = True\n+\n+_thread_local_state = _ThreadLocalState()\n+\n+def _get_current_name_stack():\n+ return _thread_local_state.name_stack\ndef _xla_disabled_error(primitive_name: str,\nextra_msg: Optional[str] = None) -> Exception:\n- assert not _enable_xla\n+ assert not _thread_local_state.enable_xla\nmsg = f\"Call to {primitive_name} cannot be converted with enable_xla=False.\"\nif extra_msg:\nmsg += f\" {extra_msg}\"\nreturn NotImplementedError(msg)\n+@contextlib.contextmanager\n+def inside_call_tf():\n+ # Set the inside_call_tf flag for a context.\n+ prev = _thread_local_state.inside_call_tf\n+ _thread_local_state.inside_call_tf = True\n+ try:\n+ yield\n+ finally:\n+ _thread_local_state.inside_call_tf = prev\n+\n@partial(api_util.api_hook, tag=\"jax2tf_convert\")\ndef convert(fun: Callable,\n*,\n@@ -214,9 +243,10 @@ def convert(fun: Callable,\nname_stack = util.extend_name_stack(util.wrap_name(fun_name, \"jax2tf\"))\ndef converted_fun(*args: TfVal, **kwargs: TfVal) -> TfVal:\n# TODO: is there a better way to check if we are inside a transformation?\n- if not core.trace_state_clean():\n+ if not core.trace_state_clean() and not _thread_local_state.inside_call_tf:\n+ # It is Ok to nest convert when we are inside a call_tf\nraise ValueError(\"convert must be used outside all JAX transformations.\" +\n- f\"Trace state: {core.thread_local_state.trace_state}\")\n+ f\"Trace state: {core.thread_local_state.trace_state.trace_stack}\")\ndef check_arg(a):\nif not _is_tfval(a):\n@@ -308,16 +338,15 @@ def convert(fun: Callable,\nreturn in_cts\ntry:\n- global _shape_env\n- assert not _shape_env, f\"Unexpected shape environment {_shape_env}\"\n- global _enable_xla\n- prev_enable_xla = _enable_xla\n- _enable_xla = enable_xla\n- global _include_xla_op_metadata\n- prev_include_xla_op_metadata = _include_xla_op_metadata\n- _include_xla_op_metadata = False\n-\n- _shape_env = shapeenv\n+ assert not _thread_local_state.shape_env, f\"Unexpected shape environment {_thread_local_state.shape_env}\"\n+\n+ prev_enable_xla = _thread_local_state.enable_xla\n+ _thread_local_state.enable_xla = enable_xla\n+\n+ prev_include_xla_op_metadata = _thread_local_state.include_xla_op_metadata\n+ _thread_local_state.include_xla_op_metadata = False\n+\n+ _thread_local_state.shape_env = shapeenv\nglobal _has_registered_tf_source_path\nif not _has_registered_tf_source_path:\nsource_info_util.register_exclusion(os.path.dirname(tf.__file__))\n@@ -345,9 +374,9 @@ def convert(fun: Callable,\nfor o, _ in out_flat_raw\n]\nfinally:\n- _shape_env = {}\n- _enable_xla = prev_enable_xla\n- _include_xla_op_metadata = prev_include_xla_op_metadata\n+ _thread_local_state.shape_env = {}\n+ _thread_local_state.enable_xla = prev_enable_xla\n+ _thread_local_state.include_xla_op_metadata = prev_include_xla_op_metadata\nout_flat = [tf.identity(x, \"jax2tf_out\") for x in out_flat]\nout = tree_util.tree_unflatten(out_tree_thunk(), out_flat)\n@@ -371,15 +400,6 @@ def dtype_of_val(val: TfVal) -> DType:\n# Internals\n-# TODO: add all globals here\n-class _ThreadLocalState(threading.local):\n- def __init__(self):\n- self.name_stack = \"\"\n-_thread_local_state = _ThreadLocalState()\n-\n-def _get_current_name_stack():\n- return _thread_local_state.name_stack\n-\n@contextlib.contextmanager\ndef _extended_name_stack(extra_name_stack: Optional[str]):\nprev_name_stack = _thread_local_state.name_stack\n@@ -526,10 +546,6 @@ def _tfval_to_tensor_jax_dtype(val: TfVal,\nreturn tf.convert_to_tensor(val, dtype=conversion_dtype), jax_dtype\n-# A dimension environment maps dimension variables to TF expressions that\n-# compute the value of the dimension. These expressions refer to the TF\n-# function arguments.\n-_ShapeEnv = Dict[str, TfVal]\ndef _args_to_avals_and_env(\nargs: Sequence[TfVal],\narg_jax_dtypes: Sequence[DType],\n@@ -573,14 +589,10 @@ def _args_to_avals_and_env(\nreturn avals, shapeenv\n-# A shape environment maps shape variables to TfVal.\n-_shape_env = {} # type: _ShapeEnv\n-\n-\ndef _eval_shape(shape: Sequence[shape_poly.DimSize]) -> Sequence[TfVal]:\nassert all(map(lambda x: x is not None, shape)), (\nf\"Argument shape should be a valid JAX shape but got {shape}\")\n- return shape_poly.eval_shape(shape, _shape_env)\n+ return shape_poly.eval_shape(shape, _thread_local_state.shape_env)\ndef shape_as_value(x):\n@@ -800,7 +812,7 @@ class TensorFlowTrace(core.Trace):\nelse:\nreturn impl(*args_tf, **params)\n- if _include_xla_op_metadata:\n+ if _thread_local_state.include_xla_op_metadata:\nop_metadata = xla.make_op_metadata(primitive, params,\nname_stack=_get_current_name_stack(),\nsource_info=source_info_util.current())\n@@ -953,7 +965,6 @@ tf_not_yet_impl = [\n\"lu_pivots_to_permutation\",\n\"rng_bit_generator\",\n\"xla_pmap\",\n- \"call_tf\",\n]\ntf_impl[ad_util.stop_gradient_p] = tf.stop_gradient\n@@ -1521,7 +1532,7 @@ def _conv_general_dilated(lhs, rhs, *,\n_out_aval: core.AbstractValue):\n\"\"\"Implementation of lax.conv_general_dilated_p using XlaConv.\"\"\"\nout_tf_shape = _aval_to_tf_shape(_out_aval)\n- if not _enable_xla:\n+ if not _thread_local_state.enable_xla:\nreturn _try_tf_conv(\nlhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\ndimension_numbers, feature_group_count, batch_group_count,\n@@ -1580,7 +1591,7 @@ def _dot_general(lhs, rhs, *, dimension_numbers,\n\"\"\"Implementation of lax.dot_general_p in terms of tf.linalg.einsum.\"\"\"\n(lhs_contracting, rhs_contracting), (lhs_batch, rhs_batch) = dimension_numbers\nlhs_ndim, rhs_ndim = len(lhs.shape), len(rhs.shape)\n- if _enable_xla:\n+ if _thread_local_state.enable_xla:\ndnums_proto = xla_data_pb2.DotDimensionNumbers()\ndnums_proto.lhs_contracting_dimensions.extend(lhs_contracting)\ndnums_proto.rhs_contracting_dimensions.extend(rhs_contracting)\n@@ -1723,7 +1734,7 @@ def _pad(operand, padding_value, *, padding_config,\n_out_aval: core.AbstractValue):\ndel _in_avals\nlow, high, interior = util.unzip3(padding_config)\n- if _enable_xla:\n+ if _thread_local_state.enable_xla:\nout = tfxla.pad(operand, padding_value, low, high, interior)\nreturn out\n@@ -1911,7 +1922,7 @@ def _reduce_window(operand, init_value, *, jaxpr, consts, window_dimensions,\n\"\"\"\nassert len(consts) == 0, \"Reduction computation cannot have constants\"\n- if not _enable_xla:\n+ if not _thread_local_state.enable_xla:\nraise _xla_disabled_error(\"reduce_window\")\ndef reducer(arg1: TfVal, arg2: TfVal) -> TfVal:\n@@ -2029,7 +2040,7 @@ def _specialized_reduce_window(reducer,\nReturns:\nThe reduced operand.\n\"\"\"\n- if not _enable_xla and name in [\"reduce_window_max\", \"reduce_window_sum\"]:\n+ if not _thread_local_state.enable_xla and name in [\"reduce_window_max\", \"reduce_window_sum\"]:\nreturn _try_tf_pool(name, operand, window_dimensions, window_strides,\npadding, base_dilation, window_dilation)\n@@ -2123,7 +2134,7 @@ tf_impl[lax.select_and_scatter_p] = _select_and_scatter\n@partial(bool_to_int8, argnums=(0, 1))\ndef _select_and_scatter_add(source, operand, *, select_prim, window_dimensions,\nwindow_strides, padding, _in_avals, _out_aval):\n- if not _enable_xla:\n+ if not _thread_local_state.enable_xla:\nraise _xla_disabled_error(\"select_and_scatter_add\")\ninit_value = tf.zeros((), operand.dtype)\nselect_fn = (\n@@ -2173,7 +2184,7 @@ def _gather(operand, start_indices, *, dimension_numbers, slice_sizes,\n_in_avals, _out_aval):\n\"\"\"Tensorflow implementation of gather.\"\"\"\ndel _in_avals, unique_indices\n- if not _enable_xla:\n+ if not _thread_local_state.enable_xla:\nraise _xla_disabled_error(\"gather\")\nproto = _gather_dimensions_proto(start_indices.shape, dimension_numbers)\nslice_sizes_tf = _eval_shape(slice_sizes)\n@@ -2209,7 +2220,7 @@ def _dynamic_slice(operand, *start_indices, slice_sizes,\nstart_indices = tf.stack(start_indices)\nslice_sizes = _eval_shape(slice_sizes)\n- if _enable_xla:\n+ if _thread_local_state.enable_xla:\nres = tfxla.dynamic_slice(operand, start_indices, size_indices=slice_sizes)\n# TODO: implement shape inference for XlaDynamicSlice\nres.set_shape(_aval_to_tf_shape(_out_aval))\n@@ -2259,7 +2270,7 @@ def _scatter(operand, scatter_indices, updates, *, update_jaxpr, update_consts,\ndel unique_indices, _in_avals\nassert len(update_consts) == 0, \"Update computation cannot have constants\"\n- if not _enable_xla:\n+ if not _thread_local_state.enable_xla:\nraise _xla_disabled_error(\"scatter\")\nproto = _scatter_dimensions_proto(scatter_indices.shape, dimension_numbers)\n@@ -2293,7 +2304,7 @@ tf_impl_with_avals[lax.scatter_add_p] = _scatter\ndef _dynamic_update_slice(operand, update, *start_indices):\n- if not _enable_xla:\n+ if not _thread_local_state.enable_xla:\nraise _xla_disabled_error(\"dynamic_update_slice\")\nreturn tfxla.dynamic_update_slice(operand, update, tf.stack(start_indices))\n@@ -2428,7 +2439,7 @@ tf_impl[lax.top_k_p] = _top_k\ndef _sort(*operands: TfVal, dimension: int, is_stable: bool,\nnum_keys: int) -> Tuple[TfVal, ...]:\n- if not _enable_xla:\n+ if not _thread_local_state.enable_xla:\nraise _xla_disabled_error(\"sort\")\nassert 1 <= num_keys <= len(operands)\nassert 0 <= dimension < len(\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/shape_poly.py",
"new_path": "jax/experimental/jax2tf/shape_poly.py",
"diff": "@@ -462,7 +462,7 @@ def parse_spec(spec: Optional[Union[str, PolyShape]],\nspec_ = spec.replace(\" \", \"\")\nif spec_[0] == \"(\":\nif spec_[-1] != \")\":\n- raise ValueError(spec)\n+ raise ValueError(f\"PolyShape '{spec}' has invalid syntax\")\nspec_ = spec_[1:-1]\nspec_ = spec_.rstrip(\",\")\nif not spec_:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/call_tf_test.py",
"new_path": "jax/experimental/jax2tf/tests/call_tf_test.py",
"diff": "@@ -25,6 +25,7 @@ from jax import numpy as jnp\nfrom jax import test_util as jtu\nfrom jax.config import config\nfrom jax.experimental import jax2tf\n+from jax.experimental.jax2tf.tests import tf_test_util\nimport numpy as np\n@@ -58,10 +59,12 @@ class CallTfTest(jtu.JaxTestCase):\n_ = tf.add(1, 1)\nsuper().setUp()\n- @parameterized_jit\n- def test_eval_scalar_arg(self, with_jit=False):\n+ #@parameterized_jit\n+ def test_eval_scalar_arg(self, with_jit=True):\n+ def f_tf(x):\n+ return tf.math.sin(x)\nx = 3.\n- res = _maybe_jit(with_jit, jax2tf.call_tf(tf.math.sin))(x)\n+ res = _maybe_jit(with_jit, jax2tf.call_tf(f_tf))(x)\nself.assertAllClose(jnp.sin(x), res, check_dtypes=False)\n@parameterized_jit\n@@ -119,6 +122,16 @@ class CallTfTest(jtu.JaxTestCase):\nres = fun_jax(x, y)\nself.assertAllClose((np.float32(12.), np.float64(11.)), res)\n+ def test_eval_non_compileable(self):\n+ # Check that in op-by-op we call a function in eager mode.\n+ def f_tf_non_compileable(x):\n+ return tf.strings.length(tf.strings.format(\"Hello {}!\", [x]))\n+\n+ f_jax = jax2tf.call_tf(f_tf_non_compileable)\n+ x = np.float32(0.7)\n+ self.assertAllClose(f_tf_non_compileable(x).numpy(), f_jax(x))\n+\n+\n@parameterized_jit\ndef test_control_flow(self, with_jit=True):\n@@ -319,6 +332,89 @@ class CallTfTest(jtu.JaxTestCase):\nres = jax.pmap(fun_jax)(x)\nself.assertAllClose(np.float32(3. * (x + 2)), res)\n+ def test_round_trip(self):\n+ f_jax = jnp.sin\n+ f_jax_rt = jax2tf.call_tf(jax2tf.convert(f_jax))\n+ x = np.float32(0.7)\n+ self.assertAllClose(f_jax(x), f_jax_rt(x))\n+\n+ def test_round_trip_custom_grad(self):\n+ @jax.custom_vjp\n+ def f(x):\n+ return x * x\n+\n+ # f_fwd: a -> (b, residual)\n+ def f_fwd(x):\n+ return f(x), np.float32(3.) * x\n+ # f_bwd: (residual, CT b) -> [CT a]\n+ def f_bwd(residual, ct_b):\n+ return residual * ct_b,\n+\n+ f.defvjp(f_fwd, f_bwd)\n+\n+ f_rt = jax2tf.call_tf(jax2tf.convert(f, with_gradient=True))\n+ x = np.float32(0.7)\n+ self.assertAllClose(f(x), f_rt(x))\n+ self.assertAllClose(jax.grad(f)(x), jax.grad(f_rt)(x))\n+\n+ def test_round_trip_shape_poly(self):\n+ f_jax = jnp.sin\n+ f_jax_rt = jax2tf.call_tf(jax2tf.convert(f_jax,\n+ polymorphic_shapes=[\"(b, ...)\"]))\n+ x = np.array([0.7, 0.8], dtype=np.float32)\n+ self.assertAllClose(f_jax(x), f_jax_rt(x))\n+\n+ def test_round_trip_saved_model_shape_poly(self):\n+ tracing_count = 0\n+ def f_jax(x):\n+ nonlocal tracing_count\n+ tracing_count += 1\n+ return jnp.sin(x)\n+\n+ f_tf = jax2tf.convert(f_jax, polymorphic_shapes=[\"(b, ...)\"])\n+ x = np.array([0.7, 0.8], dtype=np.float32)\n+ res_jax = f_jax(x)\n+ self.assertEqual(1, tracing_count)\n+ # Will trace twice, it seems. Once to get the result signature, and once again\n+ # for the actual saving.\n+ restored_f = tf_test_util.SaveAndLoadFunction(f_tf, [tf.TensorSpec([None], x.dtype)])\n+ self.assertGreaterEqual(tracing_count, 2)\n+ tracing_count = 0\n+ f_jax_rt = jax2tf.call_tf(restored_f)\n+ self.assertAllClose(res_jax, f_jax_rt(x))\n+ # Ensure that restored_f works at other batch size as well\n+ y = np.concatenate([x, x])\n+ self.assertEqual(0, tracing_count)\n+ res_jax_y = f_jax(y)\n+ self.assertEqual(1, tracing_count)\n+ # No more tracing for f_jax_rt\n+ self.assertAllClose(res_jax_y, f_jax_rt(y))\n+ self.assertEqual(1, tracing_count)\n+\n+ def test_round_trip_custom_grad_saved_model(self):\n+ @jax.custom_vjp\n+ def f(x):\n+ return x * x\n+\n+ # f_fwd: a -> (b, residual)\n+ def f_fwd(x):\n+ return f(x), np.float32(3.) * x\n+ # f_bwd: (residual, CT b) -> [CT a]\n+ def f_bwd(residual, ct_b):\n+ return residual * ct_b,\n+\n+ f.defvjp(f_fwd, f_bwd)\n+ def g(x):\n+ return jnp.sum(f(x))\n+\n+ g_tf = tf_test_util.SaveAndLoadFunction(\n+ jax2tf.convert(g, with_gradient=True, polymorphic_shapes=[\"b, ...\"]),\n+ [tf.TensorSpec([None], dtype=tf.float32)])\n+ g_rt = jax2tf.call_tf(g_tf)\n+ x = np.array([0.7], dtype=np.float32)\n+ self.assertAllClose(g(x), g_rt(x))\n+ self.assertAllClose(jax.grad(g)(x), jax.grad(g_rt)(x))\n+\ndef test_module_documentation(self):\ndef cos_tf(x):\nreturn tf.math.cos(x)\n@@ -342,6 +438,12 @@ class CallTfTest(jtu.JaxTestCase):\nprint(jax.make_jaxpr(cos_tf_sin_jax)(x))\nprint(jax.xla_computation(cos_tf_sin_jax)(x).as_hlo_text())\n+ def test_round_trip_reverse(self):\n+ f_tf = tf.math.sin\n+ f_tf_rt = jax2tf.convert(jax2tf.call_tf(f_tf))\n+ x = np.float32(0.7)\n+ self.assertAllClose(f_tf(x).numpy(), f_tf_rt(x).numpy())\n+\nif __name__ == \"__main__\":\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"new_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"diff": "# See the License for the specific language governing permissions and\n# limitations under the License.\n-import os\n-\nfrom absl.testing import absltest\nimport jax\n@@ -32,15 +30,6 @@ config.parse_flags_with_absl()\nclass SavedModelTest(tf_test_util.JaxToTfTestCase):\n- def save_and_load_model(self, model: tf.Module) -> tf.Module:\n- # Roundtrip through saved model on disk.\n- model_dir = os.path.join(absltest.get_default_test_tmpdir(), str(id(model)))\n- tf.saved_model.save(\n- model, model_dir,\n- options=tf.saved_model.SaveOptions(experimental_custom_gradients=True))\n- restored_model = tf.saved_model.load(model_dir)\n- return restored_model\n-\ndef test_eval(self):\nf_jax = jax.jit(lambda x: jnp.sin(jnp.cos(x)))\nmodel = tf.Module()\n@@ -50,7 +39,7 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\n)\nx = np.array(0.7, dtype=jnp.float32)\nself.assertAllClose(model.f(x), f_jax(x))\n- restored_model = self.save_and_load_model(model)\n+ restored_model = tf_test_util.SaveAndLoadModel(model)\nself.assertAllClose(restored_model.f(x), f_jax(x))\ndef test_gradient_disabled(self):\n@@ -62,7 +51,7 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\ninput_signature=[tf.TensorSpec([], tf.float32)])\nx = np.array(0.7, dtype=jnp.float32)\nself.assertAllClose(model.f(x), f_jax(x))\n- restored_model = self.save_and_load_model(model)\n+ restored_model = tf_test_util.SaveAndLoadModel(model)\nxv = tf.Variable(0.7, dtype=jnp.float32)\nself.assertAllClose(restored_model.f(x), f_jax(x))\n@@ -92,7 +81,7 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\ninput_signature=[tf.TensorSpec([], tf.float32)])\nx = np.array(0.7, dtype=jnp.float32)\nself.assertAllClose(model.f(x), f_jax(x))\n- restored_model = self.save_and_load_model(model)\n+ restored_model = tf_test_util.SaveAndLoadModel(model)\nxv = tf.Variable(0.7, dtype=jnp.float32)\nself.assertAllClose(restored_model.f(x), f_jax(x))\nwith tf.GradientTape() as tape:\n@@ -106,14 +95,9 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\n# JAX. We check that this information is preserved through a savedmodel\nf_tf = jax2tf.convert(f_jax)\nres = f_tf(*args)\n-\n- model = tf.Module()\ninput_signature = list(tf.TensorSpec(a.shape, a.dtype) for a in args)\n- model.f = tf.function(f_tf,\n- autograph=False,\n- input_signature=input_signature)\n- restored_model = self.save_and_load_model(model)\n- res_restored = restored_model.f(*args)\n+ restored_f = tf_test_util.SaveAndLoadFunction(f_tf, input_signature)\n+ res_restored = restored_f(*args)\nself.assertAllClose(res, res_restored)\ndef test_xla_context_preserved_slice(self):\n@@ -159,12 +143,9 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\njnp.sin(np.array([3.14, 2.78], dtype=np.float16)))\n# Save and restore SavedModel\n- model = tf.Module()\n- model.f = tf.function(\n- composed_fn,\n- input_signature=[tf.TensorSpec((2,), dtype=tf.string)])\n- restored_model = self.save_and_load_model(model)\n- res_tf_restored = restored_model.f(x_str)\n+ restored_f = tf_test_util.SaveAndLoadFunction(composed_fn,\n+ [tf.TensorSpec((2,), dtype=tf.string)])\n+ res_tf_restored = restored_f(x_str)\nself.assertAllClose(res_tf_restored.numpy(), res_tf.numpy())\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/shape_poly_test.py",
"new_path": "jax/experimental/jax2tf/tests/shape_poly_test.py",
"diff": "@@ -609,6 +609,16 @@ class ShapePolyTest(tf_test_util.JaxToTfTestCase):\nres_jax,\njax2tf.convert(f, polymorphic_shapes=[\"(b, h)\", \"h\"])(x, y))\n+ def test_saved_model_shape_poly(self):\n+ f_jax = jnp.sin\n+ f_tf = jax2tf.convert(f_jax, polymorphic_shapes=[\"(b, ...)\"])\n+ x = np.array([0.7, 0.8], dtype=np.float32)\n+ restored_f = tf_test_util.SaveAndLoadFunction(f_tf, [tf.TensorSpec([None], x.dtype)])\n+ self.assertAllClose(f_jax(x), restored_f(x))\n+ # Ensure that restored_f works at other batch size as well\n+ y = np.concatenate([x, x])\n+ self.assertAllClose(f_jax(y), restored_f(y))\n+\ndef test_readme_example(self):\n\"\"\"Some of the examples from the README.\"\"\"\ndef image_mask_jax(images, mask):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/tf_test_util.py",
"new_path": "jax/experimental/jax2tf/tests/tf_test_util.py",
"diff": "import contextlib\nimport dataclasses\nimport logging\n+import os\nfrom typing import Any, Callable, List, Optional, Sequence\n-\n+from absl.testing import absltest\nimport jax\nfrom jax import dtypes\nfrom jax import numpy as jnp\n@@ -74,6 +75,26 @@ class OpMetadataGraph:\nsource_line: str\n+def SaveAndLoadModel(model: tf.Module) -> tf.Module:\n+ # Roundtrip through saved model on disk.\n+ model_dir = os.path.join(absltest.get_default_test_tmpdir(), str(id(model)))\n+ tf.saved_model.save(\n+ model, model_dir,\n+ options=tf.saved_model.SaveOptions(experimental_custom_gradients=True))\n+ restored_model = tf.saved_model.load(model_dir)\n+ return restored_model\n+\n+def SaveAndLoadFunction(f_tf: Callable,\n+ input_signature: Sequence[tf.TensorSpec]) -> Callable:\n+ # Roundtrip through saved model on disk\n+ model = tf.Module()\n+ model.f = tf.function(f_tf,\n+ autograph=False,\n+ input_signature=input_signature)\n+ restored = SaveAndLoadModel(model)\n+ return restored.f\n+\n+\nclass JaxToTfTestCase(jtu.JaxTestCase):\ndef setUp(self):\n@@ -344,7 +365,6 @@ class JaxToTfTestCase(jtu.JaxTestCase):\nreturn tree_util.tree_multimap(polymorphic_shape_to_tensorspec, polymorphic_shapes)\n-\ndef CheckOpMetadata(self, jax_fun, x,\nexpected: Sequence[OpMetadataGraph],\ninclude_xla_op_metadata=True):\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Fix the round-trip call_tf(convert)
Also cleaned the handling of global state in jax2tf. |
260,411 | 12.06.2021 11:42:15 | -10,800 | edd96884c7309adef01d9242dbe3b71a8e983039 | [jax2tf] Extend shape polymorphism to handle add_transpose with broadcasting | [
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/lax.py",
"new_path": "jax/_src/lax/lax.py",
"diff": "@@ -656,7 +656,7 @@ def dot(lhs: Array, rhs: Array, precision: PrecisionLike = None,\nReturns:\nAn array containing the product.\n\"\"\"\n- if 1 <= lhs.ndim <= 2 and 1 <= rhs.ndim <= 2 and lhs.shape[-1] == rhs.shape[0]:\n+ if 1 <= lhs.ndim <= 2 and 1 <= rhs.ndim <= 2 and core.symbolic_equal_dim(lhs.shape[-1], rhs.shape[0]):\nreturn dot_general(lhs, rhs, (((lhs.ndim - 1,), (0,)), ((), ())),\nprecision=precision, preferred_element_type=preferred_element_type)\nelse:\n@@ -2274,22 +2274,22 @@ def _unbroadcast(aval, x):\nif not isinstance(aval, ShapedArray):\nraise TypeError(\"transpose with implicit broadcasting of unshaped values\")\nx_shape = np.shape(x)\n- if aval.shape == x_shape:\n+ if core.symbolic_equal_shape(aval.shape, x_shape):\nreturn x\nassert not aval.shape or len(x_shape) == len(aval.shape)\nif not aval.shape:\nreturn _reduce_sum(x, list(range(len(x_shape))))\nelse:\n- dims = [i for i, (a, b) in enumerate(zip(x_shape, aval.shape)) if a != b]\n+ dims = [i for i, (a, b) in enumerate(zip(x_shape, aval.shape)) if not core.symbolic_equal_dim(a, b)]\nif config.jax_enable_checks: assert all(aval.shape[i] == 1 for i in dims)\nreturn reshape(_reduce_sum(x, dims), aval.shape)\ndef _maybe_broadcast(target_shape, x):\nx_shape = np.shape(x)\n- if x_shape == target_shape:\n+ if core.symbolic_equal_shape(x_shape, target_shape):\nreturn x\nelse:\n- dims = [i for i, (a, b) in enumerate(zip(x_shape, target_shape)) if a == b]\n+ dims = [i for i, (a, b) in enumerate(zip(x_shape, target_shape)) if core.symbolic_equal_dim(a, b)]\nsqueeze_shape = [x_shape[i] for i in dims]\nreturn broadcast_in_dim(reshape(x, squeeze_shape), target_shape, dims)\n@@ -2941,7 +2941,7 @@ def _conv_general_dilated_shape_rule(\nmsg = (\"conv_general_dilated feature_group_count must divide lhs feature \"\n\"dimension size, but {} does not divide {}.\")\nraise ValueError(msg.format(feature_group_count, lhs_feature_count))\n- if quot != rhs.shape[dimension_numbers.rhs_spec[1]]:\n+ if not core.symbolic_equal_dim(quot, rhs.shape[dimension_numbers.rhs_spec[1]]):\nmsg = (\"conv_general_dilated lhs feature dimension size divided by \"\n\"feature_group_count must equal the rhs input feature dimension \"\n\"size, but {} // {} != {}.\")\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/README.md",
"new_path": "jax/experimental/jax2tf/README.md",
"diff": "@@ -880,7 +880,7 @@ the ``a_inference_cos_tf_68__``HLO function that was compiled by TF from ``cos_t\n## TensorFlow versions supported\nThe ``jax2tf.convert`` and `call_tf` require very recent versions of TensorFlow.\n-As of today, the tests are run using `tf_nightly==2.6.0-dev20210601`.\n+As of today, the tests are run using `tf_nightly==2.6.0-dev20210611`.\n## Running on GPU\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/shape_poly_test.py",
"new_path": "jax/experimental/jax2tf/tests/shape_poly_test.py",
"diff": "@@ -884,6 +884,11 @@ _POLY_SHAPE_TEST_HARNESSES = [\n[RandArg((3, 4), _f32)],\npoly_axes=[0]),\n+ _make_harness(\"add_transpose\", \"\",\n+ jax.grad(lambda x: jnp.sum(jnp.sum(x, axis=0, keepdims=0) + x)),\n+ [RandArg((3, 4), _f32)],\n+ poly_axes=[0]),\n+\n_make_harness(\"clamp\", \"\",\nlax.clamp,\n[RandArg((3, 4, 5), _f32), RandArg((3, 4, 5), _f32),\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Extend shape polymorphism to handle add_transpose with broadcasting |
260,411 | 10.06.2021 12:42:40 | -7,200 | dd8ab851214b855dd5073c1b68b1f3e89ab3cb85 | [jax2tf] Support inequality and min/max for booleans.
For inequalities we add casts to int8. For min/max we rewrite
to logical operations and/or. | [
{
"change_type": "MODIFY",
"old_path": "CHANGELOG.md",
"new_path": "CHANGELOG.md",
"diff": "@@ -11,6 +11,8 @@ PLEASE REMEMBER TO CHANGE THE '..master' WITH AN ACTUAL TAG in GITHUB LINK.\n## jax 0.2.15 (unreleased)\n* [GitHub commits](https://github.com/google/jax/compare/jax-v0.2.14...master).\n* New features:\n+ * The {func}`jax2tf.convert` supports inequalities and min/max for booleans\n+ ({jax-issue}`#6956`).\n* Breaking changes:\n* Support for NumPy 1.16 has been dropped, per the\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/_src/lax/lax.py",
"new_path": "jax/_src/lax/lax.py",
"diff": "@@ -2907,6 +2907,12 @@ def _bitcast_convert_type_shape_rule(operand, *, new_dtype):\nreturn operand.shape\ndef _bitcast_convert_type_dtype_rule(operand, *, new_dtype):\n+ old_dtype = dtypes.canonicalize_dtype(operand.dtype)\n+ if dtypes.issubdtype(old_dtype, np.bool_) or dtypes.issubdtype(old_dtype, np.complexfloating):\n+ if old_dtype != new_dtype:\n+ raise TypeError(f\"`bitcast_convert_type` for operand type ({old_dtype}) cannot have different destination type ({new_dtype})\")\n+ if np.dtype(old_dtype).itemsize != np.dtype(new_dtype).itemsize:\n+ raise TypeError(f\"`bitcast_convert_type` for operand type ({old_dtype}) must have destination type ({new_dtype}) of same size.\")\nreturn new_dtype\ndef _bitcast_convert_type_translation_rule(c, operand, *, new_dtype):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md",
"new_path": "jax/experimental/jax2tf/g3doc/jax_primitives_coverage.md",
"diff": "# Primitives with limited JAX support\n-*Last generated on: 2021-06-04* (YYYY-MM-DD)\n+*Last generated on: 2021-06-12* (YYYY-MM-DD)\n## Supported data types for primitives\n-We use a set of 2570 test harnesses to test\n+We use a set of 2604 test harnesses to test\nthe implementation of 121 numeric JAX primitives.\nWe consider a JAX primitive supported for a particular data\ntype if it is supported on at least one device type.\n@@ -77,7 +77,7 @@ be updated.\n| digamma | 4 | floating | bool, complex, integer |\n| div | 20 | inexact, integer | bool |\n| dot_general | 245 | all | |\n-| dynamic_slice | 32 | all | |\n+| dynamic_slice | 64 | all | |\n| dynamic_update_slice | 21 | all | |\n| eig | 72 | inexact | bool, integer |\n| eigh | 36 | inexact | bool, integer |\n@@ -134,10 +134,10 @@ be updated.\n| rev | 19 | all | |\n| round | 7 | floating | bool, complex, integer |\n| rsqrt | 6 | inexact | bool, integer |\n-| scatter_add | 14 | inexact, integer | bool |\n+| scatter_add | 15 | all | |\n| scatter_max | 15 | all | |\n| scatter_min | 19 | all | |\n-| scatter_mul | 14 | inexact, integer | bool |\n+| scatter_mul | 15 | all | |\n| select | 16 | all | |\n| select_and_gather_add | 15 | floating | bool, complex, integer |\n| select_and_scatter_add | 27 | bool, floating, integer | complex |\n@@ -197,8 +197,10 @@ and search for \"limitation\".\n|eigh|unimplemented|bfloat16, float16|cpu, gpu|\n|lu|unimplemented|bfloat16, float16|cpu, gpu, tpu|\n|qr|unimplemented|bfloat16, float16|cpu, gpu|\n+|scatter_add|unimplemented|bool|cpu, gpu, tpu|\n|scatter_max|unimplemented|complex64|tpu|\n|scatter_min|unimplemented|complex64|tpu|\n+|scatter_mul|unimplemented|bool|cpu, gpu, tpu|\n|select_and_scatter_add|works only for 2 or more inactive dimensions|all|tpu|\n|svd|complex not implemented. Works in JAX for CPU and GPU with custom kernels|complex|tpu|\n|svd|unimplemented|bfloat16, float16|cpu, gpu|\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md",
"new_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md",
"diff": "# Primitives with limited support for jax2tf\n-*Last generated on (YYYY-MM-DD): 2021-06-04*\n+*Last generated on (YYYY-MM-DD): 2021-06-12*\nThis document summarizes known limitations of the jax2tf conversion.\nThere are several kinds of limitations.\n@@ -33,7 +33,7 @@ The errors apply only for certain devices and compilation modes (\"eager\",\nOn TPU only the \"compiled\" mode is relevant.\nOur priority is to ensure same coverage and numerical behavior with JAX\n-in the \"compiled\" mode, **when using XLA to compile the converted program**.\n+in the \"compiled\" mode, i.e., **when using XLA to compile the converted program**.\nWe are pretty close to that goal.\nThis table only shows errors for cases that are working in JAX (see [separate\n@@ -60,20 +60,15 @@ More detailed information can be found in the\n| --- | --- | --- | --- | --- |\n| bessel_i0e | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n| bessel_i1e | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n-| bitcast_convert_type | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| cholesky | TF test skipped: Not implemented in JAX: unimplemented | float16 | cpu, gpu | compiled, eager, graph |\n| cholesky | TF error: function not compilable | complex | cpu, gpu | compiled |\n| cholesky | TF error: op not defined for dtype | complex | tpu | compiled, graph |\n| clamp | TF test skipped: Not implemented in JAX: unimplemented | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n-| clamp | TF error: op not defined for dtype | complex | cpu, gpu, tpu | compiled, eager, graph |\n| conv_general_dilated | TF test skipped: Not implemented in JAX: preferred_element_type not implemented for integers | int16, int32, int8 | gpu | compiled, eager, graph |\n| conv_general_dilated | TF test skipped: Not implemented in JAX: preferred_element_type=c128 not implemented | complex64 | tpu | compiled, eager, graph |\n| conv_general_dilated | TF test skipped: Not implemented in JAX: preferred_element_type=f64 not implemented | bfloat16, float16, float32 | tpu | compiled, eager, graph |\n| conv_general_dilated | TF test skipped: Not implemented in JAX: preferred_element_type=i64 not implemented | int16, int32, int8 | tpu | compiled, eager, graph |\n| conv_general_dilated | TF error: jax2tf BUG: batch_group_count > 1 not yet converted | all | cpu, gpu, tpu | compiled, eager, graph |\n-| cummax | TF error: op not defined for dtype | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n-| cummin | TF error: op not defined for dtype | uint64 | cpu, gpu | eager |\n-| cummin | TF error: op not defined for dtype | bool, complex | cpu, gpu, tpu | compiled, eager, graph |\n| digamma | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n| div | TF error: TF integer division fails if divisor contains 0; JAX returns NaN | integer | cpu, gpu, tpu | compiled, eager, graph |\n| dot_general | TF error: Numeric comparision disabled: Non-deterministic NaN for dot_general with preferred_element_type on GPU (b/189287598) | bfloat16, complex64, float16, float32 | gpu | compiled |\n@@ -92,42 +87,31 @@ More detailed information can be found in the\n| erf_inv | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu | eager, graph |\n| erfc | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n| fft | TF error: TF function not compileable | complex128, float64 | cpu, gpu | compiled |\n-| ge | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n-| gt | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| igamma | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu | eager, graph |\n| igammac | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu | eager, graph |\n| integer_pow | TF error: op not defined for dtype | int16, int8, unsigned | cpu, gpu | graph |\n-| le | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| lgamma | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n-| lt | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| lu | TF test skipped: Not implemented in JAX: unimplemented | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n| lu | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n-| max | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n-| min | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| neg | TF error: op not defined for dtype | unsigned | cpu, gpu, tpu | compiled, eager, graph |\n| nextafter | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n| qr | TF test skipped: Not implemented in JAX: unimplemented | bfloat16, float16 | cpu, gpu | compiled, eager, graph |\n| qr | TF error: op not defined for dtype | bfloat16 | tpu | compiled, eager, graph |\n| reduce_max | TF error: op not defined for dtype | complex | cpu, gpu, tpu | compiled, eager, graph |\n| reduce_min | TF error: op not defined for dtype | complex | cpu, gpu, tpu | compiled, eager, graph |\n-| reduce_window_max | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n-| reduce_window_min | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| regularized_incomplete_beta | TF error: op not defined for dtype | bfloat16, float16 | cpu, gpu, tpu | compiled, eager, graph |\n| rem | TF error: TF integer division fails if divisor contains 0; JAX returns NaN | integer | cpu, gpu, tpu | compiled, eager, graph |\n| round | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n| rsqrt | TF error: op not defined for dtype | bfloat16 | cpu, gpu | eager, graph |\n-| scatter_add | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n+| scatter_add | TF test skipped: Not implemented in JAX: unimplemented | bool | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_add | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n| scatter_max | TF test skipped: Not implemented in JAX: unimplemented | complex64 | tpu | compiled, eager, graph |\n-| scatter_max | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_min | TF test skipped: Not implemented in JAX: unimplemented | complex64 | tpu | compiled, eager, graph |\n-| scatter_min | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n-| scatter_mul | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n+| scatter_mul | TF test skipped: Not implemented in JAX: unimplemented | bool | cpu, gpu, tpu | compiled, eager, graph |\n| scatter_mul | TF error: op not defined for dtype | complex64 | tpu | compiled, eager, graph |\n| select_and_gather_add | TF error: jax2tf unimplemented for 64-bit inputs because the current implementation relies on packing two values into a single value. This can be fixed by using a variadic XlaReduceWindow, when available | float64 | cpu, gpu | compiled, eager, graph |\n| select_and_scatter_add | TF test skipped: Not implemented in JAX: works only for 2 or more inactive dimensions | all | tpu | compiled, eager, graph |\n| sign | TF error: sign not defined for unsigned integers | unsigned | cpu, gpu, tpu | compiled, eager, graph |\n-| sort | TF error: op not defined for dtype | bool | cpu, gpu, tpu | compiled, eager, graph |\n| svd | TF test skipped: Not implemented in JAX: complex not implemented. Works in JAX for CPU and GPU with custom kernels | complex | tpu | compiled, eager, graph |\n| svd | TF test skipped: Not implemented in JAX: unimplemented | bfloat16, float16 | cpu, gpu | compiled, eager, graph |\n| svd | TF error: function not compilable. Implemented using `tf.linalg.svd` and `tf.linalg.adjoint` | complex | cpu, gpu | compiled |\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md.template",
"new_path": "jax/experimental/jax2tf/g3doc/primitives_with_limited_support.md.template",
"diff": "@@ -33,7 +33,7 @@ The errors apply only for certain devices and compilation modes (\"eager\",\nOn TPU only the \"compiled\" mode is relevant.\nOur priority is to ensure same coverage and numerical behavior with JAX\n-in the \"compiled\" mode, **when using XLA to compile the converted program**.\n+in the \"compiled\" mode, i.e., **when using XLA to compile the converted program**.\nWe are pretty close to that goal.\nThis table only shows errors for cases that are working in JAX (see [separate\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -1162,6 +1162,8 @@ def _minmax(x: TfVal, y: TfVal, *, is_min: bool,\npartial(lax._minmax_complex_lowering,\nlax_cmp_pick_x=lax.lt if is_min else lax.gt),\nmultiple_results=False)(x, y, _in_avals=_in_avals, _out_aval=_out_aval)\n+ elif x.dtype.as_numpy_dtype == np.bool_:\n+ return (tf.math.logical_and if is_min else tf.math.logical_or)(x, y)\nelse:\nreturn (tf.math.minimum if is_min else tf.math.maximum)(x, y)\n@@ -1287,19 +1289,37 @@ def _not(x):\ntf_impl[lax.not_p] = _not\n-def bool_to_int8(f, argnums):\n- \"\"\"Computes bool valued functions using int8.\"\"\"\n+def bool_to_int8(f, argnums: Sequence[int]):\n+ \"\"\"Computes functions with some bool args and bool results using int8.\n+\n+ This is needed because some TF ops do not work for bool args, e.g.,\n+ inequalities, min/max.\n+\n+ Args:\n+ f: a TF callable to wrap. It will be called with non-boolean arguments.\n+ argnums: the positional arguments that may be booleans.\n+\n+ Returns: a TF callable that can take a mix of boolean positional arguments\n+ (in the positions specified by `argnums`) and some non-boolean positional\n+ arguments. If there are no boolean arguments, just calls `f`. Otherwise,\n+ casts the boolean arguments to `int8`, calls `f`, then casts the result to\n+ `bool`.\n+ \"\"\"\nargnums = tf.nest.flatten(argnums)\n- def wrapper(*args, **kwargs):\n- if not any(args[i].dtype == tf.bool for i in argnums):\n+ def wrapper(*args: TfVal, **kwargs):\n+ argnum_types = {args[i].dtype for i in argnums}\n+ if tf.bool not in argnum_types:\nreturn f(*args, **kwargs)\nelse:\n+ # All argnums should be boolean\n+ assert len(argnum_types) == 1, argnum_types\nargs_cast = [(tf.cast(a, tf.int8) if i in argnums else a)\nfor i, a in enumerate(args)]\nif \"_in_avals\" in kwargs:\ndef cast_aval(aval):\n+ assert aval.dtype == np.bool_\nreturn core.ShapedArray(aval.shape, np.int8)\n_in_avals_cast = [\n@@ -1321,10 +1341,11 @@ tf_impl[lax.xor_p] = bool_to_int8(tf.bitwise.bitwise_xor, argnums=(0, 1))\ntf_impl[lax.eq_p] = tf.math.equal\ntf_impl[lax.ne_p] = tf.math.not_equal\n-tf_impl[lax.ge_p] = tf.math.greater_equal\n-tf_impl[lax.gt_p] = tf.math.greater\n-tf_impl[lax.le_p] = tf.math.less_equal\n-tf_impl[lax.lt_p] = tf.math.less\n+\n+tf_impl[lax.ge_p] = bool_to_int8(tf.math.greater_equal, argnums=(0, 1))\n+tf_impl[lax.gt_p] = bool_to_int8(tf.math.greater, argnums=(0, 1))\n+tf_impl[lax.le_p] = bool_to_int8(tf.math.less_equal, argnums=(0, 1))\n+tf_impl[lax.lt_p] = bool_to_int8(tf.math.less, argnums=(0, 1))\ntf_impl[lax_linalg.cholesky_p] = tf.linalg.cholesky\n@@ -1346,6 +1367,8 @@ tf_impl[lax.convert_element_type_p] = _convert_element_type\ndef _bitcast_convert_type(operand, new_dtype):\n+ if operand.dtype == new_dtype:\n+ return operand\nreturn tf.bitcast(operand, _to_tf_dtype(new_dtype))\n@@ -1767,13 +1790,13 @@ tf_impl[lax.transpose_p] = _transpose\naxes_to_axis = lambda func: lambda operand, axes: func(operand, axis=axes)\ntf_impl[lax.reduce_sum_p] = (\n- bool_to_int8(axes_to_axis(tf.reduce_sum), argnums=0))\n+ bool_to_int8(axes_to_axis(tf.reduce_sum), argnums=[0]))\ntf_impl[lax.reduce_prod_p] = (\n- bool_to_int8(axes_to_axis(tf.reduce_prod), argnums=0))\n+ bool_to_int8(axes_to_axis(tf.reduce_prod), argnums=[0]))\ntf_impl[lax.reduce_max_p] = (\n- bool_to_int8(axes_to_axis(tf.reduce_max), argnums=0))\n+ bool_to_int8(axes_to_axis(tf.reduce_max), argnums=[0]))\ntf_impl[lax.reduce_min_p] = (\n- bool_to_int8(axes_to_axis(tf.reduce_min), argnums=0))\n+ bool_to_int8(axes_to_axis(tf.reduce_min), argnums=[0]))\ntf_impl[lax.reduce_or_p] = axes_to_axis(tf.reduce_any)\ntf_impl[lax.reduce_and_p] = axes_to_axis(tf.reduce_all)\n@@ -2178,7 +2201,7 @@ def _gather_dimensions_proto(indices_shape, dimension_numbers):\nreturn proto\n-@partial(bool_to_int8, argnums=0)\n+@partial(bool_to_int8, argnums=[0])\ndef _gather(operand, start_indices, *, dimension_numbers, slice_sizes,\nindices_are_sorted, unique_indices,\n_in_avals, _out_aval):\n@@ -2446,25 +2469,6 @@ def _sort(*operands: TfVal, dimension: int, is_stable: bool,\noperands[0].shape\n), f\"Invalid {dimension} for ndim {len(operands[0].shape)}\"\n- # The comparator is a 2N-argument TF function, with arguments [2k] and [2k +1]\n- # corresponding to two scalars from operand[k].\n- def lexicographic_comparator_old(*tf_args: TfVal) -> TfVal:\n- assert len(tf_args) == 2 * len(operands)\n- # We build a comparison:\n- # arg[0] < arg[1] or (arg[0] == arg[1] and (arg[2] < arg[3] or ...))\n- # all the way to arg[2 * num_keys - 2] < arg[2 * num_keys - 1]\n- inside_comparison = None\n- for key_idx in range(num_keys - 1, -1, -1):\n- a = tf_args[2 * key_idx]\n- b = tf_args[2 * key_idx + 1]\n- a_lt_b = tf.math.less(a, b)\n- if inside_comparison is None:\n- inside_comparison = a_lt_b\n- else:\n- inside_comparison = tf.math.logical_or(\n- a_lt_b, tf.math.logical_and(tf.math.equal(a, b), inside_comparison))\n- return inside_comparison\n-\ncomparator_spec: List[tf.TensorSpec] = []\ncomparator_jax_in_avals: List[core.AbstractValue] = []\nfor op in operands:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/jax2tf_limitations.py",
"new_path": "jax/experimental/jax2tf/tests/jax2tf_limitations.py",
"diff": "@@ -109,13 +109,13 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ngroup_method = getattr(cls, harness.group_name, None)\nif harness.group_name in cls.harness_groups_no_limitations:\nassert group_method is None, (\n- f\"Harness group {harness.group_name} is both in \"\n+ f\"Harness group '{harness.group_name}' is both in \"\nf\"'harness_groups_no_limitations' and has a custom \"\nf\"Jax2TfLimitation.classmethod defined (see module docstring)\")\nreturn []\nelse:\nassert group_method is not None, (\n- f\"Harness group {harness.group_name} must be either part of \"\n+ f\"Harness group '{harness.group_name}' must be either part of \"\nf\"'harness_groups_no_limitations' or must have a custom \"\nf\"Jax2TfLimitation.classmethod defined (see module docstring)\")\nlimitations = group_method(harness)\n@@ -124,16 +124,19 @@ class Jax2TfLimitation(primitive_harness.Limitation):\n# We keep here the explicit set of groups for which we don't have limitations\nharness_groups_no_limitations = {\n- \"abs\", \"and\", \"argmin\", \"argmax\", \"atan2\", \"broadcast\",\n- \"broadcast_in_dim\", \"ceil\",\n- \"concatenate\", \"cos\", \"cosh\", \"complex\", \"conj\",\n- \"device_put\", \"dynamic_slice\",\n- \"dynamic_update_slice\", \"exp\", \"eq\", \"floor\", \"log\", \"gather\", \"imag\",\n- \"iota\", \"is_finite\", \"ne\", \"not\", \"or\", \"pad\", \"random_split\",\n- \"reduce_and\", \"reduce_prod\", \"reduce_or\", \"reduce_sum\", \"real\", \"reshape\",\n- \"rev\",\n- \"select\", \"shift_left\", \"shift_right_logical\", \"shift_right_arithmetic\",\n- \"sin\", \"sinh\", \"slice\", \"sqrt\", \"squeeze\", \"stop_gradient\",\n+ \"abs\", \"add\", \"add_any\", \"and\", \"argmin\", \"argmax\", \"atan2\",\n+ \"bitcast_convert_type\", \"broadcast\", \"broadcast_in_dim\", \"ceil\", \"clamp\",\n+ \"concatenate\", \"cos\", \"cosh\", \"complex\", \"conj\", \"convert_element_type\",\n+ \"cummax\", \"cummin\", \"device_put\", \"dynamic_slice\",\n+ \"dynamic_update_slice\", \"exp\", \"eq\", \"floor\", \"gather\", \"ge\", \"gt\", \"imag\",\n+ \"iota\", \"is_finite\", \"le\", \"lt\", \"log\", \"mul\", \"ne\", \"not\", \"or\", \"pad\",\n+ \"population_count\", \"random_split\",\n+ \"reduce_and\", \"reduce_prod\", \"reduce_or\", \"reduce_sum\",\n+ \"reduce_window_add\", \"reduce_window_mul\", \"reduce_window_min\", \"reduce_window_max\",\n+ \"real\", \"reshape\", \"rev\", \"scatter_max\", \"scatter_min\",\n+ \"select\", \"select_and_scatter_add\",\n+ \"shift_left\", \"shift_right_logical\", \"shift_right_arithmetic\",\n+ \"sin\", \"sinh\", \"slice\", \"sqrt\", \"squeeze\", \"stop_gradient\", \"sub\",\n\"tie_in\", \"transpose\", \"xor\", \"zeros_like\"\n}\n@@ -173,15 +176,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ncls.helper_get_trig_custom_limitation(np.cosh)\n]\n- @classmethod\n- def add(cls, harness: primitive_harness.Harness):\n- return []\n-\n- @classmethod\n- # Also called add_jaxvals\n- def add_any(cls, harness: primitive_harness.Harness):\n- return []\n-\n@classmethod\ndef asin(cls, harness: primitive_harness.Harness):\nreturn [\n@@ -228,10 +222,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ndef bessel_i1e(cls, harness: primitive_harness.Harness):\nreturn cls.bessel_i0e(harness)\n- @classmethod\n- def bitcast_convert_type(cls, harness: primitive_harness.Harness):\n- return [missing_tf_kernel(dtypes=[np.bool_])]\n-\n@classmethod\ndef cholesky(cls, harness: primitive_harness.Harness):\n@@ -280,16 +270,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\nmodes=(\"eager\", \"graph\", \"compiled\"))\n]\n- @classmethod\n- def clamp(cls, harness: primitive_harness.Harness):\n- return [\n- missing_tf_kernel(dtypes=[np.complex64, np.complex128]),\n- ]\n-\n- @classmethod\n- def convert_element_type(cls, harness: primitive_harness.Harness):\n- return []\n-\n@classmethod\ndef conv_general_dilated(cls, harness: primitive_harness.Harness):\nreturn [\n@@ -307,22 +287,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ntol=5e-3)\n]\n- @classmethod\n- def cummax(cls, harness):\n- return [\n- missing_tf_kernel(dtypes=[np.bool_, np.complex64, np.complex128]),\n- ]\n-\n- @classmethod\n- def cummin(cls, harness):\n- return [\n- missing_tf_kernel(dtypes=[np.bool_, np.complex64, np.complex128]),\n- # TODO: we get jax2tf AssertionError\n- missing_tf_kernel(dtypes=[np.uint64],\n- devices=(\"cpu\", \"gpu\"),\n- modes=(\"eager\",)),\n- ]\n-\n@classmethod\ndef cumprod(cls, harness):\nreturn [\n@@ -421,9 +385,7 @@ class Jax2TfLimitation(primitive_harness.Limitation):\n@classmethod\ndef dot_general(cls, harness: primitive_harness.Harness):\nreturn [\n- missing_tf_kernel(dtypes=[\n- np.bool_,\n- ],),\n+ missing_tf_kernel(dtypes=[np.bool_],),\n# TODO(b/189287598)\nJax2TfLimitation(\n\"Non-deterministic NaN for dot_general with preferred_element_type on GPU (b/189287598)\",\n@@ -583,14 +545,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\nmodes=(\"eager\", \"graph\", \"compiled\"))\n]\n- @classmethod\n- def ge(cls, harness: primitive_harness.Harness):\n- return [missing_tf_kernel(dtypes=[np.bool_])]\n-\n- @classmethod\n- def gt(cls, harness: primitive_harness.Harness):\n- return cls.ge(harness)\n-\n@classmethod\ndef erf(cls, harness: primitive_harness.Harness):\nreturn [\n@@ -799,14 +753,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ndef pow(cls, harness: primitive_harness.Harness):\nreturn cls._pow_test_util(harness)\n- @classmethod\n- def le(cls, harness: primitive_harness.Harness):\n- return cls.ge(harness)\n-\n- @classmethod\n- def lt(cls, harness: primitive_harness.Harness):\n- return cls.ge(harness)\n-\n@classmethod\ndef lgamma(cls, harness: primitive_harness.Harness):\nreturn [\n@@ -881,8 +827,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ntst.assertAllClose(result_jax[~mask], result_tf[~mask], err_msg=err_msg)\nreturn [\n- missing_tf_kernel(\n- dtypes=[np.bool_]),\ncustom_numeric(\ncustom_assert=custom_assert,\ndescription=(\n@@ -901,7 +845,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ntst.assertAllClose(result_jax[~mask], result_tf[~mask], err_msg=err_msg)\nreturn [\n- missing_tf_kernel(dtypes=[np.bool_]),\ncustom_numeric(\ncustom_assert=custom_assert,\ndescription=(\n@@ -911,10 +854,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\nmodes=(\"eager\", \"graph\", \"compiled\"))\n]\n- @classmethod\n- def mul(cls, harness: primitive_harness.Harness):\n- return []\n-\n@classmethod\ndef neg(cls, harness: primitive_harness.Harness):\nreturn [\n@@ -925,10 +864,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ndef nextafter(cls, harness: primitive_harness.Harness):\nreturn [missing_tf_kernel(dtypes=[np.float16, dtypes.bfloat16])]\n- @classmethod\n- def population_count(cls, harness: primitive_harness.Harness):\n- return []\n-\n@classmethod\ndef qr(cls, harness: primitive_harness.Harness):\n# See https://github.com/google/jax/pull/3775#issuecomment-659407824;\n@@ -960,39 +895,14 @@ class Jax2TfLimitation(primitive_harness.Limitation):\n@classmethod\ndef reduce_max(cls, harness: primitive_harness.Harness):\n- return [\n- missing_tf_kernel(dtypes=[np.complex64, np.complex128]),\n- ]\n+ # Unlike reduce_window_max, we use a native TF op: tf.reduce_max, which\n+ # does not work for complex\n+ return [missing_tf_kernel(dtypes=[np.complex64, np.complex128])]\n@classmethod\ndef reduce_min(cls, harness: primitive_harness.Harness):\nreturn cls.reduce_max(harness)\n-\n- @classmethod\n- def reduce_window_add(cls, harness):\n- assert \"add\" == harness.params[\"computation\"].__name__\n- return []\n-\n- @classmethod\n- def reduce_window_max(cls, harness):\n- assert \"max\" == harness.params[\"computation\"].__name__\n- return [\n- missing_tf_kernel(dtypes=[np.bool_]),\n- ]\n-\n- @classmethod\n- def reduce_window_min(cls, harness):\n- assert \"min\" == harness.params[\"computation\"].__name__\n- return [\n- missing_tf_kernel(dtypes=[np.bool_]),\n- ]\n-\n- @classmethod\n- def reduce_window_mul(cls, harness):\n- assert \"mul\" == harness.params[\"computation\"].__name__\n- return []\n-\n@classmethod\ndef regularized_incomplete_beta(cls, harness: primitive_harness.Harness):\nreturn [\n@@ -1034,29 +944,15 @@ class Jax2TfLimitation(primitive_harness.Limitation):\n@classmethod\ndef scatter_add(cls, harness):\nreturn [\n- missing_tf_kernel(dtypes=[np.bool_]),\nmissing_tf_kernel(\ndtypes=[np.complex64],\ndevices=\"tpu\",\n)\n]\n- @classmethod\n- def scatter_max(cls, harness):\n- return [\n- missing_tf_kernel(dtypes=[np.bool_]),\n- ]\n-\n- @classmethod\n- def scatter_min(cls, harness):\n- return [\n- missing_tf_kernel(dtypes=[np.bool_]),\n- ]\n-\n@classmethod\ndef scatter_mul(cls, harness):\nreturn [\n- missing_tf_kernel(dtypes=[np.bool_],),\nmissing_tf_kernel(\ndtypes=[np.complex64],\ndevices=\"tpu\",\n@@ -1078,10 +974,6 @@ class Jax2TfLimitation(primitive_harness.Limitation):\ndevices=(\"cpu\", \"gpu\"))\n]\n- @classmethod\n- def select_and_scatter_add(cls, harness):\n- return []\n-\n@classmethod\ndef sign(cls, harness: primitive_harness.Harness):\nreturn [\n@@ -1101,13 +993,8 @@ class Jax2TfLimitation(primitive_harness.Limitation):\nnot harness.params[\"is_stable\"]),\nexpect_tf_error=False,\nskip_comparison=True),\n- missing_tf_kernel(dtypes=[np.bool_],),\n]\n- @classmethod\n- def sub(cls, harness):\n- return []\n-\n@classmethod\ndef svd(cls, harness: primitive_harness.Harness):\n# TODO: slow test\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/primitive_harness.py",
"new_path": "jax/experimental/jax2tf/tests/primitive_harness.py",
"diff": "@@ -1202,7 +1202,11 @@ def _make_scatter_harness(name,\n\"unimplemented\",\ndevices=\"tpu\",\ndtypes=np.complex64,\n- enabled=(f_lax in [lax.scatter_max, lax.scatter_min]))\n+ enabled=(f_lax in [lax.scatter_max, lax.scatter_min])),\n+ Limitation(\n+ \"unimplemented\",\n+ dtypes=np.bool_,\n+ enabled=(f_lax in [lax.scatter_add, lax.scatter_mul])),\n],\nf_lax=f_lax,\nshape=shape,\n@@ -1219,8 +1223,8 @@ for dtype in jtu.dtypes.all:\nfor f_lax in [\nlax.scatter_add, lax.scatter_mul, lax.scatter_max, lax.scatter_min\n]:\n- if f_lax in [lax.scatter_add, lax.scatter_mul] and dtype == np.bool_:\n- continue\n+ #if f_lax in [lax.scatter_add, lax.scatter_mul] and dtype == np.bool_:\n+ # continue\n_make_scatter_harness(\"dtypes\", dtype=dtype, f_lax=f_lax)\n# Validate f_lax/update_jaxpr\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Support inequality and min/max for booleans.
For inequalities we add casts to int8. For min/max we rewrite
to logical operations and/or. |
260,411 | 13.06.2021 17:58:22 | -10,800 | 07cc58122dc0a1b063d2b66618846a63d2163d6e | [jax2tf] Change the InconclusiveDimensionOperation error to include link to documentation | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/README.md",
"new_path": "jax/experimental/jax2tf/README.md",
"diff": "@@ -300,8 +300,8 @@ A few examples of shape specifications and uses:\n### Computing with dimension variables\n-JAX keeps track of the shape of all intermediate results. When those shapes contain\n-dimension variables JAX computes intermediate shapes as multi-variate polynomials\n+JAX keeps track of the shape of all intermediate results. When those shapes depend\n+on dimension variables JAX computes them as multi-variate polynomials\ninvolving dimension variables, which are assumed to range over strictly positive\nintegers.\nThe dimension polynomials have the following behavior for arithmetic operations:\n@@ -325,6 +325,22 @@ The dimension polynomials have the following behavior for arithmetic operations:\nintegers. E.g., `b >= 1`, `b >= 0`, `2 * a + b >= 3` are `True`, while `b >= 2`,\n`a >= b`, `a - b >= 0` are inconclusive and result in an exception.\n+For example, the following code raises the exception\n+`core.InconclusiveDimensionOperation` with the message\n+`Dimension polynomial comparison 'a + 1' == 'b' is inconclusive`.\n+\n+```\n+jax2tf.convert(lambda x: 0 if x.shape[0] + 1 == x.shape[1] else 1,\n+ polymorphic_shapes=[\"(a, b)\"])(np.ones((3, 4))\n+```\n+\n+Note that it would be unsound for JAX to compute `x.shape[0] + 1 == x.shape[1]`\n+as `False` and produce a converted function that returns `1` just because the dimension polynomials\n+are not identical: there are some concrete input shapes for which the function\n+should return `0`.\n+\n+### Dimension variables appearing in the numeric computation\n+\nThere are some situations when dimension variables arise in the staged computation itself.\nYou can see in the following example how elements from the input shapes\n`(1024, 28, 28)` and `(28, 28)` appear in the computation and specifically\n@@ -369,6 +385,9 @@ using `tf.shape` on the input parameters.\n### Errors in presence of shape polymorphism\n+In addition to the `InconclusiveDimensionOperation` error discussed above,\n+one may encounter other kinds of errors.\n+\nWhen tracing with shape polymorphism we can encounter shape errors:\n```\n@@ -420,18 +439,6 @@ jax2tf.convert(lambda x: jnp.reshape(x, (-1, x.shape[0])),\npolymorphic_shapes=[\"(b1, b2, ...)\"])(np.ones((4, 5, 6)))\n```\n-If the user code happens to perform computations directly on dimension polynomials,\n-it can expect it to work as described above for addition, subtraction, and multiplication,\n-and partially for comparisons.\n-\n-```\n-jax2tf.convert(lambda x: 0 if x.shape[0] + 1 == x.shape[1] else 1,\n- polymorphic_shapes=[\"(a, b)\"])(np.ones((3, 4))\n-```\n-\n-will raise the exception `core.InconclusiveDimensionOperation` with the message\n-`Dimension polynomial comparison 'a + 1' == 'b' is inconclusive`.\n-\nFinally, certain codes that use shapes in the actual computation may not yet work\nif those shapes are polymorphic. In the code below, the expression `x.shape[0]`\nwill have the value of the shape variable `v`. This case is not yet implemented:\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/shape_poly.py",
"new_path": "jax/experimental/jax2tf/shape_poly.py",
"diff": "@@ -34,6 +34,25 @@ import numpy as np\nDimSize = core.DimSize\nShape = core.Shape\n+\n+class InconclusiveDimensionOperation(core.InconclusiveDimensionOperation):\n+ \"\"\"Raised when we cannot conclusively compute with symbolic dimensions.\"\"\"\n+\n+ _help_msg = \"\"\"\n+This error arises for arithmetic or comparison operations with shapes that\n+are non-constant, and the result of the operation cannot be represented as\n+a polynomial of dimension variables, or a boolean constant (for comparisons).\n+\n+Please see https://github.com/google/jax/blob/master/jax/experimental/jax2tf/README.md#computing-with-dimension-variables\n+for more details.\n+\"\"\"\n+\n+ def __init__(self, message: str):\n+ error_msg = f\"{message}\\n{InconclusiveDimensionOperation._help_msg}\"\n+ # https://github.com/python/mypy/issues/5887\n+ super().__init__(error_msg) # type: ignore\n+\n+\nclass _DimMon(dict):\n\"\"\"Represents a multivariate monomial, such as n^3 * m.\n@@ -87,7 +106,7 @@ class _DimMon(dict):\ndef divide(self, divisor: '_DimMon') -> '_DimMon':\n\"\"\"\n- Divides by another monomial. Raises a core.InconclusiveDimensionOperation\n+ Divides by another monomial. Raises a InconclusiveDimensionOperation\nif the result is not a monomial.\nFor example, (n^3 * m) // n == n^2*m, but n // m fails.\n\"\"\"\n@@ -95,7 +114,7 @@ class _DimMon(dict):\nfor key, exponent in divisor.items():\ndiff = self.get(key, 0) - exponent\nif diff < 0:\n- raise core.InconclusiveDimensionOperation(f\"Cannot divide {self} by {divisor}.\")\n+ raise InconclusiveDimensionOperation(f\"Cannot divide {self} by {divisor}.\")\nelif diff == 0: del d[key]\nelif diff > 0: d[key] = diff\nreturn _DimMon(d)\n@@ -107,7 +126,7 @@ class _DimPolynomial(dict):\nThe shape variables are assumed to range over integers >= 1.\nWe overload integer operations, but we do that soundly, raising\n- :class:`core.InconclusiveDimensionOperation` when the result is not\n+ :class:`InconclusiveDimensionOperation` when the result is not\nrepresentable as a polynomial.\nThe representation of a polynomial is as a dictionary mapping _DimMonomial to\n@@ -209,7 +228,7 @@ class _DimPolynomial(dict):\nreturn False\nif ub is not None and ub < 0:\nreturn False\n- raise core.InconclusiveDimensionOperation(f\"Dimension polynomial comparison '{self}' == '{other}' is inconclusive\")\n+ raise InconclusiveDimensionOperation(f\"Dimension polynomial comparison '{self}' == '{other}' is inconclusive\")\n# We must overload __eq__ and __ne__, or else we get unsound defaults.\n__eq__ = eq\n@@ -222,7 +241,7 @@ class _DimPolynomial(dict):\nreturn True\nif ub is not None and ub < 0:\nreturn False\n- raise core.InconclusiveDimensionOperation(f\"Dimension polynomial comparison '{self}' >= '{other}' is inconclusive\")\n+ raise InconclusiveDimensionOperation(f\"Dimension polynomial comparison '{self}' >= '{other}' is inconclusive\")\n__ge__ = ge\ndef __le__(self, other: DimSize):\n@@ -253,11 +272,11 @@ class _DimPolynomial(dict):\nmon, count = dividend.leading_term\ntry:\nqmon = mon.divide(dmon)\n- except core.InconclusiveDimensionOperation:\n- raise core.InconclusiveDimensionOperation(err_msg)\n+ except InconclusiveDimensionOperation:\n+ raise InconclusiveDimensionOperation(err_msg)\nqcount, rcount = divmod(count, dcount)\nif rcount != 0:\n- raise core.InconclusiveDimensionOperation(err_msg)\n+ raise InconclusiveDimensionOperation(err_msg)\nq = _DimPolynomial.from_coeffs({qmon: qcount})\nquotient += q\n@@ -270,7 +289,7 @@ class _DimPolynomial(dict):\nremainder = r\nelse:\nif dividend != 0:\n- raise core.InconclusiveDimensionOperation(err_msg)\n+ raise InconclusiveDimensionOperation(err_msg)\nremainder = 0\nif config.jax_enable_checks:\n@@ -295,7 +314,7 @@ class _DimPolynomial(dict):\nif self.is_constant:\nreturn op.index(next(iter(self.values())))\nelse:\n- raise core.InconclusiveDimensionOperation(f\"Dimension polynomial '{self}' is not constant\")\n+ raise InconclusiveDimensionOperation(f\"Dimension polynomial '{self}' is not constant\")\ndef bounds(self) -> Tuple[Optional[int], Optional[int]]:\n\"\"\"Returns the lower and upper bounds, if defined.\"\"\"\n@@ -353,7 +372,7 @@ class DimensionHandlerPoly(core.DimensionHandler):\ndef symbolic_equal(self, d1: core.DimSize, d2: core.DimSize) -> bool:\ntry:\nreturn _ensure_poly(d1) == d2\n- except core.InconclusiveDimensionOperation:\n+ except InconclusiveDimensionOperation:\nreturn False\ndef greater_equal(self, d1: DimSize, d2: DimSize):\n@@ -367,10 +386,10 @@ class DimensionHandlerPoly(core.DimensionHandler):\nerr_msg = f\"Cannot divide evenly the sizes of shapes {tuple(s1)} and {tuple(s2)}\"\ntry:\nq, r = _ensure_poly(sz1).divmod(sz2)\n- except core.InconclusiveDimensionOperation:\n- raise core.InconclusiveDimensionOperation(err_msg)\n+ except InconclusiveDimensionOperation:\n+ raise InconclusiveDimensionOperation(err_msg)\nif r != 0:\n- raise core.InconclusiveDimensionOperation(err_msg)\n+ raise InconclusiveDimensionOperation(err_msg)\nreturn q # type: ignore[return-value]\ndef stride(self, d: DimSize, window_size: DimSize, window_stride: DimSize) -> DimSize:\n@@ -378,8 +397,8 @@ class DimensionHandlerPoly(core.DimensionHandler):\ntry:\nq, r = _ensure_poly(d - window_size).divmod(window_stride)\nreturn q + 1\n- except core.InconclusiveDimensionOperation as e:\n- raise core.InconclusiveDimensionOperation(\n+ except InconclusiveDimensionOperation as e:\n+ raise InconclusiveDimensionOperation(\nf\"Cannot compute stride for dimension '{d}', \"\nf\"window_size '{window_size}', stride '{window_stride}'. Reason: {e}.\")\nreturn d\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Change the InconclusiveDimensionOperation error to include link to documentation |
260,447 | 14.06.2021 14:51:37 | 25,200 | 095e6507b9f8c69c3a5f4c0fcd68033dae428bd6 | Support value computation of associated Legendre functions. | [
{
"change_type": "MODIFY",
"old_path": "docs/jax.scipy.rst",
"new_path": "docs/jax.scipy.rst",
"diff": "@@ -100,6 +100,7 @@ jax.scipy.special\nlogit\nlogsumexp\nlpmn\n+ lpmn_values\nmultigammaln\nndtr\nndtri\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/scipy/special.py",
"new_path": "jax/scipy/special.py",
"diff": "@@ -33,6 +33,7 @@ from jax._src.scipy.special import (\nlogit,\nlogsumexp,\nlpmn,\n+ lpmn_values,\nmultigammaln,\nlog_ndtr,\nndtr,\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/lax_scipy_test.py",
"new_path": "tests/lax_scipy_test.py",
"diff": "@@ -266,6 +266,52 @@ class LaxBackedScipyTests(jtu.JaxTestCase):\nself.assertAllClose(actual_p_derivatives,expected_p_derivatives,\nrtol=1e-6, atol=8.4e-4)\n+ with self.subTest('Test JIT compatibility'):\n+ args_maker = lambda: [z]\n+ lsp_special_fn = lambda z: lsp_special.lpmn(l_max, l_max, z)\n+ self._CompileAndCheck(lsp_special_fn, args_maker)\n+\n+ @parameterized.named_parameters(jtu.cases_from_list(\n+ {\"testcase_name\":\n+ \"_maxdegree={}_inputsize={}\".format(l_max, num_z),\n+ \"l_max\": l_max,\n+ \"num_z\": num_z}\n+ for l_max, num_z in zip([3, 4, 6, 32], [2, 3, 4, 64])))\n+ def testNormalizedLpmnValues(self, l_max, num_z):\n+ # Points on which the associated Legendre functions areevaluated.\n+ z = np.linspace(-0.2, 0.9, num_z)\n+ is_normalized = True\n+ actual_p_vals = lsp_special.lpmn_values(l_max, l_max, z, is_normalized)\n+\n+ # The expected results are obtained from scipy.\n+ expected_p_vals = np.zeros((l_max + 1, l_max + 1, num_z))\n+ for i in range(num_z):\n+ expected_p_vals[:, :, i] = osp_special.lpmn(l_max, l_max, z[i])[0]\n+\n+ def apply_normalization(a):\n+ \"\"\"Applies normalization to the associated Legendre functions.\"\"\"\n+ num_m, num_l, _ = a.shape\n+ a_normalized = np.zeros_like(a)\n+ for m in range(num_m):\n+ for l in range(num_l):\n+ c0 = (2.0 * l + 1.0) * osp_special.factorial(l - m)\n+ c1 = (4.0 * np.pi) * osp_special.factorial(l + m)\n+ c2 = np.sqrt(c0 / c1)\n+ a_normalized[m, l] = c2 * a[m, l]\n+ return a_normalized\n+\n+ # The results from scipy are not normalized and the comparison requires\n+ # normalizing the results.\n+ expected_p_vals_normalized = apply_normalization(expected_p_vals)\n+\n+ with self.subTest('Test accuracy.'):\n+ self.assertAllClose(actual_p_vals, expected_p_vals_normalized, rtol=1e-6, atol=3.2e-6)\n+\n+ with self.subTest('Test JIT compatibility'):\n+ args_maker = lambda: [z]\n+ lsp_special_fn = lambda z: lsp_special.lpmn_values(l_max, l_max, z, is_normalized)\n+ self._CompileAndCheck(lsp_special_fn, args_maker)\n+\nif __name__ == \"__main__\":\nabsltest.main(testLoader=jtu.JaxTestLoader())\n"
}
] | Python | Apache License 2.0 | google/jax | Support value computation of associated Legendre functions.
Co-authored-by: Jake VanderPlas <jakevdp@google.com> |
260,411 | 15.06.2021 11:13:20 | -10,800 | 2888e7ca81a90e989e79af0d640def549c8bd779 | [jax2tf] Add more documentation about saving models with custom gradients | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/README.md",
"new_path": "jax/experimental/jax2tf/README.md",
"diff": "@@ -95,7 +95,8 @@ is trivial:\nmy_model = tf.Module()\n# Save a function that can take scalar inputs.\nmy_model.f = tf.function(jax2tf.convert(f_jax), input_signature=[tf.TensorSpec([], tf.float32)])\n-tf.saved_model.save(my_model, '/some/directory')\n+tf.saved_model.save(my_model, '/some/directory',\n+ options=tf.saved_model.SaveOptions(experimental_custom_gradients=True))\n# Restoring (note: the restored model does *not* require JAX to run, just XLA).\nrestored_model = tf.saved_model.load('/some/directory')\n@@ -113,7 +114,8 @@ SavedModel multiple versions of a function for different input shapes, by\nmy_model.f = tf.function(jax2tf.convert(f_jax), autograph=False)\nmy_model.f(tf.ones([1, 28, 28])) # a batch size of 1\nmy_model.f(tf.ones([16, 28, 28])) # a batch size of 16\n-tf.saved_model.save(my_model, '/some/directory')\n+tf.saved_model.save(my_model, '/some/directory',\n+ options=tf.saved_model.SaveOptions(experimental_custom_gradients=True))\n```\nFor examples of how to save a Flax model as a SavedModel see the\n@@ -144,6 +146,32 @@ options = tf.saved_model.SaveOptions(experimental_custom_gradients=True)\ntf.saved_model.save(model, path, options=options)\n```\n+If you use `with_gradient=True` and forget to use the `experimental_custom_gradients=True` parameter\n+to `tf.saved_model.save` when you later load the saved model you will see a warning:\n+\n+```\n+WARNING:absl:Importing a function (__inference_converted_fun_25) with ops with unsaved custom gradients. Will likely fail if a gradient is requested.\n+```\n+\n+and if you do attempt to take a gradient of the loaded model you may get an error:\n+\n+```\n+TypeError: An op outside of the function building code is being passed\n+a \"Graph\" tensor. It is possible to have Graph tensors\n+leak out of the function building context by including a\n+tf.init_scope in your function building code.\n+For example, the following function will fail:\n+ @tf.function\n+ def has_init_scope():\n+ my_constant = tf.constant(1.)\n+ with tf.init_scope():\n+ added = my_constant * 2\n+The graph tensor has name: args_0:0\n+```\n+\n+(We are working with the TF team to give a more explicit error in this case.)\n+\n+\n## Shape-polymorphic conversion\n**The shape polymorphism support is work in progress. It is meant to be sound,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/examples/saved_model_lib.py",
"new_path": "jax/experimental/jax2tf/examples/saved_model_lib.py",
"diff": "@@ -41,7 +41,7 @@ def convert_and_save_model(\nwith_gradient: bool = False,\nenable_xla: bool = True,\ncompile_model: bool = True,\n- save_model_options: Optional[tf.saved_model.SaveOptions] = None):\n+ saved_model_options: Optional[tf.saved_model.SaveOptions] = None):\n\"\"\"Convert a JAX function and saves a SavedModel.\nThis is an example, for serious uses you will likely want to copy and\n@@ -89,7 +89,7 @@ def convert_and_save_model(\n`polymorphic_shapes` argument to jax2tf.convert for the second parameter of\n`jax_fn`. In this case, a single `input_signatures` is supported, and\nshould have `None` in the polymorphic dimensions.\n- save_model_options: options to pass to savedmodel.save.\n+ saved_model_options: options to pass to savedmodel.save.\n\"\"\"\nif not input_signatures:\nraise ValueError(\"At least one input_signature must be given\")\n@@ -124,8 +124,13 @@ def convert_and_save_model(\n# If there are more signatures, trace and cache a TF function for each one\ntf_graph.get_concrete_function(input_signature)\nwrapper = _ReusableSavedModelWrapper(tf_graph, param_vars)\n+ if with_gradient:\n+ if not saved_model_options:\n+ saved_model_options = tf.saved_model.SaveOptions(experimental_custom_gradients=True)\n+ else:\n+ saved_model_options.experimental_custom_gradients = True\ntf.saved_model.save(wrapper, model_dir, signatures=signatures,\n- options=save_model_options)\n+ options=saved_model_options)\nclass _ReusableSavedModelWrapper(tf.train.Checkpoint):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/call_tf_test.py",
"new_path": "jax/experimental/jax2tf/tests/call_tf_test.py",
"diff": "@@ -415,6 +415,48 @@ class CallTfTest(jtu.JaxTestCase):\nself.assertAllClose(g(x), g_rt(x))\nself.assertAllClose(jax.grad(g)(x), jax.grad(g_rt)(x))\n+ def test_round_trip_without_gradient_saved_model(self):\n+ # Explicitly with_gradient=False\n+ f_jax = jnp.sum\n+\n+ x = np.array([0.7, 0.8], dtype=np.float32)\n+ f_tf = tf_test_util.SaveAndLoadFunction(\n+ jax2tf.convert(f_jax, with_gradient=False),\n+ [tf.TensorSpec(x.shape, dtype=x.dtype)])\n+ f_rt = jax2tf.call_tf(f_tf)\n+\n+ self.assertAllClose(f_jax(x), f_rt(x))\n+ with self.assertRaisesRegex(Exception,\n+ \"Gradient explicitly disabled.*jax2tf-converted function does not support gradients. Use `with_gradient` parameter to enable gradients\"):\n+ jax.grad(f_rt)(x)\n+\n+ def test_round_trip_saved_model_no_gradients(self):\n+ # Save without gradients\n+ f_jax = jnp.sum\n+\n+ x = np.array([0.7, 0.8], dtype=np.float32)\n+ f_tf = tf_test_util.SaveAndLoadFunction(\n+ jax2tf.convert(f_jax, with_gradient=True),\n+ [tf.TensorSpec(x.shape, dtype=x.dtype)],\n+ save_gradients=False)\n+ f_rt = jax2tf.call_tf(f_tf)\n+\n+ self.assertAllClose(f_jax(x), f_rt(x))\n+ # TODO: clean this up b/191117111: it should fail with a clear error\n+ # The following results in a confusing error:\n+ # TypeError: An op outside of the function building code is being passed\n+ # a \"Graph\" tensor. It is possible to have Graph tensors\n+ # leak out of the function building context by including a\n+ # tf.init_scope in your function building code.\n+ # For example, the following function will fail:\n+ # @tf.function\n+ # def has_init_scope():\n+ # my_constant = tf.constant(1.)\n+ # with tf.init_scope():\n+ # added = my_constant * 2\n+ # The graph tensor has name: args_0:0\n+ # g = jax.grad(f_rt)(x)\n+\ndef test_module_documentation(self):\ndef cos_tf(x):\nreturn tf.math.cos(x)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"new_path": "jax/experimental/jax2tf/tests/savedmodel_test.py",
"diff": "@@ -42,26 +42,37 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\nrestored_model = tf_test_util.SaveAndLoadModel(model)\nself.assertAllClose(restored_model.f(x), f_jax(x))\n- def test_gradient_disabled(self):\n- f_jax = lambda x: x * x\n+ def test_gradient(self):\n+ \"\"\"Save and restore the custom gradient.\"\"\"\n+ @jax.custom_jvp\n+ def f_jax(x):\n+ return x * x\n+\n+ @f_jax.defjvp\n+ def f_jax_jvp(primals, tangents):\n+ # 3 * x * x_t\n+ x, = primals\n+ x_dot, = tangents\n+ primal_out = f_jax(x)\n+ tangent_out = x * x_dot * 3.\n+ return primal_out, tangent_out\nmodel = tf.Module()\n- model.f = tf.function(jax2tf.convert(f_jax, with_gradient=False),\n+ model.f = tf.function(jax2tf.convert(f_jax, with_gradient=True),\nautograph=False,\ninput_signature=[tf.TensorSpec([], tf.float32)])\nx = np.array(0.7, dtype=jnp.float32)\nself.assertAllClose(model.f(x), f_jax(x))\nrestored_model = tf_test_util.SaveAndLoadModel(model)\n- xv = tf.Variable(0.7, dtype=jnp.float32)\n+ xv = tf.Variable(x)\nself.assertAllClose(restored_model.f(x), f_jax(x))\n+ with tf.GradientTape() as tape:\n+ y = restored_model.f(xv)\n+ self.assertAllClose(tape.gradient(y, xv).numpy(),\n+ jax.grad(f_jax)(x))\n- with self.assertRaisesRegex(LookupError,\n- \"Gradient explicitly disabled.*The jax2tf-converted function does not support gradients\"):\n- with tf.GradientTape():\n- _ = restored_model.f(xv)\n-\n- def test_gradient(self):\n- \"\"\"Save and restore the custom gradient.\"\"\"\n+ def test_gradient_nested(self):\n+ \"\"\"Save and restore the custom gradient, when combined with other TF code.\"\"\"\n@jax.custom_jvp\ndef f_jax(x):\nreturn x * x\n@@ -76,18 +87,72 @@ class SavedModelTest(tf_test_util.JaxToTfTestCase):\nreturn primal_out, tangent_out\nmodel = tf.Module()\n- model.f = tf.function(jax2tf.convert(f_jax, with_gradient=True),\n+ # After conversion, we wrap with some pure TF code\n+ model.f = tf.function(lambda x: tf.math.sin(jax2tf.convert(f_jax, with_gradient=True)(x)),\nautograph=False,\ninput_signature=[tf.TensorSpec([], tf.float32)])\n+ f_jax_equiv = lambda x: jnp.sin(f_jax(x))\nx = np.array(0.7, dtype=jnp.float32)\n- self.assertAllClose(model.f(x), f_jax(x))\n+ self.assertAllClose(model.f(x), f_jax_equiv(x))\nrestored_model = tf_test_util.SaveAndLoadModel(model)\n- xv = tf.Variable(0.7, dtype=jnp.float32)\n- self.assertAllClose(restored_model.f(x), f_jax(x))\n+ xv = tf.Variable(x)\n+ self.assertAllClose(restored_model.f(x), f_jax_equiv(x))\nwith tf.GradientTape() as tape:\ny = restored_model.f(xv)\nself.assertAllClose(tape.gradient(y, xv).numpy(),\n- jax.grad(f_jax)(x).astype(np.float32))\n+ jax.grad(f_jax_equiv)(x))\n+\n+ def test_gradient_disabled(self):\n+ f_jax = lambda x: x * x\n+\n+ model = tf.Module()\n+ model.f = tf.function(jax2tf.convert(f_jax, with_gradient=False),\n+ autograph=False,\n+ input_signature=[tf.TensorSpec([], tf.float32)])\n+ x = np.array(0.7, dtype=jnp.float32)\n+ self.assertAllClose(model.f(x), f_jax(x))\n+ restored_model = tf_test_util.SaveAndLoadModel(model)\n+ xv = tf.Variable(0.7, dtype=jnp.float32)\n+ self.assertAllClose(restored_model.f(x), f_jax(x))\n+\n+ with self.assertRaisesRegex(LookupError,\n+ \"Gradient explicitly disabled.*The jax2tf-converted function does not support gradients\"):\n+ with tf.GradientTape():\n+ _ = restored_model.f(xv)\n+\n+ def test_save_without_gradients(self):\n+ f_jax = lambda x: x * x\n+\n+ x = np.array(0.7, dtype=jnp.float32)\n+ model = tf.Module()\n+ model.f = tf.function(jax2tf.convert(f_jax, with_gradient=True),\n+ autograph=False,\n+ input_signature=[tf.TensorSpec(x.shape, x.dtype)])\n+\n+ self.assertAllClose(model.f(x), f_jax(x))\n+ restored_model = tf_test_util.SaveAndLoadModel(model,\n+ save_gradients=False)\n+ self.assertAllClose(restored_model.f(x), f_jax(x))\n+\n+ xv = tf.Variable(x)\n+ with tf.GradientTape():\n+ _ = restored_model.f(xv)\n+ # TODO: clean this up b/191117111: it should fail with a clear error\n+ # The following results in a confusing error:\n+ # TypeError: An op outside of the function building code is being passed\n+ # a \"Graph\" tensor. It is possible to have Graph tensors\n+ # leak out of the function building context by including a\n+ # tf.init_scope in your function building code.\n+ # For example, the following function will fail:\n+ # @tf.function\n+ # def has_init_scope():\n+ # my_constant = tf.constant(1.)\n+ # with tf.init_scope():\n+ # added = my_constant * 2\n+ # The graph tensor has name: args_0:0\n+ # g = tape.gradient(res, xv)\n+ #self.assertAllClose(g.numpy(), jax.grad(f_jax)(x))\n+\ndef _compare_with_saved_model(self, f_jax, *args):\n# Certain ops are converted to ensure an XLA context, e.g.,\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/tf_test_util.py",
"new_path": "jax/experimental/jax2tf/tests/tf_test_util.py",
"diff": "@@ -75,23 +75,25 @@ class OpMetadataGraph:\nsource_line: str\n-def SaveAndLoadModel(model: tf.Module) -> tf.Module:\n+def SaveAndLoadModel(model: tf.Module,\n+ save_gradients=True) -> tf.Module:\n# Roundtrip through saved model on disk.\nmodel_dir = os.path.join(absltest.get_default_test_tmpdir(), str(id(model)))\ntf.saved_model.save(\nmodel, model_dir,\n- options=tf.saved_model.SaveOptions(experimental_custom_gradients=True))\n+ options=tf.saved_model.SaveOptions(experimental_custom_gradients=save_gradients))\nrestored_model = tf.saved_model.load(model_dir)\nreturn restored_model\ndef SaveAndLoadFunction(f_tf: Callable,\n- input_signature: Sequence[tf.TensorSpec]) -> Callable:\n+ input_signature: Sequence[tf.TensorSpec],\n+ save_gradients=True) -> Callable:\n# Roundtrip through saved model on disk\nmodel = tf.Module()\nmodel.f = tf.function(f_tf,\nautograph=False,\ninput_signature=input_signature)\n- restored = SaveAndLoadModel(model)\n+ restored = SaveAndLoadModel(model, save_gradients=save_gradients)\nreturn restored.f\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Add more documentation about saving models with custom gradients |
260,411 | 15.06.2021 15:23:27 | -10,800 | bfb3bbf859e74c172e1d6e25d1e6e24f2ab58024 | [jax2tf] Fix bug in gradient for functions with integer results
The bug is really a peculiarity of tf.function, which uses None for the
cotangents corresponding to non-float results. This does not happen
in TF eager.
Fixes: | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/jax2tf.py",
"new_path": "jax/experimental/jax2tf/jax2tf.py",
"diff": "@@ -300,6 +300,18 @@ def convert(fun: Callable,\ndef converted_grad_fn(*out_cts_flat: TfVal,\n_out_cts_avals: Sequence[core.AbstractValue],\nvariables=None):\n+ # If the function has outputs of integer or bool types, and if we are\n+ # under a tf.function context, then TF will pass None in _out_cts_flat\n+ # in place of these values. We should change these to some integers or\n+ # else JAX gets unhappy.\n+ def fix_out_ct(out_ct: TfVal, out_ct_aval: core.AbstractValue) -> TfVal:\n+ if out_ct is not None:\n+ return out_ct\n+ assert core.primal_dtype_to_tangent_dtype(out_ct_aval.dtype) == dtypes.float0, f\"out_ct={out_ct}\"\n+ return tf.zeros(_eval_shape(out_ct_aval.shape), dtype=out_ct_aval.dtype)\n+\n+ out_cts_fixed_flat = tuple(map(fix_out_ct, out_cts_flat, _out_cts_avals))\n+\nif variables:\nraise ValueError(\n\"Unexpected variables used in forward pass. \"\n@@ -315,19 +327,21 @@ def convert(fun: Callable,\n_, pullback_jax = jax.vjp(fun, *args_jax)\nreturn pullback_jax(out_cts_jax)\n+ out_tree = out_tree_thunk()\nif polymorphic_shapes is None:\nvjp_polymorphic_shapes = None\nelse:\nargs_polymorphic_shapes = tree_util.tree_unflatten(\nin_tree.children()[0], polymorphic_shapes_flat)\nout_cts_polymorphic_shapes = tree_util.tree_unflatten(\n- out_tree_thunk(),\n+ out_tree,\ntuple(str(out_aval.shape)\nfor out_aval in _out_cts_avals)) # type: ignore\nvjp_polymorphic_shapes = [\nargs_polymorphic_shapes, out_cts_polymorphic_shapes\n]\n- out_cts = tree_util.tree_unflatten(out_tree_thunk(), out_cts_flat)\n+\n+ out_cts = tree_util.tree_unflatten(out_tree, out_cts_fixed_flat)\n# TODO: enable higher-order gradients\n# TODO: I think that this does not work with kwargs\nwith tf.name_scope(\"jax2tf_vjp\"):\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/jax2tf_test.py",
"new_path": "jax/experimental/jax2tf/tests/jax2tf_test.py",
"diff": "@@ -352,6 +352,30 @@ class Jax2TfTest(tf_test_util.JaxToTfTestCase):\nself.assertAllClose(jnp.zeros(np.shape(d_dx_jax), dtypes.bfloat16),\nd_dx_tf.numpy())\n+\n+ def test_tf_gradients_int_argument(self):\n+ # https://github.com/google/jax/issues/6975\n+ # state is a pytree that contains an integer and a boolean.\n+ # The function returns an integer and a boolean.\n+ def f_jax(param, state, x):\n+ return param * x, state\n+\n+ param = np.array([0.7, 0.9], dtype=np.float32)\n+ state = dict(array=1., counter=7, truth=True)\n+ x = 3.\n+\n+ # tf.function is important, without it the bug does not appear\n+ f_tf = tf.function(jax2tf.convert(f_jax, with_gradient=True), autograph=False)\n+\n+ paramv = tf.Variable(param)\n+ with tf.GradientTape() as tape:\n+ r, _ = f_tf(paramv, state, x)\n+ loss = tf.reduce_sum(r)\n+\n+ g_tf = tape.gradient(loss, paramv)\n+ self.assertAllClose(g_tf.numpy(),\n+ jax.grad(lambda *args: jnp.sum(f_jax(*args)[0]))(param, state, x))\n+\ndef test_convert_argument_non_callable_error(self):\nwith self.assertRaisesRegex(TypeError, \"Expected a callable value\"):\njax2tf.convert(5.)\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Fix bug in gradient for functions with integer results
The bug is really a peculiarity of tf.function, which uses None for the
cotangents corresponding to non-float results. This does not happen
in TF eager.
Fixes: #6975 |
260,287 | 08.06.2021 11:53:23 | 0 | 1e288b2f422299129e10a1cde9f9c9bdd043d995 | Add support for anonymous serial loops | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/maps.py",
"new_path": "jax/experimental/maps.py",
"diff": "@@ -82,13 +82,13 @@ AxisName = core.AxisName\nResourceAxisName = AxisName # Different name just for documentation purposes\nMesh = pxla.Mesh\n-class Loop(NamedTuple):\n+class _Loop(NamedTuple):\nname: ResourceAxisName\nlength: int\nclass ResourceEnv(NamedTuple):\nphysical_mesh: Mesh\n- loops: Tuple[Loop, ...]\n+ loops: Tuple[_Loop, ...]\ndef with_mesh(self, mesh: Mesh):\noverlap = set(mesh.axis_names) & (self.resource_axes - set(self.physical_mesh.axis_names))\n@@ -98,7 +98,7 @@ class ResourceEnv(NamedTuple):\nf\"{show_axes(overlap)}\")\nreturn self._replace(physical_mesh=mesh)\n- def with_extra_loop(self, loop: Loop):\n+ def with_extra_loop(self, loop: _Loop):\nif loop.name in self.resource_axes:\nraise ValueError(f\"Cannot extend the resource environment with loop named \"\nf\"`{loop.name}`. An axis of this name is already defined!\")\n@@ -135,9 +135,45 @@ EMPTY_ENV = ResourceEnv(Mesh(np.empty((), dtype=object), ()), ())\nthread_resources = threading.local()\nthread_resources.env = EMPTY_ENV\n+\n+\"\"\"Create an anonymous serial loop resource for use in a single xmap axis.\n+\n+A use of :py:class:`SerialLoop` in :py:func:`xmap`'s ``axis_resources``\n+extends the resource environment with a new serial loop with a unique\n+unspecified name, that will only be used to partition the axis that\n+used a given instance.\n+\n+This is unlike :py:func:`serial_loop`, which makes it possible to iterate\n+jointly over chunks of multiple axes (with the usual requirement that they\n+do not coincide in a named shape of any value in the program).\n+\n+Example::\n+\n+ # Processes `x` in a vectorized way, but in 20 micro-batches.\n+ xmap(f, in_axes=['i'], out_axes=[i], axis_resources={'i': SerialLoop(20)})(x)\n+\n+ # Computes the result in a vectorized way, but in 400 micro-batches,\n+ # once for each coordinate (0, 0) <= (i, j) < (20, 20). Each `SerialLoop`\n+ # creates a fresh anonymous loop.\n+ xmap(h, in_axes=(['i'], ['j']), out_axes=['i', 'j'],\n+ axis_resources={'i': SerialLoop(20), 'j': SerialLoop(20)})(x, y)\n+\"\"\"\n+class SerialLoop:\n+ length: int\n+\n+ def __init__(self, length):\n+ self.length = length\n+\n+ def __eq__(self, other):\n+ return self.length == other.length\n+\n+ def __hash__(self):\n+ return hash(self.length)\n+\n+\n@contextlib.contextmanager\n-def loop(name: ResourceAxisName, length: int):\n- \"\"\"Define a loop resource to be available in scope of this context manager.\n+def serial_loop(name: ResourceAxisName, length: int):\n+ \"\"\"Define a serial loop resource to be available in scope of this context manager.\nThis is similar to :py:func:`mesh` in that it extends the resource\nenvironment with a resource called ``name``. But, any use of this resource\n@@ -167,7 +203,7 @@ def loop(name: ResourceAxisName, length: int):\naxis_resources={'i': 'l'})(x)\n\"\"\"\nold_env = getattr(thread_resources, \"env\", EMPTY_ENV)\n- thread_resources.env = old_env.with_extra_loop(Loop(name, length))\n+ thread_resources.env = old_env.with_extra_loop(_Loop(name, length))\ntry:\nyield\nfinally:\n@@ -293,6 +329,8 @@ def _prepare_axes(axes, arg_name):\nentries = map(partial(_parse_entry, arg_name), entries)\nreturn tree_unflatten(treedef, entries), entries, treedef\n+Resource = Union[ResourceAxisName, SerialLoop]\n+ResourceSet = Union[Resource, Tuple[Resource, ...]]\n# TODO: Some syntactic sugar to make the API more usable in a single-axis case?\n# TODO: Are the resource axes scoped lexically or dynamically? Dynamically for now!\n@@ -301,7 +339,7 @@ def xmap(fun: Callable,\nout_axes,\n*,\naxis_sizes: Dict[AxisName, int] = {},\n- axis_resources: Dict[AxisName, Union[ResourceAxisName, Tuple[ResourceAxisName, ...]]] = {},\n+ axis_resources: Dict[AxisName, ResourceSet] = {},\ndonate_argnums: Union[int, Sequence[int]] = (),\nbackend: Optional[str] = None):\n\"\"\"Assign a positional signature to a program that uses named array axes.\n@@ -486,11 +524,21 @@ def xmap(fun: Callable,\nin_axes_names = set(it.chain(*(spec.keys() for spec in in_axes_entries)))\ndefined_names = axis_sizes_names | in_axes_names\nout_axes_names = set(it.chain(*(spec.keys() for spec in out_axes_entries)))\n- normalized_axis_resources: Dict[AxisName, Tuple[ResourceAxisName, ...]] = \\\n- {axis: (resources if isinstance(resources, tuple) else (resources,))\n- for axis, resources in axis_resources.items()}\n+\n+ anon_serial_loops = []\n+ def normalize_resource(r) -> ResourceAxisName:\n+ if isinstance(r, SerialLoop):\n+ name = fresh_resource_name()\n+ anon_serial_loops.append((name, r.length))\n+ return name\n+ return r\n+\n+ normalized_axis_resources: Dict[AxisName, Tuple[ResourceAxisName, ...]] = {}\nfor axis in defined_names:\n- normalized_axis_resources.setdefault(axis, ())\n+ resources = axis_resources.get(axis, ())\n+ if not isinstance(resources, tuple):\n+ resources = (resources,)\n+ normalized_axis_resources[axis] = tuple(unsafe_map(normalize_resource, resources))\nfrozen_axis_resources = FrozenDict(normalized_axis_resources)\nnecessary_resources = set(it.chain(*frozen_axis_resources.values()))\n@@ -505,7 +553,7 @@ def xmap(fun: Callable,\nf\"{out_axes_names - defined_names}\")\nfor axis, resources in frozen_axis_resources.items():\n- if len(set(resources)) != len(resources):\n+ if len(set(resources)) != len(resources): # type: ignore\nraise ValueError(f\"Resource assignment of a single axis must be a tuple of \"\nf\"distinct resources, but specified {resources} for axis {axis}\")\n@@ -515,7 +563,6 @@ def xmap(fun: Callable,\nhas_input_rank_assertions = any(spec.expected_rank is not None for spec in in_axes_entries)\nhas_output_rank_assertions = any(spec.expected_rank is not None for spec in out_axes_entries)\n- @wraps(fun)\ndef fun_mapped(*args):\n# Putting this outside of fun_mapped would make resources lexically scoped\nresource_env = thread_resources.env\n@@ -585,6 +632,11 @@ def xmap(fun: Callable,\nf\"but the output has rank {out.ndim} (and shape {out.shape})\")\nreturn tree_unflatten(out_tree(), out_flat)\n+ # Decorate fun_mapped\n+ for loop_params in reversed(anon_serial_loops):\n+ fun_mapped = serial_loop(*loop_params)(fun_mapped)\n+ fun_mapped = wraps(fun)(fun_mapped)\n+\nreturn fun_mapped\ndef xmap_impl(fun: lu.WrappedFun, *args, name, in_axes, out_axes_thunk, donated_invars,\n@@ -1320,9 +1372,8 @@ def _jaxpr_resources(jaxpr: core.Jaxpr, resource_env) -> Set[ResourceAxisName]:\nused_resources = set()\nfor eqn in jaxpr.eqns:\nif eqn.primitive is xmap_p:\n- if eqn.params['resource_env'] != resource_env:\n- raise RuntimeError(\"Changing the resource environment (e.g. hardware mesh \"\n- \"spec) is not allowed inside xmap.\")\n+ if eqn.params['resource_env'].physical_mesh != resource_env.physical_mesh:\n+ raise RuntimeError(\"Changing the physical mesh is not allowed inside xmap.\")\nused_resources |= set(it.chain(*eqn.params['axis_resources'].values()))\nupdates = core.traverse_jaxpr_params(\npartial(_jaxpr_resources, resource_env=resource_env), eqn.params)\n"
},
{
"change_type": "MODIFY",
"old_path": "tests/xmap_test.py",
"new_path": "tests/xmap_test.py",
"diff": "@@ -39,7 +39,7 @@ from jax import lax\nfrom jax import core\nfrom jax.core import NamedShape, JaxprTypeError\nfrom jax.experimental import maps\n-from jax.experimental.maps import Mesh, mesh, xmap, loop\n+from jax.experimental.maps import Mesh, mesh, xmap, serial_loop, SerialLoop\nfrom jax.errors import JAXTypeError\nfrom jax.lib import xla_bridge\nfrom jax._src.util import curry, unzip2, split_list, prod\n@@ -548,7 +548,7 @@ class XMapTest(XMapTestCase):\ng = xmap(f, in_axes={}, out_axes=['j', ...], axis_sizes={'j': 7})\nself.assertAllClose(g(x), jnp.tile(x.reshape((1, 1)), (7, 4)))\n- @loop('l', 4)\n+ @serial_loop('l', 4)\ndef testLoopBasic(self):\nx = jnp.arange(16)\ny = xmap(lambda x: x + 4, in_axes=['i'], out_axes=['i'],\n@@ -556,13 +556,26 @@ class XMapTest(XMapTestCase):\nself.assertAllClose(y, x + 4)\n@jtu.with_mesh([('x', 2)])\n- @loop('l', 4)\n+ @serial_loop('l', 4)\ndef testLoopWithMesh(self):\nx = jnp.arange(16)\ny = xmap(lambda x: x + 4, in_axes=['i'], out_axes=['i'],\naxis_resources={'i': ('x', 'l')})(x)\nself.assertAllClose(y, x + 4)\n+ def testLoopAnonBasic(self):\n+ x = jnp.arange(16)\n+ y = xmap(lambda x: x + 4, in_axes=['i'], out_axes=['i'],\n+ axis_resources={'i': SerialLoop(4)})(x)\n+ self.assertAllClose(y, x + 4)\n+\n+ @jtu.with_mesh([('x', 2)])\n+ def testLoopAnonWithMesh(self):\n+ x = jnp.arange(16)\n+ y = xmap(lambda x: x + 4, in_axes=['i'], out_axes=['i'],\n+ axis_resources={'i': ('x', SerialLoop(4))})(x)\n+ self.assertAllClose(y, x + 4)\n+\nclass XMapTestSPMD(SPMDTestMixin, XMapTest):\n\"\"\"Re-executes all basic tests with the SPMD partitioner enabled\"\"\"\n@@ -1069,7 +1082,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nxshape = (2, 5, 6)\nx = jnp.arange(np.prod(xshape)).reshape(xshape)\nwith self.assertRaisesRegex(RuntimeError,\n- \"Changing the resource environment.*\"):\n+ \"Changing the physical mesh is not allowed.*\"):\nf(x)\ndef testEmptyArgumentTrees(self):\n@@ -1213,7 +1226,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nh(x)\n- @loop('l', 2)\n+ @serial_loop('l', 2)\ndef testResourceConflictArgsLoop(self):\nfm = xmap(lambda x: x,\nin_axes=['a', 'b'], out_axes=['a', 'b'],\n@@ -1225,7 +1238,7 @@ class XMapErrorTest(jtu.JaxTestCase):\nwith self.assertRaisesRegex(JAXTypeError, error):\nfm(x)\n- @loop('l', 2)\n+ @serial_loop('l', 2)\ndef testLoopCollectives(self):\nfm = xmap(lambda x: lax.psum(x, 'i'),\nin_axes=['i'], out_axes=[],\n"
}
] | Python | Apache License 2.0 | google/jax | Add support for anonymous serial loops |
260,411 | 16.06.2021 10:20:24 | -10,800 | 9831371e389f9226f34a53fc91790b1fc4384226 | [jax2tf] Fix the custom gradients for call_tf
The previous implementation did not work correctly when
the call_tf function was embedded in a larger JAX function
for which we take the gradient.
Also fixed handling of non-float args/results. | [
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/call_tf.py",
"new_path": "jax/experimental/jax2tf/call_tf.py",
"diff": "@@ -115,40 +115,42 @@ def call_tf(func_tf: Callable) -> Callable:\n# Define the fwd and bwd custom_vjp functions\ndef make_call_vjp_fwd(*args_jax):\n- # Return the primal argument as the residual\n+ # Return the primal arguments as the residual\nreturn make_call(*args_jax), args_jax\n- def make_call_vjp_bwd(residual, ct_res):\n- args_jax = residual # residual is the primal argument\n+ def make_call_vjp_bwd(residual_jax, ct_res_jax):\n+ args_jax = residual_jax # residual is the primal argument\n- def tf_vjp_fun(args, ct_res):\n+ def tf_vjp_fun(args_tf, ct_res_tf):\n\"\"\"Invoke TF gradient.\"\"\"\n- with tf.GradientTape(persistent=True) as tape:\n- tape.watch(args)\n- res = func_tf(*args)\n- tf.nest.assert_same_structure(res, ct_res)\n- # If the result is not a scalar, we must accumulate arguments cotangents.\n- accumulator = None # Same structure as \"arg\"\n+ # TF does not like us to watch non-float vars\n+ def replace_non_float(arg):\n+ if np.issubdtype(arg.dtype.as_numpy_dtype, np.inexact):\n+ return arg\n+ else:\n+ # When watched, this will be ignored. When use in results it will\n+ # result in a floating 0. gradient, which JAX will ignore (and\n+ # replace it with a float0)\n+ return tf.zeros((), dtype=tf.float32)\n+\n+ watched_args_tf = tf.nest.map_structure(replace_non_float, args_tf)\n+ with tf.GradientTape(persistent=True) as tape:\n+ tape.watch(watched_args_tf)\n+ res = func_tf(*args_tf)\n- def acc_ct(res_, ct_res_):\n+ tf.nest.assert_same_structure(res, ct_res_tf)\ndres_darg = tape.gradient(\n- res_,\n- sources=args,\n+ tf.nest.map_structure(replace_non_float, res),\n+ sources=watched_args_tf,\n+ output_gradients=ct_res_tf,\nunconnected_gradients=tf.UnconnectedGradients.ZERO)\n- tf.nest.assert_same_structure(dres_darg, args)\n- scaled_dres_darg = tf.nest.map_structure(lambda d: d * ct_res_,\n- dres_darg)\n- nonlocal accumulator\n- accumulator = (\n- scaled_dres_darg if accumulator is None\n- else tf.nest.map_structure(\n- lambda x, y: x + y, accumulator, scaled_dres_darg))\n-\n- tf.nest.map_structure(acc_ct, res, ct_res)\n- return accumulator\n+\n+ tf.nest.assert_same_structure(dres_darg, args_tf)\n+ return dres_darg\n+\n# Use call_tf to call the VJP function\n- return call_tf(tf_vjp_fun)(args_jax, ct_res)\n+ return call_tf(tf_vjp_fun)(args_jax, ct_res_jax)\nmake_call.defvjp(make_call_vjp_fwd, make_call_vjp_bwd)\nreturn util.wraps(func_tf)(make_call)\n"
},
{
"change_type": "MODIFY",
"old_path": "jax/experimental/jax2tf/tests/call_tf_test.py",
"new_path": "jax/experimental/jax2tf/tests/call_tf_test.py",
"diff": "# limitations under the License.\n\"\"\"Tests for call_tf.\"\"\"\n+from functools import partial\nfrom typing import Callable, Dict, Tuple\nimport unittest\n@@ -20,6 +21,7 @@ from absl.testing import absltest\nfrom absl.testing import parameterized\nimport jax\n+from jax import dtypes\nfrom jax import lax\nfrom jax import numpy as jnp\nfrom jax import test_util as jtu\n@@ -275,6 +277,76 @@ class CallTfTest(jtu.JaxTestCase):\nself.assertAllClose(\ndict(first=np.float32(4.), second=np.float32(3.)), grad_x)\n+ def test_grad_nested(self):\n+ # We embed the call_tf function in a larger function whose gradient we take\n+ # It is relevant here that the cotangents flowing through the call_tf\n+ # function are not scalars.\n+\n+ b = np.array([[11., 12., 13.], [21., 22., 23.]], dtype=np.float32) # [2, 3]\n+ c = np.array([[31., 32.], [41., 42.], [51., 52.], [61., 62.]], dtype=np.float32) # [4, 2]\n+ x_dict = dict(b=b, c=c) # b:[2, 3], c=[4, 2]\n+ # res: dict(r:[4, 3], s:[4, 2])\n+ def f_tf(x_dict):\n+ return dict(r=tf.matmul(x_dict[\"c\"], x_dict[\"b\"]), s=7. * x_dict[\"c\"])\n+\n+ @jax.jit # To recognize it in jaxpr\n+ def f_jax(x_dict):\n+ return dict(r=jnp.matmul(x_dict[\"c\"], x_dict[\"b\"]), s=7. * x_dict[\"c\"])\n+\n+ def loss(functional, x_dict):\n+ prediction = functional(x_dict) # r:[4, 3], s:[4, 2]\n+ weights = np.array([1., 2., 3., 4.], dtype=np.float32) # [4]\n+ weighted_pred = jnp.matmul(weights, prediction[\"r\"]) # [3]\n+ return jnp.sum(weighted_pred) + 4. * jnp.sum(prediction[\"s\"])\n+\n+ g_fun_with_tf = jax.grad(partial(loss, jax2tf.call_tf(f_tf)))\n+ g_fun_with_jax = jax.grad(partial(loss, f_jax))\n+\n+ g_tf = g_fun_with_tf(x_dict)\n+ g_jax = g_fun_with_jax(x_dict)\n+ self.assertAllClose(g_jax, g_tf)\n+\n+ def test_grad_int_argument(self):\n+ # Similar to https://github.com/google/jax/issues/6975\n+ # state is a pytree that contains an integer and a boolean.\n+ # The function returns an integer and a boolean.\n+ def f(param, state, x):\n+ return param * x, state\n+\n+ param = np.array([0.7, 0.9], dtype=np.float32)\n+ state = dict(array=np.float32(1.), counter=7, truth=True)\n+ x = np.float32(3.)\n+\n+ # tf.function is important, without it the bug does not appear\n+ f_call_tf = jax2tf.call_tf(f)\n+ g_call_tf = jax.grad(lambda *args: jnp.sum(f_call_tf(*args)[0]))(param, state, x)\n+ g = jax.grad(lambda *args: jnp.sum(f(*args)[0]))(param, state, x)\n+ self.assertAllClose(g_call_tf, g)\n+\n+ def test_grad_with_float0_result(self):\n+ # Gradient over integer-argument functions, with float0 result\n+ def f_jax(x, y): # x is an int, y is a float; res is a (int, float)\n+ return (2 * x, 2 * x + y * y)\n+ def f_tf(x, y):\n+ # TF needs explicit casts\n+ return (2 * x, tf.cast(2 * x, dtype=y.dtype) + y * y)\n+\n+ def wrapper(functional, x, y): # x: i32\n+ return jnp.sum(2. * functional(3 * x, 4. * y)[1])\n+\n+ grad_g = jax.grad(partial(wrapper, f_jax),\n+ allow_int=True, argnums=(0, 1))\n+ grad_g_call_tf = jax.grad(partial(wrapper, jax2tf.call_tf(f_tf)),\n+ allow_int=True, argnums=(0, 1))\n+\n+ x = np.int32(2)\n+ y = np.float32(3.)\n+ g_jax = grad_g(x, y)\n+ g_call_tf = grad_g_call_tf(x, y)\n+ self.assertEqual(g_jax[0].dtype, dtypes.float0)\n+ self.assertEqual(g_call_tf[0].dtype, dtypes.float0)\n+ self.assertAllClose(g_jax[1], g_call_tf[1])\n+\n@parameterized_jit\ndef test_grad_custom(self, with_jit=False):\n"
}
] | Python | Apache License 2.0 | google/jax | [jax2tf] Fix the custom gradients for call_tf
The previous implementation did not work correctly when
the call_tf function was embedded in a larger JAX function
for which we take the gradient.
Also fixed handling of non-float args/results. |
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