| # Owner(s): ["oncall: jit"] |
| |
| import contextlib |
| import copy |
| import itertools |
| import math |
| import operator |
| import unittest |
| |
| import numpy as np |
| import sympy |
| |
| import torch |
| import torch.fx |
| import torch.nn.functional as F |
| from torch import sym_int, SymBool, SymFloat, SymInt |
| from torch._C import _disabled_torch_function_impl |
| from torch.fx.experimental import sym_node |
| from torch.fx.experimental.proxy_tensor import make_fx |
| from torch.fx.experimental.sym_node import method_to_operator, SymNode, to_node |
| from torch.fx.experimental.symbolic_shapes import ( |
| _constrain_range_for_size, |
| DimConstraints, |
| DimDynamic, |
| expect_true, |
| guard_bool, |
| guard_float, |
| guard_int, |
| GuardOnDataDependentSymNode, |
| hint_int, |
| is_symbolic, |
| ShapeEnv, |
| StatelessSymbolicContext, |
| statically_known_true, |
| ) |
| from torch.testing._internal.common_utils import ( |
| instantiate_parametrized_tests, |
| parametrize, |
| run_tests, |
| skipIfTorchDynamo, |
| TestCase, |
| ) |
| from torch.utils import _pytree as pytree |
| from torch.utils._python_dispatch import TorchDispatchMode |
| from torch.utils._sympy.functions import ( |
| FloorDiv, |
| IsNonOverlappingAndDenseIndicator, |
| Mod, |
| ) |
| |
| |
| aten = torch.ops.aten |
| |
| meta_funcs = {} |
| |
| |
| def register_meta(op): |
| def decorator(f): |
| def add_func(op): |
| meta_funcs[op] = f |
| |
| pytree.tree_map_(add_func, op) |
| return f |
| |
| return decorator |
| |
| |
| @register_meta([aten.add.Tensor, aten.sub.Tensor]) |
| def binary_meta(a, b): |
| return a.new_empty(a.shape) |
| |
| |
| @register_meta(aten.cat.default) |
| def cat_meta(tensors, dim=0): |
| concat_length = 0 |
| shape = tensors[0].shape |
| for tensor in tensors: |
| for idx, (common_length, length) in enumerate(zip(shape, tensor.shape)): |
| if idx == dim: |
| concat_length = concat_length + length |
| else: |
| assert length == common_length |
| new_shape = list(shape) |
| new_shape[dim] = concat_length |
| return tensors[0].new_empty(new_shape) |
| |
| |
| @register_meta([aten.narrow_copy.default]) |
| def narrow_copy_symint_meta(a, dim, start, length, **kwargs): |
| shape = [] |
| for i, x in enumerate(a.shape): |
| if i == dim: |
| shape.append(length) |
| else: |
| shape.append(x) |
| return a.new_empty(tuple(shape)) |
| |
| |
| @register_meta([aten.expand.default]) |
| def expand_symint_meta(a, size, implicit=False): |
| return a.new_empty(size) |
| |
| |
| def create_contiguous(shape): |
| strides = [1] |
| for dim in reversed(shape[:-1]): |
| strides.append(dim * strides[-1]) |
| return list(reversed(strides)) |
| |
| |
| class FakeSymbolicTensor(torch.Tensor): |
| @staticmethod |
| def __new__( |
| cls, |
| sym_shape, |
| sym_strides, |
| dtype, |
| layout, |
| requires_grad, |
| device, |
| storage_offset=0, |
| ): |
| # TODO: this is wrong in general |
| sym_stride = create_contiguous(sym_shape) |
| r = torch.Tensor._make_wrapper_subclass( |
| cls, |
| sym_shape, |
| sym_stride, |
| storage_offset, |
| dtype=dtype, |
| layout=layout, |
| requires_grad=requires_grad, |
| device=device, |
| ) |
| return r |
| |
| __torch_function__ = _disabled_torch_function_impl |
| |
| def new_empty(self, shape): |
| return FakeSymbolicTensor( |
| shape, None, self.dtype, self.layout, self.requires_grad, self.device |
| ) |
| |
| @classmethod |
| def __torch_dispatch__(cls, func_overload, types, args=(), kwargs=None): |
| if func_overload in meta_funcs: |
| return meta_funcs[func_overload](*args, **kwargs) |
| |
| if func_overload == torch.ops.aten.new_empty.default: |
| self = args[0] |
| shape = args[1] |
| return FakeSymbolicTensor( |
| shape, |
| self.stride(), |
| self.dtype, |
| self.layout, |
| self.requires_grad, |
| self.device, |
| ) |
| |
| raise RuntimeError(f"operator {func_overload} not supported") |
| |
| |
| def create_symbolic_tensor(name, arg, shape_env, source=None, dynamic_dims=None): |
| from torch._dynamo.source import ConstantSource |
| |
| if source is None: |
| source = ConstantSource(name) |
| constraint_dims = [None] * arg.dim() |
| if dynamic_dims is None: |
| dynamic_dims = [DimDynamic.DUCK] * arg.dim() |
| ( |
| sym_shapes, |
| sym_strides, |
| sym_storage_offset, |
| ) = shape_env.create_symbolic_sizes_strides_storage_offset( |
| arg, |
| source=source, |
| symbolic_context=StatelessSymbolicContext( |
| dynamic_sizes=dynamic_dims, constraint_sizes=constraint_dims |
| ), |
| ) |
| return FakeSymbolicTensor( |
| sym_shapes, |
| sym_strides, |
| arg.dtype, |
| arg.layout, |
| arg.requires_grad, |
| arg.device, |
| sym_storage_offset, |
| ) |
| |
| |
| def create_symtype(cls, pytype, shape_env, val, duck=True, **kwargs): |
| from torch._dynamo.source import ConstantSource |
| |
| symbol = shape_env.create_symbol( |
| val, |
| source=ConstantSource(f"__testing_only{len(shape_env.var_to_val)}"), |
| dynamic_dim=DimDynamic.DUCK if duck else DimDynamic.DYNAMIC, |
| constraint_dim=None, |
| **kwargs, |
| ) |
| return cls(SymNode(symbol, shape_env, pytype, hint=val)) |
| |
| |
| # TODO: default duck to False |
| def create_symint(shape_env, i: int, duck=True, **kwargs) -> SymInt: |
| return create_symtype(SymInt, int, shape_env, i, duck=duck, **kwargs) |
| |
| |
| def create_symbool(shape_env, b: bool) -> SymBool: |
| return create_symtype(SymBool, bool, shape_env, b) |
| |
| |
| def create_symfloat(shape_env, f: float) -> SymFloat: |
| return create_symtype(SymFloat, float, shape_env, f) |
| |
| |
| @skipIfTorchDynamo( |
| "Creating ShapeEnv fails for confusing reasons (also we never expect dynamo to see code like this)" |
| ) |
| class TestPySymInt(TestCase): |
| def test_arith_ops(self): |
| shape_env = ShapeEnv() |
| symints = [] |
| for i in range(2, 5): |
| symints.append((i, create_symint(shape_env, i))) |
| |
| ops = [ |
| operator.add, |
| operator.sub, |
| operator.floordiv, |
| operator.mul, |
| operator.mod, |
| ] |
| |
| for op in ops: |
| for args in itertools.permutations(symints, 2): |
| if not isinstance(args[0][1], int) and ( |
| (op != operator.mod or op != operator.floordiv) and args[1][0] != 0 |
| ): |
| self.assertTrue( |
| op(args[0][1], args[1][1]) == op(args[0][0], args[1][0]) |
| ) |
| |
| def test_reverse_arith_ops(self): |
| shape_env = ShapeEnv() |
| |
| a = create_symint(shape_env, 2) |
| self.assertTrue(5 // a == 5 // 2) |
| |
| a = create_symint(shape_env, 2) |
| self.assertTrue(5 * a == 5 * 2) |
| |
| def test_sympify_symint(self): |
| shape_env = ShapeEnv() |
| a = create_symint(shape_env, 2) |
| self.assertIs(sympy.sympify(a), a.node.expr) |
| b = create_symfloat(shape_env, 3.0) |
| self.assertIs(sympy.sympify(b), b.node.expr) |
| c = create_symbool(shape_env, True) |
| self.assertIs(sympy.sympify(c), c.node.expr) |
| |
| def test_roundtrip(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env) |
| |
| self.assertTrue(not isinstance(x.shape[0], SymNode)) |
| self.assertTrue(isinstance(x.shape[0], SymInt)) |
| |
| self.assertTrue(x.shape[0] == 5) |
| self.assertTrue(x.shape[1] == 4) |
| self.assertTrue(x.shape[2], 3) |
| |
| self.assertTrue(x.size()[0], 5) |
| self.assertTrue(x.size()[1], 4) |
| # Should be simplifiable to an integer. |
| # Ref: https://github.com/pytorch/pytorch/pull/107492 |
| self.assertTrue(isinstance(x.size()[1], SymInt)) |
| self.assertTrue( |
| isinstance(x.size()[1].node.maybe_as_int(), int) |
| ) # due to guard above |
| self.assertTrue(x.size()[2] == 3) |
| |
| self.assertTrue(x.size(0) == 5) |
| self.assertTrue(x.size(1) == 4) |
| self.assertTrue(x.size(2) == 3) |
| self.assertTrue(isinstance(x.size(2), SymInt)) |
| self.assertTrue(isinstance(x.size(2).node.maybe_as_int(), int)) |
| |
| y = create_symbolic_tensor("y", torch.randn(5, 4, 3)[1:], shape_env) |
| self.assertTrue(isinstance(y.storage_offset(), SymInt)) |
| self.assertTrue(y.storage_offset() == 12) |
| |
| def test_binary(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env) |
| y = create_symbolic_tensor("y", torch.randn(5, 4, 3), shape_env) |
| |
| z = x + y |
| self.assertTrue(z.shape[0] == 5) |
| self.assertTrue(z.shape[1] == 4) |
| self.assertTrue(z.shape[2] == 3) |
| |
| # broadcasting |
| y = create_symbolic_tensor("y2", torch.randn(1, 4, 1), shape_env) |
| z = x + y |
| self.assertTrue(z.shape[0] == 5) |
| self.assertTrue(z.shape[1] == 4) |
| self.assertTrue(z.shape[2] == 3) |
| |
| def test_symint_args(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env) |
| y = create_symbolic_tensor("y", torch.randn(5, 4, 1), shape_env) |
| LAST_DIM = 2 |
| z = x.narrow_copy(LAST_DIM, 0, y.shape[LAST_DIM]) |
| self.assertTrue(z.shape[2] == y.shape[2]) |
| |
| # arithmetic expr with two symints |
| z = x.narrow_copy(LAST_DIM, 0, x.shape[LAST_DIM] - y.shape[LAST_DIM]) |
| self.assertTrue(z.shape[2] == 2) |
| |
| # arithmetic expr with a symint and python int |
| z = x.narrow_copy(LAST_DIM, 0, x.shape[LAST_DIM] - 1) |
| self.assertTrue(z.shape[2] == 2) |
| |
| def test_symint_vargs(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5, 4, 3), shape_env) |
| y = create_symbolic_tensor("y", torch.randn(1, 4, 1), shape_env) |
| |
| # varargs |
| z = y.expand(x.shape[0], y.shape[1], x.shape[2]) |
| self.assertTrue(z.shape[0] == 5) |
| self.assertTrue(z.shape[1] == 4) |
| self.assertTrue(z.shape[2] == 3) |
| |
| # shape list |
| z = y.expand((x.shape[0], y.shape[1], x.shape[2])) |
| self.assertTrue(z.shape[0] == 5) |
| self.assertTrue(z.shape[1] == 4) |
| self.assertTrue(z.shape[2] == 3) |
| |
| # mixed python symints and ints |
| z = y.expand(x.shape[0], y.shape[1], 3) |
| self.assertTrue(z.shape[0] == 5) |
| self.assertTrue(z.shape[1] == 4) |
| self.assertTrue(z.shape[2] == 3) |
| |
| # mixed python symints and ints in a list |
| z = y.expand((x.shape[0], y.shape[1], 3)) |
| self.assertTrue(z.shape[0] == 5) |
| self.assertTrue(z.shape[1] == 4) |
| self.assertTrue(z.shape[2] == 3) |
| |
| # mixed python symints and ints |
| z = y.expand(5, y.shape[1], x.shape[2]) |
| self.assertTrue(z.shape[0] == 5) |
| self.assertTrue(z.shape[1] == 4) |
| self.assertTrue(z.shape[2] == 3) |
| |
| # mixed python ints and symints in a list |
| z = y.expand((5, y.shape[1], x.shape[2])) |
| self.assertTrue(z.shape[0] == 5) |
| self.assertTrue(z.shape[1] == 4) |
| self.assertTrue(z.shape[2] == 3) |
| |
| z = y.expand((y.shape[1],)) |
| z = y.expand(y.shape[1]) |
| |
| def test_stride(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5, 5), shape_env) |
| self.assertIsInstance(x.stride()[0], SymInt) |
| |
| def test_size_expressions(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5), shape_env) |
| expand_x = x.expand(x.shape[0], x.shape[0]) |
| if expand_x.shape[0] > 3: |
| result = expand_x + expand_x |
| else: |
| result = expand_x + expand_x |
| |
| gt_op, _bt = shape_env.guards[-1] |
| self.assertTrue(isinstance(gt_op, sympy.core.relational.StrictGreaterThan)) |
| self.assertTrue(str(x.shape[0]), str(gt_op.args[0])) |
| self.assertTrue(str(expand_x.shape[1]), str(x.shape[0])) |
| self.assertTrue(str(expand_x.shape[1]), str(result.shape[0])) |
| |
| def test_floordiv_static(self): |
| shape_env = ShapeEnv() |
| s0 = create_symint(shape_env, 8) |
| # This was extracted from |
| # python test/inductor/test_cuda_cpp_wrapper.py -k |
| # DynamicShapesCudaWrapperCudaTests.test_insignificant_strides_cuda_dynamic_shapes_cuda_wrapper |
| bool(s0 % 2 == 0) |
| bool(s0 % (s0 // 2) == 0) |
| bool(2 * (s0 // 2) == s0) |
| self.assertTrue(statically_known_true(s0 // (s0 // 2) == 2)) |
| |
| def test_numel(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5), shape_env) |
| self.assertIsInstance(x.numel(), torch.SymInt) |
| self.assertIsInstance(torch.numel(x), torch.SymInt) |
| |
| x = torch.rand(3, 3) |
| self.assertIsInstance(x.numel(), int) |
| self.assertIsInstance(torch.numel(x), int) |
| |
| def test_int_to_float(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5), shape_env) |
| r = torch.sym_float(x.shape[0]) |
| self.assertIsInstance(r, torch.SymFloat, msg=type(r)) |
| |
| def test_aten_ops(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5), shape_env) |
| torch.ops.aten.narrow_copy.default(x, 0, 0, x.shape[0]) |
| |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x2", torch.randn(5, 4, 3), shape_env) |
| torch.ops.aten.expand.default(x, [x.shape[0], x.shape[1], x.shape[2]]) |
| |
| def test_fx_trace_intlist(self): |
| class CustomModule(torch.nn.Module): |
| def forward(self, x): |
| bs, c, h, w = x.shape |
| return F.pad(x, (0, w % 2, 0, h % 2, 0, 0)) |
| |
| m = CustomModule() |
| x = torch.rand(1, 3, 4, 4) |
| # should not TypeError: pad(): argument 'pad' (position 2) must be |
| # tuple of ints, not tuple |
| torch.fx.symbolic_trace(m) |
| |
| def test_meta_symint(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 2) |
| r = torch.empty(a0, device="meta") |
| self.assertIsInstance(r.shape[0], SymInt) |
| |
| def test_guard_int(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 2) |
| self.assertEqual(guard_int(a0), 2) |
| self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(s0, 2)""") |
| |
| def test_prefer_deferred_runtime_assertions_over_guards(self): |
| shape_env = ShapeEnv(prefer_deferred_runtime_asserts_over_guards=True) |
| s0 = create_symint(shape_env, 2) |
| self.assertEqual(guard_int(s0), 2) |
| self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(s0, 2)""") |
| |
| shape_env = ShapeEnv(prefer_deferred_runtime_asserts_over_guards=True) |
| s0 = create_symint(shape_env, 2) |
| self.assertTrue(expect_true(s0 == 2)) |
| self.assertEqual(len(shape_env.guards), 0) |
| self.assertExpectedInline( |
| str([ra.expr for ra in shape_env.deferred_runtime_asserts[None]]), |
| """[Eq(s0, 2)]""", |
| ) |
| |
| def test_sym_int(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 5) |
| r = sym_int(a0) |
| self.assertEqual(r, 5) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(s0, 5)""") |
| |
| a1 = create_symint(shape_env, 7) |
| r = sym_int(a1 / 2) |
| self.assertEqual(guard_int(r), 3) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[1][0]), """Eq(TruncToInt(IntTrueDiv(s1, 2)), 3)""" |
| ) |
| |
| a3 = create_symint(shape_env, 3) |
| r = sym_int(2.0 * torch.sym_float(a3)) |
| self.assertEqual(guard_int(r), 6) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[2][0]), """Eq(TruncToInt(2.0*ToFloat(s2)), 6)""" |
| ) |
| |
| def test_sym_sqrt(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 4) |
| r = torch._sym_sqrt(a0) |
| self.assertEqual(r, 2) |
| self.assertIsInstance(r, torch.SymFloat, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[0][0]), """Eq(OpaqueUnaryFn_sqrt(s0), 2.0)""" |
| ) |
| |
| def test_sym_floor(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 5) |
| r = math.floor(a0 / 2) |
| self.assertEqual(r, 2) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[0][0]), |
| """Eq(FloorToInt(IntTrueDiv(s0, 2)), 2)""", |
| ) |
| r = math.floor(3.0 * a0) |
| self.assertEqual(r, 15) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[1][0]), |
| """Eq(FloorToInt(3.0*ToFloat(s0)), 15)""", |
| ) |
| |
| def test_sym_trunc(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 5) |
| r = math.trunc(a0 / 2) |
| self.assertEqual(r, 2) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[0][0]), """Eq(TruncToInt(IntTrueDiv(s0, 2)), 2)""" |
| ) |
| r = torch.sym_int(torch.sym_sqrt(a0)) |
| self.assertEqual(r, 2) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[1][0]), """Eq(TruncToInt(OpaqueUnaryFn_sqrt(s0)), 2)""" |
| ) |
| |
| def test_sym_ceil(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 5) |
| r = math.ceil(a0 / 2) |
| self.assertEqual(r, 3) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[0][0]), |
| """Eq(CeilToInt(IntTrueDiv(s0, 2)), 3)""", |
| ) |
| r1 = 3.0 * a0 |
| r = math.floor(r1) |
| self.assertEqual(r, 15) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline( |
| str(shape_env.guards[1][0]), |
| """Eq(FloorToInt(3.0*ToFloat(s0)), 15)""", |
| ) |
| |
| def test_sym_ite(self): |
| shape_env = ShapeEnv() |
| t = create_symint(shape_env, 5) |
| f = create_symint(shape_env, 4) |
| b1 = True |
| r1 = torch.sym_ite(b1, t, f) |
| self.assertTrue(r1 is t) |
| b2 = False |
| r2 = torch.sym_ite(b2, t, f) |
| self.assertTrue(r2 is f) |
| b3 = t == 5 |
| r3 = torch.sym_ite(b3, t, f) |
| self.assertEqual(len(shape_env.guards), 0) |
| self.assertEqual(r3, 5) |
| self.assertEqual(type(t), type(r3)) |
| self.assertExpectedInline( |
| str(shape_env.guards[0][0]), |
| """Eq(Piecewise((s0, Eq(s0, 5)), (s1, True)), 5)""", |
| ) |
| b4 = f == 5 |
| r4 = torch.sym_ite(b4, t, f) |
| self.assertEqual(len(shape_env.guards), 1) |
| self.assertEqual(r4, 4) |
| self.assertEqual(type(f), type(r4)) |
| self.assertExpectedInline( |
| str(shape_env.guards[1][0]), |
| """Eq(Piecewise((s0, Eq(s1, 5)), (s1, True)), 4)""", |
| ) |
| |
| def test_tracing_sym_ite(self): |
| def f(x): |
| b = x.shape[0] == 5 |
| ret = torch.sym_ite(b, x.shape[0], x.shape[1]) |
| return ret |
| |
| gm = make_fx(f, tracing_mode="symbolic")(torch.ones(4, 5)) |
| self.assertEqual(len(gm.shape_env.guards), 0) |
| self.assertExpectedInline( |
| gm.code.strip(), |
| """\ |
| def forward(self, x_1): |
| sym_size_int = torch.ops.aten.sym_size.int(x_1, 0) |
| eq = sym_size_int == 5 |
| sym_size_int_1 = torch.ops.aten.sym_size.int(x_1, 1); x_1 = None |
| sym_ite = torch.sym_ite(eq, sym_size_int, sym_size_int_1); eq = sym_size_int = sym_size_int_1 = None |
| return sym_ite""", |
| ) |
| r1 = gm(torch.ones(4, 5)) |
| self.assertIsInstance(r1, int) |
| self.assertEqual(r1, 5) |
| r2 = gm(torch.ones(5, 4)) |
| self.assertIsInstance(r2, int) |
| self.assertEqual(r2, 5) |
| |
| def test_int_conversion(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 2) |
| int(a0) |
| self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(s0, 2)""") |
| |
| def test_data_dependent_guard(self): |
| shape_env = ShapeEnv() |
| s0 = shape_env.create_unbacked_symint() |
| self.assertRaises(GuardOnDataDependentSymNode, lambda: bool(s0 == 0)) |
| |
| def test_data_dependent_guard_propagate_real_tensors(self): |
| shape_env = ShapeEnv() |
| s0 = shape_env.create_unbacked_symint() |
| shape_env.set_unbacked_var_to_val(s0.node.expr, 0) |
| self.assertEqual(bool(s0 == 0), True) |
| |
| def test_expect_true_basic(self): |
| shape_env = ShapeEnv() |
| i0 = shape_env.create_unbacked_symint() |
| i0_sym = i0.node.expr |
| # This doesn't error |
| self.assertTrue(expect_true(i0 == 0)) |
| # This generates a deferred runtime assert via replacement |
| self.assertEqual(shape_env.replacements[i0_sym], 0) |
| # After expecting true, guards now resolve given the runtime assert |
| bool(i0 == 0) |
| |
| def test_expect_true_with_s0(self): |
| shape_env = ShapeEnv() |
| s0 = create_symint(shape_env, 5) |
| i0 = shape_env.create_unbacked_symint() |
| self.assertTrue(expect_true(i0 < s0)) |
| self.assertExpectedInline( |
| str([ra.expr for ra in shape_env.deferred_runtime_asserts[i0.node.expr]]), |
| """[u0 < s0]""", |
| ) |
| self.assertTrue(i0 < s0) |
| self.assertTrue(i0 != s0) |
| self.assertFalse(i0 > s0) |
| self.assertFalse(i0 >= s0) |
| |
| def test_expect_true_prefer_later(self): |
| shape_env = ShapeEnv() |
| i0 = shape_env.create_unbacked_symint() |
| i1 = shape_env.create_unbacked_symint() |
| i1_sym = i1.node.expr |
| self.assertTrue(expect_true(i0 + i1 == 10)) |
| # Importantly, this is put in i1, not i0! |
| self.assertExpectedInline( |
| str([ra.expr for ra in shape_env.deferred_runtime_asserts[i1_sym]]), |
| """[Eq(u0 + u1, 10)]""", |
| ) |
| self.assertTrue(i0 + i1 == 10) |
| # NB: We currently don't support deriving that we can substitute |
| # i0 + i1 with 10; maybe we should, but this means our rewriting |
| # system is no longer confluent (it's probably OK though, because |
| # you're unlikely to get other equalities like this on the |
| # unbacked SymInts.) |
| |
| def test_unbacked_substitution(self): |
| shape_env = ShapeEnv() |
| i0 = shape_env.create_unbacked_symint() |
| i1 = shape_env.create_unbacked_symint() |
| _constrain_range_for_size(i0) |
| _constrain_range_for_size(i1) |
| self.assertTrue(expect_true(i0 == i1 * 4)) |
| self.assertExpectedInline(str(i0), """u0""") |
| |
| i2 = shape_env.create_unbacked_symint() |
| i3 = shape_env.create_unbacked_symint() |
| _constrain_range_for_size(i2) |
| _constrain_range_for_size(i3) |
| self.assertTrue(expect_true(i2 * 4 == i3)) |
| self.assertExpectedInline(str(i3), """u3""") |
| |
| def test_avoid_unbacked_substitution(self): |
| shape_env = ShapeEnv() |
| i0 = shape_env.create_unbacked_symint() |
| _constrain_range_for_size(i0) |
| i1 = shape_env.create_unbacked_symint() |
| _constrain_range_for_size(i1) |
| self.assertTrue(expect_true(i0 == 10 - i1)) |
| self.assertExpectedInline(str(i0), """u0""") |
| |
| def test_expect_true_double_digits(self): |
| shape_env = ShapeEnv() |
| ia = [shape_env.create_unbacked_symint() for _ in range(11)] # allocate 10 |
| self.assertEqual(str(ia[-1]), "u10") |
| self.assertTrue(expect_true(sum(ia) == 20)) |
| self.assertEqual(len(shape_env.deferred_runtime_asserts[ia[-1].node.expr]), 1) |
| |
| def test_expect_true_refine_range(self): |
| shape_env = ShapeEnv() |
| for i, rel in enumerate( |
| [lambda x: x > 4, lambda x: 4 < x, lambda x: x >= 5, lambda x: 5 <= x] |
| ): |
| with self.subTest(f"i = {i}"): |
| i0 = shape_env.create_unbacked_symint() |
| self.assertTrue(expect_true(rel(i0))) |
| self.assertTrue(statically_known_true(i0 != 3)) |
| self.assertTrue(statically_known_true(i0 != 4)) |
| self.assertFalse(statically_known_true(i0 != 5)) |
| self.assertFalse(statically_known_true(i0 != 6)) |
| self.assertTrue(statically_known_true(i0 > 4)) |
| self.assertTrue(statically_known_true(i0 >= 5)) |
| |
| for i, rel in enumerate( |
| [lambda x: x < 4, lambda x: 4 > x, lambda x: x <= 3, lambda x: 3 >= x] |
| ): |
| with self.subTest(f"i = {i}"): |
| i0 = shape_env.create_unbacked_symint() |
| self.assertTrue(expect_true(rel(i0))) |
| self.assertFalse(statically_known_true(i0 != 2)) |
| self.assertFalse(statically_known_true(i0 != 3)) |
| self.assertTrue(statically_known_true(i0 != 4)) |
| self.assertTrue(statically_known_true(i0 != 5)) |
| self.assertTrue(statically_known_true(i0 < 4)) |
| self.assertTrue(statically_known_true(i0 <= 5)) |
| |
| def test_guard_refine_range(self): |
| shape_env = ShapeEnv() |
| for i, rel in enumerate( |
| [lambda x: x > 4, lambda x: 4 < x, lambda x: x >= 5, lambda x: 5 <= x] |
| ): |
| with self.subTest(f"i = {i}"): |
| i0 = create_symint(shape_env, 10, duck=False) |
| self.assertTrue(bool(rel(i0))) |
| self.assertTrue(statically_known_true(i0 != 3)) |
| self.assertTrue(statically_known_true(i0 != 4)) |
| self.assertFalse(statically_known_true(i0 != 5)) |
| self.assertFalse(statically_known_true(i0 != 6)) |
| self.assertTrue(statically_known_true(i0 > 4)) |
| self.assertTrue(statically_known_true(i0 >= 5)) |
| |
| for i, rel in enumerate( |
| [lambda x: x > 4, lambda x: 4 < x, lambda x: x >= 5, lambda x: 5 <= x] |
| ): |
| with self.subTest(f"i = {i}"): |
| i0 = create_symint(shape_env, 2, duck=False) |
| self.assertFalse(bool(rel(i0))) |
| self.assertFalse(statically_known_true(i0 != 3)) |
| self.assertFalse(statically_known_true(i0 != 4)) |
| self.assertTrue(statically_known_true(i0 != 5)) |
| self.assertTrue(statically_known_true(i0 != 6)) |
| self.assertTrue(statically_known_true(i0 <= 4)) |
| self.assertTrue(statically_known_true(i0 < 5)) |
| |
| for i, rel in enumerate( |
| [lambda x: x < 4, lambda x: 4 > x, lambda x: x <= 3, lambda x: 3 >= x] |
| ): |
| with self.subTest(f"i = {i}"): |
| i0 = create_symint(shape_env, 2, duck=False) |
| self.assertTrue(bool(rel(i0))) |
| self.assertFalse(statically_known_true(i0 != 2)) |
| self.assertFalse(statically_known_true(i0 != 3)) |
| self.assertTrue(statically_known_true(i0 != 4)) |
| self.assertTrue(statically_known_true(i0 != 5)) |
| self.assertTrue(statically_known_true(i0 < 4)) |
| self.assertTrue(statically_known_true(i0 <= 3)) |
| |
| for i, rel in enumerate( |
| [lambda x: x < 4, lambda x: 4 > x, lambda x: x <= 3, lambda x: 3 >= x] |
| ): |
| with self.subTest(f"i = {i}"): |
| i0 = create_symint(shape_env, 10, duck=False) |
| self.assertFalse(bool(rel(i0))) |
| self.assertTrue(statically_known_true(i0 != 2)) |
| self.assertTrue(statically_known_true(i0 != 3)) |
| self.assertFalse(statically_known_true(i0 != 4)) |
| self.assertFalse(statically_known_true(i0 != 5)) |
| self.assertTrue(statically_known_true(i0 >= 4)) |
| self.assertTrue(statically_known_true(i0 > 3)) |
| |
| def test_mul_int_oo_nan(self): |
| shape_env = ShapeEnv() |
| s0 = create_symint(shape_env, 5, duck=False) |
| s1 = create_symint(shape_env, 6, duck=False) |
| s2 = create_symint(shape_env, 5, duck=False) |
| bool(s0 * (s1 // s0) == s2) |
| |
| def test_non_overlapping_and_dense(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 5) |
| r = torch.empty_strided((a0, 7), (1, a0), device="meta") |
| self.assertTrue(torch.ops.aten.is_non_overlapping_and_dense.default(r)) |
| |
| def test_non_overlapping_and_dense_unbacked(self): |
| shape_env = ShapeEnv() |
| u0 = shape_env.create_unbacked_symint() |
| torch._check_is_size(u0) |
| cf = torch.ops.aten.is_non_overlapping_and_dense.default |
| |
| self.assertEqual(IsNonOverlappingAndDenseIndicator(u0.node.expr, 2, 2, 1), 1) |
| self.assertEqual(IsNonOverlappingAndDenseIndicator(2, u0.node.expr, 1, 2), 1) |
| self.assertTrue(cf(torch.empty_strided((u0, 2), (2, 1), device="meta"))) |
| self.assertTrue(cf(torch.empty_strided((2, u0), (1, 2), device="meta"))) |
| |
| self.assertEqual(IsNonOverlappingAndDenseIndicator(u0.node.expr, 1), 1) |
| self.assertEqual(IsNonOverlappingAndDenseIndicator(1, u0.node.expr), 1) |
| self.assertTrue(cf(torch.empty_strided((u0,), (1,), device="meta"))) |
| self.assertTrue(cf(torch.empty_strided((1,), (u0,), device="meta"))) |
| |
| Max = torch.sym_max |
| # NB: This only works because we're able to determine this tensor is |
| # contiguous. transpose(0, 1) makes it stop working |
| self.assertTrue( |
| cf( |
| torch.empty_strided( |
| (2, 3, 1, u0), |
| (3 * Max(1, u0), Max(1, u0), Max(1, u0), 1), |
| device="meta", |
| ) |
| ) |
| ) |
| |
| def test_numpy_sym_max(self): |
| self.assertEqual(torch.sym_max(np.int64(10), 12), 12) |
| self.assertEqual(torch.sym_max(np.int64(12), 10), 12) |
| self.assertEqual(torch.sym_max(np.int64(10), 12.5), 12.5) |
| self.assertEqual(torch.sym_max(np.int64(14), 12.5), 14.0) |
| self.assertEqual(torch.sym_max(np.float64(14.0), 12), 14.0) |
| self.assertEqual(torch.sym_max(np.float64(14.0), 16), 16.0) |
| |
| def test_numpy_sym_min(self): |
| self.assertEqual(torch.sym_min(np.int64(10), 12), 10) |
| self.assertEqual(torch.sym_min(np.int64(12), 10), 10) |
| self.assertEqual(torch.sym_min(np.int64(10), 12.5), 10.0) |
| self.assertEqual(torch.sym_min(np.int64(14), 12.5), 12.5) |
| self.assertEqual(torch.sym_min(np.float64(14.0), 12), 12.0) |
| self.assertEqual(torch.sym_min(np.float64(14.0), 16), 14.0) |
| |
| def test_debug_has_internal_overlap_unbacked(self): |
| shape_env = ShapeEnv() |
| u0 = shape_env.create_unbacked_symint() |
| torch._check_is_size(u0) |
| cf = torch._debug_has_internal_overlap |
| self.assertEqual(cf(torch.empty_strided((u0, 2), (2, 1), device="meta")), 0) |
| self.assertEqual(cf(torch.empty_strided((2, u0), (1, 2), device="meta")), 0) |
| self.assertEqual(cf(torch.empty_strided((u0,), (1,), device="meta")), 0) |
| self.assertEqual(cf(torch.empty_strided((1,), (u0,), device="meta")), 0) |
| Max = torch.sym_max |
| self.assertEqual( |
| cf( |
| torch.empty_strided( |
| (2, 3, 1, u0), |
| (3 * Max(1, u0), Max(1, u0), Max(1, u0), 1), |
| device="meta", |
| ) |
| ), |
| 0, |
| ) |
| |
| # Wobbling these to zero is OK too |
| self.assertEqual(cf(torch.empty_strided((u0, 2), (3, 1), device="meta")), 2) |
| self.assertEqual(cf(torch.empty_strided((2, u0), (1, 3), device="meta")), 2) |
| |
| def test_specialize_zero_one(self): |
| shape_env = ShapeEnv(specialize_zero_one=True) |
| a0 = create_symint(shape_env, 5) |
| assert a0 != 1 |
| self.assertEqual(len(shape_env.guards), 0) |
| |
| shape_env = ShapeEnv(specialize_zero_one=False) |
| a0 = create_symint(shape_env, 5) |
| assert a0 != 1 |
| self.assertEqual(len(shape_env.guards), 1) |
| |
| def test_duck_shape(self): |
| shape_env = ShapeEnv(duck_shape=True) |
| a0 = create_symint(shape_env, 5) |
| a1 = create_symint(shape_env, 5) |
| assert a0 == a1 |
| self.assertEqual(len(shape_env.guards), 0) |
| |
| shape_env = ShapeEnv(duck_shape=False) |
| a0 = create_symint(shape_env, 5) |
| a1 = create_symint(shape_env, 5) |
| assert a0 == a1 |
| self.assertEqual(len(shape_env.guards), 1) |
| |
| def test_int_bool(self): |
| # See https://github.com/pytorch/pytorch/issues/95981 |
| shape_env = ShapeEnv(duck_shape=True) |
| a0 = create_symint(shape_env, 5) |
| assert a0 |
| self.assertEqual(len(shape_env.guards), 0) |
| |
| def test_symint_as_scalar(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 2) |
| |
| sym_int_encountered = False |
| |
| class TestSymInt(TorchDispatchMode): |
| def __torch_dispatch__(self, func, types, args=(), kwargs=None): |
| assert func == torch.ops.aten.add.Tensor |
| |
| nonlocal sym_int_encountered |
| # WARNING: do not do identity tests on the outer |
| # SymInt/SymFloat, they are NOT STABLE |
| sym_int_encountered = kwargs["alpha"].node is a0.node |
| kwargs["alpha"] = 0 |
| return func(*args) |
| |
| x = torch.rand([4, 4]) |
| with TestSymInt(): |
| y = torch.add(x, x, alpha=a0) |
| |
| self.assertTrue(sym_int_encountered) |
| |
| def test_deepcopy(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 2) |
| assert a0 < 4 |
| new_shape_env = copy.deepcopy(shape_env) |
| self.assertEqual(len(new_shape_env.guards), 1) |
| |
| def test_print_readable_with_symints(self): |
| def f(a, b): |
| dim0 = a.shape[0] + b.shape[0] |
| dim1 = a.shape[1] + b.shape[1] |
| d = a.new_empty(dim0, dim1) |
| d = torch.ops.aten.native_dropout(d, 0.5, train=True) |
| return d |
| |
| fx_g = make_fx(f, tracing_mode="symbolic")(torch.randn(5, 3), torch.randn(4, 3)) |
| out = fx_g.print_readable(print_output=False) |
| |
| self.assertExpectedInline( |
| out.strip(), |
| """\ |
| class f(torch.nn.Module): |
| def forward(self, a_1: "f32[s0, s1]", b_1: "f32[s2, s1]"): |
| # No stacktrace found for following nodes |
| sym_size_int: "Sym(s0)" = torch.ops.aten.sym_size.int(a_1, 0) |
| sym_size_int_1: "Sym(s2)" = torch.ops.aten.sym_size.int(b_1, 0) |
| add: "Sym(s0 + s2)" = sym_size_int + sym_size_int_1; sym_size_int = sym_size_int_1 = None |
| sym_size_int_2: "Sym(s1)" = torch.ops.aten.sym_size.int(a_1, 1) |
| sym_size_int_3: "Sym(s1)" = torch.ops.aten.sym_size.int(b_1, 1); b_1 = None |
| add_1: "Sym(2*s1)" = sym_size_int_2 + sym_size_int_3; sym_size_int_2 = sym_size_int_3 = None |
| new_empty: "f32[s0 + s2, 2*s1]" = torch.ops.aten.new_empty.default(a_1, [add, add_1], pin_memory = False); a_1 = add = add_1 = None |
| native_dropout = torch.ops.aten.native_dropout.default(new_empty, 0.5, True); new_empty = None |
| getitem: "f32[s0 + s2, 2*s1]" = native_dropout[0] |
| getitem_1: "b8[s0 + s2, 2*s1]" = native_dropout[1]; native_dropout = None |
| return (getitem, getitem_1)""", # noqa: B950 |
| ) |
| |
| def test_statically_known_true(self): |
| shape_env = ShapeEnv() |
| s2, s3, s4 = (create_symint(shape_env, i) for i in range(2, 5)) |
| |
| # Statically known true |
| self.assertTrue(statically_known_true(True)) |
| self.assertTrue(statically_known_true(s2 == s2)) |
| self.assertTrue(statically_known_true(s2 * s3 > s3)) |
| self.assertTrue(statically_known_true(s3 * s4 > s4)) |
| self.assertTrue(statically_known_true((s3 + s3) % 2 == 0)) |
| |
| # Statically known false |
| self.assertFalse(statically_known_true(False)) |
| self.assertFalse(statically_known_true(s3 * s4 <= s4)) |
| self.assertFalse(statically_known_true((s3 + s3) % 2 == 1)) |
| |
| # True for hints, but not known statically |
| self.assertFalse(statically_known_true(s2 + s2 == s4)) |
| self.assertFalse(statically_known_true(s4 % s2 == 0)) |
| self.assertFalse(statically_known_true(s2 != s3)) |
| self.assertFalse(statically_known_true(s3 * s4 > s2)) |
| |
| # False for hints, but not known statically |
| self.assertFalse(statically_known_true(s2 == s3)) |
| self.assertFalse(statically_known_true(s2 > s3)) |
| self.assertFalse(statically_known_true(s3 + s3 == s4)) |
| |
| # No guards should be generated |
| self.assertEqual(len(shape_env.guards), 0) |
| |
| def test_ephemeral_source_simplification(self): |
| from torch._dynamo.source import EphemeralSource |
| |
| # For full robustness, ensure the ephemeral source symbols are simplified out regardless |
| # of construction order or check order. |
| for construct_ephemeral_first, x_first_in_check in itertools.product( |
| [False, True], [False, True] |
| ): |
| shape_env = ShapeEnv() |
| shape = (5, 10) |
| dynamic_dims = [DimDynamic.DYNAMIC for _ in shape] |
| x = create_symbolic_tensor( |
| "x", |
| torch.randn(*shape), |
| shape_env, |
| source=(EphemeralSource() if construct_ephemeral_first else None), |
| dynamic_dims=dynamic_dims, |
| ) |
| y = create_symbolic_tensor( |
| "y", |
| torch.randn(*shape), |
| shape_env, |
| source=(EphemeralSource() if not construct_ephemeral_first else None), |
| dynamic_dims=dynamic_dims, |
| ) |
| t_with_ephemeral = x if construct_ephemeral_first else y |
| |
| def _get_ephemeral_source_symbols(t): |
| return [ |
| s.node.expr |
| for s in itertools.chain(t.shape, t.stride(), (t.storage_offset(),)) |
| if isinstance(s, torch.SymInt) |
| and s.node.expr in shape_env.var_to_sources |
| and any( |
| source.is_ephemeral() |
| for source in shape_env.var_to_sources[s.node.expr] |
| ) |
| ] |
| |
| # these checks should simplify out the ephemeral symbols, regardless of the |
| # ordering x == y or y == x |
| self.assertTrue(len(_get_ephemeral_source_symbols(t_with_ephemeral)) > 0) |
| if x_first_in_check: |
| torch._check(x.size() == y.size()) |
| torch._check(x.stride() == y.stride()) |
| torch._check(x.storage_offset() == y.storage_offset()) |
| else: |
| torch._check(y.size() == x.size()) |
| torch._check(y.stride() == x.stride()) |
| torch._check(y.storage_offset() == x.storage_offset()) |
| self.assertEqual(len(_get_ephemeral_source_symbols(t_with_ephemeral)), 0) |
| |
| def test_ephemeral_source_unified_with_non_ephemeral_source(self): |
| from torch._dynamo.source import EphemeralSource |
| |
| for construct_ephemeral_first in (False, True): |
| shape_env = ShapeEnv() |
| shape = (5, 10) |
| # use duck sizing here to ensure symbol reuse across x and y |
| duck_dims = [DimDynamic.DUCK for _ in shape] |
| x = create_symbolic_tensor( |
| "x", |
| torch.randn(*shape), |
| shape_env, |
| source=(EphemeralSource() if construct_ephemeral_first else None), |
| dynamic_dims=duck_dims, |
| ) |
| y = create_symbolic_tensor( |
| "y", |
| torch.randn(*shape), |
| shape_env, |
| source=(EphemeralSource() if not construct_ephemeral_first else None), |
| dynamic_dims=duck_dims, |
| ) |
| |
| # regardless of construction order, non-ephemeral sources should be preferred |
| # first in the var_to_sources list for potential guarding later on |
| for source_list in shape_env.var_to_sources.values(): |
| self.assertFalse(source_list[0].is_ephemeral()) |
| |
| self.assertEqual(x.size(), y.size()) |
| self.assertEqual(x.stride(), y.stride()) |
| self.assertEqual(x.storage_offset(), y.storage_offset()) |
| |
| |
| @skipIfTorchDynamo( |
| "Creating ShapeEnv fails for confusing reasons (also we never expect dynamo to see code like this)" |
| ) |
| class TestSymNumberMagicMethods(TestCase): |
| def _do_test(self, fn, inp1, inp2, shape_env, is_unary_fn): |
| with self.subTest(fn=fn, inp1=inp1, inp2=inp2, is_unary_fn=is_unary_fn): |
| return self._do_test2(fn, inp1, inp2, shape_env, is_unary_fn) |
| |
| def _do_test2(self, fn, inp1, inp2, shape_env, is_unary_fn): |
| # Helper function |
| # NB: don't use one as that will get specialized |
| # TODO: We don't have to circuitously create the float, can just |
| # create a symfloat directly |
| seed_node = (create_symint(shape_env, 2) / 2.0).node |
| bool_seed_node = (create_symint(shape_env, 2) == 2).node |
| |
| def get_sym_inp(inp): |
| # NB: this must come before int |
| if isinstance(inp, bool): |
| return torch.SymBool(to_node(bool_seed_node, inp)) |
| elif isinstance(inp, int): |
| return torch.SymInt(to_node(seed_node, inp)) |
| else: |
| return torch.SymFloat(to_node(seed_node, inp)) |
| |
| if fn == "float_pow": |
| if inp1 < 0: |
| return |
| |
| if fn == "pow_by_natural": |
| if isinstance(inp1, float) or isinstance(inp2, float): |
| return |
| if inp2 < 0: |
| return |
| |
| def maybe_xfail(inp1, inp2): |
| if fn == "sym_sqrt" and inp1 < 0: |
| # ValueError: math domain error |
| return self.assertRaises((ValueError,)) |
| elif ( |
| fn in ("float_truediv", "int_truediv", "int_floordiv", "mod") |
| and inp2 == 0 |
| ): |
| # ZeroDivisionError: division by zero |
| return self.assertRaises((ZeroDivisionError,)) |
| elif fn in ["float_pow", "pow_by_natural"] and inp1 == 0 and inp2 < 0: |
| # ZeroDivisionError: 0.0 cannot be raised to a negative power |
| return self.assertRaises((ZeroDivisionError,)) |
| elif ( |
| # TODO: dear catastrophe waitress, |
| # this doesn't work |
| fn in ["float_pow", "pow_by_natural"] |
| and inp1 < 0 |
| and ( |
| type(inp1) is (SymInt, SymFloat) or type(inp2) is (SymInt, SymFloat) |
| ) |
| and (type(inp1) is (SymFloat, float) or type(inp2) is (SymFloat, float)) |
| ): |
| # Complex result, which we do not support: |
| # TypeError: Cannot convert complex to float |
| return self.assertRaises((RuntimeError,)) |
| elif fn in ("lshift", "rshift") and not ( |
| isinstance(inp1, (SymInt, int)) and isinstance(inp2, (SymInt, int)) |
| ): |
| # TypeError: unsupported operand type(s) |
| return self.assertRaises((TypeError,)) |
| elif fn in ("lshift", "rshift") and inp2 < 0: |
| # ValueError: math domain error |
| return self.assertRaises((ValueError,)) |
| else: |
| return contextlib.nullcontext() |
| |
| lambda_apply = method_to_operator(fn) |
| |
| def guard_fn(v): |
| if type(v) in (SymBool, bool): |
| return guard_bool(v) |
| elif type(v) in (SymFloat, float): |
| return guard_float(v) |
| else: # SymInt, int |
| return guard_int(v) |
| |
| # Get reference result |
| with maybe_xfail(inp1, inp2): |
| if is_unary_fn: |
| ref_out = lambda_apply(inp1) |
| else: |
| ref_out = lambda_apply(inp1, inp2) |
| |
| # Symified first arg |
| sym_inp1 = get_sym_inp(inp1) |
| with maybe_xfail(sym_inp1, inp2): |
| if is_unary_fn: |
| out = lambda_apply(sym_inp1) |
| else: |
| out = lambda_apply(sym_inp1, inp2) |
| if fn not in sym_node.alternate_impl_if_hinted_methods: |
| self.assertTrue(isinstance(out, (SymInt, SymFloat, SymBool))) |
| out = guard_fn(out) |
| self.assertEqual(out, ref_out) |
| |
| if is_unary_fn: |
| return |
| |
| # Symified second arg |
| sym_inp2 = get_sym_inp(inp2) |
| with maybe_xfail(inp1, sym_inp2): |
| out = lambda_apply(inp1, sym_inp2) |
| if fn not in sym_node.alternate_impl_if_hinted_methods: |
| self.assertTrue(isinstance(out, (SymInt, SymFloat, SymBool))) |
| out = guard_fn(out) |
| self.assertEqual(out, ref_out) |
| |
| # Symified both args |
| with maybe_xfail(sym_inp1, sym_inp2): |
| out = lambda_apply(sym_inp1, sym_inp2) |
| if fn not in sym_node.alternate_impl_if_hinted_methods: |
| self.assertTrue(isinstance(out, (SymInt, SymFloat, SymBool))) |
| out = guard_fn(out) |
| self.assertEqual(out, ref_out) |
| |
| @parametrize("fn", list(sym_node.magic_methods.keys())) |
| def test_bool_method(self, fn): |
| # sym_ite has its own tests |
| if fn not in sym_node.bool_magic_methods or fn == "sym_ite": |
| self.skipTest(f"{fn} is non-bool") |
| |
| is_unary_fn = fn in sym_node.unary_methods |
| shape_env = ShapeEnv() |
| self._do_test(fn, True, False, shape_env, is_unary_fn) |
| |
| @parametrize("fn", list(sym_node.magic_methods.keys())) |
| @parametrize("first_type", ["int", "float"]) |
| @parametrize("second_type", ["int", "float"]) |
| def test_method(self, fn, first_type, second_type): |
| if first_type == "float": |
| # TODO: Hmm, this looks like we skip all floats |
| self.skipTest(f"{fn} is not a float magic method") |
| |
| if ( |
| first_type == "int" or second_type == "int" |
| ) and fn in sym_node.only_float_magic_methods: |
| self.skipTest(f"{fn} is not an int method") |
| |
| if second_type == "float" and fn in ["mod"]: |
| self.skipTest(f"{fn} only handles int") |
| |
| is_unary_fn = fn in sym_node.unary_methods or fn == "round" |
| # Second argument is ignored for unary function. So only run for one type |
| if is_unary_fn and second_type == "float": |
| self.skipTest(f"{fn} is unary and already tested") |
| |
| if fn in sym_node.bool_magic_methods: |
| self.skipTest(f"{fn} is bool") |
| |
| # Only floats here since these will be converted to int if necessary. |
| # We also ignore complex and bool. |
| values = ( |
| 0.0, |
| 1.0, |
| 0.5 if fn in ("sym_acos", "sym_asin") else 2.5, # avoid math domain error |
| ) |
| |
| neg_values = tuple(-x for x in values) |
| |
| for inp1, inp2 in itertools.chain( |
| itertools.product(values, values), |
| itertools.product(values, neg_values), |
| itertools.product(neg_values, values), |
| itertools.product(neg_values, neg_values), |
| ): |
| if first_type == "int": |
| inp1 = int(inp1) |
| if second_type == "int": |
| inp2 = int(inp2) |
| |
| shape_env = ShapeEnv() |
| |
| self._do_test(fn, inp1, inp2, shape_env, is_unary_fn) |
| |
| def get_constant_bool(self, val): |
| return SymBool(torch._C._get_constant_bool_symnode(val)) |
| |
| @unittest.expectedFailure |
| def test_symint_hashing(self): |
| shape_env = ShapeEnv() |
| hash(create_symint(shape_env, 3)) |
| |
| def test_symnode_hashing(self): |
| shape_env = ShapeEnv() |
| |
| # These all trigger specialization when hashed |
| hash(create_symbool(shape_env, True)) |
| # We should be passing in float here, but create_symbol currently |
| # only supports int |
| hash(create_symfloat(shape_env, 3.0)) |
| |
| # NestedInt (SymInt), constant SymBool, SymNode are hashable |
| j1 = torch._C._get_nested_int(1, 1) |
| j1_copy = torch._C._get_nested_int(1, 1) |
| j2 = torch._C._get_nested_int(2, 1) |
| t = self.get_constant_bool(True) |
| t_copy = self.get_constant_bool(True) |
| f = self.get_constant_bool(False) |
| n = create_symint(shape_env, 3).node |
| m = self.get_constant_bool(True).node |
| |
| self.assertIs(j1 == j1_copy, True) |
| self.assertEqual(hash(j1), hash(j1_copy)) |
| self.assertIs(j1 == j2, False) |
| self.assertNotEqual(hash(j1), hash(j2)) |
| self.assertIs(t == t_copy, True) |
| self.assertEqual(hash(t), hash(t_copy)) |
| self.assertIs(t == f, False) |
| self.assertNotEqual(hash(t), hash(f)) |
| |
| hash(n) |
| hash(m) |
| |
| def test_symint_deepcopy(self): |
| shape_env = ShapeEnv() |
| |
| symnodes = (torch._C._get_nested_int(1, 1),) |
| deepcopied_symnodes = copy.deepcopy(symnodes) |
| self.assertEqual(symnodes, deepcopied_symnodes) |
| |
| def test_non_symbolic_symnode(self): |
| j1 = torch._C._get_nested_int(1, 1) |
| j2 = torch._C._get_nested_int(1, 1) |
| j3 = torch._C._get_nested_int(3, 1) |
| |
| self.assertIsInstance(j1, torch.SymInt) |
| self.assertNotIsInstance(j1, int) |
| |
| with self.assertRaisesRegex( |
| RuntimeError, "add not supported by NestedIntSymNode" |
| ): |
| j1 + 3 |
| |
| self.assertFalse(j1 == 3) |
| with self.assertRaisesRegex(RuntimeError, "indeterminate"): |
| self.assertFalse(3 >= j2) |
| |
| self.assertIs(j1 == j1, True) |
| self.assertIs(j1 == j2, True) |
| self.assertIs(j1 == j3, False) |
| self.assertIs(j1 != j3, True) |
| self.assertIs(j1 != j2, False) |
| |
| x = self.get_constant_bool(True) |
| # |
| # Unary |
| # |
| # op(constant SymBool) |
| self.assertIs(x.__sym_not__(), False) |
| |
| # |
| # Binary |
| # |
| # op(constant SymBool, bool) |
| # op(constant SymBool, constant SymBool) |
| # op(bool, constant SymBool) |
| self.assertIs(operator.and_(x, True), True) |
| self.assertIs(operator.and_(x, x), True) |
| self.assertIs(operator.and_(True, x), True) |
| |
| # op(symbolic SymBool, constant Symbool) |
| # op(constant SymBool, symbolic Symbool) |
| shape_env = ShapeEnv() |
| a = create_symint(shape_env, 2) |
| b = create_symint(shape_env, 2) |
| c = a == b # symbolic SymBool |
| d = self.get_constant_bool(True) |
| e = operator.and_(c, d) |
| f = operator.and_(d, c) |
| self.assertTrue(is_symbolic(e)) |
| self.assertTrue(is_symbolic(f)) |
| self.assertIs(e.node.guard_bool("", 0), True) |
| self.assertIs(f.node.guard_bool("", 0), True) |
| |
| # Comparing sizes |
| sz1 = torch.Size([j1, j1, j1]) |
| sz2 = torch.Size([j1, j1, j1]) |
| self.assertIs(sz1 == sz2, True) |
| |
| sz1 = torch.Size([3, j1, 4]) |
| sz2 = torch.Size([3, j2, 4]) |
| self.assertIs(sz1 == sz2, True) |
| self.assertIs(sz1 != sz2, False) |
| |
| def test_stride_symnode(self): |
| from torch._subclasses.fake_tensor import FakeTensorMode |
| |
| shape_env = ShapeEnv() |
| |
| def _create_symbolic_tensor(x, dynamic_sizes, dynamic_strides): |
| with FakeTensorMode(shape_env=shape_env) as fake_mode: |
| return fake_mode.from_tensor( |
| x, |
| symbolic_context=StatelessSymbolicContext( |
| dynamic_sizes=dynamic_sizes, |
| dynamic_strides=dynamic_strides, |
| ), |
| ) |
| |
| # check everything static |
| t = _create_symbolic_tensor( |
| x=torch.ones(3, 6), |
| dynamic_sizes=[ |
| DimDynamic.STATIC, |
| DimDynamic.STATIC, |
| ], |
| dynamic_strides=[ |
| DimDynamic.INFER_STRIDE, |
| DimDynamic.INFER_STRIDE, |
| ], |
| ) |
| self.assertTrue(all(isinstance(size, int) for size in t.size())) |
| self.assertTrue(all(isinstance(stride, int) for stride in t.stride())) |
| |
| # check dynamic size but static dims |
| t = _create_symbolic_tensor( |
| x=torch.ones(3, 6), |
| dynamic_sizes=[ |
| DimDynamic.DYNAMIC, |
| DimDynamic.DYNAMIC, |
| ], |
| dynamic_strides=[ |
| DimDynamic.INFER_STRIDE, |
| DimDynamic.INFER_STRIDE, |
| ], |
| ) |
| # Expect stride to be inferred |
| s0, s1 = t.size() |
| s2, s3 = t.stride() |
| self.assertTrue(isinstance(s0, torch.SymInt)) |
| self.assertTrue(isinstance(s1, torch.SymInt)) |
| self.assertTrue(isinstance(s2, torch.SymInt)) |
| self.assertTrue(s1 == s2) |
| self.assertEqual(s3, 1) |
| |
| # Check dynamic stride but static dims |
| t = _create_symbolic_tensor( |
| x=torch.ones(3, 6), |
| dynamic_sizes=[ |
| DimDynamic.STATIC, |
| DimDynamic.STATIC, |
| ], |
| dynamic_strides=[ |
| DimDynamic.DYNAMIC, |
| DimDynamic.INFER_STRIDE, |
| ], |
| ) |
| s0, s1 = t.size() |
| s2, s3 = t.stride() |
| self.assertTrue(isinstance(s0, int)) |
| self.assertTrue(isinstance(s1, int)) |
| self.assertTrue(isinstance(s2, torch.SymInt)) |
| self.assertTrue(isinstance(s3, int)) |
| |
| # Check dynamic sizes and dims, and ensure different symbol |
| t = _create_symbolic_tensor( |
| x=torch.ones(3, 6), |
| dynamic_sizes=[ |
| DimDynamic.DYNAMIC, |
| DimDynamic.DYNAMIC, |
| ], |
| dynamic_strides=[ |
| DimDynamic.DYNAMIC, |
| DimDynamic.INFER_STRIDE, |
| ], |
| ) |
| s0, s1 = t.size() |
| s2, s3 = t.stride() |
| self.assertTrue(isinstance(s0, torch.SymInt)) |
| self.assertTrue(isinstance(s1, torch.SymInt)) |
| self.assertTrue(isinstance(s2, torch.SymInt)) |
| self.assertTrue(isinstance(s3, int)) |
| self.assertTrue(str(s1.node.expr) != str(s2.node.expr)) |
| |
| |
| instantiate_parametrized_tests(TestSymNumberMagicMethods) |
| |
| |
| class TestFloorDiv(TestCase): |
| @staticmethod |
| def python_floordiv(x, y): |
| return x // y |
| |
| @staticmethod |
| def torch_floordiv(x, y): |
| # Note: we fully evaluate here since FloorDiv might not always do |
| # that. |
| shape_env = ShapeEnv() |
| return shape_env.evaluate_expr(FloorDiv(x, y)) |
| |
| @staticmethod |
| def yield_test_cases(values, negate=True): |
| for x, y in values: |
| yield (x, y) |
| if negate: |
| yield (-x, y) |
| yield (x, -y) |
| yield (-x, -y) |
| |
| def test_floordiv_float_int(self): |
| values = ((7, 2),) |
| |
| for x, y in TestFloorDiv.yield_test_cases(values): |
| self.assertEqual( |
| TestFloorDiv.python_floordiv(x, y), TestFloorDiv.torch_floordiv(x, y) |
| ) |
| |
| def test_floordiv_div_by_one(self): |
| values = ((2, 1),) |
| |
| for x, y in TestFloorDiv.yield_test_cases(values): |
| self.assertEqual( |
| TestFloorDiv.python_floordiv(x, y), TestFloorDiv.torch_floordiv(x, y) |
| ) |
| |
| def test_floordiv_simplify(self): |
| # Tests how we simplify or evaluate FloorDiv without free variables |
| shape_env = ShapeEnv() |
| result = 21 |
| exprs = (7 * FloorDiv(6, 2),) |
| |
| for expr in exprs: |
| self.assertEqual(expr, result) |
| self.assertEqual(expr.doit(deep=False), result) |
| self.assertEqual(expr.doit(deep=True), result) |
| self.assertEqual(sympy.simplify(expr), result) |
| self.assertEqual(shape_env.simplify(expr), result) |
| self.assertEqual(shape_env.evaluate_expr(expr), result) |
| |
| def test_floordiv_assumptions(self): |
| cases = ( |
| sympy.Symbol("i1", integer=True), |
| sympy.Symbol("i2", integer=True), |
| ) |
| |
| for base, divisor in itertools.product(cases, repeat=2): |
| |
| def op(): |
| return FloorDiv(base, divisor) |
| |
| def is_complex(x): |
| return x.is_integer is False and x.is_real is False and x.is_complex |
| |
| if is_complex(base) or is_complex(divisor): |
| self.assertRaisesRegex( |
| TypeError, |
| ( |
| r"unsupported operand type\(s\) for //: 'Symbol' and 'Symbol'," |
| r" expected integer or real" |
| ), |
| op, |
| ) |
| continue |
| |
| op = op() |
| |
| # In regular Python, x//x == 1.0 if x is a float, but FloorDiv |
| # always returns an integer 1 when both args are the same object. |
| # This even works for Symbols with no assumptions specified. |
| if base is divisor: |
| self.assertTrue(op.is_integer) |
| self.assertTrue(op.is_real) |
| elif base.is_integer and divisor.is_integer: |
| self.assertTrue(op.is_integer) |
| self.assertTrue(op.is_real) |
| else: |
| self.assertEqual(op.is_integer, None) |
| self.assertTrue(op.is_real) |
| |
| |
| class TestDimConstraints(TestCase): |
| def test_dim_constraints_reduce_congruences_simple(self): |
| from sympy import Symbol |
| |
| s = Symbol("s", positive=True, integer=True) |
| dim_constraints = DimConstraints({}, {}, set(), {}) |
| dim_constraints._congruences[s] = { |
| (s / 2) % 2, |
| (s / 2) % 8, |
| (s / 2) % 4, |
| s % 2, |
| ((s / 16) + 2) % 4, |
| } |
| congruences = dim_constraints._reduce_congruences() |
| self.assertEqual(congruences[s], {(s + 32) % 64}) |
| |
| def test_dim_constraints_reduce_inequalities_simple(self): |
| from sympy import Eq, Interval, Ne, Symbol |
| from sympy.solvers.inequalities import reduce_inequalities |
| |
| s = Symbol("s", positive=True, integer=True) |
| exprs = { |
| s >= 2, |
| Ne(8 * s, 16), |
| Ne(s / 2, 1), |
| Ne(16 * s, 32), |
| s < 16, |
| Ne(s, 2), |
| s / 2 < 16, |
| s / 2 > 1, |
| s / 2 >= 2, |
| Ne(3 * s / 2, 3), |
| } |
| solution = reduce_inequalities(exprs, s).as_set() |
| self.assertEqual(solution, Interval.Ropen(4, 16)) |
| |
| exprs.add(Eq(s / 2, 4)) |
| solution = reduce_inequalities(exprs, s).as_set() |
| self.assertEqual(solution, {8}) |
| |
| def test_dim_constraints_reduce_inequalities_error(self): |
| from collections import defaultdict |
| |
| from sympy import Symbol |
| from sympy.solvers.inequalities import reduce_inequalities |
| |
| from torch._dynamo.source import ( |
| LocalSource, |
| TensorProperty, |
| TensorPropertySource, |
| ) |
| from torch.fx.experimental.symbolic_shapes import DynamicDimConstraintPrinter |
| |
| s0 = Symbol("s0", positive=True, integer=True) |
| exprs = { |
| 4 * s0**3 - 4 * s0**2 + s0 <= 2147483647, |
| s0 >= 2, |
| s0**3 <= 2147483647, |
| s0 <= 2147483647, |
| } |
| answer = reduce_inequalities(exprs, s0) |
| |
| symbol_to_source = defaultdict(list) |
| symbol_to_source[s0].append( |
| TensorPropertySource( |
| base=LocalSource(local_name="a"), prop=TensorProperty.SIZE, idx=0 |
| ) |
| ) |
| dcp = DynamicDimConstraintPrinter(symbol_to_source, {}) |
| with self.assertRaisesRegex( |
| AssertionError, |
| "Unknown symbol.*created by constraints solver", |
| ): |
| dcp.doprint(answer) |
| |
| def test_dim_constraints_solve_full(self): |
| from sympy import Eq, Integer, Ne, Symbol |
| |
| from torch._dynamo.source import ( |
| LocalSource, |
| TensorProperty, |
| TensorPropertySource, |
| ) |
| |
| src0 = TensorPropertySource( |
| base=LocalSource(local_name="a"), prop=TensorProperty.SIZE, idx=0 |
| ) |
| src2 = TensorPropertySource( |
| base=LocalSource(local_name="b"), prop=TensorProperty.SIZE, idx=0 |
| ) |
| src3 = TensorPropertySource( |
| base=LocalSource(local_name="c"), prop=TensorProperty.SIZE, idx=0 |
| ) |
| src4 = TensorPropertySource( |
| base=LocalSource(local_name="d"), prop=TensorProperty.SIZE, idx=0 |
| ) |
| |
| src1 = TensorPropertySource( |
| base=LocalSource(local_name="a"), prop=TensorProperty.SIZE, idx=2 |
| ) |
| src7 = TensorPropertySource( |
| base=LocalSource(local_name="a"), prop=TensorProperty.SIZE, idx=3 |
| ) |
| |
| src5 = TensorPropertySource( |
| base=LocalSource(local_name="a"), prop=TensorProperty.SIZE, idx=1 |
| ) |
| src8 = TensorPropertySource( |
| base=LocalSource(local_name="b"), prop=TensorProperty.SIZE, idx=1 |
| ) |
| |
| src6 = TensorPropertySource( |
| base=LocalSource(local_name="c"), prop=TensorProperty.SIZE, idx=1 |
| ) |
| src9 = TensorPropertySource( |
| base=LocalSource(local_name="d"), prop=TensorProperty.SIZE, idx=1 |
| ) |
| src10 = TensorPropertySource( |
| base=LocalSource(local_name="e"), prop=TensorProperty.SIZE, idx=1 |
| ) |
| |
| src11 = TensorPropertySource( |
| base=LocalSource(local_name="f"), prop=TensorProperty.SIZE, idx=1 |
| ) |
| src12 = TensorPropertySource( |
| base=LocalSource(local_name="b"), prop=TensorProperty.SIZE, idx=2 |
| ) |
| |
| s0 = Symbol("s0", positive=True, integer=True) |
| s1 = Symbol("s1", positive=True, integer=True) |
| s5 = Symbol("s5", positive=True, integer=True) |
| s6 = Symbol("s6", positive=True, integer=True) |
| symbol_to_source = { |
| s0: [src0, src2, src3, src4], |
| s1: [src1, src7], |
| s5: [src5, src8], |
| s6: [src6, src9, src10], |
| } |
| var_to_val = {s0: 8, s1: 96, s5: 22, s6: 21} |
| marked_dynamic = {s0, s1, s5, s6} |
| dim_constraints = DimConstraints( |
| symbol_to_source, var_to_val, marked_dynamic, {} |
| ) |
| dim_constraints.add_equality(src2, s0) |
| dim_constraints.add_equality(src3, s0) |
| dim_constraints.add_equality(src4, s0) |
| dim_constraints.add_equality(src7, s1) |
| dim_constraints.add_equality(src8, s5) |
| dim_constraints.add_equality(src9, s6) |
| dim_constraints.add_equality(src10, s6) |
| dim_constraints.add_equality(src11, Integer(1)) |
| dim_constraints.add_equality(src12, Integer(3)) |
| |
| dim_constraints.add(s1**2 <= 2147483647) |
| dim_constraints.add(32 * s1**2 <= 2147483647) |
| dim_constraints.add(s0 < 16) |
| dim_constraints.add(Eq(Mod(s1, 2), 0)) |
| dim_constraints.add(Ne(FloorDiv(s1, 2), 1)) |
| dim_constraints.add(Ne((FloorDiv(s1, 2)) ** 2, 1)) |
| dim_constraints.add(32 * (FloorDiv(s1, 2)) ** 2 <= 2147483647) |
| dim_constraints.add((FloorDiv(s1, 2)) ** 2 > 1) |
| dim_constraints.add(Ne(FloorDiv(s1, 2), 1)) |
| dim_constraints.add( |
| 64 * (FloorDiv((FloorDiv(s1, 2) - 1), 2)) ** 2 |
| + 128 * (FloorDiv((FloorDiv(s1, 2) - 1), 2)) |
| + 64 |
| <= 2147483647 |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 2) + 1, 1)) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 2)) ** 2 |
| + 2 * (FloorDiv((FloorDiv(s1, 2) - 1), 2)) |
| + 1, |
| 1, |
| ) |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 2) + 1, 1)) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 2)) ** 2 |
| + 2 * (FloorDiv((FloorDiv(s1, 2) - 1), 2)) |
| + 1 |
| > 1 |
| ) |
| dim_constraints.add( |
| 128 * (FloorDiv((FloorDiv(s1, 2) - 1), 4)) ** 2 |
| + 256 * (FloorDiv((FloorDiv(s1, 2) - 1), 4)) |
| + 128 |
| <= 2147483647 |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 4) + 1, 1)) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 4)) ** 2 |
| + 2 * (FloorDiv((FloorDiv(s1, 2) - 1), 4)) |
| + 1, |
| 1, |
| ) |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 4) + 1, 1)) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 4)) ** 2 |
| + 2 * (FloorDiv((FloorDiv(s1, 2) - 1), 4)) |
| + 1 |
| > 1 |
| ) |
| dim_constraints.add( |
| 256 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| + 512 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) |
| + 256 |
| <= 2147483647 |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) + 1, 1)) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| + 2 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) |
| + 1, |
| 1, |
| ) |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) + 1, 1)) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| + 2 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) |
| + 1 |
| > 1 |
| ) |
| dim_constraints.add(FloorDiv((FloorDiv(s1, 2) - 1), 8) + 1 >= 3) |
| dim_constraints.add( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| <= 2147483647 |
| ) |
| dim_constraints.add(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1 >= 0) |
| dim_constraints.add(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1 >= 1) |
| dim_constraints.add( |
| Ne( |
| 60 * s0 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * s0 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * s0, |
| 0, |
| ) |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1, 1)) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1, |
| 1, |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1, |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1 |
| >= 0 |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1, 0)) |
| dim_constraints.add( |
| 1 |
| < 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1, -1)) |
| dim_constraints.add( |
| Ne( |
| 60 * s0 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * s0 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * s0, |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120, |
| ) |
| ) |
| dim_constraints.add( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120 |
| > 0 |
| ) |
| dim_constraints.add( |
| Eq( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 * (Mod(s0, 2)) |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) * Mod(s0, 2) |
| + 60 * (Mod(s0, 2)), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120, |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| 60 |
| * (FloorDiv(s0, 2)) |
| * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 |
| * FloorDiv(s0, 2) |
| * FloorDiv(s0, (FloorDiv(s0, 2))) |
| * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, 2)) * (FloorDiv(s0, (FloorDiv(s0, 2)))), |
| 0, |
| ) |
| ) |
| dim_constraints.add(Ne(FloorDiv(s0, 2), 1)) |
| dim_constraints.add( |
| Ne( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60, |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| >= 0 |
| ) |
| dim_constraints.add( |
| 1 |
| < 60 |
| * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv(s0, (FloorDiv(s0, 2))) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| ) |
| dim_constraints.add(Ne(16 * s0, 32)) |
| dim_constraints.add(Eq(16 * (Mod(s0, 2)), 0)) |
| dim_constraints.add(Ne(16 * s0, 32)) |
| dim_constraints.add(Eq(16 * (Mod(s0, 2)), 0)) |
| dim_constraints.add(FloorDiv(s0, 2) >= 2) |
| dim_constraints.add(Ne(FloorDiv(s0, 2), 1)) |
| dim_constraints.add(1 < FloorDiv(s0, 2)) |
| dim_constraints.add(Ne(s0, 2)) |
| dim_constraints.add( |
| 60 |
| * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv(s0, (FloorDiv(s0, 2))) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| >= 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| ) |
| dim_constraints.add( |
| 60 |
| * (FloorDiv(s0, 2)) |
| * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 |
| * FloorDiv(s0, 2) |
| * FloorDiv(s0, (FloorDiv(s0, 2))) |
| * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, 2)) * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| > 0 |
| ) |
| dim_constraints.add( |
| Ne( |
| 60 |
| * (FloorDiv(s0, 2)) |
| * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 |
| * FloorDiv(s0, 2) |
| * FloorDiv(s0, (FloorDiv(s0, 2))) |
| * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, 2)) * (FloorDiv(s0, (FloorDiv(s0, 2)))), |
| 3 * (FloorDiv(s0, 2)) * (FloorDiv(s0, (FloorDiv(s0, 2)))), |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| 20 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 40 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 20, |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 20 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 40 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 20 |
| >= 0 |
| ) |
| dim_constraints.add( |
| Ne( |
| 20 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 40 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 20, |
| 20, |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| 20 |
| * ( |
| Mod( |
| 1, |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1, |
| ) |
| ), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| 20 |
| * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) |
| * ( |
| Mod( |
| 1, |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1) |
| - 2 |
| * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1) |
| + 1 / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1), |
| ) |
| ) |
| - 20 |
| * Mod( |
| 1, |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1) |
| - 2 |
| * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1) |
| + 1 / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1), |
| ), |
| 0, |
| ) |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1, 1)) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1 |
| >= 1 |
| ) |
| dim_constraints.add( |
| 20 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 40 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 20 |
| >= 0 |
| ) |
| dim_constraints.add( |
| 20 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 40 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 20 |
| >= 1 |
| ) |
| dim_constraints.add( |
| 20 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 40 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 20 |
| >= 2 |
| ) |
| dim_constraints.add( |
| 20 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 40 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 20 |
| > 1 |
| ) |
| dim_constraints.add( |
| 20 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 40 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 20 |
| < 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| ) |
| dim_constraints.add( |
| Ne( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60, |
| 60, |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1, |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1, |
| ) |
| ) |
| dim_constraints.add( |
| Eq( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) |
| * ( |
| Mod( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1) |
| - 2 |
| * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1) |
| + 1 / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1), |
| 1, |
| ) |
| ) |
| - Mod( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1) |
| - 2 |
| * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1) |
| + 1 / (FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1), |
| 1, |
| ), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1, |
| FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1, |
| ) |
| ) |
| dim_constraints.add(Ne(8 * s0, 16)) |
| dim_constraints.add( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| >= (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1 |
| ) |
| dim_constraints.add( |
| 60 |
| * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv(s0, (FloorDiv(s0, 2))) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| <= 2147483647 |
| ) |
| dim_constraints.add( |
| 90 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 180 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 90 |
| <= 2147483647 |
| ) |
| dim_constraints.add(FloorDiv(s0, 2) < 16) |
| dim_constraints.add(FloorDiv(s0, 2) > 1) |
| dim_constraints.add( |
| Ne( |
| 90 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 180 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 90 * (FloorDiv(s0, 2)), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 1 |
| < 90 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 180 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 90 |
| ) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 2 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1 |
| > 1 |
| ) |
| dim_constraints.add( |
| 60 |
| * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv(s0, (FloorDiv(s0, 2))) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, (FloorDiv(s0, 2)))) |
| > 1 |
| ) |
| dim_constraints.add( |
| Ne( |
| 60 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, 2)), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 90 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 180 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 90 |
| > 1 |
| ) |
| dim_constraints.add( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| > 1 |
| ) |
| dim_constraints.add( |
| Ne( |
| 60 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, 2)), |
| 3 * (FloorDiv(s0, 2)), |
| ) |
| ) |
| dim_constraints.add( |
| 60 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 * (FloorDiv(s0, 2)) |
| > 0 |
| ) |
| dim_constraints.add( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| > 0 |
| ) |
| dim_constraints.add( |
| Ne( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120, |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 1 |
| < 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120 |
| ) |
| dim_constraints.add( |
| Ne( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120, |
| 6, |
| ) |
| ) |
| dim_constraints.add( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120 |
| > 0 |
| ) |
| dim_constraints.add( |
| Ne( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120, |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120 |
| <= 2147483647 |
| ) |
| dim_constraints.add( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120 |
| <= 20480 |
| ) |
| dim_constraints.add( |
| Ne( |
| 90 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 180 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 90, |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 120 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 240 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 120 |
| > 1 |
| ) |
| dim_constraints.add( |
| 90 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 180 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 90 |
| <= 20480 |
| ) |
| dim_constraints.add( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 120 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 60 |
| <= 20480 |
| ) |
| dim_constraints.add( |
| Ne( |
| 240 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 480 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 240, |
| 0, |
| ) |
| ) |
| dim_constraints.add(Eq(6 * s5, 132)) |
| dim_constraints.add(Eq(4, FloorDiv(s0, 2))) |
| dim_constraints.add(Eq(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1, 4)) |
| dim_constraints.add( |
| Ne( |
| 64 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 128 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 64 * (FloorDiv(s0, 2)), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 1 |
| < 64 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 128 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 64 |
| ) |
| dim_constraints.add( |
| 64 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 128 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 64 |
| <= 2147483647 |
| ) |
| dim_constraints.add( |
| 64 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 128 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 64 |
| > 1 |
| ) |
| dim_constraints.add( |
| 62 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 124 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 62 |
| <= 2147483647 |
| ) |
| dim_constraints.add( |
| Ne( |
| 62 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 124 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 62 * (FloorDiv(s0, 2)), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 1 |
| < 62 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 124 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 62 |
| ) |
| dim_constraints.add(Ne(3 * (FloorDiv(s0, 2)), 3)) |
| dim_constraints.add(Ne(3 * (FloorDiv(s0, 2)), 3)) |
| dim_constraints.add(Eq(FloorDiv(s0, 2), 4)) |
| dim_constraints.add(Eq(4, FloorDiv(s0, 2))) |
| dim_constraints.add(Eq(FloorDiv(s0, 2), 4)) |
| dim_constraints.add(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 1 >= 3) |
| dim_constraints.add( |
| 64 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 384 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 576 |
| <= 2147483647 |
| ) |
| dim_constraints.add(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 3 >= 0) |
| dim_constraints.add(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 3 >= 1) |
| dim_constraints.add( |
| Ne( |
| 64 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 384 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 576 * (FloorDiv(s0, 2)), |
| 0, |
| ) |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 3, 1)) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9, |
| 1, |
| ) |
| ) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9, |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 |
| >= 0 |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 3, 0)) |
| dim_constraints.add( |
| 1 |
| < 64 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 384 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 576 |
| ) |
| dim_constraints.add(Ne(FloorDiv((FloorDiv(s1, 2) - 1), 8) - 3, 1)) |
| dim_constraints.add( |
| Ne( |
| 64 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 384 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 576 * (FloorDiv(s0, 2)), |
| 256, |
| ) |
| ) |
| dim_constraints.add( |
| Eq( |
| 64 |
| * ( |
| Mod( |
| (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 * (FloorDiv(s0, 2)), |
| 4, |
| ) |
| ), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| Eq( |
| FloorDiv(s0, 2), |
| FloorDiv( |
| ( |
| (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 * (FloorDiv(s0, 2)) |
| ), |
| 4, |
| ), |
| ) |
| ) |
| dim_constraints.add( |
| Eq( |
| FloorDiv( |
| ( |
| (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 * (FloorDiv(s0, 2)) |
| ), |
| 4, |
| ), |
| FloorDiv(s0, 2), |
| ) |
| ) |
| dim_constraints.add( |
| Ne(64 * (Mod(FloorDiv((FloorDiv(s1, 2) - 1), 8) + 1, 4)), 0) |
| ) |
| dim_constraints.add( |
| Eq( |
| 64 |
| * ( |
| Mod( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 1, |
| 4, |
| ) |
| ), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 64 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 384 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 576 * (FloorDiv(s0, 2)) |
| > 0 |
| ) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 |
| >= 1 |
| ) |
| dim_constraints.add( |
| Eq( |
| 64 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 384 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 576, |
| 256, |
| ) |
| ) |
| dim_constraints.add( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 360 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 540 |
| <= 2147483647 |
| ) |
| dim_constraints.add( |
| Ne( |
| 60 * (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 360 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 540 * (FloorDiv(s0, 2)), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 1 |
| < 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 360 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 540 |
| ) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 |
| <= 2147483647 |
| ) |
| dim_constraints.add( |
| Ne( |
| (FloorDiv(s0, 2)) * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv(s0, 2) * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 * (FloorDiv(s0, 2)), |
| 0, |
| ) |
| ) |
| dim_constraints.add( |
| 1 |
| < (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 |
| ) |
| dim_constraints.add( |
| (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 6 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 9 |
| > 1 |
| ) |
| dim_constraints.add( |
| 60 * (FloorDiv((FloorDiv(s1, 2) - 1), 8)) ** 2 |
| - 360 * FloorDiv((FloorDiv(s1, 2) - 1), 8) |
| + 540 |
| > 1 |
| ) |
| dim_constraints.add(s0 >= 2) |
| dim_constraints.add(s1 >= 2) |
| dim_constraints.add(s6 >= 2) |
| dim_constraints.add(s5 >= 2) |
| |
| dim_constraints.solve() |
| self.assertEqual( |
| dim_constraints._static_results, |
| { |
| "L['c'].size()[0] == 8", |
| "L['d'].size()[0] == 8", |
| "L['a'].size()[2] == 96", |
| "L['f'].size()[1] == 1", |
| "L['a'].size()[3] == 96", |
| "L['b'].size()[2] == 3", |
| "L['b'].size()[1] == 22", |
| "L['b'].size()[0] == 8", |
| "L['a'].size()[1] == 22", |
| "L['a'].size()[0] == 8", |
| }, |
| ) |
| self.assertEqual( |
| dim_constraints._dynamic_results, |
| { |
| "2 <= L['c'].size()[1]", |
| "L['d'].size()[1] == L['c'].size()[1]", |
| "L['e'].size()[1] == L['c'].size()[1]", |
| }, |
| ) |
| |
| |
| class TestGuardsExpressions(TestCase): |
| """ |
| Tests the guards-related methods used by the inductor FX graph cache. |
| """ |
| |
| def test_guards_gt_lt(self): |
| shape_env = ShapeEnv() |
| s0 = create_symint(shape_env, 6) |
| s1 = create_symint(shape_env, 7) |
| s2 = create_symint(shape_env, 5) |
| |
| guard_int(sym_int(s0 > 5)) |
| guard_int(sym_int(s0 < 7)) |
| |
| guards = shape_env.produce_guards_expression([s0]) |
| |
| self.assertTrue(shape_env.evaluate_guards_expression(guards, [hint_int(s0)])) |
| self.assertFalse(shape_env.evaluate_guards_expression(guards, [hint_int(s1)])) |
| self.assertFalse(shape_env.evaluate_guards_expression(guards, [hint_int(s2)])) |
| |
| def test_guards_float_print(self): |
| shape_env = ShapeEnv() |
| s0 = create_symint(shape_env, 3) |
| guard_bool(2 / s0 == 2 / 3) |
| guards = shape_env.produce_guards_expression([s0]) |
| self.assertTrue(shape_env.evaluate_guards_expression(guards, [hint_int(s0)])) |
| |
| def test_guards_float_div(self): |
| shape_env = ShapeEnv() |
| s0 = create_symint(shape_env, 8) |
| s1 = create_symint(shape_env, 7) |
| |
| guard_int(sym_int(s0 / 2.0)) |
| guards = shape_env.produce_guards_expression([s0]) |
| |
| self.assertIn("ToFloat", guards) |
| self.assertIn("FloatTrueDiv", guards) |
| self.assertTrue(shape_env.evaluate_guards_expression(guards, [hint_int(s0)])) |
| self.assertFalse(shape_env.evaluate_guards_expression(guards, [hint_int(s1)])) |
| |
| |
| if __name__ == "__main__": |
| run_tests() |
