| # -*- coding: utf-8 -*- |
| # Owner(s): ["oncall: jit"] |
| |
| from torch._C import _disabled_torch_function_impl |
| import torch.fx |
| import torch.nn.functional as F |
| from torch.testing._internal.common_utils import run_tests, TestCase, skipIfTorchDynamo, \ |
| IS_WINDOWS, parametrize, instantiate_parametrized_tests |
| import unittest |
| import torch |
| import operator |
| import itertools |
| import random |
| import contextlib |
| import math |
| import builtins |
| import atexit |
| import io |
| import os |
| from torch.utils._pytree import tree_map |
| from torch.fx.experimental import symbolic_shapes |
| from torch.fx.experimental.proxy_tensor import make_fx |
| from torch.fx.experimental.symbolic_shapes import ShapeEnv, sym_float, guard_int, SymNode, sym_sqrt, sym_int, to_node |
| from torch.utils._python_dispatch import TorchDispatchMode |
| from torch import SymInt |
| |
| aten = torch.ops.aten |
| |
| try: |
| import sympy |
| # TODO(jansel): these tests fail on windows |
| HAS_SYMPY = not IS_WINDOWS |
| except ImportError: |
| HAS_SYMPY = False |
| skipIfNoSympy = unittest.skipIf(not HAS_SYMPY, "no sympy") |
| |
| |
| meta_funcs = {} |
| |
| |
| def register_meta(op): |
| def decorator(f): |
| def add_func(op): |
| meta_funcs[op] = f |
| 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): |
| sym_shapes, sym_strides, sym_storage_offset = shape_env.create_symbolic_sizes_strides_storage_offset(arg, sname=name) |
| return FakeSymbolicTensor(sym_shapes, sym_strides, arg.dtype, arg.layout, arg.requires_grad, arg.device, sym_storage_offset) |
| |
| def create_symint(shape_env, i): |
| return shape_env.create_symintnode(shape_env.create_symbol(i, sname=f"__testing_only{len(shape_env.var_to_val)}")) |
| |
| @skipIfTorchDynamo("Creating ShapeEnv fails for confusing reasons (also we never expect dynamo to see code like this)") |
| class TestPySymInt(TestCase): |
| |
| @skipIfNoSympy |
| 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])) |
| |
| |
| @skipIfNoSympy |
| 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) |
| |
| |
| @skipIfNoSympy |
| 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) |
| self.assertTrue(isinstance(x.size()[1], SymInt)) |
| 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)) |
| |
| 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) |
| |
| @skipIfNoSympy |
| 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) |
| |
| @skipIfNoSympy |
| 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) |
| |
| @skipIfNoSympy |
| 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]) |
| |
| @skipIfNoSympy |
| def test_stride(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5, 5), shape_env) |
| self.assertIsInstance(x.stride()[0], SymInt) |
| |
| @skipIfNoSympy |
| 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])) |
| |
| @skipIfNoSympy |
| 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) |
| |
| @skipIfNoSympy |
| def test_int_to_float(self): |
| shape_env = ShapeEnv() |
| x = create_symbolic_tensor("x", torch.randn(5), shape_env) |
| r = sym_float(x.shape[0]) |
| self.assertIsInstance(r, torch.SymFloat, msg=type(r)) |
| |
| @skipIfNoSympy |
| 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) |
| |
| @skipIfNoSympy |
| 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) |
| |
| @skipIfNoSympy |
| 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)""") |
| |
| @skipIfNoSympy |
| 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(floor(s1/2), 3)""") |
| |
| a2 = create_symint(shape_env, -3) |
| r = sym_int(a2 / 2) |
| self.assertEqual(guard_int(r), -1) |
| self.assertIsInstance(r, torch.SymInt, msg=type(r)) |
| self.assertExpectedInline(str(shape_env.guards[2][0]), """Eq(ceiling(-s2/2), -1)""") |
| |
| @skipIfNoSympy |
| def test_sym_sqrt(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 4) |
| r = sym_sqrt(a0) |
| self.assertEqual(r, 2) |
| self.assertIsInstance(r, torch.SymFloat, msg=type(r)) |
| self.assertExpectedInline(str(shape_env.guards[0][0]), """Eq(sqrt(s0), 2)""") |
| |
| @skipIfNoSympy |
| 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(floor(s0/2), 2)""") |
| |
| @skipIfNoSympy |
| def test_int_conversion(self): |
| shape_env = ShapeEnv() |
| a0 = create_symint(shape_env, 2) |
| self.assertRaisesRegex(RuntimeError, "Trying to extract", lambda: int(a0)) |
| |
| @skipIfNoSympy |
| 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) |
| |
| @skipIfNoSympy |
| @unittest.mock.patch('sys.stdout', new_callable=io.StringIO) |
| def test_print_readable_with_symints(self, mock_stdout): |
| 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)) |
| fx_g.print_readable() |
| |
| self.assertExpectedInline(mock_stdout.getvalue().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: Sym(s0) = torch.ops.aten.sym_size(a_1, 0) |
| sym_size_1: Sym(s2) = torch.ops.aten.sym_size(b_1, 0) |
| add: Sym(s0 + s2) = sym_size + sym_size_1; sym_size = sym_size_1 = None |
| sym_size_2: Sym(s1) = torch.ops.aten.sym_size(a_1, 1) |
| sym_size_3: Sym(s1) = torch.ops.aten.sym_size(b_1, 1); b_1 = None |
| add_1: Sym(2*s1) = sym_size_2 + sym_size_3; sym_size_2 = sym_size_3 = None |
| new_empty: f32[s0 + s2, 2*s1] = torch.ops.aten.new_empty.default(a_1, [add, add_1], dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), 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 |
| |
| # This environment variable controls whether or not we print expected failure |
| # lists at the end of a test suite run. The intended usage looks like this: |
| # |
| # 1. Run `PYTORCH_COLLECT_EXPECT=1 python test/test_dynamic_shapes.py -k TestSymNumberMagicMethods`. |
| # 2. Given the printed xfail list, add them to the set expected_failure_sym_magic_methods. |
| COLLECT_EXPECT = os.getenv('PYTORCH_COLLECT_EXPECT', '0') == '1' |
| |
| seen_failed = [] |
| def print_seen(): |
| out = [] |
| for key, reason in seen_failed: |
| # Make sure the generated line is lint clean |
| msg = f" {key}, # {reason}" |
| eol = msg.find("\n") |
| if eol != -1: |
| msg = msg[:eol] |
| out.append(msg[:120]) |
| |
| print("expected_failure_sym_magic_methods = {") |
| print("\n".join(out)) |
| print("}") |
| |
| if COLLECT_EXPECT: |
| atexit.register(print_seen) |
| |
| expected_failure_sym_magic_methods = { |
| ('floordiv', 'SymFloat', 'float'), # Cannot convert complex to float |
| ('floordiv', 'float', 'SymFloat'), # Cannot convert complex to float |
| ('floordiv', 'SymFloat', 'SymFloat'), # Cannot convert complex to float |
| ('floordiv', 'SymFloat', 'int'), # Scalars are not close! |
| ('floordiv', 'float', 'SymInt'), # Scalars are not close! |
| ('floordiv', 'SymFloat', 'SymInt'), # Scalars are not close! |
| ('floordiv', 'SymInt', 'float'), # Cannot convert complex to float |
| ('floordiv', 'int', 'SymFloat'), # Cannot convert complex to float |
| ('floordiv', 'SymInt', 'SymFloat'), # Cannot convert complex to float |
| } |
| |
| @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): |
| # Helper function |
| seed_node = (create_symint(shape_env, 1) / 1.).get_pyobj() |
| |
| def get_sym_inp(inp): |
| if isinstance(inp, int): |
| return torch.SymInt(to_node(seed_node, inp)) |
| else: |
| return torch.SymFloat(to_node(seed_node, inp)) |
| |
| def maybe_xfail(inp1, inp2): |
| key = (fn, type(inp1).__name__, type(inp2).__name__) |
| if COLLECT_EXPECT: |
| @contextlib.contextmanager |
| def context(): |
| try: |
| yield |
| except (TypeError, AssertionError) as e: |
| seen_failed.append((key, str(e))) |
| return context() |
| |
| if key in expected_failure_sym_magic_methods: |
| return self.assertRaises((TypeError, AssertionError)) |
| else: |
| return contextlib.nullcontext() |
| |
| # These functions might return plain int/float |
| has_valid_downcast = fn in ["min", "max"] |
| if fn in symbolic_shapes.magic_methods_on_builtins: |
| lambda_apply = getattr(builtins, fn) |
| elif fn in symbolic_shapes.magic_methods_on_math: |
| lambda_apply = getattr(math, fn) |
| elif fn in symbolic_shapes.magic_methods_on_submodule: |
| lambda_apply = getattr(symbolic_shapes, fn) |
| else: |
| lambda_apply = getattr(operator, fn) |
| |
| if fn in symbolic_shapes.always_float_magic_methods: |
| tp = "float" |
| elif fn in symbolic_shapes.always_int_magic_methods: |
| tp = "int" |
| elif is_unary_fn: |
| tp = "float" if isinstance(inp1, float) else "int" |
| else: |
| tp = "float" if any(isinstance(i, float) for i in [inp1, inp2]) else "int" |
| |
| def guard_fn(v): |
| try: |
| if fn in symbolic_shapes.always_bool_magic_methods: |
| return bool(v) |
| else: |
| return getattr(v.node, f"guard_{tp}")("", 0) |
| except Exception as e: |
| if has_valid_downcast: |
| return v |
| else: |
| raise e |
| |
| # 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) |
| self.assertEqual(guard_fn(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) |
| self.assertEqual(guard_fn(out), ref_out) |
| |
| # Symified both args |
| with maybe_xfail(sym_inp1, sym_inp2): |
| out = lambda_apply(sym_inp1, sym_inp2) |
| self.assertEqual(guard_fn(out), ref_out) |
| |
| |
| @parametrize("fn", list(symbolic_shapes.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": |
| self.skipTest(f"{fn} is not a float magic method") |
| |
| is_unary_fn = fn in symbolic_shapes.unary_magic_methods |
| # 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") |
| |
| # We could pass int/float directly for types but then the |
| # mangled test name is bad |
| inp1 = random.random() * 2.5 |
| if first_type == "int": |
| inp1 = int(inp1) |
| inp2 = random.random() * 2.5 |
| if second_type == "int": |
| inp2 = int(inp2) |
| |
| shape_env = ShapeEnv() |
| |
| self._do_test(fn, inp1, inp2, shape_env, is_unary_fn) |
| |
| instantiate_parametrized_tests(TestSymNumberMagicMethods) |
| |
| if __name__ == '__main__': |
| run_tests() |
