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# Owner(s): ["module: dynamo"]
import unittest
from contextlib import contextmanager
from importlib import import_module
import torch
import torch._prims_common as utils
from torch._dynamo.utils import preserve_rng_state
from torch._inductor import config
from torch._inductor.compiler_bisector import CompilerBisector
from torch._inductor.test_case import TestCase
from torch.library import _scoped_library, Library
from torch.testing._internal.inductor_utils import HAS_CUDA
aten = torch.ops.aten
requires_cuda = unittest.skipUnless(HAS_CUDA, "requires cuda")
f32 = torch.float32
i64 = torch.int64
i32 = torch.int32
@requires_cuda
class TestCompilerBisector(TestCase):
test_ns = "_test_bisector"
def tearDown(self):
if hasattr(torch.ops, self.test_ns):
delattr(torch.ops, self.test_ns)
if hasattr(self, "lib"):
del self.lib.m
del self.lib
def get_op(self, name):
return getattr(getattr(torch.ops, self.test_ns), name).default
def get_lib(self):
lib = Library(self.test_ns, "FRAGMENT") # noqa: TOR901
self.lib = lib
return lib
def test_bad_decomp(self):
mod = import_module("torch._inductor.compile_fx")
def bad_exp_decomp(self, rate=1, generator=None):
assert generator is None
torch._check(
not utils.is_complex_dtype(self.dtype)
and not utils.is_integer_dtype(self.dtype)
and not utils.is_boolean_dtype(self.dtype),
lambda: f"Exponential distribution is a continuous probability distribution. \
dtype must be a floating point but you specified {self.dtype}",
)
torch._check(
rate > 0.0,
lambda: f"exponential_ expects lambda > 0.0, but found lambda={rate}",
)
return torch.rand_like(self) * float("nan")
@contextmanager
def patch_exp_decomp():
from torch._inductor.compile_fx import select_decomp_table as old_decomp
def get_decomp():
out = old_decomp()
out = out.copy()
out[aten.exponential.default] = bad_exp_decomp
return out
torch._inductor.compile_fx.select_decomp_table = get_decomp
try:
yield
finally:
torch._inductor.compile_fx.select_decomp_table = old_decomp
def vq(x):
return (x + 3).exponential_() * 10.5
def test_fn():
torch._dynamo.reset()
with patch_exp_decomp():
vq_compiled = torch.compile(vq)
x = torch.randn(4, 400, 256).cuda()
with torch._dynamo.utils.preserve_rng_state():
out = vq(x)
out_compiled = vq_compiled(x)
return not out_compiled.isnan().any()
out = CompilerBisector.do_bisect(test_fn)
self.assertEqual(out.backend, "aot_eager_decomp_partition")
self.assertEqual(out.subsystem, "decomposition")
self.assertEqual(out.bisect_number, 1)
self.assertTrue("aten.exponential" in out.debug_info)
def test_joint_graph(self):
from torch._inductor import config
def pass_fn(graph: torch.fx.Graph):
nodes = graph.find_nodes(
op="call_function", target=torch.ops.aten.add.Tensor
)
assert len(nodes) == 1
args = list(nodes[0].args)
args[1] = 2
nodes[0].args = tuple(args)
config.joint_custom_post_pass = pass_fn
def foo(x):
return x + 1
def test_fn():
torch._dynamo.reset()
inp = torch.rand([10], device="cuda")
out = foo(inp)
out_c = torch.compile(foo)(inp)
return torch.allclose(out, out_c)
out = CompilerBisector.do_bisect(test_fn)
self.assertEqual(out.backend, "inductor")
self.assertEqual(out.subsystem, "joint_graph_passes")
self.assertEqual(out.bisect_number, 4)
self.assertTrue("joint_custom_post_pass" in out.debug_info)
def test_rng(self):
def foo():
return torch.rand([10], device="cuda") + 1
def test_fn():
torch._dynamo.reset()
with preserve_rng_state():
out = foo()
with preserve_rng_state():
out_c = torch.compile(foo)()
return torch.allclose(out, out_c)
out = CompilerBisector.do_bisect(test_fn)
self.assertEqual(out.backend, "inductor")
self.assertEqual(out.subsystem, "inductor_fallback_random")
self.assertTrue("inductor_fallback_random" in out.debug_info)
def test_crossref(self):
test_ns = "bisect_ops"
with _scoped_library(self.test_ns, "FRAGMENT") as lib:
lib.define("foo(Tensor x) -> Tensor")
op = self.get_op("foo")
class Foo(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
# Emulate AutoDispatchBelowADInplaceOrView, which is not bound into python
with torch._C._AutoDispatchBelowAutograd():
with torch._C._ExcludeDispatchKeyGuard(
torch._C.DispatchKeySet(
torch._C.DispatchKey.ADInplaceOrView
)
):
return op(x)
@staticmethod
def backward(ctx, gx):
return gx
def foo_impl(x):
return x.view_as(x).clone()
def foo_meta(x):
return x.view_as(x)
lib.impl("foo", Foo.apply, "Autograd")
lib.impl("foo", foo_impl, "CPU")
lib.impl("foo", foo_meta, "Meta")
x = torch.tensor(3.14159 / 3, requires_grad=True)
def test_fn():
torch._dynamo.reset()
try:
torch.testing.assert_allclose(torch.compile(op)(x), op(x))
except Exception:
return False
return True
out = CompilerBisector.do_bisect(test_fn)
self.assertEqual(out.backend, "aot_eager_decomp_partition_crossref")
def test_emulate_precision_casts(self):
def test_fn():
torch._dynamo.reset()
def calculate_scale(inp):
amax = torch.abs(torch.max(inp))
scale = 448.0 / torch.clamp(amax, min=1e-12)
scale = scale.to(torch.float32)
return scale
dtype = torch.bfloat16
torch.manual_seed(0)
inp = torch.randn(16, 16, 768, dtype=dtype, device="cuda")
eager_scale = calculate_scale(inp)
compile_scale = torch.compile(calculate_scale)(inp)
return torch.equal(eager_scale, compile_scale)
out = CompilerBisector.do_bisect(test_fn)
self.assertEqual(out.backend, "inductor")
self.assertEqual(out.subsystem, "inductor_emulate_precision_casts")
def test_bad_lowering(self):
def test_fn():
torch._dynamo.reset()
with config.patch("triton.inject_relu_bug_TESTING_ONLY", "accuracy"):
def my_func(x):
return ((x * -1) - 0.01).relu()
inp = torch.rand([100], device="cuda")
return torch.allclose(torch.compile(my_func)(inp), my_func(inp))
out = CompilerBisector.do_bisect(test_fn)
self.assertEqual(out.backend, "inductor")
self.assertEqual(out.subsystem, "lowerings")
self.assertEqual(out.bisect_number, 2)
self.assertTrue("relu" in out.debug_info)
def test_eager_backend(self):
# should indicate problem with first backend
def test_fn():
return False
out = CompilerBisector.do_bisect(test_fn)
self.assertEqual(out.backend, "eager")
self.assertEqual(out.subsystem, None)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
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