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|
# Owner(s): ["module: dynamo"]
import contextlib
import torch
import torch.fx
from torch._dynamo.test_case import TestCase
from torch._dynamo.testing import extract_graph_and_tracker
from torch.utils._pytree import tree_map
def get_nodes_by_name(graph, names):
nodes = []
for node in graph.nodes:
if node.name in names:
nodes.append(node)
return nodes
unique_ind = 0
def track_same_nodes(names, graph, region_tracker):
global unique_ind
unique_ind += 1
# find nodes in graph with names and track them
# as if they were at the same code location
nodes = get_nodes_by_name(graph, names)
for node in nodes:
region_tracker.track_node("x", unique_ind, node)
class GraphRegionTrackerTests(TestCase):
def setUp(self):
self.exit_stack = contextlib.ExitStack()
self.exit_stack.enter_context(
torch._dynamo.config.patch("track_nodes_for_deduplication", True)
)
super().setUp()
def tearDown(self):
self.exit_stack.close()
super().tearDown()
def get_result(self, fn, *args, **kwargs):
graph, region_tracker = extract_graph_and_tracker(fn, *args, **kwargs)
region_groups = region_tracker.get_identical_regions(graph)
region_groups = tree_map(lambda n: n.name, region_groups)
return str(region_groups)
def test_get_regions_single_region_group(self):
def inner_fn(x, y):
x0 = x + 1
y0 = y + 2
z = x0.sum() + y0.sum()
return z
def fn(x, y):
o0 = inner_fn(x, y)
o1 = torch.sin(y)
o2 = inner_fn(x, o1)
o3 = inner_fn(x, y)
o4 = o3 * o3
return o2 * o4
self.assertExpectedInline(
self.get_result(
fn,
torch.rand(10, 10),
torch.ones(10, 20),
),
"""[[['y0', 'x0', 'sum_2', 'sum_1', 'z'], \
['y0_1', 'x0_1', 'sum_4', 'sum_3', 'z_1'], ['y0_2', 'x0_2', 'sum_6', 'sum_5', 'z_2']]]""",
)
def test_get_regions_multiple_region_groups(self):
def inner_fn(x, y):
x1 = x + 1
y1 = y + 2
z = x1.sum() + y1.sum()
return z
def inner_fn2(a, b):
a += 2
b += 3
c = a * b.cos().sum()
return c
def fn(x, y):
x0 = torch.cos(x)
y0 = torch.sin(y)
o1 = inner_fn2(x0, y0)
o0 = inner_fn(x, y)
o1 = torch.sin(o0)
o2 = inner_fn(x, y0)
o2 = inner_fn2(x0, y0)
o3 = inner_fn(x, y)
return o1 * o2 + o3
self.assertExpectedInline(
self.get_result(
fn,
torch.rand(10, 10),
torch.ones(10, 20),
),
"""[[['y1', 'x1', 'sum_3', 'sum_2', 'z'], ['y1_1', 'x1_1', 'sum_5', 'sum_4', 'z_1'], \
['y1_2', 'x1_2', 'sum_8', 'sum_7', 'z_2']], [['b', 'cos_1', 'sum_1', 'a', 'c'], ['b_1', 'cos_2', 'sum_6', 'a_1', 'c_1']]]""",
)
def test_no_single_node_regions(self):
def inner_fn(x):
return x + 1
def fn(x):
o0 = inner_fn(x)
o1 = inner_fn(x)
o2 = inner_fn(x)
return o0 + o1 + o2
self.assertExpectedInline(self.get_result(fn, torch.ones(10, 10)), """[]""")
def test_mismatched_arg_shapes(self):
def inner_fn(x, y):
x1 = x + 1
y1 = y + 2
z = x1.sum() + y1.sum()
return z
def inner_fn2(a, b):
a += 2
b += 3
c = a * b.cos().sum()
return c
def fn(x, y):
x0 = torch.cos(x)
y0 = torch.sin(y)
o1 = inner_fn2(x0, y0)
o0 = inner_fn(x, o1)
o1 = torch.sin(o0)
o2 = inner_fn(x, y0)
o2 = inner_fn2(o2, y0)
o3 = inner_fn(x, y)
return o1 * o2 + o3
self.assertExpectedInline(
self.get_result(
fn,
torch.rand(10, 10),
torch.ones(10, 20),
),
"""[[['y1_1', 'sum_5'], ['y1_2', 'sum_8']], [['x1', 'sum_2', 'z'], ['x1_1', 'sum_4', 'z_1'], \
['x1_2', 'sum_7', 'z_2']], [['b', 'cos_1', 'sum_1'], ['b_1', 'cos_2', 'sum_6']]]""",
)
def test_mismatched_dtypes(self):
def inner_fn(x, y):
x1 = x * 1
y1 = y + 1
return x1 + y1.sum()
def fn(x, y):
x0 = torch.sin(x)
y0 = torch.cos(y)
o0 = inner_fn(x0, y0)
o2 = inner_fn(x0, y0)
o4 = inner_fn(x0, y0)
o5 = inner_fn(x0, y0)
o1 = inner_fn(x0.to(torch.bfloat16), y0.to(torch.bfloat16))
o3 = o1 + o2
return o3 * o0 + o4 + o5
self.assertExpectedInline(
self.get_result(
fn,
torch.rand(10, 10),
torch.ones(10, 20),
),
"""[[['y1', 'sum_1', 'x1', 'o0'], ['y1_1', 'sum_2', 'x1_1', 'o2'], \
['y1_2', 'sum_3', 'x1_2', 'o4'], ['y1_3', 'sum_4', 'x1_3', 'o5']]]""",
)
def test_nested_args(self):
def inner_fn(xs, ys):
out = torch._foreach_add(xs, ys)
return out[0] + out[1].sum()
def fn(x, y, z):
x0 = torch.sin(x)
y0 = torch.cos(y)
z0 = torch.sin(z)
o0 = inner_fn([x0, z0], [x0, y0])
o2 = inner_fn([x0, z0], [x0, y0])
o4 = inner_fn([x0, z0], [x0, y0])
o5 = inner_fn([x0, z0], [x0, y0])
o1 = inner_fn(
[x0.to(torch.bfloat16), z0.to(torch.bfloat16)],
[x0.to(torch.bfloat16), y0.to(torch.bfloat16)],
)
o3 = o1 + o2
return o3 * o0 + o4 + o5
self.assertExpectedInline(
self.get_result(
fn,
torch.rand(10, 10),
torch.rand(10, 20),
torch.ones(10, 20),
),
"""[[['getitem_1', '_foreach_add', 'sum_1', 'getitem', 'o0'], ['getitem_3', \
'_foreach_add_1', 'sum_2', 'getitem_2', 'o2'], ['getitem_5', '_foreach_add_2',\
'sum_3', 'getitem_4', 'o4'], ['getitem_7', '_foreach_add_3', 'sum_4', 'getitem_6', 'o5']]]""",
)
def test_mismatched_global_state(self):
def inner_fn(x, y):
x1 = x * 1
y1 = y + 1
return x1 + y1.sum()
def fn(x, y, c):
x0 = torch.sin(x)
y0 = torch.cos(y)
o4 = inner_fn(x0, y0)
o5 = inner_fn(x0, y0)
if isinstance(c, tuple):
c[0]()
o0 = inner_fn(x0, y0)
o2 = inner_fn(x0, y0)
c[1]()
else:
with c():
o0 = inner_fn(x0, y0)
o2 = inner_fn(x0, y0)
return o0 + o2 + o4 + o5
def create_toggle_fns(property):
old_value = getattr(torch.backends.cuda.matmul, property)
def toggle_property():
setattr(torch.backends.cuda.matmul, property, not old_value)
def reset_property():
setattr(torch.backends.cuda.matmul, property, old_value)
return toggle_property, reset_property
old_dtype = torch.get_default_dtype()
def set_default_dtype_bfloat16():
torch.set_default_dtype(torch.bfloat16)
def reset_default_dtype():
torch.set_default_dtype(old_dtype)
for ctx in [
lambda: torch.set_grad_enabled(False),
torch.autograd.grad_mode.inference_mode,
lambda: torch.autograd.graph.disable_saved_tensors_hooks(
"This is not supported"
),
# lambda: torch.set_num_threads(2), : Unsupported
(set_default_dtype_bfloat16, reset_default_dtype),
(
lambda: torch.use_deterministic_algorithms(True),
lambda: torch.use_deterministic_algorithms(False),
),
# (lambda: torch.use_deterministic_algorithms(True, warn_only=True),
# lambda: torch.use_deterministic_algorithms(False)), : Unsupported
create_toggle_fns("allow_bf16_reduced_precision_reduction"),
create_toggle_fns("allow_fp16_reduced_precision_reduction"),
create_toggle_fns("allow_tf32"),
]:
self.assertExpectedInline(
self.get_result(fn, torch.rand(10, 10), torch.ones(10, 20), ctx),
"""[[['y1_2', 'sum_3', 'x1_2', 'o0'], ['y1_3', 'sum_4', 'x1_3', 'o2']], \
[['y1', 'sum_1', 'x1', 'o4'], ['y1_1', 'sum_2', 'x1_1', 'o5']]]""",
)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
|
