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# Owner(s): ["module: inductor"]
import unittest
from unittest import mock
import torch
from torch._C import FileCheck
from torch._dynamo.utils import same
from torch._inductor import config, memory
from torch._inductor.test_case import TestCase
from torch._inductor.utils import run_and_get_triton_code
from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_GPU
class Foo(torch.nn.Module):
"""
The default compiled graph is
graph():
...
%op0 : [num_users=2] = call_function[...](args = (%primals_2, %primals_1), ...)
%op1 : [num_users=2] = call_function[...](args = (%primals_2, %primals_3), ...)
%op2 : [num_users=1] = call_function[...](args = (%op0, %primals_4), ...)
%op3 : [num_users=1] = call_function[...](args = (%op1, %primals_5), ...)
%op4 : [num_users=1] = call_function[...](args = (%op2,), ...)
%op5 : [num_users=1] = call_function[...](args = (%op3,), ...)
%op6_op7 : [num_users=1] = call_function[...](args = (%op5, %op4), ...)
"""
def __init__(self):
super().__init__()
self.w1 = torch.nn.Parameter(torch.ones(1, 10))
self.w2 = torch.nn.Parameter(torch.ones(1, 1))
self.w3 = torch.nn.Parameter(torch.ones(10, 1))
self.w4 = torch.nn.Parameter(torch.ones(1, 10))
def forward(self, x):
t1 = torch.matmul(x, self.w1)
t2 = torch.matmul(x, self.w2)
t3 = torch.matmul(t1, self.w3)
t4 = torch.matmul(t2, self.w4)
return t3.sum() + t4.sum()
# The tests in this class uses very small tensors. The default
# score_fusion_memory threshold will cause different fusion decisions and
# generate a different wrapper. Override the threshold to make these tests
# happy.
@config.patch("score_fusion_memory_threshold", 1)
class TestOperatorReorderForPeakMemory(TestCase):
def setUp(self):
super().setUp()
self.model = Foo().to(GPU_TYPE)
self.inputs = torch.ones((2048, 1), device=GPU_TYPE)
self.orig_reorder_method = memory.reorder_for_peak_memory
@mock.patch.object(config, "reorder_for_peak_memory", True)
def test_reorder_peak_memory(self):
outp_corr = self.model(self.inputs)
compiled_model = torch.compile(self.model)
code = run_and_get_triton_code(compiled_model, self.inputs)
(
FileCheck()
.check("def call(args):")
.check("buf1 = ")
.check("buf0 = ")
.check("buf2 = ")
.check("buf4 = ")
.check("buf3 = ")
.check("buf5 = ")
.check("buf7 = ")
.run(code)
)
# check for correctness
outp = compiled_model(self.inputs)
self.assertTrue(same(outp, outp_corr))
@mock.patch.object(config, "reorder_for_peak_memory", True)
def test_reorder_peak_memory_lpmf(self):
outp_corr = self.model(self.inputs)
def reorder_with_only_lpmf(
nodes,
name_to_buf,
name_to_fused_node,
graph_inputs,
graph_outputs,
methods=None,
):
return self.orig_reorder_method(
nodes,
name_to_buf,
name_to_fused_node,
graph_inputs,
graph_outputs,
methods=[memory.topological_sort_lpmf],
)
with mock.patch.object(
memory, "reorder_for_peak_memory", reorder_with_only_lpmf
):
compiled_model = torch.compile(self.model)
code = run_and_get_triton_code(compiled_model, self.inputs)
(
FileCheck()
.check("def call(args):")
.check("buf1 = ")
.check("buf0 = ")
.check("buf2 = ")
.check("buf4 = ")
.check("buf3 = ")
.check("buf5 = ")
.check("buf7 = ")
.run(code)
)
# check for correctness
outp = compiled_model(self.inputs)
self.assertTrue(same(outp, outp_corr))
@mock.patch.object(config, "reorder_for_peak_memory", True)
def test_reorder_peak_memory_bfs(self):
outp_corr = self.model(self.inputs)
def reorder_with_only_bfs(
nodes,
name_to_buf,
name_to_fused_node,
graph_inputs,
graph_outputs,
methods=None,
):
return self.orig_reorder_method(
nodes,
name_to_buf,
name_to_fused_node,
graph_inputs,
graph_outputs,
methods=[memory.topological_sort_bfs],
)
with mock.patch.object(
memory, "reorder_for_peak_memory", reorder_with_only_bfs
):
compiled_model = torch.compile(self.model)
code = run_and_get_triton_code(compiled_model, self.inputs)
(
FileCheck()
.check("def call(args):")
.check("buf0 = ")
.check("buf1 = ")
.check("buf2 = ")
.check("buf3 = ")
.check("buf4 = ")
.check("buf5 = ")
.check("buf7 = ")
.run(code)
)
# check for correctness
outp = compiled_model(self.inputs)
self.assertTrue(same(outp, outp_corr))
@mock.patch.object(config, "reorder_for_peak_memory", True)
def test_reorder_peak_memory_dfs(self):
outp_corr = self.model(self.inputs)
def reorder_with_only_dfs(
nodes,
name_to_buf,
name_to_fused_node,
graph_inputs,
graph_outputs,
methods=None,
):
return self.orig_reorder_method(
nodes,
name_to_buf,
name_to_fused_node,
graph_inputs,
graph_outputs,
methods=[memory.topological_sort_dfs],
)
with mock.patch.object(
memory, "reorder_for_peak_memory", reorder_with_only_dfs
):
compiled_model = torch.compile(self.model)
code = run_and_get_triton_code(compiled_model, self.inputs)
(
FileCheck()
.check("def call(args):")
.check("buf0 = ")
.check("buf2 = ")
.check("buf4 = ")
.check("buf1 = ")
.check("buf3 = ")
.check("buf5 = ")
.check("buf7 = ")
.run(code)
)
# check for correctness
outp = compiled_model(self.inputs)
self.assertTrue(same(outp, outp_corr))
@unittest.skipIf(
not torch.cuda.is_available()
or torch.cuda.get_device_properties().total_memory < int(1e10),
"Need 10GB memory to be safe to run the test",
)
def test_fusing_reductions_increase_peak_memory(self):
@torch.compile
def f(a, b, c):
return (a @ c).sum(dim=-1) + (b @ c).sum(dim=-1)
a = torch.randn(1024 * 32, 16, device=GPU_TYPE)
b = torch.randn(1024 * 32, 16, device=GPU_TYPE)
c = torch.randn(16, 1024 * 32, device=GPU_TYPE)
torch.cuda.reset_peak_memory_stats()
f(a, b, c)
peak_mem = torch.cuda.max_memory_allocated()
expected_bound = a.size(0) * c.size(1) * a.dtype.itemsize * 2
self.assertLess(peak_mem, expected_bound)
if __name__ == "__main__":
from torch._inductor.test_case import run_tests
if HAS_GPU:
run_tests()
|
