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# Owner(s): ["module: inductor"]
import logging
import os
import re
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import torch
from torch._inductor import config, test_operators
from torch._inductor.utils import fresh_inductor_cache
from torch.testing._internal.inductor_utils import GPU_TYPE, HAS_GPU
try:
try:
from . import test_torchinductor
except ImportError:
import test_torchinductor # @manual=fbcode//caffe2/test/inductor:test_inductor-library
except unittest.SkipTest:
if __name__ == "__main__":
sys.exit(0)
raise
def filesize(filename: Path):
assert filename.exists(), f"{filename} is missing"
return os.stat(filename).st_size
@config.patch("trace.enabled", True)
class TestDebugTrace(test_torchinductor.TestCase):
def test_debug_trace(self):
@torch.compile
def fn(a, b):
a = test_operators.realize(a + 1) + 2
return torch.matmul(a, b)
# TODO(aakhundov): make this work with fresh_inductor_cache
# instead of force_disable_caches. currently, with the latter
# enabled, we get `inductor [('fxgraph_cache_hit', 1)]` in
# the counters: so the cache is actually hit and the test fails.
with config.patch(
{
"trace.debug_dir": tempfile.mkdtemp(),
"force_disable_caches": True,
}
):
with self.assertLogs(
logging.getLogger("torch._inductor.debug"), level=logging.WARNING
) as cm:
fn(torch.randn(16, 16), torch.randn(16, 16))
self.assertEqual(len(cm.output), 1)
m = re.match(r"WARNING.* debug trace: (.*)", cm.output[0])
self.assertTrue(m)
filename = Path(m.group(1))
self.assertTrue(filename.is_dir())
self.assertGreater(filesize(filename / "fx_graph_readable.py"), 512)
self.assertGreater(filesize(filename / "fx_graph_runnable.py"), 512)
self.assertGreater(filesize(filename / "fx_graph_transformed.py"), 512)
self.assertGreater(filesize(filename / "output_code.py"), 1024)
self.assertExpectedInline(
open(filename / "ir_pre_fusion.txt").read().rstrip(),
"""\
op0: SchedulerNode(ComputedBuffer)
op0.writes = [MemoryDep('buf0', c0, {c0: 256})]
op0.unmet_dependencies = []
op0.met_dependencies = [MemoryDep('arg0_1', c0, {c0: 256})]
op0.outputs = [
buf0: ComputedBuffer
buf0.layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf0.users = [NodeUser(node=SchedulerNode(name='op1'), can_inplace=True, is_weak=False)]
]
op0.group.device = cpu
op0.group.iteration = ((256,), ())
op0.sizes = ([256], [])
arg0_1_layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf0_layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
class op0_loop_body:
var_ranges = {p0: 256}
index0 = p0
def body(self, ops):
get_index = self.get_index('index0')
load = ops.load('arg0_1', get_index)
constant = ops.constant(1.0, torch.float32)
add = ops.add(load, constant)
get_index_1 = self.get_index('index0')
store = ops.store('buf0', get_index_1, add, None)
return store
op1: SchedulerNode(ComputedBuffer)
op1.writes = [MemoryDep('buf1', c0, {c0: 256})]
op1.unmet_dependencies = [MemoryDep('buf0', c0, {c0: 256})]
op1.met_dependencies = []
op1.outputs = [
buf1: ComputedBuffer
buf1.layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf1.users = [NodeUser(node=ExternKernelSchedulerNode(name='op2'), can_inplace=False, is_weak=False)]
]
op1.group.device = cpu
op1.group.iteration = ((256,), ())
op1.sizes = ([256], [])
buf0_layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf1_layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
class op1_loop_body:
var_ranges = {p0: 256}
index0 = p0
def body(self, ops):
get_index = self.get_index('index0')
load = ops.load('buf0', get_index)
constant = ops.constant(2.0, torch.float32)
add = ops.add(load, constant)
get_index_1 = self.get_index('index0')
store = ops.store('buf1', get_index_1, add, None)
return store
op2: ExternKernelSchedulerNode(ExternKernelOut)
op2.writes = [StarDep(name='buf2', mode=None)]
op2.unmet_dependencies = [StarDep(name='buf1', mode=None)]
op2.met_dependencies = [StarDep(name='arg1_1', mode=None)]
op2.outputs = [
buf2: ExternKernelOut
buf2.layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf2.users = [NodeUser(node=OUTPUT, can_inplace=False, is_weak=False)]
]
op2.node.kernel = extern_kernels.mm""",
)
self.assertExpectedInline(
open(filename / "ir_post_fusion.txt").read().rstrip(),
"""\
op0_op1: FusedSchedulerNode(SchedulerNode,SchedulerNode)
op0_op1.writes = [MemoryDep('buf0', c0, {c0: 256}), MemoryDep('buf1', c0, {c0: 256})]
op0_op1.unmet_dependencies = []
op0_op1.met_dependencies = [MemoryDep('arg0_1', c0, {c0: 256})]
op0_op1.outputs = [
buf0: ComputedBuffer
buf0.layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf0.users = [NodeUser(node=SchedulerNode(name='op1'), can_inplace=True, is_weak=False)]
buf1: ComputedBuffer
buf1.layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf1.users = [NodeUser(node=ExternKernelSchedulerNode(name='op2'), can_inplace=False, is_weak=False)]
]
op0_op1.snodes[0] =
op0: SchedulerNode(ComputedBuffer)
op0.writes = [MemoryDep('buf0', c0, {c0: 256})]
op0.unmet_dependencies = []
op0.met_dependencies = [MemoryDep('arg0_1', c0, {c0: 256})]
op0.outputs = [
buf0: ComputedBuffer
buf0.layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf0.users = [NodeUser(node=SchedulerNode(name='op1'), can_inplace=True, is_weak=False)]
]
op0.group.device = cpu
op0.group.iteration = ((256,), ())
op0.sizes = ([256], [])
arg0_1_layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf0_layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
class op0_loop_body:
var_ranges = {p0: 256}
index0 = p0
def body(self, ops):
get_index = self.get_index('index0')
load = ops.load('arg0_1', get_index)
constant = ops.constant(1.0, torch.float32)
add = ops.add(load, constant)
get_index_1 = self.get_index('index0')
store = ops.store('buf0', get_index_1, add, None)
return store
op0_op1.snodes[1] =
op1: SchedulerNode(ComputedBuffer)
op1.writes = [MemoryDep('buf1', c0, {c0: 256})]
op1.unmet_dependencies = [MemoryDep('buf0', c0, {c0: 256})]
op1.met_dependencies = []
op1.outputs = [
buf1: ComputedBuffer
buf1.layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf1.users = [NodeUser(node=ExternKernelSchedulerNode(name='op2'), can_inplace=False, is_weak=False)]
]
op1.group.device = cpu
op1.group.iteration = ((256,), ())
op1.sizes = ([256], [])
buf0_layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf1_layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
class op1_loop_body:
var_ranges = {p0: 256}
index0 = p0
def body(self, ops):
get_index = self.get_index('index0')
load = ops.load('buf0', get_index)
constant = ops.constant(2.0, torch.float32)
add = ops.add(load, constant)
get_index_1 = self.get_index('index0')
store = ops.store('buf1', get_index_1, add, None)
return store
op2: ExternKernelSchedulerNode(ExternKernelOut)
op2.writes = [StarDep(name='buf2', mode=None)]
op2.unmet_dependencies = [StarDep(name='buf1', mode=None)]
op2.met_dependencies = [StarDep(name='arg1_1', mode=None)]
op2.outputs = [
buf2: ExternKernelOut
buf2.layout = FixedLayout('cpu', torch.float32, size=[16, 16], stride=[16, 1])
buf2.users = [NodeUser(node=OUTPUT, can_inplace=False, is_weak=False)]
]
op2.node.kernel = extern_kernels.mm""",
)
# intentionally only cleanup on success so debugging test is easier
shutil.rmtree(filename)
def test_debug_printer_const(self):
"""Test that having a const example_input does not break the debug printer."""
class Model(torch.nn.Module):
def forward(self, x, ks0):
return x.sum()
example_inputs = (
torch.tensor([0, 3, 6], dtype=torch.int64),
70, # const input, that will be filtered in the examples
)
_ = torch._export.aot_compile(
Model(),
example_inputs,
)
@unittest.skipIf(not HAS_GPU, "requires GPU")
def test_debug_multi_tempalte(self):
class ToyModel(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.l = torch.nn.Linear(100, 100)
self.relu = torch.nn.ReLU()
def forward(self, x):
return self.relu(self.l(x))
# no failure
with self.assertLogs(
logging.getLogger("torch._inductor.debug"), level=logging.WARNING
), fresh_inductor_cache():
m = ToyModel().to(device=GPU_TYPE)
m = torch.compile(m, mode="max-autotune")
input_tensor = torch.randn(100).to(device=GPU_TYPE)
m(input_tensor)
if __name__ == "__main__":
from torch._inductor.test_case import run_tests
from torch.testing._internal.inductor_utils import HAS_CPU
if HAS_CPU:
run_tests(needs="filelock")
|
