def test_shared_module(self) -> None:
"""
Tests the behavior of shared submodules that may have multiple
names.
"""
model = SharedModuleNet()
inputs = (torch.randn((1, *model.input_size)), )
op_handles = {
"aten::addmm": addmm_flop_jit,
} # type: Dict[str, Handle]
analyzer = JitModelAnalysis(model=model,
inputs=inputs,
op_handles=op_handles)
analyzer.skipped_ops_warnings(enabled=False)
# The names `submod2.submod` and `multiname2` are not included,
# since only the first name of a module is made the canonical one.
# The counts associated with these cases are included under
# `submod1.submod` and `multiname1` respectively.
multiname_flops = 2 * model.multiname_flops # Called under 2 names
shared_flops = 2 * model.shared_flops # Shared under 2 submodules
total_flops = multiname_flops + shared_flops
flops = {
"": total_flops,
"submod1": model.shared_flops,
"submod1.submod": shared_flops,
"submod2": model.shared_flops,
"multiname1": multiname_flops,
}
self.assertEqual(analyzer.by_module(), flops)
# Test access by alternative name
self.assertEqual(
analyzer.total("submod2.submod"),
flops["submod1.submod"],
)
self.assertEqual(
analyzer.total("multiname2"),
flops["multiname1"],
)
# Test getting canonical name
self.assertEqual(analyzer.canonical_module_name("multiname2"),
"multiname1")
self.assertEqual(analyzer.canonical_module_name("multiname1"),
"multiname1")
self.assertEqual(analyzer.canonical_module_name("submod2.submod"),
"submod1.submod")
self.assertEqual(analyzer.canonical_module_name("submod1.submod"),
"submod1.submod")
# Tests no uncalled modules
self.assertEqual(analyzer.uncalled_modules(), set())
def test_changing_handles(self) -> None:
"""
Tests .set_op_handle(), .clear_op_handles()
"""
model = NestedNet()
inputs = (torch.randn((1, *model.input_size)), )
op_handles = {
"aten::addmm": addmm_flop_jit,
} # type: Dict[str, Handle]
analyzer = JitModelAnalysis(model=model,
inputs=inputs,
op_handles=op_handles)
analyzer.skipped_ops_warnings(enabled=False)
# Request a result once to cache flop counts
_ = analyzer.total("")
# Add an op handle
analyzer.set_op_handle("aten::_convolution", conv_flop_jit)
self.assertEqual(analyzer.by_module_and_operator(), model.flops)
# Overwrite an op handle
def make_dummy_op(name: str, output: int) -> Handle:
def dummy_ops_handle(inputs: List[Any],
outputs: List[Any]) -> typing.Counter[str]:
return Counter({name: output})
return dummy_ops_handle
dummy_name = "dummy_op"
dummy_out = 1000
analyzer.set_op_handle("aten::addmm",
make_dummy_op(dummy_name, dummy_out))
dummy_flops = {}
for name, counts in model.flops.items():
dummy_flops[name] = Counter(
{op: flop
for op, flop in counts.items() if op != "addmm"})
dummy_flops[""][dummy_name] = 2 * dummy_out
dummy_flops["fc"][dummy_name] = dummy_out
dummy_flops["submod"][dummy_name] = dummy_out
dummy_flops["submod.fc"][dummy_name] = dummy_out
self.assertEqual(analyzer.by_module_and_operator(), dummy_flops)
# Clear ops handles
analyzer.clear_op_handles()
empty_flops = {name: Counter() for name in model.flops}
self.assertEqual(analyzer.by_module_and_operator(), empty_flops)
def test_disable_warnings(self) -> None:
"""
Tests .skipped_ops_warnings(...) and .tracer_warnings(...)
"""
model = TraceWarningNet()
inputs = (torch.randn((1, *model.input_size)), )
op_handles = {
"aten::addmm": addmm_flop_jit,
} # type: Dict[str, Handle]
analyzer = JitModelAnalysis(model=model,
inputs=inputs,
op_handles=op_handles)
# Tracer warnings
analyzer.tracer_warnings(mode="all")
analyzer._stats = None # Manually clear cache so trace is rerun
self.assertWarns(torch.jit._trace.TracerWarning, analyzer.total)
analyzer._stats = None # Manually clear cache so trace is rerun
self.assertWarns(RuntimeWarning, analyzer.total)
analyzer.tracer_warnings(mode="none")
analyzer._stats = None # Manually clear cache so trace is rerun
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
_ = analyzer.total()
if w:
warning_types = [s.category for s in w]
self.assertFalse(
torch.jit._trace.TracerWarning in warning_types)
self.assertFalse(RuntimeWarning in warning_types)
analyzer.tracer_warnings(mode="no_tracer_warning")
analyzer._stats = None # Manually clear cache so trace is rerun
self.assertWarns(RuntimeWarning, analyzer.total)
analyzer._stats = None # Manually clear cache so trace is rerun
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
_ = analyzer.total()
if w:
warning_types = [s.category for s in w]
self.assertFalse(
torch.jit._trace.TracerWarning in warning_types)
# Skipped ops and uncalled modules warnings
logger = logging.getLogger()
skipped_string = "Skipped operation aten::add 1 time(s)"
uncalled_string = "Module never called: fc1"
analyzer.uncalled_modules_warnings(enabled=False)
analyzer.skipped_ops_warnings(enabled=False)
analyzer._stats = None # Manually clear cache so trace is rerun
with self.assertLogs(logger, logging.WARN) as cm:
logger.warning("Dummy warning.")
_ = analyzer.total()
self.assertFalse(any(skipped_string in s for s in cm.output))
self.assertFalse(any(uncalled_string in s for s in cm.output))
analyzer.skipped_ops_warnings(enabled=True)
analyzer.uncalled_modules_warnings(enabled=True)
analyzer._stats = None # Manually clear cache so trace is rerun
with self.assertLogs(logger, logging.WARN) as cm:
_ = analyzer.total()
self.assertTrue(any(skipped_string in s for s in cm.output))
self.assertTrue(any(uncalled_string in s for s in cm.output))
def test_copy(self) -> None:
"""
Tests .copy(...)
"""
model = RepeatedNet()
inputs = (torch.randn((1, *model.input_size)), )
op_handles = {
"aten::addmm": addmm_flop_jit,
} # type: Dict[str, Handle]
analyzer = JitModelAnalysis(model=model,
inputs=inputs,
op_handles=op_handles)
analyzer.skipped_ops_warnings(enabled=False)
analyzer.tracer_warnings(mode="none")
repeated_net_flops = model.fc1_num * model.fc1_flops
repeated_net_flops += model.fc2_num * model.fc2_flops
analyzer_copy = analyzer.copy()
# Outputs are the same
self.assertEqual(
analyzer.by_module_and_operator(),
analyzer_copy.by_module_and_operator(),
)
# Settings match
self.assertEqual(analyzer._enable_warn_skipped_ops,
analyzer_copy._enable_warn_skipped_ops)
self.assertEqual(
analyzer._enable_warn_uncalled_mods,
analyzer_copy._enable_warn_uncalled_mods,
)
self.assertEqual(analyzer._warn_trace, analyzer_copy._warn_trace)
# Changing copy does not change original
analyzer_copy.skipped_ops_warnings(enabled=True)
self.assertNotEqual(analyzer._enable_warn_skipped_ops,
analyzer_copy._enable_warn_skipped_ops)
# Copy with new model and inputs
new_model = NonForwardNet()
bs = 5
new_inputs = (torch.randn((bs, *new_model.input_size)), )
analyzer_new = analyzer.copy(new_model=new_model,
new_inputs=new_inputs)
non_forward_flops = new_model.fc_flops + new_model.submod.fc_flops
# Total is correct for new model and inputs
self.assertEqual(analyzer_new.total(), non_forward_flops * bs)
# Original is unaffected
self.assertEqual(analyzer.total(), repeated_net_flops)
# Settings match
self.assertEqual(analyzer._enable_warn_skipped_ops,
analyzer_new._enable_warn_skipped_ops)
self.assertEqual(analyzer._warn_trace, analyzer_new._warn_trace)