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# Owner(s): ["oncall: quantization"]
import copy
import unittest
from collections import Counter
from typing import Dict
import torch
from torch.ao.quantization import (
compare_results,
CUSTOM_KEY,
extract_results_from_loggers,
generate_numeric_debug_handle,
NUMERIC_DEBUG_HANDLE_KEY,
prepare_for_propagation_comparison,
)
from torch.ao.quantization.pt2e.graph_utils import get_control_flow_submodules
from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
get_symmetric_quantization_config,
XNNPACKQuantizer,
)
from torch.export import export_for_training
from torch.testing._internal.common_quantization import TestHelperModules
from torch.testing._internal.common_utils import IS_WINDOWS, skipIfCrossRef, TestCase
def _extract_debug_handles(model) -> Dict[str, int]:
debug_handle_map: Dict[str, int] = {}
m_queue = [model]
while m_queue:
cur_m = m_queue.pop(0)
for n in cur_m.graph.nodes:
if CUSTOM_KEY in n.meta and NUMERIC_DEBUG_HANDLE_KEY in n.meta[CUSTOM_KEY]:
debug_handle_map[str(n)] = n.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY]
control_flow_submodules = [
submodule for _, submodule, _ in get_control_flow_submodules(cur_m)
]
m_queue.extend(control_flow_submodules)
return debug_handle_map
@unittest.skipIf(IS_WINDOWS, "Windows not yet supported for torch.compile")
class TestNumericDebugger(TestCase):
def test_simple(self):
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = export_for_training(m, example_inputs)
generate_numeric_debug_handle(ep)
debug_handle_map = _extract_debug_handles(ep.module())
self.assertEqual(len(set(debug_handle_map.values())), len(debug_handle_map))
def test_control_flow(self):
m = TestHelperModules.ControlFlow()
example_inputs = m.example_inputs()
ep = export_for_training(m, example_inputs)
generate_numeric_debug_handle(ep)
debug_handle_map = _extract_debug_handles(ep.module())
self.assertEqual(len(set(debug_handle_map.values())), len(debug_handle_map))
def test_quantize_pt2e_preserve_handle(self):
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = export_for_training(m, example_inputs)
generate_numeric_debug_handle(ep)
m = ep.module()
quantizer = XNNPACKQuantizer().set_global(
get_symmetric_quantization_config(is_per_channel=False)
)
m = prepare_pt2e(m, quantizer)
debug_handle_map = _extract_debug_handles(m)
res_counter = Counter(debug_handle_map.values())
repeated_debug_handle_ids = [1, 2, 3]
# 3 ids were repeated because we copy over the id from node to its output observer
# torch.ops.aten.conv2d.default, torch.ops.aten.squeeze.dim and torch.ops.aten.conv1d.default
for dh_id in repeated_debug_handle_ids:
self.assertEqual(res_counter[dh_id], 2)
m(*example_inputs)
m = convert_pt2e(m)
debug_handle_map = _extract_debug_handles(m)
res_counter = Counter(debug_handle_map.values())
# same set of ids where repeated, because we copy over the id from observer/fake_quant to
# dequantize node
repeated_debug_handle_ids = [1, 2, 3]
for dh_id in repeated_debug_handle_ids:
self.assertEqual(res_counter[dh_id], 2)
def test_copy_preserve_handle(self):
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = torch.export.export(m, example_inputs)
generate_numeric_debug_handle(ep)
debug_handle_map_ref = _extract_debug_handles(ep)
ep_copy = copy.copy(ep)
debug_handle_map = _extract_debug_handles(ep_copy)
self.assertEqual(debug_handle_map, debug_handle_map_ref)
def test_deepcopy_preserve_handle(self):
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = torch.export.export(m, example_inputs)
generate_numeric_debug_handle(ep)
debug_handle_map_ref = _extract_debug_handles(ep)
ep_copy = copy.deepcopy(ep)
debug_handle_map = _extract_debug_handles(ep_copy)
self.assertEqual(debug_handle_map, debug_handle_map_ref)
@skipIfCrossRef # mlazos: retracing FX graph with torch function mode doesn't propagate metadata, because the stack
# trace of the mode torch function impl doesn't match the traced graph stored lineno.
def test_re_export_preserve_handle(self):
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = export_for_training(m, example_inputs)
generate_numeric_debug_handle(ep)
m = ep.module()
debug_handle_map_ref = _extract_debug_handles(m)
m_export = export_for_training(m, example_inputs).module()
debug_handle_map = _extract_debug_handles(m_export)
self.assertEqual(debug_handle_map, debug_handle_map_ref)
def test_run_decompositions_preserve_handle(self):
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = export_for_training(m, example_inputs)
generate_numeric_debug_handle(ep)
debug_handle_map_ref = _extract_debug_handles(ep)
ep_copy = copy.copy(ep)
ep_copy = ep_copy.run_decompositions()
debug_handle_map = _extract_debug_handles(ep_copy)
# checking the map still has the same ids, the node may change
self.assertEqual(
set(debug_handle_map.values()), set(debug_handle_map_ref.values())
)
def test_prepare_for_propagation_comparison(self):
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = export_for_training(m, example_inputs)
generate_numeric_debug_handle(ep)
m = ep.module()
m_logger = prepare_for_propagation_comparison(m)
ref = m(*example_inputs)
res = m_logger(*example_inputs)
from torch.ao.quantization.pt2e._numeric_debugger import OutputLogger
loggers = [m for m in m_logger.modules() if isinstance(m, OutputLogger)]
self.assertEqual(len(loggers), 3)
self.assertTrue("conv2d" in [logger.node_name for logger in loggers])
self.assertEqual(res, ref)
def test_extract_results_from_loggers(self):
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = export_for_training(m, example_inputs)
generate_numeric_debug_handle(ep)
m = ep.module()
m_ref_logger = prepare_for_propagation_comparison(m)
quantizer = XNNPACKQuantizer().set_global(
get_symmetric_quantization_config(is_per_channel=False)
)
m = prepare_pt2e(m, quantizer)
m(*example_inputs)
m = convert_pt2e(m)
m_quant_logger = prepare_for_propagation_comparison(m)
m_ref_logger(*example_inputs)
m_quant_logger(*example_inputs)
ref_results = extract_results_from_loggers(m_ref_logger)
quant_results = extract_results_from_loggers(m_quant_logger)
comparison_results = compare_results(ref_results, quant_results)
for node_summary in comparison_results.values():
if len(node_summary.results) > 0:
self.assertGreaterEqual(node_summary.results[0].sqnr, 35)
def test_added_node_gets_unique_id(self) -> None:
m = TestHelperModules.Conv2dThenConv1d()
example_inputs = m.example_inputs()
ep = export_for_training(m, example_inputs)
generate_numeric_debug_handle(ep)
ref_handles = _extract_debug_handles(ep)
ref_counter = Counter(ref_handles.values())
for k, v in ref_counter.items():
self.assertEqual(
v,
1,
msg=f"For handle {k}, there were {v} nodes with that handle, but expected only 1",
)
# Now that we have unique ids, add a new node into the graph and re-generate
# to make sure that the new node gets a unique id.
last_node = next(iter(reversed(ep.graph.nodes)))
with ep.graph.inserting_before(last_node):
arg = last_node.args[0]
self.assertIsInstance(arg, (list, tuple))
arg = arg[0]
# Add a function that only requires a single tensor input.
n = ep.graph.call_function(torch.ops.aten.relu.default, args=(arg,))
arg.replace_all_uses_with(n, lambda x: x != n)
ep.graph_module.recompile()
# Regenerate handles, make sure only the new relu node has a new id, and
# it doesn't clash with any of the existing ids.
generate_numeric_debug_handle(ep)
handles_after_modification = _extract_debug_handles(ep)
handles_counter = Counter(handles_after_modification.values())
for name, handle in ref_handles.items():
self.assertIn(name, handles_after_modification)
# Check that handle was unchanged.
self.assertEqual(handles_after_modification[name], handle)
# Check that total count was unchanged.
ref_count = ref_counter[handle]
after_count = handles_counter[handle]
self.assertEqual(
after_count,
ref_count,
msg=f"For handle {handle}, there were {after_count} nodes with that handle, but expected only {ref_count}",
)
# Check for relu specifically. Avoid hardcoding the handle id since it
# may change with future node ordering changes.
self.assertNotEqual(handles_after_modification["relu_default"], 0)
self.assertEqual(handles_counter[handles_after_modification["relu_default"]], 1)
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