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# Owner(s): ["oncall: quantization"]
import copy
import unittest
from typing import Any, Dict
import torch
from torch.ao.quantization.observer import (
HistogramObserver,
MinMaxObserver,
PlaceholderObserver,
)
from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
from torch.ao.quantization.quantizer import (
QuantizationAnnotation,
QuantizationSpec,
Quantizer,
SharedQuantizationSpec,
)
from torch.ao.quantization.quantizer.xnnpack_quantizer import (
get_symmetric_quantization_config,
)
from torch.ao.quantization.quantizer.xnnpack_quantizer_utils import (
OP_TO_ANNOTATOR,
QuantizationConfig,
)
from torch.export import export_for_training
from torch.testing._internal.common_quantization import QuantizationTestCase
from torch.testing._internal.common_utils import IS_WINDOWS
class TestHelperModules:
class Conv2dWithObsSharingOps(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv = torch.nn.Conv2d(3, 3, 3)
self.hardtanh = torch.nn.Hardtanh()
self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
self.linear = torch.nn.Linear(3, 3)
def forward(self, x):
x = self.conv(x)
x = self.adaptive_avg_pool2d(x)
x = self.hardtanh(x)
x = x.view(-1, 3)
x = self.linear(x)
return x
class Conv2dWithSharedDQ(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = torch.nn.Conv2d(3, 3, 3)
self.conv2 = torch.nn.Conv2d(3, 3, 1)
self.linear = torch.nn.Linear(3, 3)
def forward(self, x):
x = self.conv1(x)
z = x.view(-1, 3)
w = self.linear(z)
y = self.conv2(x)
add_output = x + y
extra_output = x * 2
return w, add_output, extra_output
class ModuleForDifferentQconfig(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv1 = torch.nn.Conv2d(3, 3, 3)
self.conv2 = torch.nn.Conv2d(3, 3, 1)
self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1))
def forward(self, x):
x = self.conv1(x)
w = self.adaptive_avg_pool2d(x)
y = self.conv2(x)
add_output = x + y
extra_output = x + 2
return w, add_output, extra_output
_DEQUANTIZE_OPS = [
torch.ops.quantized_decomposed.dequantize_per_tensor.default,
torch.ops.quantized_decomposed.dequantize_per_tensor.tensor,
torch.ops.quantized_decomposed.dequantize_per_channel.default,
]
@unittest.skipIf(IS_WINDOWS, "Windows not yet supported for torch.compile")
class TestDuplicateDQPass(QuantizationTestCase):
def _test_duplicate_dq(
self,
model,
example_inputs,
quantizer,
):
m_eager = model.eval()
# program capture
m = copy.deepcopy(m_eager)
m = export_for_training(
m,
example_inputs,
).module()
m = prepare_pt2e(m, quantizer)
# Calibrate
m(*example_inputs)
m = convert_pt2e(m)
pt2_quant_output = m(*example_inputs)
for n in m.graph.nodes:
annotation = n.meta.get("quantization_annotation", None)
if annotation is not None:
for arg in n.args:
if isinstance(arg, torch.fx.Node) and arg.target in _DEQUANTIZE_OPS:
self.assertEqual(len(arg.users.keys()), 1)
def test_no_need_for_duplicate_dq(self):
"""
Model under test
conv2d -> avgpool -> hardtanh -> linear
Check quantization tags on conv2d, avgpool and linear are correctly set
"""
class BackendAQuantizer(Quantizer):
def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
backend_string = "BackendA"
quantization_config = get_symmetric_quantization_config(
is_per_channel=True
)
OP_TO_ANNOTATOR["linear"](gm, quantization_config)
OP_TO_ANNOTATOR["conv"](gm, quantization_config)
OP_TO_ANNOTATOR["adaptive_avg_pool2d"](gm, quantization_config)
def validate(self, model: torch.fx.GraphModule) -> None:
pass
example_inputs = (torch.randn(1, 3, 5, 7),)
self._test_duplicate_dq(
TestHelperModules.Conv2dWithObsSharingOps(),
example_inputs,
BackendAQuantizer(),
)
def test_simple_duplicate_dq(self):
"""
Model under test
conv2d -> conv2d -> add
| |
--------->
|
-----> view_copy --> linear
|
-----> mul
There should be three dq nodes because output for the
first conv2d is fed to next conv2d, add, and view_copy + linear.
All three are quantized.
Thus DQ node is not duplicated for those three uses
"""
class BackendAQuantizer(Quantizer):
def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
backend_string = "BackendA"
quantization_config = get_symmetric_quantization_config(
is_per_channel=True
)
OP_TO_ANNOTATOR["linear"](gm, quantization_config)
OP_TO_ANNOTATOR["conv"](gm, quantization_config)
OP_TO_ANNOTATOR["add"](gm, quantization_config)
def validate(self, model: torch.fx.GraphModule) -> None:
pass
example_inputs = (torch.randn(1, 3, 5, 7),)
self._test_duplicate_dq(
TestHelperModules.Conv2dWithSharedDQ(),
example_inputs,
BackendAQuantizer(),
)
def test_no_add_quant_duplicate_dq(self):
"""
Model under test
conv2d -> conv2d -> add
| |
--------->
|
-----> view_copy --> linear
|
-----> mul
There should be three dq nodes because output for the
first conv2d is fed to next conv2d, and view_copy + linear.
Both are quantized.
However the skip connection to add and mul are not quantized.
Thus DQ node is not duplicated for those two uses
"""
class BackendAQuantizer(Quantizer):
def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
backend_string = "BackendA"
quantization_config = get_symmetric_quantization_config(
is_per_channel=True
)
OP_TO_ANNOTATOR["linear"](gm, quantization_config)
OP_TO_ANNOTATOR["conv"](gm, quantization_config)
def validate(self, model: torch.fx.GraphModule) -> None:
pass
example_inputs = (torch.randn(1, 3, 5, 7),)
self._test_duplicate_dq(
TestHelperModules.Conv2dWithSharedDQ(),
example_inputs,
BackendAQuantizer(),
)
def test_avgpool_use_different_qconfig(self):
"""
Model under test
conv2d -> conv2d -> add
| |
--------->
|
-----> adaptive_avgpool2d (different qconfig)
|
-----> add
output
conv2d -> dq -> conv2d -> add
| |
-------> dq ----->
|
-> dq -> q -> dq -----> adaptive_avgpool2d (different qconfig)
|
-> dq -----> add
"""
def _get_uint8_quantization_config():
act_observer_or_fake_quant_ctr = HistogramObserver # type: ignore[assignment]
act_quantization_spec = QuantizationSpec(
dtype=torch.uint8,
quant_min=0,
quant_max=255,
qscheme=torch.per_tensor_affine,
observer_or_fake_quant_ctr=act_observer_or_fake_quant_ctr.with_args(
eps=2**-12
),
)
weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = ( # noqa: F821
MinMaxObserver
)
extra_args: Dict[str, Any] = {"eps": 2**-12}
weight_quantization_spec = QuantizationSpec(
dtype=torch.uint8,
quant_min=0,
quant_max=255,
qscheme=torch.per_tensor_affine,
ch_axis=0,
is_dynamic=False,
observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args(
**extra_args
),
)
bias_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = ( # noqa: F821
PlaceholderObserver
)
bias_quantization_spec = QuantizationSpec(
dtype=torch.float,
observer_or_fake_quant_ctr=bias_observer_or_fake_quant_ctr,
)
quantization_config = QuantizationConfig(
act_quantization_spec,
act_quantization_spec,
weight_quantization_spec,
bias_quantization_spec,
)
return quantization_config
class BackendAQuantizer(Quantizer):
def annotate(self, gm: torch.fx.GraphModule) -> torch.fx.GraphModule:
backend_string = "BackendA"
quantization_config = get_symmetric_quantization_config(
is_per_channel=True
)
avgpool_qconfig = _get_uint8_quantization_config()
OP_TO_ANNOTATOR["conv"](gm, quantization_config)
OP_TO_ANNOTATOR["add"](gm, quantization_config)
for n in gm.graph.nodes:
if n.op == "call_function" and n.target == torch.ops.aten.mean.dim:
qspec = avgpool_qconfig.input_activation
input_act = n.args[0]
output_qspec = SharedQuantizationSpec((input_act, n))
n.meta["quantization_annotation"] = QuantizationAnnotation(
input_qspec_map={input_act: qspec},
output_qspec=output_qspec,
_annotated=True,
)
def validate(self, model: torch.fx.GraphModule) -> None:
pass
example_inputs = (torch.randn(1, 3, 5, 7),)
self._test_duplicate_dq(
TestHelperModules.ModuleForDifferentQconfig(),
example_inputs,
BackendAQuantizer(),
)
|
