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|
# Owner(s): ["oncall: quantization"]
import copy
from typing import Any, Dict, Optional, Tuple
import torch
from torch._higher_order_ops.out_dtype import out_dtype # noqa: F401
from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
from torch.ao.quantization.quantizer import Quantizer
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 (
NodeSpec as ns,
QuantizationTestCase,
skipIfNoQNNPACK,
TestHelperModules,
)
@skipIfNoQNNPACK
class TestPT2ERepresentation(QuantizationTestCase):
def _test_representation(
self,
model: torch.nn.Module,
example_inputs: Tuple[Any, ...],
quantizer: Quantizer,
ref_node_occurrence: Dict[ns, int],
non_ref_node_occurrence: Dict[ns, int],
fixed_output_tol: Optional[float] = None,
output_scale_idx: int = 2,
) -> torch.nn.Module:
# resetting dynamo cache
torch._dynamo.reset()
model = export_for_training(
model,
example_inputs,
).module()
model_copy = copy.deepcopy(model)
model = prepare_pt2e(model, quantizer)
# Calibrate
model(*example_inputs)
model = convert_pt2e(model, use_reference_representation=True)
self.checkGraphModuleNodes(model, expected_node_occurrence=ref_node_occurrence)
# make sure it runs
pt2e_quant_output = model(*example_inputs)
# TODO: torchdynamo times out when we do this, we can enable numerical checking
# after that is fixed
model_copy = prepare_pt2e(model_copy, quantizer)
# Calibrate
model_copy(*example_inputs)
model_copy = convert_pt2e(model_copy, use_reference_representation=False)
self.checkGraphModuleNodes(
model_copy, expected_node_occurrence=non_ref_node_occurrence
)
pt2e_quant_output_copy = model_copy(*example_inputs)
output_tol = None
if fixed_output_tol is not None:
output_tol = fixed_output_tol
else:
idx = 0
for n in model_copy.graph.nodes:
if (
n.target
== torch.ops.quantized_decomposed.quantize_per_tensor.default
):
idx += 1
if idx == output_scale_idx:
output_tol = n.args[1]
assert output_tol is not None
# make sure the result is off by one at most in the quantized integer representation
self.assertTrue(
torch.max(torch.abs(pt2e_quant_output_copy - pt2e_quant_output))
<= (2 * output_tol + 1e-5)
)
def test_static_linear(self):
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(5, 5)
def forward(self, x):
return self.linear(x)
quantizer = XNNPACKQuantizer()
operator_config = get_symmetric_quantization_config(is_per_channel=False)
quantizer.set_global(operator_config)
example_inputs = (torch.randn(2, 5),)
self._test_representation(
M().eval(),
example_inputs,
quantizer,
ref_node_occurrence={},
non_ref_node_occurrence={},
)
def test_dynamic_linear(self):
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(5, 5)
def forward(self, x):
return self.linear(x)
quantizer = XNNPACKQuantizer()
operator_config = get_symmetric_quantization_config(
is_per_channel=False, is_dynamic=True
)
quantizer.set_global(operator_config)
example_inputs = (torch.randn(2, 5),)
self._test_representation(
M().eval(),
example_inputs,
quantizer,
ref_node_occurrence={},
non_ref_node_occurrence={},
fixed_output_tol=1e-4,
)
def test_conv2d(self):
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.conv2d = torch.nn.Conv2d(3, 3, 3)
def forward(self, x):
return self.conv2d(x)
quantizer = XNNPACKQuantizer()
operator_config = get_symmetric_quantization_config(is_per_channel=False)
quantizer.set_global(operator_config)
example_inputs = (torch.randn(1, 3, 3, 3),)
self._test_representation(
M().eval(),
example_inputs,
quantizer,
ref_node_occurrence={},
non_ref_node_occurrence={},
)
def test_add(self):
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, x, y):
return x + y
quantizer = XNNPACKQuantizer()
quantization_config = get_symmetric_quantization_config(is_per_channel=True)
quantizer.set_global(quantization_config)
m_eager = M().eval()
example_inputs = (
torch.randn(1, 3, 3, 3),
torch.randn(1, 3, 3, 3),
)
self._test_representation(
M().eval(),
example_inputs,
quantizer,
ref_node_occurrence={},
non_ref_node_occurrence={},
)
def test_add_relu(self):
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, x, y):
out = x + y
out = torch.nn.functional.relu(out)
return out
quantizer = XNNPACKQuantizer()
operator_config = get_symmetric_quantization_config(is_per_channel=True)
quantizer.set_global(operator_config)
example_inputs = (
torch.randn(1, 3, 3, 3),
torch.randn(1, 3, 3, 3),
)
ref_node_occurrence = {
ns.call_function(out_dtype): 2,
}
self._test_representation(
M().eval(),
example_inputs,
quantizer,
ref_node_occurrence=ref_node_occurrence,
non_ref_node_occurrence={},
)
def test_maxpool2d(self):
quantizer = XNNPACKQuantizer()
operator_config = get_symmetric_quantization_config(is_per_channel=True)
quantizer.set_global(operator_config)
m_eager = TestHelperModules.ConvMaxPool2d().eval()
example_inputs = (torch.randn(1, 2, 2, 2),)
self._test_representation(
m_eager,
example_inputs,
quantizer,
ref_node_occurrence={},
non_ref_node_occurrence={},
)
def test_qdq_per_channel(self):
"""Test representation for quantize_per_channel and dequantize_per_channel op"""
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.linear = torch.nn.Linear(5, 5)
def forward(self, x):
return self.linear(x)
quantizer = XNNPACKQuantizer()
# use per channel quantization for weight
operator_config = get_symmetric_quantization_config(is_per_channel=True)
quantizer.set_global(operator_config)
m_eager = M().eval()
inputs = [
(torch.randn(1, 5),),
(torch.randn(1, 3, 5),),
(torch.randn(1, 3, 3, 5),),
(torch.randn(1, 3, 3, 3, 5),),
]
for example_inputs in inputs:
ref_node_occurrence = {
ns.call_function(
torch.ops.quantized_decomposed.quantize_per_channel.default
): 0,
ns.call_function(
torch.ops.quantized_decomposed.dequantize_per_channel.default
): 0,
}
non_ref_node_occurrence = {
# quantize_per_channel is folded
ns.call_function(
torch.ops.quantized_decomposed.quantize_per_channel.default
): 0,
ns.call_function(
torch.ops.quantized_decomposed.dequantize_per_channel.default
): 1,
}
self._test_representation(
M().eval(),
example_inputs,
quantizer,
ref_node_occurrence,
non_ref_node_occurrence,
output_scale_idx=2,
)
def test_qdq(self):
"""Test representation for quantize and dequantize op"""
class M(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, x, y):
return x + y
quantizer = XNNPACKQuantizer()
quantization_config = get_symmetric_quantization_config(is_per_channel=True)
quantizer.set_global(quantization_config)
m_eager = M().eval()
example_inputs = (
torch.randn(1, 3, 3, 3),
torch.randn(1, 3, 3, 3),
)
ref_node_occurrence = {
ns.call_function(torch.ops.quantized_decomposed.quantize_per_tensor): 0,
ns.call_function(torch.ops.quantized_decomposed.dequantize_per_tensor): 0,
}
non_ref_node_occurrence = {
ns.call_function(
torch.ops.quantized_decomposed.quantize_per_tensor.default
): 3,
ns.call_function(
torch.ops.quantized_decomposed.dequantize_per_tensor.default
): 3,
}
self._test_representation(
M().eval(),
example_inputs,
quantizer,
ref_node_occurrence,
non_ref_node_occurrence,
)
|
