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Change default quant scheme #3687

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merged 2 commits into from
Dec 20, 2024
Merged

Change default quant scheme #3687

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a0f2162
Change default quant scheme to min-max
quic-kyunggeu Dec 20, 2024
56b1daf
Resolve test failures
quic-kyunggeu Dec 20, 2024
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8 changes: 4 additions & 4 deletions TrainingExtensions/torch/src/python/aimet_torch/v2/quantsim/quantsim.py
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Original file line number Diff line number Diff line change
Expand Up @@ -196,12 +196,12 @@ def __init__(self, # pylint: disable=too-many-arguments, too-many-locals, too-ma
if not quant_scheme:
old_default = QuantScheme.post_training_tf_enhanced
new_default = QuantScheme.training_range_learning_with_tf_init
msg = _red(f"The default value of 'quant_scheme' will change from '{old_default}' "
f"to '{new_default}' in the later versions. "
"If you wish to maintain the legacy behavior in the future, "
msg = _red(f"The default value of 'quant_scheme' has changed from '{old_default}' "
f"to '{new_default}' since aimet-torch==2.0.0. "
"If you wish to maintain the legacy default behavior, "
f"please explicitly pass 'quant_scheme={old_default}'")
warnings.warn(msg, DeprecationWarning, stacklevel=2)
quant_scheme = old_default
quant_scheme = new_default

if rounding_mode:
if rounding_mode == 'nearest':
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5 changes: 3 additions & 2 deletions TrainingExtensions/torch/test/python/v2/ab_test/test_quantsim_export.py
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Expand Up @@ -55,6 +55,7 @@
from aimet_torch.v2.quantsim import QuantizationSimModel
from aimet_torch.v1.quantsim import OnnxExportApiArgs
from aimet_torch.v1.qc_quantize_op import QcQuantizeWrapper
from aimet_common.defs import QuantScheme
from aimet_torch.utils import get_layer_by_name

from ..models_.models_to_test import (
Expand Down Expand Up @@ -371,8 +372,8 @@ def test_json_interchangeable(self):
model = resnet18().eval()
dummy_input = torch.randn(1, 3, 224, 224)

sim_v1 = QuantizationSimModelV1(model, dummy_input)
sim_v2 = QuantizationSimModel(model, dummy_input)
sim_v1 = QuantizationSimModelV1(model, dummy_input, quant_scheme=QuantScheme.post_training_tf_enhanced)
sim_v2 = QuantizationSimModel(model, dummy_input, quant_scheme=QuantScheme.post_training_tf_enhanced)

sim_v1.compute_encodings(lambda model, _: model(dummy_input), None)
sim_v2.compute_encodings(lambda model, _: model(dummy_input), None)
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4 changes: 2 additions & 2 deletions TrainingExtensions/torch/test/python/v2/quantsim/test_quantsim.py
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Expand Up @@ -783,7 +783,7 @@ def test_save_and_load_gbbq(self):

assert qsim.model.fc.param_quantizers['weight'].get_max()[0][0] == old_max
out3 = qsim.model(dummy_input)
assert torch.equal(out1, out3)
assert torch.allclose(out1, out3)

qsim.model.fc.weight = torch.nn.Parameter(torch.randn(old_weight.shape))
qsim.compute_encodings(lambda m, _: m(dummy_input_2), None)
Expand All @@ -793,7 +793,7 @@ def test_save_and_load_gbbq(self):
qsim.load_encodings(os.path.join(temp_dir, 'exported_encodings_torch.encodings'))

out4 = qsim.model(dummy_input)
assert torch.equal(out1, out4)
assert torch.allclose(out1, out4)


def test_quantsim_with_unused_modules(self):
Expand Down
5 changes: 3 additions & 2 deletions TrainingExtensions/torch/test/python/v2/test_quant_analyzer_.py
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Expand Up @@ -236,7 +236,7 @@ def test_export_per_layer_stats_histogram(self):
input_shape = (1, 3, 32, 32)
dummy_input = torch.randn(*input_shape)
model = TinyModel().eval()
sim = QuantizationSimModel(model, dummy_input)
sim = QuantizationSimModel(model, dummy_input, quant_scheme=QuantScheme.post_training_tf_enhanced)
sim.compute_encodings(evaluate, dummy_input)
forward_pass_callback = CallbackFunc(calibrate, dummy_input)
eval_callback = CallbackFunc(evaluate, dummy_input)
Expand Down Expand Up @@ -276,7 +276,8 @@ def test_export_per_layer_stats_histogram_per_channel(self):
input_shape = (1, 3, 32, 32)
dummy_input = torch.randn(*input_shape)
model = TinyModel().eval()
sim = QuantizationSimModel(model, dummy_input, config_file=os.path.join(tmp_dir, "quantsim_config.json"))
sim = QuantizationSimModel(model, dummy_input, config_file=os.path.join(tmp_dir, "quantsim_config.json"),
quant_scheme=QuantScheme.post_training_tf_enhanced)
sim.compute_encodings(evaluate, dummy_input)
forward_pass_callback = CallbackFunc(calibrate, dummy_input)
eval_callback = CallbackFunc(evaluate, dummy_input)
Expand Down
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