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andrewor14 merged 36 commits into from
Oct 15, 2024
Merged

Add generic fake quantized linear for QAT #1020

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0756f39
Make module swap the main QAT flow again
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Add generic fake quantized linear for QAT
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10 changes: 8 additions & 2 deletions test/dtypes/test_affine_quantized_float.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,11 +26,17 @@
float8_weight_only,
quantize_,
)
from torchao.quantization.observer import PerRow, PerTensor
from torchao.quantization.granularity import (
PerRow,
PerTensor,
)
from torchao.quantization.quant_api import (
float8_static_activation_float8_weight,
)
from torchao.quantization.quant_primitives import MappingType, choose_qparams_affine
from torchao.quantization.quant_primitives import (
MappingType,
choose_qparams_affine,
)

random.seed(0)
torch.manual_seed(0)
Expand Down
22 changes: 12 additions & 10 deletions test/quantization/test_observer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,11 +9,13 @@
from torch.testing._internal import common_utils
from torch.testing._internal.common_utils import TestCase

from torchao.quantization.observer import (
AffineQuantizedMinMaxObserver,
from torchao.quantization.granularity import (
PerAxis,
PerTensor,
)
from torchao.quantization.observer import (
AffineQuantizedMinMaxObserver,
)
from torchao.quantization.quant_api import (
insert_observers_,
)
Expand Down Expand Up @@ -42,7 +44,7 @@ def test_min_max_per_tensor_affine(self):
obs = AffineQuantizedMinMaxObserver(
MappingType.ASYMMETRIC,
torch.uint8,
granularity_type=PerTensor(),
granularity=PerTensor(),
eps=torch.finfo(torch.float32).eps,
scale_dtype=torch.float,
zero_point_dtype=torch.int,
Expand All @@ -54,7 +56,7 @@ def test_min_max_per_channel_affine(self):
obs = AffineQuantizedMinMaxObserver(
MappingType.ASYMMETRIC,
torch.uint8,
granularity_type=PerAxis(axis=0),
granularity=PerAxis(axis=0),
eps=torch.finfo(torch.float32).eps,
scale_dtype=torch.float,
zero_point_dtype=torch.int,
Expand All @@ -68,7 +70,7 @@ def test_block_size_calc_success(self):
obs = AffineQuantizedMinMaxObserver(
MappingType.SYMMETRIC,
torch.float8_e4m3fn,
granularity_type=PerTensor(),
granularity=PerTensor(),
eps=torch.finfo(torch.float32).eps,
scale_dtype=torch.float,
zero_point_dtype=torch.int,
Expand All @@ -87,7 +89,7 @@ def test_block_size_calc_success(self):
obs = AffineQuantizedMinMaxObserver(
MappingType.SYMMETRIC,
torch.float8_e4m3fn,
granularity_type=PerAxis(1),
granularity=PerAxis(1),
eps=torch.finfo(torch.float32).eps,
scale_dtype=torch.float,
zero_point_dtype=torch.int,
Expand All @@ -102,7 +104,7 @@ def test_block_size_row_errors(self):
obs = AffineQuantizedMinMaxObserver(
MappingType.SYMMETRIC,
torch.float8_e4m3fn,
granularity_type=PerAxis(0),
granularity=PerAxis(0),
eps=torch.finfo(torch.float32).eps,
scale_dtype=torch.float,
zero_point_dtype=torch.int,
Expand All @@ -121,7 +123,7 @@ def test_block_size_row_errors(self):
obs = AffineQuantizedMinMaxObserver(
MappingType.SYMMETRIC,
torch.float8_e4m3fn,
granularity_type=PerAxis(1),
granularity=PerAxis(1),
eps=torch.finfo(torch.float32).eps,
scale_dtype=torch.float,
zero_point_dtype=torch.int,
Expand Down Expand Up @@ -149,7 +151,7 @@ def test_linear_observer_tensor(self, observe_weight: bool):
input_observer = AffineQuantizedMinMaxObserver(
MappingType.SYMMETRIC,
torch.float8_e4m3fn,
granularity_type=PerTensor(),
granularity=PerTensor(),
eps=torch.finfo(torch.float32).eps,
scale_dtype=torch.float,
zero_point_dtype=torch.int,
Expand All @@ -159,7 +161,7 @@ def test_linear_observer_tensor(self, observe_weight: bool):
weight_observer = AffineQuantizedMinMaxObserver(
MappingType.SYMMETRIC,
torch.float8_e4m3fn,
granularity_type=PerTensor(),
granularity=PerTensor(),
eps=torch.finfo(torch.float32).eps,
scale_dtype=torch.float,
zero_point_dtype=torch.int,
Expand Down
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