Skip to content

Qualcomm AI Engine Direct - Reduce redundant observers #6351

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Nov 7, 2024
Merged

Qualcomm AI Engine Direct - Reduce redundant observers #6351

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 24 additions & 8 deletions backends/qualcomm/quantizer/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -229,14 +229,29 @@ def get_default_8bit_qnn_ptq_config(
) -> QuantizationConfig:
extra_args: Dict[str, Any] = {"eps": 2**-12}

act_quantization_spec = QuantizationSpec(
dtype=torch.uint8,
qscheme=(
torch.per_tensor_symmetric if act_symmetric else torch.per_tensor_affine
),
ch_axis=0,
observer_or_fake_quant_ctr=act_observer.with_args(**extra_args),
)
if act_symmetric:
# If zero_point is 128, htp can do optimizations.
# If we keep quant_min and quant_max none, observer will default use 128 as zero_point.
# If we provide uint8 quant_min/max, it will use 127 as zero_point, which is undesired.
act_quantization_spec = QuantizationSpec(
dtype=torch.uint8,
qscheme=torch.per_tensor_symmetric,
ch_axis=0,
observer_or_fake_quant_ctr=act_observer.with_args(**extra_args),
)
else:
# PyTorch will remove redundant observers based on attributes such as:
# dtype, quant_min, quant_max, ch_axis, etc.
# Providing values like quant_min and quant_max can help observers compare
# and further reduce the number of observers.
act_quantization_spec = QuantizationSpec(
dtype=torch.uint8,
quant_min=torch.iinfo(torch.uint8).min,
quant_max=torch.iinfo(torch.uint8).max,
qscheme=torch.per_tensor_affine,
ch_axis=0,
observer_or_fake_quant_ctr=act_observer.with_args(**extra_args),
)

weight_quantization_spec = QuantizationSpec(
dtype=torch.int8,
Expand Down Expand Up @@ -409,6 +424,7 @@ def get_ptq_per_channel_quant_config(
quant_min=torch.iinfo(act_dtype).min,
quant_max=torch.iinfo(act_dtype).max,
qscheme=torch.per_tensor_affine,
ch_axis=0,
observer_or_fake_quant_ctr=MovingAverageMinMaxObserver.with_args(**extra_args),
)

Expand Down
6 changes: 4 additions & 2 deletions examples/qualcomm/utils.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -348,7 +348,9 @@ def histogram(golden, predict):
return (pa, mpa, miou, cls_iou)


def get_imagenet_dataset(dataset_path, data_size, image_shape, crop_size=None):
def get_imagenet_dataset(
dataset_path, data_size, image_shape, crop_size=None, shuffle=True
):
from torchvision import datasets, transforms

def get_data_loader():
Expand All @@ -365,7 +367,7 @@ def get_data_loader():
imagenet_data = datasets.ImageFolder(dataset_path, transform=preprocess)
return torch.utils.data.DataLoader(
imagenet_data,
shuffle=True,
shuffle=shuffle,
)

# prepare input data
Expand Down
Loading