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Qualcomm AI Engine Direct - Fixed uint16 tensor and linear op #3196

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Qualcomm AI Engine Direct - Fixed uint16 tensor and linear op #3196

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22 changes: 7 additions & 15 deletions backends/qualcomm/builders/node_visitor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,8 +14,6 @@

from executorch.exir.dialects._ops import ops as exir_ops

from .qnn_constants import QNN_uint16

from .utils import get_parameter, is_graph_input, is_graph_output, is_parameter


Expand All @@ -26,7 +24,7 @@
# Note that there is no int64 tensor data type in Qnn.
torch.int64: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UNDEFINED,
torch.uint8: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UFIXED_POINT_8,
QNN_uint16: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UFIXED_POINT_16,
torch.uint16: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UFIXED_POINT_16,
}
QNN_TENSOR_TYPE_MAP = {
torch.bool: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_BOOL_8,
Expand All @@ -36,7 +34,7 @@
torch.int32: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_INT_32,
torch.int64: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_INT_64,
torch.uint8: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_8,
QNN_uint16: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_16,
torch.uint16: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_16,
float: PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_FLOAT_32,
}

Expand Down Expand Up @@ -170,7 +168,7 @@ def get_quant_encoding_conf(
return self.make_qnn_per_tensor_config(quant_attrs)

def get_quant_tensor_value(
self, tensor: torch.Tensor, quant_attrs: Dict, dtype, bitwidth
self, tensor: torch.Tensor, quant_attrs: Dict, quant_configs: Dict
) -> torch.Tensor:
if quant_attrs["encoding"] in PER_TENSOR_ENCODING:
scale = quant_attrs["scale"]
Expand All @@ -179,16 +177,11 @@ def get_quant_tensor_value(
scale = quant_attrs["scales"]
zero_point = quant_attrs["zero_points"]

# To bypass torch.uint16 quantization is not supported
dtype = (
torch.int32
if dtype == PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_16
else quant_attrs["dtype"]
)
dtype = quant_configs["dtype"]

tensor = tensor.div(scale).add(zero_point).round().to(dtype)
# Make the backends access data correctly
if bitwidth == 4:
if quant_configs.get("bitwidth") == 4:
mask = torch.full(tensor.size(), 0x0F, dtype=torch.int8)
tensor = torch.bitwise_and(mask, tensor)
return tensor
Expand Down Expand Up @@ -237,7 +230,7 @@ def get_data_type(
<= torch.iinfo(torch.int16).max - torch.iinfo(torch.int16).min
):
if unsigned:
quant_config["dtype"] = QNN_uint16
quant_config["dtype"] = torch.uint16
else:
quant_config["dtype"] = torch.int16
return QNN_QUANT_TYPE_MAP[quant_config["dtype"]]
Expand Down Expand Up @@ -328,8 +321,7 @@ def define_tensor(
tensor = self.get_quant_tensor_value(
tensor,
node.meta["quant_attrs"],
dtype,
quant_configs.get("bitwidth"),
quant_configs,
)
tensor_wrapper = PyQnnWrapper.TensorWrapper(
tensor_name,
Expand Down
1 change: 0 additions & 1 deletion backends/qualcomm/builders/qnn_constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
from enum import IntEnum, unique

QNN_OP_PACKAGE_NAME_QTI_AISW = "qti.aisw"
QNN_uint16 = "uint16"

# Below constants should be same as those in QNN headers.
# Maybe someday we should expose these constants by pybind
Expand Down
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