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Qualcomm AI Engine Direct - Fixed layer norm quantization annotation for 16bit #5927

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Qualcomm AI Engine Direct - Fixed layer norm quantization annotation for 16bit #5927

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20 changes: 14 additions & 6 deletions backends/qualcomm/quantizer/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1055,17 +1055,25 @@ def annotate_layer_norm(node: Node, quantization_config: QuantizationConfig) ->

if _is_annotated([node]):
return
input_act_qspec = quantization_config.input_activation

_annotate_input_qspec_map(
node,
act_node,
quantization_config.input_activation,
)
_annotate_input_qspec_map(
node,
weight_node,
quantization_config.input_activation,
input_act_qspec,
)
if input_act_qspec.dtype == torch.int32:
_annotate_input_qspec_map(
node,
weight_node,
get_default_16bit_qnn_ptq_config().weight,
)
else:
_annotate_input_qspec_map(
node,
weight_node,
input_act_qspec,
)
nodes_to_mark_annotated = [node, weight_node]
if bias_node:
_annotate_input_qspec_map(
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10 changes: 10 additions & 0 deletions backends/qualcomm/tests/test_qnn_delegate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -634,6 +634,16 @@ def test_qnn_backend_16a4w_conv2d(self):
)
self.lower_module_and_test_output(module, sample_input)

def test_qnn_backend_16a4w_layer_norm(self):
module = LayerNorm() # noqa: F405
sample_input = (torch.randn(196, 768),)
module = self.get_qdq_module(
module,
sample_input,
quant_dtype=QuantDtype.use_16a4w,
)
self.lower_module_and_test_output(module, sample_input)

def test_qnn_backend_16a4w_linear(self):
module = Linear() # noqa: F405
sample_input = (torch.randn([3, 4]),)
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