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Gate FuseQATConvBN behind is_qat=True; opt in from QAT deployments #19601

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Gate FuseQATConvBN behind is_qat=True; opt in from QAT deployments #19601

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25 changes: 16 additions & 9 deletions backends/cadence/aot/compiler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,6 +152,7 @@ def convert_pt2(
def apply_pre_edge_transform_passes(
converted_program: ExportedProgram,
quantizer: CadenceQuantizer,
is_qat: bool = False,
) -> ExportedProgram:
"""
Apply pre-edge transform passes including QuantFusion and torch ops passes.
Expand All @@ -166,12 +167,11 @@ def apply_pre_edge_transform_passes(
"""
# pyre-ignore[16]: no attribute
patterns = [q.pattern for q in quantizer.quantizers]
PassManager(
[
FuseQATConvBN(converted_program),
QuantFusion(patterns),
]
)(converted_program.graph_module)
passes = []
if is_qat:
passes.append(FuseQATConvBN(converted_program))
passes.append(QuantFusion(patterns))
PassManager(passes)(converted_program.graph_module)

# Apply torch ops passes (e.g., ReplaceMulTensorWithMulAndFullOpsPass)
fused_program = apply_torch_ops_passes(converted_program)
Expand All @@ -187,19 +187,24 @@ def get_fake_quant_model(
quantizer: CadenceQuantizer,
calibration_data: Optional[list[tuple[object, ...]]] = None,
dump_graphs: bool = False,
is_qat: bool = False,
) -> torch.fx.GraphModule:
# Make the model inference mode by calling model.eval()
model.eval()

ops_to_keep = quantizer.get_ops_to_preserve_from_decomposition()
program = trace(model, inputs, dump_graphs=dump_graphs, ops_to_keep=ops_to_keep)
program = trace(
model, inputs, dump_graphs=dump_graphs, ops_to_keep=ops_to_keep, is_qat=is_qat
)

if dump_graphs:
logging.info("Graph after trace:")
logging.info(program.graph.print_tabular())

# Get prepared graph module
prepared_gm = prepare_traced_pt2(program, quantizer, dump_graphs=dump_graphs)
prepared_gm = prepare_traced_pt2(
program, quantizer, dump_graphs=dump_graphs, is_qat=is_qat
)

# Calibrate
# If no calibration data is provided, use the inputs
Expand All @@ -221,6 +226,7 @@ def quantize_pt2(
calibration_data: Optional[list[tuple[object, ...]]] = None,
dump_graphs: bool = False,
quant_input_args: Optional[list[str]] = None,
is_qat: bool = False,
) -> ExportedProgram:
"""
Trace, prepare, convert and fuse the model using the given quantizer.
Expand All @@ -242,6 +248,7 @@ def quantize_pt2(
quantizer=quantizer,
calibration_data=calibration_data,
dump_graphs=dump_graphs,
is_qat=is_qat,
)
# Wrap the model to handle quantized inputs if provided
if quant_input_args is not None:
Expand All @@ -254,7 +261,7 @@ def quantize_pt2(
if quant_input_args is not None:
QuantizedInputWrapper.sink_dequants(program)

fused_program = apply_pre_edge_transform_passes(program, quantizer)
fused_program = apply_pre_edge_transform_passes(program, quantizer, is_qat=is_qat)

if dump_graphs:
logging.info("Graph after quantization and fusion:")
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