Skip to content

Fix mobile bert fine tune #9915

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

Closed
wants to merge 1 commit into from
Closed

Fix mobile bert fine tune #9915

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
42 changes: 27 additions & 15 deletions examples/qualcomm/scripts/mobilebert_fine_tune.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,17 +17,17 @@
generate_htp_compiler_spec,
generate_qnn_executorch_compiler_spec,
skip_annotation,
to_edge_transform_and_lower_to_qnn,
)
from executorch.examples.qualcomm.utils import (
build_executorch_binary,
make_output_dir,
make_quantizer,
parse_skip_delegation_node,
QnnPartitioner,
setup_common_args_and_variables,
SimpleADB,
)
from executorch.exir import to_edge
from executorch.exir import ExecutorchBackendConfig
from transformers import BertTokenizer, MobileBertForSequenceClassification


Expand Down Expand Up @@ -273,30 +273,42 @@ def calibrator(gm):

quantizer = make_quantizer(quant_dtype=quant_dtype)
backend_options = generate_htp_compiler_spec(quant_dtype is not None)
partitioner = QnnPartitioner(
generate_qnn_executorch_compiler_spec(
soc_model=getattr(QcomChipset, args.model),
backend_options=backend_options,
),
skip_node_id_set=skip_node_id_set,
skip_node_op_set=skip_node_op_set,
# partitioner = QnnPartitioner(
# generate_qnn_executorch_compiler_spec(
# soc_model=getattr(QcomChipset, args.model),
# backend_options=backend_options,
# ),
# skip_node_id_set=skip_node_id_set,
# skip_node_op_set=skip_node_op_set,
# )
backend_options = generate_htp_compiler_spec(
use_fp16=False,
)
compile_spec = generate_qnn_executorch_compiler_spec(
soc_model=QcomChipset.SM8550,
backend_options=backend_options,
)
# skip embedding layer cause it's quantization sensitive
graph_module, _ = skip_annotation(
nn_module=model,
quantizer=quantizer,
partitioner=partitioner,
compiler_specs=compile_spec,
sample_input=inputs[0],
calibration_cb=calibrator,
fp_node_op_set={torch.ops.aten.embedding.default},
)
# lower all graph again, the skipped operators will be left in CPU
exec_prog = to_edge(
torch.export.export(graph_module, inputs[0], strict=True),
).to_executorch()

# exec_prog = to_edge(
# torch.export.export(graph_module, inputs[0], strict=True),
# ).to_executorch()
delegated_program = to_edge_transform_and_lower_to_qnn(
graph_module, inputs[0], compile_spec
)
executorch_program = delegated_program.to_executorch(
config=ExecutorchBackendConfig(extract_delegate_segments=True)
)
with open(f"{args.artifact}/{pte_filename}.pte", "wb") as file:
file.write(exec_prog.buffer)
file.write(executorch_program.buffer)

if args.compile_only:
return
Expand Down
Loading