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Refactor _to_edge_and_lower_llama to remove args #10520

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May 13, 2025
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Refactor _to_edge_and_lower_llama to remove args #10520

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98 changes: 66 additions & 32 deletions examples/models/llama/export_llama_lib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
from executorch.backends.vulkan._passes.remove_asserts import remove_asserts
from executorch.devtools.backend_debug import print_delegation_info

from executorch.devtools.etrecord import generate_etrecord
from executorch.devtools.etrecord import generate_etrecord as generate_etrecord_func
from executorch.examples.models.llama.hf_download import (
download_and_convert_hf_checkpoint,
)
Expand Down Expand Up @@ -749,7 +749,9 @@ def _to_edge_and_lower_llama_xnnpack(
pt2e_quant_params,
quantizers,
quant_dtype,
args,
xnnpack_extended_ops: bool = False,
generate_etrecord: bool = False,
verbose: bool = False,
) -> LLMEdgeManager: # noqa: C901
partitioners = []

Expand All @@ -758,7 +760,7 @@ def _to_edge_and_lower_llama_xnnpack(

modelname = f"xnnpack_dq_{modelname}"

if args.xnnpack_extended_ops:
if xnnpack_extended_ops:
partitioners.append(
get_xnnpack_partitioner(dynamic_quant_only_partitioner=False)
)
Expand All @@ -769,15 +771,15 @@ def _to_edge_and_lower_llama_xnnpack(
logging.info(f"--> {partitioner.__class__.__name__}")

# TODO: Enable generating ETRecord with XNNPack and to_edge_transform_and_lower().
if args.generate_etrecord:
if generate_etrecord:
raise NotImplementedError(
"export_llama does not support XNNPack and generating ETRecord at the moment."
)

builder = builder_exported.pt2e_quantize(quantizers).to_edge_transform_and_lower(
partitioners
)
if args.verbose:
if verbose:
print_delegation_info(builder.edge_manager.exported_program().graph_module)

return builder.to_executorch(passes=additional_passes)
Expand All @@ -790,52 +792,66 @@ def _to_edge_and_lower_llama( # noqa: C901
pt2e_quant_params,
quantizers,
quant_dtype,
args,
vulkan: bool = False,
mps: bool = False,
coreml: bool = False,
qnn: bool = False,
dtype_override: str = "fp32",
enable_dynamic_shape: bool = True,
use_kv_cache: bool = False,
embedding_quantize: Optional[str] = None,
pt2e_quantize: Optional[str] = None,
coreml_ios: int = 15,
coreml_quantize: Optional[str] = None,
coreml_compute_units: str = "cpu_only",
use_qnn_sha: bool = False,
num_sharding: int = 0,
soc_model: str = "SM8650",
generate_etrecord: bool = False,
verbose: bool = False,
):
builder_exported_to_edge = builder_exported.pt2e_quantize(
quantizers
).export_to_edge()

# to_backend
partitioners = []
if args.vulkan:
if vulkan:
partitioners.append(
get_vulkan_partitioner(
args.dtype_override,
args.enable_dynamic_shape,
dtype_override,
enable_dynamic_shape,
)
)
modelname = f"vulkan_{modelname}"

# Need to remove asserts from the graph to prevent graph breaks
remove_asserts(builder_exported_to_edge.edge_manager.exported_program())

if args.mps:
partitioners.append(get_mps_partitioner(args.use_kv_cache))
if mps:
partitioners.append(get_mps_partitioner(use_kv_cache))
modelname = f"mps_{modelname}"

if args.coreml:
if coreml:
coreml_partitioner = get_coreml_partitioner(
args.coreml_ios,
args.embedding_quantize,
args.pt2e_quantize,
args.coreml_quantize,
args.coreml_compute_units,
coreml_ios,
embedding_quantize,
pt2e_quantize,
coreml_quantize,
coreml_compute_units,
)
partitioners.append(coreml_partitioner)
modelname = f"coreml_{modelname}"

if args.qnn:
if qnn:
logging.warning(
"The model definition in current repro is not performant, please refer to the instruction"
" in https://github.com/pytorch/executorch/tree/main/examples/qualcomm/oss_scripts/llama/README.md for better performance."
)
from executorch.extension.llm.custom_ops import model_sharding

partitioners.append(
get_qnn_partitioner(
args.use_kv_cache, args.pt2e_quantize, args.num_sharding, args.soc_model
)
get_qnn_partitioner(use_kv_cache, pt2e_quantize, num_sharding, soc_model)
)
# pyre-ignore: Undefined import [21]: Could not find a module corresponding to import `executorch.backends.qualcomm._passes`
from executorch.backends.qualcomm._passes import (
Expand Down Expand Up @@ -864,7 +880,7 @@ def _to_edge_and_lower_llama( # noqa: C901
)

atten = builder_exported_to_edge.model.layers[0].attention
if args.use_qnn_sha:
if use_qnn_sha:
cache_shape = torch.Size(
(atten.max_batch_size, atten.max_context_len, atten.head_dim)
)
Expand All @@ -887,10 +903,10 @@ def _to_edge_and_lower_llama( # noqa: C901
passes_job[TagQuantIO][QCOM_PASS_ARGS_KWARGS_DEFAULTS_KEY][
"get_quant_io_dtype_fn"
] = partial(get_custom_quant_ios_dtype, cache_shape)
if args.num_sharding > 0:
if num_sharding > 0:
SplitGraph, setting = model_sharding.get_split_graph_pass(
builder_exported_to_edge.metadata["get_n_layers"],
shares=args.num_sharding,
shares=num_sharding,
)
passes_job[SplitGraph] = setting
dep_table[SplitGraph] = [FoldQDQ]
Expand All @@ -905,17 +921,17 @@ def _to_edge_and_lower_llama( # noqa: C901
for partitioner in partitioners:
logging.info(f"--> {partitioner.__class__.__name__}")

if args.generate_etrecord:
if generate_etrecord:
if not builder_exported_to_edge.edge_manager:
raise ValueError("Unable to generate etrecord due to missing edge manager.")

logging.info("Generating etrecord")
# Copy the edge manager which will be serialized into etrecord. This is memory-wise expensive.
edge_manager_copy = copy.deepcopy(builder_exported_to_edge.edge_manager)
builder = builder_exported_to_edge.to_backend(partitioners)
if args.verbose:
if verbose:
print_delegation_info(builder.edge_manager.exported_program().graph_module)
if args.num_sharding > 0 and args.qnn:
if num_sharding > 0 and qnn:
# pyre-ignore: Undefined import [21]: Could not find a module corresponding to import `executorch.backends.qualcomm.utils.utils`.
from executorch.backends.qualcomm.utils.utils import canonicalize_program

Expand All @@ -927,17 +943,17 @@ def _to_edge_and_lower_llama( # noqa: C901

# Generate ETRecord
if edge_manager_copy:
generate_etrecord(
generate_etrecord_func(
et_record="etrecord.bin",
edge_dialect_program=edge_manager_copy,
executorch_program=builder.export_program,
)
logging.info("Generated etrecord.bin")
else:
builder = builder_exported_to_edge.to_backend(partitioners)
if args.verbose:
if verbose:
print_delegation_info(builder.edge_manager.exported_program().graph_module)
if args.num_sharding > 0 and args.qnn:
if num_sharding > 0 and qnn:
from executorch.backends.qualcomm.utils.utils import canonicalize_program

canonicalize_program(builder.edge_manager.exported_program())
Expand Down Expand Up @@ -976,7 +992,9 @@ def _export_llama(args) -> LLMEdgeManager: # noqa: C901
pt2e_quant_params,
quantizers,
quant_dtype,
args,
xnnpack_extended_ops=args.xnnpack_extended_ops,
generate_etrecord=args.generate_etrecord,
verbose=args.verbose,
)
else:
builder = _to_edge_and_lower_llama(
Expand All @@ -986,7 +1004,23 @@ def _export_llama(args) -> LLMEdgeManager: # noqa: C901
pt2e_quant_params,
quantizers,
quant_dtype,
args,
vulkan=args.vulkan,
mps=args.mps,
coreml=args.coreml,
qnn=args.qnn,
dtype_override=args.dtype_override,
enable_dynamic_shape=args.enable_dynamic_shape,
use_kv_cache=args.use_kv_cache,
embedding_quantize=args.embedding_quantize,
pt2e_quantize=args.pt2e_quantize,
coreml_ios=args.coreml_ios,
coreml_quantize=args.coreml_quantize,
coreml_compute_units=args.coreml_compute_units,
use_qnn_sha=args.use_qnn_sha,
num_sharding=args.num_sharding,
soc_model=args.soc_model,
generate_etrecord=args.generate_etrecord,
verbose=args.verbose,
)

if args.profile_memory:
Expand Down
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