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Feb 26, 2025
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Revert #8501 and #8624 #8716

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adb897c
Revert "Switch to new ao quant api for 8da4w (#8501)"
swolchok Feb 26, 2025
8a95288
Revert "Use to_edge_lower_and_transform for XNNPack (#8624)"
swolchok Feb 26, 2025
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132 changes: 41 additions & 91 deletions examples/models/llama/export_llama_lib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -676,62 +676,47 @@ def _validate_args(args):
)


def _to_edge_and_lower_llama_xnnpack(
builder_exported,
modelname,
additional_passes,
pt2e_quant_params,
quantizers,
quant_dtype,
args,
) -> LLMEdgeManager: # noqa: C901
partitioners = []

# Order matters here, dynamic quantization should be applied first when both xnnpack and xnnpack_extended_ops are enabled
partitioners.append(get_xnnpack_partitioner(dynamic_quant_only_partitioner=True))

modelname = f"xnnpack_dq_{modelname}"

if args.xnnpack_extended_ops:
partitioners.append(
get_xnnpack_partitioner(dynamic_quant_only_partitioner=False)
)
modelname = f"xnnpack_{modelname}"

logging.info("Lowering model using following partitioner(s): ")
for partitioner in partitioners:
logging.info(f"--> {partitioner.__class__.__name__}")
def _export_llama(args) -> LLMEdgeManager: # noqa: C901
_validate_args(args)

# TODO: Enable generating ETRecord with XNNPack and to_edge_transform_and_lower().
if args.generate_etrecord:
raise NotImplementedError(
"export_llama does not support XNNPack and generating ETRecord at the moment."
)
pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

return builder.to_executorch(passes=additional_passes)
builder_exported.run_canonical_optimizations()

if args.export_only:
exit()

def _to_edge_and_lower_llama( # noqa: C901
builder_exported,
modelname,
additional_passes,
pt2e_quant_params,
quantizers,
quant_dtype,
args,
):
builder_exported_to_edge = builder_exported.pt2e_quantize(
quantizers
).export_to_edge()

modelname = builder_exported_to_edge.modelname

# to_backend
partitioners = []

# Order matters here, dynamic quantization should be applied first when both xnnpack and xnnpack_extended_ops are enabled
if (
pt2e_quant_params is not None and pt2e_quant_params.quantize_linear is not None
) or (args.xnnpack):
partitioners.append(
get_xnnpack_partitioner(dynamic_quant_only_partitioner=True)
)

# force xnnpack to be true if pt2e_quant_params is not None and args.xnnpack is False
args.xnnpack = True
modelname = f"xnnpack_dq_{modelname}"

if args.xnnpack_extended_ops:
assert args.xnnpack, "xnnpack_extended_ops requires xnnpack to be enabled"
partitioners.append(
get_xnnpack_partitioner(dynamic_quant_only_partitioner=False)
)
modelname = f"xnnpack_{modelname}"

if args.vulkan:
partitioners.append(
get_vulkan_partitioner(
Expand All @@ -746,6 +731,7 @@ def _to_edge_and_lower_llama( # noqa: C901
modelname = f"vulkan_{modelname}"

# Need to remove asserts from the graph to prevent graph breaks
# pyre-ignore: Undefined attribute [16]: `Optional` has no attribute `exported_program`.
remove_asserts(builder_exported_to_edge.edge_manager.exported_program())

if args.mps:
Expand Down Expand Up @@ -774,11 +760,13 @@ def _to_edge_and_lower_llama( # noqa: C901
# 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 _transform, tag_quant_io

# pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`, Optional type has no attribute `exported_program`
_transform(builder_exported_to_edge.edge_manager.exported_program())

if args.num_sharding > 0:
model_sharding.split_graph(
builder_exported_to_edge.edge_manager.exported_program(),
# pyre-fixme[16]: `Optional` has no attribute `__getitem__`.
builder_exported_to_edge.metadata["get_n_layers"],
shares=args.num_sharding,
)
Expand All @@ -804,15 +792,19 @@ def _to_edge_and_lower_llama( # noqa: C901
atten.head_dim,
)
)
# pyre-ignore
tag_quant_io(
builder_exported_to_edge.edge_manager.exported_program().graph_module,
partial(get_custom_quant_ios_dtype, cache_shape),
partial(get_custom_quant_ios_dtype, cache_shape), # pyre-ignore
)

logging.info("Lowering model using following partitioner(s): ")
for partitioner in partitioners:
logging.info(f"--> {partitioner.__class__.__name__}")

additional_passes = []
if args.model in TORCHTUNE_DEFINED_MODELS:
additional_passes = [InitializedMutableBufferPass(["kv_cache_pos"])]
if args.generate_etrecord:
if not builder_exported_to_edge.edge_manager:
raise ValueError("Unable to generate etrecord due to missing edge manager.")
Expand All @@ -826,6 +818,7 @@ def _to_edge_and_lower_llama( # noqa: C901
if args.num_sharding > 0 and args.qnn:
from executorch.backends.qualcomm.utils.utils import canonicalize_program

# pyre-fixme[16]: Module `backends` has no attribute `qualcomm`.
canonicalize_program(builder.edge_manager.exported_program())

builder = builder.to_executorch(
Expand All @@ -847,55 +840,11 @@ def _to_edge_and_lower_llama( # noqa: C901
if args.num_sharding > 0 and args.qnn:
from executorch.backends.qualcomm.utils.utils import canonicalize_program

# pyre-fixme[16]: Module `backends` has no attribute `qualcomm`.
canonicalize_program(builder.edge_manager.exported_program())

builder = builder.to_executorch(passes=additional_passes)

return builder


def _export_llama(args) -> LLMEdgeManager: # noqa: C901
_validate_args(args)

pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

additional_passes = []
if args.model in TORCHTUNE_DEFINED_MODELS:
additional_passes = [InitializedMutableBufferPass(["kv_cache_pos"])]

# export_to_edge
builder_exported = _prepare_for_llama_export(args).export()
builder_exported.run_canonical_optimizations()
modelname = builder_exported.modelname

if args.export_only:
exit()

if pt2e_quant_params is not None and pt2e_quant_params.quantize_linear is not None:
# Force xnnpack to be true if pt2e_quant_params is not None and args.xnnpack is False
args.xnnpack = True

if args.xnnpack:
builder = _to_edge_and_lower_llama_xnnpack(
builder_exported,
modelname,
additional_passes,
pt2e_quant_params,
quantizers,
quant_dtype,
args,
)
else:
builder = _to_edge_and_lower_llama(
builder_exported,
modelname,
additional_passes,
pt2e_quant_params,
quantizers,
quant_dtype,
args,
)

if args.profile_memory:
generate_memory_trace(builder.export_program, "memory_profile.json")

Expand All @@ -917,6 +866,7 @@ def _export_llama(args) -> LLMEdgeManager: # noqa: C901
output_file = f"{builder.output_dir}/{modelname}.pte"

builder.save_to_pte(output_file)

return builder


Expand Down
9 changes: 5 additions & 4 deletions examples/models/llama/source_transformation/quantize.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -119,10 +119,11 @@ def quantize( # noqa C901
# Check for required args
if group_size is None:
raise Exception("For 8da4w quantization, group size must be specified.")
from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer

from torchao.quantization import int8_dynamic_activation_int4_weight, quantize_

quantize_(model, int8_dynamic_activation_int4_weight(group_size=group_size))
model = Int8DynActInt4WeightQuantizer(
precision=torch_dtype, groupsize=group_size
).quantize(model)

if verbose:
print("quantized model:", model)
Expand Down Expand Up @@ -662,7 +663,7 @@ def convert_for_runtime(self) -> nn.Module:
def quantized_model(self) -> nn.Module:
model_updated_state_dict = self.create_quantized_state_dict(self.packed)
self.convert_for_runtime()
self.mod.load_state_dict(model_updated_state_dict, assign=True)
self.mod.load_state_dict(model_updated_state_dict)
return self.mod


Expand Down
1 change: 1 addition & 0 deletions examples/models/llava/export_llava.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,7 @@ def export(self) -> "LlavaEdgeManager":
dynamic_shapes=dynamic_shape,
strict=False,
)
# pyre-ignore: Incompatible attribute type [8]: Attribute `pre_autograd_graph_module` declared in class `LLMEdgeManager` has type `Optional[GraphModule]` but is used as type `Module`.
self.pre_autograd_graph_module = self.export_program.module()
return self

Expand Down
28 changes: 6 additions & 22 deletions extension/llm/export/builder.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@
DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
from executorch.exir import EdgeProgramManager
from executorch.exir.backend.partitioner import Partitioner

from executorch.exir.backend.utils import format_delegated_graph
Expand All @@ -39,7 +39,7 @@
from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e
from torch.ao.quantization.quantizer import Quantizer
from torch.ao.quantization.quantizer.composable_quantizer import ComposableQuantizer
from torch.export import export_for_training, ExportedProgram
from torch.export import export_for_training
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand Down Expand Up @@ -89,8 +89,8 @@ def __init__(
dynamic_shapes: Optional[Any] = None,
):
self.model = model
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
# graph module returned from export()
self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.modelname = modelname
self.max_seq_len = max_seq_len
self.dtype = dtype
Expand Down Expand Up @@ -218,8 +218,8 @@ def export(self) -> "LLMEdgeManager":
kwargs=self.example_kwarg_inputs,
dynamic_shapes=dynamic_shape,
)
# pyre-fixme[8]: Attribute has type `Optional[GraphModule]`; used as
# `Module`.
self.pre_autograd_exported_program = exported_module
self.pre_autograd_graph_module = exported_module.module()
if hasattr(self.args, "export_only") and self.args.export_only:
torch.export.save(exported_module, self.args.output_name)
Expand Down Expand Up @@ -330,10 +330,7 @@ def pt2e_quantize(self, quantizers: Optional[List[Quantizer]]) -> "LLMEdgeManage
assert (
self.pre_autograd_graph_module is not None
), "Please run export() first"
m = prepare_pt2e(
self.pre_autograd_graph_module, # pyre-ignore[6]
composed_quantizer,
)
m = prepare_pt2e(self.pre_autograd_graph_module, composed_quantizer)
logging.info(
f"Calibrating with tasks: {self.calibration_tasks}, limit: {self.calibration_limit}, calibration_data: {self.calibration_data}, tokenizer_path: {self.tokenizer_path}, seq_length: {self.calibration_seq_length}"
)
Expand Down Expand Up @@ -433,19 +430,6 @@ def to_backend(self, partitioners: Optional[List[Partitioner]]) -> "LLMEdgeManag

return self

def to_edge_transform_and_lower(
self, partitioners: Optional[List[Partitioner]]
) -> "LLMEdgeManager":
if partitioners is None:
logging.info("No partitioner provided, skipping backend lowering...")
edge_config = self._get_edge_config()
self.edge_manager = to_edge_transform_and_lower(
self.pre_autograd_exported_program,
partitioner=partitioners,
compile_config=edge_config,
)
return self

def to_executorch(
self, passes: Optional[List[ExportPass]] = None
) -> "LLMEdgeManager":
Expand Down
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