Use to_edge_lower_and_transform for XNNPack (#8624)#8717

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Merged
facebook-github-bot merged 1 commit into from
Feb 27, 2025
Merged

Use to_edge_lower_and_transform for XNNPack (#8624) #8717

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132 changes: 91 additions & 41 deletions examples/models/llama/export_llama_lib.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -676,47 +676,62 @@ def _validate_args(args):
)


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

pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

builder_exported.run_canonical_optimizations()

if args.export_only:
exit()

builder_exported_to_edge = builder_exported.pt2e_quantize(
quantizers
).export_to_edge()

modelname = builder_exported_to_edge.modelname

# to_backend
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
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)
)
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}"
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}"

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

# 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."
)

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)

return builder.to_executorch(passes=additional_passes)


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()

# to_backend
partitioners = []
if args.vulkan:
partitioners.append(
get_vulkan_partitioner(
Expand All@@ -731,7 +746,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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 DownExpand Up@@ -760,13 +774,11 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -792,19 +804,15 @@ def _export_llama(args) -> LLMEdgeManager: # 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), # pyre-ignore
partial(get_custom_quant_ios_dtype, cache_shape),
)

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@@ -818,7 +826,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -840,11 +847,55 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -866,7 +917,6 @@ 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
1 change: 0 additions & 1 deletion examples/models/llava/export_llava.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,6 @@ 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
29 changes: 23 additions & 6 deletions extension/llm/export/builder.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,7 @@
DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
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
from torch.export import export_for_training, ExportedProgram
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand DownExpand Up@@ -89,8 +89,8 @@ def __init__(
dynamic_shapes: Optional[Any] = None,
):
self.model = model
# graph module returned from export()
self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
self.modelname = modelname
self.max_seq_len = max_seq_len
self.dtype = dtype
Expand DownExpand 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 DownExpand Up@@ -330,7 +330,10 @@ 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, composed_quantizer)
m = prepare_pt2e(
self.pre_autograd_graph_module, # pyre-ignore[6]
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 DownExpand Up@@ -430,6 +433,20 @@ 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,
constant_methods=self.metadata,
)
return self

def to_executorch(
self, passes: Optional[List[ExportPass]] = None
) -> "LLMEdgeManager":
Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Use to_edge_lower_and_transform for XNNPack (#8624)#8717

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to your account

 Merged

facebook-github-bot
merged 1 commit into

from

Feb 27, 2025
 Merged


Use to_edge_lower_and_transform for XNNPack (#8624)
#8717
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132 changes: 91 additions & 41 deletions
examples/models/llama/export_llama_lib.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -676,47 +676,62 @@ def _validate_args(args):
 )


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

 pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

 builder_exported.run_canonical_optimizations()

 if args.export_only:
 exit()

 builder_exported_to_edge = builder_exported.pt2e_quantize(
 quantizers
 ).export_to_edge()

 modelname = builder_exported_to_edge.modelname

 # to_backend
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
 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)
 )
 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}"
 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}"

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

 # 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."
 )

 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)

 return builder.to_executorch(passes=additional_passes)


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()

 # to_backend
 partitioners = []
 if args.vulkan:
 partitioners.append(
 get_vulkan_partitioner(
Expand All@@ -731,7 +746,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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 DownExpand Up@@ -760,13 +774,11 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -792,19 +804,15 @@ def _export_llama(args) -> LLMEdgeManager: # 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), # pyre-ignore
 partial(get_custom_quant_ios_dtype, cache_shape),
 )

 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@@ -818,7 +826,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -840,11 +847,55 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -866,7 +917,6 @@ 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

1 change: 0 additions & 1 deletion
examples/models/llava/export_llava.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,6 @@ 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

29 changes: 23 additions & 6 deletions
extension/llm/export/builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,7 @@
 DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
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
from torch.export import export_for_training, ExportedProgram
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand DownExpand Up@@ -89,8 +89,8 @@ def __init__(
 dynamic_shapes: Optional[Any] = None,
 ):
 self.model = model
# graph module returned from export()
 self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
 self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
 self.modelname = modelname
 self.max_seq_len = max_seq_len
 self.dtype = dtype
Expand DownExpand 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 DownExpand Up@@ -330,7 +330,10 @@ 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, composed_quantizer)
 m = prepare_pt2e(
 self.pre_autograd_graph_module, # pyre-ignore[6]
 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 DownExpand Up@@ -430,6 +433,20 @@ 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,
 constant_methods=self.metadata,
 )
 return self

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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Use to_edge_lower_and_transform for XNNPack (#8624)#8717

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Feb 27, 2025
 Merged


Use to_edge_lower_and_transform for XNNPack (#8624)
#8717
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132 changes: 91 additions & 41 deletions
examples/models/llama/export_llama_lib.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -676,47 +676,62 @@ def _validate_args(args):
 )


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

 pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

 builder_exported.run_canonical_optimizations()

 if args.export_only:
 exit()

 builder_exported_to_edge = builder_exported.pt2e_quantize(
 quantizers
 ).export_to_edge()

 modelname = builder_exported_to_edge.modelname

 # to_backend
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
 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)
 )
 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}"
 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}"

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

 # 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."
 )

 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)

 return builder.to_executorch(passes=additional_passes)


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()

 # to_backend
 partitioners = []
 if args.vulkan:
 partitioners.append(
 get_vulkan_partitioner(
Expand All@@ -731,7 +746,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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 DownExpand Up@@ -760,13 +774,11 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -792,19 +804,15 @@ def _export_llama(args) -> LLMEdgeManager: # 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), # pyre-ignore
 partial(get_custom_quant_ios_dtype, cache_shape),
 )

 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@@ -818,7 +826,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -840,11 +847,55 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -866,7 +917,6 @@ 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

1 change: 0 additions & 1 deletion
examples/models/llava/export_llava.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,6 @@ 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

29 changes: 23 additions & 6 deletions
extension/llm/export/builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,7 @@
 DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
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
from torch.export import export_for_training, ExportedProgram
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand DownExpand Up@@ -89,8 +89,8 @@ def __init__(
 dynamic_shapes: Optional[Any] = None,
 ):
 self.model = model
# graph module returned from export()
 self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
 self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
 self.modelname = modelname
 self.max_seq_len = max_seq_len
 self.dtype = dtype
Expand DownExpand 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 DownExpand Up@@ -330,7 +330,10 @@ 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, composed_quantizer)
 m = prepare_pt2e(
 self.pre_autograd_graph_module, # pyre-ignore[6]
 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 DownExpand Up@@ -430,6 +433,20 @@ 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,
 constant_methods=self.metadata,
 )
 return self

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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Use to_edge_lower_and_transform for XNNPack (#8624)#8717

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 Merged

facebook-github-bot
merged 1 commit into

from

Feb 27, 2025
 Merged


Use to_edge_lower_and_transform for XNNPack (#8624)
#8717
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132 changes: 91 additions & 41 deletions
examples/models/llama/export_llama_lib.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -676,47 +676,62 @@ def _validate_args(args):
 )


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

 pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

 builder_exported.run_canonical_optimizations()

 if args.export_only:
 exit()

 builder_exported_to_edge = builder_exported.pt2e_quantize(
 quantizers
 ).export_to_edge()

 modelname = builder_exported_to_edge.modelname

 # to_backend
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
 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)
 )
 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}"
 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}"

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

 # 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."
 )

 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)

 return builder.to_executorch(passes=additional_passes)


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()

 # to_backend
 partitioners = []
 if args.vulkan:
 partitioners.append(
 get_vulkan_partitioner(
Expand All@@ -731,7 +746,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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 DownExpand Up@@ -760,13 +774,11 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -792,19 +804,15 @@ def _export_llama(args) -> LLMEdgeManager: # 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), # pyre-ignore
 partial(get_custom_quant_ios_dtype, cache_shape),
 )

 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@@ -818,7 +826,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -840,11 +847,55 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -866,7 +917,6 @@ 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

1 change: 0 additions & 1 deletion
examples/models/llava/export_llava.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,6 @@ 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

29 changes: 23 additions & 6 deletions
extension/llm/export/builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,7 @@
 DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
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
from torch.export import export_for_training, ExportedProgram
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand DownExpand Up@@ -89,8 +89,8 @@ def __init__(
 dynamic_shapes: Optional[Any] = None,
 ):
 self.model = model
# graph module returned from export()
 self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
 self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
 self.modelname = modelname
 self.max_seq_len = max_seq_len
 self.dtype = dtype
Expand DownExpand 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 DownExpand Up@@ -330,7 +330,10 @@ 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, composed_quantizer)
 m = prepare_pt2e(
 self.pre_autograd_graph_module, # pyre-ignore[6]
 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 DownExpand Up@@ -430,6 +433,20 @@ 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,
 constant_methods=self.metadata,
 )
 return self

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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Use to_edge_lower_and_transform for XNNPack (#8624)#8717

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Feb 27, 2025
 Merged


Use to_edge_lower_and_transform for XNNPack (#8624)
#8717
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132 changes: 91 additions & 41 deletions
examples/models/llama/export_llama_lib.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -676,47 +676,62 @@ def _validate_args(args):
 )


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

 pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

 builder_exported.run_canonical_optimizations()

 if args.export_only:
 exit()

 builder_exported_to_edge = builder_exported.pt2e_quantize(
 quantizers
 ).export_to_edge()

 modelname = builder_exported_to_edge.modelname

 # to_backend
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
 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)
 )
 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}"
 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}"

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

 # 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."
 )

 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)

 return builder.to_executorch(passes=additional_passes)


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()

 # to_backend
 partitioners = []
 if args.vulkan:
 partitioners.append(
 get_vulkan_partitioner(
Expand All@@ -731,7 +746,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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 DownExpand Up@@ -760,13 +774,11 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -792,19 +804,15 @@ def _export_llama(args) -> LLMEdgeManager: # 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), # pyre-ignore
 partial(get_custom_quant_ios_dtype, cache_shape),
 )

 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@@ -818,7 +826,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -840,11 +847,55 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -866,7 +917,6 @@ 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

1 change: 0 additions & 1 deletion
examples/models/llava/export_llava.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,6 @@ 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

29 changes: 23 additions & 6 deletions
extension/llm/export/builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,7 @@
 DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
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
from torch.export import export_for_training, ExportedProgram
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand DownExpand Up@@ -89,8 +89,8 @@ def __init__(
 dynamic_shapes: Optional[Any] = None,
 ):
 self.model = model
# graph module returned from export()
 self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
 self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
 self.modelname = modelname
 self.max_seq_len = max_seq_len
 self.dtype = dtype
Expand DownExpand 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 DownExpand Up@@ -330,7 +330,10 @@ 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, composed_quantizer)
 m = prepare_pt2e(
 self.pre_autograd_graph_module, # pyre-ignore[6]
 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 DownExpand Up@@ -430,6 +433,20 @@ 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,
 constant_methods=self.metadata,
 )
 return self

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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Use to_edge_lower_and_transform for XNNPack (#8624)#8717

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

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Sign in
to your account

 Merged

facebook-github-bot
merged 1 commit into

from

Feb 27, 2025
 Merged


Use to_edge_lower_and_transform for XNNPack (#8624)
#8717
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132 changes: 91 additions & 41 deletions
examples/models/llama/export_llama_lib.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -676,47 +676,62 @@ def _validate_args(args):
 )


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

 pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

 builder_exported.run_canonical_optimizations()

 if args.export_only:
 exit()

 builder_exported_to_edge = builder_exported.pt2e_quantize(
 quantizers
 ).export_to_edge()

 modelname = builder_exported_to_edge.modelname

 # to_backend
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
 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)
 )
 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}"
 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}"

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

 # 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."
 )

 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)

 return builder.to_executorch(passes=additional_passes)


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()

 # to_backend
 partitioners = []
 if args.vulkan:
 partitioners.append(
 get_vulkan_partitioner(
Expand All@@ -731,7 +746,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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 DownExpand Up@@ -760,13 +774,11 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -792,19 +804,15 @@ def _export_llama(args) -> LLMEdgeManager: # 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), # pyre-ignore
 partial(get_custom_quant_ios_dtype, cache_shape),
 )

 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@@ -818,7 +826,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -840,11 +847,55 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -866,7 +917,6 @@ 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

1 change: 0 additions & 1 deletion
examples/models/llava/export_llava.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,6 @@ 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

29 changes: 23 additions & 6 deletions
extension/llm/export/builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,7 @@
 DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
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
from torch.export import export_for_training, ExportedProgram
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand DownExpand Up@@ -89,8 +89,8 @@ def __init__(
 dynamic_shapes: Optional[Any] = None,
 ):
 self.model = model
# graph module returned from export()
 self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
 self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
 self.modelname = modelname
 self.max_seq_len = max_seq_len
 self.dtype = dtype
Expand DownExpand 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 DownExpand Up@@ -330,7 +330,10 @@ 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, composed_quantizer)
 m = prepare_pt2e(
 self.pre_autograd_graph_module, # pyre-ignore[6]
 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 DownExpand Up@@ -430,6 +433,20 @@ 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,
 constant_methods=self.metadata,
 )
 return self

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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Use to_edge_lower_and_transform for XNNPack (#8624)#8717

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merged 1 commit into

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Feb 27, 2025
 Merged


Use to_edge_lower_and_transform for XNNPack (#8624)
#8717
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132 changes: 91 additions & 41 deletions
examples/models/llama/export_llama_lib.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -676,47 +676,62 @@ def _validate_args(args):
 )


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

 pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

 builder_exported.run_canonical_optimizations()

 if args.export_only:
 exit()

 builder_exported_to_edge = builder_exported.pt2e_quantize(
 quantizers
 ).export_to_edge()

 modelname = builder_exported_to_edge.modelname

 # to_backend
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
 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)
 )
 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}"
 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}"

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

 # 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."
 )

 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)

 return builder.to_executorch(passes=additional_passes)


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()

 # to_backend
 partitioners = []
 if args.vulkan:
 partitioners.append(
 get_vulkan_partitioner(
Expand All@@ -731,7 +746,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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 DownExpand Up@@ -760,13 +774,11 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -792,19 +804,15 @@ def _export_llama(args) -> LLMEdgeManager: # 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), # pyre-ignore
 partial(get_custom_quant_ios_dtype, cache_shape),
 )

 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@@ -818,7 +826,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -840,11 +847,55 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -866,7 +917,6 @@ 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

1 change: 0 additions & 1 deletion
examples/models/llava/export_llava.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,6 @@ 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

29 changes: 23 additions & 6 deletions
extension/llm/export/builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,7 @@
 DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
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
from torch.export import export_for_training, ExportedProgram
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand DownExpand Up@@ -89,8 +89,8 @@ def __init__(
 dynamic_shapes: Optional[Any] = None,
 ):
 self.model = model
# graph module returned from export()
 self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
 self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
 self.modelname = modelname
 self.max_seq_len = max_seq_len
 self.dtype = dtype
Expand DownExpand 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 DownExpand Up@@ -330,7 +330,10 @@ 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, composed_quantizer)
 m = prepare_pt2e(
 self.pre_autograd_graph_module, # pyre-ignore[6]
 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 DownExpand Up@@ -430,6 +433,20 @@ 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,
 constant_methods=self.metadata,
 )
 return self

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

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Use to_edge_lower_and_transform for XNNPack (#8624)#8717

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 Merged

facebook-github-bot
merged 1 commit into

from

Feb 27, 2025
 Merged


Use to_edge_lower_and_transform for XNNPack (#8624)
#8717
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132 changes: 91 additions & 41 deletions
examples/models/llama/export_llama_lib.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -676,47 +676,62 @@ def _validate_args(args):
 )


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

 pt2e_quant_params, quantizers, quant_dtype = get_quantizer_and_quant_params(args)

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

 builder_exported.run_canonical_optimizations()

 if args.export_only:
 exit()

 builder_exported_to_edge = builder_exported.pt2e_quantize(
 quantizers
 ).export_to_edge()

 modelname = builder_exported_to_edge.modelname

 # to_backend
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
 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)
 )
 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}"
 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}"

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

 # 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."
 )

 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)

 return builder.to_executorch(passes=additional_passes)


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()

 # to_backend
 partitioners = []
 if args.vulkan:
 partitioners.append(
 get_vulkan_partitioner(
Expand All@@ -731,7 +746,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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 DownExpand Up@@ -760,13 +774,11 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -792,19 +804,15 @@ def _export_llama(args) -> LLMEdgeManager: # 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), # pyre-ignore
 partial(get_custom_quant_ios_dtype, cache_shape),
 )

 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@@ -818,7 +826,6 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -840,11 +847,55 @@ def _export_llama(args) -> LLMEdgeManager: # 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@@ -866,7 +917,6 @@ 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

1 change: 0 additions & 1 deletion
examples/models/llava/export_llava.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -67,7 +67,6 @@ 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

29 changes: 23 additions & 6 deletions
extension/llm/export/builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,7 @@
 DuplicateDynamicQuantChainPass,
)
from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
from executorch.exir import EdgeProgramManager
from executorch.exir import EdgeProgramManager, to_edge_transform_and_lower
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
from torch.export import export_for_training, ExportedProgram
from torch.nn.attention import SDPBackend

FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
Expand DownExpand Up@@ -89,8 +89,8 @@ def __init__(
 dynamic_shapes: Optional[Any] = None,
 ):
 self.model = model
# graph module returned from export()
 self.pre_autograd_graph_module: Optional[torch.fx.GraphModule] = None
self.pre_autograd_exported_program: Optional[ExportedProgram] = None
 self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
 self.modelname = modelname
 self.max_seq_len = max_seq_len
 self.dtype = dtype
Expand DownExpand 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 DownExpand Up@@ -330,7 +330,10 @@ 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, composed_quantizer)
 m = prepare_pt2e(
 self.pre_autograd_graph_module, # pyre-ignore[6]
 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 DownExpand Up@@ -430,6 +433,20 @@ 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,
 constant_methods=self.metadata,
 )
 return self

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