def torchscript_export(context, model, output_path, quantize=False):
"""Convert a pytext model snapshot to a torchscript model."""
config = context.obj.load_config()
model = model or config.save_snapshot_path
output_path = output_path or f"{config.save_snapshot_path}.torchscript"
print(f"Exporting {model} to torchscript file: {output_path}")
export_saved_model_to_torchscript(model, output_path, quantize)
def torchscript_export(context, export_json, model, output_path, quantize):
"""Convert a pytext model snapshot to a torchscript model."""
export_config = ExportConfig()
# only populate from export_json if no export option is configured from the command line.
if export_json:
export_json_config = _load_and_validate_export_json_config(export_json)
read_chunk_size = export_json_config.pop("read_chunk_size", None)
if read_chunk_size is not None:
print("Warning: Ignoring read_chunk_size.")
if export_json_config.get("read_chunk_size", None) is not None:
print(
"Error: Do not know what to do with read_chunk_size. Ignoring."
)
if "export_list" not in export_json_config.keys():
export_section_config_list = [export_json_config["export"]]
else:
export_section_config_list = export_json_config["export_list"]
for export_section_config in export_section_config_list:
if not quantize and not output_path:
export_config.export_caffe2_path = export_section_config.get(
"export_caffe2_path", None)
export_config.export_onnx_path = export_section_config.get(
"export_onnx_path", "/tmp/model.onnx")
export_config.torchscript_quantize = export_section_config.get(
"torchscript_quantize", False)
else:
print(
"the export-json config is ignored because export options are found the command line"
)
export_config.torchscript_quantize = quantize
export_config.export_torchscript_path = export_section_config.get(
"export_torchscript_path", None)
# if config has export_torchscript_path, use export_torchscript_path from config, otherwise keep the default from CLI
if export_config.export_torchscript_path is not None:
output_path = export_config.export_torchscript_path
export_config.export_lite_path = export_section_config.get(
"export_lite_path", None)
export_config.inference_interface = export_section_config.get(
"inference_interface", None)
export_config.accelerate = export_section_config.get(
"accelerate", [])
export_config.seq_padding_control = export_section_config.get(
"seq_padding_control", None)
export_config.batch_padding_control = export_section_config.get(
"batch_padding_control", None)
if not model or not output_path:
config = context.obj.load_config()
model = model or config.save_snapshot_path
output_path = output_path or f"{config.save_snapshot_path}.torchscript"
print(f"Exporting {model} to torchscript file: {output_path}")
export_saved_model_to_torchscript(model, output_path,
export_config)
def torchscript_export(context, export_json, model, output_path, quantize):
"""Convert a pytext model snapshot to a torchscript model."""
kwargs = {}
# only populate from export_json if no export option is configured from the command line.
if export_json:
if not quantize and not output_path:
export_json_config = _load_and_validate_export_json_config(
export_json)
kwargs = export_json_config["export"]
else:
print(
"the export-json config is ignored because export options are found the command line"
)
kwargs = {"quantize": quantize}
if "export_torchscript_path" in export_json_config["export"]:
output_path = export_json_config["export"][
"export_torchscript_path"]
if not model or not output_path:
config = context.obj.load_config()
model = model or config.save_snapshot_path
output_path = output_path or f"{config.save_snapshot_path}.torchscript"
print(f"Exporting {model} to torchscript file: {output_path}")
export_saved_model_to_torchscript(model, output_path, **kwargs)
def torchscript_export(context, export_json, model, output_path, quantize,
target):
"""Convert a pytext model snapshot to a torchscript model."""
export_cfg = ExportConfig()
# only populate from export_json if no export option is configured from the command line.
if export_json:
export_json_config = _load_and_validate_export_json_config(export_json)
read_chunk_size = export_json_config.pop("read_chunk_size", None)
if read_chunk_size is not None:
print("Warning: Ignoring read_chunk_size.")
if export_json_config.get("read_chunk_size", None) is not None:
print(
"Error: Do not know what to do with read_chunk_size. Ignoring."
)
if "export" in export_json_config.keys():
export_cfgs = [export_json_config["export"]]
else:
export_cfgs = export_json_config["export_list"]
if target:
print(
"A single export was specified in the command line. Filtering out all other export options"
)
export_cfgs = [
cfg for cfg in export_cfgs if cfg["target"] == target
]
if export_cfgs == []:
print(
"No ExportConfig matches the target name specified in the command line."
)
for partial_export_cfg in export_cfgs:
if not quantize and not output_path:
export_cfg = config_from_json(ExportConfig, partial_export_cfg)
else:
print(
"the export-json config is ignored because export options are found the command line"
)
export_cfg = config_from_json(
ExportConfig,
partial_export_cfg,
("export_caffe2_path", "export_onnx_path"),
)
export_cfg.torchscript_quantize = quantize
# if config has export_torchscript_path, use export_torchscript_path from config, otherwise keep the default from CLI
if export_cfg.export_torchscript_path is not None:
output_path = export_cfg.export_torchscript_path
if not model or not output_path:
config = context.obj.load_config()
model = model or config.save_snapshot_path
output_path = output_path or f"{config.save_snapshot_path}.torchscript"
print(f"Exporting {model} to torchscript file: {output_path}")
print(export_cfg)
export_saved_model_to_torchscript(model, output_path, export_cfg)
