def step_target_fps_parallelization(model: ModelWrapper, cfg: DataflowBuildConfig):
"""If target_fps was specified, use the SetFolding transformation to determine
parallelization attributes. The auto-generated config will be saved under
auto_folding_config.json under the outputs, which can serve as a basis for
customizing the folding factors further."""
target_cycles_per_frame = cfg._resolve_cycles_per_frame()
if target_cycles_per_frame is not None:
model = model.transform(
SetFolding(
target_cycles_per_frame,
mvau_wwidth_max=cfg.mvau_wwidth_max,
two_pass_relaxation=cfg.folding_two_pass_relaxation,
)
)
# extract the suggested configuration and save it as json
hw_attrs = [
"PE",
"SIMD",
"ram_style",
"resType",
"mem_mode",
"runtime_writeable_weights",
]
extract_model_config_to_json(
model, cfg.output_dir + "/auto_folding_config.json", hw_attrs
)
return model
def step_target_fps_parallelization(model: ModelWrapper,
cfg: DataflowBuildConfig):
"""If target_fps was specified, use the SetFolding transformation to determine
parallelization attributes."""
target_cycles_per_frame = cfg._resolve_cycles_per_frame()
if target_cycles_per_frame is not None:
model = model.transform(
SetFolding(target_cycles_per_frame,
mvau_wwidth_max=cfg.mvau_wwidth_max))
return model
