release_platform_name = vitis_platform
else:
vitis_platform = None
# for Zynq, use the board name as the release name
# e.g. ZCU104
release_platform_name = platform_name
platform_dir = "release/%s" % release_platform_name
os.makedirs(platform_dir, exist_ok=True)
for model_name in models:
# set up the build configuration for this model
cfg = build_cfg.DataflowBuildConfig(
output_dir="output_%s_%s" % (model_name, release_platform_name),
folding_config_file="folding_config/%s_folding_config.json" %
model_name,
synth_clk_period_ns=10.0,
board=platform_name,
shell_flow_type=shell_flow_type,
vitis_platform=vitis_platform,
generate_outputs=[build_cfg.DataflowOutputType.BITFILE],
save_intermediate_models=True,
)
model_file = "models/%s.onnx" % model_name
# launch FINN compiler to build
build.build_dataflow_cfg(model_file, cfg)
# copy bitfiles into release dir if found
bitfile_gen_dir = cfg.output_dir + "/bitfile"
files_to_check_and_copy = [
"finn-accel.bit",
"finn-accel.hwh",
"finn-accel.xclbin",
]
"step_hls_ipgen",
"step_set_fifo_depths",
"step_create_stitched_ip",
"step_make_pynq_driver",
"step_synthesize_bitfile",
"step_deployment_package",
]
cfg = build_cfg.DataflowBuildConfig(
steps=mobilenet_build_steps,
output_dir="output_%s_%s" % (model_name, board),
folding_config_file="folding_config/%s_folding_config.json" % board,
synth_clk_period_ns=synth_clk_period_ns,
board=board,
shell_flow_type=build_cfg.ShellFlowType.VITIS_ALVEO,
# folding config comes with FIFO depths already
auto_fifo_depths=False,
vitis_platform=vitis_platform,
# enable extra performance optimizations (physopt)
vitis_opt_strategy=build_cfg.VitisOptStrategyCfg.PERFORMANCE_BEST,
generate_outputs=[
build_cfg.DataflowOutputType.PYNQ_DRIVER,
build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
build_cfg.DataflowOutputType.BITFILE,
build_cfg.DataflowOutputType.DEPLOYMENT_PACKAGE,
],
)
model_file = "models/%s_pre_post_tidy.onnx" % model_name
build.build_dataflow_cfg(model_file, cfg)
print("DoublePackedConv detected")
except:
warn(
" FINN Experimental not available. Using non-packed folded down convolution. This is 16 times slower per MHz "
)
folding_config_file = "folding_config/U250_folding_config_no_doublepack_pe_folded_16.json"
cfg = build_cfg.DataflowBuildConfig(
steps=resnet50_build_steps,
output_dir="output_%s_%s" % (model_name, board),
synth_clk_period_ns=synth_clk_period_ns,
board=board,
shell_flow_type=build_cfg.ShellFlowType.VITIS_ALVEO,
vitis_platform=vitis_platform,
# throughput parameters (auto-folding)
mvau_wwidth_max=24,
target_fps=target_fps,
folding_config_file=folding_config_file,
# enable extra performance optimizations (physopt)
vitis_opt_strategy=build_cfg.VitisOptStrategyCfg.PERFORMANCE_BEST,
generate_outputs=[
build_cfg.DataflowOutputType.PYNQ_DRIVER,
build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
build_cfg.DataflowOutputType.BITFILE,
build_cfg.DataflowOutputType.DEPLOYMENT_PACKAGE,
],
)
model_file = "models/%s_exported.onnx" % model_name
build.build_dataflow_cfg(model_file, cfg)
model = ModelWrapper(model_file)
model.model.opset_import[0].version = 11
model_file = model_file.replace(".onnx", "_opset-11.onnx")
model.save(model_file)
platform_name = "Pynq-Z1"
output_dir = f"{time.time():.2f}_output_{model_name.replace('/','_')}_{platform_name}"
# Configure build
cfg = build_cfg.DataflowBuildConfig(
# steps=estimate_steps, generate_outputs=estimate_outputs,
verify_steps=verification_steps,
steps=build_steps,
generate_outputs=build_outputs,
output_dir=output_dir,
target_fps=200000,
synth_clk_period_ns=10.0,
board=platform_name,
shell_flow_type=build_cfg.ShellFlowType.VIVADO_ZYNQ,
save_intermediate_models=True,
stitched_ip_gen_dcp=True,
verify_save_full_context=True,
)
# Build the model
build.build_dataflow_cfg(model_file, cfg)
# Save Build config
config_json_path = f"{output_dir}/DataflowBuildConfig.json"
with open(config_json_path, "w") as f:
f.write(cfg.to_json())
print(f"Saved DataflowBuildConfig to: {config_json_path}")
release_platform_name = platform_name
platform_dir = "release/%s" % release_platform_name
os.makedirs(platform_dir, exist_ok=True)
cfg = build_cfg.DataflowBuildConfig(
steps=select_build_steps(platform_name),
output_dir="output_%s_%s" % (model_name, release_platform_name),
synth_clk_period_ns=select_clk_period(platform_name),
board=platform_name,
shell_flow_type=shell_flow_type,
vitis_platform=vitis_platform,
folding_config_file="folding_config/%s_folding_config.json" %
platform_name,
auto_fifo_depths=True,
standalone_thresholds=False,
# enable extra performance optimizations (physopt)
vitis_opt_strategy=build_cfg.VitisOptStrategyCfg.PERFORMANCE_BEST,
generate_outputs=[
build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
build_cfg.DataflowOutputType.STITCHED_IP,
# build_cfg.DataflowOutputType.OOC_SYNTH,
# build_cfg.DataflowOutputType.RTLSIM_PERFORMANCE,
build_cfg.DataflowOutputType.BITFILE,
build_cfg.DataflowOutputType.DEPLOYMENT_PACKAGE,
build_cfg.DataflowOutputType.PYNQ_DRIVER,
],
)
model_file = "models/%s.onnx" % model_name
build.build_dataflow_cfg(model_file, cfg)
# copy bitfiles and runtime weights into release dir if found
# e.g. ZCU104
release_platform_name = platform_name
platform_dir = "release/%s" % release_platform_name
os.makedirs(platform_dir, exist_ok=True)
for model_name in models:
# set up the build configuration for this model
cfg = build_cfg.DataflowBuildConfig(
output_dir="output_%s_%s" % (model_name, release_platform_name),
synth_clk_period_ns=5.0,
target_fps=10000000,
board=platform_name,
shell_flow_type=shell_flow_type,
vitis_platform=vitis_platform,
enable_build_pdb_debug=True,
generate_outputs=[
build_cfg.DataflowOutputType.ESTIMATE_REPORTS,
build_cfg.DataflowOutputType.BITFILE,
# build_cfg.DataflowOutputType.STITCHED_IP,
# build_cfg.DataflowOutputType.RTLSIM_PERFORMANCE,
# build_cfg.DataflowOutputType.OOC_SYNTH,
build_cfg.DataflowOutputType.PYNQ_DRIVER,
build_cfg.DataflowOutputType.DEPLOYMENT_PACKAGE,
],
save_intermediate_models=True,
)
# cfg = build_cfg.DataflowBuildConfig(
# output_dir="output_%s_%s" % (model_name, release_platform_name),
# folding_config_file=None,
# synth_clk_period_ns=1.0,
# hls_clk_period_ns=1.0,
# target_fps=10000000,