def test_fused_operational(caplog, mnist_fused_8bit_state):
caplog.set_level(logging.INFO)
G = load_state(mnist_fused_8bit_state)
opts = {
'default_input_location': 'ARG_LOC_L2',
'default_output_location': 'ARG_LOC_L2',
'default_global_location': 'ARG_LOC_L3_HFLASH',
'default_local_location': '0',
}
code_gen = CodeGenerator(G, DefaultNamingConvension(G), opts)
default_template(G, code_generator=code_gen)
def test_fake_values_concat(concat_test_graph):
G = create_graph(concat_test_graph, opts={"load_tensors": True})
G.add_dimensions()
G.adjust_order()
matcher = get_std_match_group()
matcher.match(G)
G.add_dimensions()
G.constant_store.fake = True
stats_collector = ActivationStatsCollector()
stats_collector.collect_stats(
G, [np.random.rand(*node.dims.shape) for node in G.inputs()])
astats = stats_collector.reduce_stats()
stats_collector = FilterStatsCollector()
fstats = stats_collector.collect_stats(G)
quantizer = SimpleQuantizer(astats, fstats, force_width=8)
qrecs = quantizer.quantize(G)
G.quantization = qrecs
with tempfile.TemporaryDirectory() as tempdir:
opts = {
'default_input_location': 'ARG_LOC_L2',
'default_output_location': 'ARG_LOC_L2',
'default_global_location': 'ARG_LOC_L3_HFLASH',
'default_local_location': '0',
'at_ver': 3,
'tensor_directory': tempdir
}
code_gen = CodeGenerator(G, DefaultNamingConvension(G), opts)
print(default_template(G, code_generator=code_gen))
code_gen.write_constants()
def test_tensor_dump(mnist_fused_8bit_state):
with tempfile.TemporaryDirectory() as tempdir:
G = load_state(mnist_fused_8bit_state)
opts = {
'default_input_location': 'ARG_LOC_L2',
'default_output_location': 'ARG_LOC_L2',
'default_global_location': 'ARG_LOC_L3_HFLASH',
'default_local_location': '0',
'tensor_directory': tempdir
}
code_gen = CodeGenerator(G, DefaultNamingConvension(G), opts)
default_template(G, code_generator=code_gen)
code_gen.write_constants()
files_list = [
f for f in os.listdir(tempdir)
if os.path.isfile(os.path.join(tempdir, f))
]
assert set(files_list) == set([
'Step2Weights.tensor', 'Step1Weights.tensor', 'Step1Biases.tensor',
'Step3Weights.tensor', 'Step2Biases.tensor', 'Step3Biases.tensor'
])
def test_gen_vergesense(caplog):
caplog.set_level(logging.DEBUG)
tfi = TfliteImporter()
G = tfi.create_graph("tests/graph/marco_17_04.tflite", {
'load_tensors': True,
'load_quantization': True
})
G.add_dimensions()
G.adjust_order()
matcher = get_scale8_match_group()
matcher.match(G)
G.add_dimensions()
with tempfile.TemporaryDirectory() as tempdir:
opts = {
'default_input_location': 'ARG_LOC_L2',
'default_output_location': 'ARG_LOC_L2',
'default_global_location': 'ARG_LOC_L3_HFLASH',
'default_local_location': 'AT_MEM_UNDEF',
'tensor_directory': tempdir
}
code_gen = CodeGenerator(G, DefaultNamingConvension(G), opts)
default_template(G, code_generator=code_gen)
code_gen.write_constants()
def test_activatiofusion(actfusion_graph):
G = actfusion_graph
matcher = get_fusion('scale8_match_group')
matcher.match(G)
G.add_dimensions()
astat_col = ActivationStatsCollector()
astats = astat_col.collect_stats(
G, [np.full([10, 10, 2], 1),
np.full([10, 10, 2], 1)])
astats = astat_col.reduce_stats()
quantizer = MultQuantizer(astats,
force_width=8,
quantized_dimension="channel")
G.quantization = quantizer.quantize(G)
with tempfile.TemporaryDirectory() as tempdir:
opts = {
'default_input_location': 'ARG_LOC_L2',
'default_output_location': 'ARG_LOC_L2',
'default_global_location': 'ARG_LOC_L3_HFLASH',
'default_local_location': 'AT_MEM_UNDEF',
'tensor_directory': tempdir
}
code_gen = CodeGenerator(G, DefaultNamingConvension(G), opts)
ATModel_code = default_template(G, code_generator=code_gen)
def gen_project(G,
settings,
project_folder,
script_commands,
overwrite=False,
performance=False,
quantized=False,
test_results=False,
save_inputs=False,
input_file=None,
input_args=None,
gen_atproject=False,
dump_tensors=False,
input_tensors=None,
tolerance=0.0):
settings = deepcopy(settings)
settings['graph_monitor_cycles'] = True
settings['graph_produce_node_names'] = True
settings['graph_produce_operinfos'] = True
code_gen = CodeGenerator(G, DefaultNamingConvension(G), settings)
if not os.path.exists(project_folder):
os.mkdir(project_folder)
qoutputs = None
if test_results:
np.random.seed(12345)
finput_tensors = []
input_tensors = []
for i, node in enumerate(G.input_nodes()):
out_q = G.quantization[NodeId(node)].out_qs[0]
if input_file:
file_per_input = glob_input_files(input_file, G.num_inputs)[0]
finput = import_data(file_per_input[i], **input_args)
else:
min_val = out_q.min if not out_q.is_floating else -1.0
max_val = out_q.max if not out_q.is_floating else 1.0
finput = get_rand(node.out_dims[0].shape,
low_high=(min_val, max_val))
finput_tensors.append(finput)
executer = GraphExecuter(G, qrecs=G.quantization)
qoutput_tensors = executer.execute(finput_tensors.copy(),
qmode=QuantizationMode.all())
qoutputs = []
for params in G.outputs():
outp = qoutput_tensors[params.step_idx][0]
qoutputs.append(outp)
for i, params in enumerate(G.input_nodes()):
inp = qoutput_tensors[params.step_idx][0]
input_tensors.append(inp)
if save_inputs:
nodeq = G.quantization[NodeId(params, None)].out_qs[0]
np.save(os.path.join(project_folder, f"fake_input_{i}.npy"),
nodeq.dequantize(inp))
main = os.path.join(project_folder, f"{code_gen.project_name}")
main_c = main + '.c'
main_h = main + '.h'
common_mk = os.path.join(project_folder, "common.mk")
nntool_script = os.path.join(project_folder, "nntool_script")
if overwrite or not os.path.exists(main_c):
with open(os.path.join(project_folder, f"{code_gen.project_name}.c"),
"w") as output_fp:
output_fp.write(
generate_main_appl_template(G, code_gen, input_tensors,
qoutputs, tolerance))
if overwrite or not os.path.exists(main_h):
with open(os.path.join(project_folder, f"{code_gen.project_name}.h"),
"w") as output_fp:
output_fp.write(generate_main_appl_header(G, code_gen))
if overwrite or not os.path.exists(common_mk):
open_args = parse_last_open(script_commands)
open_args = build_last_open_args(open_args) if open_args else ""
with open(os.path.join(project_folder, "common.mk"), "w") as output_fp:
if gen_atproject:
output_fp.write(
generate_main_appl_make_atproject(G, code_gen, quantized,
'Model.c'))
else:
output_fp.write(
generate_main_appl_make(G,
code_gen,
quantized,
open_args=open_args))
if overwrite or not os.path.exists(nntool_script):
with open(nntool_script, 'w') as fp:
# NOTE - gen_template_project is excluded so that tests work. Normally it will not be in the
# history.
fp.writelines(process_script(script_commands))
# always add performance since the main template uses it
for setting in [
'set graph_produce_node_names true',
'set graph_produce_operinfos true',
'set graph_monitor_cycles true'
]:
fp.write(f'{setting}\n')
if dump_tensors:
fp.write('set graph_dump_tensor 7\n')
if script_commands[-1] != "save_state":
fp.write('save_state\n')
if gen_atproject:
code_gen = CodeGenerator(G, DefaultNamingConvension(G), settings)
with open(os.path.join(project_folder, 'Model.c'), "w") as output_fp:
output_fp.write(default_template(G, code_generator=code_gen))
if G.has_expressions:
with open(os.path.join(project_folder, "Expression_Kernels.c"),
"w") as output_fp:
output_fp.write(
basic_kernel_source_template(G, code_generator=code_gen))
with open(os.path.join(project_folder, "Expression_Kernels.h"),
"w") as output_fp:
output_fp.write(
basic_kernel_header_template(G, code_generator=code_gen))
code_gen.write_constants(tensor_directory=project_folder)
ignore_function = None if overwrite else skip_existing_files(
project_folder)
shutil.copytree(os.path.join(os.environ.get("NNTOOL_PATH"),
'generation/project_template'),
project_folder,
dirs_exist_ok=True,
ignore=ignore_function)
if not gen_atproject:
try:
shutil.copy(
G.graph_identity.filename,
os.path.join(project_folder,
os.path.split(G.graph_identity.filename)[1]))
except shutil.SameFileError:
pass
def test_fused_operational(caplog, mnist_fused_8bit_state):
caplog.set_level(logging.INFO)
G = load_state(mnist_fused_8bit_state)
default_template(G)
