def test_pipe_runtime_error_check():
# This function is used to trigger runtime error by applying wrong logic.
if pipeline_executor_build.pipeline_executor_build_enabled():
# Get three pipeline modules here.
(mod1, mod2, mod3), dshape = get_split_mod()
# The input or output name is illegal and expects a runtime error.
pipe_error = pipeline_executor_build.PipelineConfig()
with pytest.raises(RuntimeError):
pipe_error[mod1]["output"][9]
with pytest.raises(RuntimeError):
pipe_error[mod1]["input"]["data_9"]
# The module connection will cause a cycle in DAG and expects runtime error.
with pytest.raises(RuntimeError):
pipe_error[mod1]["output"][0].connect(pipe_error[mod2]["input"]["data_0"])
pipe_error[mod2]["output"][0].connect(pipe_error[mod1]["input"]["data_0"])
# The module connection is illegal and expects runtime error.
with pytest.raises(RuntimeError):
pipe_error[mod1]["output"][0].connect(pipe_error[mod1]["input"]["data_0"])
with pytest.raises(RuntimeError):
pipe_error[mod1]["input"]["data_0"].connect(pipe_error[mod1]["input"]["data_0"])
with pytest.raises(RuntimeError):
pipe_error[mod1]["input"]["data_0"].connect(pipe_error[mod2]["input"]["data_0"])
with pytest.raises(RuntimeError):
pipe_error[mod1]["output"][0].connect(pipe_error["input"]["data_0"])
with pytest.raises(RuntimeError):
pipe_error["input"]["data_0"].connect(pipe_error[mod1]["output"][0])
with pytest.raises(RuntimeError):
pipe_error["output"]["0"].connect(pipe_error[mod1]["output"][0])
# Create pipeline executor to check the executor runtime errors.
pipe_config = pipeline_executor_build.PipelineConfig()
pipe_config[mod1].target = "llvm"
pipe_config[mod1].dev = tvm.cpu(0)
pipe_config["param_group"]["param_0"].connect(pipe_config[mod1]["param"])
pipe_config[mod1]["output"][0].connect(pipe_config["output"]["0"])
# Build and create a pipeline module.
with tvm.transform.PassContext(opt_level=3):
pipeline_mod_factory = pipeline_executor_build.build(pipe_config)
pipeline_module = pipeline_executor.PipelineModule(pipeline_mod_factory)
customized_parameters, _ = recreate_parameters(mod1)
# Checking the pipeline executor runtime errors.
with pytest.raises(RuntimeError):
pipeline_module.set_params("param_0", None)
with pytest.raises(RuntimeError):
pipeline_module.set_params("param_1", customized_parameters)
def test_pipeline():
if pipeline_executor.pipeline_executor_enabled():
target_list = tvm.testing.enabled_targets()
for target in target_list:
affinity = os.sched_getaffinity(0)
# Get the three pipeline modules here.
(mod1, mod2, mod3), dshape = get_mannual_mod()
# Prepare batch data for pipeline computation.
datas = []
for i in range(5):
datas.append(np.full(dshape, 3 + i).astype("float32"))
pipe_config = pipeline_executor.PipelineConfig()
customized_parameters, customized_parameters_mod = recreate_parameters(
mod1)
assert customized_parameters_mod == mod1
# The global parameters group named "param_0" will be connected to "mod1" as parameters.
pipe_config["param_group"]["param_0"].connect(
pipe_config[mod1]["param"])
# The pipeline input named "data_0" will be connected to a input named "data_0"
# of mod1.
pipe_config["input"]["data_a"].connect(
pipe_config[mod1]["input"]["data_0"])
# The pipeline Input named "data_1" will be connected to a input named "data_1"
# of mod2.
pipe_config["input"]["data_b"].connect(
pipe_config[mod2]["input"]["data_1"])
# The mod1 output[0] will be connected to a input named "data_0" of mod2.
pipe_config[mod1]["output"][0].connect(
pipe_config[mod2]["input"]["data_0"])
# The mod1 output[1] will be connected to a input named "data_0" of mod3.
pipe_config[mod1]["output"][1].connect(
pipe_config[mod3]["input"]["data_0"])
# The mod2 output[2] will be connected to a input named "data_1" of mod3.
pipe_config[mod2]["output"][0].connect(
pipe_config[mod3]["input"]["data_1"])
# The mod1 output[2] will be connected to pipeline output[0].
pipe_config[mod1]["output"][2].connect(pipe_config["output"]["0"])
# The mod3 output[0] will be connected to pipeline output[1].
pipe_config[mod3]["output"][0].connect(pipe_config["output"]["1"])
# Print configueration (print(pipe_config)), the result looks like following.
#
# Inputs
# |data_a: mod1:data_0
# |data_b: mod2:data_1
#
# output
# |output(1) : mod1.output(2)
# |output(2) : mod3.output(0)
#
# connections
# |mod1.output(0)-> mod2.data_0
# |mod1.output(1)-> mod3.data_0
# |mod2.output(0)-> mod3.data_1
# Set other parameters.
pipe_config[mod1].target = target[0]
pipe_config[mod1].dev = target[1]
pipe_config[mod1].cpu_affinity = "0"
pipe_config[mod2].target = "llvm"
pipe_config[mod2].dev = tvm.cpu(0)
pipe_config[mod2].cpu_affinity = "0"
pipe_config[mod3].target = "llvm"
pipe_config[mod3].dev = tvm.cpu(0)
pipe_config[mod3].cpu_affinity = "0"
# Checking the configuration of modules dependency.
mconfig = pipe_config.get_config()
assert mconfig["module_connection"] == get_manual_conf(
[mod1, mod2, mod3], target)
# Build and create a pipeline module.
with tvm.transform.PassContext(opt_level=3):
pipeline_mod_factory = pipeline_executor.build(pipe_config)
# Export the parameter configuration to a file.
directory_path = tvm.contrib.utils.tempdir().temp_dir
# If the directory does not exist, create it.
if not os.path.exists(directory_path):
os.makedirs(directory_path)
config_file_name = pipeline_mod_factory.export_library(
directory_path)
# Use the output of build to create and initialize PipelineModule.
pipeline_module = pipeline_executor.PipelineModule(
pipeline_mod_factory)
assert pipeline_module
# Use the import function to create and initialize PipelineModule.
pipeline_module_test = pipeline_executor.PipelineModule.load_library(
config_file_name)
assert pipeline_module_test.num_outputs == 2
input_map = pipeline_module_test.get_input_pipeline_map("data_b")
assert input_map[0] == "1" and input_map[1] == "data_1"
input_map = pipeline_module_test.get_input_pipeline_map("data_a")
assert input_map[0] == "0" and input_map[1] == "data_0"
module_index = pipeline_module_test.get_params_group_pipeline_map(
"param_0")
assert module_index == 0
# Using the parameters group name to set parameters.
pipeline_module_test.set_params("param_0", customized_parameters)
normal_outputs = []
for round in range(0, len(datas)):
data = datas[round]
# Getting the result without setting customized parameters.
wrong_output = run_modules(
mconfig["module_connection"],
tvm.cpu(),
"llvm",
"data_0",
data,
mod2,
"data_1",
data,
)
# Getting the result with setting customized parameters.
normal_output = run_modules(
mconfig["module_connection"],
tvm.cpu(),
"llvm",
"data_0",
data,
mod2,
"data_1",
data,
customized_parameters_mod,
customized_parameters,
)
# Appending the normal output into the list in order to do future correctness
# checking.
normal_outputs.append(normal_output)
# Setting the input data into the pipeline executor.
pipeline_module_test.set_input("data_a", data)
pipeline_module_test.set_input("data_b", data)
input_map = pipeline_module_test.get_input_pipeline_map(
"data_a")
# Checking whether the input setting of the first runtime is successful.
# The input of the rest of runtime will go into a queue and we can not check
# these input data here.
if input_map[0] == "0":
input_data = pipeline_module_test.get_input("data_a")
tvm.testing.assert_allclose(data, input_data.numpy())
# Running the pipeline executor in the pipeline mode.
pipeline_module_test.run()
for k in range(0, len(datas)):
statistic_time = 0
outputs = pipeline_module_test.get_output()
while len(outputs) == 0:
outputs = pipeline_module_test.get_output()
statistic_time = statistic_time + 1
# Setting the timeout to 10 seconds.
assert statistic_time < 5
time.sleep(1)
for i in range(len(outputs)):
tvm.testing.assert_allclose(normal_outputs[k][i],
outputs[i].numpy())
assert not (normal_output[i] == wrong_output[i]).all()
assert pipeline_module_test.num_executing_pipeline == round + 1
# Reset the cpu affinity after a test.
reset_cpu_affinity(affinity)
def test_pipeline():
if pipeline_executor.pipeline_executor_enabled():
target_list = tvm.testing.enabled_targets()
for target in target_list:
# Get the three pipeline modules here.
(mod1, mod2, mod3), dshape = get_mannual_mod()
# Prepare batch data for pipeline computation.
datas = []
for i in range(5):
datas.append(np.full(dshape, 3 + i).astype("float32"))
pipe_config = pipeline_executor.PipelineConfig()
customized_parameters = recreate_parameters(mod2)
# The global parameters group named "param_0" will be connected to "mod1" as parameters.
pipe_config["param_group"]["param_0"].connect(
pipe_config[mod2]["param"])
# The pipeline input named "data_0" will be connected to a input named "data_0"
# of mod1.
pipe_config["input"]["data_a"].connect(
pipe_config[mod1]["input"]["data_0"])
# The pipeline Input named "data_1" will be connected to a input named "data_1"
# of mod2.
pipe_config["input"]["data_b"].connect(
pipe_config[mod2]["input"]["data_1"])
# The mod1 output[0] will be connected to a input named "data_0" of mod2.
pipe_config[mod1]["output"][0].connect(
pipe_config[mod2]["input"]["data_0"])
# The mod1 output[1] will be connected to a input named "data_0" of mod3.
pipe_config[mod1]["output"][1].connect(
pipe_config[mod3]["input"]["data_0"])
# The mod2 output[2] will be connected to a input named "data_1" of mod3.
pipe_config[mod2]["output"][0].connect(
pipe_config[mod3]["input"]["data_1"])
# The mod1 output[2] will be connected to pipeline output[0].
pipe_config[mod1]["output"][2].connect(pipe_config["output"]["0"])
# The mod3 output[0] will be connected to pipeline output[1].
pipe_config[mod3]["output"][0].connect(pipe_config["output"]["1"])
print(pipe_config)
# Print configueration (print(pipe_config)), the result looks like following.
#
# Inputs
# |data_a: mod1:data_0
# |data_b: mod2:data_1
#
# output
# |output(1) : mod1.output(2)
# |output(2) : mod3.output(0)
#
# connections
# |mod1.output(0)-> mod2.data_0
# |mod1.output(1)-> mod3.data_0
# |mod2.output(0)-> mod3.data_1
# Set other parameters.
pipe_config[mod1].target = target[0]
pipe_config[mod1].dev = target[1]
pipe_config[mod2].target = "llvm"
pipe_config[mod2].dev = tvm.cpu(0)
pipe_config[mod3].target = "llvm"
pipe_config[mod3].dev = tvm.cpu(0)
# Here is to check the correctness of the configuration generated by API.
mconfig = pipe_config.get_config()
assert mconfig["module_connection"] == get_manual_conf(
[mod1, mod2, mod3], target)
# Build and create a pipeline module.
with tvm.transform.PassContext(opt_level=3):
pipeline_mod_factory = pipeline_executor.build(pipe_config)
# Export the parameter configuration to a file.
directory_path = tvm.contrib.utils.tempdir().temp_dir
# If the directory does not exist, create it.
if not os.path.exists(directory_path):
os.makedirs(directory_path)
config_file_name = pipeline_mod_factory.export_library(
directory_path)
# Use the output of build to create and initialize PipelineModule.
pipeline_module = pipeline_executor.PipelineModule(
pipeline_mod_factory)
assert pipeline_module
# Use the import function to create and initialize PipelineModule.
pipeline_module_test = pipeline_executor.PipelineModule.load_library(
config_file_name)
assert pipeline_module_test.num_outputs == 2
input_map = pipeline_module_test.get_input_pipeline_map("data_b")
assert input_map[0] == "1" and input_map[1] == "data_1"
input_map = pipeline_module_test.get_input_pipeline_map("data_a")
assert input_map[0] == "0" and input_map[1] == "data_0"
module_index = pipeline_module_test.get_params_group_pipeline_map(
"param_0")
assert module_index == 1
# Use the parameters group name to set parameters.
pipeline_module_test.set_params("param_0", customized_parameters)