def test_invalid_create_shm(self):
# Raises error since tried to create invalid cuda shared memory region
try:
shm_op0_handle = cshm.create_shared_memory_region("dummy_data", -1, 0)
cshm.destroy_shared_memory_region(shm_op0_handle)
except Exception as ex:
self.assertTrue(str(ex) == "unable to create cuda shared memory handle")
def cleanup_shm_regions(self, shm_handles):
# Make sure unregister is before shared memory destruction
self.triton_client_.unregister_system_shared_memory()
self.triton_client_.unregister_cuda_shared_memory()
for shm_tmp_handle in shm_handles:
if _test_system_shared_memory:
shm.destroy_shared_memory_region(shm_tmp_handle[2])
elif _test_cuda_shared_memory:
cudashm.destroy_shared_memory_region(shm_tmp_handle[2])
def test_unregister_before_register(self):
# Create a valid cuda shared memory region and unregister before register
if _protocol == "http":
triton_client = httpclient.InferenceServerClient(_url, verbose=True)
else:
triton_client = grpcclient.InferenceServerClient(_url, verbose=True)
shm_op0_handle = cshm.create_shared_memory_region("dummy_data", 8, 0)
triton_client.unregister_cuda_shared_memory("dummy_data")
shm_status = triton_client.get_cuda_shared_memory_status()
if _protocol == "http":
self.assertEqual(len(shm_status), 0)
else:
self.assertEqual(len(shm_status.regions), 0)
cshm.destroy_shared_memory_region(shm_op0_handle)
def test_valid_create_set_register(self):
# Create a valid cuda shared memory region, fill data in it and register
if _protocol == "http":
triton_client = httpclient.InferenceServerClient(_url, verbose=True)
else:
triton_client = grpcclient.InferenceServerClient(_url, verbose=True)
shm_op0_handle = cshm.create_shared_memory_region("dummy_data", 8, 0)
cshm.set_shared_memory_region(shm_op0_handle,
[np.array([1, 2], dtype=np.float32)])
triton_client.register_cuda_shared_memory(
"dummy_data", cshm.get_raw_handle(shm_op0_handle), 0, 8)
shm_status = triton_client.get_cuda_shared_memory_status()
if _protocol == "http":
self.assertEqual(len(shm_status), 1)
else:
self.assertEqual(len(shm_status.regions), 1)
cshm.destroy_shared_memory_region(shm_op0_handle)
def test_reregister_after_register(self):
# Create a valid cuda shared memory region and unregister after register
if _protocol == "http":
triton_client = httpclient.InferenceServerClient(_url, verbose=True)
else:
triton_client = grpcclient.InferenceServerClient(_url, verbose=True)
shm_op0_handle = cshm.create_shared_memory_region("dummy_data", 8, 0)
triton_client.register_cuda_shared_memory(
"dummy_data", cshm.get_raw_handle(shm_op0_handle), 0, 8)
try:
triton_client.register_cuda_shared_memory(
"dummy_data", cshm.get_raw_handle(shm_op0_handle), 0, 8)
except Exception as ex:
self.assertIn(
"shared memory region 'dummy_data' already in manager", str(ex))
shm_status = triton_client.get_cuda_shared_memory_status()
if _protocol == "http":
self.assertEqual(len(shm_status), 1)
else:
self.assertEqual(len(shm_status.regions), 1)
cshm.destroy_shared_memory_region(shm_op0_handle)
def unregister_cleanup_shm_regions(shm_regions, shm_handles,
precreated_shm_regions, outputs,
use_system_shared_memory,
use_cuda_shared_memory):
if not (use_system_shared_memory or use_cuda_shared_memory):
return None
triton_client = httpclient.InferenceServerClient("localhost:8000")
if use_cuda_shared_memory:
triton_client.unregister_cuda_shared_memory(shm_regions[0] + '_data')
triton_client.unregister_cuda_shared_memory(shm_regions[1] + '_data')
cudashm.destroy_shared_memory_region(shm_handles[0])
cudashm.destroy_shared_memory_region(shm_handles[1])
else:
triton_client.unregister_system_shared_memory(shm_regions[0] + '_data')
triton_client.unregister_system_shared_memory(shm_regions[1] + '_data')
shm.destroy_shared_memory_region(shm_handles[0])
shm.destroy_shared_memory_region(shm_handles[1])
if precreated_shm_regions is None:
i = 0
if "OUTPUT0" in outputs:
if use_cuda_shared_memory:
triton_client.unregister_cuda_shared_memory(shm_regions[2] +
'_data')
cudashm.destroy_shared_memory_region(shm_handles[2])
else:
triton_client.unregister_system_shared_memory(shm_regions[2] +
'_data')
shm.destroy_shared_memory_region(shm_handles[2])
i += 1
if "OUTPUT1" in outputs:
if use_cuda_shared_memory:
triton_client.unregister_cuda_shared_memory(shm_regions[2 +
i] +
'_data')
cudashm.destroy_shared_memory_region(shm_handles[3])
else:
triton_client.unregister_system_shared_memory(shm_regions[2 +
i] +
'_data')
shm.destroy_shared_memory_region(shm_handles[3])
if output1 is not None:
output1_data = cudashm.get_contents_as_numpy(
shm_op1_handle, utils.triton_to_np_dtype(output1['datatype']),
output1['shape'])
else:
print("OUTPUT1 is missing in the response.")
sys.exit(1)
for i in range(16):
print(
str(input0_data[i]) + " + " + str(input1_data[i]) + " = " +
str(output0_data[0][i]))
print(
str(input0_data[i]) + " - " + str(input1_data[i]) + " = " +
str(output1_data[0][i]))
if (input0_data[i] + input1_data[i]) != output0_data[0][i]:
print("cudashm infer error: incorrect sum")
sys.exit(1)
if (input0_data[i] - input1_data[i]) != output1_data[0][i]:
print("cudashm infer error: incorrect difference")
sys.exit(1)
print(triton_client.get_cuda_shared_memory_status())
triton_client.unregister_cuda_shared_memory()
cudashm.destroy_shared_memory_region(shm_ip0_handle)
cudashm.destroy_shared_memory_region(shm_ip1_handle)
cudashm.destroy_shared_memory_region(shm_op0_handle)
cudashm.destroy_shared_memory_region(shm_op1_handle)
print('PASS: cuda shared memory')
def infer_zero(tester, pf, batch_size, tensor_dtype, input_shapes, output_shapes,
model_version=None, use_http=True, use_grpc=True,
use_http_json_tensors=True, use_streaming=True, shm_region_name_prefix=None,
use_system_shared_memory=False, use_cuda_shared_memory=False,
priority=0, timeout_us=0):
tester.assertTrue(
use_http or use_grpc or use_http_json_tensors or use_streaming)
configs = []
if use_http:
configs.append(("localhost:8000", "http", False, True))
if use_http_json_tensors and (tensor_dtype != np.float16):
configs.append(("localhost:8000", "http", False, False))
if use_grpc:
configs.append(("localhost:8001", "grpc", False, False))
if use_streaming:
configs.append(("localhost:8001", "grpc", True, False))
tester.assertEqual(len(input_shapes), len(output_shapes))
io_cnt = len(input_shapes)
if shm_region_name_prefix is None:
shm_region_name_prefix = ["input", "output"]
input_dict = {}
expected_dict = {}
shm_ip_handles = list()
shm_op_handles = list()
for io_num in range(io_cnt):
if pf == "libtorch" or pf == "libtorch_nobatch":
input_name = "INPUT__{}".format(io_num)
output_name = "OUTPUT__{}".format(io_num)
else:
input_name = "INPUT{}".format(io_num)
output_name = "OUTPUT{}".format(io_num)
input_shape = input_shapes[io_num]
output_shape = output_shapes[io_num]
rtensor_dtype = _range_repr_dtype(tensor_dtype)
if (rtensor_dtype != np.bool):
input_array = np.random.randint(low=np.iinfo(rtensor_dtype).min,
high=np.iinfo(rtensor_dtype).max,
size=input_shape, dtype=rtensor_dtype)
else:
input_array = np.random.choice(a=[False, True], size=input_shape)
if tensor_dtype != np.object:
input_array = input_array.astype(tensor_dtype)
expected_array = np.ndarray.copy(input_array)
else:
expected_array = np.array([unicode(str(x), encoding='utf-8')
for x in input_array.flatten()], dtype=object)
input_array = np.array([str(x) for x in input_array.flatten()],
dtype=object).reshape(input_array.shape)
expected_array = expected_array.reshape(output_shape)
expected_dict[output_name] = expected_array
output_byte_size = expected_array.nbytes
if batch_size == 1:
input_list = [input_array]
else:
input_list = [x for x in input_array]
# Serialization of string tensors in the case of shared memory must be done manually
if tensor_dtype == np.object:
input_list_tmp = serialize_byte_tensor_list(input_list)
else:
input_list_tmp = input_list
input_byte_size = sum([ip.nbytes for ip in input_list_tmp])
# create and register shared memory region for inputs and outputs
shm_io_handles = su.create_set_either_shm_region([shm_region_name_prefix[0]+str(io_num),
shm_region_name_prefix[1]+str(io_num)],
input_list_tmp, input_byte_size, output_byte_size,
use_system_shared_memory, use_cuda_shared_memory)
if len(shm_io_handles) != 0:
shm_ip_handles.append(shm_io_handles[0])
shm_op_handles.append(shm_io_handles[1])
input_dict[input_name] = input_array
if model_version is not None:
model_version = str(model_version)
else:
model_version = ""
# Run inference and check results for each config
for config in configs:
model_name = tu.get_zero_model_name(pf, io_cnt, tensor_dtype)
if config[1] == "http":
triton_client = httpclient.InferenceServerClient(
config[0], verbose=True)
else:
triton_client = grpcclient.InferenceServerClient(
config[0], verbose=True)
inputs = []
output_req = []
for io_num, (input_name, output_name) in enumerate(zip(input_dict.keys(), expected_dict.keys())):
input_data = input_dict[input_name]
input_byte_size = input_data.nbytes
output_byte_size = expected_dict[output_name].nbytes
if config[1] == "http":
inputs.append(httpclient.InferInput(
input_name, input_data.shape, np_to_triton_dtype(tensor_dtype)))
output_req.append(httpclient.InferRequestedOutput(
output_name, binary_data=config[3]))
else:
inputs.append(grpcclient.InferInput(
input_name, input_data.shape, np_to_triton_dtype(tensor_dtype)))
output_req.append(
grpcclient.InferRequestedOutput(output_name))
if not (use_cuda_shared_memory or use_system_shared_memory):
if config[1] == "http":
inputs[-1].set_data_from_numpy(input_data, binary_data=config[3])
else:
inputs[-1].set_data_from_numpy(input_data)
else:
# Register necessary shared memory regions/handles
su.register_add_either_shm_regions(inputs, output_req, shm_region_name_prefix,
(shm_ip_handles, shm_op_handles), io_num, input_byte_size, output_byte_size,
use_system_shared_memory, use_cuda_shared_memory, triton_client)
if config[2]:
user_data = UserData()
triton_client.start_stream(partial(completion_callback, user_data))
try:
results = triton_client.async_stream_infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()),
priority=priority, timeout=timeout_us)
except Exception as e:
triton_client.stop_stream()
raise e
triton_client.stop_stream()
(results, error) = user_data._completed_requests.get()
if error is not None:
raise error
else:
results = triton_client.infer(model_name,
inputs,
model_version=model_version,
outputs=output_req,
request_id=str(_unique_request_id()),
priority=priority, timeout=timeout_us)
last_response = results.get_response()
if config[1] == "http":
response_model_name = last_response["model_name"]
if model_version != "":
response_model_version = last_response["model_version"]
response_outputs = last_response["outputs"]
else:
response_model_name = last_response.model_name
if model_version != "":
response_model_version = last_response.model_version
response_outputs = last_response.outputs
tester.assertEqual(response_model_name, model_name)
if model_version != "":
tester.assertEqual(response_model_version, model_version)
tester.assertEqual(len(response_outputs), io_cnt)
for result in response_outputs:
if config[1] == "http":
result_name = result["name"]
else:
result_name = result.name
tester.assertTrue(result_name in expected_dict)
if use_system_shared_memory or use_cuda_shared_memory:
if pf == "libtorch" or pf == "libtorch_nobatch":
io_num = int(result_name.split("OUTPUT__")[1])
else:
io_num = int(result_name.split("OUTPUT")[1])
shm_handle = shm_op_handles[io_num]
output = results.get_output(result_name)
if config[1] == "http":
output_datatype = output['datatype']
output_shape = output['shape']
else:
output_datatype = output.datatype
output_shape = output.shape
output_dtype = triton_to_np_dtype(output_datatype)
if use_system_shared_memory:
output_data = shm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
elif use_cuda_shared_memory:
output_data = cudashm.get_contents_as_numpy(
shm_handle, output_dtype, output_shape)
else:
output_data = results.as_numpy(result_name)
if (output_data.dtype == np.object) and (config[3] == False):
output_data = output_data.astype(np.bytes_)
expected = expected_dict[result_name]
tester.assertEqual(output_data.shape, expected.shape)
tester.assertTrue(np.array_equal(output_data, expected),
"{}, {}, expected: {}, got {}".format(
model_name, result_name, expected, output_data))
if len(shm_ip_handles) != 0:
for io_num in range(io_cnt):
if use_cuda_shared_memory:
triton_client.unregister_cuda_shared_memory(
shm_region_name_prefix[0]+str(io_num)+'_data')
triton_client.unregister_cuda_shared_memory(
shm_region_name_prefix[0]+str(io_num)+'_data')
cudashm.destroy_shared_memory_region(shm_ip_handles[io_num])
cudashm.destroy_shared_memory_region(shm_op_handles[io_num])
else:
triton_client.unregister_system_shared_memory(
shm_region_name_prefix[1]+str(io_num)+'_data')
triton_client.unregister_system_shared_memory(
shm_region_name_prefix[1]+str(io_num)+'_data')
shm.destroy_shared_memory_region(shm_ip_handles[io_num])
shm.destroy_shared_memory_region(shm_op_handles[io_num])
return results
def _cleanup_server(self, shm_handles):
for shm_handle in shm_handles:
cshm.destroy_shared_memory_region(shm_handle)
def infer_shape_tensor(tester,
pf,
tensor_dtype,
input_shape_values,
dummy_input_shapes,
use_http=True,
use_grpc=True,
use_streaming=True,
shm_suffix="",
use_system_shared_memory=False,
use_cuda_shared_memory=False,
priority=0,
timeout_us=0,
batch_size=1):
tester.assertTrue(use_http or use_grpc or use_streaming)
tester.assertTrue(pf == "plan" or pf == "plan_nobatch")
tester.assertEqual(len(input_shape_values), len(dummy_input_shapes))
if use_system_shared_memory and use_cuda_shared_memory:
raise ValueError(
"Cannot set both System and CUDA shared memory flags to 1")
configs = []
if use_http:
configs.append(("localhost:8000", "http", False))
if use_grpc:
configs.append(("localhost:8001", "grpc", False))
if use_streaming:
configs.append(("localhost:8001", "grpc", True))
io_cnt = len(input_shape_values)
# FIXME wrap up shm handle cleanup
# For (cuda) shared memory, it's only set for shape tensor for simplicity.
# Regular tensor with (cuda) shared memory should be well-tested in other
# tests.
# item is (handle, byte_size, is_cuda)
input_shm_handle_list = []
output_shm_handle_list = []
dummy_input_list = []
input_list = []
expected_dict = dict()
# Prepare IO in advance
for io_num in range(io_cnt):
dummy_input_name = "DUMMY_INPUT{}".format(io_num)
input_name = "INPUT{}".format(io_num)
dummy_output_name = "DUMMY_OUTPUT{}".format(io_num)
output_name = "OUTPUT{}".format(io_num)
# Prepare the dummy tensor
rtensor_dtype = _range_repr_dtype(tensor_dtype)
if (rtensor_dtype != np.bool):
dummy_in0 = np.random.randint(low=np.iinfo(rtensor_dtype).min,
high=np.iinfo(rtensor_dtype).max,
size=dummy_input_shapes[io_num],
dtype=rtensor_dtype)
else:
dummy_in0 = np.random.choice(a=[False, True],
size=dummy_input_shapes[io_num])
if tensor_dtype != np.object:
dummy_in0 = dummy_in0.astype(tensor_dtype)
else:
dummy_in0 = np.array([str(x) for x in dummy_in0.flatten()],
dtype=object).reshape(dummy_in0.shape)
dummy_input_list.append(dummy_in0)
# Prepare shape input tensor
in0 = np.asarray(input_shape_values[io_num], dtype=np.int32)
input_list.append(in0)
# Prepare the expected value for the output. Skip dummy output as we
# only care about its shape (== value of OUTPUT*)
expected_dict[output_name] = np.ndarray.copy(in0)
# Only need to create region once
input_byte_size = in0.size * np.dtype(np.int32).itemsize
output_byte_size = input_byte_size * batch_size
if use_system_shared_memory:
input_shm_handle_list.append(
(shm.create_shared_memory_region(input_name + shm_suffix,
'/' + input_name + shm_suffix,
input_byte_size),
input_byte_size, False))
output_shm_handle_list.append((shm.create_shared_memory_region(
output_name + shm_suffix, '/' + output_name + shm_suffix,
output_byte_size), output_byte_size, False))
shm.set_shared_memory_region(input_shm_handle_list[-1][0], [
in0,
])
elif use_cuda_shared_memory:
input_shm_handle_list.append(
(cudashm.create_shared_memory_region(input_name + shm_suffix,
input_byte_size, 0),
input_byte_size, True))
output_shm_handle_list.append(
(cudashm.create_shared_memory_region(output_name + shm_suffix,
output_byte_size, 0),
output_byte_size, True))
cudashm.set_shared_memory_region(input_shm_handle_list[-1][0], [
in0,
])
model_name = tu.get_zero_model_name(pf, io_cnt, tensor_dtype)
# Run inference and check results for each config
for config in configs:
client_utils = grpcclient if config[1] == "grpc" else httpclient
triton_client = client_utils.InferenceServerClient(config[0],
verbose=True)
inputs = []
outputs = []
# Set IOs
for io_num in range(io_cnt):
dummy_input_name = "DUMMY_INPUT{}".format(io_num)
input_name = "INPUT{}".format(io_num)
dummy_output_name = "DUMMY_OUTPUT{}".format(io_num)
output_name = "OUTPUT{}".format(io_num)
inputs.append(
client_utils.InferInput(dummy_input_name,
dummy_input_shapes[io_num],
np_to_triton_dtype(tensor_dtype)))
inputs.append(
client_utils.InferInput(input_name, input_list[io_num].shape,
"INT32"))
outputs.append(
client_utils.InferRequestedOutput(dummy_output_name))
outputs.append(client_utils.InferRequestedOutput(output_name))
# -2: dummy; -1: input
inputs[-2].set_data_from_numpy(dummy_input_list[io_num])
if (not use_system_shared_memory) and (not use_cuda_shared_memory):
inputs[-1].set_data_from_numpy(input_list[io_num])
else:
input_byte_size = input_shm_handle_list[io_num][1]
output_byte_size = output_shm_handle_list[io_num][1]
if use_system_shared_memory:
triton_client.register_system_shared_memory(
input_name + shm_suffix, "/" + input_name + shm_suffix,
input_byte_size)
triton_client.register_system_shared_memory(
output_name + shm_suffix,
"/" + output_name + shm_suffix, output_byte_size)
else:
triton_client.register_cuda_shared_memory(
input_name + shm_suffix,
cudashm.get_raw_handle(
input_shm_handle_list[io_num][0]), 0,
input_byte_size)
triton_client.register_cuda_shared_memory(
output_name + shm_suffix,
cudashm.get_raw_handle(
output_shm_handle_list[io_num][0]), 0,
output_byte_size)
inputs[-1].set_shared_memory(input_name + shm_suffix,
input_byte_size)
outputs[-1].set_shared_memory(output_name + shm_suffix,
output_byte_size)
if config[2]:
user_data = UserData()
triton_client.start_stream(partial(completion_callback, user_data))
try:
results = triton_client.async_stream_infer(model_name,
inputs,
outputs=outputs,
priority=priority,
timeout=timeout_us)
except Exception as e:
triton_client.stop_stream()
raise e
triton_client.stop_stream()
(results, error) = user_data._completed_requests.get()
if error is not None:
raise error
else:
results = triton_client.infer(model_name,
inputs,
outputs=outputs,
priority=priority,
timeout=timeout_us)
for io_num in range(io_cnt):
output_name = "OUTPUT{}".format(io_num)
dummy_output_name = "DUMMY_OUTPUT{}".format(io_num)
expected = expected_dict[output_name]
# get outputs as numpy array
dummy_out = results.as_numpy(dummy_output_name)
if (not use_system_shared_memory) and (not use_cuda_shared_memory):
out = results.as_numpy(output_name)
else:
output = results.get_output(output_name)
if config[1] == "grpc":
output_shape = output.shape
else:
output_shape = output["shape"]
if use_system_shared_memory:
out = shm.get_contents_as_numpy(
output_shm_handle_list[io_num][0], np.int32,
output_shape)
else:
out = cudashm.get_contents_as_numpy(
output_shm_handle_list[io_num][0], np.int32,
output_shape)
# if out shape is 2D, it is batched
if (len(out.shape) == 2):
# The shape of the dummy output should be equal to the shape values
# specified in the shape tensor
tester.assertTrue(
np.array_equal(dummy_out.shape[1:], out[0]),
"{}, {} shape, expected: {}, got {}".format(
model_name, dummy_output_name, out[0],
dummy_out.shape[1:]))
for b in range(1, out.shape[0]):
tester.assertTrue(
np.array_equal(out[b - 1], out[b]),
"expect shape tensor has consistent value, "
"expected: {}, got {}".format(out[b - 1], out[b]))
out = out[0]
else:
tester.assertTrue(
np.array_equal(dummy_out.shape, out),
"{}, {} shape, expected: {}, got {}".format(
model_name, dummy_output_name, out, dummy_out.shape))
tester.assertTrue(
np.array_equal(out, expected),
"{}, {}, expected: {}, got {}".format(model_name, output_name,
expected, out))
# unregister shared memory region for next config
if use_system_shared_memory:
triton_client.unregister_system_shared_memory(input_name +
shm_suffix)
triton_client.unregister_system_shared_memory(output_name +
shm_suffix)
elif use_cuda_shared_memory:
triton_client.unregister_cuda_shared_memory(input_name +
shm_suffix)
triton_client.unregister_cuda_shared_memory(output_name +
shm_suffix)
for handle in input_shm_handle_list:
if (handle[2]):
cudashm.destroy_shared_memory_region(handle[0])
else:
shm.destroy_shared_memory_region(handle[0])
for handle in output_shm_handle_list:
if (handle[2]):
cudashm.destroy_shared_memory_region(handle[0])
else:
shm.destroy_shared_memory_region(handle[0])
