Skip to content

Commit 45347ef

Browse files
author
DvirDukhan
committed
fixed tests_onnx.py
1 parent dd1bb66 commit 45347ef

File tree

1 file changed

+0
-192
lines changed

1 file changed

+0
-192
lines changed

‎tests/flow/tests_onnx.py‎

Lines changed: 0 additions & 192 deletions
Original file line numberDiff line numberDiff line change
@@ -463,195 +463,3 @@ def test_onnx_use_custom_allocator_with_GPU(env):
463463
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
464464
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 11)
465465

466-
def tests_onnx_info(env):
467-
if not TEST_ONNX:
468-
env.debugPrint("skipping {} since TEST_ONNX=0".format(sys._getframe().f_code.co_name), force=True)
469-
return
470-
con = env.getConnection()
471-
472-
ret = con.execute_command('AI.INFO')
473-
env.assertEqual(6, len(ret))
474-
475-
test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
476-
linear_model_filename = os.path.join(test_data_path, 'linear_iris.onnx')
477-
478-
with open(linear_model_filename, 'rb') as f:
479-
model_pb = f.read()
480-
481-
con.execute_command('AI.MODELSET', 'linear{1}', 'ONNX', DEVICE, 'BLOB', model_pb)
482-
483-
ret = con.execute_command('AI.INFO')
484-
env.assertEqual(8, len(ret))
485-
env.assertEqual(b'ONNX version', ret[6])
486-
487-
488-
def test_parallelism():
489-
env = Env(moduleArgs='INTRA_OP_PARALLELISM 1 INTER_OP_PARALLELISM 1')
490-
if not TEST_ONNX:
491-
env.debugPrint("skipping {} since TEST_ONNX=0".format(sys._getframe().f_code.co_name), force=True)
492-
return
493-
494-
con = env.getConnection()
495-
test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
496-
model_filename = os.path.join(test_data_path, 'mnist.onnx')
497-
sample_filename = os.path.join(test_data_path, 'one.raw')
498-
with open(model_filename, 'rb') as f:
499-
model_pb = f.read()
500-
with open(sample_filename, 'rb') as f:
501-
sample_raw = f.read()
502-
503-
ret = con.execute_command('AI.MODELSET', 'm{1}', 'ONNX', DEVICE, 'BLOB', model_pb)
504-
env.assertEqual(ret, b'OK')
505-
con.execute_command('AI.TENSORSET', 'a{1}', 'FLOAT', 1, 1, 28, 28, 'BLOB', sample_raw)
506-
507-
con.execute_command('AI.MODELRUN', 'm{1}', 'INPUTS', 'a{1}', 'OUTPUTS', 'b{1}')
508-
ensureSlaveSynced(con, env)
509-
values = con.execute_command('AI.TENSORGET', 'b{1}', 'VALUES')
510-
argmax = max(range(len(values)), key=lambda i: values[i])
511-
env.assertEqual(argmax, 1)
512-
513-
load_time_config = {k.split(":")[0]: k.split(":")[1]
514-
for k in con.execute_command("INFO MODULES").decode().split("#")[3].split()[1:]}
515-
env.assertEqual(load_time_config["ai_inter_op_parallelism"], "1")
516-
env.assertEqual(load_time_config["ai_intra_op_parallelism"], "1")
517-
518-
env = Env(moduleArgs='INTRA_OP_PARALLELISM 2 INTER_OP_PARALLELISM 2')
519-
load_time_config = {k.split(":")[0]: k.split(":")[1]
520-
for k in con.execute_command("INFO MODULES").decode().split("#")[3].split()[1:]}
521-
env.assertEqual(load_time_config["ai_inter_op_parallelism"], "2")
522-
env.assertEqual(load_time_config["ai_intra_op_parallelism"], "2")
523-
524-
525-
def test_onnx_use_custom_allocator(env):
526-
if not TEST_ONNX:
527-
env.debugPrint("skipping {} since TEST_ONNX=0".format(sys._getframe().f_code.co_name), force=True)
528-
return
529-
530-
con = env.getConnection()
531-
test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
532-
model_filename = os.path.join(test_data_path, 'mul_1.onnx')
533-
with open(model_filename, 'rb') as f:
534-
model_pb = f.read()
535-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
536-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
537-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory"]), 0)
538-
539-
# Expect using the allocator during model set for allocating the model, its input name and output name:
540-
# overall 3 allocations. The model raw size is 130B ,and the names are 2B each. In practice we allocate
541-
# more than 134B as Redis allocator will use additional memory for its internal management and for the
542-
# 64-Byte alignment. When the test runs with valgrind, redis will use malloc for the allocations
543-
# (hence will not use additional memory).
544-
ret = con.execute_command('AI.MODELSET', 'm{1}', 'ONNX', 'CPU', 'BLOB', model_pb)
545-
env.assertEqual(ret, b'OK')
546-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
547-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
548-
549-
# Expect using at least 130+63+(size of an address) + 2*(2+63+(size of an address)) bytes.
550-
model_allocation_bytes_used = int(ai_memory_config["ai_onnxruntime_memory"])
551-
env.assertTrue(model_allocation_bytes_used > 334)
552-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 3)
553-
con.execute_command('AI.TENSORSET', 'a_mul{1}', 'FLOAT', 3, 2, 'VALUES', 1.0, 2.0, 3.0, 4.0, 5.0, 6.0)
554-
555-
# Running the model should access the allocator 6 times: allocating+freeing input+output names,
556-
# and allocating+freeing the output as OrtValue.
557-
con.execute_command('AI.MODELRUN', 'm{1}', 'INPUTS', 'a_mul{1}', 'OUTPUTS', 'b{1}')
558-
values = con.execute_command('AI.TENSORGET', 'b{1}', 'VALUES')
559-
env.assertEqual(values, [b'1', b'4', b'9', b'16', b'25', b'36'])
560-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
561-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
562-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 9)
563-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory"]), model_allocation_bytes_used)
564-
565-
# Expect using the allocator free function 3 times: when releasing the model, input name and output name.
566-
con.execute_command('AI.MODELDEL', 'm{1}')
567-
env.assertFalse(con.execute_command('EXISTS', 'm{1}'))
568-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
569-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
570-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory"]), 0)
571-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 12)
572-
573-
# test the use of Redis allocator in model run op.
574-
model_filename = os.path.join(test_data_path, 'mnist.onnx')
575-
sample_filename = os.path.join(test_data_path, 'one.raw')
576-
577-
with open(model_filename, 'rb') as f:
578-
model_pb = f.read()
579-
with open(sample_filename, 'rb') as f:
580-
sample_raw = f.read()
581-
582-
ret = con.execute_command('AI.MODELSET', 'm{1}', 'ONNX', 'CPU', 'BLOB', model_pb)
583-
env.assertEqual(ret, b'OK')
584-
con.execute_command('AI.TENSORSET', 'a{1}', 'FLOAT', 1, 1, 28, 28, 'BLOB', sample_raw)
585-
586-
# Expect 18 allocator's access from onnx during the run (in addition to the allocations that were made while
587-
# creating the model).
588-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
589-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
590-
allocator_access_num_before = ai_memory_config["ai_onnxruntime_memory_access_num"]
591-
con.execute_command('AI.MODELRUN', 'm{1}', 'INPUTS', 'a{1}', 'OUTPUTS', 'b{1}')
592-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
593-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
594-
allocator_access_num_after = ai_memory_config["ai_onnxruntime_memory_access_num"]
595-
env.assertEqual(int(allocator_access_num_after) - int(allocator_access_num_before), 18)
596-
597-
values = con.execute_command('AI.TENSORGET', 'b{1}', 'VALUES')
598-
argmax = max(range(len(values)), key=lambda i: values[i])
599-
env.assertEqual(argmax, 1)
600-
601-
602-
def test_onnx_use_custom_allocator_with_GPU(env):
603-
if not TEST_ONNX:
604-
env.debugPrint("skipping {} since TEST_ONNX=0".format(sys._getframe().f_code.co_name), force=True)
605-
return
606-
if DEVICE == 'CPU':
607-
env.debugPrint("skipping {} since this test if for GPU only".format(sys._getframe().f_code.co_name), force=True)
608-
return
609-
610-
con = env.getConnection()
611-
test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
612-
model_filename = os.path.join(test_data_path, 'mul_1.onnx')
613-
with open(model_filename, 'rb') as f:
614-
model_pb = f.read()
615-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
616-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
617-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory"]), 0)
618-
619-
# Expect using the allocator during model set for allocating the model, its input name and output name:
620-
# overall 3 allocations. The model raw size is 130B ,and the names are 2B each. In practice we allocate
621-
# more than 134B as Redis allocator will use additional memory for its internal management and for the
622-
# 64-Byte alignment. When the test runs with valgrind, redis will use malloc for the allocations.
623-
ret = con.execute_command('AI.MODELSET', 'm_gpu{1}', 'ONNX', DEVICE, 'BLOB', model_pb)
624-
env.assertEqual(ret, b'OK')
625-
626-
# but for GPU, expect using the allocator only for allocating input and output names (not the model itself).
627-
ret = con.execute_command('AI.MODELSET', 'm_cpu{1}', 'ONNX', 'CPU', 'BLOB', model_pb)
628-
env.assertEqual(ret, b'OK')
629-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
630-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
631-
632-
# Expect using at least 130+63+(size of an address) + 4*(2+63+(size of an address)) bytes.
633-
model_allocation_bytes_used = int(ai_memory_config["ai_onnxruntime_memory"])
634-
env.assertTrue(model_allocation_bytes_used > 472)
635-
env.assertTrue(model_allocation_bytes_used < 705)
636-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 5)
637-
638-
# Make sure that allocator is not used for running and freeing the GPU model, except for
639-
# the input and output names allocations (and deallocations).
640-
con.execute_command('AI.TENSORSET', 'a{1}', 'FLOAT', 3, 2, 'VALUES', 1.0, 2.0, 3.0, 4.0, 5.0, 6.0)
641-
con.execute_command('AI.MODELRUN', 'm_gpu{1}', 'INPUTS', 'a{1}', 'OUTPUTS', 'b{1}')
642-
values = con.execute_command('AI.TENSORGET', 'b{1}', 'VALUES')
643-
env.assertEqual(values, [b'1', b'4', b'9', b'16', b'25', b'36'])
644-
# Expect that memory usage didn't change, and for another 4 accesses to the allocator (input and output names
645-
# allocation and free)
646-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
647-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
648-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory"]), model_allocation_bytes_used)
649-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 9)
650-
651-
# Expect only 2 more accesses in delete - for deallocating input and output names
652-
con.execute_command('AI.MODELDEL', 'm_gpu{1}')
653-
env.assertFalse(con.execute_command('EXISTS', 'm_gpu{1}'))
654-
ai_memory_config = {k.split(":")[0]: k.split(":")[1]
655-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
656-
env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 11)
657-

0 commit comments

Comments
 (0)