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DvirDukhanDvirDukhan
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fixed tests_onnx.py
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‎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):
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for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
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env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 11)
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def tests_onnx_info(env):
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if not TEST_ONNX:
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env.debugPrint("skipping {} since TEST_ONNX=0".format(sys._getframe().f_code.co_name), force=True)
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return
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con = env.getConnection()
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ret = con.execute_command('AI.INFO')
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env.assertEqual(6, len(ret))
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test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
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linear_model_filename = os.path.join(test_data_path, 'linear_iris.onnx')
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with open(linear_model_filename, 'rb') as f:
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model_pb = f.read()
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con.execute_command('AI.MODELSET', 'linear{1}', 'ONNX', DEVICE, 'BLOB', model_pb)
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ret = con.execute_command('AI.INFO')
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env.assertEqual(8, len(ret))
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env.assertEqual(b'ONNX version', ret[6])
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def test_parallelism():
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env = Env(moduleArgs='INTRA_OP_PARALLELISM 1 INTER_OP_PARALLELISM 1')
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if not TEST_ONNX:
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env.debugPrint("skipping {} since TEST_ONNX=0".format(sys._getframe().f_code.co_name), force=True)
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return
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con = env.getConnection()
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test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
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model_filename = os.path.join(test_data_path, 'mnist.onnx')
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sample_filename = os.path.join(test_data_path, 'one.raw')
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with open(model_filename, 'rb') as f:
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model_pb = f.read()
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with open(sample_filename, 'rb') as f:
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sample_raw = f.read()
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ret = con.execute_command('AI.MODELSET', 'm{1}', 'ONNX', DEVICE, 'BLOB', model_pb)
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env.assertEqual(ret, b'OK')
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con.execute_command('AI.TENSORSET', 'a{1}', 'FLOAT', 1, 1, 28, 28, 'BLOB', sample_raw)
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con.execute_command('AI.MODELRUN', 'm{1}', 'INPUTS', 'a{1}', 'OUTPUTS', 'b{1}')
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ensureSlaveSynced(con, env)
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values = con.execute_command('AI.TENSORGET', 'b{1}', 'VALUES')
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argmax = max(range(len(values)), key=lambda i: values[i])
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env.assertEqual(argmax, 1)
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load_time_config = {k.split(":")[0]: k.split(":")[1]
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for k in con.execute_command("INFO MODULES").decode().split("#")[3].split()[1:]}
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env.assertEqual(load_time_config["ai_inter_op_parallelism"], "1")
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env.assertEqual(load_time_config["ai_intra_op_parallelism"], "1")
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env = Env(moduleArgs='INTRA_OP_PARALLELISM 2 INTER_OP_PARALLELISM 2')
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load_time_config = {k.split(":")[0]: k.split(":")[1]
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for k in con.execute_command("INFO MODULES").decode().split("#")[3].split()[1:]}
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env.assertEqual(load_time_config["ai_inter_op_parallelism"], "2")
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env.assertEqual(load_time_config["ai_intra_op_parallelism"], "2")
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def test_onnx_use_custom_allocator(env):
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if not TEST_ONNX:
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env.debugPrint("skipping {} since TEST_ONNX=0".format(sys._getframe().f_code.co_name), force=True)
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return
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530-
con = env.getConnection()
531-
test_data_path = os.path.join(os.path.dirname(__file__), 'test_data')
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model_filename = os.path.join(test_data_path, 'mul_1.onnx')
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with open(model_filename, 'rb') as f:
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model_pb = f.read()
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ai_memory_config = {k.split(":")[0]: k.split(":")[1]
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for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
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env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory"]), 0)
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# Expect using the allocator during model set for allocating the model, its input name and output name:
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# overall 3 allocations. The model raw size is 130B ,and the names are 2B each. In practice we allocate
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# more than 134B as Redis allocator will use additional memory for its internal management and for the
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# 64-Byte alignment. When the test runs with valgrind, redis will use malloc for the allocations
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# (hence will not use additional memory).
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ret = con.execute_command('AI.MODELSET', 'm{1}', 'ONNX', 'CPU', 'BLOB', model_pb)
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env.assertEqual(ret, b'OK')
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ai_memory_config = {k.split(":")[0]: k.split(":")[1]
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for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
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# Expect using at least 130+63+(size of an address) + 2*(2+63+(size of an address)) bytes.
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model_allocation_bytes_used = int(ai_memory_config["ai_onnxruntime_memory"])
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env.assertTrue(model_allocation_bytes_used > 334)
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env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 3)
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con.execute_command('AI.TENSORSET', 'a_mul{1}', 'FLOAT', 3, 2, 'VALUES', 1.0, 2.0, 3.0, 4.0, 5.0, 6.0)
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# Running the model should access the allocator 6 times: allocating+freeing input+output names,
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# and allocating+freeing the output as OrtValue.
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con.execute_command('AI.MODELRUN', 'm{1}', 'INPUTS', 'a_mul{1}', 'OUTPUTS', 'b{1}')
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values = con.execute_command('AI.TENSORGET', 'b{1}', 'VALUES')
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env.assertEqual(values, [b'1', b'4', b'9', b'16', b'25', b'36'])
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ai_memory_config = {k.split(":")[0]: k.split(":")[1]
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for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
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env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 9)
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env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory"]), model_allocation_bytes_used)
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# Expect using the allocator free function 3 times: when releasing the model, input name and output name.
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con.execute_command('AI.MODELDEL', 'm{1}')
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env.assertFalse(con.execute_command('EXISTS', 'm{1}'))
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ai_memory_config = {k.split(":")[0]: k.split(":")[1]
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for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
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env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory"]), 0)
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env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 12)
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# test the use of Redis allocator in model run op.
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model_filename = os.path.join(test_data_path, 'mnist.onnx')
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sample_filename = os.path.join(test_data_path, 'one.raw')
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with open(model_filename, 'rb') as f:
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model_pb = f.read()
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with open(sample_filename, 'rb') as f:
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sample_raw = f.read()
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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:]}
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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}')
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ai_memory_config = {k.split(":")[0]: k.split(":")[1]
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for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
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allocator_access_num_after = ai_memory_config["ai_onnxruntime_memory_access_num"]
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env.assertEqual(int(allocator_access_num_after) - int(allocator_access_num_before), 18)
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597-
values = con.execute_command('AI.TENSORGET', 'b{1}', 'VALUES')
598-
argmax = max(range(len(values)), key=lambda i: values[i])
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env.assertEqual(argmax, 1)
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601-
602-
def test_onnx_use_custom_allocator_with_GPU(env):
603-
if not TEST_ONNX:
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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)
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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)
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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.
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model_allocation_bytes_used = int(ai_memory_config["ai_onnxruntime_memory"])
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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')
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env.assertEqual(values, [b'1', b'4', b'9', b'16', b'25', b'36'])
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# Expect that memory usage didn't change, and for another 4 accesses to the allocator (input and output names
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# 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}'))
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ai_memory_config = {k.split(":")[0]: k.split(":")[1]
655-
for k in con.execute_command("INFO MODULES").decode().split("#")[4].split()[1:]}
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env.assertEqual(int(ai_memory_config["ai_onnxruntime_memory_access_num"]), 11)
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