def test_correctness_use_adaptive_pooling():
if mge.is_cuda_available():
model_name = "mnist_model_with_test.mge"
else:
model_name = "mnist_model_with_test_cpu.mge"
model_path = os.path.join(os.path.dirname(__file__), model_name)
set_execution_strategy("HEURISTIC_REPRODUCIBLE")
run_train(model_path, False, False, max_err=1e-5, use_adaptive_pooling=True)
run_train(model_path, True, False, max_err=1e-5, use_adaptive_pooling=True)
run_train(model_path, True, True, max_err=1e-5, use_adaptive_pooling=True)
# sublinear
config = SublinearMemoryConfig(genetic_nr_iter=10)
run_train(
model_path,
True,
True,
sublinear_memory_config=config,
max_err=1e-5,
use_adaptive_pooling=True,
)
run_eval(model_path, False, max_err=1e-7, use_adaptive_pooling=True)
run_eval(model_path, True, max_err=1e-7, use_adaptive_pooling=True)
def test_matmul(execution_strategy):
@trace(symbolic=True, capture_as_const=True)
def fwd(data1, data2):
return F.matmul(data1, data2)
old = get_execution_strategy()
set_execution_strategy(execution_strategy)
max_err = None
if execution_strategy == "PROFILE_REPRODUCIBLE":
max_err = 1e-5
data1 = Tensor(np.random.random((32, 64)))
data2 = Tensor(np.random.random((64, 16)))
result = fwd(data1, data2)
check_pygraph_dump(fwd, [data1, data2], [result], max_err=max_err)
set_execution_strategy(old)
def reproducible():
old = get_execution_strategy()
set_execution_strategy("HEURISTIC_REPRODUCIBLE")
yield
set_execution_strategy(old)
def test_dp_correctness():
model_name = "mnist_model_with_test.mge"
model_path = os.path.join(os.path.dirname(__file__), model_name)
set_execution_strategy("HEURISTIC_REPRODUCIBLE")
run_test(model_path, False, False, max_err=5e-5)