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[PIR Unittest] fix pir ut in (test_quant_linear_op) #66959

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merged 2 commits into from
Aug 5, 2024
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[PIR Unittest] fix pir ut in (test_quant_linear_op) #66959

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c78928b
fix pir ut test_quant_linear_op
ooooo-create Aug 2, 2024
d7e7eaa
merge
ooooo-create Aug 2, 2024
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295 changes: 148 additions & 147 deletions test/legacy_test/test_quant_linear_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -549,7 +549,7 @@ def run_program(num_flatten_dims):
startup_program = Program()
main_program = Program()

with paddle_static_guard():
with paddle.pir_utils.OldIrGuard():
with program_guard(main_program, startup_program):
quant_round_type = 0
quant_max_bound = 127.0
Expand Down Expand Up @@ -611,153 +611,154 @@ def run_program(num_flatten_dims):
)
class TestQuantLinearOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
quant_round_type = 0
quant_max_bound = 127.0
quant_min_bound = -127.0

input_data = np.random.random((2, 4)).astype("float32")
scale_in = get_scale_in(input_data)

weight = np.random.random([25, 1]).astype("float32")
scale_weight = get_scale_weights(weight)
weight = quant_weights(
weight,
scale_weight,
quant_round_type,
quant_max_bound,
quant_min_bound,
)

def test_Variable():
with paddle_static_guard():
w2 = paddle.static.data(
name='w2', shape=[25, 1], dtype='int8'
)
quant_linear(
x=input_data,
size=1,
num_flatten_dims=1,
w=w2,
scale_in=scale_in,
scale_weight=scale_weight.tolist(),
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_Variable)

def test_type():
with paddle_static_guard():
x2 = paddle.static.data(
name='x2', shape=[-1, 4], dtype='int32'
)
w2 = paddle.static.data(
name='w2', shape=[25, 1], dtype='int8'
)
paddle.static.nn.fc(
x=x2,
size=1,
num_flatten_dims=1,
w=w2,
scale_in=scale_in,
scale_weight=scale_weight.tolist(),
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_type)

def test_Variable():
with paddle_static_guard():
x3 = paddle.static.data(
name='x3', shape=[-1, 4], dtype='float32'
)
quant_linear(
x=x3,
size=1,
num_flatten_dims=1,
w=weight,
scale_in=scale_in,
scale_weight=scale_weight.tolist(),
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_Variable)

def test_type():
with paddle_static_guard():
x3 = paddle.static.data(
name='x3', shape=[-1, 4], dtype='float32'
)
w3 = paddle.static.data(
name='w3', shape=[25, 1], dtype='int32'
)
paddle.static.nn.fc(
x=x3,
size=1,
num_flatten_dims=1,
w=w3,
scale_in=scale_in,
scale_weight=scale_weight.tolist(),
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_type)

scale_weight = 1.0

def test_type():
with paddle_static_guard():
x4 = paddle.static.data(
name='x4', shape=[-1, 4], dtype='float32'
)
w4 = paddle.static.data(
name='w4', shape=[25, 1], dtype='int8'
)
paddle.static.nn.fc(
x=x4,
size=1,
num_flatten_dims=1,
w=w4,
scale_in=scale_in,
scale_weight=scale_weight,
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_type)

scale_weight = []

def test_param_length():
with paddle_static_guard():
x4 = paddle.static.data(
name='x4', shape=[-1, 4], dtype='float32'
)
w4 = paddle.static.data(
name='w4', shape=[25, 1], dtype='int8'
)
paddle.static.nn.fc(
x=x4,
size=1,
num_flatten_dims=1,
w=w4,
scale_in=scale_in,
scal=scale_weight,
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)
with paddle.pir_utils.OldIrGuard():
with program_guard(Program(), Program()):
quant_round_type = 0
quant_max_bound = 127.0
quant_min_bound = -127.0

input_data = np.random.random((2, 4)).astype("float32")
scale_in = get_scale_in(input_data)

weight = np.random.random([25, 1]).astype("float32")
scale_weight = get_scale_weights(weight)
weight = quant_weights(
weight,
scale_weight,
quant_round_type,
quant_max_bound,
quant_min_bound,
)

self.assertRaises(TypeError, test_param_length)
def test_Variable():
with paddle_static_guard():
w2 = paddle.static.data(
name='w2', shape=[25, 1], dtype='int8'
)
quant_linear(
x=input_data,
size=1,
num_flatten_dims=1,
w=w2,
scale_in=scale_in,
scale_weight=scale_weight.tolist(),
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_Variable)

def test_type():
with paddle_static_guard():
x2 = paddle.static.data(
name='x2', shape=[-1, 4], dtype='int32'
)
w2 = paddle.static.data(
name='w2', shape=[25, 1], dtype='int8'
)
paddle.static.nn.fc(
x=x2,
size=1,
num_flatten_dims=1,
w=w2,
scale_in=scale_in,
scale_weight=scale_weight.tolist(),
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_type)

def test_Variable():
with paddle_static_guard():
x3 = paddle.static.data(
name='x3', shape=[-1, 4], dtype='float32'
)
quant_linear(
x=x3,
size=1,
num_flatten_dims=1,
w=weight,
scale_in=scale_in,
scale_weight=scale_weight.tolist(),
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_Variable)

def test_type():
with paddle_static_guard():
x3 = paddle.static.data(
name='x3', shape=[-1, 4], dtype='float32'
)
w3 = paddle.static.data(
name='w3', shape=[25, 1], dtype='int32'
)
paddle.static.nn.fc(
x=x3,
size=1,
num_flatten_dims=1,
w=w3,
scale_in=scale_in,
scale_weight=scale_weight.tolist(),
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_type)

scale_weight = 1.0

def test_type():
with paddle_static_guard():
x4 = paddle.static.data(
name='x4', shape=[-1, 4], dtype='float32'
)
w4 = paddle.static.data(
name='w4', shape=[25, 1], dtype='int8'
)
paddle.static.nn.fc(
x=x4,
size=1,
num_flatten_dims=1,
w=w4,
scale_in=scale_in,
scale_weight=scale_weight,
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_type)

scale_weight = []

def test_param_length():
with paddle_static_guard():
x4 = paddle.static.data(
name='x4', shape=[-1, 4], dtype='float32'
)
w4 = paddle.static.data(
name='w4', shape=[25, 1], dtype='int8'
)
paddle.static.nn.fc(
x=x4,
size=1,
num_flatten_dims=1,
w=w4,
scale_in=scale_in,
scal=scale_weight,
quant_round_type=quant_round_type,
quant_max_bound=quant_max_bound,
quant_min_bound=quant_min_bound,
)

self.assertRaises(TypeError, test_param_length)


if __name__ == "__main__":
Expand Down
28 changes: 16 additions & 12 deletions test/xpu/test_while_op_xpu.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -98,24 +98,28 @@ def test_simple_net(self):
)
self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)

@test_with_pir_api
def test_simple_net_forward(self):
with paddle.pir_utils.OldIrGuard():
main_program = base.Program()
startup_program = base.Program()
with base.program_guard(main_program, startup_program):
self.simple_net()
main_program = base.Program()
startup_program = base.Program()
with base.program_guard(main_program, startup_program):
self.simple_net()
if paddle.framework.in_pir_mode():
binary = main_program
else:
binary = base.compiler.CompiledProgram(main_program)

xpu_place = paddle.XPUPlace(0)
exe = Executor(xpu_place)
d = []
xpu_place = paddle.XPUPlace(0)
exe = Executor(xpu_place)
d = []

for i in range(3):
d.append(numpy.random.random(size=[10]).astype('float32'))
for i in range(3):
d.append(numpy.random.random(size=[10]).astype('float32'))

for _ in range(2):
exe.run(binary, feed={'d0': d[0], 'd1': d[1], 'd2': d[2]})
for _ in range(2):
exe.run(binary, feed={'d0': d[0], 'd1': d[1], 'd2': d[2]})

@test_with_pir_api
def test_exceptions(self):
i = paddle.zeros(shape=[2], dtype='int64')
array_len = paddle.tensor.fill_constant(
Expand Down