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[AMP OP&Test] Append bf16/fp16 support 4 elementwise_max #51151

Merged
merged 11 commits into from
Mar 14, 2023
Merged

[AMP OP&Test] Append bf16/fp16 support 4 elementwise_max #51151

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a81b623
wisemax fp16 support
piDack Mar 3, 2023
6b660dd
add bf16 support 4 elementwise_max
piDack Mar 3, 2023
ade5ccc
append broadcast 4 op 4 fp16 / bf16
piDack Mar 3, 2023
00c18b5
fix elewise_max ut bf16 numeric delta
piDack Mar 6, 2023
8d2427d
append fp/bf16 uts
piDack Mar 9, 2023
c6c2829
add fp/bf16 uts
piDack Mar 9, 2023
f6d2889
Merge branch 'develop' of github.com:piDack/Paddle into wise_max
piDack Mar 10, 2023
0d42ba0
change bf16 uts delta
piDack Mar 13, 2023
b0c2bf8
fix some issue
piDack Mar 13, 2023
adf970f
fix confict
piDack Mar 13, 2023
e45e05f
add prim 4 fp16
piDack Mar 14, 2023
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219 changes: 217 additions & 2 deletions python/paddle/fluid/tests/unittests/test_elementwise_max_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,15 +48,32 @@ def test_check_grad_normal(self):

def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X")
['Y'],
'Out',
max_relative_error=0.005,
no_grad_set=set("X"),
)

def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y')
['X'],
'Out',
max_relative_error=0.005,
no_grad_set=set('Y'),
)


class TestElementwiseFP16Op(TestElementwiseOp):
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改一下类名，用TestElementwiseMaxFP16Op代替

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done

def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.1, 1, [13, 17]).astype(np.float16)
sgn = np.random.choice([-1, 1], [13, 17]).astype(np.float16)
y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype(np.float16)
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxOp_ZeroDim1(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -67,6 +84,16 @@ def setUp(self):
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxFP16Op_ZeroDim1(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.1, 1, []).astype("float16")
y = np.random.uniform(0.1, 1, []).astype("float16")
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxOp_ZeroDim2(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -77,6 +104,16 @@ def setUp(self):
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxFP16Op_ZeroDim2(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.1, 1, [13, 17]).astype("float16")
y = np.random.uniform(0.1, 1, []).astype("float16")
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxOp_ZeroDim3(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -87,6 +124,16 @@ def setUp(self):
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxFP16Op_ZeroDim3(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.1, 1, []).astype("float16")
y = np.random.uniform(0.1, 1, [13, 17]).astype("float16")
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


@unittest.skipIf(
core.is_compiled_with_cuda()
and (
Expand Down Expand Up @@ -131,6 +178,53 @@ def test_check_grad_ingore_y(self):
self.check_grad(['X'], 'Out', no_grad_set=set('Y'))


class TestElementwiseMaxBF16Op_ZeroDim1(TestElementwiseBF16Op):
def setUp(self):
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很多子类的setUp都复用了基类的一些代码，建议不同点用init_data等函数单独提取出来，减少重复代码

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done

self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.1, 1, []).astype("float32")
y = np.random.uniform(0.1, 1, []).astype("float32")
self.inputs = {
'X': convert_float_to_uint16(x),
'Y': convert_float_to_uint16(y),
}
self.outputs = {'Out': convert_float_to_uint16(np.maximum(x, y))}

def test_check_grad_normal(self):
if hasattr(self, 'attrs'):
self.check_grad(
['X', 'Y'], 'Out', numeric_grad_delta=0.05, check_eager=False
)
else:
self.check_grad(
['X', 'Y'], 'Out', numeric_grad_delta=0.05, check_eager=True
)

def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', numeric_grad_delta=0.05, no_grad_set=set("X")
)

def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', numeric_grad_delta=0.05, no_grad_set=set('Y')
)


class TestElementwiseMaxBF16Op_scalar(TestElementwiseBF16Op):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.random_integers(-5, 5, [2, 3, 20]).astype("float32")
y = np.array([0.5]).astype("float32")
self.inputs = {
'X': convert_float_to_uint16(x),
'Y': convert_float_to_uint16(y),
}
self.outputs = {'Out': convert_float_to_uint16(np.maximum(x, y))}
self.__class__.no_need_check_grad = True


@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast."
)
Expand All @@ -144,6 +238,16 @@ def setUp(self):
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxFP16Op_scalar(TestElementwiseMaxOp_scalar):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.random_integers(-5, 5, [2, 3, 20]).astype("float16")
y = np.array([0.5]).astype("float16")
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxOp_Vector(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -155,6 +259,31 @@ def setUp(self):
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxFP16Op_Vector(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.random((100,)).astype("float16")
sgn = np.random.choice([-1, 1], (100,)).astype("float16")
y = x + sgn * np.random.uniform(0.1, 1, (100,)).astype("float16")
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseMaxBF16Op_Vector(TestElementwiseBF16Op):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.random((100,)).astype("float32")
sgn = np.random.choice([-1, 1], (100,)).astype("float32")
y = x + sgn * np.random.uniform(0.1, 1, (100,)).astype("float32")
self.inputs = {
'X': convert_float_to_uint16(x),
'Y': convert_float_to_uint16(y),
}
self.outputs = {'Out': convert_float_to_uint16(np.maximum(x, y))}


class TestElementwiseMaxOp_broadcast_0(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -174,6 +303,25 @@ def setUp(self):
}


class TestElementwiseMaxFP16Op_broadcast_0(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.5, 1, (100, 5, 2)).astype(np.float16)
sgn = np.random.choice([-1, 1], (100,)).astype(np.float16)
y = x[:, 0, 0] + sgn * np.random.uniform(1, 2, (100,)).astype(
np.float16
)
self.inputs = {'X': x, 'Y': y}

self.attrs = {'axis': 0}
self.outputs = {
'Out': np.maximum(
self.inputs['X'], self.inputs['Y'].reshape(100, 1, 1)
)
}


class TestElementwiseMaxOp_broadcast_1(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -193,6 +341,25 @@ def setUp(self):
}


class TestElementwiseMaxFP16Op_broadcast_1(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.5, 1, (2, 100, 3)).astype(np.float16)
sgn = np.random.choice([-1, 1], (100,)).astype(np.float16)
y = x[0, :, 0] + sgn * np.random.uniform(1, 2, (100,)).astype(
np.float16
)
self.inputs = {'X': x, 'Y': y}

self.attrs = {'axis': 1}
self.outputs = {
'Out': np.maximum(
self.inputs['X'], self.inputs['Y'].reshape(1, 100, 1)
)
}


class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -211,6 +378,24 @@ def setUp(self):
}


class TestElementwiseMaxFP16Op_broadcast_2(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.5, 1, (1, 3, 100)).astype(np.float16)
sgn = np.random.choice([-1, 1], (100,)).astype(np.float16)
y = x[0, 0, :] + sgn * np.random.uniform(1, 2, (100,)).astype(
np.float16
)
self.inputs = {'X': x, 'Y': y}

self.outputs = {
'Out': np.maximum(
self.inputs['X'], self.inputs['Y'].reshape(1, 1, 100)
)
}


class TestElementwiseMaxOp_broadcast_3(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -230,6 +415,25 @@ def setUp(self):
}


class TestElementwiseMaxFP16Op_broadcast_3(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.5, 1, (2, 50, 2, 1)).astype(np.float16)
sgn = np.random.choice([-1, 1], (50, 2)).astype(np.float16)
y = x[0, :, :, 0] + sgn * np.random.uniform(1, 2, (50, 2)).astype(
np.float16
)
self.inputs = {'X': x, 'Y': y}

self.attrs = {'axis': 1}
self.outputs = {
'Out': np.maximum(
self.inputs['X'], self.inputs['Y'].reshape(1, 50, 2, 1)
)
}


class TestElementwiseMaxOp_broadcast_4(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
Expand All @@ -242,5 +446,16 @@ def setUp(self):
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseFP16Op_broadcast_4(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_max"
self.python_api = paddle.maximum
x = np.random.uniform(0.5, 1, (2, 3, 4, 5)).astype(np.float16)
sgn = np.random.choice([-1, 1], (2, 3, 1, 5)).astype(np.float16)
y = x + sgn * np.random.uniform(1, 2, (2, 3, 1, 5)).astype(np.float16)
self.inputs = {'X': x, 'Y': y}
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


if __name__ == '__main__':
unittest.main()