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elementwise_pow, square, sin and cos support bfloat16 for xpu #60402

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Merged
QingshuChen merged 1 commit into from
Dec 28, 2023
Merged

elementwise_pow, square, sin and cos support bfloat16 for xpu #60402

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18 changes: 14 additions & 4 deletions paddle/phi/backends/xpu/xpu3_op_list.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -293,7 +293,9 @@ XPUOpMap& get_kl3_ops() {
phi::DataType::INT32,
phi::DataType::INT64})},
{"elementwise_pow",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"elementwise_sub_grad",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
{"elementwise_sub",
Expand Down Expand Up @@ -885,7 +887,9 @@ XPUOpMap& get_kl3_ops() {
{"square_grad",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
{"square",
XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"squared_l2_norm",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
Expand Down Expand Up @@ -1136,9 +1140,15 @@ XPUOpMap& get_kl3_ops() {
phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"sin", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
{"sin",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"sin_grad", XPUKernelSet({phi::DataType::FLOAT32})},
{"cos", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})},
{"cos",
XPUKernelSet({phi::DataType::FLOAT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16})},
{"cos_grad", XPUKernelSet({phi::DataType::FLOAT32})},
{"linspace",
XPUKernelSet({phi::DataType::FLOAT32,
Expand Down
3 changes: 2 additions & 1 deletion paddle/phi/kernels/legacy/xpu/elementwise_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -153,4 +153,5 @@ PD_REGISTER_KERNEL(elementwise_pow_raw,
ALL_LAYOUT,
phi::ElementwisePowRawKernel,
float,
phi::dtype::float16) {}
phi::dtype::float16,
phi::dtype::bfloat16) {}
27 changes: 21 additions & 6 deletions paddle/phi/kernels/xpu/activation_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -624,19 +624,34 @@ PD_REGISTER_KERNEL(sqrt,
PD_REGISTER_KERNEL(
tanh, XPU, ALL_LAYOUT, phi::TanhKernel, float, phi::dtype::float16) {}

PD_REGISTER_KERNEL(
square, XPU, ALL_LAYOUT, phi::SquareKernel, float, phi::dtype::float16) {}
PD_REGISTER_KERNEL(square,
XPU,
ALL_LAYOUT,
phi::SquareKernel,
float,
phi::dtype::float16,
phi::dtype::bfloat16) {}

PD_REGISTER_KERNEL(
log, XPU, ALL_LAYOUT, phi::LogKernel, float, phi::dtype::float16) {}

PD_REGISTER_KERNEL(
relu6, XPU, ALL_LAYOUT, phi::Relu6Kernel, float, phi::dtype::float16) {}

PD_REGISTER_KERNEL(
sin, XPU, ALL_LAYOUT, phi::SinKernel, float, phi::dtype::float16) {}
PD_REGISTER_KERNEL(
cos, XPU, ALL_LAYOUT, phi::CosKernel, float, phi::dtype::float16) {}
PD_REGISTER_KERNEL(sin,
XPU,
ALL_LAYOUT,
phi::SinKernel,
float,
phi::dtype::float16,
phi::dtype::bfloat16) {}
PD_REGISTER_KERNEL(cos,
XPU,
ALL_LAYOUT,
phi::CosKernel,
float,
phi::dtype::float16,
phi::dtype::bfloat16) {}

#define PD_REGISTER_ACTIVATION_KERNEL(name, func) \
PD_REGISTER_KERNEL(name, XPU, ALL_LAYOUT, phi::func, float) {}
Expand Down
3 changes: 2 additions & 1 deletion paddle/phi/kernels/xpu/elementwise_kernel.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -114,4 +114,5 @@ PD_REGISTER_KERNEL(elementwise_pow,
ALL_LAYOUT,
phi::ElementwisePowKernel,
float,
phi::dtype::float16) {}
phi::dtype::float16,
phi::dtype::bfloat16) {}
63 changes: 31 additions & 32 deletions test/xpu/test_activation_op_xpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -521,34 +521,39 @@ def set_case(self):
self.op_type = "square"
self.dtype = self.in_type
self.init_config()
if self.dtype == np.uint16:
# bfloat16 actually
self.x = convert_float_to_uint16(self.tmp_x)
else:
self.x = self.tmp_x.astype(self.dtype)
out = np.square(self.x)

self.attrs = {'use_xpu': True}
self.inputs = {'X': OpTest.np_dtype_to_base_dtype(self.x)}
self.outputs = {'Out': out}

def init_config(self):
self.x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
self.tmp_x = np.random.uniform(-1, 1, [11, 17])

class XPUTestSquare_ZeroDim(XPUTestSquare):
def init_config(self):
self.x = np.random.uniform(-2, 2, []).astype(self.dtype)
self.tmp_x = np.random.uniform(-2, 2, [])

class XPUTestSquare2(XPUTestSquare):
def init_config(self):
self.x = np.random.uniform(-2, 2, [100]).astype(self.dtype)
self.tmp_x = np.random.uniform(-2, 2, [100])

class XPUTestSquare3(XPUTestSquare):
def init_config(self):
self.x = np.random.uniform(-2, 2, [1, 15, 19]).astype(self.dtype)
self.tmp_x = np.random.uniform(-2, 2, [1, 15, 19])

class XPUTestSquare4(XPUTestSquare):
def init_config(self):
self.x = np.random.uniform(-2, 2, [100, 10]).astype(self.dtype)
self.tmp_x = np.random.uniform(-2, 2, [100, 10])

class XPUTestSquare5(XPUTestSquare):
def init_config(self):
self.x = np.random.uniform(-2, 2, [1, 2, 5, 17]).astype(self.dtype)
self.tmp_x = np.random.uniform(-2, 2, [1, 2, 5, 17])


support_types = get_xpu_op_support_types('square')
Expand Down Expand Up @@ -1297,38 +1302,35 @@ def set_case(self):
self.dtype = self.in_type

self.init_config()
if self.dtype == np.uint16:
# bfloat16 actually
self.x = convert_float_to_uint16(self.tmp_x)
else:
self.x = self.tmp_x.astype(self.dtype)
out = np.sin(self.x)

self.inputs = {'X': self.x}
self.outputs = {'Out': out}
self.attrs = {'use_xpu': True}

def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, [11, 17]).astype(
self.dtype
)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [11, 17])

class XPUTestSin_ZeroDim(XPUTestSinBase):
def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, []).astype(self.dtype)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [])

class XPUTestSin2(XPUTestSinBase):
def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, [1024, 8]).astype(
self.dtype
)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [1024, 8])

class XPUTestSin3(XPUTestSinBase):
def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, [4, 512, 15, 15]).astype(
self.dtype
)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [4, 512, 15, 15])

class XPUTestSin4(XPUTestSinBase):
def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, [4, 256, 22, 22]).astype(
self.dtype
)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [4, 256, 22, 22])


support_types = get_xpu_op_support_types('sin')
Expand All @@ -1347,38 +1349,35 @@ def set_case(self):
self.dtype = self.in_type

self.init_config()
if self.dtype == np.uint16:
# bfloat16 actually
self.x = convert_float_to_uint16(self.tmp_x)
else:
self.x = self.tmp_x.astype(self.dtype)
out = np.cos(self.x)

self.inputs = {'X': self.x}
self.outputs = {'Out': out}
self.attrs = {'use_xpu': True}

def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, [11, 17]).astype(
self.dtype
)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [11, 17])

class XPUTestCos_ZeroDim(XPUTestCosBase):
def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, []).astype(self.dtype)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [])

class XPUTestCos2(XPUTestCosBase):
def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, [1024, 8]).astype(
self.dtype
)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [1024, 8])

class XPUTestCos3(XPUTestCosBase):
def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, [4, 512, 15, 15]).astype(
self.dtype
)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [4, 512, 15, 15])

class XPUTestCos4(XPUTestCosBase):
def init_config(self):
self.x = np.random.uniform(-np.pi, np.pi, [4, 256, 22, 22]).astype(
self.dtype
)
self.tmp_x = np.random.uniform(-np.pi, np.pi, [4, 256, 22, 22])


support_types = get_xpu_op_support_types('cos')
Expand Down
65 changes: 26 additions & 39 deletions test/xpu/test_elementwise_pow_op_xpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@
create_test_class,
get_xpu_op_support_types,
)
from op_test import OpTest, skip_check_grad_ci
from op_test import OpTest, convert_float_to_uint16, skip_check_grad_ci
from op_test_xpu import XPUOpTest

import paddle
Expand All @@ -40,73 +40,60 @@ def setUp(self):
self.dtype = self.in_type
self.__class__.no_need_check_grad = True
self.compute_input_output()

def compute_input_output(self):
if self.dtype == np.uint16:
# bfloat16 actually
self.x = convert_float_to_uint16(self.tmp_x)
self.y = convert_float_to_uint16(self.tmp_y)
else:
self.x = self.tmp_x.astype(self.dtype)
self.y = self.tmp_y.astype(self.dtype)
self.inputs = {
'X': np.random.uniform(1, 2, [20, 5]).astype(self.dtype),
'Y': np.random.uniform(1, 2, [20, 5]).astype(self.dtype),
'X': self.x,
'Y': self.y,
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}

def compute_input_output(self):
self.tmp_x = np.random.uniform(1, 2, [20, 5])
self.tmp_y = np.random.uniform(1, 2, [20, 5])

def test_check_output(self):
if paddle.is_compiled_with_xpu():
place = paddle.XPUPlace(0)
self.check_output_with_place(place, check_dygraph=False)

class TestElementwisePowOp_big_shape_1(TestElementwisePowOp):
def compute_input_output(self):
self.inputs = {
'X': np.random.uniform(1, 2, [10, 10]).astype(self.dtype),
'Y': np.random.uniform(0.1, 1, [10, 10]).astype(self.dtype),
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
self.tmp_x = np.random.uniform(1, 2, [10, 10])
self.tmp_y = np.random.uniform(0.1, 1, [10, 10])

class TestElementwisePowOp_big_shape_2(TestElementwisePowOp):
def compute_input_output(self):
self.inputs = {
'X': np.random.uniform(1, 2, [10, 10]).astype(self.dtype),
'Y': np.random.uniform(0.2, 2, [10, 10]).astype(self.dtype),
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
self.tmp_x = np.random.uniform(1, 2, [10, 10])
self.tmp_y = np.random.uniform(0.2, 2, [10, 10])

@skip_check_grad_ci(
reason="[skip shape check] Use y_shape(1) to test broadcast."
)
class TestElementwisePowOp_scalar(TestElementwisePowOp):
def compute_input_output(self):
self.inputs = {
'X': np.random.uniform(0.1, 1, [3, 3, 4]).astype(self.dtype),
'Y': np.random.uniform(0.1, 1, [1]).astype(self.dtype),
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
self.tmp_x = np.random.uniform(0.1, 1, [3, 3, 4])
self.tmp_y = np.random.uniform(0.1, 1, [1])

class TestElementwisePowOp_tensor(TestElementwisePowOp):
def compute_input_output(self):
self.inputs = {
'X': np.random.uniform(0.1, 1, [100]).astype(self.dtype),
'Y': np.random.uniform(1, 3, [100]).astype(self.dtype),
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
self.tmp_x = np.random.uniform(0.1, 1, [100])
self.tmp_y = np.random.uniform(1, 3, [100])

class TestElementwisePowOp_broadcast_0(TestElementwisePowOp):
def compute_input_output(self):
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 1, 100]).astype(self.dtype),
'Y': np.random.uniform(0.1, 1, [100]).astype(self.dtype),
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
self.tmp_x = np.random.uniform(0.1, 1, [2, 1, 100])
self.tmp_y = np.random.uniform(0.1, 1, [100])

class TestElementwisePowOp_broadcast_4(TestElementwisePowOp):
def compute_input_output(self):
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 10, 3, 5]).astype(
self.dtype
),
'Y': np.random.uniform(0.1, 1, [2, 10, 1, 5]).astype(
self.dtype
),
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
self.tmp_x = np.random.uniform(0.1, 1, [2, 10, 3, 5])
self.tmp_y = np.random.uniform(0.1, 1, [2, 10, 1, 5])

class TestElementwisePowOpInt(OpTest):
def setUp(self):
Expand Down