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[Accuracy diff No.181、182、183] fix maximum_grad minimum_grad bug #71716

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wanghuancoder merged 15 commits into from
Mar 20, 2025
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[Accuracy diff No.181、182、183] fix maximum_grad minimum_grad bug #71716

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0afb843
fix maximum_grad minimum_grad bug
wanghuancoder Mar 17, 2025
d05b0a0
refine
wanghuancoder Mar 17, 2025
128cfff
refine
wanghuancoder Mar 17, 2025
afa0bff
refine
wanghuancoder Mar 17, 2025
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into…
wanghuancoder Mar 17, 2025
2d75d9c
refine
wanghuancoder Mar 18, 2025
eb63f7d
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wanghuancoder Mar 18, 2025
ee06371
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61 changes: 53 additions & 8 deletions paddle/fluid/prim/api/composite_backward/composite_backward_api.h
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Original file line number Diff line number Diff line change
Expand Up @@ -1413,9 +1413,28 @@ void maximum_grad(const Tensor& x,
const Tensor& out_grad,
Tensor* x_grad,
Tensor* y_grad) {
Tensor half_tensor;
Tensor out_grad_copy = out_grad;
if (x_grad || y_grad) {
// cast, because divide and add kernel is not support bf16 and fp16 on CPU
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
out_grad_copy = cast<T>(out_grad, phi::DataType::FLOAT32);
}
auto equal_tensor = cast<T>(equal<T>(x, y), out_grad_copy.dtype());
auto tmp_tensor =
full<T>({1}, 2.0, out_grad_copy.dtype(), out_grad_copy.place());
half_tensor = (out_grad_copy / tmp_tensor) * equal_tensor;
}

if (x_grad) {
auto x_tmp = cast<T>(greater_than<T>(x, y), out_grad.dtype());
auto dx_res = out_grad * x_tmp;
auto x_tmp = cast<T>(greater_than<T>(x, y), out_grad_copy.dtype());
auto dx_res = out_grad_copy * x_tmp + half_tensor;
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
dx_res = cast<T>(dx_res, out_grad.dtype());
}

if (out_grad.dims() != x.dims()) {
// Maybe need reduce here
auto reduce_dim = get_reduce_dims(x.dims(), out_grad.dims());
Expand All @@ -1438,8 +1457,12 @@ void maximum_grad(const Tensor& x,
}

if (y_grad) {
auto y_tmp = cast<T>(less_equal<T>(x, y), out_grad.dtype());
auto dy_res = out_grad * y_tmp;
auto y_tmp = cast<T>(less_than<T>(x, y), out_grad_copy.dtype());
auto dy_res = out_grad_copy * y_tmp + half_tensor;
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
dy_res = cast<T>(dy_res, out_grad.dtype());
}
if (out_grad.dims() != y.dims()) {
// Maybe need reduce here
phi::DDim reduce_dim = get_reduce_dims(y.dims(), out_grad.dims());
Expand Down Expand Up @@ -1897,9 +1920,27 @@ void minimum_grad(const Tensor& x,
const Tensor& out_grad,
Tensor* x_grad,
Tensor* y_grad) {
Tensor half_tensor;
Tensor out_grad_copy = out_grad;
if (x_grad || y_grad) {
// cast, because divide and add kernel is not support bf16 and fp16 on CPU
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
out_grad_copy = cast<T>(out_grad, phi::DataType::FLOAT32);
}
auto equal_tensor = cast<T>(equal<T>(x, y), out_grad_copy.dtype());
auto tmp_tensor =
full<T>({1}, 2.0, out_grad_copy.dtype(), out_grad_copy.place());
half_tensor = (out_grad_copy / tmp_tensor) * equal_tensor;
}

if (x_grad) {
auto x_tmp = cast<T>(less_than<T>(x, y), out_grad.dtype());
auto dx_res = out_grad * x_tmp;
auto x_tmp = cast<T>(less_than<T>(x, y), out_grad_copy.dtype());
auto dx_res = out_grad_copy * x_tmp + half_tensor;
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
dx_res = cast<T>(dx_res, out_grad.dtype());
}
if (out_grad.dims() != x.dims()) {
// Maybe need reduce here
auto reduce_dim = get_reduce_dims(x.dims(), out_grad.dims());
Expand All @@ -1922,8 +1963,12 @@ void minimum_grad(const Tensor& x,
}

if (y_grad) {
auto y_tmp = cast<T>(greater_equal<T>(x, y), out_grad.dtype());
auto dy_res = out_grad * y_tmp;
auto y_tmp = cast<T>(greater_than<T>(x, y), out_grad_copy.dtype());
auto dy_res = out_grad_copy * y_tmp + half_tensor;
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
dy_res = cast<T>(dy_res, out_grad.dtype());
}
if (out_grad.dims() != y.dims()) {
// Maybe need reduce here
phi::DDim reduce_dim = get_reduce_dims(y.dims(), out_grad.dims());
Expand Down
60 changes: 52 additions & 8 deletions paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1217,9 +1217,27 @@ void maximum_grad(const Tensor& x,
const Tensor& out_grad,
Tensor* x_grad,
Tensor* y_grad) {
Tensor half_tensor;
Tensor out_grad_copy = out_grad;
if (x_grad || y_grad) {
// cast, because divide and add kernel is not support bf16 and fp16 on CPU
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
out_grad_copy = cast<T>(out_grad, phi::DataType::FLOAT32);

@xiaoguoguo626807 xiaoguoguo626807 Mar 20, 2025

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这个cast 有个统一的库函数，后面可以替换下

}
auto equal_tensor = cast<T>(equal<T>(x, y), out_grad_copy.dtype());
auto tmp_tensor =
full<T>({1}, 2.0, out_grad_copy.dtype(), out_grad_copy.place());
half_tensor = (out_grad_copy / tmp_tensor) * equal_tensor;
}

if (x_grad) {
auto x_tmp = cast<T>(greater_than<T>(x, y), out_grad.dtype());
auto dx_res = out_grad * x_tmp;
auto x_tmp = cast<T>(greater_than<T>(x, y), out_grad_copy.dtype());
auto dx_res = out_grad_copy * x_tmp + half_tensor;
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
dx_res = cast<T>(dx_res, out_grad.dtype());
}
if (has_dynamic_shape(x.shape()) || has_dynamic_shape(out_grad.shape()) ||
out_grad.dims() != x.dims()) {
auto dx_reduce_res = reduce_as<T>(dx_res, x);
Expand All @@ -1230,8 +1248,12 @@ void maximum_grad(const Tensor& x,
}

if (y_grad) {
auto y_tmp = cast<T>(less_equal<T>(x, y), out_grad.dtype());
auto dy_res = out_grad * y_tmp;
auto y_tmp = cast<T>(less_than<T>(x, y), out_grad_copy.dtype());
auto dy_res = out_grad_copy * y_tmp + half_tensor;
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
dy_res = cast<T>(dy_res, out_grad.dtype());
}
if (has_dynamic_shape(y.shape()) || has_dynamic_shape(out_grad.shape()) ||
out_grad.dims() != y.dims()) {
auto dy_reduce_res = reduce_as<T>(dy_res, y);
Expand Down Expand Up @@ -2243,9 +2265,27 @@ void minimum_grad(const Tensor& x,
const Tensor& out_grad,
Tensor* x_grad,
Tensor* y_grad) {
Tensor half_tensor;
Tensor out_grad_copy = out_grad;
if (x_grad || y_grad) {
// cast, because divide and add kernel is not support bf16 and fp16 on CPU
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
out_grad_copy = cast<T>(out_grad, phi::DataType::FLOAT32);
}
auto equal_tensor = cast<T>(equal<T>(x, y), out_grad_copy.dtype());
auto tmp_tensor =
full<T>({1}, 2.0, out_grad_copy.dtype(), out_grad_copy.place());
half_tensor = (out_grad_copy / tmp_tensor) * equal_tensor;
}

if (x_grad) {
auto x_tmp = cast<T>(less_than<T>(x, y), out_grad.dtype());
auto dx_res = out_grad * x_tmp;
auto x_tmp = cast<T>(less_than<T>(x, y), out_grad_copy.dtype());
auto dx_res = out_grad_copy * x_tmp + half_tensor;
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
dx_res = cast<T>(dx_res, out_grad.dtype());
}
if (has_dynamic_shape(x.shape()) || has_dynamic_shape(out_grad.shape()) ||
out_grad.dims() != x.dims()) {
auto dx_reduce_res = reduce_as<T>(dx_res, x);
Expand All @@ -2256,8 +2296,12 @@ void minimum_grad(const Tensor& x,
}

if (y_grad) {
auto y_tmp = cast<T>(greater_equal<T>(x, y), out_grad.dtype());
auto dy_res = out_grad * y_tmp;
auto y_tmp = cast<T>(greater_than<T>(x, y), out_grad_copy.dtype());
auto dy_res = out_grad_copy * y_tmp + half_tensor;
if (out_grad.dtype() == phi::DataType::BFLOAT16 ||
out_grad.dtype() == phi::DataType::FLOAT16) {
dy_res = cast<T>(dy_res, out_grad.dtype());
}
if (has_dynamic_shape(y.shape()) || has_dynamic_shape(out_grad.shape()) ||
out_grad.dims() != y.dims()) {
auto dy_reduce_res = reduce_as<T>(dy_res, y);
Expand Down
36 changes: 24 additions & 12 deletions paddle/phi/kernels/funcs/elementwise_functor.h
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -477,14 +477,16 @@ struct MaximumFunctor {
template <typename T>
struct MaxGradXFunctor {
inline HOSTDEVICE T operator()(const T x, const T y, const T dout) const {
return dout * static_cast<T>(x > y);
return dout * static_cast<T>(x > y) +
(dout / static_cast<T>(2)) * static_cast<T>(x == y);
}
};

template <typename T>
struct MaxGradYFunctor {
inline HOSTDEVICE T operator()(const T x, const T y, const T dout) const {
return dout * static_cast<T>(x <= y);
return dout * static_cast<T>(x < y) +
(dout / static_cast<T>(2)) * static_cast<T>(x == y);
}
};

Expand All @@ -494,10 +496,14 @@ struct MaxGradXYFunctor {
const InT y,
const InT dout) {
phi::Array<OutT, 2> outs;
// dx = dout * (x > y)
outs[0] = static_cast<OutT>(dout * static_cast<InT>(x > y));
// dy = dout * (x <= y)
outs[1] = static_cast<OutT>(dout * static_cast<InT>(x <= y));
// dx = dout * (x > y) + dout / 2 * (x == y)
outs[0] = static_cast<OutT>(dout * static_cast<InT>(x > y) +
(dout / static_cast<InT>(2)) *
static_cast<InT>(x == y));
// dy = dout * (x < y) + dout / 2 * (x == y)
outs[1] = static_cast<OutT>(dout * static_cast<InT>(x < y) +
(dout / static_cast<InT>(2)) *
static_cast<InT>(x == y));
return outs;
}
};
Expand All @@ -512,13 +518,15 @@ struct MinimumFunctor {
template <typename T>
struct MinGradXFunctor {
inline HOSTDEVICE T operator()(const T x, const T y, const T dout) const {
return dout * static_cast<T>(x < y);
return dout * static_cast<T>(x < y) +
(dout / static_cast<T>(2)) * static_cast<T>(x == y);
}
};
template <typename T>
struct MinGradYFunctor {
inline HOSTDEVICE T operator()(const T x, const T y, const T dout) const {
return dout * static_cast<T>(x >= y);
return dout * static_cast<T>(x > y) +
(dout / static_cast<T>(2)) * static_cast<T>(x == y);
}
};

Expand All @@ -528,10 +536,14 @@ struct MinGradXYFunctor {
const InT y,
const InT dout) {
phi::Array<OutT, 2> outs;
// dx = dout * (x < y)
outs[0] = static_cast<OutT>(dout * static_cast<InT>(x < y));
// dy = dout * (x >= y)
outs[1] = static_cast<OutT>(dout * static_cast<InT>(x >= y));
// dx = dout * (x < y) + dout / 2 * (x == y)
outs[0] = static_cast<OutT>(dout * static_cast<InT>(x < y) +
(dout / static_cast<InT>(2)) *
static_cast<InT>(x == y));
// dy = dout * (x > y) + dout / 2 * (x == y)
outs[1] = static_cast<OutT>(dout * static_cast<InT>(x > y) +
(dout / static_cast<InT>(2)) *
static_cast<InT>(x == y));
return outs;
}
};
Expand Down
12 changes: 8 additions & 4 deletions paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h
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Original file line number Diff line number Diff line change
Expand Up @@ -1305,14 +1305,16 @@ void MultiplyTripleGradKernel(const Context& dev_ctx,
template <typename T>
struct MaxGradDx {
HOSTDEVICE T operator()(T x, T y, T out UNUSED, T dout) const {
return dout * static_cast<T>(x > y);
return dout * static_cast<T>(x > y) +
(dout / static_cast<T>(2)) * static_cast<T>(x == y);
}
};

template <typename T>
struct MaxGradDy {
HOSTDEVICE T operator()(T x, T y, T out UNUSED, T dout) const {
return dout * static_cast<T>(x <= y);
return dout * static_cast<T>(x < y) +
(dout / static_cast<T>(2)) * static_cast<T>(x == y);
}
};

Expand All @@ -1324,14 +1326,16 @@ struct MaxGradDy {
template <typename T>
struct MinGradDx {
HOSTDEVICE T operator()(T x, T y, T out UNUSED, T dout) const {
return dout * static_cast<T>(x < y);
return dout * static_cast<T>(x < y) +
(dout / static_cast<T>(2)) * static_cast<T>(x == y);
}
};

template <typename T>
struct MinGradDy {
HOSTDEVICE T operator()(T x, T y, T out UNUSED, T dout) const {
return dout * static_cast<T>(x >= y);
return dout * static_cast<T>(x > y) +
(dout / static_cast<T>(2)) * static_cast<T>(x == y);
}
};

Expand Down
17 changes: 17 additions & 0 deletions test/legacy_test/test_elementwise_max_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -370,5 +370,22 @@ def setUp(self):
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


class TestElementwiseOpEqualInput(TestElementwiseOp):
def init_data(self):
self.x = np.ones([13, 17]).astype(np.float32)
self.y = np.ones([13, 17]).astype(np.float32)

def setUp(self):
self.init_data()
self.op_type = "elementwise_max"
self.prim_op_type = "prim"
self.if_enable_cinn()
self.python_api = paddle.maximum
self.dtype = np.float32
self.public_python_api = paddle.maximum
self.inputs = {'X': self.x, 'Y': self.y}
self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}


if __name__ == '__main__':
unittest.main()
17 changes: 17 additions & 0 deletions test/legacy_test/test_elementwise_min_op.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -451,5 +451,22 @@ def init_data(self):
)


class TestElementwiseOpEqualInput(TestElementwiseOp):
def init_data(self):
self.x = np.ones([13, 17]).astype(np.float32)
self.y = np.ones([13, 17]).astype(np.float32)

def setUp(self):
self.init_data()
self.op_type = "elementwise_min"
self.prim_op_type = "prim"
self.if_enable_cinn()
self.python_api = paddle.minimum
self.dtype = np.float32
self.public_python_api = paddle.minimum
self.inputs = {'X': self.x, 'Y': self.y}
self.outputs = {'Out': np.minimum(self.inputs['X'], self.inputs['Y'])}


if __name__ == '__main__':
unittest.main()
44 changes: 44 additions & 0 deletions test/legacy_test/test_maximum_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,6 +120,50 @@ def test_dynamic_api(self):
res = res.numpy()
np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05)

@unittest.skipIf(
core.is_compiled_with_xpu(),
"XPU need fix the bug",
)
def test_equal_tensors(self):
numpy_tensor = np.ones([10000]).astype("float32")
paddle_x = paddle.to_tensor(numpy_tensor)
paddle_x.stop_gradient = False
numpy_tensor = np.ones([10000]).astype("float32")
paddle_x2 = paddle.to_tensor(numpy_tensor)
paddle_x2.stop_gradient = False

numpy_tensor = np.ones([10000]).astype("float32")
paddle_outgrad = paddle.to_tensor(numpy_tensor)

paddle_out = paddle.maximum(paddle_x, paddle_x2)
paddle_x_grad, paddle_x2_grad = paddle.grad(
[paddle_out],
[paddle_x, paddle_x2],
grad_outputs=[paddle_outgrad],
allow_unused=True,
)

np.testing.assert_allclose(
paddle_out.numpy(),
numpy_tensor,
1e-2,
1e-2,
)

np.testing.assert_allclose(
paddle_x_grad.numpy(),
numpy_tensor * 0.5,
1e-2,
1e-2,
)

np.testing.assert_allclose(
paddle_x2_grad.numpy(),
numpy_tensor * 0.5,
1e-2,
1e-2,
)


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