Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[XPU] fix performance degradation of sharding_stage1_overlap #63920

Merged
merged 1 commit into from
Apr 30, 2024
Merged

[XPU] fix performance degradation of sharding_stage1_overlap #63920

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 4 additions & 0 deletions paddle/phi/backends/xpu/xpu2_op_list.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -386,12 +386,14 @@ XPUOpMap& get_kl2_ops() {
XPUKernelSet({phi::DataType::INT64,
phi::DataType::INT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16,
phi::DataType::FLOAT64,
phi::DataType::FLOAT32})},
{"fill_any",
XPUKernelSet({phi::DataType::INT64,
phi::DataType::INT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16,
phi::DataType::FLOAT64,
phi::DataType::FLOAT32})},
{"fill_any_like",
Expand Down Expand Up @@ -430,12 +432,14 @@ XPUOpMap& get_kl2_ops() {
phi::DataType::INT32,
phi::DataType::INT8,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16,
phi::DataType::FLOAT32})},
{"flatten_contiguous_range",
XPUKernelSet({phi::DataType::INT64,
phi::DataType::INT32,
phi::DataType::INT8,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16,
phi::DataType::FLOAT32})},
{"flatten_grad",
XPUKernelSet({phi::DataType::INT64,
Expand Down
4 changes: 4 additions & 0 deletions paddle/phi/backends/xpu/xpu3_op_list.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -389,12 +389,14 @@ XPUOpMap& get_kl3_ops() {
XPUKernelSet({phi::DataType::INT64,
phi::DataType::INT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16,
phi::DataType::FLOAT64,
phi::DataType::FLOAT32})},
{"fill_any",
XPUKernelSet({phi::DataType::INT64,
phi::DataType::INT32,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16,
phi::DataType::FLOAT64,
phi::DataType::FLOAT32})},
{"fill_any_like",
Expand Down Expand Up @@ -447,12 +449,14 @@ XPUOpMap& get_kl3_ops() {
phi::DataType::INT32,
phi::DataType::INT8,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16,
phi::DataType::FLOAT32})},
{"flatten_contiguous_range",
XPUKernelSet({phi::DataType::INT64,
phi::DataType::INT32,
phi::DataType::INT8,
phi::DataType::FLOAT16,
phi::DataType::BFLOAT16,
phi::DataType::FLOAT32})},
{"flatten_grad",
XPUKernelSet({phi::DataType::INT64,
Expand Down
46 changes: 12 additions & 34 deletions paddle/phi/kernels/funcs/math_function.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -121,50 +121,28 @@ struct TensorSetConstantXPU {
auto* ctx = phi::DeviceContextPool::Instance().Get(place_);
auto begin = ctx->Alloc<T>(tensor_);
int numel = tensor_->numel();
std::unique_ptr<T[]> data_cpu(new T[numel]);
std::fill(data_cpu.get(), data_cpu.get() + numel, static_cast<T>(value_));
memory_utils::Copy(place_,
begin,
phi::CPUPlace(),
static_cast<void*>(data_cpu.get()),
numel * sizeof(T));
}
phi::DenseTensor* tensor_;
U value_;
phi::Place place_;
};

template <>
struct TensorSetConstantXPU<float> {
TensorSetConstantXPU(phi::DenseTensor* tensor, float value, phi::Place place)
: tensor_(tensor), value_(value), place_(place) {}
template <typename T>
void apply() const {
auto* ctx = phi::DeviceContextPool::Instance().Get(place_);
auto begin = ctx->Alloc<T>(tensor_);
int numel = tensor_->numel();
if ((std::is_same<T, float>::value) ||
(std::is_same<T, phi::dtype::bfloat16>::value) ||
(std::is_same<T, phi::dtype::float16>::value)) {
using XPUType = typename XPUTypeTrait<T>::Type;
auto* dev_ctx = static_cast<phi::XPUContext*>(ctx);
int r = xpu::constant<XPUType>(dev_ctx->x_context(),
reinterpret_cast<XPUType*>(begin),
numel,
static_cast<XPUType>(value_));
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
} else {
if (std::is_same<T, phi::dtype::complex<float>>::value ||
std::is_same<T, phi::dtype::complex<double>>::value) {
std::unique_ptr<T[]> data_cpu(new T[numel]);
std::fill(data_cpu.get(), data_cpu.get() + numel, static_cast<T>(value_));
memory_utils::Copy(place_,
begin,
phi::CPUPlace(),
static_cast<void*>(data_cpu.get()),
numel * sizeof(T));
} else {
auto* dev_ctx = static_cast<phi::XPUContext*>(ctx);
using XPUType = typename XPUTypeTrait<T>::Type;
T val = static_cast<T>(value_);
int r = xpu::constant<XPUType>(dev_ctx->x_context(),
reinterpret_cast<XPUType*>(begin),
numel,
static_cast<XPUType>(val));
PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
}
}
phi::DenseTensor* tensor_;
float value_;
U value_;
phi::Place place_;
};
#endif
Expand Down
97 changes: 44 additions & 53 deletions test/xpu/test_fill_any_op_xpu.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
create_test_class,
get_xpu_op_support_types,
)
from op_test import convert_float_to_uint16
from op_test_xpu import XPUOpTest

import paddle
Expand All @@ -35,15 +36,17 @@ def __init__(self):
class TestFillAnyOp(XPUOpTest):
def setUp(self):
self.op_type = "fill_any"
self.dtype = 'float64'
self.dtype = self.in_type
self.value = 0.0
self.init()
self.inputs = {'X': np.random.random((20, 30)).astype(self.dtype)}
self.attrs = {'value': float(self.value)}
self.outputs = {
'Out': self.value
* np.ones_like(self.inputs["X"]).astype(self.dtype)
}
out_np = self.value * np.ones_like(self.inputs["X"])
if self.dtype == np.uint16:
out_np = convert_float_to_uint16(out_np)
else:
out_np = out_np.astype(self.dtype)
self.outputs = {'Out': out_np}

def init(self):
pass
Expand All @@ -54,61 +57,49 @@ def test_check_output(self):
def test_check_grad(self):
self.check_grad_with_place(paddle.XPUPlace(0), ['X'], 'Out')

class TestFillAnyOpFloat32(TestFillAnyOp):
def init(self):
self.dtype = np.float32
self.value = 0.0

class TestFillAnyOpFloat16(TestFillAnyOp):
def init(self):
self.dtype = np.float16

class TestFillAnyOpvalue1(TestFillAnyOp):
def init(self):
self.dtype = np.float32
self.value = 111111555
self.value = 11555

class TestFillAnyOpvalue2(TestFillAnyOp):
def init(self):
self.dtype = np.float32
self.value = 11111.1111

class TestFillAnyInplace(unittest.TestCase):
def test_fill_any_version(self):
with paddle.base.dygraph.guard():
var = paddle.to_tensor(np.ones((4, 2, 3)).astype(np.float32))
self.assertEqual(var.inplace_version, 0)

var.fill_(0)
self.assertEqual(var.inplace_version, 1)

var.fill_(0)
self.assertEqual(var.inplace_version, 2)

var.fill_(0)
self.assertEqual(var.inplace_version, 3)

def test_fill_any_equal(self):
with paddle.base.dygraph.guard():
tensor = paddle.to_tensor(
np.random.random((20, 30)).astype(np.float32)
)
target = tensor.numpy()
target[...] = 1

tensor.fill_(1)
self.assertEqual((tensor.numpy() == target).all().item(), True)

def test_backward(self):
with paddle.base.dygraph.guard():
x = paddle.full([10, 10], -1.0, dtype='float32')
x.stop_gradient = False
y = 2 * x
y.fill_(1)
y.backward()
np.testing.assert_array_equal(
x.grad.numpy(), np.zeros([10, 10])
)

class TestFillAnyInplace(unittest.TestCase):
def test_fill_any_version(self):
with paddle.base.dygraph.guard():
var = paddle.to_tensor(np.ones((4, 2, 3)).astype(np.float32))
self.assertEqual(var.inplace_version, 0)

var.fill_(0)
self.assertEqual(var.inplace_version, 1)

var.fill_(0)
self.assertEqual(var.inplace_version, 2)

var.fill_(0)
self.assertEqual(var.inplace_version, 3)

def test_fill_any_equal(self):
with paddle.base.dygraph.guard():
tensor = paddle.to_tensor(
np.random.random((20, 30)).astype(np.float32)
)
target = tensor.numpy()
target[...] = 1

tensor.fill_(1)
self.assertEqual((tensor.numpy() == target).all().item(), True)

def test_backward(self):
with paddle.base.dygraph.guard():
x = paddle.full([10, 10], -1.0, dtype='float32')
x.stop_gradient = False
y = 2 * x
y.fill_(1)
y.backward()
np.testing.assert_array_equal(x.grad.numpy(), np.zeros([10, 10]))


class TestFillAnyLikeOpSpecialValue(unittest.TestCase):
Expand Down