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未验证 提交 b6bf650a 编写于 作者: F fwenguang 提交者： GitHub
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[MLU]support calling mlu op from python interface (#38292) 

* [MLU]support calling mlu op from python interface

* [MLU]fix

* fix

* [mlu]fix mlu_places

* [mlu]fix required mlu

* fix

* [MLU]fix tensor copy

* [mlu] fix MLUPlace call path
上级 1df354e7
隐藏空白更改
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Showing with 409 addition and 17 deletion
paddle/fluid/framework/tensor_util.cc
浏览文件 @ b6bf650a
......@@ -357,6 +357,36 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
"Copying from %s to %s is not supported.", src_place, dst_place));
}
#endif
#ifdef PADDLE_WITH_MLU
else if (platform::is_mlu_place(src_place) && // NOLINT
platform::is_cpu_place(dst_place)) {
auto src_mlu_place = BOOST_GET_CONST(platform::MLUPlace, src_place);
auto dst_cpu_place = BOOST_GET_CONST(platform::CPUPlace, dst_place);
auto stream =
reinterpret_cast<const platform::MLUDeviceContext&>(ctx).stream();
memory::Copy(dst_cpu_place, dst_ptr, src_mlu_place, src_ptr, size, stream);
}
else if (platform::is_cpu_place(src_place) && // NOLINT
platform::is_mlu_place(dst_place)) {
auto src_cpu_place = BOOST_GET_CONST(platform::CPUPlace, src_place);
auto dst_mlu_place = BOOST_GET_CONST(platform::MLUPlace, dst_place);
auto stream =
reinterpret_cast<const platform::MLUDeviceContext&>(ctx).stream();
memory::Copy(dst_mlu_place, dst_ptr, src_cpu_place, src_ptr, size, stream);
}
else if (platform::is_mlu_place(src_place) && // NOLINT
platform::is_mlu_place(dst_place)) {
auto src_mlu_place = BOOST_GET_CONST(platform::MLUPlace, src_place);
auto dst_mlu_place = BOOST_GET_CONST(platform::MLUPlace, dst_place);
auto stream =
reinterpret_cast<const platform::MLUDeviceContext&>(ctx).stream();
memory::Copy(dst_mlu_place, dst_ptr, src_mlu_place, src_ptr, size, stream);
}
else { // NOLINT
PADDLE_THROW(platform::errors::Unimplemented(
"Copying from %s to %s is not supported.", src_place, dst_place));
}
#endif
}
void TensorCopy(const Tensor& src, const platform::Place& dst_place,
......@@ -526,6 +556,35 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
"Copy from %s to %s is not supported.", src_place, dst_place));
}
#endif
#ifdef PADDLE_WITH_MLU
else if (platform::is_mlu_place(src_place) && // NOLINT
platform::is_cpu_place(dst_place)) {
memory::Copy(BOOST_GET_CONST(platform::CPUPlace, dst_place), dst_ptr,
BOOST_GET_CONST(platform::MLUPlace, src_place), src_ptr, size,
nullptr);
}
else if (platform::is_cpu_place(src_place) && // NOLINT
platform::is_mlu_place(dst_place)) {
memory::Copy(BOOST_GET_CONST(platform::MLUPlace, dst_place), dst_ptr,
BOOST_GET_CONST(platform::CPUPlace, src_place), src_ptr, size,
nullptr);
}
else if (platform::is_mlu_place(src_place) && // NOLINT
platform::is_mlu_place(dst_place)) {
if (src_ptr == dst_ptr) {
VLOG(3) << "Skip copy the same data async from " << src_place << " to "
<< dst_place;
return;
}
memory::Copy(BOOST_GET_CONST(platform::MLUPlace, dst_place), dst_ptr,
BOOST_GET_CONST(platform::MLUPlace, src_place), src_ptr, size,
nullptr);
}
else { // NOLINT
PADDLE_THROW(platform::errors::Unimplemented(
"Copy from %s to %s is not supported.", src_place, dst_place));
}
#endif
}
template <typename Predicate, typename DevCtx>
......
paddle/fluid/imperative/prepared_operator.cc
浏览文件 @ b6bf650a
......@@ -217,6 +217,16 @@ PreparedOp PrepareImpl(const NameVarMap<VarType>& ins,
expected_kernel_key.place_ = platform::CPUPlace();
kernel_iter = kernels.find(expected_kernel_key);
}
#endif
#ifdef PADDLE_WITH_MLU
if (kernel_iter == kernels.end() &&
is_mlu_place(expected_kernel_key.place_)) {
VLOG(3) << "missing MLU kernel: " << op.Type()
<< ", expected_kernel_key:" << expected_kernel_key
<< ", fallbacking to CPU one!";
expected_kernel_key.place_ = platform::CPUPlace();
kernel_iter = kernels.find(expected_kernel_key);
}
#endif
// TODO(jiabin): Add operator.cc's line 1000 part back when we need that
// case
......
paddle/fluid/memory/allocation/allocator_facade.cc
浏览文件 @ b6bf650a
......@@ -690,7 +690,7 @@ class AllocatorFacadePrivate {
#ifdef PADDLE_WITH_MLU
int device_count = platform::GetMLUDeviceCount();
for (int i = 0; i < device_count; ++i) {
platform::XPUPlace p(i);
platform::MLUPlace p(i);
system_allocators_[p] = std::make_shared<NaiveBestFitAllocator>(p);
}
#endif
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ b6bf650a
......@@ -137,6 +137,10 @@ limitations under the License. */
#include "paddle/fluid/platform/ipu_info.h"
#endif
#ifdef PADDLE_WITH_MLU
#include "paddle/fluid/platform/device/mlu/mlu_info.h"
#endif
#ifdef PADDLE_WITH_CRYPTO
#include "paddle/fluid/pybind/crypto.h"
#endif
......@@ -164,6 +168,7 @@ PyTypeObject *g_cpuplace_pytype = nullptr;
PyTypeObject *g_xpuplace_pytype = nullptr;
PyTypeObject *g_npuplace_pytype = nullptr;
PyTypeObject *g_cudapinnedplace_pytype = nullptr;
PyTypeObject *g_mluplace_pytype = nullptr;
PyTypeObject *g_framework_tensor_pytype = nullptr;
bool IsCompiledWithCUDA() {
......@@ -230,6 +235,14 @@ bool IsCompiledWithCINN() {
#endif
}
bool IsCompiledWithMLU() {
#ifndef PADDLE_WITH_MLU
return false;
#else
return true;
#endif
}
bool IsCompiledWithHETERPS() {
#ifndef PADDLE_WITH_HETERPS
return false;
......@@ -295,10 +308,9 @@ OpSupportedInfos(const std::string &place,
[](unsigned char c) { return std::toupper(c); });
using fn_type = std::add_pointer<bool(const platform::Place &)>::type;
std::unordered_map<std::string, fn_type> is_target_place{
{"GPU", &platform::is_gpu_place},
{"CPU", &platform::is_cpu_place},
{"XPU", &platform::is_xpu_place},
{"NPU", &platform::is_npu_place},
{"GPU", &platform::is_gpu_place}, {"CPU", &platform::is_cpu_place},
{"XPU", &platform::is_xpu_place}, {"NPU", &platform::is_npu_place},
{"MLU", &platform::is_mlu_place},
};
PADDLE_ENFORCE_NE(
is_target_place.count(query_place), 0,
......@@ -769,6 +781,10 @@ PYBIND11_MODULE(core_noavx, m) {
[](framework::Tensor &self, paddle::platform::NPUPlace &place) {
self.mutable_data<float>(place);
})
.def("_alloc_float",
[](framework::Tensor &self, paddle::platform::MLUPlace &place) {
self.mutable_data<float>(place);
})
.def("_alloc_double",
[](framework::Tensor &self, paddle::platform::CPUPlace &place) {
self.mutable_data<double>(place);
......@@ -785,6 +801,10 @@ PYBIND11_MODULE(core_noavx, m) {
[](framework::Tensor &self, paddle::platform::CUDAPlace &place) {
self.mutable_data<int>(place);
})
.def("_alloc_int",
[](framework::Tensor &self, paddle::platform::MLUPlace &place) {
self.mutable_data<int>(place);
})
.def("_alloc_int",
[](framework::Tensor &self,
paddle::platform::CUDAPinnedPlace &place) {
......@@ -815,6 +835,11 @@ PYBIND11_MODULE(core_noavx, m) {
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(self.mutable_data(place, type));
})
.def("_mutable_data",
[](framework::Tensor &self, paddle::platform::MLUPlace &place,
paddle::framework::proto::VarType::Type type) {
return reinterpret_cast<uintptr_t>(self.mutable_data(place, type));
})
.def("_clear", &framework::Tensor::clear)
.def("_mutable_data",
[](framework::Tensor &self, paddle::platform::NPUPlace &place,
......@@ -831,6 +856,8 @@ PYBIND11_MODULE(core_noavx, m) {
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::CUDAPinnedPlace>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::MLUPlace>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("_copy_from", &TensorCopyFrom<paddle::platform::Place>,
py::arg("tensor"), py::arg("place"), py::arg("batch_size") = -1)
.def("set", SetTensorFromPyArray<paddle::platform::CPUPlace>,
......@@ -843,6 +870,8 @@ PYBIND11_MODULE(core_noavx, m) {
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::IPUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::MLUPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false)
.def("set", SetTensorFromPyArray<paddle::platform::CUDAPinnedPlace>,
py::arg("array"), py::arg("place"), py::arg("zero_copy") = false,
R"DOC(
......@@ -850,7 +879,7 @@ PYBIND11_MODULE(core_noavx, m) {
Args:
lod (numpy.ndarray): The data to set.
place (CPUPlace|CUDAPlace|XPUPlace|IPUPlace|CUDAPinnedPlace|NPUPlace): The place where the
place (CPUPlace|CUDAPlace|XPUPlace|IPUPlace|CUDAPinnedPlace|NPUPlace|MLUPlace): The place where the
LoDTensor is to be set.
zero_copy (bool, optional): Whether to share memory with the input numpy array.
This parameter only works with CPUPlace. Default: False.
......@@ -1619,6 +1648,18 @@ All parameter, weight, gradient are variables in Paddle.
"Please recompile or reinstall Paddle with XPU support."));
#else
return new paddle::platform::XPUDeviceContext(place);
#endif
})
.def_static("create",
[](paddle::platform::MLUPlace& place)
-> paddle::platform::DeviceContext* {
#ifndef PADDLE_WITH_MLU
PADDLE_THROW(
platform::errors::PermissionDenied(
"Cannot use MLUPlace in CPU/GPU version, "
"Please recompile or reinstall Paddle with MLU support."));
#else
return new paddle::platform::MLUDeviceContext(place);
#endif
})
.def_static("create",
......@@ -1736,6 +1777,7 @@ All parameter, weight, gradient are variables in Paddle.
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::CPUPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::XPUPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::NPUPlace>)
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::MLUPlace>)
.def("_equals",
&IsSamePlace<platform::CUDAPlace, platform::CUDAPinnedPlace>)
.def("_get_device_id",
......@@ -2004,6 +2046,75 @@ All parameter, weight, gradient are variables in Paddle.
#endif
.def("__str__", string::to_string<const platform::IPUPlace &>);
// MLUPlace
py::class_<platform::MLUPlace> mluplace(m, "MLUPlace", R"DOC(
MLUPlace is a descriptor of a device.
It represents a MLU device on which a tensor will be allocated and a model will run.
Examples:
.. code-block:: python
import paddle
# required: mlu
mlu_place = paddle.MLUPlace(0)
)DOC");
g_mluplace_pytype = reinterpret_cast<PyTypeObject *>(mluplace.ptr());
mluplace
.def("__init__",
[](platform::MLUPlace &self, int dev_id) {
#ifdef PADDLE_WITH_MLU
if (UNLIKELY(dev_id < 0)) {
LOG(ERROR) << string::Sprintf(
"Invalid MLUPlace(%d), device id must be 0 or "
"positive integer",
dev_id);
std::exit(-1);
}
if (UNLIKELY(dev_id >= platform::GetMLUDeviceCount())) {
if (platform::GetMLUDeviceCount() == 0) {
LOG(ERROR) << "Cannot use MLU because there is no MLU "
"detected on your "
"machine.";
std::exit(-1);
} else {
LOG(ERROR) << string::Sprintf(
"Invalid MLUPlace(%d), must inside [0, %d), because MLU "
"number on your machine is %d",
dev_id, platform::GetMLUDeviceCount(),
platform::GetMLUDeviceCount());
std::exit(-1);
}
}
new (&self) platform::MLUPlace(dev_id);
#else
LOG(ERROR) << string::Sprintf(
"Cannot use MLU because you have installed CPU/GPU/... "
"version "
"PaddlePaddle.\n"
"If you want to use MLU, please try to install MLU version "
"PaddlePaddle by: pip install paddlepaddle-mlu\n"
"If you only have CPU, please change MLUPlace(%d) to be "
"CPUPlace().\n",
dev_id);
std::exit(-1);
#endif
})
.def("_type", &PlaceIndex<platform::MLUPlace>)
#ifdef PADDLE_WITH_MLU
.def("_equals", &IsSamePlace<platform::MLUPlace, platform::Place>)
.def("_equals", &IsSamePlace<platform::MLUPlace, platform::CUDAPlace>)
.def("_equals", &IsSamePlace<platform::MLUPlace, platform::CPUPlace>)
.def("_equals", &IsSamePlace<platform::MLUPlace, platform::XPUPlace>)
.def("_equals", &IsSamePlace<platform::MLUPlace, platform::NPUPlace>)
.def("_equals", &IsSamePlace<platform::MLUPlace, platform::IPUPlace>)
.def("_equals", &IsSamePlace<platform::MLUPlace, platform::MLUPlace>)
.def("_equals",
&IsSamePlace<platform::MLUPlace, platform::CUDAPinnedPlace>)
.def("get_device_id",
[](const platform::MLUPlace &self) { return self.GetDeviceId(); })
#endif
.def("__str__", string::to_string<const platform::MLUPlace &>);
py::class_<platform::Place> platformplace(m, "Place");
g_place_pytype = reinterpret_cast<PyTypeObject *>(platformplace.ptr());
platformplace.def(py::init<>())
......@@ -2015,6 +2126,7 @@ All parameter, weight, gradient are variables in Paddle.
.def("_equals", &IsSamePlace<platform::Place, platform::NPUPlace>)
.def("_equals", &IsSamePlace<platform::Place, platform::IPUPlace>)
.def("_equals", &IsSamePlace<platform::Place, platform::CUDAPinnedPlace>)
.def("_equals", &IsSamePlace<platform::Place, platform::MLUPlace>)
.def("is_gpu_place",
[](platform::Place &self) { return platform::is_gpu_place(self); })
.def("is_cpu_place",
......@@ -2029,6 +2141,8 @@ All parameter, weight, gradient are variables in Paddle.
[](platform::Place &self) {
return platform::is_cuda_pinned_place(self);
})
.def("is_mlu_place",
[](platform::Place &self) { return platform::is_mlu_place(self); })
.def("gpu_device_id",
[](platform::Place &self) {
return BOOST_GET_CONST(platform::CUDAPlace, self).device;
......@@ -2045,6 +2159,10 @@ All parameter, weight, gradient are variables in Paddle.
[](platform::Place &self) {
return BOOST_GET_CONST(platform::IPUPlace, self).device;
})
.def("mlu_device_id",
[](platform::Place &self) {
return BOOST_GET_CONST(platform::MLUPlace, self).device;
})
.def("set_place", [](platform::Place &self,
const platform::Place &other) { self = other; })
.def("set_place",
......@@ -2072,6 +2190,10 @@ All parameter, weight, gradient are variables in Paddle.
[](platform::Place &self, const platform::IPUPlace &ipu_place) {
self = ipu_place;
})
.def("set_place",
[](platform::Place &self, const platform::MLUPlace &mlu_place) {
self = mlu_place;
})
.def("__repr__", string::to_string<const platform::Place &>)
.def("__str__", string::to_string<const platform::Place &>);
......@@ -2120,6 +2242,12 @@ All parameter, weight, gradient are variables in Paddle.
pybind11::gil_scoped_release release;
self.Run(scope, place);
})
.def("run",
[](OperatorBase &self, const Scope &scope,
const platform::MLUPlace &place) {
pybind11::gil_scoped_release release;
self.Run(scope, place);
})
.def("type",
[](const OperatorBase &op) -> std::string { return op.Type(); })
.def("outputs",
......@@ -2307,6 +2435,7 @@ All parameter, weight, gradient are variables in Paddle.
m.def("is_compiled_with_xpu", IsCompiledWithXPU);
m.def("is_compiled_with_mkldnn", IsCompiledWithMKLDNN);
m.def("is_compiled_with_cinn", IsCompiledWithCINN);
m.def("is_compiled_with_mlu", IsCompiledWithMLU);
m.def("_is_compiled_with_heterps", IsCompiledWithHETERPS);
m.def("supports_bfloat16", SupportsBfloat16);
m.def("supports_bfloat16_fast_performance", SupportsBfloat16FastPerformance);
......@@ -2627,6 +2756,10 @@ All parameter, weight, gradient are variables in Paddle.
m.def("get_ipu_device_count", platform::GetIPUDeviceCount);
#endif
#ifdef PADDLE_WITH_MLU
m.def("get_mlu_device_count", platform::GetMLUDeviceCount);
#endif
py::enum_<platform::TracerOption>(m, "TracerOption", py::arithmetic())
.value("kDefault", platform::TracerOption::kDefault)
.value("kOpDetail", platform::TracerOption::kOpDetail)
......
paddle/fluid/pybind/tensor_py.h
浏览文件 @ b6bf650a
......@@ -345,6 +345,18 @@ void SetTensorFromPyArrayT(
PADDLE_THROW(platform::errors::PermissionDenied(
"Cannot use NPUPlace in CPU/GPU/XPU version. "
"Please recompile or reinstall Paddle with NPU support."));
#endif
} else if (paddle::platform::is_mlu_place(place)) {
#ifdef PADDLE_WITH_MLU
platform::Place tmp_place = place;
platform::MLUDeviceGuard guard(
BOOST_GET_CONST(platform::MLUPlace, tmp_place).device);
auto dst = self->mutable_data<T>(place);
paddle::platform::MLUMemcpyH2DSync(dst, array.data(), array.nbytes());
#else
PADDLE_THROW(platform::errors::PermissionDenied(
"Cannot use MLUPlace in CPU/GPU version, "
"Please recompile or reinstall Paddle with MLU support."));
#endif
} else {
#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
......@@ -702,6 +714,7 @@ inline py::array TensorToPyArray(const framework::Tensor &tensor,
bool is_gpu_tensor = platform::is_gpu_place(tensor.place());
bool is_xpu_tensor = platform::is_xpu_place(tensor.place());
bool is_npu_tensor = platform::is_npu_place(tensor.place());
bool is_mlu_tensor = platform::is_mlu_place(tensor.place());
const auto &tensor_dims = tensor.dims();
auto tensor_dtype = tensor.type();
size_t sizeof_dtype = framework::SizeOfType(tensor_dtype);
......@@ -720,7 +733,7 @@ inline py::array TensorToPyArray(const framework::Tensor &tensor,
std::string py_dtype_str = details::TensorDTypeToPyDTypeStr(tensor.type());
if (!is_gpu_tensor && !is_xpu_tensor && !is_npu_tensor) {
if (!is_gpu_tensor && !is_xpu_tensor && !is_npu_tensor && !is_mlu_tensor) {
if (!need_deep_copy) {
auto base = py::cast(std::move(tensor));
return py::array(py::dtype(py_dtype_str.c_str()), py_dims, py_strides,
......@@ -816,6 +829,29 @@ inline py::array TensorToPyArray(const framework::Tensor &tensor,
PADDLE_THROW(platform::errors::PermissionDenied(
"Cannot use NPUPlace in CPU/GPU/XPU version, "
"Please recompile or reinstall Paddle with NPU support."));
#endif
} else if (is_mlu_tensor) {
#ifdef PADDLE_WITH_MLU
py::array py_arr(py::dtype(py_dtype_str.c_str()), py_dims, py_strides);
PADDLE_ENFORCE_EQ(py_arr.writeable(), true,
platform::errors::InvalidArgument(
"PyArray is not writable, in which case memory leak "
"or double free would occur"));
PADDLE_ENFORCE_EQ(
py_arr.owndata(), true,
platform::errors::InvalidArgument(
"PyArray does not own data, in which case memory leak "
"or double free would occur"));
size_t copy_bytes = sizeof_dtype * numel;
auto p = BOOST_GET_CONST(platform::MLUPlace, tensor.place());
paddle::memory::Copy(platform::CPUPlace(), py_arr.mutable_data(), p,
tensor_buf_ptr, copy_bytes, nullptr);
return py_arr;
#else
PADDLE_THROW(platform::errors::PermissionDenied(
"Cannot use MLUPlace in CPU/GPU/XPU/NPU version, "
"Please recompile or reinstall Paddle with MLU support."));
#endif
}
PADDLE_THROW(platform::errors::Unimplemented("Place is not supported"));
......
python/CMakeLists.txt
浏览文件 @ b6bf650a
......@@ -6,6 +6,8 @@ set(PY_FILES paddle/__init__.py
if(WITH_GPU)
SET(PACKAGE_NAME "paddlepaddle-gpu")
elseif(WITH_MLU)
SET(PACKAGE_NAME "paddlepaddle-mlu")
elseif(WITH_ROCM)
SET(PACKAGE_NAME "paddlepaddle-rocm")
elseif(WITH_ASCEND_CL)
......
python/paddle/__init__.py
浏览文件 @ b6bf650a
......@@ -291,6 +291,7 @@ from .framework import IPUPlace # noqa: F401
from .framework import CUDAPlace # noqa: F401
from .framework import NPUPlace # noqa: F401
from .framework import CUDAPinnedPlace # noqa: F401
from .framework import MLUPlace # noqa: F401
from .autograd import grad # noqa: F401
from .autograd import no_grad # noqa: F401
......@@ -322,6 +323,7 @@ from .fluid.framework import set_flags # noqa: F401
from .device import is_compiled_with_xpu # noqa: F401
from .device import is_compiled_with_npu # noqa: F401
from .device import is_compiled_with_ipu # noqa: F401
from .device import is_compiled_with_mlu # noqa: F401
from .device import XPUPlace # noqa: F401
from .fluid.dygraph.base import enable_dygraph as disable_static # noqa: F401
......
python/paddle/device/__init__.py
浏览文件 @ b6bf650a
......@@ -29,12 +29,14 @@ __all__ = [ # noqa
'get_device',
'XPUPlace',
'IPUPlace',
'MLUPlace',
'is_compiled_with_xpu',
'is_compiled_with_ipu',
'is_compiled_with_cinn',
'is_compiled_with_cuda',
'is_compiled_with_rocm',
'is_compiled_with_npu'
'is_compiled_with_npu',
'is_compiled_with_mlu'
]
_cudnn_version = None
......@@ -120,6 +122,41 @@ def XPUPlace(dev_id):
return core.XPUPlace(dev_id)
def is_compiled_with_mlu():
"""
Whether paddle was built with WITH_MLU=ON to support Cambricon MLU
Returns (bool): whether paddle was built with WITH_MLU=ON
Examples:
.. code-block:: python
# required: mlu
import paddle
support_mlu = paddle.device.is_compiled_with_mlu()
"""
return core.is_compiled_with_mlu()
def MLUPlace(dev_id):
"""
Return a Cambricon MLU Place
Parameters:
dev_id(int): MLU device id
Examples:
.. code-block:: python
# required: mlu
import paddle
place = paddle.device.MLUPlace(0)
"""
return core.MLUPlace(dev_id)
def get_cudnn_version():
"""
This funciton return the version of cudnn. the retuen value is int which represents the
......@@ -181,13 +218,21 @@ def _convert_to_place(device):
"The device should not be 'ipu', " \
"since PaddlePaddle is not compiled with IPU")
place = core.IPUPlace()
elif lower_device == 'mlu':
if not core.is_compiled_with_mlu():
raise ValueError("The device should not be 'mlu', "
"since PaddlePaddle is not compiled with MLU")
selected_mlus = os.getenv("FLAGS_selected_mlus", "0").split(",")
device_id = int(selected_mlus[0])
place = core.MLUPlace(device_id)
else:
avaliable_gpu_device = re.match(r'gpu:\d+', lower_device)
avaliable_xpu_device = re.match(r'xpu:\d+', lower_device)
avaliable_npu_device = re.match(r'npu:\d+', lower_device)
if not avaliable_gpu_device and not avaliable_xpu_device and not avaliable_npu_device:
avaliable_mlu_device = re.match(r'mlu:\d+', lower_device)
if not avaliable_gpu_device and not avaliable_xpu_device and not avaliable_npu_device and not avaliable_mlu_device:
raise ValueError(
"The device must be a string which is like 'cpu', 'gpu', 'gpu:x', 'xpu', 'xpu:x', 'npu', 'npu:x' or ipu"
"The device must be a string which is like 'cpu', 'gpu', 'gpu:x', 'xpu', 'xpu:x', 'mlu', 'mlu:x', 'npu', 'npu:x' or ipu"
)
if avaliable_gpu_device:
if not core.is_compiled_with_cuda():
......@@ -216,19 +261,28 @@ def _convert_to_place(device):
device_id = device_info_list[1]
device_id = int(device_id)
place = core.NPUPlace(device_id)
if avaliable_mlu_device:
if not core.is_compiled_with_mlu():
raise ValueError(
"The device should not be {}, since PaddlePaddle is "
"not compiled with mlu".format(avaliable_mlu_device))
device_info_list = device.split(':', 1)
device_id = device_info_list[1]
device_id = int(device_id)
place = core.MLUPlace(device_id)
return place
def set_device(device):
"""
Paddle supports running calculations on various types of devices, including CPU, GPU, XPU, NPU and IPU.
Paddle supports running calculations on various types of devices, including CPU, GPU, XPU, NPU, MLU and IPU.
They are represented by string identifiers. This function can specify the global device
which the OP will run.
Parameters:
device(str): This parameter determines the specific running device.
It can be ``cpu``, ``gpu``, ``xpu``, ``npu``, ``gpu:x``, ``xpu:x``, ``npu:x`` and ``ipu``,
where ``x`` is the index of the GPUs, XPUs or NPUs.
It can be ``cpu``, ``gpu``, ``xpu``, ``npu``, ``mlu``, ``gpu:x``, ``xpu:x``, ``npu:x``, ``mlu:x`` and ``ipu``,
where ``x`` is the index of the GPUs, XPUs, NPUs or MLUs.
Examples:
......@@ -249,7 +303,7 @@ def set_device(device):
def get_device():
"""
This funciton can get the current global device of the program is running.
It's a string which is like 'cpu', 'gpu:x', 'xpu:x' and 'npu:x'. if the global device is not
It's a string which is like 'cpu', 'gpu:x', 'xpu:x', 'mlu:x' and 'npu:x'. if the global device is not
set, it will return a string which is 'gpu:x' when cuda is avaliable or it
will return a string which is 'cpu' when cuda is not avaliable.
......@@ -277,6 +331,9 @@ def get_device():
elif isinstance(place, core.IPUPlace):
num_devices = core.get_ipu_device_count()
device = "ipus:{{0-{}}}".format(num_devices - 1)
elif isinstance(place, core.MLUPlace):
device_id = place.get_device_id()
device = 'mlu:' + str(device_id)
else:
raise ValueError("The device specification {} is invalid".format(place))
......
python/paddle/fluid/__init__.py
浏览文件 @ b6bf650a
......@@ -71,7 +71,7 @@ from . import distribute_lookup_table
from .param_attr import ParamAttr, WeightNormParamAttr
from .data_feeder import DataFeeder
from .core import LoDTensor, LoDTensorArray, Scope, _Scope
from .core import CPUPlace, XPUPlace, CUDAPlace, CUDAPinnedPlace, NPUPlace, IPUPlace
from .core import CPUPlace, XPUPlace, CUDAPlace, CUDAPinnedPlace, NPUPlace, IPUPlace, MLUPlace
from .incubate import fleet
from .transpiler import DistributeTranspiler, \
memory_optimize, release_memory, DistributeTranspilerConfig
......@@ -133,6 +133,7 @@ __all__ = framework.__all__ + executor.__all__ + \
'CUDAPinnedPlace',
'NPUPlace',
'IPUPlace',
'MLUPlace',
'Tensor',
'ParamAttr',
'WeightNormParamAttr',
......
python/paddle/fluid/framework.py
浏览文件 @ b6bf650a
......@@ -51,6 +51,7 @@ __all__ = [
'cuda_places',
'cpu_places',
'xpu_places',
'mlu_places',
'cuda_pinned_places',
'in_dygraph_mode',
'is_compiled_with_cinn',
......@@ -347,6 +348,18 @@ def _current_expected_place():
"You are using XPU version Paddle, but your XPU device is not set properly. CPU device will be used by default."
)
_global_expected_place_ = core.CPUPlace()
elif core.is_compiled_with_mlu():
try:
device_count = core.get_mlu_device_count()
except Exception as e:
device_count = 0
if device_count > 0:
_global_expected_place_ = core.MLUPlace(0)
else:
warnings.warn(
"You are using MLU version Paddle, but your MLU device is not set properly. CPU device will be used by default."
)
_global_expected_place_ = core.CPUPlace()
else:
_global_expected_place_ = core.CPUPlace()
......@@ -426,6 +439,15 @@ def _npu_ids():
return device_ids
def _mlu_ids():
mlus_env = os.getenv("FLAGS_selected_mlus")
if mlus_env:
device_ids = [int(s) for s in mlus_env.split(",")]
else:
device_ids = six.moves.range(core.get_mlu_device_count())
return device_ids
def is_compiled_with_xpu():
"""
Whether this whl package can be used to run the model on XPU.
......@@ -721,6 +743,48 @@ def cuda_pinned_places(device_count=None):
return [core.CUDAPinnedPlace()] * device_count
def mlu_places(device_ids=None):
"""
**Note**:
For multi-card tasks, please use `FLAGS_selected_mlus` environment variable to set the visible MLU device.
This function creates a list of :code:`paddle.device.MLUPlace` objects.
If :code:`device_ids` is None, environment variable of
:code:`FLAGS_selected_mlus` would be checked first. For example, if
:code:`FLAGS_selected_mlus=0,1,2`, the returned list would
be [paddle.device.MLUPlace(0), paddle.device.MLUPlace(1), paddle.device.MLUPlace(2)].
If :code:`FLAGS_selected_mlus` is not set, all visible
mlu places would be returned.
If :code:`device_ids` is not None, it should be the device
ids of MLUs. For example, if :code:`device_ids=[0,1,2]`,
the returned list would be
[paddle.device.MLUPlace(0), paddle.device.MLUPlace(1), paddle.device.MLUPlace(2)].
Parameters:
device_ids (list or tuple of int, optional): list of MLU device ids.
Returns:
list of paddle.device.MLUPlace: Created MLU place list.
Examples:
.. code-block:: python
# required: mlu
import paddle
import paddle.static as static
paddle.enable_static()
mlu_places = static.mlu_places()
"""
assert core.is_compiled_with_mlu(), \
"Not compiled with MLU"
if device_ids is None:
device_ids = _mlu_ids()
elif not isinstance(device_ids, (list, tuple)):
device_ids = [device_ids]
return [core.MLUPlace(dev_id) for dev_id in device_ids]
class NameScope(object):
def __init__(self, name="", parent=None):
self._children = dict()
......@@ -2090,6 +2154,10 @@ class Variable(object):
p = core.Place()
p.set_place(t._place())
place = core.NPUPlace(p.npu_device_id())
elif p.is_mlu_place():
p = core.Place()
p.set_place(t._place())
place = core.MLUPlace(p.mlu_device_id())
else:
p = core.Place()
p.set_place(t._place())
......@@ -6768,7 +6836,8 @@ def _get_paddle_place(place):
if place is None:
return place
if isinstance(place, (core.Place, core.XPUPlace, core.CPUPlace,
core.CUDAPinnedPlace, core.CUDAPlace, core.NPUPlace)):
core.CUDAPinnedPlace, core.CUDAPlace, core.NPUPlace,
core.MLUPlace)):
return place
if not isinstance(place, str):
......@@ -6823,8 +6892,20 @@ def _get_paddle_place(place):
device_id = int(device_id)
return core.NPUPlace(device_id)
# MLU
avaliable_mlu_place = re.match(r'mlu:\d+', place)
if avaliable_mlu_place:
if not core.is_compiled_with_mlu():
raise ValueError(
"The device should not be {}, since PaddlePaddle is " \
"not compiled with MLU".format(avaliable_mlu_place))
place_info_list = place.split(':', 1)
device_id = place_info_list[1]
device_id = int(device_id)
return core.MLUPlace(device_id)
raise ValueError(
"Paddle supports CPUPlace, CUDAPlace,CUDAPinnedPlace, XPUPlace and NPUPlace, but received {}.".
"Paddle supports CPUPlace, CUDAPlace,CUDAPinnedPlace, XPUPlace, MLUPlace and NPUPlace, but received {}.".
format(place))
......
python/paddle/fluid/io.py
浏览文件 @ b6bf650a
......@@ -2100,6 +2100,10 @@ def load(program, model_path, executor=None, var_list=None):
p = paddle.fluid.core.Place()
p.set_place(t._place())
place = paddle.fluid.NPUPlace(p.npu_device_id())
elif p.is_mlu_place():
p = paddle.fluid.core.Place()
p.set_place(t._place())
place = paddle.fluid.MLUPlace(p.mlu_device_id())
else:
p = paddle.fluid.core.Place()
p.set_place(t._place())
......@@ -2394,6 +2398,10 @@ def set_program_state(program, state_dict):
p = paddle.fluid.core.Place()
p.set_place(ten_place)
py_place = paddle.fluid.NPUPlace(p.npu_device_id())
elif ten_place.is_mlu_place():
p = paddle.fluid.core.Place()
p.set_place(ten_place)
py_place = paddle.fluid.MLUPlace(p.mlu_device_id())
ten.set(new_para_np, py_place)
......
python/paddle/framework/__init__.py
浏览文件 @ b6bf650a
......@@ -28,6 +28,7 @@ from ..fluid.core import IPUPlace # noqa: F401
from ..fluid.core import CUDAPlace # noqa: F401
from ..fluid.core import CUDAPinnedPlace # noqa: F401
from ..fluid.core import NPUPlace # noqa: F401
from ..fluid.core import MLUPlace # noqa: F401
from ..fluid.core import VarBase # noqa: F401
from paddle.fluid import core # noqa: F401
......
python/paddle/static/__init__.py
浏览文件 @ b6bf650a
......@@ -45,6 +45,7 @@ from ..fluid.framework import program_guard # noqa: F401
from ..fluid.framework import cpu_places # noqa: F401
from ..fluid.framework import cuda_places # noqa: F401
from ..fluid.framework import xpu_places # noqa: F401
from ..fluid.framework import mlu_places # noqa: F401
from ..fluid.framework import npu_places # noqa: F401
from ..fluid.framework import Variable # noqa: F401
from ..fluid.layers.control_flow import Print # noqa: F401
......@@ -103,6 +104,7 @@ __all__ = [ #noqa
'cuda_places',
'xpu_places',
'npu_places',
'mlu_places',
'Variable',
'create_global_var',
'accuracy',
......
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