[CustomDevice] add sharding support

paddle/fluid/distributed/collective/process_group_custom.cc

@@ -722,6 +722,43 @@ std::shared_ptr<ProcessGroup::Task> ProcessGroupCustom::Send(
       false,
       false);
 }
+
+std::shared_ptr<ProcessGroup::Task> ProcessGroupCustom::Reduce(
+    phi::DenseTensor* out_tensor,
+    const phi::DenseTensor& in_tensor,
+    const ReduceOptions& opts,
+    bool sync_op,
+    bool use_calc_stream) {
+  phi::distributed::CommStaticCheck::SameShape(*out_tensor,
+                                               in_tensor,
+                                               /*dst_rank*/ opts.root_rank,
+                                               /*cur_rank*/ rank_,
+                                               size_,
+                                               phi::AllocationType::CUSTOM);
+  std::vector<phi::DenseTensor> in_wrapper{in_tensor};
+  std::vector<phi::DenseTensor> out_wrapper{*out_tensor};
+  return Collective(
+      in_wrapper,
+      out_wrapper,
+      [&](phi::DenseTensor& input,
+          phi::DenseTensor& output,
+          phi::ccl::CCLComm comm,
+          const phi::stream::Stream& stream) {
+        phi::DeviceManager::CCLReduce(device_type_,
+                                      input.data(),
+                                      output.data(),
+                                      input.numel(),
+                                      phi::ccl::ToCCLDataType(input.dtype()),
+                                      ToCustomCCLRedType(opts.reduce_op),
+                                      opts.root_rank,
+                                      comm,
+                                      stream);
+      },
+      CommType::REDUCE,
+      sync_op,
+      use_calc_stream);
+}
+
 std::shared_ptr<ProcessGroupCustom>
 ProcessGroupCustom::CreateProcessGroupCustom(
     const std::shared_ptr<phi::distributed::Store>& store,

paddle/fluid/distributed/collective/process_group_custom.h

@@ -163,6 +163,12 @@ class ProcessGroupCustom : public ProcessGroupWithStream {
   std::shared_ptr<ProcessGroup::Task> Recv(
       std::vector<phi::DenseTensor>& tensors, int src_rank) override;
 
+  std::shared_ptr<ProcessGroup::Task> Reduce(phi::DenseTensor* out_tensor,
+                                             const phi::DenseTensor& in_tensor,
+                                             const ReduceOptions& opts,
+                                             bool sync_op,
+                                             bool use_calc_stream) override;
+
  protected:
   virtual std::shared_ptr<ProcessGroupCustom::CustomTask> CreateTask(
       std::vector<Place> places,

paddle/fluid/pybind/custom_device_py.cc

@@ -29,6 +29,10 @@ namespace pybind {
 void BindCustomDevicePy(py::module *m_ptr) {
   auto &m = *m_ptr;
   // Bind Methods
+  m.def("_get_device_min_chunk_size", [](const std::string &device_type) {
+    auto place = paddle::platform::CustomPlace(device_type);
+    return phi::DeviceManager::GetMinChunkSize(place);
+  });
   m.def(
       "_get_device_total_memory",
       [](const std::string &device_type, int device_id) {

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py

@@ -82,8 +82,10 @@ def __init__(
 
         super().__init__(learning_rate=optim._learning_rate, parameters=params)
         assert (
-            core.is_compiled_with_cuda() or core.is_compiled_with_xpu()
-        ), "Only GPU and XPU is supported now"
+            core.is_compiled_with_cuda()
+            or core.is_compiled_with_xpu()
+            or (device in core.get_all_custom_device_type())
+        ), "Only GPU and XPU and CustomDevice is supported now"
 
         # Segmentation information
         self._dtype_rank_params = (
@@ -371,6 +373,13 @@ def rank_buffer_size(self):
         Count the memory size of the parameters corresponding to rank under the corresponding dtype.
         """
         # CUDA alignment 256 bytes
+        if self._default_device in core.get_all_custom_device_type():
+            device_alignment = core.libpaddle._get_device_min_chunk_size(
+                self._default_device
+            )
+        else:
+            device_alignment = alignment[self._default_device]
+
         if len(self._rank_buffer_size) == 0:
             for dtype in self.dtype_rank_params.keys():
                 if dtype not in self._rank_buffer_size.keys():
@@ -384,11 +393,11 @@ def rank_buffer_size(self):
                         if not param.trainable:
                             continue
                         size = param._numel() * align[dtype]
-                        remaining = size % alignment[self._default_device]
+                        remaining = size % device_alignment
                         ali = (
                             0
                             if remaining == 0
-                            else alignment[self._default_device] - remaining
+                            else device_alignment - remaining
                         )
                         align_ = ali // align[dtype]
                         self._rank_buffer_size[dtype][dst_rank] += (
@@ -439,14 +448,17 @@ def _integration_params(self):
         if self.offload:
             self._optim._master_weights = self._master_params
             cpu_master_params = list(self._master_params.values())
+            if self._default_device in core.get_all_custom_device_type():
+                device_alignment = core.libpaddle._get_device_min_chunk_size(
+                    self._default_device
+                )
+            else:
+                device_alignment = alignment[self._default_device]
+
             for param in cpu_master_params:
                 size = param._numel() * align[Type.fp32.value]
-                remaining = size % alignment[self.offload_device]
-                ali = (
-                    0
-                    if remaining == 0
-                    else alignment[self.offload_device] - remaining
-                )
+                remaining = size % device_alignment
+                ali = 0 if remaining == 0 else device_alignment - remaining
                 align_ = ali // align[Type.fp32.value]
                 self.offload_buffer_size += param._numel() + align_
                 self.offload_param2align[param.name] = align_
@@ -528,11 +540,26 @@ def _step(self):
 
             for param in self._local_params:
                 if param.name in self._master_params.keys():
-                    param.set_value(
-                        self._master_params[param.name]
-                        .cuda(self.dev_id)
-                        .cast(dtype=param.dtype)
-                    )
+                    if (
+                        self._default_device
+                        in core.get_all_custom_device_type()
+                    ):
+                        param.set_value(
+                            self._master_params[param.name]
+                            ._copy_to(
+                                paddle.CustomPlace(
+                                    self._default_device, self.dev_id
+                                ),
+                                True,
+                            )
+                            .cast(dtype=param.dtype)
+                        )
+                    else:
+                        param.set_value(
+                            self._master_params[param.name]
+                            .cuda(self.dev_id)
+                            .cast(dtype=param.dtype)
+                        )
         else:
             self._optim.step()
 

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py

@@ -89,7 +89,10 @@ def __init__(
         super().__init__()
 
         # Default configs
-        assert core.is_compiled_with_cuda(), "Only support CUDA."
+        assert core.is_compiled_with_cuda() or (
+            device in core.get_all_custom_device_type()
+        ), "Only support CUDA / CustomDevice."
+
         self._layer = layer
         self._default_device = device
         self.__sync_buffers = sync_buffers
@@ -243,7 +246,15 @@ def _clear_gradients(self):
         else:
             for param in list(self._unslice_params):
                 param.clear_gradient(False)
-                tmp_var = param.cuda(DEV_ID)
+                if (
+                    self._default_device
+                    in paddle.device.get_all_custom_device_type()
+                ):
+                    tmp_var = param._copy_to(
+                        paddle.CustomPlace(self._default_device, DEV_ID), True
+                    )
+                else:
+                    tmp_var = param.cuda(DEV_ID)
 
                 if (
                     tmp_var.dtype == Type.fp32.value
@@ -718,10 +729,14 @@ def allreduce_(*_):
     def _param2align(self, param):
         # CUDA alignment 256 bytes
         size = param._numel() * align[param.dtype]
-        remaining = size % alignment[self._default_device]
-        ali = (
-            0 if remaining == 0 else alignment[self._default_device] - remaining
-        )
+        if self._default_device in core.get_all_custom_device_type():
+            device_alignment = core.libpaddle._get_device_min_chunk_size(
+                self._default_device
+            )
+        else:
+            device_alignment = alignment[self._default_device]
+        remaining = size % device_alignment
+        ali = 0 if remaining == 0 else device_alignment - remaining
         align_ = ali // align[param.dtype]
         return align_
 
@@ -1095,7 +1110,10 @@ def _device2cpu(trans_param, convert_dtype=False):
 
 
 def _cpu2device(param):
-    tmp_p = param.fw_storage.cuda(DEV_ID)
+    if DEV in paddle.device.get_all_custom_device_type():
+        tmp_p = param.fw_storage._copy_to(paddle.CustomPlace(DEV, DEV_ID), True)
+    else:
+        tmp_p = param.fw_storage.cuda(DEV_ID)
     if (
         tmp_p.dtype == Type.fp32.value
         and param2dtype[param.name] == Type.fp16.value

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_storage.py

@@ -76,11 +76,16 @@ def to(self, device, dtype=None, keep_alignment=True):
         ), "Conversion type is not supported now"
 
         if self._device != device:
-            tmp_buffer = (
-                cvt_to_device(self.buffer, self.dev_id)
-                if device in ["gpu", "xpu"]
-                else self.buffer.cpu()
-            )
+            if device in paddle.device.get_all_custom_device_type():
+                tmp_buffer = self.buffer._copy_to(
+                    paddle.CustomPlace(device, self.dev_id), True
+                )
+            else:
+                tmp_buffer = (
+                    cvt_to_device(self.buffer, self.dev_id)
+                    if device in ["gpu", "xpu"]
+                    else self.buffer.cpu()
+                )
             for param in self._params:
                 param.clear_gradient(False)
 
@@ -133,8 +138,13 @@ def add_rank_params(self, trainable_params, param2align, convert_gpu=True):
             cpu_param_shape.append(p_shape)
 
         if convert_gpu:
-            # buffer convert from cpu to cuda
-            self.buffer = cvt_to_device(self.buffer, self.dev_id)
+            if self._device in paddle.device.get_all_custom_device_type():
+                self.buffer = self.buffer._copy_to(
+                    paddle.CustomPlace(self._device, self.dev_id), True
+                )
+            else:
+                # buffer convert from cpu to cuda
+                self.buffer = cvt_to_device(self.buffer, self.dev_id)
 
         self._fill = 0
 

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py

@@ -162,8 +162,14 @@ def _dygraph_clip(self, params_grads):
 
         # add all reduce to get global norm of distributed params_and_grads
         dev_id = int(self._device.split(":")[1])
+        dev_type = self._device.split(':')[0]
         if paddle.device.get_device() == "cpu":
-            global_norm_var = global_norm_var.cuda(dev_id)
+            if dev_type in paddle.device.get_all_custom_device_type():
+                global_norm_var = global_norm_var._copy_to(
+                    paddle.CustomPlace(dev_type, dev_id), True
+                )
+            else:
+                global_norm_var = global_norm_var.cuda(dev_id)
 
         with device_guard(dev_id, self._device.split(":")[0]):
             paddle.distributed.all_reduce(global_norm_var, group=self._group)
@@ -207,6 +213,8 @@ def device_guard(dev_id=0, device="cpu"):
         paddle.set_device(device)
     elif device in ["gpu", "xpu"]:
         paddle.set_device(f"{device}:{dev_id}")
+    elif device in paddle.device.get_all_custom_device_type():
+        paddle.set_device(f"{device}:{dev_id}")
 
     try:
         yield