[npu][hybrid] support offload

Author: wangxicoding

paddle/fluid/framework/tensor_util.cc

@@ -151,6 +151,52 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
                  BOOST_GET_CONST(platform::NPUPlace, src_place), src_ptr, size,
                  stream);
   }
+  else if (platform::is_npu_pinned_place(src_place) &&  // NOLINT
+           platform::is_npu_place(dst_place)) {         /* npu_pinned->npu */
+    auto src_npu_pinned_place =
+        BOOST_GET_CONST(platform::NPUPinnedPlace, src_place);
+    auto dst_npu_place = BOOST_GET_CONST(platform::NPUPlace, dst_place);
+    auto ctx_place = ctx.GetPlace();
+    PADDLE_ENFORCE_EQ(platform::is_npu_place(ctx_place), true,
+                      platform::errors::PreconditionNotMet(
+                          "Device context place mismatch. When copying Tensor "
+                          "data from NPU Pinned memory to NPU memory, current "
+                          "device context place should be NPU."));
+    auto ctx_npu_place = BOOST_GET_CONST(platform::NPUPlace, ctx_place);
+    PADDLE_ENFORCE_EQ(dst_npu_place, ctx_npu_place,
+                      platform::errors::PreconditionNotMet(
+                          "The target NPU device and current device context do "
+                          "not match. The target NPU device number is %d, but "
+                          "device context NPU number is %d.",
+                          dst_npu_place.device, ctx_npu_place.device));
+    auto stream =
+        reinterpret_cast<const platform::NPUDeviceContext&>(ctx).stream();
+    memory::Copy(dst_npu_place, dst_ptr, src_npu_pinned_place, src_ptr, size,
+                 stream);
+  }
+  else if (platform::is_npu_place(src_place) &&        // NOLINT
+           platform::is_npu_pinned_place(dst_place)) { /* npu->npu_pinned */
+    auto src_npu_place = BOOST_GET_CONST(platform::NPUPlace, src_place);
+    auto dst_npu_pinned_place =
+        BOOST_GET_CONST(platform::NPUPinnedPlace, dst_place);
+    auto ctx_place = ctx.GetPlace();
+    PADDLE_ENFORCE_EQ(platform::is_npu_place(ctx_place), true,
+                      platform::errors::PreconditionNotMet(
+                          "Device context place mismatch. When copying Tensor "
+                          "data from NPU memory to NPU Pinned memory, current "
+                          "device context place should be NPU."));
+    auto ctx_npu_place = BOOST_GET_CONST(platform::NPUPlace, ctx_place);
+    PADDLE_ENFORCE_EQ(src_place, ctx_npu_place,
+                      platform::errors::PreconditionNotMet(
+                          "The source NPU device and current device context do "
+                          "not match. The source NPU device number is %d, but "
+                          "device context NPU number is %d.",
+                          src_npu_place.device, ctx_npu_place.device));
+    auto stream =
+        reinterpret_cast<const platform::NPUDeviceContext&>(ctx).stream();
+    memory::Copy(dst_npu_pinned_place, dst_ptr, src_npu_place, src_ptr, size,
+                 stream);
+  }
   else {  // NOLINT
     PADDLE_THROW(platform::errors::Unimplemented(
         "Copy from %s to %s is not supported.", src_place, dst_place));

paddle/fluid/memory/allocation/npu_pinned_allocator.cc

@@ -54,6 +54,17 @@ void NPUPinnedAllocator::FreeImpl(Allocation *allocation) {
   std::lock_guard<std::mutex> lock(mtx_);
   void *ptr = allocation->ptr();
   auto iter = npu_events_.find(allocation);
+
+  // Managed by GC if not called RecordEvent.
+  if (iter == npu_events_.end()) {
+    // double free? No such problem has been found so far.
+    // Or maybe we need a set<Allocation*> to record which
+    // Allocation managed by GC.
+    free(ptr);
+    delete allocation;
+    return;
+  }
+
   aclrtEvent event = iter->second;
   aclrtEventStatus status = ACL_EVENT_STATUS_COMPLETE;
   PADDLE_ENFORCE_NPU_SUCCESS(aclrtQueryEvent(event, &status));

paddle/fluid/operators/memcpy_op.h

@@ -36,6 +36,16 @@ class SelectedRows;
 namespace paddle {
 namespace operators {
 class MemcpyFunctor {
+ private:
+  enum DeviceType {
+    CPU = 0,
+    CUDA = 1,
+    CUDA_PINNED = 2,
+    XPU = 3,
+    NPU = 4,
+    NPU_PINNED = 5,
+  };
+
  public:
   MemcpyFunctor(framework::Variable *out,
                 const platform::DeviceContext &dev_ctx,
@@ -45,18 +55,21 @@ class MemcpyFunctor {
   void operator()(const framework::LoDTensor &lod_tensor) const {
     auto &out_tensor = *out_->GetMutable<framework::LoDTensor>();
 
-    if (dst_place_type_ == 2) {
+    if (dst_place_type_ == DeviceType::CUDA_PINNED) {
       framework::TensorCopy(lod_tensor, platform::CUDAPinnedPlace(), dev_ctx_,
                             &out_tensor);
-    } else if (dst_place_type_ == 1) {
+    } else if (dst_place_type_ == DeviceType::CUDA) {
       framework::TensorCopy(lod_tensor, dev_ctx_.GetPlace(), dev_ctx_,
                             &out_tensor);
-    } else if (dst_place_type_ == 0) {
+    } else if (dst_place_type_ == DeviceType::CPU) {
       framework::TensorCopySync(lod_tensor, platform::CPUPlace(), &out_tensor);
 #ifdef PADDLE_WITH_ASCEND_CL
-    } else if (dst_place_type_ == 4) {
+    } else if (dst_place_type_ == DeviceType::NPU) { /* npu_pin->npu */
       framework::TensorCopy(lod_tensor, dev_ctx_.GetPlace(), dev_ctx_,
                             &out_tensor);
+    } else if (dst_place_type_ == DeviceType::NPU_PINNED) { /* npu->npu_pin */
+      framework::TensorCopy(lod_tensor, platform::NPUPinnedPlace(), dev_ctx_,
+                            &out_tensor);
 #endif
     } else {
       PADDLE_THROW(platform::errors::Unimplemented(

python/paddle/distributed/fleet/meta_optimizers/sharding/offload_helper.py

@@ -20,10 +20,36 @@
 __all__ = []
 
 
+class PlaceType:
+    # sync with memcpy op, maybe not a good design
+    CPU = 0
+    CUDA = 1
+    CUDA_PINNED = 2
+    XPU = 3  # unsupport for now
+    NPU = 4
+    NPU_PINNED = 5
+
+    @staticmethod
+    def default_device():
+        if core.is_compiled_with_cuda():
+            return PlaceType.CUDA
+        elif core.is_compiled_with_npu():
+            return PlaceType.NPU
+        return PlaceType.CPU
+
+    @staticmethod
+    def default_pinned():
+        if core.is_compiled_with_cuda():
+            return PlaceType.CUDA_PINNED
+        elif core.is_compiled_with_npu():
+            return PlaceType.NPU_PINNED
+        return PlaceType.CPU
+
+
 class OffloadHelper(object):
     cpu_place_type = 0
-    cuda_place_type = 1
-    cuda_pinned_place_type = 2
+    cuda_place_type = PlaceType.default_device()
+    cuda_pinned_place_type = PlaceType.default_pinned()
 
     def __init__(self, mp_ring_id=None, dp_ring_id=None):
         self.mp_ring_id = mp_ring_id