[2/N][SymmMem] Add MemPool allocator and tests (pytorch#161471)

(Porting most of pytorch#161008)

Hooking SymmetricMemory Allocator to MemPool so that user can create symmetric tensors with regular `torch.zeros`, `torch.arange` etc factories. Also so that our ops can have functional variants that create `out` tensors on symmetric memory.

To end users, this PR supports a python UI as follows:
```
allocator = symm_mem.get_mempool_allocator(device)
mempool = torch.cuda.MemPool(allocator)
with torch.cuda.use_mem_pool(mempool):
    tensor = torch.arange(numel, dtype=dtype, device=device)
```

Added tests for both use cases above.

Pull Request resolved: pytorch#161471
Approved by: https://github.com/ngimel
ghstack dependencies: pytorch#161470

Author: kwen2501

BUILD.bazel

@@ -747,6 +747,7 @@ cc_library(
             "torch/csrc/distributed/c10d/symm_mem/CUDASymmetricMemory.cu",
             "torch/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryOps.cu",
             "torch/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryUtils.cpp",
+            "torch/csrc/distributed/c10d/symm_mem/cuda_mem_pool.cpp",
             "torch/csrc/distributed/c10d/symm_mem/intra_node_comm.cu",
         ],
     )) + torch_sources,

build_variables.bzl

@@ -755,6 +755,7 @@ libtorch_cuda_distributed_extra_sources = [
     "torch/csrc/distributed/c10d/symm_mem/NCCLSymmetricMemory.cu",
     "torch/csrc/distributed/c10d/symm_mem/intra_node_comm.cpp",
     "torch/csrc/distributed/c10d/symm_mem/intra_node_comm.cu",
+    "torch/csrc/distributed/c10d/symm_mem/cuda_mem_pool.cpp",
     "torch/csrc/distributed/rpc/tensorpipe_cuda.cpp",
 ]
 

caffe2/CMakeLists.txt

@@ -581,6 +581,7 @@ if(USE_CUDA)
         ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryOps.cu
         ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryUtils.cpp
         ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/NCCLSymmetricMemory.cu
+        ${TORCH_SRC_DIR}/csrc/distributed/c10d/symm_mem/cuda_mem_pool.cpp
         PROPERTIES COMPILE_FLAGS "-DPYTORCH_C10_DRIVER_API_SUPPORTED=1"
       )
     endif()

test/distributed/test_nvshmem.py

@@ -65,6 +65,58 @@ def foo():
         out = symm_mem.empty(numel, dtype=dtype, device=self.device)
         symm_mem.rendezvous(out, group=group_name)
 
+    @skipIfRocm
+    def test_mempool_tensor_factory(self) -> None:
+        """
+        Test the effectiveness of MemPool on tensor factory ops.
+        """
+        self._init_device()
+        group_name = dist.group.WORLD.group_name
+        symm_mem.enable_symm_mem_for_group(group_name)
+
+        dtype = torch.float
+        numel = 1024
+        src_rank = 0
+
+        allocator = symm_mem.get_mempool_allocator(self.device)
+        mempool = torch.cuda.MemPool(allocator)
+
+        with torch.cuda.use_mem_pool(mempool):
+            if self.rank == src_rank:
+                tensor = torch.arange(numel, dtype=dtype, device=self.device)
+            else:
+                tensor = torch.zeros(numel, dtype=dtype, device=self.device)
+
+        symm_mem.rendezvous(tensor, group=group_name)
+        torch.ops.symm_mem.nvshmem_broadcast(tensor, group_name)
+        self.assertEqual(tensor, torch.arange(numel, dtype=dtype, device=self.device))
+
+    @skipIfRocm
+    def test_mempool_compute_ops(self) -> None:
+        """
+        Apply MemPool context to a compute op that creates input to collective.
+        """
+        self._init_device()
+        group_name = dist.group.WORLD.group_name
+        symm_mem.enable_symm_mem_for_group(group_name)
+
+        dtype = torch.float
+        dim = 1024
+        w = torch.ones(dim, dim, dtype=dtype, device=self.device)
+        x0 = torch.ones(1, dim, dtype=dtype, device=self.device)
+
+        allocator = symm_mem.get_mempool_allocator(self.device)
+        mempool = torch.cuda.MemPool(allocator)
+
+        with torch.cuda.use_mem_pool(mempool):
+            x = x0 + self.rank
+            y = torch.mm(x, w)
+
+        # y should be a symm tensor
+        torch.ops.symm_mem.nvshmem_broadcast(y, group_name)
+        expected = torch.mm(x0, w)
+        self.assertEqual(y, expected)
+
     @skipIfRocm
     def test_nvshmem_put(self) -> None:
         self._init_device()

torch/_C/_distributed_c10d.pyi

@@ -769,6 +769,8 @@ class _SymmetricMemory:
     def set_backend(name: str) -> None: ...
     @staticmethod
     def get_backend(device: torch.device) -> Optional[str]: ...
+    @staticmethod
+    def get_mempool_allocator(device: torch.device) -> Any: ...
     @property
     def rank(self) -> int: ...
     @property

torch/csrc/distributed/c10d/init.cpp

@@ -1128,6 +1128,9 @@ This class does not support ``__members__`` property.)");
           &::c10d::symmetric_memory::has_multicast_support)
       .def_static("set_backend", &::c10d::symmetric_memory::set_backend)
       .def_static("get_backend", &::c10d::symmetric_memory::get_backend)
+      .def_static(
+          "get_mempool_allocator",
+          &::c10d::symmetric_memory::get_mempool_allocator)
       .def_property_readonly("rank", &SymmetricMemory::get_rank)
       .def_property_readonly("world_size", &SymmetricMemory::get_world_size)
       .def_property_readonly(

torch/csrc/distributed/c10d/symm_mem/SymmetricMemory.cpp

@@ -266,6 +266,28 @@ TORCH_API bool has_multicast_support(
     return allocator->has_multicast_support(device_idx);
   }
 }
+
+static std::unordered_map<c10::DeviceType, std::shared_ptr<c10::Allocator>>
+    _mempool_allocators;
+
+void register_mempool_allocator(
+    c10::DeviceType device_type,
+    std::shared_ptr<c10::Allocator> allocator) {
+  _mempool_allocators[device_type] = std::move(allocator);
+}
+
+// Get allocator for MemPool given device
+std::shared_ptr<c10::Allocator> get_mempool_allocator(c10::Device device) {
+  auto it = _mempool_allocators.find(device.type());
+  if (it == _mempool_allocators.end()) {
+    TORCH_CHECK(
+        false,
+        "SymmetricMemory MemPool did not find backend for device type ",
+        device.type());
+  }
+  return it->second;
+}
+
 } // namespace c10d::symmetric_memory
 
 namespace {

torch/csrc/distributed/c10d/symm_mem/SymmetricMemory.hpp

@@ -184,4 +184,11 @@ TORCH_API void set_backend(const std::string& name);
 
 TORCH_API std::optional<std::string> get_backend(c10::Device device);
 
+C10_EXPORT void register_mempool_allocator(
+    c10::DeviceType device_type,
+    std::shared_ptr<c10::Allocator> allocator);
+
+TORCH_API std::shared_ptr<c10::Allocator> get_mempool_allocator(
+    c10::Device device);
+
 } // namespace c10d::symmetric_memory

torch/csrc/distributed/c10d/symm_mem/cuda_mem_pool.cpp

@@ -0,0 +1,39 @@
+#include <torch/csrc/cuda/CUDAPluggableAllocator.h>
+#include <torch/csrc/distributed/c10d/symm_mem/SymmetricMemory.hpp>
+
+namespace {
+using namespace c10d::symmetric_memory;
+
+// Alloc functor for MemPool
+void* cuda_symm_alloc(size_t size, int device, void* stream) {
+  static auto allocator = get_allocator(c10::DeviceType::CUDA);
+  TORCH_CHECK(
+      allocator->name() == "NVSHMEM", "Only NVSHMEM backend is supported");
+  // Note: this alloc functor works for the NVSHMEM and NCCL backends only,
+  // because only these backends takes `nullopt` for the `group` argument which
+  // is not given by MemPool's invocation (actually these two backends requires
+  // it to be `nullopt`).
+  return allocator->alloc(size, device, /*group_name=*/std::nullopt);
+}
+
+// Free functor for MemPool
+void cuda_symm_free(void* ptr, size_t size, int device, void* stream) {
+  static auto allocator = get_allocator(c10::DeviceType::CUDA);
+  TORCH_CHECK(
+      allocator->name() == "NVSHMEM", "Only NVSHMEM backend is supported");
+  allocator->free(ptr);
+}
+
+// Register allocator for CUDA MemPool
+struct RegisterCUDAMemPoolAllocator {
+  RegisterCUDAMemPoolAllocator() {
+    std::shared_ptr<c10::cuda::CUDACachingAllocator::CUDAAllocator> allocator =
+        torch::cuda::CUDAPluggableAllocator::createCustomAllocator(
+            cuda_symm_alloc, cuda_symm_free);
+    register_mempool_allocator(c10::DeviceType::CUDA, allocator);
+  }
+};
+
+static RegisterCUDAMemPoolAllocator register_cuda_mempool_allocator_;
+
+} // namespace

torch/distributed/_symmetric_memory/__init__.py

@@ -1781,4 +1781,14 @@ def get_backend(device: _device) -> Optional[str]:
     return _SymmetricMemory.get_backend(torch.device(device))
 
 
+def get_mempool_allocator(device: _device):  # type: ignore[no-untyped-def]
+    r"""
+    Get the MemPool allocator for symmetric memory for a given device.
+    Args:
+        device (class:`torch.device` or str): the device for which to get the
+        MemPool allocator.
+    """
+    return _SymmetricMemory.get_mempool_allocator(torch.device(device))
+
+
 __all__ = ["empty", "rendezvous", "is_nvshmem_available", "set_backend", "get_backend"]