NCCL EP zero copy additional support

tests/pytorch/distributed/run_ep.py

@@ -19,6 +19,7 @@
     ep_dispatch,
     ep_combine,
     symm_mem_alloc,
+    is_symm_backed,
     _ep_combine_raw,
     _ep_dispatch_raw,
 )
@@ -155,22 +156,14 @@ def setUp(self):
         ):
             self.skipTest("not exercised in zero-copy mode")
 
-    def _make_buffer(
-        self,
-        alignment=0,
-        top_k=TOP_K,
-        dispatch_recv_tokens=None,
-        combine_grad_expert_out=None,
-    ):
+    def _make_buffer(self, alignment=0, top_k=TOP_K):
         return EpBuffer(
             top_k=top_k,
             max_tokens_per_rank=TOKENS_PER_RANK,
             recv_capacity_per_rank=self.cfg.recv_capacity_per_rank,
             hidden_dim=HIDDEN_DIM,
             num_local_experts=NUM_LOCAL_EXPERTS,
             alignment=alignment,
-            dispatch_recv_tokens=dispatch_recv_tokens,
-            combine_grad_expert_out=combine_grad_expert_out,
         )
 
     def _expert_out(self, expert_out):
@@ -253,10 +246,10 @@ def test_dispatch_autograd(self):
             ]
         for label, recv_tokens in cases:
             with self.subTest(case=label):
-                buf = self._make_buffer(dispatch_recv_tokens=recv_tokens)
+                buf = self._make_buffer()
                 topk_idx, tokens, w = _make_identity_inputs(self.cfg.rank, self.cfg.ep_size)
                 tokens_p = tokens.detach().clone().requires_grad_(True)
-                rt, rw, _tc = ep_dispatch(buf, tokens_p, topk_idx, w)
+                rt, rw, _tc = ep_dispatch(buf, tokens_p, topk_idx, w, recv_tokens=recv_tokens)
                 if recv_tokens is not None:  # caller-supplied recv_tokens must be used in place
                     self.assertEqual(rt.data_ptr(), recv_tokens.data_ptr())
                 rt = self._stage_grad_symm(rt)
@@ -269,20 +262,17 @@ def test_dispatch_autograd(self):
 
     @_zero_copy_test_include
     def test_caller_provides_dispatch_recv_tokens(self):
-        """Caller-supplied recv_tokens: EpBuffer adopts it (recv_topk_weights stays
-        owned) and ep_dispatch returns a view of the caller's buffer."""
+        """Caller-supplied recv_tokens (symm-mem-backed in zero-copy): ep_dispatch
+        writes into it and returns a view of the caller's buffer."""
         if ZERO_COPY:
             rc = self.cfg.recv_capacity_per_rank
             rt_buf = symm_mem_alloc((rc, HIDDEN_DIM), torch.bfloat16, self.ep_group)
         else:
             rt_buf, _rw_buf, _ = self._make_raw_recv()
-        buf = self._make_buffer(dispatch_recv_tokens=rt_buf)
-        self.assertEqual(buf.recv_tokens_symm_buf.data_ptr(), rt_buf.data_ptr())
-        if ZERO_COPY:  # recv_topk_weights is always buffer-owned in zero-copy
-            self.assertIsNotNone(buf.recv_topk_weights_symm_buf)
+        buf = self._make_buffer()
         topk_idx, tokens, w = _make_identity_inputs(self.cfg.rank, self.cfg.ep_size)
         tokens_p = tokens.detach().clone().requires_grad_(True)
-        rt, rw, _ = ep_dispatch(buf, tokens_p, topk_idx, w)
+        rt, rw, _ = ep_dispatch(buf, tokens_p, topk_idx, w, recv_tokens=rt_buf)
         self.assertEqual(rt.data_ptr(), rt_buf.data_ptr())
         rt = self._stage_grad_symm(rt)
         rw = self._stage_grad_symm(rw)
@@ -294,22 +284,45 @@ def test_caller_provides_dispatch_recv_tokens(self):
 
     @_zero_copy_test_include
     def test_caller_provides_grad_expert_out(self):
-        """Caller-supplied grad_expert_out: EpBuffer adopts it as the combine
-        backward grad target (symm-mem under zero-copy)."""
+        """Caller-supplied grad_out (symm-mem-backed in zero-copy): ep_combine's
+        backward scatters the expert-out grad into it."""
         rc = self.cfg.recv_capacity_per_rank
         if ZERO_COPY:
             gbuf = symm_mem_alloc((rc, HIDDEN_DIM), torch.bfloat16, self.ep_group)
         else:
             gbuf = torch.empty(rc, HIDDEN_DIM, dtype=torch.bfloat16, device=self.cfg.device)
-        buf = self._make_buffer(combine_grad_expert_out=gbuf)
-        self.assertEqual(buf.grad_expert_out_symm_buf.data_ptr(), gbuf.data_ptr())
+        gbuf.zero_()
+        buf = self._make_buffer()
         topk_idx, tokens, w = _make_identity_inputs(self.cfg.rank, self.cfg.ep_size)
         tokens_p = tokens.detach().clone().requires_grad_(True)
         recv_t, recv_w, _ = ep_dispatch(buf, tokens_p, topk_idx, w)
         recv_t = self._stage_grad_symm(recv_t)
         recv_w = self._stage_grad_symm(recv_w)
         expert_out = self._expert_out(self._weighted(recv_t, recv_w))
-        out = ep_combine(buf, expert_out)
+        out = ep_combine(buf, expert_out, grad_out=gbuf)
+        (0.5 * (out.float() ** 2).sum()).backward()
+        torch.cuda.synchronize()
+        torch.testing.assert_close(out.float(), tokens.float(), atol=5e-2, rtol=5e-2)
+        torch.testing.assert_close(tokens_p.grad.float(), tokens.float(), atol=5e-2, rtol=5e-2)
+        # the caller-owned buffer was used as the combine-bwd scatter target
+        self.assertGreater(gbuf.abs().sum().item(), 0.0)
+
+    @_zero_copy_test_include
+    def test_zero_copy_pool_auto_alloc(self):
+        """Zero-copy with recv/grad left None: ep_dispatch/ep_combine allocate their IO
+        tensors from the symm-mem pool (is_symm_backed). This is the primary mcore
+        path — mcore hands no caller buffers, TE pools them on the fly."""
+        if not ZERO_COPY:
+            self.skipTest("zero-copy pool auto-alloc only")
+        buf = self._make_buffer()
+        topk_idx, tokens, w = _make_identity_inputs(self.cfg.rank, self.cfg.ep_size)
+        tokens_p = tokens.detach().clone().requires_grad_(True)
+        recv_t, recv_w, _ = ep_dispatch(buf, tokens_p, topk_idx, w)  # recv_tokens=None -> pool
+        self.assertTrue(is_symm_backed(recv_t))  # dispatch recv came from the symm-mem pool
+        recv_t = self._stage_grad_symm(recv_t)
+        recv_w = self._stage_grad_symm(recv_w)
+        expert_out = self._expert_out(self._weighted(recv_t, recv_w))
+        out = ep_combine(buf, expert_out)  # grad_out=None -> bwd allocs the grad from the pool
         (0.5 * (out.float() ** 2).sum()).backward()
         torch.cuda.synchronize()
         torch.testing.assert_close(out.float(), tokens.float(), atol=5e-2, rtol=5e-2)

transformer_engine/pytorch/distributed.py

@@ -1842,24 +1842,63 @@ def get_symmetric_memory_tensor(tensor_numel, tensor_dtype, tensor_device, tp_gr
     return msg
 
 
+_SYMM_MEM_POOL = None
+
+
+def _get_symm_mem_pool(device: torch.device, backend: str = "NCCL"):
+    """Process-wide torch MemPool backed by the symmetric-memory allocator, created once (each rank
+    drives one device). The pool/allocator captures the backend at creation and there is no per-pool
+    backend arg, so the (process-global) backend is always set before the pool is created. The
+    collective rendezvous cost is amortized across allocations (paid per new segment, not per buffer).
+    """
+    global _SYMM_MEM_POOL
+    if _SYMM_MEM_POOL is None:
+        symm_mem.set_backend(backend)
+        if hasattr(symm_mem, "get_mem_pool"):
+            _SYMM_MEM_POOL = symm_mem.get_mem_pool(device)
+        elif hasattr(torch.cuda, "MemPool") and hasattr(symm_mem, "get_mempool_allocator"):
+            _SYMM_MEM_POOL = torch.cuda.MemPool(symm_mem.get_mempool_allocator(device))
+        else:
+            raise RuntimeError(
+                "No symmetric-memory MemPool API available (need torch symm-mem get_mem_pool, or "
+                "torch.cuda.MemPool + get_mempool_allocator)."
+            )
+    return _SYMM_MEM_POOL
+
+
 def symm_mem_alloc(
     shape,
     dtype: torch.dtype,
     ep_group: dist_group_type,
     device: Optional[torch.device] = None,
+    use_pool: bool = False,
+    backend: str = "NCCL",
 ) -> torch.Tensor:
-    """Allocate and rendezvous a symm-mem buffer on ep_group. Collective on ep_group."""
+    """Allocate a symm-mem buffer on ep_group.
+
+    ``use_pool=False`` (default): freshly allocate and do one explicit collective ``rendezvous`` per
+    buffer, for caller-owned static buffers (fp8 zero-copy). ``use_pool=True``: allocate from a
+    process-wide symm-mem MemPool whose segments are auto-registered (implicit mempool), so no explicit
+    rendezvous is needed and torch manages the tensor lifecycle (freed back to the pool) — for
+    lifecycle-managed zero-copy, e.g. bf16, where the recv buffer is saved for backward and so cannot
+    be a shared static buffer. ``backend`` selects the symm-mem backend (default NCCL; for the pool it
+    is captured at pool creation)."""
     if device is None:
         device = torch.device("cuda", torch.cuda.current_device())
     if not HAS_TORCH_SYMMETRIC:
         raise RuntimeError(
             "torch.distributed._symmetric_memory is unavailable; symm_mem_alloc "
             "requires PyTorch built with NCCL symm-mem support."
         )
-    if symm_mem.get_backend(device) != "NCCL":
-        symm_mem.set_backend("NCCL")
-    t = symm_mem.empty(*shape, dtype=dtype, device=device)
-    symm_mem.rendezvous(t, group=ep_group)
+    if use_pool:
+        pool = _get_symm_mem_pool(device, backend)
+        with torch.cuda.use_mem_pool(pool):
+            t = torch.empty(*shape, dtype=dtype, device=device)
+    else:
+        if symm_mem.get_backend(device) != backend:
+            symm_mem.set_backend(backend)
+        t = symm_mem.empty(*shape, dtype=dtype, device=device)
+        symm_mem.rendezvous(t, group=ep_group)
     return t