Add all-gather coalesce tests; fix all-gather coalesce bug "len(input)"

test/test_mp_all_gather.py

@@ -13,7 +13,8 @@ def all_gather(tensor, dim):
 def _mp_fn(index):
   device = xm.xla_device()
   world_size = xm.xrt_world_size()
-  if xm.xla_device_hw(device) in ('TPU', 'GPU', 'CUDA', 'ROCM'):
+  input_list_size = 5
+  if xm.xla_device_hw(device) in ('TPU', 'GPU', 'CUDA', 'ROCM', 'NEURON'):
     # Testing with a single replica group
     ordinal_tensor = torch.tensor([index], dtype=torch.float).to(device)
     result = xm.all_gather(ordinal_tensor, dim=0)
@@ -57,6 +58,48 @@ def _mp_fn(index):
           f'Failed to create two replica groups with {world_size} replicas',
           file=sys.stderr)
 
+    # Testing with a single replica group and tensor list as input
+    ordinal_tensors = [
+        torch.tensor([i * 1000 + index], dtype=torch.float).to(device)
+        for i in range(input_list_size)
+    ]
+    # TODO: add support for list input with pin_layout=True and output=None
+    result_list = xm.all_gather(ordinal_tensors, dim=0, pin_layout=False)
+
+    for i, result in enumerate(result_list):
+      cpu_result = result.cpu()
+      expected = i * 1000 + torch.arange(world_size, dtype=torch.float)
+      if not cpu_result.allclose(expected):
+        print(
+            'xm.all_gather() produced wrong reductions for item {i} in result list',
+            file=sys.stderr)
+        print(f'[{index}] {cpu_result}', file=sys.stderr)
+        sys.exit(1)
+
+    # Testing with a single replica group and tensor list as input and output!=None (out-of-place)
+    ordinal_tensors = [
+        torch.tensor([i * 1000 + index], dtype=torch.float).to(device)
+        for i in range(input_list_size)
+    ]
+    output_tensors = [
+        torch.zeros([world_size], dtype=torch.float).to(device)
+        for i in range(input_list_size)
+    ]
+    # TODO: add support for list input with pin_layout=True and output!=None
+    result_list = xm.all_gather(
+        ordinal_tensors, dim=0, output=output_tensors, pin_layout=False)
+
+    for i, result in enumerate(result_list):
+      cpu_result = result.cpu()
+      expected = i * 1000 + torch.arange(world_size, dtype=torch.float)
+      if not cpu_result.allclose(expected):
+        print(
+            'xm.all_gather() produced wrong reductions for item {i} in result list',
+            file=sys.stderr)
+        print(f'[{index}] {cpu_result}', file=sys.stderr)
+        sys.exit(1)
+    # TODO: add test for torch.compile when support for list input is ready
+
   else:
     print(f'{device} is not a TPU or GPU device', file=sys.stderr)
 

torch_xla/core/xla_model.py

@@ -553,7 +553,7 @@ def all_gather(value, dim=0, groups=None, output=None, pin_layout=True):
     A tensor which has, in the ``dim`` dimension, all the values from the
     participating replicas.
   """
-  if pin_layout and (output == None or xla_device_hw(value.device) == 'NEURON'):
+  if pin_layout and output == None and isinstance(value, torch.Tensor):
     # There is not an easy way to pin the all_gather layout on TPU, GPU and NEURON,
     # use all_reduce based all_gather for this purpose.
     return _all_gather_using_all_reduce(
@@ -587,13 +587,17 @@ def all_gather(value, dim=0, groups=None, output=None, pin_layout=True):
   # Now the input should be a list of Tensors.
   elif isinstance(value, list) and all(
       isinstance(v, torch.Tensor) for v in value):
+    if pin_layout:
+      raise RuntimeError(
+          "For xm.all_gather with list of tensors input, pin_layout=True is not yet supported."
+      )
     if output != None:
       if not isinstance(output, list) or any(
           not isinstance(v, torch.Tensor) for v in output):
         raise TypeError(
             f"`output` needs to be a list of Tensors, but given {type(output)}."
         )
-      if len(output) != len(input):
+      if len(output) != len(value):
         raise ValueError("`output` length doesn't match `input` length: "
                          f"{len(output)} vs {len(input)}.")
       # Call the out of place version of the reduce_scatter