add context parallel group init to mp init

fairscale/nn/model_parallel/initialize.py

@@ -34,17 +34,21 @@
 _DATA_PARALLEL_GROUP = None
 # Pipeline parallel group that the current rank belongs to.
 _PIPELINE_PARALLEL_GROUP = None
-
 _PIPELINE_PARALLEL_RANKS = None
 
+_CONTEXT_PARALLEL_GROUP = None
+_CONTEXT_PARALLEL_GROUP_RANKS = None
+
 
 def initialize_model_parallel(
-    model_parallel_size_: int,
+    model_parallel_size: int,
+    context_parallel_size: int = 1,
     pipeline_length: int = 1,
     *,
     model_parallel_backend: Optional[str] = None,
+    cp_backend: Optional[str] = None,
     pipeline_backend: Optional[str] = None,
-    ddp_backend: Optional[str] = None
+    ddp_backend: Optional[str] = None,
 ) -> None:
     """
     Initialize model data parallel groups.
@@ -67,19 +71,21 @@ def initialize_model_parallel(
     # Get world size and rank. Ensure some consistencies.
     assert torch.distributed.is_initialized()
     world_size = torch.distributed.get_world_size()
-    model_parallel_size = int(min(model_parallel_size_, world_size))
+    model_parallel_size = int(min(model_parallel_size, world_size))
     ensure_divisibility(world_size, model_parallel_size)
-    ensure_divisibility(world_size, model_parallel_size * pipeline_length)
+    ensure_divisibility(world_size, context_parallel_size)
+    ensure_divisibility(world_size, model_parallel_size * pipeline_length * context_parallel_size)
     rank = torch.distributed.get_rank()
 
-    data_parallel_size = int(world_size / (model_parallel_size * pipeline_length))
+    data_parallel_size = int(world_size / (model_parallel_size * pipeline_length * context_parallel_size))
 
     if torch.distributed.get_rank() == 0:
-        print("> initializing model parallel with size {}".format(model_parallel_size_))
-        print("> initializing ddp with size {}".format(data_parallel_size))
+        print("> initializing model parallel with size {}".format(model_parallel_size))
+        print("> initializing context parallel with size {}".format(context_parallel_size))
         print("> initializing pipeline with size {}".format(pipeline_length))
+        print("> initializing ddp with size {}".format(data_parallel_size))
 
-    groups = torch.LongTensor(range(world_size)).reshape(data_parallel_size, pipeline_length, model_parallel_size)
+    groups = torch.LongTensor(range(world_size)).reshape(data_parallel_size, pipeline_length, context_parallel_size, model_parallel_size)
 
     found = torch.where(groups == rank)
     assert all(len(x) == 1 for x in found)
@@ -88,41 +94,81 @@ def initialize_model_parallel(
     # Build the data parallel groups.
     global _DATA_PARALLEL_GROUP
     assert _DATA_PARALLEL_GROUP is None, "data parallel group is already initialized"
-    for j in range(pipeline_length):
-        for k in range(model_parallel_size):
-            group = torch.distributed.new_group(groups[:, j, k].tolist(), backend=ddp_backend)
-            if j == found[1] and k == found[2]:
-                _DATA_PARALLEL_GROUP = group
+    for i in range(pipeline_length):
+        for j in range(context_parallel_size):
+            for k in range(model_parallel_size):
+                group = torch.distributed.new_group(groups[:, i, j, k].tolist(), backend=ddp_backend)
+                if i == found[1] and j == found[2] and k == found[3]:
+                    _DATA_PARALLEL_GROUP = group
+
 
     # Build the model parallel groups.
     global _MODEL_PARALLEL_GROUP
-    assert _MODEL_PARALLEL_GROUP is None, "model parallel group is already initialized"
+    assert _MODEL_PARALLEL_GROUP is None, "Model parallel group is already initialized"
     for i in range(data_parallel_size):
         for j in range(pipeline_length):
-            group = torch.distributed.new_group(groups[i, j, :].tolist(), backend=model_parallel_backend)
-            if i == found[0] and j == found[1]:
-                _MODEL_PARALLEL_GROUP = group
+            for k in range(context_parallel_size):
+                group = torch.distributed.new_group(groups[i, j, k, :].tolist(), backend=model_parallel_backend)
+                if i == found[0] and j == found[1] and k == found[2]:
+                    _MODEL_PARALLEL_GROUP = group
 
+
+    # Build the pipeline parallel groups.
     global _PIPELINE_PARALLEL_GROUP
-    assert _PIPELINE_PARALLEL_GROUP is None, "model parallel group is already initialized"
     global _PIPELINE_PARALLEL_RANKS
-    assert _PIPELINE_PARALLEL_RANKS is None, "model parallel group is already initialized"
+    assert _PIPELINE_PARALLEL_GROUP is None, "Pipeline parallel group is already initialized"
+    for i in range(data_parallel_size):
+        for j in range(context_parallel_size):
+            for k in range(model_parallel_size):
+                ranks = groups[i, :, j, k].tolist()
+                group = torch.distributed.new_group(ranks, backend=pipeline_backend)
+                if i == found[0] and j == found[2] and k == found[3]:
+                    _PIPELINE_PARALLEL_GROUP = group
+                    _PIPELINE_PARALLEL_RANKS = ranks
+
+
+    # Build the context parallel groups.
+    global _CONTEXT_PARALLEL_GROUP
+    global _CONTEXT_PARALLEL_GROUP_RANKS
+
+    assert (
+        _CONTEXT_PARALLEL_GROUP is None
+    ), "Context parallelism is already initialized."
     for i in range(data_parallel_size):
-        for k in range(model_parallel_size):
-            ranks = groups[i, :, k].tolist()
-            group = torch.distributed.new_group(ranks, backend=pipeline_backend)
-            if i == found[0] and k == found[2]:
-                _PIPELINE_PARALLEL_GROUP = group
-                _PIPELINE_PARALLEL_RANKS = ranks
+        for j in range(pipeline_length):
+            for k in range(model_parallel_size):
+                ranks = groups[i, j, :, k].tolist()
+                group = torch.distributed.new_group(ranks, backend=cp_backend)
+                if i == found[0] and j == found[1] and k == found[3]:
+                    _CONTEXT_PARALLEL_GROUP = group
+                    _CONTEXT_PARALLEL_GROUP_RANKS = ranks
 
 
 def model_parallel_is_initialized() -> bool:
     """Check if model and data parallel groups are initialized."""
-    if _MODEL_PARALLEL_GROUP is None or _DATA_PARALLEL_GROUP is None or _PIPELINE_PARALLEL_GROUP is None:
+    if _MODEL_PARALLEL_GROUP is None or _DATA_PARALLEL_GROUP is None or _PIPELINE_PARALLEL_GROUP is None or _CONTEXT_PARALLEL_GROUP is None:
         return False
     return True
 
 
+def get_context_parallel_group() -> torch.distributed.ProcessGroup:
+    """Get the context parallel group the caller rank belongs to."""
+    assert (
+        _CONTEXT_PARALLEL_GROUP is not None
+    ), "context parallel group is not initialized"
+    return _CONTEXT_PARALLEL_GROUP
+
+
+def get_context_parallel_world_size() -> int:
+    """Return world size for the context parallel group."""
+    return torch.distributed.get_world_size(group=get_context_parallel_group())
+
+
+def get_context_parallel_rank() -> int:
+    """Return my rank for the context parallel group."""
+    return torch.distributed.get_rank(group=get_context_parallel_group())
+
+
 def get_model_parallel_group() -> torch.distributed.ProcessGroup:
     """Get the model parallel group the caller rank belongs to."""
     assert _MODEL_PARALLEL_GROUP is not None, "model parallel group is not initialized"
@@ -179,10 +225,16 @@ def destroy_model_parallel() -> None:
     """Set the groups to none."""
     global _MODEL_PARALLEL_GROUP
     _MODEL_PARALLEL_GROUP = None
+
     global _DATA_PARALLEL_GROUP
     _DATA_PARALLEL_GROUP = None
+
     global _PIPELINE_PARALLEL_GROUP
     _PIPELINE_PARALLEL_GROUP = None
-
     global _PIPELINE_PARALLEL_RANKS
     _PIPELINE_PARALLEL_RANKS = None
+
+    global _CONTEXT_PARALLEL_GROUP
+    _CONTEXT_PARALLEL_GROUP = None
+    global _CONTEXT_PARALLEL_GROUP_RANKS
+    _CONTEXT_PARALLEL_GROUP_RANKS = None