sync fbcode cp pg initialize

fairscale/nn/model_parallel/initialize.py

@@ -20,8 +20,6 @@
 # limitations under the License.
 
 
-"""Model and data parallel groups."""
-
 from typing import List, Optional
 
 import torch
@@ -41,13 +39,13 @@
 
 
 def initialize_model_parallel(
-    model_parallel_size: int,
-    context_parallel_size: int = 1,
+    model_parallel_size_: int,
     pipeline_length: int = 1,
+    context_parallel_size: int = 1,
     *,
     model_parallel_backend: Optional[str] = None,
-    cp_backend: Optional[str] = None,
     pipeline_backend: Optional[str] = None,
+    cp_backend: Optional[str] = None,
     ddp_backend: Optional[str] = None,
 ) -> None:
     """
@@ -67,11 +65,28 @@ def initialize_model_parallel(
     are on the same DGX box. For example if we are using 2 DGX-1 boxes
     with a total of 16 GPUs, rank 0 to 7 belong to the first box and
     ranks 8 to 15 belong to the second box.
+
+    process groups initialized in the order of MP, CP, PP, DP.
+
+    Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
+    use 2 GPUs to parallelize the model tensor, 2 GPUs to parallelize context(seq len), and 2 GPUs to parallelize
+    the model pipeline. The present function will
+    create 8 tensor model-parallel groups, 8 context-parallel group, 8 pipeline model-parallel groups
+    and 8 data-parallel groups as:
+    when alternate_pp_config = False,
+        8 data_parallel groups:
+            [g0, g4], [g1, g5], [g2, g6], [g3, g7], [g8, g12], [g9, g13], [g10, g14], [g11, g15]
+        8 tensor model-parallel groups:
+            [g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]
+        8 context-parallel groups:
+            [g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]
+        8 pipeline model-parallel groups:
+            [g0, g8], [g1, g9], [g2, g10], [g3, g11], [g4, g12], [g5, g13], [g6, g16], [g7, g15]
     """
     # 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, context_parallel_size)
     ensure_divisibility(world_size, model_parallel_size * pipeline_length * context_parallel_size)