fix

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -1180,7 +1180,9 @@ class HybridParallelPlugin(PipelinePluginBase):
             gradient_checkpoint_config=gradient_checkpoint_config,
             fp8_communication=fp8_communication,
             inner_ring_size=inner_ring_size,
+            pg_mesh=self.pg_mesh,
         )
+
         self.amp_config = dict(
             initial_scale=initial_scale,
             growth_factor=growth_factor,

colossalai/shardformer/layer/attn.py

@@ -7,7 +7,6 @@ import torch.distributed as dist
 import torch.nn.functional as F
 from einops import rearrange
 
-from colossalai.cluster import ProcessGroupMesh
 from colossalai.kernel.kernel_loader import (
     FlashAttentionDaoLoader,
     FlashAttentionForFloatAndCustomMaskLoader,
@@ -432,7 +431,7 @@ class RingAttention(torch.autograd.Function):
     INTER_RING_GROUP_COPY: dist.ProcessGroup = None
 
     @staticmethod
-    def get_double_ring_groups(sp_group, inner_ring_size=None):
+    def get_double_ring_groups(sp_group, pg_mesh, inner_ring_size=None):
         """
         Get 2D ring groups for the given process group. Generally, to avoid congestion, the inner ring size
         shouldn't be larger than the number of NICs on each node.
@@ -465,20 +464,17 @@ class RingAttention(torch.autograd.Function):
         inner_ring_group = None
         inter_ring_group = None
 
-        inter_axis, inner_axis = 0, 1
-        pg_mesh = ProcessGroupMesh(num_rings, inner_ring_size)
-
         # Create inner ring groups
         for i in range(inner_ring_size):
             ranks = list(range(i * inner_ring_size, (i + 1) * inner_ring_size))
-            group = pg_mesh.get_group_along_axis(inner_axis)
+            group = pg_mesh.get_group_along_axis(2, ranks)
             if sp_rank in ranks:
                 inner_ring_group = group
 
         # Create inter ring groups
         for i in range(num_rings):
             ranks = list(range(i, sp_size, num_rings))
-            group = pg_mesh.get_group_along_axis(inter_axis)
+            group = pg_mesh.get_group_along_axis(2, ranks)
             if sp_rank in ranks:
                 inter_ring_group = group
 
@@ -499,6 +495,7 @@ class RingAttention(torch.autograd.Function):
         deterministic=False,
         return_softmax=False,
         inner_ring_size=None,
+        pg_mesh=None,
         **kwargs,
     ):
         """
@@ -546,7 +543,9 @@ class RingAttention(torch.autograd.Function):
 
         if inner_ring_size != None:
             RingAttention.SP_GROUP = sp_group
-            inner_ring_group, inter_ring_group = RingAttention.get_double_ring_groups(sp_group, inner_ring_size)
+            inner_ring_group, inter_ring_group = RingAttention.get_double_ring_groups(
+                sp_group, pg_mesh, inner_ring_size
+            )
             RingAttention.INNER_RING_GROUP = inner_ring_group
             RingAttention.INTER_RING_GROUP = inter_ring_group
         else:

colossalai/shardformer/modeling/gpt2.py

@@ -868,6 +868,7 @@ def get_gpt2_flash_attention_forward(shard_config: Optional[ShardConfig] = None)
                 dropout_p=dropout_p,
                 scale=scale,
                 inner_ring_size=shard_config.inner_ring_size,
+                pg_mesh=shard_config.pg_mesh,
             )
         else:
             attn_output = ColoAttention.attention(query, key, value, **attention_mask, dropout_p=dropout_p, scale=scale)

colossalai/shardformer/modeling/llama.py

@@ -571,6 +571,7 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
                 sp_group,
                 **attention_mask,
                 inner_ring_size=shard_config.inner_ring_size,
+                pg_mesh=shard_config.pg_mesh,
             )
 
         elif shard_config.enable_flash_attention:

colossalai/shardformer/shard/shard_config.py

@@ -51,6 +51,7 @@ class ShardConfig:
     extra_kwargs: Dict[str, Any] = field(default_factory=dict)
 
     # For ring attention
+    pg_mesh: Optional[int] = None
     inner_ring_size: Optional[int] = None
     # for moe related
     moe_dp_group: Optional[ProcessGroup] = None

tests/test_shardformer/test_layer/test_ring_attn.py

@@ -5,6 +5,7 @@ from flash_attn import flash_attn_qkvpacked_func, flash_attn_varlen_qkvpacked_fu
 from torch.testing import assert_close
 
 import colossalai
+from colossalai.cluster import ProcessGroupMesh
 from colossalai.shardformer.layer import AttnMaskType
 from colossalai.shardformer.layer.attn import AttnMaskType, RingAttention
 from colossalai.shardformer.layer.utils import split_batch_zigzag, split_varlen_zigzag
@@ -21,6 +22,9 @@ def check_ring_attn(seq_len, bs, nheads, d, dtype, inner_ring_size):
     torch.cuda.manual_seed(2)
     device = get_current_device()
     sp_group = dist.group.WORLD
+    dp_size, pp_size, tp_size = 1, 1, 1
+    sp_size = dist.get_world_size()
+    pg_mesh = ProcessGroupMesh(dp_size, pp_size, sp_size, tp_size)
     # Some outliers may seem large, but our errors are still lower than
     # than Megatron-LM context parallel's
     # (https://github.com/NVIDIA/TransformerEngine/blob/33a3d02f81c56e6f7b542c09bfa86657078d57fb/tests/pytorch/fused_attn/run_fused_attn_with_cp.py#L215)
@@ -36,13 +40,7 @@ def check_ring_attn(seq_len, bs, nheads, d, dtype, inner_ring_size):
 
     # Ring attention vs single GPU
     ring_out, ring_lse = RingAttention.attention(
-        q,
-        k,
-        v,
-        sp_group,
-        AttnMaskType.CAUSAL,
-        return_softmax=True,
-        inner_ring_size=inner_ring_size,
+        q, k, v, sp_group, AttnMaskType.CAUSAL, return_softmax=True, inner_ring_size=inner_ring_size, pg_mesh=pg_mesh
     )
     ring_out = ring_out.transpose(1, 2)
     out, lse, _ = flash_attn_qkvpacked_func(
@@ -125,6 +123,7 @@ def check_packed_seq(seqlen, bs, nheads, d, dtype):
         **mask_info,
         pad_output=False,
         return_softmax=True,
+        pg_mesh=dist.group.WORLD,
         # deterministic=True
     )
     ring_out = ring_out.transpose(1, 2).reshape(-1, nheads, d)