Merge pull request #6071 from wangbluo/ring_attention

[Ring Attention] fix the 2d ring attn when using multiple machine

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -1177,7 +1177,10 @@ class HybridParallelPlugin(PipelinePluginBase):
             gradient_checkpoint_config=gradient_checkpoint_config,
             fp8_communication=fp8_communication,
             inner_ring_size=inner_ring_size,
+            pg_mesh=self.pg_mesh,
+            sp_axis=self.sp_axis,
         )
+
         self.amp_config = dict(
             initial_scale=initial_scale,
             growth_factor=growth_factor,

colossalai/shardformer/layer/attn.py

@@ -431,7 +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_axis, 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.
@@ -441,21 +441,17 @@ class RingAttention(torch.autograd.Function):
         Returns:
             Tuple[dist.ProcessGroup, dist.ProcessGroup]: Inner-ring process group and inter-ring process group.
         """
+        assert pg_mesh is not None, f"Error: The pg mesh is None! please check the process group initialization."
+
+        sp_group = pg_mesh.get_group_along_axis(sp_axis)
         sp_size = dist.get_world_size(sp_group)
         sp_rank = dist.get_rank(sp_group)
 
-        if inner_ring_size is None:
-            if torch.cuda.device_count() >= dist.get_world_size():
-                # single node, no need to consider NICs
-                return sp_group, sp_group
-            if sp_size <= 4:
-                inner_ring_size = min(2, sp_size)
-            else:
-                inner_ring_size = min(4, sp_size)
-        else:
-            assert (
-                inner_ring_size <= sp_size and sp_size % inner_ring_size == 0
-            ), f"Error: sp_size {sp_size} should be divisible by inner_ring_size {inner_ring_size}"
+        assert inner_ring_size is not None
+
+        assert (
+            inner_ring_size <= sp_size and sp_size % inner_ring_size == 0
+        ), f"Error: sp_size {sp_size} should be divisible by inner_ring_size {inner_ring_size}"
 
         if inner_ring_size == sp_size:
             return sp_group, sp_group
@@ -474,14 +470,14 @@ class RingAttention(torch.autograd.Function):
         # Create inner ring groups
         for i in range(inner_ring_size):
             ranks = list(range(i * inner_ring_size, (i + 1) * inner_ring_size))
-            group = dist.new_group(ranks)
+            group = pg_mesh.get_group_along_axis(sp_axis, 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 = dist.new_group(ranks)
+            group = pg_mesh.get_group_along_axis(sp_axis, ranks)
             if sp_rank in ranks:
                 inter_ring_group = group
 
@@ -492,7 +488,7 @@ class RingAttention(torch.autograd.Function):
         q,  # (B, H, Sq, D)
         k,
         v,
-        sp_group,
+        sp_axis,
         attention_mask_type,
         cu_seqlens=None,
         max_seqlen=None,
@@ -502,6 +498,7 @@ class RingAttention(torch.autograd.Function):
         deterministic=False,
         return_softmax=False,
         inner_ring_size=None,
+        pg_mesh=None,
         **kwargs,
     ):
         """
@@ -512,7 +509,7 @@ class RingAttention(torch.autograd.Function):
             q (torch.Tensor): Query tensor. Shape should be [B, nHeads, Sq, D]
             k (torch.Tensor): Key tensor. Shape should be [B, nHeads, Sq, Sq, D]
             v (torch.Tensor): Value tensor. Shape should be [B, nHeads, Sq, Sq, D]
-            sp_group (Optional[dist.ProcessGroup]): Process group for sequence parallelism
+            sp_axis (Optional[int]): Sp axis for the global pg mesh.
             sp_tream (torch.cuda.Stream): An different stream for output correction.
             cu_seqlens (Optional[torch.Tensor], optional): The cumulative sequence lengths
                 of the sequences in the batch, used to index into q.
@@ -537,7 +534,6 @@ class RingAttention(torch.autograd.Function):
             RingAttention.ATTN_DONE = torch.cuda.Event()
         if RingAttention.SP_STREAM is None:
             RingAttention.SP_STREAM = torch.cuda.Stream()
-
         assert (
             q.shape[2] == k.shape[2]
         ), "Q, K and V having different sequence lengths (inference or cross-attn)\
@@ -546,11 +542,13 @@ class RingAttention(torch.autograd.Function):
             attention_mask_type in RingAttention.SUPPORTED_MASK_TYPES
         ), f"Mask type {attention_mask_type} is not supported yet."
 
-        clone_pg = lambda pg: dist.new_group(dist.get_process_group_ranks(pg))
+        assert pg_mesh is not None, f"Error: The pg mesh is None! please check the process group initialization."
 
-        if RingAttention.SP_GROUP is not sp_group:
+        clone_pg = lambda pg: dist.new_group(dist.get_process_group_ranks(pg))
+        sp_group = pg_mesh.get_group_along_axis(sp_axis)
+        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_axis, 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

@@ -857,17 +857,17 @@ def get_gpt2_flash_attention_forward(shard_config: Optional[ShardConfig] = None)
         dropout_p = self.attn_dropout.p if self.training else 0.0
 
         sp_mode = shard_config.sequence_parallelism_mode
-        sp_group = shard_config.sequence_parallel_process_group
         if sp_mode == "ring_attn":
             attn_output = RingAttention.attention(
                 query,
                 key,
                 value,
-                sp_group,
+                sp_axis=shard_config.sp_axis,
                 **attention_mask,
                 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

@@ -569,9 +569,10 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
                 query_states,
                 key_states,
                 value_states,
-                sp_group,
+                sp_axis=shard_config.sp_axis,
                 **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

@@ -49,6 +49,8 @@ class ShardConfig:
     extra_kwargs: Dict[str, Any] = field(default_factory=dict)
 
     # For ring attention
+    sp_axis: Optional[int] = None
+    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
@@ -17,11 +18,14 @@ from colossalai.utils import get_current_device
 @parameterize("nheads", [5])
 @parameterize("d", [128])
 @parameterize("dtype", [torch.bfloat16, torch.float16])
-def check_ring_attn(seq_len, bs, nheads, d, dtype):
+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()
+    sp_axis = 2
+    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)
@@ -40,11 +44,11 @@ def check_ring_attn(seq_len, bs, nheads, d, dtype):
         q,
         k,
         v,
-        sp_group,
+        sp_axis,
         AttnMaskType.CAUSAL,
         return_softmax=True,
-        inner_ring_size=max(2, sp_size // 2),
-        # inner_ring_size=4
+        inner_ring_size=inner_ring_size,
+        pg_mesh=pg_mesh,
     )
     ring_out = ring_out.transpose(1, 2)
     out, lse, _ = flash_attn_qkvpacked_func(
@@ -83,6 +87,7 @@ def check_packed_seq(seqlen, bs, nheads, d, dtype):
     device = get_current_device()
     sp_group = dist.group.WORLD
     sp_size = dist.get_world_size()
+    sp_axis = 2
     atol = rtol = 7e-3
     torch.cuda.manual_seed(2)
     # Prepare varlen attention mask
@@ -123,10 +128,11 @@ def check_packed_seq(seqlen, bs, nheads, d, dtype):
         q_ring,
         k_ring,
         v_ring,
-        sp_group,
+        sp_axis,
         **mask_info,
         pad_output=False,
         return_softmax=True,
+        pg_mesh=ProcessGroupMesh(1, 1, sp_size, 1),
         # deterministic=True
     )
     ring_out = ring_out.transpose(1, 2).reshape(-1, nheads, d)
@@ -161,12 +167,12 @@ def check_packed_seq(seqlen, bs, nheads, d, dtype):
 def launch_single_ring(rank, world_size, port):
     colossalai.launch(rank, world_size, "localhost", port)
     check_packed_seq()
-    check_ring_attn()
+    check_ring_attn(inner_ring_size=None)
 
 
 def launch_double_ring(rank, world_size, port):
     colossalai.launch(rank, world_size, "localhost", port)
-    check_ring_attn()
+    check_ring_attn(inner_ring_size=2)
 
 
 @rerun_if_address_is_in_use()