mirror of
https://github.com/hpcaitech/ColossalAI.git
synced 2025-09-04 02:26:51 +00:00
fix
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@@ -1180,7 +1180,9 @@ class HybridParallelPlugin(PipelinePluginBase):
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gradient_checkpoint_config=gradient_checkpoint_config,
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fp8_communication=fp8_communication,
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inner_ring_size=inner_ring_size,
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pg_mesh=self.pg_mesh,
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)
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self.amp_config = dict(
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initial_scale=initial_scale,
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growth_factor=growth_factor,
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@@ -7,7 +7,6 @@ import torch.distributed as dist
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import torch.nn.functional as F
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from einops import rearrange
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from colossalai.cluster import ProcessGroupMesh
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from colossalai.kernel.kernel_loader import (
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FlashAttentionDaoLoader,
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FlashAttentionForFloatAndCustomMaskLoader,
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@@ -432,7 +431,7 @@ class RingAttention(torch.autograd.Function):
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INTER_RING_GROUP_COPY: dist.ProcessGroup = None
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@staticmethod
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def get_double_ring_groups(sp_group, inner_ring_size=None):
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def get_double_ring_groups(sp_group, pg_mesh, inner_ring_size=None):
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"""
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Get 2D ring groups for the given process group. Generally, to avoid congestion, the inner ring size
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shouldn't be larger than the number of NICs on each node.
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@@ -465,20 +464,17 @@ class RingAttention(torch.autograd.Function):
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inner_ring_group = None
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inter_ring_group = None
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inter_axis, inner_axis = 0, 1
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pg_mesh = ProcessGroupMesh(num_rings, inner_ring_size)
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# Create inner ring groups
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for i in range(inner_ring_size):
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ranks = list(range(i * inner_ring_size, (i + 1) * inner_ring_size))
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group = pg_mesh.get_group_along_axis(inner_axis)
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group = pg_mesh.get_group_along_axis(2, ranks)
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if sp_rank in ranks:
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inner_ring_group = group
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# Create inter ring groups
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for i in range(num_rings):
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ranks = list(range(i, sp_size, num_rings))
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group = pg_mesh.get_group_along_axis(inter_axis)
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group = pg_mesh.get_group_along_axis(2, ranks)
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if sp_rank in ranks:
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inter_ring_group = group
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@@ -499,6 +495,7 @@ class RingAttention(torch.autograd.Function):
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deterministic=False,
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return_softmax=False,
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inner_ring_size=None,
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pg_mesh=None,
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**kwargs,
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):
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"""
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@@ -546,7 +543,9 @@ class RingAttention(torch.autograd.Function):
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if inner_ring_size != None:
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RingAttention.SP_GROUP = sp_group
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inner_ring_group, inter_ring_group = RingAttention.get_double_ring_groups(sp_group, inner_ring_size)
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inner_ring_group, inter_ring_group = RingAttention.get_double_ring_groups(
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sp_group, pg_mesh, inner_ring_size
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)
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RingAttention.INNER_RING_GROUP = inner_ring_group
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RingAttention.INTER_RING_GROUP = inter_ring_group
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else:
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@@ -868,6 +868,7 @@ def get_gpt2_flash_attention_forward(shard_config: Optional[ShardConfig] = None)
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dropout_p=dropout_p,
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scale=scale,
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inner_ring_size=shard_config.inner_ring_size,
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pg_mesh=shard_config.pg_mesh,
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)
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else:
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attn_output = ColoAttention.attention(query, key, value, **attention_mask, dropout_p=dropout_p, scale=scale)
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@@ -571,6 +571,7 @@ def get_llama_flash_attention_forward(shard_config: ShardConfig, sp_mode=None, s
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sp_group,
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**attention_mask,
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inner_ring_size=shard_config.inner_ring_size,
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pg_mesh=shard_config.pg_mesh,
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)
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elif shard_config.enable_flash_attention:
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@@ -51,6 +51,7 @@ class ShardConfig:
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extra_kwargs: Dict[str, Any] = field(default_factory=dict)
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# For ring attention
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pg_mesh: Optional[int] = None
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inner_ring_size: Optional[int] = None
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# for moe related
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moe_dp_group: Optional[ProcessGroup] = None
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