Skip to content

[Ring Attention] Overlap kv comm with output rescale #6017

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Edenzzzz merged 1 commit into from
Aug 19, 2024
Merged

[Ring Attention] Overlap kv comm with output rescale #6017

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
30 changes: 19 additions & 11 deletions colossalai/shardformer/layer/attn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -690,6 +690,13 @@ def _forward(q, k, v, causal):
)
return out, softmax_lse, rng_state

def _kv_comm(i):
# Avoid overwriting attn input when it shares mem with buffer
if not RingAttention.ATTN_DONE.query():
kv_buffers[(i + 1) % 2] = torch.empty_like(kv_buffers[i % 2])
if i < local_sp_size - 1:
local_kv_comms[i % 2].send_recv(kv_buffers[i % 2], kv_buffers[(i + 1) % 2])

def _local_ring_forward():
# (Hopefully) overlap output correction with next flash attn
for i in range(local_sp_size):
Expand All @@ -698,12 +705,8 @@ def _local_ring_forward():
# NOTE: waiting outside the current stream will NOT correctly synchronize.
if i > 0:
local_kv_comms[(i + 1) % 2].wait()

# Avoid overwriting attn input when it shares mem with buffer
if not RingAttention.ATTN_DONE.query():
kv_buffers[(i + 1) % 2] = torch.empty_like(kv_buffers[i % 2])
if i < local_sp_size - 1:
local_kv_comms[i % 2].send_recv(kv_buffers[i % 2], kv_buffers[(i + 1) % 2])
if i == 0:
_kv_comm(i)

if i == 0:
# Compute with local KV; no mask
Expand Down Expand Up @@ -734,6 +737,9 @@ def _local_ring_forward():
rng_states[i],
) = _forward(q_block, kv_block[0], kv_block[1], causal=False)
RingAttention.ATTN_DONE.record()
# Pipeline the next KV comm with output correction instead of the next flash attn
# to minimize idle time when comm takes longer than attn.
_kv_comm(i + 1)

block_softmax_lse[i % 2] = (
block_softmax_lse[i % 2].transpose(0, 1).unsqueeze(-1).contiguous().float()
Expand Down Expand Up @@ -761,15 +767,13 @@ def _other_ring_forward(ring_num_idx, out, softmax_lse):
# all new KVs from the previous inner ring
for i in range(local_sp_size):
with torch.cuda.stream(sp_streams[i % 2]):
if not RingAttention.ATTN_DONE.query():
kv_buffers[(i + 1) % 2] = torch.empty_like(kv_buffers[i % 2])
if i < local_sp_size - 1:
local_kv_comms[i % 2].send_recv(kv_buffers[i % 2], kv_buffers[(i + 1) % 2])

# Send & recv KV
if i > 0:
local_kv_comms[(i + 1) % 2].wait()

if i == 0:
_kv_comm(i)

if ring_num_idx > inter_ring_rank:
kv_block = kv_buffers[i % 2]
(
Expand All @@ -778,6 +782,8 @@ def _other_ring_forward(ring_num_idx, out, softmax_lse):
rng_states[i + local_sp_size * ring_num_idx],
) = _forward(q1, kv_block[0], kv_block[1], causal=False)
RingAttention.ATTN_DONE.record()

_kv_comm(i + 1)
block_softmax_lse[i % 2] = (
block_softmax_lse[i % 2].transpose(0, 1).unsqueeze(-1).contiguous().float()
)
Expand All @@ -792,6 +798,8 @@ def _other_ring_forward(ring_num_idx, out, softmax_lse):
rng_states[i + local_sp_size * ring_num_idx],
) = _forward(q, kv_block[0], kv_block[1], causal=False)
RingAttention.ATTN_DONE.record()

_kv_comm(i + 1)
block_softmax_lse[i % 2] = (
block_softmax_lse[i % 2].transpose(0, 1).unsqueeze(-1).contiguous().float()
)
Expand Down