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[LoRA, Performance] Speedup multi-LoRA serving - Step 1 (#1587)

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This commit is contained in:
Ying Sheng
2024-10-06 10:33:44 -07:00
committed by GitHub
parent 58d1082e39
commit 9c064bf78a
3 changed files with 34 additions and 32 deletions
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27
python/sglang/srt/lora/lora.py
View File

@@ -101,12 +101,12 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
) -> None:
super().__init__(base_layer, segment_gemm, lora_rank, scaling)
def set_lora_info(self, A_buffer, B_buffer, bs, seq_lens, weight_indices):
def set_lora_info(self, A_buffer, B_buffer, bs, seg_indptr, weight_indices):
self.set_lora = True
self.A_buffer = A_buffer
self.B_buffer = B_buffer
self.bs = bs
self.seq_lens = seq_lens
self.seg_indptr = seg_indptr
self.weight_indices = weight_indices
def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
@@ -115,11 +115,10 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
weights=self.A_buffer,
batch_size=self.bs,
weight_column_major=True,
seg_lens=self.seq_lens,
seg_indptr=self.seg_indptr,
weight_indices=self.weight_indices,
)
# FIXME
assert lora_a_output.shape[-1] == self.lora_rank * 2
lora_output = torch.empty_like(base_output)
output_dim = lora_output.shape[-1] // 2
for i in range(2):
@@ -132,7 +131,7 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
weights=self.B_buffer[:, left:right, :].contiguous(),
batch_size=self.bs,
weight_column_major=True,
seg_lens=self.seq_lens,
seg_indptr=self.seg_indptr,
weight_indices=self.weight_indices,
)
return base_output + lora_output * self.scaling
@@ -145,14 +144,14 @@ class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
super().__init__(base_layer, segment_gemm, lora_rank, scaling)
def set_lora_info(
self, A_buffer_qkv, B_buffer_q, B_buffer_kv, bs, seq_lens, weight_indices
self, A_buffer_qkv, B_buffer_q, B_buffer_kv, bs, seg_indptr, weight_indices
):
self.set_lora = True
self.A_buffer_qkv = A_buffer_qkv
self.B_buffer_q = B_buffer_q
self.B_buffer_kv = B_buffer_kv
self.bs = bs
self.seq_lens = seq_lens
self.seg_indptr = seg_indptr
self.weight_indices = weight_indices
def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
@@ -161,7 +160,7 @@ class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
weights=self.A_buffer_qkv,
batch_size=self.bs,
weight_column_major=True,
seg_lens=self.seq_lens,
seg_indptr=self.seg_indptr,
weight_indices=self.weight_indices,
)
# FIXME parallelize qkv
@@ -173,7 +172,7 @@ class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
weights=self.B_buffer_q,
batch_size=self.bs,
weight_column_major=True,
seg_lens=self.seq_lens,
seg_indptr=self.seg_indptr,
weight_indices=self.weight_indices,
)
# kv
@@ -189,7 +188,7 @@ class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
weights=self.B_buffer_kv[:, left:right, :].contiguous(),
batch_size=self.bs,
weight_column_major=True,
seg_lens=self.seq_lens,
seg_indptr=self.seg_indptr,
weight_indices=self.weight_indices,
)
)
@@ -202,12 +201,12 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
) -> None:
super().__init__(base_layer, segment_gemm, lora_rank, scaling)
def set_lora_info(self, A_buffer, B_buffer, bs, seq_lens, weight_indices):
def set_lora_info(self, A_buffer, B_buffer, bs, seg_indptr, weight_indices):
self.set_lora = True
self.A_buffer = A_buffer
self.B_buffer = B_buffer
self.bs = bs
self.seq_lens = seq_lens
self.seg_indptr = seg_indptr
self.weight_indices = weight_indices
def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
@@ -216,7 +215,7 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
weights=self.A_buffer,
batch_size=self.bs,
weight_column_major=True,
seg_lens=self.seq_lens,
seg_indptr=self.seg_indptr,
weight_indices=self.weight_indices,
)
lora_output = self.segment_gemm.run(
@@ -224,7 +223,7 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
weights=self.B_buffer,
batch_size=self.bs,
weight_column_major=True,
seg_lens=self.seq_lens,
seg_indptr=self.seg_indptr,
weight_indices=self.weight_indices,
)
return base_output + lora_output * self.scaling

27
python/sglang/srt/lora/lora_manager.py
View File

@@ -274,18 +274,24 @@ class LoRAManager:
cur_uids = set(forward_batch.lora_paths)
assert len(cur_uids) <= self.max_loras_per_batch
i = 0
j = len(self.active_uids)
evictable_uids = list(self.active_uids)
for uid in cur_uids:
if uid not in self.active_uids:
while i < len(evictable_uids) and evictable_uids[i] in cur_uids:
i += 1
if i < len(evictable_uids):
if j < self.max_loras_per_batch:
index = j
j += 1
else:
while i < len(evictable_uids) and evictable_uids[i] in cur_uids:
i += 1
assert i < len(evictable_uids)
self.active_uids.remove(evictable_uids[i])
self.buffer_id.pop(evictable_uids[i])
self.load_lora(uid, i)
index = i
i += 1
self.load_lora(uid, index)
self.active_uids.add(uid)
self.buffer_id[uid] = i
i += 1
self.buffer_id[uid] = index
if cur_uids == set([None]):
return
@@ -295,8 +301,11 @@ class LoRAManager:
seg_lens = (
forward_batch.extend_seq_lens
if forward_batch.forward_mode.is_extend()
else torch.ones(bs)
else torch.ones(bs, device="cuda")
)
# FIXME: reuse the data rather than recompute
seg_indptr = torch.zeros((bs + 1,), dtype=torch.int32, device="cuda")
seg_indptr[1:] = torch.cumsum(seg_lens, dim=0)
weight_indices = torch.empty((bs,), dtype=torch.int64, device="cuda")
for i, lora_path in enumerate(forward_batch.lora_paths):
weight_indices[i] = self.buffer_id[lora_path]
@@ -310,7 +319,7 @@ class LoRAManager:
self.A_buffer[weight_name][layer_id],
self.B_buffer[weight_name][layer_id],
bs,
seg_lens,
seg_indptr,
weight_indices,
)
else:
@@ -319,6 +328,6 @@ class LoRAManager:
self.B_buffer["q_proj"][layer_id],
self.B_buffer["kv_proj"][layer_id],
bs,
seg_lens,
seg_indptr,
weight_indices,
)