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[Infer] Revise and Adapt Triton Kernels for Spec-Dec (#5401)

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* [Infer/Fix] Fix Dependency in test - RMSNorm kernel (#5399)

fix dependency in pytest

* resolve conflicts for revising flash-attn

* adapt kv cache copy kernel for spec-dec

* fix seqlen-n kvcache copy kernel/tests

* test kvcache copy - use torch.equal

* add assertions

* (trivial) comment out
This commit is contained in:
Yuanheng Zhao
2024-02-28 13:47:00 +08:00
committed by Yuanheng
parent d56c96334e
commit d63c469f45
6 changed files with 274 additions and 122 deletions
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3
colossalai/kernel/triton/__init__.py
View File

@@ -11,7 +11,7 @@ if HAS_TRITON:
from .context_attn_unpad import context_attention_unpadded
from .flash_decoding import flash_decoding_attention
from .fused_rotary_embedding import fused_rotary_embedding
from .kvcache_copy import copy_kv_to_blocked_cache
from .kvcache_copy import copy_k_to_blocked_cache, copy_kv_to_blocked_cache
from .no_pad_rotary_embedding import decoding_fused_rotary_embedding, rotary_embedding
from .rms_layernorm import rms_layernorm
from .rotary_cache_copy import get_xine_cache
@@ -20,6 +20,7 @@ if HAS_TRITON:
__all__ = [
"context_attention_unpadded",
"flash_decoding_attention",
"copy_k_to_blocked_cache",
"copy_kv_to_blocked_cache",
"softmax",
"rms_layernorm",

110
colossalai/kernel/triton/flash_decoding.py
View File

@@ -9,13 +9,14 @@ import triton.language as tl
# Triton 2.1.0
@triton.jit
def _flash_decoding_fwd_kernel(
Q, # [batch_size, head_num, q_len(1), head_dim]
Q, # [batch_size * q_len, head_num, head_dim]
KCache, # [num_blocks, num_kv_heads, block_size, head_dim]
VCache, # [num_blocks, num_kv_heads, block_size, head_dim]
block_tables, # [batch_size, max_blocks_per_sequence]
mid_o, # [batch_size, head_num, kv_split_num, head_dim]
mid_o_lse, # [batch_size, head_num, kv_split_num]
mid_o, # [batch_size * q_len, head_num, kv_split_num, head_dim]
mid_o_lse, # [batch_size * q_len, head_num, kv_split_num]
kv_seq_len, # [batch_size]
q_len,
batch_size,
stride_qt,
stride_qh,
@@ -39,44 +40,37 @@ def _flash_decoding_fwd_kernel(
BLOCK_SIZE: tl.constexpr,
HEAD_DIM: tl.constexpr,
):
cur_seq_idx = tl.program_id(0)
cur_token_idx = tl.program_id(0)
cur_seq_idx = cur_token_idx // q_len
if cur_seq_idx >= batch_size:
return
cur_head_idx = tl.program_id(1)
block_start_kv = tl.program_id(2) # for splitting k/v
cur_kv_head_idx = cur_head_idx // KV_GROUPS
offsets_dmodel = tl.arange(0, HEAD_DIM)
# NOTE It requires BLOCK_KV and BLOCK_SIZE to be the same
# TODO might want to replace with BLOCK_KV % BLOCK_SIZE == 0 (optimize BLOCK_KV as multiple of BLOCK_SIZE)
# and then support calculating multiple kv cache blocks on an instance
tl.static_assert(BLOCK_KV == BLOCK_SIZE)
# get the current (kv) sequence length from provided context lengths tensor
# get the current (kv) sequence length
cur_kv_seq_len = tl.load(kv_seq_len + cur_seq_idx)
offsets_q = cur_seq_idx * stride_qt + cur_head_idx * stride_qh + offsets_dmodel * stride_qd
q = tl.load(Q + offsets_q)
# block table for the current sequence
block_table_ptr = block_tables + cur_seq_idx * stride_bts
# actually current block table current block start idx
# cur_bt_start_idx = block_start_kv * (BLOCK_KV // BLOCK_SIZE)
cur_bt_start_idx = block_start_kv
cur_block_id = tl.load(block_table_ptr + cur_bt_start_idx * stride_btb)
if block_start_kv * BLOCK_KV >= cur_kv_seq_len:
return
offsets_dmodel = tl.arange(0, HEAD_DIM)
offsets_q = cur_token_idx * stride_qt + cur_head_idx * stride_qh + offsets_dmodel * stride_qd
q = tl.load(Q + offsets_q)
# block table for the current sequence
block_table_ptr = block_tables + cur_seq_idx * stride_bts
# cur_bt_start_idx = block_start_kv * (BLOCK_KV // BLOCK_SIZE)
# cur_block_id = tl.load(block_table_ptr + cur_bt_start_idx * stride_btb)
cur_block_id = tl.load(block_table_ptr + block_start_kv * stride_btb)
cur_occupied_size = tl.where(
(block_start_kv + 1) * BLOCK_SIZE <= cur_kv_seq_len, BLOCK_SIZE, cur_kv_seq_len - block_start_kv * BLOCK_SIZE
)
tl.device_assert(cur_occupied_size >= 0)
cur_kv_head_idx = cur_head_idx // KV_GROUPS
offset_kvcache = cur_block_id * stride_cacheb + cur_kv_head_idx * stride_cacheh
K_block_ptr = tl.make_block_ptr(
base=KCache + offset_kvcache,
shape=(cur_occupied_size, HEAD_DIM),
@@ -115,14 +109,14 @@ def _flash_decoding_fwd_kernel(
acc = acc / l
offsets_mid_o = (
cur_seq_idx * stride_mid_ot
cur_token_idx * stride_mid_ot
+ cur_head_idx * stride_mid_oh
+ block_start_kv * stride_mid_ob
+ offsets_dmodel * stride_mid_od
)
tl.store(mid_o + offsets_mid_o, acc)
offsets_mid_o_lse = (
cur_seq_idx * stride_mid_o_lset + cur_head_idx * stride_mid_o_lseh + block_start_kv * stride_mid_o_lseb
cur_token_idx * stride_mid_o_lset + cur_head_idx * stride_mid_o_lseh + block_start_kv * stride_mid_o_lseb
)
# logsumexp L^(j) = m^(j) + log(l^(j))
tl.store(mid_o_lse + offsets_mid_o_lse, m + tl.log(l))
@@ -135,6 +129,7 @@ def _flash_decoding_fwd_reduce_kernel(
mid_o_lse, # [batch_size, head_num, kv_split_num]
O, # [batch_size, num_heads, head_dim] or [batch_size, 1, num_heads, head_dim]
kv_seq_len,
q_len,
batch_size,
stride_mid_ot,
stride_mid_oh,
@@ -149,7 +144,8 @@ def _flash_decoding_fwd_reduce_kernel(
BLOCK_KV: tl.constexpr,
HEAD_DIM: tl.constexpr,
):
cur_seq_idx = tl.program_id(0)
cur_token_idx = tl.program_id(0)
cur_seq_idx = cur_token_idx // q_len
if cur_seq_idx >= batch_size:
return
cur_head_idx = tl.program_id(1)
@@ -164,8 +160,8 @@ def _flash_decoding_fwd_reduce_kernel(
l = 0.0 # sum exp
acc = tl.zeros([HEAD_DIM], dtype=tl.float32)
offsets_mid_o = cur_seq_idx * stride_mid_ot + cur_head_idx * stride_mid_oh + offsets_dmodel
offset_mid_lse = cur_seq_idx * stride_o_lset + cur_head_idx * stride_o_lseh
offsets_mid_o = cur_token_idx * stride_mid_ot + cur_head_idx * stride_mid_oh + offsets_dmodel
offset_mid_lse = cur_token_idx * stride_o_lset + cur_head_idx * stride_o_lseh
for block_i in range(0, kv_split_num, 1):
mid_o_block = tl.load(mid_o + offsets_mid_o + block_i * stride_mid_ob)
lse = tl.load(mid_o_lse + offset_mid_lse + block_i * stride_o_lseb)
@@ -179,7 +175,7 @@ def _flash_decoding_fwd_reduce_kernel(
m_i = m_ij
acc = acc / l
offsets_O = cur_seq_idx * stride_ot + cur_head_idx * stride_oh + offsets_dmodel
offsets_O = cur_token_idx * stride_ot + cur_head_idx * stride_oh + offsets_dmodel
tl.store(O + offsets_O, acc.to(O.type.element_ty))
return
@@ -199,12 +195,14 @@ def flash_decoding_attention(
mid_output_lse: torch.Tensor = None,
sm_scale: int = None,
kv_group_num: int = 1,
q_len: int = 1,
):
"""
Flash decoding implemented with a blocked KV Cache (PagedAttention) during decoding stage.
Args:
q (torch.Tensor): [bsz, num_heads, head_dim]
q (torch.Tensor): [bsz * q_len, num_heads, head_dim]
q_len > 1 only for verification process in speculative-decoding.
k_cache (torch.Tensor): [num_blocks, num_kv_heads, block_size, head_dim]
v_cache (torch.Tensor): [num_blocks, num_kv_heads, block_size, head_dim]
kv_seq_len (torch.Tensor): [batch_size]
@@ -212,19 +210,25 @@ def flash_decoding_attention(
block_tables (torch.Tensor): [batch_size, max_blocks_per_sequence]
max_seq_len_in_batch (int): Maximum sequence length in the batch.
output (torch.Tensor): [bsz, num_heads * head_dim]
mid_output (torch.Tensor): [ max_bsz , num_heads, kv_max_split_num, head_dim]
mid_output (torch.Tensor): [max_bsz * q_len, num_heads, kv_max_split_num, head_dim]
Intermediate output tensor. `max_bsz` should be greater than or equal to `bsz`.
mid_output_lse (torch.Tensor): [ max_bsz , num_heads, kv_max_split_num]
q_len > 1 only for verification process in speculative-decoding.
mid_output_lse (torch.Tensor): [max_bsz * q_len, num_heads, kv_max_split_num]
Log-sum-exp of intermediate output. `max_bsz` should be greater than or equal to `bsz`.
q_len > 1 only for verification process in speculative-decoding.
block_size (int): Size of each block in the blocked key/value cache.
num_kv_group (int, optional): Number of key/value groups. Defaults to 1.
q_length (int): Query length. Use for speculative decoding when `q_length` > 1 (i.e. the last n tokens).
Defaults to 1.
Returns:
Output tensor with shape [bsz, num_heads * head_dim]
Output tensor with shape [bsz * q_len, num_heads * head_dim]
"""
q = q.squeeze() if q.dim() == 4 else q
assert q.dim() == 3, f"Incompatible q dim: {q.dim()}"
bsz, num_heads, head_dim = q.shape
n_tokens, num_heads, head_dim = q.shape
assert n_tokens % q_len == 0, "Invalid q_len"
bsz = n_tokens // q_len
assert head_dim in {32, 64, 128, 256}
assert kv_seq_len.shape[0] == block_tables.shape[0] == bsz, (
@@ -247,22 +251,31 @@ def flash_decoding_attention(
max_seq_len_in_batch = kv_seq_len.max().item() if max_seq_len_in_batch is None else max_seq_len_in_batch
# For compatibility (TODO revise modeling in future)
kv_max_split_num = (max_seq_len_in_batch + BLOCK_KV - 1) // BLOCK_KV
mid_output = (
torch.zeros(size=(bsz, num_heads, kv_max_split_num, head_dim), dtype=torch.float32, device=q.device)
if mid_output is None
else mid_output
)
mid_output_lse = (
torch.zeros(size=(bsz, num_heads, kv_max_split_num), dtype=torch.float32, device=q.device)
if mid_output_lse is None
else mid_output_lse
)
if mid_output is None:
mid_output = torch.empty(
(bsz * q_len, num_heads, kv_max_split_num, head_dim), dtype=torch.float32, device=q.device
)
if mid_output_lse is None:
mid_output_lse = torch.empty((bsz * q_len, num_heads, kv_max_split_num), dtype=torch.float32, device=q.device)
if output is None:
# A hack to prevent `view` operation in modeling
output = torch.empty((bsz * q_len, num_heads * head_dim), dtype=q.dtype, device=q.device)
assert (
mid_output.size(2) == mid_output_lse.size(2) >= kv_max_split_num
), "Incompatible kv split number of intermediate output tensors"
assert (
mid_output.size(0) == mid_output_lse.size(0) >= output.size(0) == n_tokens
), f"Incompatible first dimension of output tensors"
# NOTE use `triton.next_power_of_2` here to utilize the cache mechanism of triton
# To optimize, revise batching/scheduling to batch 2^n sequences in a batch (preferred)
grid = (triton.next_power_of_2(bsz), num_heads, triton.cdiv(triton.next_power_of_2(max_seq_len_in_batch), BLOCK_KV))
output = torch.empty((bsz, num_heads * head_dim), dtype=q.dtype, device=q.device) if output is None else output
grid = (
triton.next_power_of_2(bsz * q_len),
num_heads,
triton.cdiv(triton.next_power_of_2(max_seq_len_in_batch), BLOCK_KV),
)
_flash_decoding_fwd_kernel[grid](
q,
k_cache,
@@ -271,6 +284,7 @@ def flash_decoding_attention(
mid_output,
mid_output_lse,
kv_seq_len,
q_len,
bsz,
q.stride(0),
q.stride(1),
@@ -295,13 +309,13 @@ def flash_decoding_attention(
HEAD_DIM=head_dim,
)
grid = (triton.next_power_of_2(bsz), num_heads)
grid = (triton.next_power_of_2(bsz * q_len), num_heads)
_flash_decoding_fwd_reduce_kernel[grid](
mid_output,
mid_output_lse,
output,
kv_seq_len,
q_len,
bsz,
mid_output.stride(0),
mid_output.stride(1),

109
colossalai/kernel/triton/kvcache_copy.py
View File

@@ -3,6 +3,50 @@ import triton
import triton.language as tl
# Triton 2.1.0
@triton.jit
def _copy_to_kcache_seqlen_n_kernel(
KV, # K or V
KVCache, # KCache or VCache
BLOCK_TABLES,
context_lengths,
stride_kt,
stride_kh,
stride_kd,
stride_cacheb,
stride_cacheh,
stride_cachebs,
stride_cached,
stride_bts,
stride_btb,
block_size,
n,
HEAD_DIM: tl.constexpr,
):
cur_token_idx = tl.program_id(0)
cur_seq_idx = cur_token_idx // n
cur_token_shift = cur_token_idx - (n * (cur_seq_idx + 1))
# cur_token_shift = cur_token_idx - n * cur_seq_idx
cur_kv_head_idx = tl.program_id(1)
past_kv_seq_len = tl.load(context_lengths + cur_seq_idx) + cur_token_shift
last_bt_block_idx = past_kv_seq_len // block_size
block_table_ptr = BLOCK_TABLES + cur_seq_idx * stride_bts
block_id = tl.load(block_table_ptr + last_bt_block_idx * stride_btb)
offset_last_block = past_kv_seq_len % block_size
offsets_dmodel = tl.arange(0, HEAD_DIM)
offsets_kv = cur_token_idx * stride_kt + cur_kv_head_idx * stride_kh + offsets_dmodel * stride_kd
kv = tl.load(KV + offsets_kv)
offsets_kvcache = (
block_id * stride_cacheb
+ cur_kv_head_idx * stride_cacheh
+ offset_last_block * stride_cachebs
+ offsets_dmodel * stride_cached
)
tl.store(KVCache + offsets_kvcache, kv)
return
# Triton 2.1.0
@triton.jit
def _copy_to_kvcache_seqlen1_kernel(
@@ -40,10 +84,11 @@ def _copy_to_kvcache_seqlen1_kernel(
block_id = tl.load(block_table_ptr + last_bt_block_idx * stride_btb)
offsets_in_last_block = past_kv_seq_len % block_size
offsets_dmodel = tl.arange(0, HEAD_DIM)
offsets_kv = cur_seq_idx * stride_kt + cur_kv_head_idx * stride_kh + offsets_dmodel * stride_kd
offsets_k = cur_seq_idx * stride_kt + cur_kv_head_idx * stride_kh + offsets_dmodel * stride_kd
offsets_v = cur_seq_idx * stride_vt + cur_kv_head_idx * stride_vh + offsets_dmodel * stride_vd
k = tl.load(K + offsets_kv)
v = tl.load(V + offsets_kv)
k = tl.load(K + offsets_k)
v = tl.load(V + offsets_v)
offsets_kcache = (
block_id * stride_cachekb
@@ -63,6 +108,64 @@ def _copy_to_kvcache_seqlen1_kernel(
return
def copy_k_to_blocked_cache(
k: torch.Tensor, k_cache: torch.Tensor, kv_lengths: torch.Tensor, block_tables: torch.Tensor, n: int = 1
):
"""
Copy keys or values to the blocked key/value cache during decoding stage.
Args:
k (torch.Tensor): [bsz, 1, num_kv_heads, head_dim]/[bsz, num_kv_heads, head_dim] - Keys or values during decoding with seq len 1.
[bsz * n, num_kv_heads, head_dim] - Keys or values with seq len n
k_cache (torch.Tensor): [num_blocks, num_kv_heads, block_size, head_dim] - Blocked key or value cache.
kv_lengths (torch.Tensor): [bsz] - Past key/value sequence lengths plus current sequence length for each sequence.
block_tables (torch.Tensor): [bsz, max_blocks_per_sequence] - Block tables for each sequence.
n (int): Number of tokens to copy for each sequence. Default to 1.
"""
assert k.size(-1) == k_cache.size(-1), "Incompatible head dim"
assert k.dtype == k_cache.dtype, "Expected consistent dtype for tensor and cache."
k = k.reshape(-1, k.size(-2), k.size(-1)) if k.dim() == 4 else k
assert k.dim() == 3, f"Invalid k dim {k.dim()}"
bsz, num_kv_heads, head_dim = k.shape
# NOTE when n > 1, the shape of k is [bsz * n, num_kv_heads, head_dim]
if n > 1:
assert bsz % n == 0, "Each sequence should have the same number of tokens to be copied"
bsz = bsz // n
assert kv_lengths.shape[0] == block_tables.shape[0] == bsz, (
f"Got incompatible batch size (number of seqs):\n"
f" Past kv sequence lengths bsz {kv_lengths.shape[0]}; "
f" block tables bsz {block_tables.shape[0]}, input k batch size {bsz}"
)
# Modify if the shape of kv cahce is changed.
block_size = k_cache.size(-2)
num_warps = 8 if head_dim > 128 else 4
grid = (bsz * n, num_kv_heads)
_copy_to_kcache_seqlen_n_kernel[grid](
k,
k_cache,
block_tables,
kv_lengths,
k.stride(0),
k.stride(1),
k.stride(2),
k_cache.stride(0),
k_cache.stride(1),
k_cache.stride(2),
k_cache.stride(3),
block_tables.stride(0),
block_tables.stride(1),
block_size,
n=n,
HEAD_DIM=head_dim,
num_warps=num_warps,
)
def copy_kv_to_blocked_cache(
k: torch.Tensor,
v: torch.Tensor,