from typing import Optional
import torch
import triton
import triton.language as tl
from mindspeed.ops.triton.utils import prepare_chunk_indices
@triton.heuristics({
'HAS_SCALE': lambda args: args['scale'] is not None,
'IS_VARLEN': lambda args: args['cu_seqlens'] is not None
})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps)
for num_warps in [1, 2, 4, 8]
],
key=['B', 'H', 'BT', 'IS_VARLEN', 'REVERSE']
)
@triton.jit(do_not_specialize=['T'])
def chunk_local_cumsum_scalar_kernel(
s,
o,
scale,
cu_seqlens,
chunk_indices,
T,
B: tl.constexpr,
H: tl.constexpr,
BLOCK_T: tl.constexpr,
REVERSE: tl.constexpr,
HAS_SCALE: tl.constexpr,
IS_VARLEN: tl.constexpr,
HEAD_FIRST: tl.constexpr,
CHUNK_SIZE: tl.constexpr = 64,
):
i_block, i_b = tl.program_id(0), tl.program_id(1)
N_CHUNKS: tl.constexpr = BLOCK_T // CHUNK_SIZE
if IS_VARLEN:
i_s, i_block = tl.load(chunk_indices + i_block * 2).to(tl.int32), tl.load(
chunk_indices + i_block * 2 + 1
).to(tl.int32)
bos, eos = tl.load(cu_seqlens + i_s).to(tl.int32), tl.load(
cu_seqlens + i_s + 1
).to(tl.int32)
T = eos - bos
else:
bos, eos = i_b * T, i_b * T + T
if HEAD_FIRST:
ptr_s = tl.make_block_ptr(
s + bos * H, (H, T), (T, 1), (0, i_block * BLOCK_T), (H, BLOCK_T), (1, 0)
)
ptr_o = tl.make_block_ptr(
o + bos * H, (H, T), (T, 1), (0, i_block * BLOCK_T), (H, BLOCK_T), (1, 0)
)
b_s = tl.load(ptr_s, boundary_check=(0,)).to(tl.float32)
b_s = tl.reshape(b_s, (H, N_CHUNKS, CHUNK_SIZE))
b_s = tl.trans(b_s, (2, 0, 1))
b_o = tl.cumsum(b_s, axis=0)
if REVERSE:
b_z = tl.sum(b_s, axis=0)
b_o = -b_o + b_z[None] + b_s
if HAS_SCALE:
b_o *= scale
b_o = tl.trans(b_o, (1, 2, 0))
b_o = tl.reshape(b_o, (H, BLOCK_T))
else:
ptr_s = tl.make_block_ptr(
s + bos * H, (T, H), (H, 1), (i_block * BLOCK_T, 0), (BLOCK_T, H), (1, 0)
)
ptr_o = tl.make_block_ptr(
o + bos * H, (T, H), (H, 1), (i_block * BLOCK_T, 0), (BLOCK_T, H), (1, 0)
)
b_s = tl.load(ptr_s, boundary_check=(0,)).to(tl.float32)
b_s = tl.reshape(b_s, (N_CHUNKS, CHUNK_SIZE, H))
b_s = tl.trans(b_s, (1, 0, 2))
b_o = tl.cumsum(b_s, axis=0)
if REVERSE:
b_z = tl.sum(b_s, axis=0)
b_o = -b_o + b_z[None] + b_s
if HAS_SCALE:
b_o *= scale
b_o = tl.trans(b_o, (1, 0, 2))
b_o = tl.reshape(b_o, (BLOCK_T, H))
tl.store(ptr_o, b_o.to(ptr_o.dtype.element_ty), boundary_check=(0,))
return
def chunk_local_cumsum_scalar(
g: torch.Tensor,
chunk_size: int,
reverse: bool = False,
scale: float = None,
cu_seqlens: Optional[torch.Tensor] = None,
head_first: bool = False,
output_dtype: Optional[torch.dtype] = torch.float
) -> torch.Tensor:
if head_first:
g = g.transpose(1, 2).contiguous()
B, H, T = g.shape
else:
B, T, H = g.shape
if chunk_size != 2 ** (chunk_size.bit_length() - 1):
raise ValueError(
f"chunk_size must be a power of 2, chunk_size is{chunk_size}"
)
BT = triton.next_power_of_2((2 ** 18) // (H * chunk_size))
chunk_indices = prepare_chunk_indices(cu_seqlens, BT) if cu_seqlens is not None else None
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
grid = (NT, B)
chunk_local_cumsum_scalar_kernel[grid](
s=g_org,
o=g,
scale=scale,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
B=B,
H=H,
BLOCK_T=BT,
HEAD_FIRST=head_first,
REVERSE=reverse,
CHUNK_SIZE=chunk_size,
)
if head_first:
g = g.transpose(1, 2).contiguous()
return g
def chunk_local_cumsum(
g: torch.Tensor,
chunk_size: int,
reverse: bool = False,
scale: float = None,
cu_seqlens: Optional[torch.Tensor] = None,
head_first: bool = False,
output_dtype: Optional[torch.dtype] = torch.float,
**kwargs
) -> torch.Tensor:
if cu_seqlens is not None:
if g.shape[0] != 1:
raise ValueError(
f"Only batch size 1 is supported when cu_seqlens are provided, current size is{g.shape[0]}"
)
if len(g.shape) == 3:
return chunk_local_cumsum_scalar(
g=g,
chunk_size=chunk_size,
reverse=reverse,
scale=scale,
cu_seqlens=cu_seqlens,
head_first=head_first,
output_dtype=output_dtype
)
else:
raise ValueError(
f"Unsupported input shape {g.shape}, "
f"which should be (B, T, H, D) if `head_first=False` "
f"or (B, H, T, D) otherwise"
)
