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Copyright (c) 2024 by SageAttention team.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import torch
import triton
import triton.language as tl
@triton.jit
def quant_per_block_int8_kernel(Input, Output, Scale, L,
stride_iz, stride_ih, stride_in,
stride_oz, stride_oh, stride_on,
stride_sz, stride_sh,
sm_scale,
C: tl.constexpr, BLK: tl.constexpr):
off_blk = tl.program_id(0)
off_h = tl.program_id(1)
off_b = tl.program_id(2)
offs_n = off_blk * BLK + tl.arange(0, BLK)
offs_k = tl.arange(0, C)
input_ptrs = Input + off_b * stride_iz + off_h * stride_ih + offs_n[:, None] * stride_in + offs_k[None, :]
output_ptrs = Output + off_b * stride_oz + off_h * stride_oh + offs_n[:, None] * stride_on + offs_k[None, :]
scale_ptrs = Scale + off_b * stride_sz + off_h * stride_sh + off_blk
x = tl.load(input_ptrs, mask=offs_n[:, None] < L)
x = x.to(tl.float32)
x *= sm_scale
scale = tl.max(tl.abs(x)) / 127.
x_int8 = x / scale
x_int8 += 0.5 * tl.where(x_int8 >= 0, 1, -1)
x_int8 = x_int8.to(tl.int8)
tl.store(output_ptrs, x_int8, mask=offs_n[:, None] < L)
tl.store(scale_ptrs, scale)
def per_block_int8(q, k, km=None, BLKQ=128, BLKK=64, sm_scale=None, tensor_layout="HND"):
q_int8 = torch.empty(q.shape, dtype=torch.int8, device=q.device)
k_int8 = torch.empty(k.shape, dtype=torch.int8, device=k.device)
if km is not None:
k = k - km
if tensor_layout == "HND":
b, h_qo, qo_len, head_dim = q.shape
_, h_kv, kv_len, _ = k.shape
stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(1), q.stride(2)
stride_bz_qo, stride_h_qo, stride_seq_qo = q_int8.stride(0), q_int8.stride(1), q_int8.stride(2)
stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(1), k.stride(2)
stride_bz_ko, stride_h_ko, stride_seq_ko = k_int8.stride(0), k_int8.stride(1), k_int8.stride(2)
elif tensor_layout == "NHD":
b, qo_len, h_qo, head_dim = q.shape
_, kv_len, h_kv, _ = k.shape
stride_bz_q, stride_h_q, stride_seq_q = q.stride(0), q.stride(2), q.stride(1)
stride_bz_qo, stride_h_qo, stride_seq_qo = q_int8.stride(0), q_int8.stride(2), q_int8.stride(1)
stride_bz_k, stride_h_k, stride_seq_k = k.stride(0), k.stride(2), k.stride(1)
stride_bz_ko, stride_h_ko, stride_seq_ko = k_int8.stride(0), k_int8.stride(2), k_int8.stride(1)
else:
raise ValueError(f"Unknown tensor layout: {tensor_layout}")
q_scale = torch.empty((b, h_qo, (qo_len + BLKQ - 1) // BLKQ), device=q.device, dtype=torch.float32)
k_scale = torch.empty((b, h_kv, (kv_len + BLKK - 1) // BLKK), device=q.device, dtype=torch.float32)
if sm_scale is None:
sm_scale = head_dim**-0.5
grid = ((qo_len + BLKQ - 1) // BLKQ, h_qo, b)
quant_per_block_int8_kernel[grid](
q, q_int8, q_scale, qo_len,
stride_bz_q, stride_h_q, stride_seq_q,
stride_bz_qo, stride_h_qo, stride_seq_qo,
q_scale.stride(0), q_scale.stride(1),
sm_scale=(sm_scale * 1.44269504),
C=head_dim, BLK=BLKQ
)
grid = ((kv_len + BLKK - 1) // BLKK, h_kv, b)
quant_per_block_int8_kernel[grid](
k, k_int8, k_scale, kv_len,
stride_bz_k, stride_h_k, stride_seq_k,
stride_bz_ko, stride_h_ko, stride_seq_ko,
k_scale.stride(0), k_scale.stride(1),
sm_scale=1.0,
C=head_dim, BLK=BLKK
)
return q_int8, q_scale, k_int8, k_scale
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