File size: 4,189 Bytes
6f0b660 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
# 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 math
import os
import torch
from ..utils.import_utils import is_torch_npu_available
if is_torch_npu_available():
from torch_npu import npu_fusion_attention
# FlashAttention2 is supported on Ascend NPU with down-right aligned causal mask by default.
# Set environment variable `NPU_FA2_SPARSE_MODE` to 2 when using top-left aligned causal mask.
TOP_LEFT_ALIGNED_CAUSAL_MASK_MODE = 2
DOWN_RIGHT_ALIGNED_CAUSAL_MASK_MODE = 3
SPARSE_MODE = int(os.getenv("NPU_FA2_SPARSE_MODE", default=DOWN_RIGHT_ALIGNED_CAUSAL_MASK_MODE))
if SPARSE_MODE not in [TOP_LEFT_ALIGNED_CAUSAL_MASK_MODE, DOWN_RIGHT_ALIGNED_CAUSAL_MASK_MODE]:
raise ValueError(
"Environment variable `NPU_FA2_SPARSE_MODE` can only be set as 2 (top-left aligned causal mask) "
"or 3 (down-right aligned causal mask)."
)
ATTN_MASK_NPU_CACHE = {}
def get_attn_mask_npu(device):
"""Get or create attention mask for the specified device."""
if device not in ATTN_MASK_NPU_CACHE:
ATTN_MASK_NPU_CACHE[device] = torch.triu(torch.ones([2048, 2048], device=device), diagonal=1).bool()
return ATTN_MASK_NPU_CACHE[device]
def is_npu_fa2_top_left_aligned_causal_mask():
return SPARSE_MODE == TOP_LEFT_ALIGNED_CAUSAL_MASK_MODE if is_torch_npu_available() else False
def npu_flash_attn_func(
q,
k,
v,
dropout_p=0.0,
softmax_scale=None,
causal=False,
**kwargs,
):
keep_prob = 1.0 - dropout_p
if softmax_scale is None:
softmax_scale = 1.0 / math.sqrt(q.shape[-1])
if not causal:
head_num = q.shape[2]
output = npu_fusion_attention(q, k, v, head_num, "BSND", keep_prob=keep_prob, scale=softmax_scale)[0]
else:
attn_mask_npu = get_attn_mask_npu(q.device)
head_num = q.shape[2]
output = npu_fusion_attention(
q,
k,
v,
head_num,
"BSND",
keep_prob=keep_prob,
scale=softmax_scale,
atten_mask=attn_mask_npu,
sparse_mode=SPARSE_MODE,
)[0]
return output
def npu_flash_attn_varlen_func(
q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
max_seqlen_q=None, # defined for aligning params order with corresponding function in `flash-attn`
max_seqlen_k=None, # defined for aligning params order with corresponding function in `flash-attn`
dropout_p=0.0,
softmax_scale=None,
causal=False,
**kwargs,
):
keep_prob = 1.0 - dropout_p
if softmax_scale is None:
softmax_scale = 1.0 / math.sqrt(q.shape[-1])
if not causal:
head_num = q.shape[1]
output = npu_fusion_attention(
q,
k,
v,
head_num,
pse=None,
atten_mask=None,
scale=softmax_scale,
keep_prob=keep_prob,
input_layout="TND",
actual_seq_qlen=tuple(cu_seqlens_q[1:].cpu().numpy().tolist()),
actual_seq_kvlen=tuple(cu_seqlens_k[1:].cpu().numpy().tolist()),
)[0]
else:
attn_mask_npu = get_attn_mask_npu(q.device)
head_num = q.shape[1]
output = npu_fusion_attention(
q,
k,
v,
head_num,
pse=None,
padding_mask=None,
atten_mask=attn_mask_npu,
scale=softmax_scale,
keep_prob=keep_prob,
input_layout="TND",
actual_seq_qlen=tuple(cu_seqlens_q[1:].cpu().numpy().tolist()),
actual_seq_kvlen=tuple(cu_seqlens_k[1:].cpu().numpy().tolist()),
sparse_mode=SPARSE_MODE,
)[0]
return output
|