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  1. build/torch-stable-abi210-cu126-x86_64-linux/__init__.py +23 -0
  2. build/torch-stable-abi210-cu126-x86_64-linux/_custom_ops.py +173 -0
  3. build/torch-stable-abi210-cu126-x86_64-linux/_ops.py +9 -0
  4. build/torch-stable-abi210-cu126-x86_64-linux/_paged_attention_cuda_4b8a7b6.abi3.so +3 -0
  5. build/torch-stable-abi210-cu126-x86_64-linux/_private_for_testing.py +7 -0
  6. build/torch-stable-abi210-cu126-x86_64-linux/metadata.json +44 -0
  7. build/torch-stable-abi210-cu126-x86_64-linux/metadata.json.sigstore +1 -0
  8. build/torch-stable-abi210-cu126-x86_64-linux/platforms.py +92 -0
  9. build/torch-stable-abi210-cu130-x86_64-linux/__init__.py +23 -0
  10. build/torch-stable-abi210-cu130-x86_64-linux/_custom_ops.py +173 -0
  11. build/torch-stable-abi210-cu130-x86_64-linux/_ops.py +9 -0
  12. build/torch-stable-abi210-cu130-x86_64-linux/_paged_attention_cuda_4b8a7b6.abi3.so +3 -0
  13. build/torch-stable-abi210-cu130-x86_64-linux/_private_for_testing.py +7 -0
  14. build/torch-stable-abi210-cu130-x86_64-linux/metadata.json +47 -0
  15. build/torch-stable-abi210-cu130-x86_64-linux/metadata.json.sigstore +1 -0
  16. build/torch-stable-abi210-cu130-x86_64-linux/platforms.py +92 -0
  17. build/torch-stable-abi210-cu132-x86_64-linux/__init__.py +23 -0
  18. build/torch-stable-abi210-cu132-x86_64-linux/_custom_ops.py +173 -0
  19. build/torch-stable-abi210-cu132-x86_64-linux/_ops.py +9 -0
  20. build/torch-stable-abi210-cu132-x86_64-linux/_paged_attention_cuda_4b8a7b6.abi3.so +3 -0
  21. build/torch-stable-abi210-cu132-x86_64-linux/_private_for_testing.py +7 -0
  22. build/torch-stable-abi210-cu132-x86_64-linux/metadata.json +47 -0
  23. build/torch-stable-abi210-cu132-x86_64-linux/metadata.json.sigstore +1 -0
  24. build/torch-stable-abi210-cu132-x86_64-linux/platforms.py +92 -0
build/torch-stable-abi210-cu126-x86_64-linux/__init__.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from . import platforms
2
+ from ._custom_ops import (
3
+ convert_fp8,
4
+ copy_blocks,
5
+ paged_attention_v1,
6
+ paged_attention_v2,
7
+ reshape_and_cache,
8
+ reshape_and_cache_flash,
9
+ swap_blocks,
10
+ )
11
+
12
+ from . import _private_for_testing # noqa: F401
13
+
14
+ __all__ = [
15
+ "_private_for_testing",
16
+ "convert_fp8",
17
+ "copy_blocks",
18
+ "paged_attention_v1",
19
+ "paged_attention_v2",
20
+ "reshape_and_cache",
21
+ "reshape_and_cache_flash",
22
+ "swap_blocks",
23
+ ]
build/torch-stable-abi210-cu126-x86_64-linux/_custom_ops.py ADDED
@@ -0,0 +1,173 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List, Optional
2
+
3
+ import torch
4
+
5
+ from ._ops import ops
6
+
7
+
8
+ # page attention ops
9
+ def paged_attention_v1(
10
+ out: torch.Tensor,
11
+ query: torch.Tensor,
12
+ key_cache: torch.Tensor,
13
+ value_cache: torch.Tensor,
14
+ num_kv_heads: int,
15
+ scale: float,
16
+ block_tables: torch.Tensor,
17
+ seq_lens: torch.Tensor,
18
+ block_size: int,
19
+ max_seq_len: int,
20
+ alibi_slopes: Optional[torch.Tensor],
21
+ kv_cache_dtype: str,
22
+ k_scale: float,
23
+ v_scale: float,
24
+ tp_rank: int = 0,
25
+ blocksparse_local_blocks: int = 0,
26
+ blocksparse_vert_stride: int = 0,
27
+ blocksparse_block_size: int = 64,
28
+ blocksparse_head_sliding_step: int = 0,
29
+ ) -> None:
30
+ ops.paged_attention_v1(
31
+ out,
32
+ query,
33
+ key_cache,
34
+ value_cache,
35
+ num_kv_heads,
36
+ scale,
37
+ block_tables,
38
+ seq_lens,
39
+ block_size,
40
+ max_seq_len,
41
+ alibi_slopes,
42
+ kv_cache_dtype,
43
+ k_scale,
44
+ v_scale,
45
+ tp_rank,
46
+ blocksparse_local_blocks,
47
+ blocksparse_vert_stride,
48
+ blocksparse_block_size,
49
+ blocksparse_head_sliding_step,
50
+ )
51
+
52
+
53
+ def paged_attention_v2(
54
+ out: torch.Tensor,
55
+ exp_sum: torch.Tensor,
56
+ max_logits: torch.Tensor,
57
+ tmp_out: torch.Tensor,
58
+ query: torch.Tensor,
59
+ key_cache: torch.Tensor,
60
+ value_cache: torch.Tensor,
61
+ num_kv_heads: int,
62
+ scale: float,
63
+ block_tables: torch.Tensor,
64
+ seq_lens: torch.Tensor,
65
+ block_size: int,
66
+ max_seq_len: int,
67
+ alibi_slopes: Optional[torch.Tensor],
68
+ kv_cache_dtype: str,
69
+ k_scale: float,
70
+ v_scale: float,
71
+ tp_rank: int = 0,
72
+ blocksparse_local_blocks: int = 0,
73
+ blocksparse_vert_stride: int = 0,
74
+ blocksparse_block_size: int = 64,
75
+ blocksparse_head_sliding_step: int = 0,
76
+ ) -> None:
77
+ ops.paged_attention_v2(
78
+ out,
79
+ exp_sum,
80
+ max_logits,
81
+ tmp_out,
82
+ query,
83
+ key_cache,
84
+ value_cache,
85
+ num_kv_heads,
86
+ scale,
87
+ block_tables,
88
+ seq_lens,
89
+ block_size,
90
+ max_seq_len,
91
+ alibi_slopes,
92
+ kv_cache_dtype,
93
+ k_scale,
94
+ v_scale,
95
+ tp_rank,
96
+ blocksparse_local_blocks,
97
+ blocksparse_vert_stride,
98
+ blocksparse_block_size,
99
+ blocksparse_head_sliding_step,
100
+ )
101
+
102
+
103
+ def reshape_and_cache(
104
+ key: torch.Tensor,
105
+ value: torch.Tensor,
106
+ key_cache: torch.Tensor,
107
+ value_cache: torch.Tensor,
108
+ slot_mapping: torch.Tensor,
109
+ kv_cache_dtype: str,
110
+ k_scale: float,
111
+ v_scale: float,
112
+ ) -> None:
113
+ ops.reshape_and_cache(
114
+ key,
115
+ value,
116
+ key_cache,
117
+ value_cache,
118
+ slot_mapping,
119
+ kv_cache_dtype,
120
+ k_scale,
121
+ v_scale,
122
+ )
123
+
124
+
125
+ def reshape_and_cache_flash(
126
+ key: torch.Tensor,
127
+ value: torch.Tensor,
128
+ key_cache: torch.Tensor,
129
+ value_cache: torch.Tensor,
130
+ slot_mapping: torch.Tensor,
131
+ kv_cache_dtype: str,
132
+ k_scale: torch.Tensor,
133
+ v_scale: torch.Tensor,
134
+ ) -> None:
135
+ ops.reshape_and_cache_flash(
136
+ key,
137
+ value,
138
+ key_cache,
139
+ value_cache,
140
+ slot_mapping,
141
+ kv_cache_dtype,
142
+ k_scale,
143
+ v_scale,
144
+ )
145
+
146
+
147
+ def copy_blocks(
148
+ key_caches: List[torch.Tensor],
149
+ value_caches: List[torch.Tensor],
150
+ block_mapping: torch.Tensor,
151
+ ) -> None:
152
+ ops.copy_blocks(key_caches, value_caches, block_mapping)
153
+
154
+
155
+ def swap_blocks(
156
+ src: torch.Tensor, dst: torch.Tensor, block_mapping: torch.Tensor
157
+ ) -> None:
158
+ ops.swap_blocks(src, dst, block_mapping)
159
+
160
+
161
+ def convert_fp8(
162
+ output: torch.Tensor, input: torch.Tensor, scale: float = 1.0, kv_dtype: str = "fp8"
163
+ ) -> None:
164
+ ops.convert_fp8(output, input, scale, kv_dtype)
165
+
166
+
167
+ __all__ = [
168
+ "convert_fp8",
169
+ "paged_attention_v1",
170
+ "paged_attention_v2",
171
+ "reshape_and_cache",
172
+ "copy_blocks",
173
+ ]
build/torch-stable-abi210-cu126-x86_64-linux/_ops.py ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from . import _paged_attention_cuda_4b8a7b6
3
+ ops = torch.ops._paged_attention_cuda_4b8a7b6
4
+
5
+ def add_op_namespace_prefix(op_name: str):
6
+ """
7
+ Prefix op by namespace.
8
+ """
9
+ return f"_paged_attention_cuda_4b8a7b6::{op_name}"
build/torch-stable-abi210-cu126-x86_64-linux/_paged_attention_cuda_4b8a7b6.abi3.so ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:465ad3baeb4ca55824956916df7fb110d8b4f1251c4cfd1974de808c4f3adcbd
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+ size 138139456
build/torch-stable-abi210-cu126-x86_64-linux/_private_for_testing.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ """Internal symbols that need to be exposed for the test suite."""
2
+
3
+ from ._ops import ops
4
+
5
+ __all__ = [
6
+ "ops",
7
+ ]
build/torch-stable-abi210-cu126-x86_64-linux/metadata.json ADDED
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+ "name": "paged-attention",
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+ "id": "_paged_attention_cuda_4b8a7b6",
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build/torch-stable-abi210-cu126-x86_64-linux/platforms.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import random
3
+ from abc import ABC, abstractmethod
4
+ from functools import lru_cache, wraps
5
+ from typing import Callable, ParamSpec, TypeVar
6
+
7
+ import numpy as np
8
+ import torch
9
+
10
+ IS_ROCM = torch.version.hip is not None
11
+ IS_MPS = torch.backends.mps.is_available()
12
+
13
+
14
+ class Platform(ABC):
15
+ @classmethod
16
+ def seed_everything(cls, seed: int) -> None:
17
+ """
18
+ Set the seed of each random module.
19
+ `torch.manual_seed` will set seed on all devices.
20
+
21
+ Loosely based on: https://github.com/Lightning-AI/pytorch-lightning/blob/2.4.0/src/lightning/fabric/utilities/seed.py#L20
22
+ """
23
+ random.seed(seed)
24
+ np.random.seed(seed)
25
+ torch.manual_seed(seed)
26
+
27
+ @abstractmethod
28
+ def get_device_name(self, device_id: int = 0) -> str: ...
29
+
30
+ @abstractmethod
31
+ def is_cuda(self) -> bool: ...
32
+
33
+ @abstractmethod
34
+ def is_rocm(self) -> bool: ...
35
+
36
+ @abstractmethod
37
+ def is_mps(self) -> bool: ...
38
+
39
+
40
+ class CudaPlatform(Platform):
41
+ @classmethod
42
+ @lru_cache(maxsize=8)
43
+ def get_device_name(cls, device_id: int = 0) -> str:
44
+ return torch.cuda.get_device_name(0)
45
+
46
+ def is_cuda(self) -> bool:
47
+ return True
48
+
49
+ def is_rocm(self) -> bool:
50
+ return False
51
+
52
+ def is_mps(self) -> bool:
53
+ return False
54
+
55
+
56
+ class RocmPlatform(Platform):
57
+ @classmethod
58
+ @lru_cache(maxsize=8)
59
+ def get_device_name(cls, device_id: int = 0) -> str:
60
+ return torch.cuda.get_device_name(device_id)
61
+
62
+ def is_cuda(self) -> bool:
63
+ return False
64
+
65
+ def is_rocm(self) -> bool:
66
+ return True
67
+
68
+ def is_mps(self) -> bool:
69
+ return False
70
+
71
+
72
+ class MpsPlatform(Platform):
73
+ @classmethod
74
+ @lru_cache(maxsize=8)
75
+ def get_device_name(cls, device_id: int = 0) -> str:
76
+ return torch.cuda.get_device_name(device_id)
77
+
78
+ def is_cuda(self) -> bool:
79
+ return False
80
+
81
+ def is_rocm(self) -> bool:
82
+ return False
83
+
84
+ def is_mps(self) -> bool:
85
+ return True
86
+
87
+ current_platform = (
88
+ RocmPlatform() if IS_ROCM else
89
+ MpsPlatform() if IS_MPS else
90
+ CudaPlatform() if torch.cuda.is_available() else
91
+ None
92
+ )
build/torch-stable-abi210-cu130-x86_64-linux/__init__.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from . import platforms
2
+ from ._custom_ops import (
3
+ convert_fp8,
4
+ copy_blocks,
5
+ paged_attention_v1,
6
+ paged_attention_v2,
7
+ reshape_and_cache,
8
+ reshape_and_cache_flash,
9
+ swap_blocks,
10
+ )
11
+
12
+ from . import _private_for_testing # noqa: F401
13
+
14
+ __all__ = [
15
+ "_private_for_testing",
16
+ "convert_fp8",
17
+ "copy_blocks",
18
+ "paged_attention_v1",
19
+ "paged_attention_v2",
20
+ "reshape_and_cache",
21
+ "reshape_and_cache_flash",
22
+ "swap_blocks",
23
+ ]
build/torch-stable-abi210-cu130-x86_64-linux/_custom_ops.py ADDED
@@ -0,0 +1,173 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List, Optional
2
+
3
+ import torch
4
+
5
+ from ._ops import ops
6
+
7
+
8
+ # page attention ops
9
+ def paged_attention_v1(
10
+ out: torch.Tensor,
11
+ query: torch.Tensor,
12
+ key_cache: torch.Tensor,
13
+ value_cache: torch.Tensor,
14
+ num_kv_heads: int,
15
+ scale: float,
16
+ block_tables: torch.Tensor,
17
+ seq_lens: torch.Tensor,
18
+ block_size: int,
19
+ max_seq_len: int,
20
+ alibi_slopes: Optional[torch.Tensor],
21
+ kv_cache_dtype: str,
22
+ k_scale: float,
23
+ v_scale: float,
24
+ tp_rank: int = 0,
25
+ blocksparse_local_blocks: int = 0,
26
+ blocksparse_vert_stride: int = 0,
27
+ blocksparse_block_size: int = 64,
28
+ blocksparse_head_sliding_step: int = 0,
29
+ ) -> None:
30
+ ops.paged_attention_v1(
31
+ out,
32
+ query,
33
+ key_cache,
34
+ value_cache,
35
+ num_kv_heads,
36
+ scale,
37
+ block_tables,
38
+ seq_lens,
39
+ block_size,
40
+ max_seq_len,
41
+ alibi_slopes,
42
+ kv_cache_dtype,
43
+ k_scale,
44
+ v_scale,
45
+ tp_rank,
46
+ blocksparse_local_blocks,
47
+ blocksparse_vert_stride,
48
+ blocksparse_block_size,
49
+ blocksparse_head_sliding_step,
50
+ )
51
+
52
+
53
+ def paged_attention_v2(
54
+ out: torch.Tensor,
55
+ exp_sum: torch.Tensor,
56
+ max_logits: torch.Tensor,
57
+ tmp_out: torch.Tensor,
58
+ query: torch.Tensor,
59
+ key_cache: torch.Tensor,
60
+ value_cache: torch.Tensor,
61
+ num_kv_heads: int,
62
+ scale: float,
63
+ block_tables: torch.Tensor,
64
+ seq_lens: torch.Tensor,
65
+ block_size: int,
66
+ max_seq_len: int,
67
+ alibi_slopes: Optional[torch.Tensor],
68
+ kv_cache_dtype: str,
69
+ k_scale: float,
70
+ v_scale: float,
71
+ tp_rank: int = 0,
72
+ blocksparse_local_blocks: int = 0,
73
+ blocksparse_vert_stride: int = 0,
74
+ blocksparse_block_size: int = 64,
75
+ blocksparse_head_sliding_step: int = 0,
76
+ ) -> None:
77
+ ops.paged_attention_v2(
78
+ out,
79
+ exp_sum,
80
+ max_logits,
81
+ tmp_out,
82
+ query,
83
+ key_cache,
84
+ value_cache,
85
+ num_kv_heads,
86
+ scale,
87
+ block_tables,
88
+ seq_lens,
89
+ block_size,
90
+ max_seq_len,
91
+ alibi_slopes,
92
+ kv_cache_dtype,
93
+ k_scale,
94
+ v_scale,
95
+ tp_rank,
96
+ blocksparse_local_blocks,
97
+ blocksparse_vert_stride,
98
+ blocksparse_block_size,
99
+ blocksparse_head_sliding_step,
100
+ )
101
+
102
+
103
+ def reshape_and_cache(
104
+ key: torch.Tensor,
105
+ value: torch.Tensor,
106
+ key_cache: torch.Tensor,
107
+ value_cache: torch.Tensor,
108
+ slot_mapping: torch.Tensor,
109
+ kv_cache_dtype: str,
110
+ k_scale: float,
111
+ v_scale: float,
112
+ ) -> None:
113
+ ops.reshape_and_cache(
114
+ key,
115
+ value,
116
+ key_cache,
117
+ value_cache,
118
+ slot_mapping,
119
+ kv_cache_dtype,
120
+ k_scale,
121
+ v_scale,
122
+ )
123
+
124
+
125
+ def reshape_and_cache_flash(
126
+ key: torch.Tensor,
127
+ value: torch.Tensor,
128
+ key_cache: torch.Tensor,
129
+ value_cache: torch.Tensor,
130
+ slot_mapping: torch.Tensor,
131
+ kv_cache_dtype: str,
132
+ k_scale: torch.Tensor,
133
+ v_scale: torch.Tensor,
134
+ ) -> None:
135
+ ops.reshape_and_cache_flash(
136
+ key,
137
+ value,
138
+ key_cache,
139
+ value_cache,
140
+ slot_mapping,
141
+ kv_cache_dtype,
142
+ k_scale,
143
+ v_scale,
144
+ )
145
+
146
+
147
+ def copy_blocks(
148
+ key_caches: List[torch.Tensor],
149
+ value_caches: List[torch.Tensor],
150
+ block_mapping: torch.Tensor,
151
+ ) -> None:
152
+ ops.copy_blocks(key_caches, value_caches, block_mapping)
153
+
154
+
155
+ def swap_blocks(
156
+ src: torch.Tensor, dst: torch.Tensor, block_mapping: torch.Tensor
157
+ ) -> None:
158
+ ops.swap_blocks(src, dst, block_mapping)
159
+
160
+
161
+ def convert_fp8(
162
+ output: torch.Tensor, input: torch.Tensor, scale: float = 1.0, kv_dtype: str = "fp8"
163
+ ) -> None:
164
+ ops.convert_fp8(output, input, scale, kv_dtype)
165
+
166
+
167
+ __all__ = [
168
+ "convert_fp8",
169
+ "paged_attention_v1",
170
+ "paged_attention_v2",
171
+ "reshape_and_cache",
172
+ "copy_blocks",
173
+ ]
build/torch-stable-abi210-cu130-x86_64-linux/_ops.py ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from . import _paged_attention_cuda_4b8a7b6
3
+ ops = torch.ops._paged_attention_cuda_4b8a7b6
4
+
5
+ def add_op_namespace_prefix(op_name: str):
6
+ """
7
+ Prefix op by namespace.
8
+ """
9
+ return f"_paged_attention_cuda_4b8a7b6::{op_name}"
build/torch-stable-abi210-cu130-x86_64-linux/_paged_attention_cuda_4b8a7b6.abi3.so ADDED
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+ size 97624248
build/torch-stable-abi210-cu130-x86_64-linux/_private_for_testing.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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+ """Internal symbols that need to be exposed for the test suite."""
2
+
3
+ from ._ops import ops
4
+
5
+ __all__ = [
6
+ "ops",
7
+ ]
build/torch-stable-abi210-cu130-x86_64-linux/metadata.json ADDED
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build/torch-stable-abi210-cu130-x86_64-linux/platforms.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import random
3
+ from abc import ABC, abstractmethod
4
+ from functools import lru_cache, wraps
5
+ from typing import Callable, ParamSpec, TypeVar
6
+
7
+ import numpy as np
8
+ import torch
9
+
10
+ IS_ROCM = torch.version.hip is not None
11
+ IS_MPS = torch.backends.mps.is_available()
12
+
13
+
14
+ class Platform(ABC):
15
+ @classmethod
16
+ def seed_everything(cls, seed: int) -> None:
17
+ """
18
+ Set the seed of each random module.
19
+ `torch.manual_seed` will set seed on all devices.
20
+
21
+ Loosely based on: https://github.com/Lightning-AI/pytorch-lightning/blob/2.4.0/src/lightning/fabric/utilities/seed.py#L20
22
+ """
23
+ random.seed(seed)
24
+ np.random.seed(seed)
25
+ torch.manual_seed(seed)
26
+
27
+ @abstractmethod
28
+ def get_device_name(self, device_id: int = 0) -> str: ...
29
+
30
+ @abstractmethod
31
+ def is_cuda(self) -> bool: ...
32
+
33
+ @abstractmethod
34
+ def is_rocm(self) -> bool: ...
35
+
36
+ @abstractmethod
37
+ def is_mps(self) -> bool: ...
38
+
39
+
40
+ class CudaPlatform(Platform):
41
+ @classmethod
42
+ @lru_cache(maxsize=8)
43
+ def get_device_name(cls, device_id: int = 0) -> str:
44
+ return torch.cuda.get_device_name(0)
45
+
46
+ def is_cuda(self) -> bool:
47
+ return True
48
+
49
+ def is_rocm(self) -> bool:
50
+ return False
51
+
52
+ def is_mps(self) -> bool:
53
+ return False
54
+
55
+
56
+ class RocmPlatform(Platform):
57
+ @classmethod
58
+ @lru_cache(maxsize=8)
59
+ def get_device_name(cls, device_id: int = 0) -> str:
60
+ return torch.cuda.get_device_name(device_id)
61
+
62
+ def is_cuda(self) -> bool:
63
+ return False
64
+
65
+ def is_rocm(self) -> bool:
66
+ return True
67
+
68
+ def is_mps(self) -> bool:
69
+ return False
70
+
71
+
72
+ class MpsPlatform(Platform):
73
+ @classmethod
74
+ @lru_cache(maxsize=8)
75
+ def get_device_name(cls, device_id: int = 0) -> str:
76
+ return torch.cuda.get_device_name(device_id)
77
+
78
+ def is_cuda(self) -> bool:
79
+ return False
80
+
81
+ def is_rocm(self) -> bool:
82
+ return False
83
+
84
+ def is_mps(self) -> bool:
85
+ return True
86
+
87
+ current_platform = (
88
+ RocmPlatform() if IS_ROCM else
89
+ MpsPlatform() if IS_MPS else
90
+ CudaPlatform() if torch.cuda.is_available() else
91
+ None
92
+ )
build/torch-stable-abi210-cu132-x86_64-linux/__init__.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from . import platforms
2
+ from ._custom_ops import (
3
+ convert_fp8,
4
+ copy_blocks,
5
+ paged_attention_v1,
6
+ paged_attention_v2,
7
+ reshape_and_cache,
8
+ reshape_and_cache_flash,
9
+ swap_blocks,
10
+ )
11
+
12
+ from . import _private_for_testing # noqa: F401
13
+
14
+ __all__ = [
15
+ "_private_for_testing",
16
+ "convert_fp8",
17
+ "copy_blocks",
18
+ "paged_attention_v1",
19
+ "paged_attention_v2",
20
+ "reshape_and_cache",
21
+ "reshape_and_cache_flash",
22
+ "swap_blocks",
23
+ ]
build/torch-stable-abi210-cu132-x86_64-linux/_custom_ops.py ADDED
@@ -0,0 +1,173 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import List, Optional
2
+
3
+ import torch
4
+
5
+ from ._ops import ops
6
+
7
+
8
+ # page attention ops
9
+ def paged_attention_v1(
10
+ out: torch.Tensor,
11
+ query: torch.Tensor,
12
+ key_cache: torch.Tensor,
13
+ value_cache: torch.Tensor,
14
+ num_kv_heads: int,
15
+ scale: float,
16
+ block_tables: torch.Tensor,
17
+ seq_lens: torch.Tensor,
18
+ block_size: int,
19
+ max_seq_len: int,
20
+ alibi_slopes: Optional[torch.Tensor],
21
+ kv_cache_dtype: str,
22
+ k_scale: float,
23
+ v_scale: float,
24
+ tp_rank: int = 0,
25
+ blocksparse_local_blocks: int = 0,
26
+ blocksparse_vert_stride: int = 0,
27
+ blocksparse_block_size: int = 64,
28
+ blocksparse_head_sliding_step: int = 0,
29
+ ) -> None:
30
+ ops.paged_attention_v1(
31
+ out,
32
+ query,
33
+ key_cache,
34
+ value_cache,
35
+ num_kv_heads,
36
+ scale,
37
+ block_tables,
38
+ seq_lens,
39
+ block_size,
40
+ max_seq_len,
41
+ alibi_slopes,
42
+ kv_cache_dtype,
43
+ k_scale,
44
+ v_scale,
45
+ tp_rank,
46
+ blocksparse_local_blocks,
47
+ blocksparse_vert_stride,
48
+ blocksparse_block_size,
49
+ blocksparse_head_sliding_step,
50
+ )
51
+
52
+
53
+ def paged_attention_v2(
54
+ out: torch.Tensor,
55
+ exp_sum: torch.Tensor,
56
+ max_logits: torch.Tensor,
57
+ tmp_out: torch.Tensor,
58
+ query: torch.Tensor,
59
+ key_cache: torch.Tensor,
60
+ value_cache: torch.Tensor,
61
+ num_kv_heads: int,
62
+ scale: float,
63
+ block_tables: torch.Tensor,
64
+ seq_lens: torch.Tensor,
65
+ block_size: int,
66
+ max_seq_len: int,
67
+ alibi_slopes: Optional[torch.Tensor],
68
+ kv_cache_dtype: str,
69
+ k_scale: float,
70
+ v_scale: float,
71
+ tp_rank: int = 0,
72
+ blocksparse_local_blocks: int = 0,
73
+ blocksparse_vert_stride: int = 0,
74
+ blocksparse_block_size: int = 64,
75
+ blocksparse_head_sliding_step: int = 0,
76
+ ) -> None:
77
+ ops.paged_attention_v2(
78
+ out,
79
+ exp_sum,
80
+ max_logits,
81
+ tmp_out,
82
+ query,
83
+ key_cache,
84
+ value_cache,
85
+ num_kv_heads,
86
+ scale,
87
+ block_tables,
88
+ seq_lens,
89
+ block_size,
90
+ max_seq_len,
91
+ alibi_slopes,
92
+ kv_cache_dtype,
93
+ k_scale,
94
+ v_scale,
95
+ tp_rank,
96
+ blocksparse_local_blocks,
97
+ blocksparse_vert_stride,
98
+ blocksparse_block_size,
99
+ blocksparse_head_sliding_step,
100
+ )
101
+
102
+
103
+ def reshape_and_cache(
104
+ key: torch.Tensor,
105
+ value: torch.Tensor,
106
+ key_cache: torch.Tensor,
107
+ value_cache: torch.Tensor,
108
+ slot_mapping: torch.Tensor,
109
+ kv_cache_dtype: str,
110
+ k_scale: float,
111
+ v_scale: float,
112
+ ) -> None:
113
+ ops.reshape_and_cache(
114
+ key,
115
+ value,
116
+ key_cache,
117
+ value_cache,
118
+ slot_mapping,
119
+ kv_cache_dtype,
120
+ k_scale,
121
+ v_scale,
122
+ )
123
+
124
+
125
+ def reshape_and_cache_flash(
126
+ key: torch.Tensor,
127
+ value: torch.Tensor,
128
+ key_cache: torch.Tensor,
129
+ value_cache: torch.Tensor,
130
+ slot_mapping: torch.Tensor,
131
+ kv_cache_dtype: str,
132
+ k_scale: torch.Tensor,
133
+ v_scale: torch.Tensor,
134
+ ) -> None:
135
+ ops.reshape_and_cache_flash(
136
+ key,
137
+ value,
138
+ key_cache,
139
+ value_cache,
140
+ slot_mapping,
141
+ kv_cache_dtype,
142
+ k_scale,
143
+ v_scale,
144
+ )
145
+
146
+
147
+ def copy_blocks(
148
+ key_caches: List[torch.Tensor],
149
+ value_caches: List[torch.Tensor],
150
+ block_mapping: torch.Tensor,
151
+ ) -> None:
152
+ ops.copy_blocks(key_caches, value_caches, block_mapping)
153
+
154
+
155
+ def swap_blocks(
156
+ src: torch.Tensor, dst: torch.Tensor, block_mapping: torch.Tensor
157
+ ) -> None:
158
+ ops.swap_blocks(src, dst, block_mapping)
159
+
160
+
161
+ def convert_fp8(
162
+ output: torch.Tensor, input: torch.Tensor, scale: float = 1.0, kv_dtype: str = "fp8"
163
+ ) -> None:
164
+ ops.convert_fp8(output, input, scale, kv_dtype)
165
+
166
+
167
+ __all__ = [
168
+ "convert_fp8",
169
+ "paged_attention_v1",
170
+ "paged_attention_v2",
171
+ "reshape_and_cache",
172
+ "copy_blocks",
173
+ ]
build/torch-stable-abi210-cu132-x86_64-linux/_ops.py ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from . import _paged_attention_cuda_4b8a7b6
3
+ ops = torch.ops._paged_attention_cuda_4b8a7b6
4
+
5
+ def add_op_namespace_prefix(op_name: str):
6
+ """
7
+ Prefix op by namespace.
8
+ """
9
+ return f"_paged_attention_cuda_4b8a7b6::{op_name}"
build/torch-stable-abi210-cu132-x86_64-linux/_paged_attention_cuda_4b8a7b6.abi3.so ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ oid sha256:7f652685222ac9e6af2183ec9320bd3f4e883c802e870df3861d6b46cd439d73
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+ size 96451400
build/torch-stable-abi210-cu132-x86_64-linux/_private_for_testing.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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+ """Internal symbols that need to be exposed for the test suite."""
2
+
3
+ from ._ops import ops
4
+
5
+ __all__ = [
6
+ "ops",
7
+ ]
build/torch-stable-abi210-cu132-x86_64-linux/metadata.json ADDED
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build/torch-stable-abi210-cu132-x86_64-linux/platforms.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import random
3
+ from abc import ABC, abstractmethod
4
+ from functools import lru_cache, wraps
5
+ from typing import Callable, ParamSpec, TypeVar
6
+
7
+ import numpy as np
8
+ import torch
9
+
10
+ IS_ROCM = torch.version.hip is not None
11
+ IS_MPS = torch.backends.mps.is_available()
12
+
13
+
14
+ class Platform(ABC):
15
+ @classmethod
16
+ def seed_everything(cls, seed: int) -> None:
17
+ """
18
+ Set the seed of each random module.
19
+ `torch.manual_seed` will set seed on all devices.
20
+
21
+ Loosely based on: https://github.com/Lightning-AI/pytorch-lightning/blob/2.4.0/src/lightning/fabric/utilities/seed.py#L20
22
+ """
23
+ random.seed(seed)
24
+ np.random.seed(seed)
25
+ torch.manual_seed(seed)
26
+
27
+ @abstractmethod
28
+ def get_device_name(self, device_id: int = 0) -> str: ...
29
+
30
+ @abstractmethod
31
+ def is_cuda(self) -> bool: ...
32
+
33
+ @abstractmethod
34
+ def is_rocm(self) -> bool: ...
35
+
36
+ @abstractmethod
37
+ def is_mps(self) -> bool: ...
38
+
39
+
40
+ class CudaPlatform(Platform):
41
+ @classmethod
42
+ @lru_cache(maxsize=8)
43
+ def get_device_name(cls, device_id: int = 0) -> str:
44
+ return torch.cuda.get_device_name(0)
45
+
46
+ def is_cuda(self) -> bool:
47
+ return True
48
+
49
+ def is_rocm(self) -> bool:
50
+ return False
51
+
52
+ def is_mps(self) -> bool:
53
+ return False
54
+
55
+
56
+ class RocmPlatform(Platform):
57
+ @classmethod
58
+ @lru_cache(maxsize=8)
59
+ def get_device_name(cls, device_id: int = 0) -> str:
60
+ return torch.cuda.get_device_name(device_id)
61
+
62
+ def is_cuda(self) -> bool:
63
+ return False
64
+
65
+ def is_rocm(self) -> bool:
66
+ return True
67
+
68
+ def is_mps(self) -> bool:
69
+ return False
70
+
71
+
72
+ class MpsPlatform(Platform):
73
+ @classmethod
74
+ @lru_cache(maxsize=8)
75
+ def get_device_name(cls, device_id: int = 0) -> str:
76
+ return torch.cuda.get_device_name(device_id)
77
+
78
+ def is_cuda(self) -> bool:
79
+ return False
80
+
81
+ def is_rocm(self) -> bool:
82
+ return False
83
+
84
+ def is_mps(self) -> bool:
85
+ return True
86
+
87
+ current_platform = (
88
+ RocmPlatform() if IS_ROCM else
89
+ MpsPlatform() if IS_MPS else
90
+ CudaPlatform() if torch.cuda.is_available() else
91
+ None
92
+ )