attention.py 395 KB
Newer Older
1
# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
2
3
4
5
#
# See LICENSE for license information.

"""Attention."""
6
import collections
7
from contextlib import nullcontext
8
from importlib.metadata import version as get_pkg_version
9
from importlib.metadata import PackageNotFoundError
10
import math
11
import os
12
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
13
import warnings
14
import logging
15
import functools
16

17
from dataclasses import dataclass, fields
cyanguwa's avatar
cyanguwa committed
18
import numpy as np
19
from packaging.version import Version as PkgVersion
20
21

import torch
22
import torch.nn.functional as F
23

24
import transformer_engine_torch as tex
25
26
import transformer_engine as te
from transformer_engine.pytorch.utils import get_cudnn_version
27
28
29
30
from transformer_engine.pytorch.cpp_extensions import (
    cast_to_fp8,
    cast_from_fp8,
)
31
32
33
34
35
from transformer_engine.pytorch.cpp_extensions.fused_attn import (
    fused_attn_fwd_qkvpacked,
    fused_attn_bwd_qkvpacked,
    fused_attn_fwd_kvpacked,
    fused_attn_bwd_kvpacked,
36
37
    fused_attn_fwd,
    fused_attn_bwd,
38
39
40
41
    QKVLayout,
    AttnBiasType,
    AttnMaskType,
    FusedAttnBackend,
42
43
44
45
46
47
48
49
50
51
52
53
54
    META_QKV,
    META_DQKV,
    META_O,
    META_DO,
    META_S,
    META_DP,
    META_O_CP,
    META_DQKV_CP,
)
from transformer_engine.pytorch.fp8 import (
    FP8GlobalStateManager,
    get_fp8_te_dtype,
    get_fp8_torch_dtype,
55
)
56
from transformer_engine.pytorch.float8_tensor import Float8Tensor
57
from transformer_engine.pytorch.module import LayerNormLinear, Linear
58
from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
59
60
61
62
63
from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
64
    get_default_init_method,
65
66
67
68
)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
69
    AttnBiasTypes,
70
    QKVLayouts,
71
    dist_group_type,
72
    TE_DType,
73
74
75
76
)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
77
    get_distributed_rank,
78
    checkpoint,
79
80
81
    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
82
83
    gather_along_first_dim,
    reduce_scatter_along_first_dim,
84
85
)
from transformer_engine.pytorch.export import is_in_onnx_export_mode
86
from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
87
88
from transformer_engine.pytorch.graph import is_graph_capturing

89

90
91
92
93
94
95
96
97
98
99
# NVTE_DEBUG = 0/1 # disables/enables debug mode, default = 0
_NVTE_DEBUG = int(os.getenv("NVTE_DEBUG", "0"))
# NVTE_DEBUG_LEVEL = 0/1/2 # enables more and more verbose debug mode, default = 0
_NVTE_DEBUG_LEVEL = int(os.getenv("NVTE_DEBUG_LEVEL", "0"))
_log_level = _NVTE_DEBUG * _NVTE_DEBUG_LEVEL
_log_levels = {0: logging.WARNING, 1: logging.INFO, 2: logging.DEBUG}
_log_level = _log_levels[_log_level if _log_level in [0, 1, 2] else 2]
_formatter = logging.Formatter("[%(levelname)-8s | %(name)-19s]: %(message)s")
_stream_handler = logging.StreamHandler()
_stream_handler.setFormatter(_formatter)
100
101
102
103
104
105
106
107
108
109
fa_logger = logging.getLogger()
fa_logger.setLevel(_log_level)
if not fa_logger.hasHandlers():
    fa_logger.addHandler(_stream_handler)


@functools.lru_cache(maxsize=None)
def _get_supported_versions(version_min, version_max):
    return ">= " + str(version_min) + ", " + "<= " + str(version_max)

110

111
112
113
_NVTE_FLASH_ATTN = int(os.getenv("NVTE_FLASH_ATTN", "1"))
_NVTE_FUSED_ATTN = int(os.getenv("NVTE_FUSED_ATTN", "1"))
_NVTE_UNFUSED_ATTN = int(os.getenv("NVTE_UNFUSED_ATTN", "1"))
114
115
116
117
118

# Detect flash-attn v2 in the environment
_flash_attn_is_installed = False
_flash_attn_version = PkgVersion("0")
_flash_attn_version_required = PkgVersion("2.1.1")
119
_flash_attn_max_version = PkgVersion("2.6.3")
120
121
122
123
124
125
126
_flash_attn_2_plus = False
_flash_attn_2_1_plus = False
_flash_attn_2_3_plus = False
_flash_attn_2_4_plus = False
_flash_attn_2_4_1_plus = False
_flash_attn_2_5_7_plus = False
_flash_attn_2_6_0_plus = False
127
128
129
130
131
132
133

flash_attn_func = None
flash_attn_varlen_func = None
flash_attn_varlen_fwd = None
flash_attn_varlen_bwd = None
flash_attn_cuda_bwd = None

134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
try:
    _flash_attn_version = PkgVersion(get_pkg_version("flash-attn"))
except PackageNotFoundError:
    if get_device_compute_capability() >= (8, 0) and _NVTE_FLASH_ATTN:
        fa_logger.debug(
            "flash-attn v2 is not installed. To use, please install it by"
            """ "pip install flash-attn".""",
        )
else:
    if _flash_attn_version_required <= _flash_attn_version <= _flash_attn_max_version:
        from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_varlen_func
        from flash_attn.flash_attn_interface import (
            _flash_attn_varlen_forward as flash_attn_varlen_fwd,
        )
        from flash_attn.flash_attn_interface import (
            _flash_attn_varlen_backward as flash_attn_varlen_bwd,
        )
        from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd

        _flash_attn_is_installed = True
        _flash_attn_2_plus = _flash_attn_version >= PkgVersion("2")
        _flash_attn_2_1_plus = _flash_attn_version >= PkgVersion("2.1")
        _flash_attn_2_3_plus = _flash_attn_version >= PkgVersion("2.3")
        _flash_attn_2_4_plus = _flash_attn_version >= PkgVersion("2.4")
        _flash_attn_2_4_1_plus = _flash_attn_version >= PkgVersion("2.4.1")
        _flash_attn_2_5_7_plus = _flash_attn_version >= PkgVersion("2.5.7")
        _flash_attn_2_6_0_plus = _flash_attn_version >= PkgVersion("2.6.0")
    elif get_device_compute_capability() >= (8, 0) and _NVTE_FLASH_ATTN:
        fa_logger.warning(
            "Supported flash-attn versions are %s. Found flash-attn %s.",
            _get_supported_versions(
                _flash_attn_version_required,
                _flash_attn_max_version,
            ),
            _flash_attn_version,
        )

# Detect flash-attn v3 in the environment
# This section will be removed when FA3 is released as a regular FA package,
# i.e. flashattn-hopper 3.0.0 as flash-attn 3.0.0
_flash_attn_3_is_installed = False
_flash_attn_3_version = PkgVersion("0")
_flash_attn_3_0_0_beta = False
177
_use_flash_attn_3 = False
178
179
180
181
182
_flash_attn_3_installation_steps = """\
(1) pip install "git+https://github.com/Dao-AILab/flash-attention.git#egg=flashattn-hopper&subdirectory=hopper"
(2) python_path=`python -c "import site; print(site.getsitepackages()[0])"`
(3) mkdir -p $python_path/flashattn_hopper
(4) wget -P $python_path/flashattn_hopper https://raw.githubusercontent.com/Dao-AILab/flash-attention/main/hopper/flash_attn_interface.py"""
183
try:
184
    _flash_attn_3_version = PkgVersion(get_pkg_version("flashattn-hopper"))
185
except PackageNotFoundError:
186
187
188
    if get_device_compute_capability() >= (9, 0) and _NVTE_FLASH_ATTN:
        fa_logger.debug(
            "flash-attn v3 is not installed. To use, please install it by \n%s",
189
            _flash_attn_3_installation_steps,
190
        )
191
192
193
194
195
else:
    from flashattn_hopper.flash_attn_interface import flash_attn_func as flash_attn_func_v3
    from flashattn_hopper.flash_attn_interface import (
        flash_attn_varlen_func as flash_attn_varlen_func_v3,
    )
196
197
198
199
200
201
    from flashattn_hopper.flash_attn_interface import (
        _flash_attn_varlen_forward as flash_attn_varlen_fwd_v3,
    )
    from flashattn_hopper.flash_attn_interface import (
        _flash_attn_varlen_backward as flash_attn_varlen_bwd_v3,
    )
202

203
204
    _flash_attn_3_is_installed = True
    _flash_attn_3_0_0_beta = PkgVersion("3.0.0b") < _flash_attn_3_version < PkgVersion("3.0.0")
205
    _use_flash_attn_3 = True
206

207
208
209
210
211
212
213
_attention_backends = {
    "attention_params": None,
    "use_flash_attention": None,
    "use_fused_attention": None,
    "fused_attention_backend": None,
    "use_unfused_attention": None,
    "backend_selection_requires_update": False,
214
}
215
216


217
218
@dataclass(eq=True)
class AttentionParams:
219
    """
220
    Attention parameters used to determine which backend to be used.
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239

    Parameters
    ----------
    qkv_type: Union[torch.Tensor, Float8Tensor], default = `torch.Tensor`
        Type of query/key/value tensors, {`torch.Tensor`, `Float8Tensor`}.
    qkv_dtype: torch.dtype, default = `torch.bfloat16`
        Data type of query/key/value tensors.
    qkv_layout: str, default = "sbh3d"
        Query/key/value tensor memory layout.
    batch_size: int, default = 1
        Batch size.
    num_heads: int, default = 16
        Number of attention heads in the query tensor.
    num_gqa_groups: int, default = 16
        Number of attention heads in key and value tensors.
    max_seqlen_q: int, default = 128
        Maximum sequence length of the query tensor.
    max_seqlen_kv: int, default = 128
        Maximum sequence length of the key and value tensors.
240
241
242
243
    head_dim_qk: int, default = 64
        The size of each attention head in query and key tensors.
    head_dim_v: int, default = 64
        The size of each attention head in the value tensor.
244
245
246
    attn_mask_type: str, default = `no_mask`
        Attention mask type, {`no_mask`, `padding`, `causal`, `padding_causal`,
        `causal_bottom_right`, `padding_causal_bottom_right`, `arbitrary`}
247
    window_size: Tuple[int, int], default = None
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
        Sliding window attention size.
    alibi_slopes_shape: Optional[Union[torch.Size, List]], default = `None`
        Tensor shape of :attr:`alibi_slopes` in `DotProductAttention`.
    core_attention_bias_type: str, default = `no_bias`
        Attention bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}.
    core_attention_bias_shape: str, default = `1hss`
        Attention bias shape, {`1hss`, `b1ss`, `bhss`}.
    core_attention_bias_requires_grad: bool, default = `True`
        Whether attention bias requires gradient.
    pad_between_seqs: bool, default = `False`
        Whether there is padding between sequences in a batch.
        This only applies to `qkv_format=thd`.
    attention_dropout: float, default = 0.0
        Attention dropout.
    context_parallel: bool, default = `False`
        Whether context parallelism is used or not.
    deterministic: bool, default = `False`
        Whether to run `DotProductAttention` with determinism or not.
266
267
    is_training: bool, default = `True`
        Whether in training mode (`True`) or inference mode (`False`)
268
269
270
271
    fp8: bool, default = `False`
        Whether `DotProductAttention` is in an `fp8_autocast` region.
    fp8_meta: Optional[Dict[str Any]], default = `None`
        The FP8 metadata tensor of `DotProductAttention`.
272
273
274
275
276
277
278
279
280
281
    """

    qkv_type: Union[torch.Tensor, Float8Tensor] = torch.Tensor
    qkv_dtype: torch.dtype = torch.bfloat16
    qkv_layout: str = "sbh3d"
    batch_size: int = 1
    num_heads: int = 16
    num_gqa_groups: int = 16
    max_seqlen_q: int = 128
    max_seqlen_kv: int = 128
282
283
    head_dim_qk: int = 64
    head_dim_v: int = 64
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
    attn_mask_type: str = "no_mask"
    window_size: Union[Tuple[int, int], None] = None
    alibi_slopes_shape: Union[torch.Size, List, None] = None
    core_attention_bias_type: str = "no_bias"
    core_attention_bias_shape: str = "1hss"
    core_attention_bias_requires_grad: bool = True
    pad_between_seqs: bool = False
    attention_dropout: float = 0.0
    context_parallel: bool = False
    deterministic: bool = False
    is_training: bool = True
    fp8: bool = False
    fp8_meta: Union[Dict[str, Any], None] = None


_alibi_cache = {
    "_num_heads": None,
    "_alibi_slopes": None,
    "_max_seqlen_q": None,
    "_max_seqlen_kv": None,
    "_bottom_right_alignment": True,
    "_alibi_bias": None,
    "_alibi_slopes_require_update": False,
    "_alibi_bias_require_update": False,
}


__all__ = ["DotProductAttention", "InferenceParams", "MultiheadAttention"]


314
315
316
317
318
def maybe_contiguous(tensor: torch.Tensor) -> torch.Tensor:
    """Make tensor contiguous if final stride is not 1."""
    return tensor.contiguous() if tensor.stride(-1) != 1 else tensor


319
320
321
322
323
324
325
326
327
def get_attention_backend(
    attention_params: AttentionParams = None,
):
    """
    Select the appropriate attention backend/sub-backend based on user input and runtime environment.

    Parameters
    ----------
    See `AttentionParams`.
328
329
330
331
332
333
334

    Returns
    ----------
    use_flash_attention: bool
        Whether the `FlashAttention` backend has been selected.
    use_fused_attention: bool
        Whether the `FusedAttention` backend has been selected.
335
336
    fused_attention_backend: tex.NVTE_Fused_Attn_Backend
        If `use_fused_attention = True`, one of `FusedAttention` three sub-backends, else `None`.
337
338
339
340
341
342
    use_unfused_attention: bool
        Whether the `UnfusedDotProductAttention` backend has been selected.
    available_backends: List[bool]
        All available backends that could support the provided input. A list of Booleans
        in the form of [use_flash_attention, use_fused_attention, use_unfused_attention].
    """
343
344
345
346
347
348
349
350
    qkv_type = attention_params.qkv_type
    qkv_dtype = attention_params.qkv_dtype
    qkv_layout = attention_params.qkv_layout
    batch_size = attention_params.batch_size
    num_heads = attention_params.num_heads
    num_gqa_groups = attention_params.num_gqa_groups
    max_seqlen_q = attention_params.max_seqlen_q
    max_seqlen_kv = attention_params.max_seqlen_kv
351
352
    head_dim_qk = attention_params.head_dim_qk
    head_dim_v = attention_params.head_dim_v
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
    attn_mask_type = attention_params.attn_mask_type
    window_size = attention_params.window_size
    alibi_slopes_shape = attention_params.alibi_slopes_shape
    core_attention_bias_type = attention_params.core_attention_bias_type
    core_attention_bias_shape = attention_params.core_attention_bias_shape
    core_attention_bias_requires_grad = attention_params.core_attention_bias_requires_grad
    pad_between_seqs = attention_params.pad_between_seqs
    attention_dropout = attention_params.attention_dropout
    context_parallel = attention_params.context_parallel
    deterministic = attention_params.deterministic
    is_training = attention_params.is_training
    fp8 = attention_params.fp8
    fp8_meta = attention_params.fp8_meta

    # Run config
368
    logger = logging.getLogger("DotProductAttention")
369
370
371
    logger.setLevel(_log_level)
    if not logger.hasHandlers():
        logger.addHandler(_stream_handler)
372
373
374
375
376
    device_compute_capability = get_device_compute_capability()
    cudnn_version = get_cudnn_version()
    run_config = {
        "transformer_engine_version": te.__version__,
        "compute_capability": "sm"
377
        + str(10 * device_compute_capability[0] + device_compute_capability[1]),
378
379
380
381
382
383
        "flash_attn_version": (
            str(_flash_attn_version) if _flash_attn_is_installed else "not installed"
        ),
        "flash_attn_3_version": (
            str(_flash_attn_3_version) if _flash_attn_3_is_installed else "not installed"
        ),
384
385
386
387
388
389
390
391
392
        "cudnn_version": ".".join([str(i) for i in cudnn_version]),
    }
    attention_params_dict = {
        field.name: getattr(attention_params, field.name) for field in fields(attention_params)
    }
    run_config.update(attention_params_dict)
    if fp8:
        run_config["NVTE_FP8_DPA_BWD"] = int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
    logger.debug("Running with config=%s", run_config)
393

394
395
396
397
398
399
    # The following sections check if `FlashAttention` supports the provided attention params,
    # regardless of whether FA2 or FA3 is installed. If FA2 or FA3 is not installed but is
    # necessary for performance/functionality, a warning will be issued to prompt users to
    # install an appropriate FA version.
    global _flash_attn_version_required, _flash_attn_max_version, _use_flash_attn_3

400
    # Filter: Environment variables
401
402
403
404
    use_flash_attention = int(os.getenv("NVTE_FLASH_ATTN", "1"))
    use_fused_attention = int(os.getenv("NVTE_FUSED_ATTN", "1"))
    use_unfused_attention = int(os.getenv("NVTE_UNFUSED_ATTN", "1"))
    if not use_flash_attention and _flash_attn_is_installed:
405
406
407
408
409
410
411
412
        logger.debug("Disabling FlashAttention due to NVTE_FLASH_ATTN=0")
    if not use_fused_attention:
        logger.debug("Disabling FusedAttention due to NVTE_FUSED_ATTN=0")
    if not use_unfused_attention:
        logger.debug("Disabling UnfusedDotProductAttention due to NVTE_UNFUSED_ATTN=0")

    # Filter: ONNX mode
    if is_in_onnx_export_mode():
413
        if use_flash_attention and _flash_attn_is_installed:
414
415
416
417
418
419
420
421
            logger.debug("Disabling FlashAttention due to ONNX mode")
        use_flash_attention = False
        if use_fused_attention:
            logger.debug("Disabling FusedAttention due to ONNX mode")
        use_fused_attention = False

    # Filter: Compute capability
    if device_compute_capability < (8, 0):
422
        if use_flash_attention and _flash_attn_is_installed:
423
            logger.debug("Disabling FlashAttention as it requires compute capability sm80+")
424
        use_flash_attention = False
425
426
427
        if use_fused_attention:
            logger.debug("Disabling FusedAttention as it requires compute capability sm80+")
            use_fused_attention = False
428
    if device_compute_capability < (9, 0):
429
        if use_flash_attention and _flash_attn_3_is_installed:
430
            logger.debug("Disabling FlashAttention 3 as it requires compute capability sm90+")
431
        _use_flash_attn_3 = False
432
433

    # Filter: Data type
434
435
436
437
    if qkv_dtype not in [torch.bfloat16, torch.float16] or qkv_type not in [
        torch.Tensor,
        Float8Tensor,
    ]:
438
        if use_flash_attention and _flash_attn_is_installed:
439
440
441
442
443
444
            logger.debug(
                "Disabling FlashAttention due to unsupported QKV data type. "
                "Supported: qkv_dtype = {torch.bfloat16, torch.float16}. "
                "Found: qkv_dtype = %s.",
                qkv_dtype,
            )
445
        use_flash_attention = False
446
447
448
449
450
451
452
453
        if use_fused_attention:
            logger.debug(
                "Disabling FusedAttention due to unsupported QKV data type. "
                "Supported: qkv_dtype = {torch.bfloat16, torch.float16}. "
                "Found: qkv_dtype = %s.",
                qkv_dtype,
            )
            use_fused_attention = False
454
455
456

    # Filter: Execution type
    if fp8 and fp8_meta["recipe"].fp8_dpa:
457
        if use_flash_attention and not _use_flash_attn_3:
458
459
            if _flash_attn_is_installed:
                logger.debug("Disabling FlashAttention as FlashAttention 2 does not support FP8")
460
461
462
463
464
            use_flash_attention = False
        if use_flash_attention and _use_flash_attn_3 and is_training:
            logger.debug(
                "Disabling FlashAttention as FlashAttention 3 does not support FP8 training"
            )
465
466
467
468
469
470
            use_flash_attention = False
        if use_unfused_attention:
            logger.debug("Disabling UnfusedDotProductAttention as it does not support FP8")
            use_unfused_attention = False

    # Filter: Head dimension
471
    if use_flash_attention and head_dim_qk != head_dim_v:
472
473
        if _flash_attn_is_installed:
            logger.debug("Disabling FlashAttention as it does not support MLA.")
474
        use_flash_attention = False
475
    if use_flash_attention and (
476
477
478
        head_dim_qk > 256
        or head_dim_qk % 8 != 0
        or (head_dim_qk > 192 and device_compute_capability not in ((8, 0), (9, 0)))
479
    ):
480
481
482
483
484
485
486
487
488
489
        if _flash_attn_is_installed:
            logger.debug(
                "Disabling FlashAttention due to unsupported head_dim_qk and head_dim_v. "
                "Supported: head_dim_qk = head_dim_v, head_dim_qk %%8 = 0, "
                "head_dim_qk <= 256 (>192 requires sm80/90). "
                "Found: head_dim_qk = %s, head_dim_v = %s, on sm%s.",
                head_dim_qk,
                head_dim_v,
                ".".join([str(i) for i in device_compute_capability]),
            )
490
        use_flash_attention = False
491
492
493
494
495
496
497
    qkv_layout_group = qkv_layout.replace("b", "").replace("s", "").replace("t", "")
    if use_fused_attention and head_dim_qk != head_dim_v and qkv_layout_group != "hd_hd_hd":
        logger.debug(
            "Disabling FusedAttention as MLA is not supported with qkv_layout = %s",
            qkv_layout,
        )
        use_fused_attention = False
498
499
500
501
502
503
504
505

    # Filter: QKV layout
    qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
    if qkv_format == "thd":
        if use_unfused_attention:
            logger.debug("Disabling UnfusedDotProductAttention for qkv_format = thd")
            use_unfused_attention = False
        if use_flash_attention and pad_between_seqs:
506
507
508
509
510
            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention for qkv_format = thd when there is "
                    "padding between sequences, i.e. [a, a, PAD, b, b, b, PAD, c, PAD]"
                )
511
512
            use_flash_attention = False

513
    # Filter: Dropout
514
515
516
    if attention_dropout != 0.0 and use_flash_attention and _use_flash_attn_3:
        logger.debug("Disabling FlashAttention 3 for dropout")
        _use_flash_attn_3 = False
517

518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
    # Filter: Context parallelism
    # qkv_format | attn_mask_type              | attn_bias_type           | supported backends
    # ----------------------------------------------------------------------------------------------------
    # bshd, sbhd | self-attention:             | no_bias, post_scale_bias | FlashAttention, FusedAttention
    #            |     no_mask, causal         |                          |
    #            | cross-attention:            |                          |
    #            |     no_mask                 |                          |
    # thd        | self-attention:             | no_bias                  | FlashAttention, FusedAttention
    #            |     padding, padding_causal |                          | if no padding between sequences,
    #            | cross-attention:            |                          | FusedAttention
    #            |     padding                 |                          | if there is padding between sequences
    # Note: context parallelism requires seq_len % (cp_size * 2) == 0 for each sequence in q, k, v.
    if context_parallel and use_unfused_attention:
        logger.debug(
            "Disabling UnfusedDotProductAttention as it does not support context parallelism"
        )
        use_unfused_attention = False
    if context_parallel and use_flash_attention:
536
        if fp8 and fp8_meta["recipe"].fp8_dpa:
537
538
539
540
            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with FP8"
                )
541
            use_flash_attention = False
542
        if "bottom_right" in attn_mask_type:
543
544
545
546
547
            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with"
                    " causal_bottom_right masking"
                )
548
549
            use_flash_attention = False
        elif "causal" in attn_mask_type and max_seqlen_q != max_seqlen_kv:
550
551
552
553
554
            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with"
                    " causal masking for cross-attention"
                )
555
556
            use_flash_attention = False
        elif core_attention_bias_type not in ["no_bias", "post_scale_bias"]:
557
558
559
560
561
562
            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with bias"
                    " type of %s",
                    core_attention_bias_type,
                )
563
564
            use_flash_attention = False
        elif qkv_format == "thd" and core_attention_bias_type != "no_bias":
565
566
567
568
569
            if _flash_attn_is_installed:
                logger.debug(
                    "Disabling FlashAttention as it does not support context parallelism with"
                    " attention bias for THD format"
                )
570
            use_flash_attention = False
571

572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
    if context_parallel and use_fused_attention:
        if "bottom_right" in attn_mask_type:
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with"
                " causal_bottom_right masking"
            )
            use_fused_attention = False
        elif "causal" in attn_mask_type and max_seqlen_q != max_seqlen_kv:
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with causal"
                " masking for cross-attention"
            )
            use_fused_attention = False
        elif core_attention_bias_type not in ["no_bias", "post_scale_bias"]:
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with bias type"
                " of %s",
                core_attention_bias_type,
            )
            use_fused_attention = False
        elif qkv_format == "thd" and core_attention_bias_type != "no_bias":
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with attention"
                " bias for THD format"
            )
            use_fused_attention = False
        elif head_dim_qk != head_dim_v:
            logger.debug(
                "Disabling FusedAttention as it does not support context parallelism with MLA"
            )
            use_fused_attention = False

604
    # Filter: Attention mask
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
    # attn_mask_type              | attention_mask                       | supported backends
    # ----------------------------------------------------------------------------------------
    # no_mask                     | None                                 | All
    # padding                     |                                      | All
    #     self-attention          | One tensor in shape [b, 1, 1, sq]    |
    #     cross-attention         | Tuple of two tensors in shapes       |
    #                             | [b, 1, 1, sq] and [b, 1, 1, skv]     |
    # causal                      | None                                 |
    #     self-attention          |                                      | All
    #     cross-attention         |                                      | FusedAttention, UnfusedDotProductAttention
    # padding_causal              | Same as "padding"                    |
    #     self-attention          |                                      | All
    #     cross-attention         |                                      | FusedAttention, UnfusedDotProductAttention
    # causal_bottom_right         | None                                 | All
    # padding_causal_bottom_right | Same as "padding"                    |
    #     self-attention          |                                      | All
    #     cross-attention         |                                      | FlashAttention, UnfusedDotProductAttention
    # arbitrary                   | One tensor in shape broadcastable to | UnfusedDotProductAttention
    #                             | [b, h, sq, skv]                      |
624
    if attn_mask_type == "arbitrary":
625
        if use_flash_attention and _flash_attn_is_installed:
626
627
628
629
630
            logger.debug("Disabling FlashAttention for arbitrary mask")
        use_flash_attention = False
        if use_fused_attention:
            logger.debug("Disabling FusedAttention for arbitrary mask")
        use_fused_attention = False
631
632
    if (
        use_flash_attention
633
        and _use_flash_attn_3
634
635
636
637
638
639
640
641
642
        and attn_mask_type in ["causal", "padding_causal"]
        and max_seqlen_q != max_seqlen_kv
    ):
        logger.warning(
            "Disabling FlashAttention 3 as it only supports bottom-right-diagonal "
            "causal mask since flash-attn 2.1. See "
            "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
        )
        _use_flash_attn_3 = False
643
644
645
646
647
    if (
        use_flash_attention
        and attn_mask_type in ["causal", "padding_causal"]
        and max_seqlen_q != max_seqlen_kv
    ):
648
649
650
651
652
653
654
655
656
        if _flash_attn_2_1_plus:
            logger.warning(
                "Disabling FlashAttention as it only supports bottom-right-diagonal "
                "causal mask since flash-attn 2.1. See "
                "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
            )
            use_flash_attention = False
        if not _flash_attn_is_installed:
            _flash_attn_max_version = PkgVersion("2.1")
657
658
659
660
661
    if (
        use_flash_attention
        and attn_mask_type in ["causal_bottom_right", "padding_causal_bottom_right"]
        and max_seqlen_q != max_seqlen_kv
    ):
662
663
664
665
666
667
668
669
670
        if not _flash_attn_is_installed:
            _flash_attn_version_required = PkgVersion("2.1")
        elif not _flash_attn_2_1_plus and not _use_flash_attn_3:
            logger.warning(
                "Disabling FlashAttention as it only supports top-left-diagonal "
                "causal mask before flash-attn 2.1. See "
                "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
            )
            use_flash_attention = False
671
672
673
674
675
676
677
678
679
    if (
        use_flash_attention
        and _use_flash_attn_3
        and fp8
        and fp8_meta["recipe"].fp8_dpa
        and "padding" in attn_mask_type
    ):
        logger.debug("Disabling FlashAttention 3 for FP8 and padding masks")
        _use_flash_attn_3 = False
680
681

    # Filter: Sliding window attention
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
    #    backend                 |      window_size       | diagonal alignment
    # ---------------------------------------------------------------------------------
    # FlashAttention             | (-1, -1) or (>=0, >=0) | bottom right
    # FusedAttention             | (-1,  0) or (>=0, 0)   | top left
    # UnfusedDotProductAttention | (-1, -1) or (>=0, >=0) | both;
    #                            |                        | converts window_size to an 'arbitrary' mask
    if window_size is None:
        window_size = check_set_window_size(attn_mask_type, window_size)
    else:
        if use_fused_attention and (window_size[0] != -1 or window_size[1] not in [-1, 0]):
            if fp8 and (fp8_meta["recipe"].fp8_dpa or fp8_meta["recipe"].fp8_mha):
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention"
                    " for FP8"
                )
                use_fused_attention = False
            elif window_size[1] != 0 or attention_dropout != 0.0 or qkv_format == "thd":
                logger.debug(
                    "Disabling FusedAttention as it only supports sliding window attention "
                    "with causal mask, no dropout, and qkv_format = bshd/sbhd"
                )
                use_fused_attention = False
            elif max_seqlen_q != max_seqlen_kv and attn_mask_type in [
                "no_mask",
                "padding",
                "causal_bottom_right",
                "padding_causal_bottom_right",
            ]:
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
                    "with attn_mask_type = %s for cross-attention",
                    attn_mask_type,
                )
                use_fused_attention = False
            elif "padding" in attn_mask_type:
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
                    "with attn_mask_type = %s",
                    attn_mask_type,
                )
                use_fused_attention = False
723
724
725
726
727
728
729
730
731
732
733
734
735
        if use_flash_attention and (window_size[0] != -1 or window_size[1] not in [-1, 0]):
            if _use_flash_attn_3:
                logger.debug(
                    "Disabling FlashAttention 3 as it does not support sliding window attention"
                )
                _use_flash_attn_3 = False
            if not _flash_attn_is_installed:
                _flash_attn_version_required = PkgVersion("2.3")
            elif not _flash_attn_2_3_plus:
                logger.debug(
                    "Disabling FlashAttention as sliding window attention requires flash-attn 2.3+"
                )
                use_flash_attention = False
736
737

    # Filter: Attention bias
738
739
740
741
742
743
744
745
    #    backend                 |      bias types              | ALiBi diagonal alignment
    # ---------------------------------------------------------------------------------
    # FlashAttention             | no_bias, alibi/alibi_slopes  | bottom right
    # FusedAttention             | no_bias, post_scale_bias     |
    #                            | alibi/alibi_slopes           | top left,
    #                            |                              | bottom_right (converts to a 'post_scale_bias' bias)
    # UnfusedDotProductAttention | no_bias, pre/post_scale_bias |
    #                            | alibi/alibi_slopes           | both; converts to a 'post_scale_bias' bias
746
    if use_flash_attention and core_attention_bias_type == "alibi":
747
        if _use_flash_attn_3:
748
749
            logger.debug("Disabling FlashAttention 3 for ALiBi")
            _use_flash_attn_3 = False
750
751
752
        if not _flash_attn_is_installed:
            _flash_attn_version_required = PkgVersion("2.4")
        elif not _flash_attn_2_4_plus:
753
754
            logger.debug("Disabling FlashAttention as ALiBi requires flash-attn 2.4+")
            use_flash_attention = False
755

756
757
758
759
    if use_flash_attention and (
        core_attention_bias_type not in ["no_bias", "alibi"]
        or core_attention_bias_shape is not None
    ):
760
761
        if _flash_attn_is_installed:
            logger.debug("Disabling FlashAttention for pre/post_scale_bias")
762
763
764
765
766
767
768
769
        use_flash_attention = False

    fu_core_attention_bias_type = core_attention_bias_type
    fu_core_attention_bias_shape = core_attention_bias_shape
    fu_core_attention_bias_requires_grad = core_attention_bias_requires_grad
    if (
        use_fused_attention
        and core_attention_bias_type == "alibi"
770
        and (alibi_slopes_shape is not None or max_seqlen_q != max_seqlen_kv)
771
772
773
    ):
        fu_core_attention_bias_type = "post_scale_bias"
        fu_core_attention_bias_requires_grad = False
774
775
776
777
778
        if alibi_slopes_shape is None:
            fu_core_attention_bias_shape = "1hss"
        elif len(alibi_slopes_shape) == 1 and alibi_slopes_shape[0] == num_heads:
            fu_core_attention_bias_shape = "1hss"
        elif (
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
            len(alibi_slopes_shape) == 2
            and alibi_slopes_shape[0] == batch_size
            and alibi_slopes_shape[1] == num_heads
        ):
            fu_core_attention_bias_shape = "bhss"

    if (
        use_fused_attention
        and fu_core_attention_bias_type == "post_scale_bias"
        and fu_core_attention_bias_shape != "1hss"
    ):
        if fu_core_attention_bias_requires_grad:
            # remove this line when cuDNN adds bwd support for
            # [1, 1, s, s], [b, 1, s, s] and [b, h, s, s]
            logger.debug("Disabling FusedAttention for dBias in [1, H, S, S] shape")
            use_fused_attention = False
        else:
            # max512 backend will only support [1, h, s, s]
            os.environ["NVTE_FUSED_ATTN_BACKEND"] = "1"

    # Filter: cuDNN support
    fused_attention_backend = None
    if use_fused_attention:
        q_type = TE_DType[qkv_dtype]
        kv_type = q_type
        if fp8 and fp8_meta["recipe"].fp8_dpa:
            q_type = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            kv_type = q_type
        fused_attention_backend = tex.get_fused_attn_backend(
            q_type,
            kv_type,
            QKVLayout[qkv_layout],
            AttnBiasType[fu_core_attention_bias_type],
            AttnMaskType[attn_mask_type],
            attention_dropout,
            num_heads,
            num_gqa_groups,
            max_seqlen_q,
            max_seqlen_kv,
818
819
            head_dim_qk,
            head_dim_v,
820
821
            window_size[0],
            window_size[1],
822
        )
823
        if fused_attention_backend == FusedAttnBackend["No_Backend"]:
824
825
            logger.debug("Disabling FusedAttention as no backend supports the provided input")
            use_fused_attention = False
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
            fused_attention_backend = None
        if (
            use_fused_attention
            and window_size is not None
            and window_size[0] != -1
            and fused_attention_backend != FusedAttnBackend["F16_arbitrary_seqlen"]
        ):
            logger.debug(
                "Disabling FusedAttention as only sub-backend %s does not support "
                "slidng window attention",
                int(fused_attention_backend),
            )
            use_fused_attention = False
            fused_attention_backend = None
        if (
            use_fused_attention
            and fused_attention_backend == FusedAttnBackend["F16_max512_seqlen"]
843
844
845
846
847
848
849
850
            and fu_core_attention_bias_type == "post_scale_bias"
            and fu_core_attention_bias_shape != "1hss"
        ):
            logger.debug(
                "Disabling FusedAttention as cuDNN sub-backend 0 only supports post_scale_bias in"
                " [1, H, S, S] shape"
            )
            use_fused_attention = False
851
            fused_attention_backend = None
852
853
854
855
856
857
858
859
860
861
862
863
864

    # Filter: Determinism
    # backend                      | deterministic
    # ---------------------------------------------
    # FlashAttention               |
    #     flash-attn >=2.0, <2.4.1 | no
    #     flash-attn >=2.4.1       | yes
    # FusedAttention               |
    #     sub-backend 0            | yes
    #     sub-backend 1            | workspace optimization path and sm90+: yes;
    #                              | otherwise: no
    #     sub-backend 2            | no
    # UnfusedDotProductAttention   | yes
865
866
867
868
869
870
871
872
873
874
    if use_flash_attention and deterministic:
        if not _flash_attn_is_installed:
            _flash_attn_version_required = PkgVersion("2.4.1")
        elif not _flash_attn_2_4_1_plus and not _use_flash_attn_3:
            logger.warning(
                "Disabling FlashAttention as version <2.4.1 does not support deterministic "
                "execution. To use FlashAttention with deterministic behavior, "
                "please install flash-attn >= 2.4.1."
            )
            use_flash_attention = False
875
876
877
878
879
880
881
882
883
884
885
    if use_fused_attention and deterministic:
        if fused_attention_backend == FusedAttnBackend["FP8"] and is_training:
            logger.debug("Disabling FusedAttention for determinism reasons")
            use_fused_attention = False
        if (
            fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]
            and is_training
            and (
                device_compute_capability < (9, 0)
                or core_attention_bias_requires_grad
                or cudnn_version < (8, 9, 5)
886
            )
887
888
889
        ):
            logger.debug("Disabling FusedAttention for determinism reasons")
            use_fused_attention = False
890
891
892

    # All available backends
    available_backends = [use_flash_attention, use_fused_attention, use_unfused_attention]
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909

    # `FusedAttention` and `FlashAttention` are faster backends than `UnfusedDotProductAttention`.
    # When `FusedAttention` does not support the provided attention params, and `FlashAttention`
    # does, we recommend users to install flash-attn if not installed already.
    if not use_fused_attention and use_flash_attention and not _flash_attn_is_installed:
        logger.warning(
            "flash-attn may provide important feature support or performance improvement."
            " Please install flash-attn %s.",
            _get_supported_versions(
                _flash_attn_version_required,
                _flash_attn_max_version,
            ),
        )
    if use_flash_attention and not _flash_attn_is_installed:
        use_flash_attention = False
        available_backends[0] = False

910
911
912
913
914
915
916
917
918
919
920
921
    logger.debug(
        "Available backends = {FlashAttention=%s, FusedAttention=%s%s,"
        " UnfusedDotProductAttention=%s}",
        bool(available_backends[0]),
        bool(available_backends[1]),
        (
            f" (sub-backend {int(fused_attention_backend)})"
            if fused_attention_backend is not None
            else ""
        ),
        bool(available_backends[2]),
    )
922
923
924
925
926
927
928
929
930
931
932
933
934

    # Select FusedAttention for performance
    if (
        use_flash_attention
        and use_fused_attention
        and fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]
    ):
        if device_compute_capability == (9, 0):
            logger.debug(
                "Disabling FlashAttention to give FusedAttention preference on Hopper+ "
                "for performance reasons"
            )
            use_flash_attention = False
935
936
937
938
939
940
941
    if (
        use_flash_attention
        and use_fused_attention
        and fused_attention_backend == FusedAttnBackend["FP8"]
        and _use_flash_attn_3
    ):
        logger.debug(
942
943
            "Disabling FlashAttention 3 to give FusedAttention preference for performance reasons "
            "in FP8 execution"
944
945
946
        )
        use_flash_attention = False

947
948
949
950
951
952
    # Selected backend
    if use_flash_attention:
        use_fused_attention = False
        use_unfused_attention = False
    elif use_fused_attention:
        use_unfused_attention = False
953
    selected_backend = "NoBackend"
954
955
956
957
958
959
    if use_flash_attention:
        selected_backend = "FlashAttention"
    elif use_fused_attention:
        selected_backend = f"FusedAttention (sub-backend {int(fused_attention_backend)})"
    elif use_unfused_attention:
        selected_backend = "UnfusedDotProductAttention"
960
    logger.debug("Selected backend = %s", selected_backend)
961

962
963
964
965
966
967
    global _attention_backends
    _attention_backends["use_flash_attention"] = use_flash_attention
    _attention_backends["use_fused_attention"] = use_fused_attention
    _attention_backends["fused_attention_backend"] = fused_attention_backend
    _attention_backends["use_unfused_attention"] = use_unfused_attention
    _attention_backends["backend_selection_requires_update"] = False
968
969
970
971

    return (
        use_flash_attention,
        use_fused_attention,
972
        fused_attention_backend,
973
974
975
976
977
        use_unfused_attention,
        available_backends,
    )


978
class InferenceParams:  # pylint: disable=too-few-public-methods
979
980
    """
    Inference parameters that are passed to the main model in order
981
    to efficiently calculate and store the context during inference.
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021

    Parameters
    ----------
    max_batch_size : int
                    maximum batch size during inference.
    max_sequence_length : int
                         maximum sequence length during inference.
    """

    def __init__(self, max_batch_size, max_sequence_length):
        self.max_sequence_length = max_sequence_length
        self.max_batch_size = max_batch_size
        self.sequence_len_offset = 0
        self.batch_size_offset = 0
        self.key_value_memory_dict = {}

    def swap_key_value_dict(self, batch_indices):
        """
        Reorders the KV cache using the specified batch indices.

        Parameters
        ----------
        batch_indices : List[int]
                       Sequence of indices to reorder along the batch dimensions of
                       the KV cache. Must have a length equal to the batch size.
        """
        if len(self.key_value_memory_dict) == 0:
            raise ValueError("should not swap when dict in empty")

        for layer_number, inference_memory in self.key_value_memory_dict.items():
            inference_key_memory, inference_value_memory = inference_memory
            assert (
                len(batch_indices) == inference_key_memory.shape[1]
            )  # make sure batch size is the same
            new_inference_key_memory = inference_key_memory[:, batch_indices]
            new_inference_value_memory = inference_value_memory[:, batch_indices]
            self.key_value_memory_dict[layer_number] = (
                new_inference_key_memory,
                new_inference_value_memory,
            )
1022

1023

1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
@torch.no_grad()
def get_swa_mask(
    window_size: Tuple[int, int],
    max_seqlen_q: int,
    max_seqlen_kv: int,
    attn_mask_type: str = "no_mask",
    attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
) -> torch.Tensor:
    """
    Convert sliding window `window_size` to an equivalent "`arbitrary`" mask.
    For "`causal`" mask type, the sliding window diagonal is aligned to the top left corner,
    and for other mask types, the bottom right corner.

    Parameters
    ----------
    window_size: Tuple[int, int]
        Sliding window size for local attention, where query at position i attends to keys
        in [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q
        + window_size[1]] inclusive. Special cases (-1, -1) and (-1, 0) mean no sliding
        window and causal mask specifically. Both `causal` and `causal_bottom_right` masks
        map to `window_size = (-1, 0)` and Transformer Engine distinguishes them based on
        `attn_mask_type`.
    max_seqlen_q: int
        Maximum sequence length for queries.
    max_seqlen_kv: int
        Maximum sequence length for keys and values.
    attn_mask_type: str, default = `no_mask`
        Attention mask type, {"`no_mask`", "`padding`", "`causal`", "`padding_causal`",
        "`causal_bottom_right`", "`padding_causal_bottom_right`", "`arbitrary`"}
    attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
        default = `None`
        Boolean tensor(s) used to mask out attention softmax input.

    Returns
    ----------
    attention_mask: torch.Tensor
        Combined `attention_mask` (input) and sliding window attention mask.
        The shape is [max_seqlen_q, max_seqlen_kv] when input `attention_mask` is None;
        else, the same shape as input `attention_mask`.
    """
    mask = torch.ones(max_seqlen_q, max_seqlen_kv, dtype=torch.bool, device="cuda")
    if attn_mask_type in ["causal"]:
        left = window_size[0] if window_size[0] != -1 else max_seqlen_q
        right = window_size[1] if window_size[1] != -1 else max_seqlen_q
        mask_upper = torch.triu(mask, diagonal=-left)
        mask_lower = torch.tril(mask_upper, diagonal=right)
    else:
        left = window_size[0] if window_size[0] != -1 else max_seqlen_kv
        right = window_size[1] if window_size[1] != -1 else max_seqlen_kv
        mask_upper = torch.triu(mask, diagonal=max_seqlen_kv - max_seqlen_q - left)
        mask_lower = torch.tril(mask_upper, diagonal=max_seqlen_kv - max_seqlen_q + right)
    attn_mask_type = "arbitrary"
    mask = mask_lower.logical_not()
    if attention_mask is not None:
        mask = torch.logical_and(attention_mask, mask)
    return attn_mask_type, mask


1082
1083
1084
1085
1086
@torch.no_grad()
def get_alibi(
    num_heads: int,
    max_seqlen_q: int,
    max_seqlen_kv: int,
1087
1088
    actual_seqlens_q: Optional[torch.Tensor] = None,
    actual_seqlens_kv: Optional[torch.Tensor] = None,
1089
1090
    alibi_slopes: Optional[torch.Tensor] = None,
    bias_dtype: Optional[torch.dtype] = None,
1091
    bottom_right_alignment: bool = True,
1092
) -> Tuple[torch.Tensor, torch.Tensor]:
1093
    """
1094
1095
1096
1097
1098
1099
1100
1101
    Parameters
    ----------
    num_heads: int
        Number of heads.
    max_seqlen_q: int
        Maximum sequence length for queries.
    max_seqlen_kv: int
        Maximum sequence length for keys and values.
1102
1103
1104
1105
    actual_seqlens_q: Optional[torch.Tensor], default = `None`
        Actual sequence lengths for queries, in shape [batch_size].
    actual_seqlens_kv: Optional[torch.Tensor], default = `None`
        Actual sequence lengths for keys and values, in shape [batch_size].
1106
1107
1108
1109
    alibi_slopes: Optional[torch.Tensor], default = `None`
        Custom ALiBi slopes, FP32, CUDA tensor, in shape [num_heads] or [batch_size, num_heads].
    bias_dtype: Optional[torch.dtype], default = `None`
        Dtype of the generated ALiBi bias. If None, use torch.float32.
1110
1111
1112
    bottom_right_alignment: bool, default = `True`
        Whether to align the diagonal of the ALiBi bias to the bottom right corner of
        the matrix (`True`) or top left (`False`).
1113

1114
1115
1116
1117
1118
    Returns
    ----------
    alibi_slopes: torch.Tensor
        ALiBi slopes in FP32 and shape [num_heads] or [batch_size, num_heads].
    alibi_bias: torch.Tensor
1119
1120
1121
1122
1123
1124
        ALiBi bias in FP32 or `bias_dtype`. Its shape is
        (1) [1, num_heads, max_seqlen_q, max_seqlen_kv] if `alibi_slopes` is in [num_heads] shape,
        and `actual_seqlens_q` and `actual_seqlens_kv` are `None`; or
        (2) [batch_size, num_heads, max_seqlen_q, max_seqlen_kv] if `alibi_slopes` is in
        [batch_size, num_heads] shape, or, if `alibi_slopes` is in [num_heads] shape and
        `actual_seqlens_q` and `actual_seqlens_kv` are not `None`.
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
    """
    global _alibi_cache
    if _alibi_cache["_alibi_slopes_require_update"]:
        if alibi_slopes is not None:
            _alibi_cache["_alibi_slopes"] = alibi_slopes
        else:
            n = 2 ** math.floor(math.log2(num_heads))
            m_0 = 2.0 ** (-8.0 / n)
            m = torch.pow(m_0, torch.arange(1, 1 + n))

            if n < num_heads:
                m_hat_0 = 2.0 ** (-4.0 / n)
                m_hat = torch.pow(m_hat_0, torch.arange(1, 1 + 2 * (num_heads - n), 2))
                m = torch.cat([m, m_hat])

            _alibi_cache["_alibi_slopes"] = m.to(dtype=torch.float32, device="cuda")
        _alibi_cache["_num_heads"] = num_heads
        _alibi_cache["_alibi_slopes_require_update"] = False

    if _alibi_cache["_alibi_bias_require_update"]:
        assert _alibi_cache["_alibi_slopes"] is not None, "ALiBi slopes can not be None!"
        if _alibi_cache["_alibi_slopes"].dim() == 1:
            slopes_shape = torch.Size([1, _alibi_cache["_alibi_slopes"].shape[0], 1, 1])
1148
        elif _alibi_cache["_alibi_slopes"].dim() == 2:
1149
            slopes_shape = torch.Size([*_alibi_cache["_alibi_slopes"].shape[:], 1, 1])
1150
1151
1152
        else:
            raise ValueError("ALiBi slopes cannot exceed 2 dimensions.")

1153
        bias = torch.arange(max_seqlen_q, dtype=torch.int32, device="cuda").view(
1154
            1, 1, max_seqlen_q, 1
1155
1156
        ) - torch.arange(max_seqlen_kv, dtype=torch.int32, device="cuda").view(
            1, 1, 1, max_seqlen_kv
1157
        )
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
        if actual_seqlens_q is None and actual_seqlens_kv is None:
            if bottom_right_alignment:
                bias = bias + max_seqlen_kv - max_seqlen_q
        elif actual_seqlens_q is not None and actual_seqlens_kv is not None:
            batch_size = actual_seqlens_q.shape[0]
            bias = bias.expand(batch_size, 1, max_seqlen_q, max_seqlen_kv)
            if bottom_right_alignment:
                bias = bias + (actual_seqlens_kv - actual_seqlens_q).view(batch_size, 1, 1, 1)
        else:
            assert (
                False
            ), "actual_seqlens_q and actual_seqlens_kv need to be both None or torch.Tensors!"
1170
1171
1172
        bias = bias.abs().mul(-1)
        bias = bias * _alibi_cache["_alibi_slopes"].view(slopes_shape)
        _alibi_cache["_max_seqlen_q"], _alibi_cache["_max_seqlen_kv"] = max_seqlen_q, max_seqlen_kv
1173
        _alibi_cache["_bottom_right_alignment"] = bottom_right_alignment
1174
1175
1176
1177
1178
        bias_dtype = torch.float32 if bias_dtype is None else bias_dtype
        _alibi_cache["_alibi_bias"] = bias.contiguous().to(dtype=bias_dtype, device="cuda")
        _alibi_cache["_alibi_bias_require_update"] = False

    return _alibi_cache["_alibi_slopes"], _alibi_cache["_alibi_bias"]
1179
1180
1181
1182
1183
1184
1185
1186
1187


def get_cu_seqlens(mask: torch.Tensor) -> torch.Tensor:
    """
    Given a padding mask of shape [batch_size, 1, 1, max_seqlen], returns an int32
    tensor of shape [batch_size + 1] containing the cumulative sequence lengths of
    the samples in a batch.
    """
    mask = mask.squeeze(1).squeeze(1)
1188
    reduced_mask = mask.logical_not().sum(dim=1)
1189
1190
1191
1192
1193
1194
    cu_seqlens = reduced_mask.cumsum(dim=0).to(torch.int32)
    zero = torch.zeros(1, dtype=torch.int32, device="cuda")
    cu_seqlens = torch.cat((zero, cu_seqlens))

    return cu_seqlens

1195

1196
1197
1198
def get_cu_seqlens_and_indices(mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Given a padding mask of shape [batch_size, 1, 1, max_seqlen], returns an int32
1199
1200
1201
    tensor of shape [batch_size + 1] containing the cumulative sequence lengths of
    the samples in a batch, and another int32 tensor of shape [batch_size * max_seqlen, 1, 1]
    containing the indices for the valid tokens.
1202
1203
1204
1205
    """
    mask = mask.squeeze(1).squeeze(1)
    bs, seqlen = mask.shape

1206
    reduced_mask = mask.logical_not().sum(dim=1)
1207
1208
1209
1210
1211
    cu_seqlens = reduced_mask.cumsum(dim=0).to(torch.int32)
    zero = torch.zeros(1, dtype=torch.int32, device="cuda")
    cu_seqlens = torch.cat((zero, cu_seqlens))

    mask = mask.reshape(-1)
1212
    indices = mask.logical_not().nonzero()
1213
1214
1215
1216
    indices = indices.unsqueeze(-1)

    num_nonzeros = indices.shape[0]
    pad_amount = bs * seqlen - num_nonzeros
1217
1218
1219
    indices = F.pad(
        input=indices, pad=(0, 0, 0, 0, 0, pad_amount), mode="constant", value=float(bs * seqlen)
    )
1220
1221
1222
1223

    return cu_seqlens, indices


1224
1225
1226
1227
1228
1229
1230
1231
def get_indices(max_seqlen: int, cu_seqlens: torch.Tensor) -> torch.Tensor:
    """
    Given max_seqlen and cu_seqlens of shape [batch_size + 1], returns an int32
    tensor of shape [batch_size * max_seqlen, 1, 1] containing the indices for
    the valid tokens in a batch.
    """
    bs = len(cu_seqlens) - 1
    seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
1232
1233
    indices = [i * max_seqlen + ii for i, j in enumerate(seqlens) for ii in range(j)]
    indices = torch.Tensor(indices).unsqueeze(1).unsqueeze(1).to(dtype=torch.int64, device="cuda")
1234
1235
1236

    num_nonzeros = indices.shape[0]
    pad_amount = bs * max_seqlen - num_nonzeros
1237
1238
1239
1240
1241
1242
    indices = F.pad(
        input=indices,
        pad=(0, 0, 0, 0, 0, pad_amount),
        mode="constant",
        value=float(bs * max_seqlen),
    )
1243
1244
1245

    return indices

1246

1247
_cu_seqlens_cache = {}
1248
1249


1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
def _get_full_cu_seqlens(
    batch_size: int,
    max_seqlen: int,
    device: torch.device,
) -> torch.Tensor:
    """Cumulative sequence lengths in full data batch

    All sequences in batch have the maximum sequence length.

    """
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
    global _cu_seqlens_cache
    if (batch_size, max_seqlen) not in _cu_seqlens_cache:
        _cu_seqlens_cache[(batch_size, max_seqlen)] = torch.arange(
            0,
            (batch_size + 1) * max_seqlen,
            step=max_seqlen,
            dtype=torch.int32,
            device=device,
        )
    return _cu_seqlens_cache[(batch_size, max_seqlen)]
1270
1271


1272
@torch.compile
1273
1274
1275
1276
1277
1278
1279
1280
def pack_tensor(
    indices: torch.Tensor,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Packs the given tensor using the `indices`.
    """
    padding_indice = torch.zeros(
1281
1282
        1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device
    )
1283
    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
1284
1285
1286
1287
1288
1289
1290
1291
    if isinstance(tensor, Float8Tensor):
        tensor_data = torch.cat((tensor._data, padding_indice), dim=0)

        packed = Float8Tensor.make_like(tensor, data=torch.gather(tensor_data, 0, indices))
    else:
        tensor = torch.cat((tensor, padding_indice), dim=0)

        packed = torch.gather(tensor, 0, indices)
1292
1293
1294
    return packed


1295
@torch.compile
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
def pack_2_tensors(
    indices: torch.Tensor,
    t1: torch.Tensor,
    t2: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Packs the given 2 tensors using the `indices`.
    """
    t1_packed = pack_tensor(indices, t1)
    t2_packed = pack_tensor(indices, t2)
    return t1_packed, t2_packed


1309
@torch.compile
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
def pack_3_tensors(
    indices: torch.Tensor,
    t1: torch.Tensor,
    t2: torch.Tensor,
    t3: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Packs the given 3 tensors using the `indices`.
    """
    t1_packed = pack_tensor(indices, t1)
    t2_packed = pack_tensor(indices, t2)
    t3_packed = pack_tensor(indices, t3)
    return t1_packed, t2_packed, t3_packed


1325
@torch.compile
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
def unpack_tensor(
    indices: torch.Tensor,
    dim0: int,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Inverse of `pack_tensor`.
    """
    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
    unpacked = torch.zeros(
1336
1337
        dim0 + 1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device
    )
1338
1339
1340
1341
1342
1343
    if isinstance(tensor, Float8Tensor):
        unpacked.scatter_(0, indices, tensor._data)
        unpacked = Float8Tensor.make_like(tensor, data=unpacked[0:-1, :, :])
    else:
        unpacked.scatter_(0, indices, tensor)
        unpacked = unpacked[0:-1, :, :]
1344
1345
1346
    return unpacked


1347
@torch.compile
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
def unpack_2_tensors(
    indices: torch.Tensor,
    dim0: int,
    t1: torch.Tensor,
    t2: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Inverse of `pack_2_tensors`.
    """
    t1_unpacked = unpack_tensor(indices, dim0, t1)
    t2_unpacked = unpack_tensor(indices, dim0, t2)
    return t1_unpacked, t2_unpacked


1362
@torch.compile
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
def unpack_3_tensors(
    indices: torch.Tensor,
    dim0: int,
    t1: torch.Tensor,
    t2: torch.Tensor,
    t3: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Inverse of `pack_3_tensors`.
    """
    t1_unpacked = unpack_tensor(indices, dim0, t1)
    t2_unpacked = unpack_tensor(indices, dim0, t2)
    t3_unpacked = unpack_tensor(indices, dim0, t3)
    return t1_unpacked, t2_unpacked, t3_unpacked


class PackTensors(torch.autograd.Function):
    """
    Autograd function to pack tensors.
    """
1383

1384
1385
    @staticmethod
    def forward(
1386
        ctx, indices: torch.Tensor, *tensors: Tuple[torch.Tensor, ...]
1387
    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
1388
        # pylint: disable=missing-function-docstring
1389
        assert 1 <= len(tensors) <= 3, f"Packing {len(tensors)} tensors not supported."
1390
        ctx.save_for_backward(indices)
1391
1392
1393
1394
1395
1396
1397
1398
1399
        ctx.dim0 = tensors[0].shape[0]
        if len(tensors) == 1:
            return pack_tensor(indices, *tensors)
        if len(tensors) == 2:
            return pack_2_tensors(indices, *tensors)
        return pack_3_tensors(indices, *tensors)

    @staticmethod
    def backward(ctx, *grad_outputs: Tuple[torch.Tensor, ...]):
1400
        # pylint: disable=missing-function-docstring
1401
        (indices,) = ctx.saved_tensors
1402
        if len(grad_outputs) == 1:
1403
            return None, unpack_tensor(indices, ctx.dim0, *grad_outputs)
1404
        if len(grad_outputs) == 2:
1405
1406
            return None, *unpack_2_tensors(indices, ctx.dim0, *grad_outputs)
        return None, *unpack_3_tensors(indices, ctx.dim0, *grad_outputs)
1407
1408
1409
1410
1411
1412


class UnpackTensor(torch.autograd.Function):
    """
    Autograd function to unpack a tensor.
    """
1413

1414
1415
1416
1417
1418
1419
1420
    @staticmethod
    def forward(
        ctx,
        indices: torch.Tensor,
        dim0: int,
        tensor: torch.Tensor,
    ) -> torch.Tensor:
1421
        # pylint: disable=missing-function-docstring
1422
        ctx.save_for_backward(indices)
1423
1424
1425
1426
        return unpack_tensor(indices, dim0, tensor)

    @staticmethod
    def backward(ctx, grad_output):
1427
        # pylint: disable=missing-function-docstring
1428
1429
        (indices,) = ctx.saved_tensors
        return None, None, pack_tensor(indices, grad_output)
1430
1431


1432
1433
1434
def flash_attn_p2p_communicate(
    rank, send_tensor, send_dst, recv_tensor, recv_src, cp_group, batch_p2p_comm
):
1435
    """Point-to-point communications of KV and dKV in Attention with context parallelism"""
1436
1437
1438
1439
    send_recv_ops = []

    if batch_p2p_comm:
        if rank % 2 == 0:
1440
1441
1442
1443
1444
1445
            send_op = torch.distributed.P2POp(
                torch.distributed.isend, send_tensor, send_dst, cp_group
            )
            recv_op = torch.distributed.P2POp(
                torch.distributed.irecv, recv_tensor, recv_src, cp_group
            )
1446
1447
1448
            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
1449
1450
1451
1452
1453
1454
            recv_op = torch.distributed.P2POp(
                torch.distributed.irecv, recv_tensor, recv_src, cp_group
            )
            send_op = torch.distributed.P2POp(
                torch.distributed.isend, send_tensor, send_dst, cp_group
            )
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
            send_recv_ops.append(recv_op)
            send_recv_ops.append(send_op)
        send_recv_reqs = torch.distributed.batch_isend_irecv(send_recv_ops)
    else:
        if rank % 2 == 0:
            send_op = torch.distributed.isend(send_tensor, send_dst, cp_group)
            recv_op = torch.distributed.irecv(recv_tensor, recv_src, cp_group)
            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
            recv_op = torch.distributed.irecv(recv_tensor, recv_src, cp_group)
            send_op = torch.distributed.isend(send_tensor, send_dst, cp_group)
            send_recv_ops.append(recv_op)
            send_recv_ops.append(send_op)
        send_recv_reqs = send_recv_ops

    return send_recv_reqs


1474
@jit_fuser
1475
1476
1477
1478
1479
def flash_attn_fwd_out_correction(
    out: torch.Tensor,
    out_per_step: torch.Tensor,
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
1480
1481
    movedim_src: int,
    movedim_dst: int,
1482
):
1483
    """Merge partial outputs of each step in Attention with context parallelism"""
1484
1485
1486
    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse).movedim(
        movedim_src, movedim_dst
    )
1487
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
1488
    out_corrected = out_per_step * softmax_lse_corrected_exp
1489
1490
1491
    out.add_(out_corrected)


1492
@jit_fuser
1493
1494
1495
1496
def flash_attn_fwd_softmax_lse_correction(
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
):
1497
    """Merge softmax stats of each step in Attention with context parallelism"""
1498
1499
1500
1501
    max_scale = torch.max(softmax_lse, softmax_lse_per_step)
    min_scale = torch.min(softmax_lse, softmax_lse_per_step)
    new_scale = max_scale + torch.log(1 + torch.exp(min_scale - max_scale))
    softmax_lse.copy_(new_scale)
1502
1503


1504
1505
@jit_fuser
def get_cu_seqlens_on_cp_rank(
1506
1507
1508
1509
1510
1511
    cu_seqlens: torch.Tensor,
    cu_seqlens_padded_on_cp_rank: torch.Tensor,
    cp_size: int,
    cp_rank: int,
    first_half: bool,
    second_half: bool,
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
):
    """Compute cu_seqlens of a context parallelism rank"""
    seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
    seqlens_padded = (cu_seqlens_padded_on_cp_rank[1:] - cu_seqlens_padded_on_cp_rank[:-1]) // 2
    zeros = torch.zeros_like(seqlens)
    cu_seqlens_on_cp_rank = torch.zeros_like(cu_seqlens)
    if first_half:
        seqlens_1 = seqlens - cp_rank * seqlens_padded
        seqlens_1 = seqlens_1.clamp(zeros, seqlens_padded)
        cu_seqlens_on_cp_rank[1:].add_(seqlens_1)
    if second_half:
        seqlens_2 = seqlens - (2 * cp_size - cp_rank - 1) * seqlens_padded
        seqlens_2 = seqlens_2.clamp(zeros, seqlens_padded)
        cu_seqlens_on_cp_rank[1:].add_(seqlens_2)
    cu_seqlens_on_cp_rank.cumsum_(dim=0)
    return cu_seqlens_on_cp_rank


1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
@torch.compile
def get_seq_chunk_ids_for_reordering(cp_size, device, to_contiguous):
    """
    Context parallelism assigns two discontiguous sequence chunks to each GPU for load balancing.
    To make sure tokens are ordered correctly for compute, we need to reorder sequence chunks
    before or after CP communications (e.g., all-gather, all-to-all). This function is to compute
    sequence chunk ids for reordering.
    """
    chunk_ids = torch.empty(2 * cp_size, dtype=torch.int32, device=device)
    if to_contiguous:
        for rank in range(cp_size):
            chunk_ids[rank] = 2 * rank
            chunk_ids[rank + cp_size] = 2 * cp_size - 2 * rank - 1
    else:
        for rank in range(cp_size):
            chunk_ids[2 * rank] = rank
            chunk_ids[2 * rank + 1] = 2 * cp_size - rank - 1
    return chunk_ids


@torch.compile
def reorder_seq_chunks_for_a2a(x, chunk_ids_for_a2a, seq_dim, cp_size, before_attn):
    """Reorder sequence chunk for A2A communication."""
    if before_attn:
        # [cp, b, s, np//cp, hn] -> [b, cp, s, np//cp, hn]
        # or [cp, s, b, np//cp, hn] -> [cp, s, b, np//cp, hn]
        x = x.movedim(0, seq_dim).contiguous()
        # [b, cp, s, np//cp, hn] -> [b, cp*2, s//2, np//cp, hn]
        # or [cp, s, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
        x = x.view(*x.shape[:seq_dim], cp_size * 2, -1, *x.shape[(seq_dim + 2) :])
        # reorder the sequence chunks
        x = torch.index_select(x, dim=seq_dim, index=chunk_ids_for_a2a)
    else:
        # [b, cp*2, s//2, np//cp, hn] -> [cp*2, b, s//2, np//cp, hn]
        # or [cp*2, s//2, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
        x = x.movedim(seq_dim, 0).contiguous()
        # reorder the sequence chunks
        x = torch.index_select(x, dim=0, index=chunk_ids_for_a2a)
        # [cp*2, b, s//2, np//cp, hn] -> [cp, 2, b, s//2, np//cp, hn]
        # or [cp*2, s//2, b, np//cp, hn] -> [cp, 2, s//2, b, np//cp, hn]
        x = x.view(cp_size, 2, *x.shape[1:])
    return x


def flash_attn_a2a_communicate(
    a2a_inputs: Union[torch.Tensor, List[torch.Tensor]],
    chunk_ids_for_a2a: torch.Tensor,
    seq_dim: int,
    cp_size: int,
    cp_group: dist_group_type,
    cp_stream: torch.cuda.Stream,
    before_attn: bool,
) -> Union[torch.Tensor, List[torch.Tensor]]:
    """A2A communication for context parallelism."""
    a2a_inputs = [a2a_inputs] if not isinstance(a2a_inputs, list) else a2a_inputs
    a2a_outputs, a2a_reqs = [None] * len(a2a_inputs), [None] * len(a2a_inputs)
    if before_attn:
        for i in range(len(a2a_inputs) + 2):
            if 0 < i < len(a2a_inputs) + 1:
                a2a_outputs[i - 1] = torch.empty_like(a2a_inputs[i - 1])
                a2a_reqs[i - 1] = torch.distributed.all_to_all_single(
                    a2a_outputs[i - 1], a2a_inputs[i - 1], group=cp_group, async_op=True
                )
            if i > 1:
                with torch.cuda.stream(cp_stream):
                    a2a_reqs[i - 2].wait()
                    x = a2a_outputs[i - 2]
                    # reorder the sequence chunks
                    x = reorder_seq_chunks_for_a2a(
                        x, chunk_ids_for_a2a, seq_dim, cp_size, before_attn
                    )
                    # [b, cp*2, s//2, np//cp, hn] -> [b, cp*s, np//cp, hn]
                    # or [cp*2, s//2, b, np//cp, hn] -> [cp*s, b, np//cp, hn]
                    a2a_outputs[i - 2] = x.view(*x.shape[:seq_dim], -1, *x.shape[(seq_dim + 2) :])
            if i < len(a2a_inputs):
                x = a2a_inputs[i]
                # [b, s, np, hn] -> [b, s, cp, np//cp, hn]
                # or [s, b, np, hn] -> [s, b, cp, np//cp, hn]
                x = x.view(*x.shape[:-2], cp_size, x.shape[-2] // cp_size, x.shape[-1])
                # [b, s, cp, np//cp, hn] -> [cp, b, s, np//cp, hn]
                # or [s, b, cp, np//cp, hn] -> [cp, s, b, np//cp, hn]
                a2a_inputs[i] = x.movedim(-3, 0).contiguous()
    else:
        for i in range(len(a2a_inputs) + 2):
            if 0 < i < len(a2a_inputs) + 1:
                a2a_outputs[i - 1] = torch.empty_like(a2a_inputs[i - 1])
                a2a_reqs[i - 1] = torch.distributed.all_to_all_single(
                    a2a_outputs[i - 1], a2a_inputs[i - 1], group=cp_group, async_op=True
                )
            if i < len(a2a_inputs):
                x = a2a_inputs[i]
                # [b, cp*s, np//cp, hn] -> [b, cp*2, s//2, np//cp, hn]
                # or [cp*s, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
                x = x.view(*x.shape[:seq_dim], cp_size * 2, -1, *x.shape[(seq_dim + 1) :])
                # reorder the sequence chunks
                a2a_inputs[i] = reorder_seq_chunks_for_a2a(
                    x, chunk_ids_for_a2a, seq_dim, cp_size, before_attn
                )
            if i > 1:
                with torch.cuda.stream(cp_stream):
                    a2a_reqs[i - 2].wait()
                    x = a2a_outputs[i - 2]
                    # [cp, 2, b, s//2, np//cp, hn] -> [b, 2, s//2, cp, np//cp, hn]
                    # or [cp, 2, s//2, b, np//cp, hn] -> [2, s//2, b, cp, np//cp, hn]
                    x = x.movedim(0, -3).movedim(0, seq_dim).contiguous()
                    # [b, 2, s//2, cp, np//cp, hn] -> [b*s, np, hn]
                    # or [2, s//2, b, cp, np//cp, hn] -> [s*b, np, hn]
                    a2a_outputs[i - 2] = x.view(-1, x.shape[-3] * x.shape[-2], x.shape[-1])
    torch.cuda.current_stream().wait_stream(cp_stream)
    return a2a_outputs[0] if len(a2a_inputs) == 1 else a2a_outputs


1642
class AttnFuncWithCPAndKVP2P(torch.autograd.Function):
1643
    """
1644
1645
1646
    Attention implementation with context parallelism. Exchange KV between CP ranks
    with P2P in ring topology. Split attention compute into multiple steps, and overlap
    current-step compute with next-step communication.
1647
1648
1649
1650
1651

    This implementation also supports hierarchical CP, which parallelizes attention
    heads in low-level CP groups and parallelizes sequence dimension in high-level CP
    groups. For more details, please refer to `LongVILA <https://arxiv.org/abs/2408.10188>`_
    and `USP <https://arxiv.org/abs/2405.07719>`_.
1652
1653
1654
    """

    @staticmethod
1655
1656
1657
1658
1659
1660
1661
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
1662
        cu_seqlens_kv,
1663
        max_seqlen_q,
1664
        max_seqlen_kv,
1665
1666
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
1667
1668
1669
1670
1671
1672
1673
1674
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
1675
1676
        fp8,
        fp8_meta,
1677
1678
1679
        cp_group,
        cp_global_ranks,
        cp_stream,
1680
    ):
1681
        # pylint: disable=missing-function-docstring
1682
1683
1684
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
        if isinstance(cp_group, list):
            assert (
                qkv_format != "thd"
            ), f"{qkv_format} format is not supported with hierarchical CP implementation yet!"
            assert attn_bias_type == "no_bias", (
                f"{attn_bias_type} bias type is not supported with hierarchical CP implementation"
                " yet!"
            )
            cp_group_a2a = cp_group[0]
            cp_size_a2a = get_distributed_world_size(cp_group_a2a)
            rank_a2a = get_distributed_rank(cp_group_a2a)
            cp_group = cp_group[1]
        else:
            cp_group_a2a = None
            cp_size_a2a = 1
            rank_a2a = 0

1702
1703
        cp_size = get_distributed_world_size(cp_group)
        rank = get_distributed_rank(cp_group)
1704
1705
        send_dst = cp_global_ranks[(rank + 1) % cp_size * cp_size_a2a + rank_a2a]
        recv_src = cp_global_ranks[(rank - 1) % cp_size * cp_size_a2a + rank_a2a]
1706
1707
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

1708
1709
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
1710

1711
        seq_dim = None
1712
        if qkv_format in ["bshd", "sbhd"]:
1713
            seq_dim = qkv_format.index("s")
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
        else:
            qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

        pad_between_seqs_q = not torch.equal(cu_seqlens_q_padded, cu_seqlens_q)
        pad_between_seqs_kv = not torch.equal(cu_seqlens_kv_padded, cu_seqlens_kv)
        max_seqlen_q = max_seqlen_q // cp_size
        max_seqlen_kv = max_seqlen_kv // cp_size
        cu_seqlens_q_padded = cu_seqlens_q_padded // cp_size
        cu_seqlens_kv_padded = cu_seqlens_kv_padded // cp_size
        cu_seqlens_q_per_step = [None for _ in range(cp_size)]
        cu_seqlens_kv_per_step = [None for _ in range(cp_size)]
1726

1727
1728
1729
        fused_attn_qkv_dtype = None
        fused_attn_backend = None
        amax_per_step = None
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
        if fp8:
            if use_fused_attention:
                fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
                fused_attn_qkv_dtype = fp8_dtype_forward
                fused_attn_backend = FusedAttnBackend["FP8"]
                if fp8_meta["recipe"].fp8_mha:
                    assert (
                        isinstance(q, Float8Tensor)
                        and isinstance(k, Float8Tensor)
                        and isinstance(v, Float8Tensor)
                    ), "q/k/v must be Float8Tensors for FP8 MHA!"
                    fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                    q_fp8, k_fp8, v_fp8 = q, k, v
                    q, k, v = q_fp8._data, k_fp8._data, v_fp8._data
                else:
                    q_f16, k_f16, v_f16 = q, k, v
                    if cp_size_a2a == 1 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                        q = cast_to_fp8(q_f16, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)
                    if int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                        k, v = [
                            cast_to_fp8(x, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)
                            for x in [k_f16, v_f16]
                        ]
                fp8_meta_kwargs = {}
                fp8_meta_kwargs["d_scale_qkv"] = fp8_meta["scaling_fwd"].scale_inv
                fp8_meta_kwargs["d_scale_qkv_offset"] = META_QKV
                fp8_meta_kwargs["d_scale_s"] = fp8_meta["scaling_fwd"].scale_inv
                fp8_meta_kwargs["d_scale_s_offset"] = META_S
                fp8_meta_kwargs["q_scale_s"] = fp8_meta["scaling_fwd"].scale
                fp8_meta_kwargs["q_scale_s_offset"] = META_S
                fp8_meta_kwargs["q_scale_o"] = fp8_meta["scaling_fwd"].scale
                fp8_meta_kwargs["q_scale_o_offset"] = META_O_CP
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            q_f16 = q
            if use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_qkv_dtype = TE_DType[q.dtype]
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

        if cp_size_a2a > 1:
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size_a2a, q.device, True)
            q, k, v = flash_attn_a2a_communicate(
                [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size_a2a, cp_group_a2a, cp_stream, True
            )
            if not fp8:
                q_f16 = q
            elif not fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                q_f16 = q
                q = cast_to_fp8(q_f16, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)

1783
1784
1785
        assert qkv_format == "thd" or (
            q.shape[seq_dim] % 2 == 0 and k.shape[seq_dim] % 2 == 0
        ), "Sequence length per GPU needs to be divisible by 2!"
1786
        if causal:
1787
1788
            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
1789
                q, k, v = [x.view(x.shape[0], 2, x.shape[1] // 2, *x.shape[2:]) for x in [q, k, v]]
1790
1791
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
1792
                q, k, v = [x.view(2, x.shape[0] // 2, *x.shape[1:]) for x in [q, k, v]]
1793
1794
1795
        total_tokens_kv = None if qkv_format != "thd" else k.shape[0]
        # remove padded tokens at the end
        k, v = [x if qkv_format != "thd" else x[: cu_seqlens_kv_padded[-1]] for x in [k, v]]
1796
        if attn_bias is not None:
1797
            assert len(attn_bias.shape) == 4, (
1798
1799
1800
                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
1801
1802
1803
            assert (
                attn_bias.shape[-2] % 2 == 0 and attn_bias.shape[-1] % (2 * cp_size) == 0
            ), "Sequence length does not meet divisible requirements!"
1804
            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
1805
1806
1807
1808
1809
1810
            attn_bias_ = attn_bias.view(
                *attn_bias.shape[:-2],
                2,
                attn_bias.shape[-2] // 2,
                2 * cp_size,
                attn_bias.shape[-1] // (2 * cp_size),
1811
1812
            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
1813
1814
            attn_bias = attn_bias.view(
                *attn_bias.shape[:-1], 2 * cp_size, attn_bias.shape[-1] // (2 * cp_size)
1815
            )
1816
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
1817
1818
1819
1820

        softmax_lse_in_packed_format = not use_fused_attention and (
            _flash_attn_2_6_0_plus or _use_flash_attn_3
        )
1821
        flash_attn_fwd = None
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
                flash_attn_fwd = flash_attn_varlen_fwd_v3
                fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
            else:
                flash_attn_fwd = flash_attn_varlen_fwd
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
                if _flash_attn_2_3_plus:
                    fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
                if _flash_attn_2_4_plus:
                    fa_forward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_5_7_plus:
                    fa_forward_kwargs["block_table"] = None
1837

1838
1839
1840
        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
1841
        attn_bias_inputs = [None, None]
1842
1843
1844
1845
        # Flash Attn outputs
        out_per_step = [None for _ in range(cp_size)]
        softmax_lse_per_step = [None for _ in range(cp_size)]
        rng_states = [None for _ in range(cp_size)]
1846
        attn_biases = [None for _ in range(cp_size)]
1847
1848
1849
1850
1851
1852
1853

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), cp_stream]
        # synchronize fwd results correction across steps
        fwd_results_correction_done = torch.cuda.Event()

        p2p_comm_buffers = [None for _ in range(cp_size)]
1854
1855
1856
1857
        if use_fused_attention and qkv_format in ["bshd", "sbhd"]:
            p2p_comm_buffers[0] = torch.cat((k.unsqueeze(-3), v.unsqueeze(-3)), dim=-3)
        else:
            p2p_comm_buffers[0] = torch.cat((k.unsqueeze(0), v.unsqueeze(0)), dim=0)
1858
1859
        send_recv_reqs = [[], []]

1860
1861
        softmax_lse_ = None
        out = None
1862
        for i in range(cp_size + 1):
1863
            if i < cp_size:
1864
                with torch.cuda.stream(flash_attn_streams[i % 2]):
1865
                    # wait until KV is received
1866
                    for req in send_recv_reqs[(i + 1) % 2]:
1867
1868
                        req.wait()

1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
                    if i < (cp_size - 1):
                        p2p_comm_buffers[i + 1] = torch.empty_like(p2p_comm_buffers[i])
                        send_recv_reqs[i % 2] = flash_attn_p2p_communicate(
                            rank,
                            p2p_comm_buffers[i],
                            send_dst,
                            p2p_comm_buffers[i + 1],
                            recv_src,
                            cp_group,
                            batch_p2p_comm,
                        )

1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
                    if (
                        not fp8
                        or fp8_meta["recipe"].fp8_mha
                        or int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
                    ):
                        kv_inputs[i % 2] = p2p_comm_buffers[i]
                    else:
                        # KV exchange is in BF16/FP16, cast received KV in each step
                        kv_inputs[i % 2] = cast_to_fp8(
                            p2p_comm_buffers[i],
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                        )
                    if fp8 and use_fused_attention:
1896
1897
1898
1899
                        fp8_meta_kwargs["amax_s"] = amax_per_step
                        fp8_meta_kwargs["amax_s_offset"] = i
                        fp8_meta_kwargs["amax_o"] = amax_per_step
                        fp8_meta_kwargs["amax_o_offset"] = cp_size + i
1900
1901
                    if causal:
                        if i == 0:
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv, cu_seqlens_kv_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1914
                            if use_fused_attention:
1915
1916
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
1917
                                    q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
1918
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
1919
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
1920
                                        k.shape[0], -1, 2, *k.shape[-2:]
1921
                                    )
1922
1923
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
1924
                                    q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
1925
1926
1927
1928
                                    # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                        -1, k.shape[2], 2, *k.shape[-2:]
                                    )
1929
                                elif qkv_format == "thd":
1930
                                    q_inputs[i % 2] = q
1931
1932
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1933
1934
1935
1936
1937
1938
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1939
                                    ).contiguous()
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q,
                                    max_seqlen_kv,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
                                    q_inputs[i % 2],
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    fused_attn_qkv_dtype,
                                    fused_attn_backend,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type=attn_mask_type,
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                    **fp8_meta_kwargs,
1968
                                )
1969
1970
1971
1972
1973
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
1974
1975
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
1976
                                q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
1977
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
1978
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
1979
                                fa_outputs = flash_attn_fwd(
1980
1981
1982
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
1983
1984
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1985
                                    max_seqlen_q,
1986
                                    max_seqlen_kv,
1987
                                    causal=True,
1988
                                    **fa_forward_kwargs,
1989
                                )
1990
1991
1992
1993
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
                                if not _use_flash_attn_3:
                                    rng_states[i] = fa_outputs[7]
1994
                        elif i <= rank:
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv,
                                    cu_seqlens_kv_padded,
                                    cp_size,
                                    (rank - i) % cp_size,
                                    True,
                                    False,
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // (cp_size * 2)
2012
                            if use_fused_attention:
2013
2014
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
2015
                                    q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
2016
2017
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2][:, 0, ...].contiguous()
2018
2019
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
2020
                                    q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
2021
2022
                                    # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2][0].contiguous()
2023
                                elif qkv_format == "thd":
2024
                                    q_inputs[i % 2] = q
2025
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
2026
                                    kv_inputs[i % 2] = tex.thd_read_half_tensor(
2027
                                        kv_inputs[i % 2], cu_seqlens_kv_padded, 0
2028
                                    )
2029
2030
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
2031
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q,
                                    max_seqlen_kv // 2,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
                                    q_inputs[i % 2],
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    fused_attn_qkv_dtype,
                                    fused_attn_backend,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type="padding" if padding else "no_mask",
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                                    cu_seqlens_kv_padded=(
                                        None
                                        if cu_seqlens_kv_padded is None
                                        else cu_seqlens_kv_padded // 2
                                    ),
                                    **fp8_meta_kwargs,
2064
                                )
2065
2066
2067
2068
2069
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
2070
2071
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
2072
                                q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
2073
2074
                                if qkv_format == "thd":
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
2075
                                    kv_inputs[i % 2] = tex.thd_read_half_tensor(
2076
                                        kv_inputs[i % 2], cu_seqlens_kv_padded, 0
2077
                                    )
2078
2079
                                else:
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
2080
                                    kv_inputs[i % 2] = kv_inputs[i % 2][:, :, 0, ...].contiguous()
2081
                                # [2, b, sk//2, np, hn] -> [2, b*sk//2, np, hn]
2082
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
2083
2084
2085
                                if _use_flash_attn_3 or _flash_attn_2_3_plus:
                                    fa_forward_kwargs["window_size"] = (-1, -1)
                                fa_outputs = flash_attn_fwd(
2086
2087
2088
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
2089
2090
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
2091
                                    max_seqlen_q,
2092
                                    max_seqlen_kv // 2,
2093
                                    causal=False,
2094
                                    **fa_forward_kwargs,
2095
                                )
2096
2097
2098
2099
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
                                if not _use_flash_attn_3:
                                    rng_states[i] = fa_outputs[7]
2100
                        else:
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
                            if pad_between_seqs_q:
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, False, True
                                )
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // (cp_size * 2)
                            if pad_between_seqs_kv:
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv,
                                    cu_seqlens_kv_padded,
                                    cp_size,
                                    (rank - i) % cp_size,
                                    True,
                                    True,
                                )
                            else:
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
2118
                            if use_fused_attention:
2119
2120
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
2121
                                    q_inputs[i % 2] = q[:, 1, ...].contiguous()
2122
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
2123
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
2124
                                        k.shape[0], -1, 2, *k.shape[-2:]
2125
                                    )
2126
2127
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2128
                                    q_inputs[i % 2] = q[1].contiguous()
2129
2130
2131
2132
                                    # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                        -1, k.shape[2], 2, *k.shape[-2:]
                                    )
2133
2134
                                elif qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
2135
2136
2137
                                    q_inputs[i % 2] = tex.thd_read_half_tensor(
                                        q, cu_seqlens_q_padded, 1
                                    )
2138
2139
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
2140
2141
2142
2143
2144
2145
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
2146
                                    ).contiguous()
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
                                out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                    is_training,
                                    max_seqlen_q // 2,
                                    max_seqlen_kv,
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
                                    q_inputs[i % 2],
                                    (
                                        kv_inputs[i % 2][..., 0, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][0]
                                    ),
                                    (
                                        kv_inputs[i % 2][..., 1, :, :]
                                        if qkv_format in ["bshd", "sbhd"]
                                        else kv_inputs[i % 2][1]
                                    ),
                                    fused_attn_qkv_dtype,
                                    fused_attn_backend,
                                    attn_scale=softmax_scale,
                                    dropout=dropout_p,
                                    qkv_layout=qkv_layout,
                                    attn_mask_type="padding" if padding else "no_mask",
                                    attn_bias_type=attn_bias_type,
                                    attn_bias=attn_bias_inputs[i % 2],
                                    cu_seqlens_q_padded=(
                                        None
                                        if cu_seqlens_q_padded is None
                                        else cu_seqlens_q_padded // 2
                                    ),
                                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                    **fp8_meta_kwargs,
2179
                                )
2180
2181
2182
2183
2184
                                if fp8:
                                    softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                                else:
                                    softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                    attn_biases[i] = rest[0] if len(rest) > 0 else None
2185
                            else:
2186
2187
                                if qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
2188
2189
2190
                                    q_inputs[i % 2] = tex.thd_read_half_tensor(
                                        q, cu_seqlens_q_padded, 1
                                    )
2191
2192
                                else:
                                    # [b, 2, sq//2, np, hn]->[b, sq//2, np, hn]->[b*sq//2, np, hn]
2193
                                    q_inputs[i % 2] = (
2194
                                        q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
2195
                                    )
2196
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
2197
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
2198
2199
2200
                                if _use_flash_attn_3 or _flash_attn_2_3_plus:
                                    fa_forward_kwargs["window_size"] = (-1, -1)
                                fa_outputs = flash_attn_fwd(
2201
2202
2203
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
2204
2205
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
2206
                                    max_seqlen_q // 2,
2207
                                    max_seqlen_kv,
2208
                                    causal=False,
2209
                                    **fa_forward_kwargs,
2210
                                )
2211
2212
2213
2214
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
                                if not _use_flash_attn_3:
                                    rng_states[i] = fa_outputs[7]
2215
                    else:
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
                        if pad_between_seqs_q:
                            cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                            )
                        else:
                            cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
                        if pad_between_seqs_kv:
                            cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                cu_seqlens_kv,
                                cu_seqlens_kv_padded,
                                cp_size,
                                (rank - i) % cp_size,
                                True,
                                True,
                            )
                        else:
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
2233
                        if use_fused_attention:
2234
2235
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
2236
2237
2238
2239
2240
2241
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
2242
                                ).contiguous()
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
                            out_per_step[i], aux_ctx_tensors = fused_attn_fwd(
                                is_training,
                                max_seqlen_q,
                                max_seqlen_kv,
                                cu_seqlens_q_per_step[i],
                                cu_seqlens_kv_per_step[i],
                                q,
                                (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                ),
                                (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                ),
                                fused_attn_qkv_dtype,
                                fused_attn_backend,
                                attn_scale=softmax_scale,
                                dropout=dropout_p,
                                qkv_layout=qkv_layout,
                                attn_mask_type=attn_mask_type,
                                attn_bias_type=attn_bias_type,
                                attn_bias=attn_bias_inputs[i % 2],
                                cu_seqlens_q_padded=cu_seqlens_q_padded,
                                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                                **fp8_meta_kwargs,
2271
                            )
2272
2273
2274
2275
2276
                            if fp8:
                                softmax_lse_per_step[i], _, rng_states[i] = aux_ctx_tensors
                            else:
                                softmax_lse_per_step[i], rng_states[i], *rest = aux_ctx_tensors
                                attn_biases[i] = rest[0] if len(rest) > 0 else None
2277
                        else:
2278
                            # [b, sq, np, hn] -> [b*sq, np, hn]
2279
                            q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
2280
                            # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
2281
                            kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
2282
                            fa_outputs = flash_attn_fwd(
2283
2284
2285
                                q_inputs[i % 2],
                                kv_inputs[i % 2][0],
                                kv_inputs[i % 2][1],
2286
2287
                                cu_seqlens_q_per_step[i],
                                cu_seqlens_kv_per_step[i],
2288
                                max_seqlen_q,
2289
                                max_seqlen_kv,
2290
                                causal=False,
2291
                                **fa_forward_kwargs,
2292
                            )
2293
2294
2295
2296
                            out_per_step[i] = fa_outputs[4]
                            softmax_lse_per_step[i] = fa_outputs[5]
                            if not _use_flash_attn_3:
                                rng_states[i] = fa_outputs[7]
2297
2298
2299
2300

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
2301
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
2302

2303
2304
                if use_fused_attention:
                    # [b, np, sq, 1] -> [b, np, sq]
2305
                    softmax_lse_per_step[i - 1].squeeze_(-1)
2306
2307
2308
2309
2310
                if qkv_format != "thd" and softmax_lse_in_packed_format:
                    # [np, t] -> [np, b, sq]
                    softmax_lse_per_step[i - 1] = softmax_lse_per_step[i - 1].view(
                        q.shape[-2], q.shape[0], -1
                    )
2311

2312
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
2313
2314
2315
2316
2317
2318
2319
2320
                    if fp8:
                        out_per_step[i - 1] = cast_from_fp8(
                            out_per_step[i - 1],
                            fp8_meta["scaling_fwd"],
                            META_O_CP,
                            fp8_dtype_forward,
                            TE_DType[torch.float32],
                        )
2321
                    if i == 1:
2322
                        out = torch.zeros_like(q if not fp8 else out_per_step[0]).view(q.shape)
2323
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
2324
                        if causal and qkv_format != "thd":
2325
2326
                            # [b, np, sq] -> [b, np, 2, sq//2] lse not in packed format
                            # [np, b, sq] -> [np, b, 2, sq//2] lse in packed format
2327
                            softmax_lse_ = softmax_lse.view(
2328
                                *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
2329
                            )
2330
2331
2332
2333
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
2334
                    else:
2335
                        if qkv_format == "thd":
2336
                            tex.thd_second_half_lse_correction(
2337
2338
2339
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
2340
                                softmax_lse_in_packed_format,
2341
                            )
2342
                        else:
2343
2344
2345
                            flash_attn_fwd_softmax_lse_correction(
                                softmax_lse_[..., 1, :], softmax_lse_per_step[i - 1]
                            )
2346
2347

                if i < cp_size:
2348
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
2349
2350
2351
2352
2353

        torch.cuda.current_stream().wait_stream(flash_attn_streams[1])

        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
2354
            out_ = None
2355
            if qkv_format == "bshd":
2356
2357
2358
                out_per_step[i] = out_per_step[i].view(
                    out.shape[0], -1, *out.shape[-2:]
                )  # pylint: disable=used-before-assignment
2359
2360
2361
2362
                out_ = out[:, 1, ...]
            elif qkv_format == "sbhd":
                out_per_step[i] = out_per_step[i].view(-1, *out.shape[-3:])
                out_ = out[1]
2363

2364
            if i <= rank or not causal:
2365
                if qkv_format in ["bshd", "sbhd"]:
2366
2367
2368
2369
2370
                    flash_attn_fwd_out_correction(
                        out.view(*out_per_step[i].shape),
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2371
2372
                        0 if softmax_lse_in_packed_format else 2,
                        2 if softmax_lse_in_packed_format else seq_dim,
2373
                    )
2374
                elif qkv_format == "thd":
2375
2376
2377
2378
2379
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2380
                        cu_seqlens_q_padded,
2381
                        False,
2382
                        softmax_lse_in_packed_format,
2383
                    )
2384
            else:
2385
                if qkv_format in ["bshd", "sbhd"]:
2386
2387
2388
2389
2390
                    flash_attn_fwd_out_correction(
                        out_,
                        out_per_step[i],
                        softmax_lse_[..., 1, :],
                        softmax_lse_per_step[i],
2391
2392
                        0 if softmax_lse_in_packed_format else 2,
                        2 if softmax_lse_in_packed_format else seq_dim,
2393
                    )
2394
                elif qkv_format == "thd":
2395
2396
2397
2398
2399
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2400
                        cu_seqlens_q_padded,
2401
                        True,
2402
                        softmax_lse_in_packed_format,
2403
                    )
2404

2405
2406
2407
        if qkv_format != "thd" and softmax_lse_in_packed_format:
            # [np, b, sq] -> [np, t]
            softmax_lse = softmax_lse.view(softmax_lse.shape[0], -1)
2408
        kv = p2p_comm_buffers[-1]
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
        if qkv_format == "bshd":
            out = out.view(out.shape[0], -1, *out.shape[-2:])
            ctx.batch_size = out.shape[0]
        elif qkv_format == "sbhd":
            out = out.view(-1, *out.shape[-3:])
            ctx.batch_size = out.shape[1]

        if cp_size_a2a > 1:
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size_a2a, out.device, False)
            out = flash_attn_a2a_communicate(
                out, chunk_ids_for_a2a, seq_dim, cp_size_a2a, cp_group_a2a, cp_stream, False
            )
            if use_fused_attention:
                if qkv_format == "bshd":
                    # [b*s, np, hn] -> [b, s, np, hn]
                    out = out.view(ctx.batch_size, -1, *out.shape[-2:])
                elif qkv_format == "sbhd":
                    # [s*b, np, hn] -> [s, b, np, hn]
                    out = out.view(-1, ctx.batch_size, *out.shape[-2:])
        elif not use_fused_attention:
2429
            out = out.view(-1, *out.shape[-2:])
2430

2431
2432
2433
2434
2435
        if fp8 and use_fused_attention:
            amax_cp_fwd = amax_per_step.amax(dim=1)
            fp8_meta["scaling_fwd"].amax_history[0][META_S] = amax_cp_fwd[0]
            fp8_meta["scaling_fwd"].amax_history[0][META_O_CP] = amax_cp_fwd[1]

2436
        out_fp8 = None
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
        out_f16 = out.to(q_fp8.dtype if fp8 and fp8_meta["recipe"].fp8_mha else q_f16.dtype)
        if fp8 and (fp8_meta["recipe"].fp8_mha or int(os.getenv("NVTE_FP8_DPA_BWD", "1"))):
            out_fp8 = cast_to_fp8(out_f16, fp8_meta["scaling_fwd"], META_O, fp8_dtype_forward)

        if fp8 and fp8_meta["recipe"].fp8_mha:
            out_ret = Float8Tensor(
                data=out_fp8,
                fp8_meta=fp8_meta,
                fp8_meta_forward=True,
                fp8_meta_index=META_O,
                fp8_dtype=fp8_dtype_forward,
                dtype=q_fp8.dtype,
            )
        else:
            out_ret = out_f16

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
            q_save, kv_save, out_save = q, kv, out_fp8
            fp8_fwd_scales = fp8_meta["scaling_fwd"].scale.clone()
            fp8_fwd_scale_invs = fp8_meta["scaling_fwd"].scale_inv.clone()
        elif fp8 and fp8_meta["recipe"].fp8_mha:
2458
2459
2460
2461
2462
2463
2464
2465
            q_fp8 = Float8Tensor(
                data=q,
                fp8_meta=fp8_meta,
                fp8_meta_forward=True,
                fp8_meta_index=META_QKV,
                fp8_dtype=fp8_dtype_forward,
                dtype=q_fp8.dtype,
            )
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
            kv_fp8 = Float8Tensor(
                data=kv,
                fp8_meta=fp8_meta,
                fp8_meta_forward=True,
                fp8_meta_index=META_QKV,
                fp8_dtype=fp8_dtype_forward,
                dtype=k_fp8.dtype,
            )
            q_save, kv_save, out_save = q_fp8, kv_fp8, out_f16
            fp8_fwd_scales, fp8_fwd_scale_invs = None, None
        else:
2477
            q_f16 = q_f16.view(q.shape)
2478
2479
2480
            q_save, kv_save, out_save = q_f16, kv, out_f16
            fp8_fwd_scales, fp8_fwd_scale_invs = None, None

2481
        ctx.save_for_backward(
2482
2483
2484
            q_save,
            kv_save,
            out_save,
2485
            softmax_lse,
2486
2487
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
2488
2489
            fp8_fwd_scales,
            fp8_fwd_scale_invs,
2490
2491
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
2492
2493
            *rng_states,
            *attn_biases,
2494
        )
2495
2496
2497
        ctx.cp_group_a2a = cp_group_a2a
        ctx.cp_size_a2a = cp_size_a2a
        ctx.rank_a2a = rank_a2a
2498
2499
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
2500
        ctx.cp_stream = cp_stream
2501
        ctx.dropout_p = dropout_p
2502
        ctx.total_tokens_kv = total_tokens_kv
2503
        ctx.max_seqlen_q = max_seqlen_q
2504
        ctx.max_seqlen_kv = max_seqlen_kv
2505
        ctx.softmax_scale = softmax_scale
2506
        ctx.qkv_format = qkv_format
2507
        ctx.attn_mask_type = attn_mask_type
2508
2509
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
2510
        ctx.deterministic = deterministic
2511
        ctx.use_fused_attention = use_fused_attention
2512
2513
2514
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
        return out_ret
2515
2516
2517

    @staticmethod
    def backward(ctx, dout):
2518
        # pylint: disable=missing-function-docstring
2519
2520
2521
        cp_size_a2a = ctx.cp_size_a2a
        rank_a2a = ctx.rank_a2a

2522
2523
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
2524
2525
        send_dst = ctx.cp_global_ranks[(rank - 1) % cp_size * cp_size_a2a + rank_a2a]
        recv_src = ctx.cp_global_ranks[(rank + 1) % cp_size * cp_size_a2a + rank_a2a]
2526
2527
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

2528
        (q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded) = ctx.saved_tensors[:6]
2529
2530
2531
2532
2533
        (fp8_fwd_scales, fp8_fwd_scale_invs) = ctx.saved_tensors[6:8]
        cu_seqlens_q_per_step = ctx.saved_tensors[8 : 8 + cp_size]
        cu_seqlens_kv_per_step = ctx.saved_tensors[8 + cp_size : 8 + cp_size * 2]
        rng_states = ctx.saved_tensors[8 + cp_size * 2 : 8 + cp_size * 3]
        attn_biases = ctx.saved_tensors[8 + cp_size * 3 : 8 + cp_size * 4]
2534

2535
2536
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
2537
        if ctx.qkv_format in ["bshd", "sbhd"]:
2538
            seq_dim = ctx.qkv_format.index("s")
2539
2540
2541
            qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format[:-2] + "2" + ctx.qkv_format[-2:]
        else:
            qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format
2542

2543
        if attn_biases[0] is not None:
2544
2545
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
2546
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
2547
2548
2549
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
2550
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
2551
2552
2553
            )
        else:
            attn_dbias = None
2554
            attn_dbias_ = None
2555

2556
2557
2558
2559
        softmax_lse_in_packed_format = not ctx.use_fused_attention and (
            _flash_attn_2_6_0_plus or _use_flash_attn_3
        )

2560
        if causal:
2561
            if ctx.qkv_format == "thd" or softmax_lse_in_packed_format:
2562
                softmax_lse_ = tex.thd_read_second_half_lse(
2563
                    softmax_lse, cu_seqlens_q_padded, softmax_lse_in_packed_format
2564
                )
2565
2566
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
2567
2568
2569
                softmax_lse_ = softmax_lse.view(
                    *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
                )
2570
2571
2572
2573
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
                if ctx.use_fused_attention:
                    # [b, np, sq//2] -> [b, np, sq//2, 1]
                    softmax_lse_.unsqueeze_(-1)
2574
2575
2576
        if ctx.use_fused_attention:
            # [b, np, sq] -> [b, np, sq, 1]
            softmax_lse.unsqueeze_(-1)
2577

2578
        dout_dtype = dout.dtype
2579
2580
2581
2582
2583
2584
        fused_attn_backend = None
        fused_attn_qkv_dtype = None
        fused_attn_dqkv_dtype = None
        amax_per_step = None
        seq_dim = None
        dout_fp8_dtype = None
2585
2586
        if ctx.fp8:
            if ctx.use_fused_attention:
2587
                fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
2588
                fp8_dtype_backward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=False)
2589
                fused_attn_qkv_dtype = fp8_dtype_forward
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
                fused_attn_dqkv_dtype = fp8_dtype_backward
                fused_attn_backend = FusedAttnBackend["FP8"]
                dq_fp8 = torch.empty((cp_size, *q.shape), dtype=q.dtype, device=q.device)
                dkv_fp8 = torch.empty((cp_size, *kv.shape), dtype=kv.dtype, device=kv.device)
                dkv_fp8_ = torch.empty_like(dkv_fp8)
                if ctx.fp8_meta["recipe"].fp8_mha:
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = dout._scale_inv
                    dout = dout._data
                else:
                    dout = cast_to_fp8(
                        dout, ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                    )
                p2p_comm_buffers = [[kv, dkv_fp8], [torch.empty_like(kv), dkv_fp8_]]
                fp8_meta_kwargs = {}
                fp8_meta_kwargs["d_scale_qkv"] = fp8_fwd_scale_invs[META_QKV]
                fp8_meta_kwargs["d_scale_s"] = fp8_fwd_scale_invs[META_S]
                fp8_meta_kwargs["d_scale_o"] = fp8_fwd_scale_invs[META_O]
                fp8_meta_kwargs["d_scale_do"] = ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO]
                fp8_meta_kwargs["d_scale_dp"] = ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP]
                fp8_meta_kwargs["q_scale_s"] = fp8_fwd_scales[META_S]
                fp8_meta_kwargs["q_scale_dp"] = ctx.fp8_meta["scaling_bwd"].scale[META_DP]
                fp8_meta_kwargs["q_scale_dqkv"] = ctx.fp8_meta["scaling_bwd"].scale[META_DQKV_CP]
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            if ctx.fp8_meta is not None and ctx.fp8_meta["recipe"].fp8_mha:
2618
2619
2620
2621
2622
2623
2624
                q, kv = [x.from_float8(x.dtype) for x in [q, kv]]
                if cp_size_a2a == 1:
                    dout = dout.from_float8(dout_dtype)
                else:
                    dout_fp8_dtype = dout._fp8_dtype
                    dout_scale_inv = dout._scale_inv
                    dout = dout._data
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
            dq = torch.empty_like(q)
            if ctx.qkv_format == "thd" and causal:
                dq[cu_seqlens_q_padded[-1] :].fill_(0)
            p2p_comm_buffers = [
                torch.empty((2, *kv.shape), dtype=kv.dtype, device=kv.device),
                torch.empty((2, *kv.shape), dtype=kv.dtype, device=kv.device),
            ]
            p2p_comm_buffers[0][0].copy_(kv)
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_qkv_dtype = TE_DType[q.dtype]
2636
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
2637
2638
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
        if cp_size_a2a > 1:
            if not ctx.use_fused_attention:
                out = out.view(ctx.batch_size, -1, *out.shape[-2:])
                dout = dout.view(*out.shape)
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size_a2a, out.device, True)
            out, dout = flash_attn_a2a_communicate(
                [out, dout],
                chunk_ids_for_a2a,
                seq_dim,
                cp_size_a2a,
                ctx.cp_group_a2a,
                ctx.cp_stream,
                True,
            )
            if not ctx.fp8 and ctx.fp8_meta is not None and ctx.fp8_meta["recipe"].fp8_mha:
                dout = cast_from_fp8(
2655
2656
2657
2658
2659
2660
                    dout,
                    None,
                    None,
                    dout_fp8_dtype,
                    TE_DType[dout_dtype],
                    scale_inv=dout_scale_inv,  # pylint: disable=used-before-assignment
2661
2662
                )

2663
2664
2665
2666
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

2667
        flash_attn_bwd = None
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
                flash_attn_bwd = flash_attn_varlen_bwd_v3
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
                flash_attn_bwd = flash_attn_varlen_bwd
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
                if _flash_attn_2_4_plus:
                    fa_backward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_4_1_plus:
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
2680

2681
2682
2683
2684
2685
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

2686
2687
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
            if ctx.fp8:
                if i < cp_size - 1:
                    send_recv_reqs = flash_attn_p2p_communicate(
                        rank,
                        send_tensor[0],
                        send_dst,
                        recv_tensor[0],
                        recv_src,
                        ctx.cp_group,
                        batch_p2p_comm,
                    )
                else:
                    dkv_a2a_req = torch.distributed.all_to_all_single(
                        dkv_fp8,
                        dkv_fp8_,
                        group=ctx.cp_group,
                        async_op=True,
                    )
                    send_recv_reqs = [dkv_a2a_req]
            else:
                if i == 0:
                    send_tensor = send_tensor[0]
                    recv_tensor = recv_tensor[0]
                if i == (cp_size - 1):
                    send_tensor = send_tensor[1]
                    recv_tensor = recv_tensor[1]
                send_recv_reqs = flash_attn_p2p_communicate(
                    rank, send_tensor, send_dst, recv_tensor, recv_src, ctx.cp_group, batch_p2p_comm
                )
2717

2718
            kv = p2p_comm_buffers[i % 2][0]
2719
            dk_, dv_ = None, None
2720
2721
2722
            if ctx.fp8 and ctx.use_fused_attention:
                fp8_meta_kwargs["amax_dp"] = amax_per_step[0][i]
                fp8_meta_kwargs["amax_dqkv"] = amax_per_step[0][i]
2723
            # In reversed order of fwd
2724
            if causal:
2725
                if i == (cp_size - 1):
2726
                    if ctx.use_fused_attention:
2727
2728
2729
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
2730
2731
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2732
2733
2734
2735
2736
2737
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
2738
2739
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2740
2741
2742
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2743
2744
                        elif ctx.qkv_format == "thd":
                            q_, kv_, out_, dout_ = q, kv, out, dout
2745
2746
2747
2748
2749
2750
2751
2752
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse,
                                softmax_lse,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
2753
                        if attn_dbias is not None:
2754
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2755
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2756
                            ctx.max_seqlen_q,
2757
2758
2759
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2760
                            q_,
2761
2762
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2763
2764
                            out_,
                            dout_,
2765
2766
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2767
                            aux_ctx_tensors,
2768
                            fused_attn_backend,
2769
2770
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2771
2772
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2773
                            qkv_layout=qkv_layout,
2774
                            attn_mask_type=ctx.attn_mask_type,
2775
                            attn_bias_type=ctx.attn_bias_type,
2776
2777
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2778
2779
2780
2781
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2782
                        dq_ = torch.zeros_like(q_)
2783
2784
2785
2786
2787
2788
                        # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                        kv_ = kv.view(2, -1, *kv.shape[-2:])
                        dkv_ = torch.empty_like(kv_)
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        out_ = out.view(-1, *out.shape[-2:])
                        dout_ = dout.view(-1, *dout.shape[-2:])
2789
2790
2791
2792
2793
                        if _use_flash_attn_3 or _flash_attn_2_3_plus:
                            fa_backward_kwargs["window_size"] = (-1, 0)
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2794
2795
2796
2797
2798
2799
2800
2801
2802
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2803
2804
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2805
                            ctx.max_seqlen_q,
2806
                            ctx.max_seqlen_kv,
2807
2808
                            causal=True,
                            **fa_backward_kwargs,
2809
                        )
2810
                elif i >= (cp_size - rank - 1):
2811
                    if ctx.use_fused_attention:
2812
2813
2814
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
2815
2816
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                            kv_ = kv[:, 0, ...].contiguous()
2817
2818
2819
2820
2821
2822
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
2823
2824
                            # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                            kv_ = kv[0].contiguous()
2825
2826
2827
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2828
2829
2830
                        elif ctx.qkv_format == "thd":
                            q_, out_, dout_ = q, out, dout
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2831
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2832
2833
2834
2835
2836
2837
2838
2839
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse,
                                softmax_lse,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
2840
                        if attn_dbias is not None:
2841
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2842
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2843
                            ctx.max_seqlen_q,
2844
2845
2846
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2847
                            q_,
2848
2849
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2850
2851
                            out_,
                            dout_,
2852
2853
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2854
                            aux_ctx_tensors,
2855
                            fused_attn_backend,
2856
2857
2858
2859
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=(
                                None if cu_seqlens_kv_padded is None else cu_seqlens_kv_padded // 2
                            ),
2860
2861
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2862
                            qkv_layout=qkv_layout,
2863
                            attn_mask_type="padding" if padding else "no_mask",
2864
                            attn_bias_type=ctx.attn_bias_type,
2865
2866
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2867
2868
2869
2870
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2871
                        dq_ = torch.zeros_like(q_)
2872
2873
                        if ctx.qkv_format == "thd":
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2874
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2875
2876
2877
                        else:
                            # [2, b, 2, sk//2, np, hn]->[2, b, sk//2, np, hn]->[2, b*sk//2, np, hn]
                            kv_ = kv[:, :, 0, ...].contiguous().view(2, -1, *kv.shape[-2:])
2878
2879
2880
2881
                        dkv_ = torch.empty_like(kv_)
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        out_ = out.view(-1, *out.shape[-2:])
                        dout_ = dout.view(-1, *dout.shape[-2:])
2882
2883
2884
2885
2886
                        if _use_flash_attn_3 or _flash_attn_2_3_plus:
                            fa_backward_kwargs["window_size"] = (-1, -1)
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2887
2888
2889
2890
2891
2892
2893
2894
2895
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2896
2897
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2898
                            ctx.max_seqlen_q,
2899
                            ctx.max_seqlen_kv // 2,
2900
2901
                            causal=False,
                            **fa_backward_kwargs,
2902
2903
2904
                        )
                else:
                    if ctx.use_fused_attention:
2905
2906
2907
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous()
2908
2909
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2910
2911
2912
2913
2914
2915
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous()
                            dout_ = dout[:, 1, ...].contiguous()
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            q_ = q[1].contiguous()
2916
2917
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2918
2919
2920
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            out_ = out[1].contiguous()
                            dout_ = dout[1].contiguous()
2921
2922
                        elif ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2923
2924
2925
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q_padded, 1)
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q_padded, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q_padded, 1)
2926
                            kv_ = kv
2927
2928
2929
2930
2931
2932
2933
2934
                        if ctx.fp8:
                            aux_ctx_tensors = [
                                softmax_lse_,
                                softmax_lse_,
                                rng_states[cp_size - i - 1],
                            ]
                        else:
                            aux_ctx_tensors = [softmax_lse_, rng_states[cp_size - i - 1]]
2935
                        if attn_dbias is not None:
2936
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2937
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2938
                            ctx.max_seqlen_q // 2,
2939
2940
2941
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2942
                            q_,
2943
2944
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2945
2946
                            out_,
                            dout_,
2947
2948
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2949
                            aux_ctx_tensors,
2950
                            fused_attn_backend,
2951
2952
2953
2954
                            cu_seqlens_q_padded=(
                                None if cu_seqlens_q_padded is None else cu_seqlens_q_padded // 2
                            ),
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2955
2956
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2957
                            qkv_layout=qkv_layout,
2958
                            attn_mask_type="padding" if padding else "no_mask",
2959
                            attn_bias_type=ctx.attn_bias_type,
2960
2961
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2962
2963
                        )
                    else:
2964
2965
                        if ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2966
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q_padded, 1)
2967
2968
2969
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
2970
                        dq_ = torch.zeros_like(q_)
2971
2972
2973
                        # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                        kv_ = kv.view(2, -1, *kv.shape[-2:])
                        dkv_ = torch.empty_like(kv_)
2974
                        if ctx.qkv_format == "thd":
2975
2976
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q_padded, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q_padded, 1)
2977
2978
2979
2980
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous().view(-1, *out.shape[-2:])
                            dout_ = dout[:, 1, ...].contiguous().view(-1, *dout.shape[-2:])
2981
2982
2983
2984
2985
                        if _use_flash_attn_3 or _flash_attn_2_3_plus:
                            fa_backward_kwargs["window_size"] = (-1, -1)
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2986
2987
2988
2989
2990
2991
2992
2993
2994
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse_,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2995
2996
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2997
                            ctx.max_seqlen_q // 2,
2998
                            ctx.max_seqlen_kv,
2999
3000
                            causal=False,
                            **fa_backward_kwargs,
3001
3002
3003
                        )
            else:
                if ctx.use_fused_attention:
3004
3005
3006
3007
                    if ctx.fp8:
                        aux_ctx_tensors = [softmax_lse, softmax_lse, rng_states[cp_size - i - 1]]
                    else:
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size - i - 1]]
3008
                    if attn_dbias is not None:
3009
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
3010
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
3011
                        ctx.max_seqlen_q,
3012
3013
3014
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
3015
                        q,
3016
3017
                        kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0],
                        kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1],
3018
3019
                        out,
                        dout,
3020
3021
                        fused_attn_qkv_dtype,
                        fused_attn_dqkv_dtype,
3022
                        aux_ctx_tensors,
3023
                        fused_attn_backend,
3024
3025
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
3026
3027
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
3028
                        qkv_layout=qkv_layout,
3029
                        attn_mask_type=ctx.attn_mask_type,
3030
                        attn_bias_type=ctx.attn_bias_type,
3031
3032
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
3033
3034
3035
                    )
                else:
                    # [b, sq, np, hn] -> [b*sq, np, hn]
3036
                    q_ = q.view(-1, *q.shape[-2:])
3037
                    dq_ = torch.zeros_like(q_)
3038
                    # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
3039
3040
                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
3041
                    # [b, sq, np, hn] -> [b*sq, np, hn]
3042
3043
                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
3044
3045
3046
3047
3048
                    if _use_flash_attn_3 or _flash_attn_2_3_plus:
                        fa_backward_kwargs["window_size"] = (-1, -1)
                    if not _use_flash_attn_3:
                        fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                    flash_attn_bwd(
3049
3050
3051
3052
3053
3054
3055
3056
3057
                        dout_,
                        q_,
                        kv_[0],
                        kv_[1],
                        out_,
                        softmax_lse,
                        dq_,
                        dkv_[0],
                        dkv_[1],
3058
3059
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
3060
                        ctx.max_seqlen_q,
3061
                        ctx.max_seqlen_kv,
3062
3063
                        causal=False,
                        **fa_backward_kwargs,
3064
3065
                    )

3066
3067
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
3068
            if i >= (cp_size - rank - 1) or not causal:
3069
3070
3071
3072
                # [b*sq, np, hn] -> [b, 2, sq//2, np, hn] if causal
                # [b*sq, np, hn] -> [b, sq, np, hn] if not causal
                dq_ = dq_.view(*dq.shape)
            else:
3073
3074
3075
3076
3077
3078
                if ctx.qkv_format == "bshd":
                    # [b*sq//2, np, hn] -> [b, sq//2, np, hn]
                    dq_ = dq_.view(dq.shape[0], *dq.shape[2:])
                elif ctx.qkv_format == "sbhd":
                    # [b*sq//2, np, hn] -> [sq//2, b, np, hn]
                    dq_ = dq_.view(-1, *dq.shape[-3:])
3079

3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
            if ctx.fp8:
                if i >= (cp_size - rank - 1) or not causal:
                    dq.copy_(dq_)
                else:
                    if ctx.qkv_format == "bshd":
                        dq[:, 0, ...].fill_(0)
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[0].fill_(0)
                        dq[1].copy_(dq_)
            elif causal:
3091
                if i > (cp_size - rank - 1):
3092
                    dq.add_(dq_)
3093
3094
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
3095
3096
                        dq.copy_(dq_)
                    else:
3097
3098
3099
3100
3101
3102
                        if ctx.qkv_format == "bshd":
                            dq[:, 0, ...].copy_(dq_[:, 0, ...])
                            dq[:, 1, ...].add_(dq_[:, 1, ...])
                        elif ctx.qkv_format == "sbhd":
                            dq[0].copy_(dq_[0])
                            dq[1].add_(dq_[1])
3103
                        elif ctx.qkv_format == "thd":
3104
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
3105
                elif i > 0:
3106
3107
3108
3109
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
3110
                    elif ctx.qkv_format == "thd":
3111
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
3112
                else:
3113
3114
3115
3116
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
3117
                    elif ctx.qkv_format == "thd":
3118
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
3119
3120
3121
3122
3123
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
3124

3125
            if attn_dbias is not None:
3126
                idx = (rank + i + 1) % cp_size
3127
                if i == (cp_size - 1) or not causal:
3128
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
3129
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
3130
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
3131
3132
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
3133
3134
3135
3136
                    # [b, np, sq, sk//(2*cp)]
                    attn_dbias[..., idx, :].copy_(dbias_)
                else:
                    # [b, np, sq//2, sk//cp] -> [b, np, sq//2, 2, sk//(2*cp)]
3137
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
3138
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
3139
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
3140

3141
3142
3143
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
3144

3145
3146
3147
3148
3149
3150
3151
            if ctx.fp8:
                if i < cp_size - 1:
                    dkv = dkv_fp8_[(rank + i + 1) % cp_size]
                else:
                    dkv = dkv_fp8[(rank + i + 1) % cp_size]
            else:
                dkv = p2p_comm_buffers[(i + 1) % 2][1]
3152
            if ctx.use_fused_attention:
3153
3154
3155
                dkv_ = torch.cat(
                    (dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0
                )  # pylint: disable=used-before-assignment
3156
3157
3158
3159
                if ctx.qkv_format in ["bshd", "sbhd"]:
                    # [b, 2, sk//2, 2, np, hn] -> [2, b, 2, sk//2, np, hn] or
                    # [2, sk//2, b, 2, np, hn] -> [2, 2, sk//2, b, np, hn]
                    dkv = dkv.view(2, *dkv.shape[0:-3], *dkv.shape[-2:])
3160
            if causal and i >= (cp_size - rank - 1) and i != (cp_size - 1):
3161
3162
3163
3164
3165
3166
                if ctx.qkv_format == "bshd":
                    # [2, b*sk//2, np, hn] -> [2, b, sk//2, np, hn]
                    dkv_ = dkv_.view(*dkv.shape[0:2], *dkv.shape[3:])
                elif ctx.qkv_format == "sbhd":
                    # [2, b*sk//2, np, hn] -> [2, sk//2, b, np, hn]
                    dkv_ = dkv_.view(dkv.shape[0], -1, *dkv.shape[-3:])
3167
3168
3169
3170
            else:
                # [2, b*sk, np, hn] -> [2, b, 2, sk//2, np, hn] if causal
                # [2, b*sk, np, hn] -> [2, b, sk, np, hn] if not causal
                dkv_ = dkv_.view(*dkv.shape)
3171

3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
            if ctx.fp8:
                if causal and i >= (cp_size - rank - 1) and i != (cp_size - 1):
                    if ctx.qkv_format == "bshd":
                        dkv[:, :, 0, ...].copy_(dkv_)
                        dkv[:, :, 1, ...].fill_(0)
                    elif ctx.qkv_format == "sbhd":
                        dkv[:, 0, ...].copy_(dkv_)
                        dkv[:, 1, ...].fill_(0)
                else:
                    dkv.copy_(dkv_)
            elif causal:
3183
                if i == (cp_size - 1):
3184
                    if rank == 0:
3185
3186
3187
3188
3189
3190
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_[:, :, 0, ...])
                            dkv[:, :, 1, ...].copy_(dkv_[:, :, 1, ...])
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_[:, 0, ...])
                            dkv[:, 1, ...].copy_(dkv_[:, 1, ...])
3191
                        elif ctx.qkv_format == "thd":
3192
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
3193
3194
                    else:
                        dkv.add_(dkv_)
3195
3196
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
3197
3198
3199
3200
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
3201
                        elif ctx.qkv_format == "thd":
3202
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
3203
                    else:
3204
3205
3206
3207
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
3208
                        elif ctx.qkv_format == "thd":
3209
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
3210
3211
3212
3213
3214
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
3215
3216
3217
3218
3219
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
        if ctx.fp8 and ctx.use_fused_attention:
            amax_cp_bwd = amax_per_step.amax(dim=1)
            ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP] = amax_cp_bwd[0]
            ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV_CP] = amax_cp_bwd[1]
            if ctx.qkv_format in ["bshd", "sbhd"]:
                # [cp, b, 2, sk//2, 2, np, hn] -> [cp, 2, b, 2, sk//2, np, hn] or
                # [cp, 2, sk//2, b, 2, np, hn] -> [cp, 2, 2, sk//2, b, np, hn]
                dkv_fp8 = dkv_fp8.view(cp_size, 2, *dkv_fp8.shape[1:-3], *dkv_fp8.shape[-2:])
            dq, dkv = [
                cast_from_fp8(
                    x,
                    ctx.fp8_meta["scaling_bwd"],
                    META_DQKV_CP,
                    fp8_dtype_backward,
                    TE_DType[torch.float32],
                )
                for x in [dq_fp8, dkv_fp8]
            ]
            dq, dkv = [x.sum(dim=0).to(dout_dtype) for x in [dq, dkv]]

3240
        if causal:
3241
3242
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
3243
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
3244
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
3245
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
3246
3247
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
3248
                dq = dq.view(-1, *dq.shape[-3:])
3249
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
3250
3251
3252
3253
3254
3255
3256
3257
3258
                dkv = dkv.view(dkv.shape[0], -1, *dkv.shape[-3:])

        if ctx.qkv_format == "thd":
            dkv_ = torch.empty(
                2, ctx.total_tokens_kv, *dkv.shape[-2:], dtype=dkv.dtype, device=dkv.device
            )
            dkv_[:, : cu_seqlens_kv_padded[-1]].copy_(dkv)
            dkv_[:, cu_seqlens_kv_padded[-1] :].fill_(0)
            dkv = dkv_
3259

3260
3261
3262
3263
3264
        if ctx.fp8 and ctx.fp8_meta["recipe"].fp8_mha:
            dq, dkv = [
                cast_to_fp8(x, ctx.fp8_meta["scaling_bwd"], META_DQKV, fp8_dtype_backward)
                for x in [dq, dkv]
            ]
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
        dk, dv = dkv[0], dkv[1]

        if cp_size_a2a > 1:
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size_a2a, q.device, False)
            dq, dk, dv = flash_attn_a2a_communicate(
                [dq, dk, dv],
                chunk_ids_for_a2a,
                seq_dim,
                cp_size_a2a,
                ctx.cp_group_a2a,
                ctx.cp_stream,
                False,
            )
            if ctx.qkv_format == "bshd":
                dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
            elif ctx.qkv_format == "sbhd":
                dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

        if ctx.fp8 and ctx.fp8_meta["recipe"].fp8_mha:
3284
3285
3286
3287
3288
3289
3290
3291
3292
            dq, dk, dv = [
                Float8Tensor(
                    data=x,
                    fp8_meta=ctx.fp8_meta,
                    fp8_meta_forward=False,
                    fp8_meta_index=META_DQKV,
                    fp8_dtype=fp8_dtype_backward,
                    dtype=dout_dtype,
                )
3293
                for x in [dq, dk, dv]
3294
3295
            ]

3296
3297
3298
3299
        if attn_dbias is not None:
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, sq, sk]
            attn_dbias = attn_dbias.view(*attn_dbias.shape[:-2], -1)

3300
3301
3302
        return (
            None,
            dq,
3303
3304
            dk,
            dv,
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
3316
            attn_dbias,
3317
3318
3319
3320
3321
            None,
            None,
            None,
            None,
            None,
3322
3323
            None,
            None,
3324
        )
3325
3326


3327
3328
def get_kv_seq_info_after_all_gather(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size, causal
3329
):
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
    """Compute KV sequence index range and update window size after all-gather."""
    local_chunk_end_idx = (local_chunk_id + 1) * max_seqlen_kv
    full_seq_end_idx = max_seqlen_kv * cp_size * 2

    if window_size is None:
        window_size = (-1, 0) if causal else (-1, -1)

    if window_size[1] == -1:
        seq_end_idx = full_seq_end_idx
        window_size_right = -1
    else:
        seq_end_idx = min(full_seq_end_idx, local_chunk_end_idx + window_size[1])
        window_size_right = local_chunk_end_idx + window_size[1] - seq_end_idx

    if window_size[0] == -1:
        seq_start_idx = 0
        window_size_left = -1
    else:
        seq_start_idx = max(0, local_chunk_end_idx - max_seqlen_q - window_size[0])
        window_size_left = window_size[0] + seq_end_idx - local_chunk_end_idx

    return (seq_start_idx, seq_end_idx), (window_size_left, window_size_right)
3352
3353
3354
3355


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
3356
3357
    Attention implementation with context parallelism. KV all-gather between CP ranks is exposed.
    Refer section 3.3.2 of `The Llama 3 Herd of Models <https://arxiv.org/abs/2407.21783>`_.
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
    """

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
3380
3381
        cp_group,
        cp_stream,
3382
    ):
3383
        # pylint: disable=missing-function-docstring
3384
3385
3386
3387
3388
3389
3390
3391
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
        rank = get_distributed_rank(cp_group)

        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
3392
        assert not padding, f"{attn_mask_type} mask type is not supported!"
3393
3394
3395
3396
3397
3398
3399
        if use_fused_attention and causal and "bottom_right" not in attn_mask_type:
            attn_mask_type = attn_mask_type + "_bottom_right"
        assert attn_bias_type == "no_bias", f"{attn_bias_type} bias type is not supported!"
        assert q.shape[-1] % 8 == 0, "Hidden size per attention head should be multiple of 8!"
        assert (
            use_fused_attention or _flash_attn_2_3_plus
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
3400

3401
        flash_attn_fwd = None
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
                flash_attn_fwd = flash_attn_varlen_fwd_v3
            else:
                flash_attn_fwd = flash_attn_varlen_fwd
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
                if _flash_attn_2_4_plus:
                    fa_forward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_5_7_plus:
                    fa_forward_kwargs["block_table"] = None
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427

        assert qkv_format != "thd", f"{qkv_format} format is not supported!"
        qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

        seq_dim = qkv_format.index("s")
        assert (
            q.shape[seq_dim] % 2 == 0 and k.shape[seq_dim] % 2 == 0
        ), "Sequence length per GPU needs to be divisible by 2!"

        max_seqlen_q = max_seqlen_q // (2 * cp_size)
        max_seqlen_kv = max_seqlen_kv // (2 * cp_size)
        cu_seqlens_q = cu_seqlens_q // (2 * cp_size)
        cu_seqlens_q_padded = cu_seqlens_q_padded // (2 * cp_size)

3428
3429
3430
3431
        # [b, s, np, hn] -> [b, 2, s//2, np, hn] or [s, b, np, hn] -> [2, s//2, b, np, hn]
        q = q.view(*q.shape[:seq_dim], 2, q.shape[seq_dim] // 2, *q.shape[(seq_dim + 1) :])
        # [b, s, np, hn] or [s, b, np, hn] -> [s, b, np, hn]
        k, v = [x.movedim(seq_dim, 0).contiguous() for x in [k, v]]
3432

3433
        # [s, b, np, hn] -> [cp, s, b, np, hn]
3434
3435
        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
3436
3437

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
3438
3439
        k_ag = k_ag.view(2 * cp_size, k.shape[0] // 2, *k.shape[1:])
        v_ag = v_ag.view(2 * cp_size, v.shape[0] // 2, *v.shape[1:])
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering(cp_size, k.device, True)
        k_ag = torch.index_select(k_ag, dim=0, index=chunk_ids_for_kv_ag)
        v_ag = torch.index_select(v_ag, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
        k_ag = k_ag.view(-1, *k.shape[1:])
        v_ag = v_ag.view(-1, *v.shape[1:])
        cp_stream.wait_stream(torch.cuda.current_stream())

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), cp_stream]
3450
3451

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
3452
3453
3454
        kv_seq_range_per_step = [None, None]
        window_size_per_step = [None, None]
        cu_seqlens_kv_per_step = [None, None]
3455
3456
3457
3458
3459
3460
3461
3462
        out_per_step = [None, None]
        softmax_lse_per_step = [None, None]
        rng_states = [None, None]
        out = torch.empty_like(q)

        for i in range(len(local_seq_chunk_ids) + 1):
            if i < len(local_seq_chunk_ids):
                with torch.cuda.stream(flash_attn_streams[i]):
3463
3464
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
3465
3466
3467
3468
3469
3470
3471
3472
3473
                    q_ = q.select(seq_dim, i).contiguous()
                    kv_seq_range_per_step[i], window_size_per_step[i] = (
                        get_kv_seq_info_after_all_gather(
                            local_seq_chunk_ids[i],
                            cp_size,
                            max_seqlen_q,
                            max_seqlen_kv,
                            window_size,
                            causal,
3474
                        )
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
                    )
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i][0],
                        kv_seq_range_per_step[i][1],
                    )
                    max_seqlen_kv_ = seq_end_idx - seq_start_idx
                    cu_seqlens_kv_per_step[i] = _get_full_cu_seqlens(
                        k.shape[1], max_seqlen_kv_, k.device
                    )
                    k_, v_ = [x[seq_start_idx:seq_end_idx] for x in [k_ag, v_ag]]
                    # [s_range, b, np, hn] -> [b, s_range, np, hn] or [s_range, b, np, hn]
                    k_, v_ = [x.movedim(0, seq_dim).contiguous() for x in [k_, v_]]
3487
3488
3489
3490
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
3491
                            max_seqlen_kv_,
3492
                            cu_seqlens_q,
3493
                            cu_seqlens_kv_per_step[i],
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
                            q_,
                            k_,
                            v_,
                            TE_DType[q.dtype],
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            attn_scale=softmax_scale,
                            dropout=dropout_p,
                            qkv_layout=qkv_layout,
                            attn_mask_type=attn_mask_type,
                            attn_bias_type=attn_bias_type,
                            attn_bias=attn_bias,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
3506
3507
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
                            window_size=window_size_per_step[i],
3508
3509
3510
                        )
                    else:
                        q_, k_, v_ = [x.view(-1, *x.shape[-2:]) for x in [q_, k_, v_]]
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
                        fa_outputs = flash_attn_fwd(
                            q_,
                            k_,
                            v_,
                            cu_seqlens_q,
                            cu_seqlens_kv_per_step[i],
                            max_seqlen_q,
                            max_seqlen_kv_,
                            causal=causal,
                            window_size=window_size_per_step[i],
                            **fa_forward_kwargs,
3522
                        )
3523
3524
3525
3526
                        out_per_step[i] = fa_outputs[4]
                        softmax_lse_per_step[i] = fa_outputs[5]
                        if not _use_flash_attn_3:
                            rng_states[i] = fa_outputs[7]
3527
3528
3529
3530

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
3531
                        out[:, i - 1].copy_(out_per_step[i - 1].view(out[:, i - 1].shape))
3532
                    elif qkv_format == "sbhd":
3533
                        out[i - 1].copy_(out_per_step[i - 1].view(out[i - 1].shape))
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550

        torch.cuda.current_stream().wait_stream(cp_stream)

        if use_fused_attention:
            if qkv_format == "bshd":
                out = out.view(out.shape[0], -1, *out.shape[-2:])
            elif qkv_format == "sbhd":
                out = out.view(-1, *out.shape[-3:])
        else:
            out = out.view(-1, *out.shape[-2:])

        ctx.save_for_backward(
            q,
            k,
            v,
            cu_seqlens_q,
            cu_seqlens_q_padded,
3551
            *cu_seqlens_kv_per_step,
3552
3553
3554
3555
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
3556
3557
        ctx.kv_seq_range_per_step = kv_seq_range_per_step
        ctx.window_size_per_step = window_size_per_step
3558
3559
3560
3561
3562
3563
3564
        ctx.cp_group = cp_group
        ctx.cp_stream = cp_stream
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.softmax_scale = softmax_scale
        ctx.qkv_format = qkv_format
        ctx.attn_bias_type = attn_bias_type
3565
        ctx.attn_mask_type = attn_mask_type
3566
3567
3568
3569
3570
3571
        ctx.deterministic = deterministic
        ctx.use_fused_attention = use_fused_attention
        return out

    @staticmethod
    def backward(ctx, dout):
3572
        # pylint: disable=missing-function-docstring
3573
3574
3575
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

3576
3577
3578
3579
3580
3581
3582
        (q, k, v, cu_seqlens_q, cu_seqlens_q_padded) = ctx.saved_tensors[:5]
        cu_seqlens_kv_per_step = ctx.saved_tensors[5:7]
        out_per_step = ctx.saved_tensors[7:9]
        softmax_lse_per_step = ctx.saved_tensors[9:11]
        rng_states = ctx.saved_tensors[11:13]
        kv_seq_range_per_step = ctx.kv_seq_range_per_step
        window_size_per_step = ctx.window_size_per_step
3583

3584
        seq_dim = ctx.qkv_format.index("s")
3585
3586
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

3587
        dout = dout.view(q.shape)
3588
        dq = torch.empty_like(q)
3589
        dk = torch.zeros((k.shape[0] * cp_size, *k.shape[1:]), dtype=k.dtype, device=k.device)
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
        dv = torch.zeros_like(dk)
        dq_per_step = [None, None]
        dk_per_step = [None, None]
        dv_per_step = [None, None]

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), ctx.cp_stream]
        # synchronize dkv update across steps
        dkv_update_done = torch.cuda.Event()

3600
        # [s, b, np, hn] -> [cp, s, b, np, hn]
3601
3602
        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
3603
3604

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
3605
3606
        k_ag = k_ag.view(2 * cp_size, k.shape[0] // 2, *k.shape[1:])
        v_ag = v_ag.view(2 * cp_size, v.shape[0] // 2, *v.shape[1:])
3607
3608
3609
3610
3611
3612
3613
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering(cp_size, k.device, True)
        k_ag = torch.index_select(k_ag, dim=0, index=chunk_ids_for_kv_ag)
        v_ag = torch.index_select(v_ag, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
        k_ag = k_ag.view(-1, *k.shape[1:])
        v_ag = v_ag.view(-1, *v.shape[1:])
        ctx.cp_stream.wait_stream(torch.cuda.current_stream())
3614
3615
3616

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]

3617
        flash_attn_bwd = None
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
                flash_attn_bwd = flash_attn_varlen_bwd_v3
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
                flash_attn_bwd = flash_attn_varlen_bwd
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
                if _flash_attn_2_4_plus:
                    fa_backward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_4_1_plus:
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
3630
3631
3632
3633

        for i in range(len(local_seq_chunk_ids) + 1):
            if i < len(local_seq_chunk_ids):
                with torch.cuda.stream(flash_attn_streams[i]):
3634
3635
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
3636
3637
3638
3639
3640
3641
3642
3643
3644
                    q_ = q.select(seq_dim, i).contiguous()
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i][0],
                        kv_seq_range_per_step[i][1],
                    )
                    max_seqlen_kv = seq_end_idx - seq_start_idx
                    k_, v_ = [x[seq_start_idx:seq_end_idx] for x in [k_ag, v_ag]]
                    # [cp*s, b, np, hn] -> [b, s_range, np, hn] or [s_range, b, np, hn]
                    k_, v_ = [x.movedim(0, seq_dim).contiguous() for x in [k_, v_]]
3645
                    out_ = out_per_step[i]
3646
                    dout_ = dout.select(seq_dim, i).contiguous().view(out_.shape)
3647
3648
3649
3650
                    if ctx.use_fused_attention:
                        aux_ctx_tensors = [softmax_lse_per_step[i], rng_states[i]]
                        dq_per_step[i], dk_per_step[i], dv_per_step[i], _ = fused_attn_bwd(
                            ctx.max_seqlen_q,
3651
                            max_seqlen_kv,
3652
                            cu_seqlens_q,
3653
                            cu_seqlens_kv_per_step[i],
3654
3655
3656
3657
3658
3659
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
                            TE_DType[q.dtype],
3660
                            TE_DType[dout.dtype],
3661
3662
3663
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
3664
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
3665
3666
3667
3668
3669
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
                            qkv_layout=qkv_layout,
                            attn_mask_type=ctx.attn_mask_type,
                            attn_bias_type=ctx.attn_bias_type,
3670
3671
                            window_size=window_size_per_step[i],
                            deterministic=ctx.deterministic,
3672
3673
                        )
                    else:
3674
                        batch_size = k_.shape[0]
3675
3676
3677
3678
                        q_, k_, v_ = [x.view(-1, *x.shape[-2:]) for x in [q_, k_, v_]]
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
3679
3680
3681
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[i]
                        flash_attn_bwd(
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
                            dout_,
                            q_,
                            k_,
                            v_,
                            out_,
                            softmax_lse_per_step[i],
                            dq_per_step[i],
                            dk_per_step[i],
                            dv_per_step[i],
                            cu_seqlens_q,
3692
                            cu_seqlens_kv_per_step[i],
3693
                            ctx.max_seqlen_q,
3694
                            max_seqlen_kv,
3695
                            causal="causal" in ctx.attn_mask_type,
3696
                            window_size=window_size_per_step[i],
3697
                            **fa_backward_kwargs,
3698
                        )
3699
3700
3701
3702
3703
3704
3705
                        # [b*sq//2, np, hn] -> [b, sq//2, np, hn]
                        dq_per_step[i] = dq_per_step[i].view(dq[:, i].shape)
                        # [b*s_range, np, hn] -> [b, s_range, np, hn]
                        dk_per_step[i], dv_per_step[i] = [
                            x.view(batch_size, -1, *x.shape[-2:])
                            for x in [dk_per_step[i], dv_per_step[i]]
                        ]
3706
3707
3708
3709

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if ctx.qkv_format == "bshd":
3710
                        dq[:, i - 1].copy_(dq_per_step[i - 1])
3711
                    elif ctx.qkv_format == "sbhd":
3712
3713
3714
3715
3716
3717
                        dq[i - 1].copy_(dq_per_step[i - 1])
                    # [b, s_range, np, hn] or [s_range, b, np, hn] -> [s_range, b, np, hn]
                    dk_per_step[i - 1], dv_per_step[i - 1] = [
                        x.movedim(seq_dim, 0).contiguous()
                        for x in [dk_per_step[i - 1], dv_per_step[i - 1]]
                    ]
3718
3719
3720
                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
3721
3722
3723
3724
3725
3726
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i - 1][0],
                        kv_seq_range_per_step[i - 1][1],
                    )
                    dk[seq_start_idx:seq_end_idx].add_(dk_per_step[i - 1])
                    dv[seq_start_idx:seq_end_idx].add_(dv_per_step[i - 1])
3727
3728
3729
3730
3731
                    if i < len(local_seq_chunk_ids):
                        flash_attn_streams[i - 1].record_event(dkv_update_done)

        torch.cuda.current_stream().wait_stream(ctx.cp_stream)

3732
3733
3734
3735
3736
3737
3738
        # [cp*s, b, np, hn] -> [cp*2, s//2, b, np, hn]
        dk = dk.view(2 * cp_size, -1, *dk.shape[-3:])
        dv = dv.view(2 * cp_size, -1, *dv.shape[-3:])
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering(cp_size, dk.device, False)
        dk = torch.index_select(dk, dim=0, index=chunk_ids_for_kv_ag)
        dv = torch.index_select(dv, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
3739
3740
3741
3742
3743
        dk = dk.view(-1, *dk.shape[-3:])
        dv = dv.view(-1, *dv.shape[-3:])
        dk, _ = reduce_scatter_along_first_dim(dk, ctx.cp_group)
        dv, _ = reduce_scatter_along_first_dim(dv, ctx.cp_group)

3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
        dq = dq.view(*dq.shape[:seq_dim], -1, *dq.shape[(seq_dim + 2) :])
        dk = dk.movedim(0, seq_dim).contiguous()
        dv = dv.movedim(0, seq_dim).contiguous()

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
        )


class AttnFuncWithCPAndQKVOA2A(torch.autograd.Function):
    """
    Attention implementation with context parallelism. Like Ulysses, applying A2A to QKVO.
    Refer the paper `DeepSpeed Ulysses <https://arxiv.org/abs/2309.14509>`_.
    """

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_kv,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
        fp8,
        fp8_meta,
        cp_group,
        cp_stream,
    ):
3804
        # pylint: disable=missing-function-docstring
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)

        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
        assert not padding, f"{attn_mask_type} mask type is not supported!"
        assert attn_bias_type == "no_bias", f"{attn_bias_type} bias type is not supported!"
        assert q.shape[-1] % 8 == 0, "Hidden size per attention head should be multiple of 8!"
        assert (
            window_size == (-1, 0)
            or window_size == (-1, -1)
            or use_fused_attention
            or _flash_attn_2_3_plus
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
3821

3822
        flash_attn_fwd = None
3823
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
            if _use_flash_attn_3:
                flash_attn_fwd = flash_attn_varlen_fwd_v3
                fa_forward_kwargs["window_size"] = window_size
            else:
                flash_attn_fwd = flash_attn_varlen_fwd
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
                if _flash_attn_2_3_plus:
                    fa_forward_kwargs["window_size"] = window_size
                if _flash_attn_2_4_plus:
                    fa_forward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_5_7_plus:
                    fa_forward_kwargs["block_table"] = None
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851

        assert (
            q.shape[-2] % cp_size == 0 and k.shape[-2] % cp_size == 0
        ), "The number of attention heads needs to be divisible by CP size!"

        assert qkv_format != "thd", f"{qkv_format} format is not supported!"
        qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

        batch_dim = qkv_format.index("b")
        seq_dim = qkv_format.index("s")
        assert (
            q.shape[seq_dim] % 2 == 0 and k.shape[seq_dim] % 2 == 0
        ), "Sequence length per GPU needs to be divisible by 2!"

3852
3853
        fused_attn_backend = None
        fused_attn_qkv_dtype = None
3854
3855
3856
3857
3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
3933
        if fp8:
            if use_fused_attention:
                fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
                fused_attn_qkv_dtype = fp8_dtype_forward
                fused_attn_backend = FusedAttnBackend["FP8"]
                if fp8_meta["recipe"].fp8_mha:
                    assert (
                        isinstance(q, Float8Tensor)
                        and isinstance(k, Float8Tensor)
                        and isinstance(v, Float8Tensor)
                    ), "q/k/v must be Float8Tensors for FP8 MHA!"
                    fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                    q_fp8, k_fp8, v_fp8 = q, k, v
                    q, k, v = q_fp8._data, k_fp8._data, v_fp8._data
                elif int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                    q_f16, k_f16, v_f16 = q, k, v
                    q, k, v = [
                        cast_to_fp8(x, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)
                        for x in [q_f16, k_f16, v_f16]
                    ]
                fp8_meta_kwargs = {}
                fp8_meta_kwargs["d_scale_qkv"] = fp8_meta["scaling_fwd"].scale_inv
                fp8_meta_kwargs["d_scale_qkv_offset"] = META_QKV
                fp8_meta_kwargs["d_scale_s"] = fp8_meta["scaling_fwd"].scale_inv
                fp8_meta_kwargs["d_scale_s_offset"] = META_S
                fp8_meta_kwargs["q_scale_s"] = fp8_meta["scaling_fwd"].scale
                fp8_meta_kwargs["q_scale_s_offset"] = META_S
                fp8_meta_kwargs["q_scale_o"] = fp8_meta["scaling_fwd"].scale
                fp8_meta_kwargs["q_scale_o_offset"] = META_O
                fp8_meta_kwargs["amax_s"] = fp8_meta["scaling_fwd"].amax_history
                fp8_meta_kwargs["amax_s_offset"] = META_S
                fp8_meta_kwargs["amax_o"] = fp8_meta["scaling_fwd"].amax_history
                fp8_meta_kwargs["amax_o_offset"] = META_O
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            if use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_qkv_dtype = TE_DType[q.dtype]
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size, q.device, True)
        q, k, v = flash_attn_a2a_communicate(
            [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, True
        )

        if fp8 and not fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
            q_f16, k_f16, v_f16 = q, k, v
            q, k, v = [
                cast_to_fp8(x, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward)
                for x in [q_f16, k_f16, v_f16]
            ]

        batch_size = q.shape[batch_dim]
        if use_fused_attention:
            out, aux_ctx_tensors = fused_attn_fwd(
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                fused_attn_qkv_dtype,
                fused_attn_backend,
                attn_scale=softmax_scale,
                dropout=dropout_p,
                qkv_layout=qkv_layout,
                attn_mask_type=attn_mask_type,
                attn_bias_type=attn_bias_type,
                attn_bias=attn_bias,
                cu_seqlens_q_padded=cu_seqlens_q_padded,
                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                window_size=window_size,
                **fp8_meta_kwargs,
            )
        else:
            # [b, cp*s, np//cp, hn] -> [b*cp*s, np//cp, hn]
            q, k, v = [x.view(-1, *x.shape[-2:]) for x in [q, k, v]]
3934
            fa_outputs = flash_attn_fwd(
3935
3936
3937
3938
3939
3940
3941
3942
                q,
                k,
                v,
                cu_seqlens_q,
                cu_seqlens_kv,
                max_seqlen_q,
                max_seqlen_kv,
                causal=causal,
3943
                **fa_forward_kwargs,
3944
            )
3945
3946
            out, softmax_lse = fa_outputs[4], fa_outputs[5]
            rng_state = fa_outputs[7] if not _use_flash_attn_3 else None
3947
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
3993
3994
3995
3996
3997
3998
3999
4000
4001
4002
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
4021
4022
4023
4024
4025
4026
4027
4028
4029
4030
4031
4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
            aux_ctx_tensors = [softmax_lse, rng_state]
            # [b*cp*s, np//cp, hn] -> [b, cp*s, np//cp, hn]
            out = out.view(batch_size, -1, *out.shape[-2:])

        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size, out.device, False)
        out = flash_attn_a2a_communicate(
            out, chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, False
        )

        if use_fused_attention:
            if qkv_format == "bshd":
                # [b*s, np, hn] -> [b, s, np, hn]
                out = out.view(batch_size, -1, *out.shape[-2:])
            elif qkv_format == "sbhd":
                # [s*b, np, hn] -> [s, b, np, hn]
                out = out.view(-1, batch_size, *out.shape[-2:])

        if fp8:
            if fp8_meta["recipe"].fp8_mha:
                out_fp8 = Float8Tensor(
                    data=out,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q_fp8.dtype,
                )
                out = out_fp8._data
                out_ret = out_fp8
            else:
                out_f16 = cast_from_fp8(
                    out,
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    TE_DType[q_f16.dtype],
                )
                out_ret = out_f16
        else:
            out_ret = out

        if fp8:
            if int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                q_save, k_save, v_save, out_save = q, k, v, out
            elif fp8_meta["recipe"].fp8_mha:
                q_fp8, k_fp8, v_fp8 = [
                    Float8Tensor(
                        data=x,
                        fp8_meta=fp8_meta,
                        fp8_meta_forward=True,
                        fp8_meta_index=META_QKV,
                        fp8_dtype=fp8_dtype_forward,
                        dtype=out_fp8.dtype,
                    )
                    for x in [q, k, v]
                ]
                q_save, k_save, v_save, out_save = q_fp8, k_fp8, v_fp8, out_fp8
            else:
                q_save, k_save, v_save, out_save = q_f16, k_f16, v_f16, out_f16
        else:
            q_save, k_save, v_save, out_save = q, k, v, out

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
            fp8_fwd_scales = fp8_meta["scaling_fwd"].scale.clone()
            fp8_fwd_scale_invs = fp8_meta["scaling_fwd"].scale_inv.clone()
        else:
            fp8_fwd_scales, fp8_fwd_scale_invs = None, None

        ctx.save_for_backward(
            q_save,
            k_save,
            v_save,
            out_save,
            cu_seqlens_q,
            cu_seqlens_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            fp8_fwd_scales,
            fp8_fwd_scale_invs,
            *aux_ctx_tensors,
        )
        ctx.batch_size = batch_size
        ctx.cp_group = cp_group
        ctx.cp_stream = cp_stream
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.softmax_scale = softmax_scale
        ctx.qkv_format = qkv_format
        ctx.attn_mask_type = attn_mask_type
        ctx.attn_bias_type = attn_bias_type
        ctx.deterministic = deterministic
        ctx.window_size = window_size
        ctx.use_fused_attention = use_fused_attention
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
        return out_ret

    @staticmethod
    def backward(ctx, dout):
4047
        # pylint: disable=missing-function-docstring
4048
4049
4050
4051
4052
4053
4054
4055
4056
4057
4058
4059
4060
        cp_size = get_distributed_world_size(ctx.cp_group)

        q, k, v, out = ctx.saved_tensors[:4]
        cu_seqlens_q, cu_seqlens_kv, cu_seqlens_q_padded, cu_seqlens_kv_padded = ctx.saved_tensors[
            4:8
        ]
        fp8_fwd_scales, fp8_fwd_scale_invs = ctx.saved_tensors[8:10]
        aux_ctx_tensors = ctx.saved_tensors[10:]

        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format
        causal = "causal" in ctx.attn_mask_type
        seq_dim = ctx.qkv_format.index("s")

4061
4062
4063
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
        fused_attn_qkv_dtype = None
4064
4065
4066
4067
4068
4069
4070
4071
4072
4073
4074
4075
4076
4077
4078
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
4089
4090
4091
4092
4093
4094
4095
4096
4097
4098
4099
4100
4101
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114
        if ctx.fp8:
            if ctx.use_fused_attention:
                fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
                fp8_dtype_backward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=False)
                fused_attn_qkv_dtype = fp8_dtype_forward
                fused_attn_dqkv_dtype = fp8_dtype_backward
                fused_attn_backend = FusedAttnBackend["FP8"]
                if ctx.fp8_meta["recipe"].fp8_mha:
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = dout._scale_inv
                    dout_fp8 = dout
                    dout = dout_fp8._data
                else:
                    dout_f16 = dout
                    dout = cast_to_fp8(
                        dout_f16, ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                    )
                fp8_meta_kwargs = {}
                fp8_meta_kwargs["d_scale_qkv"] = fp8_fwd_scale_invs[META_QKV]
                fp8_meta_kwargs["d_scale_s"] = fp8_fwd_scale_invs[META_S]
                fp8_meta_kwargs["d_scale_o"] = fp8_fwd_scale_invs[META_O]
                fp8_meta_kwargs["d_scale_do"] = ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO]
                fp8_meta_kwargs["d_scale_dp"] = ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP]
                fp8_meta_kwargs["q_scale_s"] = fp8_fwd_scales[META_S]
                fp8_meta_kwargs["q_scale_dp"] = ctx.fp8_meta["scaling_bwd"].scale[META_DP]
                fp8_meta_kwargs["q_scale_dqkv"] = ctx.fp8_meta["scaling_bwd"].scale[META_DQKV]
                fp8_meta_kwargs["amax_dp"] = ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP]
                fp8_meta_kwargs["amax_dqkv"] = ctx.fp8_meta["scaling_bwd"].amax_history[0][
                    META_DQKV
                ]
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            if ctx.fp8_meta is not None and ctx.fp8_meta["recipe"].fp8_mha:
                assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                q, k, v, out, dout = [x.from_float8(x.dtype) for x in [q, k, v, out, dout]]
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_qkv_dtype = TE_DType[q.dtype]
                fused_attn_dqkv_dtype = TE_DType[dout.dtype]
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

        if not ctx.use_fused_attention:
            out = out.view(ctx.batch_size, -1, *out.shape[-2:])
        dout = dout.view(*out.shape)

        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size, out.device, True)
        out, dout = flash_attn_a2a_communicate(
            [out, dout], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, True
        )

4115
        flash_attn_bwd = None
4116
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
4127
4128
4129
4130
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
            if _use_flash_attn_3:
                flash_attn_bwd = flash_attn_varlen_bwd_v3
                fa_backward_kwargs["window_size"] = ctx.window_size
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
                flash_attn_bwd = flash_attn_varlen_bwd
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
                if _flash_attn_2_3_plus:
                    fa_backward_kwargs["window_size"] = ctx.window_size
                if _flash_attn_2_4_plus:
                    fa_backward_kwargs["alibi_slopes"] = None
                if _flash_attn_2_4_1_plus:
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
4131
4132
4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161

        if ctx.use_fused_attention:
            dq, dk, dv, _ = fused_attn_bwd(
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                out,
                dout,
                fused_attn_qkv_dtype,
                fused_attn_dqkv_dtype,
                aux_ctx_tensors,
                fused_attn_backend,
                cu_seqlens_q_padded=cu_seqlens_q_padded,
                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                attn_scale=ctx.softmax_scale,
                dropout=ctx.dropout_p,
                qkv_layout=qkv_layout,
                attn_mask_type=ctx.attn_mask_type,
                attn_bias_type=ctx.attn_bias_type,
                window_size=ctx.window_size,
                deterministic=ctx.deterministic,
                **fp8_meta_kwargs,
            )
        else:
            softmax_lse, rng_state = aux_ctx_tensors
            out, dout = [x.view(-1, *x.shape[-2:]) for x in [out, dout]]
            dq, dk, dv = [torch.empty_like(x) for x in [q, k, v]]
4162
4163
4164
            if not _use_flash_attn_3:
                fa_backward_kwargs["rng_state"] = rng_state
            flash_attn_bwd(
4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
                dout,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dk,
                dv,
                cu_seqlens_q,
                cu_seqlens_kv,
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
4178
4179
                causal=causal,
                **fa_backward_kwargs,
4180
4181
4182
4183
4184
4185
4186
4187
            )
            dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]

        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering(cp_size, q.device, False)
        dq, dk, dv = flash_attn_a2a_communicate(
            [dq, dk, dv], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, False
        )

4188
        if ctx.qkv_format == "bshd":
4189
            dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
4190
        elif ctx.qkv_format == "sbhd":
4191
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
            dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

        if ctx.fp8:
            if ctx.fp8_meta["recipe"].fp8_mha:
                dq, dk, dv = [
                    Float8Tensor(
                        data=x,
                        fp8_meta=ctx.fp8_meta,
                        fp8_meta_forward=False,
                        fp8_meta_index=META_DQKV,
                        fp8_dtype=fp8_dtype_backward,
                        dtype=dout_fp8.dtype,
                    )
                    for x in [dq, dk, dv]
                ]
            else:
                dq, dk, dv = [
                    cast_from_fp8(
                        x,
                        ctx.fp8_meta["scaling_bwd"],
                        META_DQKV,
                        fp8_dtype_backward,
                        TE_DType[dout_f16.dtype],
                    )
                    for x in [dq, dk, dv]
                ]
4217
4218
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4240
4241
4242
            None,
            None,
            None,
4243
4244
4245
        )


4246
def attn_forward_func_with_cp(
4247
4248
4249
4250
4251
    is_training,
    q,
    k,
    v,
    cu_seqlens_q,
4252
    cu_seqlens_kv,
4253
    max_seqlen_q,
4254
    max_seqlen_kv,
4255
4256
    cu_seqlens_q_padded,
    cu_seqlens_kv_padded,
4257
4258
4259
4260
    dropout_p,
    cp_group,
    cp_global_ranks,
    cp_stream,
4261
    cp_comm_type,
4262
4263
4264
4265
4266
4267
4268
    softmax_scale=None,
    qkv_format="bshd",
    attn_mask_type="causal",
    attn_bias_type="no_bias",
    attn_bias=None,
    deterministic=False,
    use_fused_attention=False,
4269
    window_size=None,
4270
4271
    fp8=False,
    fp8_meta=None,
4272
) -> torch.Tensor:
4273
4274
4275
4276
    """
    Attention implementation with context parallelism.
    """

4277
4278
4279
4280
4281
4282
4283
4284
4285
4286
4287
4288
4289
4290
4291
4292
    if cp_comm_type == "a2a+p2p":
        assert isinstance(
            cp_group, list
        ), "Hierarchical CP implementation needs multi-level CP groups!"
        assert len(cp_group) == 2, "Current implementation only supports two-level CP groups!"
        if get_distributed_world_size(cp_group[0]) == 1:
            cp_group = cp_group[1]
            cp_comm_type = "p2p"
        elif get_distributed_world_size(cp_group[1]) == 1:
            cp_group = cp_group[0]
            cp_comm_type = "a2a"
    else:
        assert isinstance(
            cp_group, dist_group_type
        ), f"Unsupported process group for CP communication type {cp_comm_type}!"

4293
4294
4295
4296
4297
4298
4299
4300
4301
4302
4303
4304
4305
4306
4307
4308
4309
4310
4311
4312
    assert qkv_format in [
        "bshd",
        "sbhd",
        "thd",
    ], f"QKV format of {qkv_format} is not supported with context parallelism!"
    assert (
        qkv_format != "sbhd" or use_fused_attention
    ), "FlashAttention does not support sbhd format!"
    assert (
        qkv_format != "thd"
        or not use_fused_attention
        or attn_mask_type in ["padding", "padding_causal"]
    ), (
        f"Context parallelism is not supported for {attn_mask_type} mask type and "
        f"{qkv_format} format with {'FusedAttention' if use_fused_attention else 'FlashAttention'}!"
    )
    assert attn_bias is None or (use_fused_attention and "padding" not in attn_mask_type), (
        """Attention bias is only supported with FusedAttention and "causal" """
        """or "no_mask" mask types!"""
    )
4313
4314
4315
    assert (
        cu_seqlens_q_padded is not None and cu_seqlens_kv_padded is not None
    ), "cu_seqlens_q_padded and cu_seqlens_kv_padded cannot be None with context parallelism!"
4316
4317
4318

    sliding_window_attn = (
        window_size is not None and window_size != (-1, 0) and window_size != (-1, -1)
4319
    )
4320
4321
4322
4323
4324
    assert (
        not sliding_window_attn
        or cp_comm_type == "a2a"
        or (cp_comm_type == "all_gather" and not use_fused_attention)
    ), "The context parallel running configs cannot support sliding window attetnion!"
4325

4326
4327
4328
4329
4330
4331
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
4344
4345
4346
    args = [
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_kv,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
    ]

4347
    if cp_comm_type in ["p2p", "a2a+p2p"]:
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
        args += [fp8, fp8_meta, cp_group, cp_global_ranks, cp_stream]
        out = AttnFuncWithCPAndKVP2P.apply(*args)
    elif cp_comm_type == "all_gather":
        args.pop(5)
        args.pop(8)
        args += [window_size, cp_group, cp_stream]
        out = AttnFuncWithCPAndKVAllGather.apply(*args)
    elif cp_comm_type == "a2a":
        args += [window_size, fp8, fp8_meta, cp_group, cp_stream]
        out = AttnFuncWithCPAndQKVOA2A.apply(*args)
4358
4359
4360
    else:
        raise ValueError(f"Unsupported communication type: {cp_comm_type}!")

4361
4362
4363
    return out


4364
4365
4366
4367
class RotaryPositionEmbedding(torch.nn.Module):
    """
    Implements Rotary Position Embedding from https://arxiv.org/abs/2104.09864.
    """
4368

4369
4370
4371
    def __init__(
        self,
        dim: int,
4372
        rotary_percent: float = 1.0,
4373
4374
        seq_len_interpolation_factor: Optional[int] = None,
        pretrained_max_position_embeddings: Optional[int] = None,
4375
        rotary_base: float = 10000.0,
4376
4377
4378
4379
4380
4381
    ):
        """
        Parameters
        ----------
        dim: int
            rotary embedding dimension
4382
4383
        rotary_percent: float
            Percent of rotary dimension to use for rotary position embeddings.
4384
4385
4386
4387
4388
4389
4390
        seq_len_interpolation_factor: int
            if not None, discrete positions will be interpolated by this factor via the trick in
            https://arxiv.org/abs/2306.15595
        pretrained_max_position_embeddings: int
            pre-trained max_position_embeddings before position interpolation
        """
        super().__init__()
4391
4392
        if rotary_percent < 1.0:
            dim = int(dim * rotary_percent)
4393
        self.seq_len_interpolation_factor = seq_len_interpolation_factor
4394
        self.rotary_base = rotary_base
4395
        inv_freq = 1.0 / (
4396
            self.rotary_base
4397
4398
4399
4400
4401
            ** (
                torch.arange(0, dim, 2, dtype=torch.float32, device=torch.cuda.current_device())
                / dim
            )
        )
4402
        self.register_buffer("inv_freq", inv_freq)
4403
4404
4405
4406
4407
4408
4409
4410
4411
4412
4413
4414
4415
        self.pretrained_max_position_embeddings = pretrained_max_position_embeddings

    def forward(self, max_seq_len: int, offset: int = 0):
        """
        Create rotary position embedding frequencies

        Parameters
        ----------
        max_seq_len: int
            sequence length of a sample
        offset: int, default = 0
            fixed offset for freqencies
        """
4416
4417
4418
4419
        seq = (
            torch.arange(max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
            + offset
        )
4420

4421
4422
4423
4424
4425
4426
4427
4428
        if (
            self.pretrained_max_position_embeddings is not None
            and self.seq_len_interpolation_factor is not None
        ):
            if (
                max_seq_len
                > self.pretrained_max_position_embeddings * self.seq_len_interpolation_factor
            ):
4429
4430
4431
4432
4433
4434
                # dynamic linear scaling (length > position we have learned)
                seq *= 1 / (max_seq_len / self.pretrained_max_position_embeddings)
            else:
                # fixed linear scaling
                seq *= 1 / self.seq_len_interpolation_factor

4435
        freqs = torch.einsum("i , j -> i j", seq, self.inv_freq)
4436
4437
4438
4439
4440
4441
        # first part even vector components, second part odd vector components,
        #  2 * dim in dimension size
        emb = torch.cat((freqs, freqs), dim=-1)
        # emb [seq_length, .., dim]
        return emb.reshape(emb.size(0), 1, 1, emb.size(1))

4442
4443
4444
4445
4446
4447
4448
4449
4450
4451
4452
4453
4454
4455
4456
4457
4458

class FusedRoPEFunc(torch.autograd.Function):
    """
    Function for FusedRoPE

    This implementation assumes the input tensor to be in `sbhd`, `bshd` or `thd` format and
    the RoPE tensor to be of shape (s, 1, 1, d). It accepts arbitrary memory layouts to avoid
    the expensive `.contiguous()` calls, thus it may not achieve the best memory access pattern.
    """

    @staticmethod
    def forward(
        ctx,
        t: torch.Tensor,
        freqs: torch.Tensor,
        tensor_format: str = "sbhd",
        cu_seqlens: Union[torch.Tensor, None] = None,
4459
4460
        cp_size: int = 1,
        cp_rank: int = 0,
4461
    ) -> torch.Tensor:
4462
        # pylint: disable=missing-function-docstring
4463
4464
        if freqs.dtype != torch.float32:
            freqs = freqs.float()
4465
4466
4467
        if tensor_format == "sbhd":
            output = tex.fused_rope_forward(t, freqs, False)
        elif tensor_format == "bshd":
4468
            output = tex.fused_rope_forward(t.transpose(0, 1), freqs, True).transpose(0, 1)
4469
        elif tensor_format == "thd":
4470
            output = tex.fused_rope_thd_forward(t, cu_seqlens, freqs, cp_size, cp_rank)
4471
4472
4473
4474
        else:
            raise ValueError(f"Unsupported tensor_format: {tensor_format}.")
        ctx.save_for_backward(freqs, cu_seqlens)
        ctx.tensor_format = tensor_format
4475
4476
        ctx.cp_size = cp_size
        ctx.cp_rank = cp_rank
4477
4478
4479
4480

        return output

    @staticmethod
4481
    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
4482
        # pylint: disable=missing-function-docstring
4483
4484
4485
4486
4487
4488
4489
4490
        freqs, cu_seqlens = ctx.saved_tensors
        if ctx.tensor_format == "sbhd":
            grad_input = tex.fused_rope_backward(grad_output, freqs, False)
        elif ctx.tensor_format == "bshd":
            grad_input = tex.fused_rope_backward(
                grad_output.transpose(0, 1), freqs, True
            ).transpose(0, 1)
        elif ctx.tensor_format == "thd":
4491
4492
4493
            grad_input = tex.fused_rope_thd_backward(
                grad_output, cu_seqlens, freqs, ctx.cp_size, ctx.cp_rank
            )
4494
4495
4496
        else:
            raise ValueError(f"Unsupported tensor_format: {ctx.tensor_format}.")

4497
        return grad_input, None, None, None, None, None
4498
4499


4500
4501
4502
4503
4504
4505
4506
4507
4508
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
    """
    change sign so the last dimension becomes [-odd, +even]
    """
    x = x.view(x.shape[:-1] + torch.Size((2, x.shape[-1] // 2)))
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


4509
def apply_rotary_pos_emb(
4510
4511
4512
4513
4514
    t: torch.Tensor,
    freqs: torch.Tensor,
    tensor_format: str = "sbhd",
    fused: bool = False,
    cu_seqlens: Union[torch.Tensor, None] = None,
4515
4516
    cp_size: int = 1,
    cp_rank: int = 0,
4517
) -> torch.Tensor:
4518
    """
4519
    Apply rotary positional embedding tensor to the input tensor.
4520

4521
4522
4523
    Parameters
    ----------
    t: torch.Tensor
4524
        Input tensor of shape `[s, b, h, d]`, `[b, s, h, d]` or `[t, h, d]`, on which
4525
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
        rotary positional embedding will be applied.
    freqs: torch.Tensor
        Rotary positional embedding tensor of shape `[s2, 1, 1, d2]` and dtype 'float',
        with `s2 >= s` and `d2 <= d`.
    fused: bool, default = False
        Whether to use a fused applying RoPE implementation.
    tensor_format: {'sbhd', 'bshd', 'thd'}, default = 'sbhd'
        is `bshd` if `t` is of shape `[bs, seq, ...]`, or `sbhd` if `t` is
        of shape `[seq, bs, ...]`. 'thd' is only supported when `fused` is True.
    cu_seqlens: torch.Tensor, default = None.
        Cumulative sum of sequence lengths in a batch for `t`, with shape [b + 1] and
        dtype torch.int32. Only valid when `tensor_format` is 'thd'.
4537
4538
4539
4540
4541
        Should be `cu_seqlens_padded` when cp_size > 1.
    cp_size: int, default = 1.
        Context parallel world size. Only valid when `tensor_format` is 'thd' and `fused` is True.
    cp_rank: int, default = 0.
        Context parallel rank. Only valid when `tensor_format` is 'thd' and `fused` is True.
4542
    """
4543
4544
4545
4546
    if fused:
        assert (
            tensor_format != "thd" or cu_seqlens is not None
        ), "cu_seqlens must not be None when tensor_format is 'thd'."
4547
        return FusedRoPEFunc.apply(t, freqs, tensor_format, cu_seqlens, cp_size, cp_rank)
4548
4549
4550
4551
4552
4553

    assert tensor_format in ("sbhd", "bshd"), (
        "Only formats `sbhd` or `bshd` are supported for input tensor `t` "
        f"when fused is False, got {tensor_format}."
    )

4554
4555
4556
4557
4558
    max_seq_len = freqs.shape[0]
    cur_seq_len = t.shape[1] if tensor_format == "bshd" else t.shape[0]

    # Only apply the rotary embeddings up to the sequence length of the running
    # input.
4559
4560
4561
    assert (
        cur_seq_len <= max_seq_len
    ), f"Rotary Embeddings only supported up to {max_seq_len} sequence length!"
4562
    freqs = freqs[:cur_seq_len]
4563
    if tensor_format == "bshd":
4564
4565
4566
4567
        freqs = freqs.transpose(0, 1)  # [seq, 1, 1, dim] -> [1, seq, 1, dim]
    # cos/sin first then dtype conversion for better precision
    cos_ = torch.cos(freqs).to(t.dtype)
    sin_ = torch.sin(freqs).to(t.dtype)
4568

4569
4570
4571
4572
4573
4574
    rot_dim = freqs.shape[-1]
    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]

    # first part is cosine component
    # second part is sine component, need to change signs with _rotate_half method
4575
    t = (t * cos_) + (_rotate_half(t) * sin_)
4576
4577
4578
    return torch.cat((t, t_pass), dim=-1)


cyanguwa's avatar
cyanguwa committed
4579
class _SplitAlongDim(torch.autograd.Function):
4580
4581
4582
    """"""

    @staticmethod
4583
4584
4585
4586
4587
    def forward(
        ctx,
        mixed_x_layer: torch.Tensor,
        split_dim: int,
        split_size_or_sections: Union[int, List[int], Tuple[int]],
4588
    ) -> Tuple[torch.Tensor, ...]:
4589
        # pylint: disable=missing-function-docstring
cyanguwa's avatar
cyanguwa committed
4590
4591
        ctx.split_dim = split_dim
        ctx.split_size_or_sections = split_size_or_sections
4592
        if isinstance(mixed_x_layer, Float8Tensor):
4593
4594
4595
4596
4597
4598
            return tuple(
                Float8Tensor.make_like(
                    mixed_x_layer,
                    data=x,
                )
                for x in torch.split(
4599
4600
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
4601
4602
4603
4604
                    dim=split_dim,
                )
            )
        return torch.split(mixed_x_layer, split_size_or_sections, dim=split_dim)
4605
4606

    @staticmethod
4607
    def backward(ctx, *grad_outputs):
4608
        # pylint: disable=missing-function-docstring
4609
4610
        assert len(grad_outputs) > 0, "No gradients received for backprop!"

cyanguwa's avatar
cyanguwa committed
4611
4612
        if isinstance(ctx.split_size_or_sections, (list, tuple)):
            split_sizes = ctx.split_size_or_sections
4613
4614
4615
            assert len(grad_outputs) == len(
                split_sizes
            ), "Unequal number of gradients vs split sections for backprop!"
cyanguwa's avatar
cyanguwa committed
4616
4617
4618
4619
4620
        if isinstance(ctx.split_size_or_sections, int):
            split_sizes = [ctx.split_size_or_sections] * len(grad_outputs)
        dims = len(grad_outputs[0].shape)
        split_dim = (ctx.split_dim + dims) % dims

4621
4622
4623
4624
4625
4626
4627
4628
        if isinstance(grad_outputs[0], Float8Tensor):
            noop_ok = True
            strides = grad_outputs[0].stride()
            data_ptr = grad_outputs[0]._data.untyped_storage().data_ptr()
            shape = list(grad_outputs[0].shape)
            for i, tensor in enumerate(grad_outputs):
                shape_i = shape
                shape_i[split_dim] = split_sizes[i]
4629
4630
4631
4632
4633
4634
4635
                offset_size = sum(split_sizes[:i]) * np.prod(shape[split_dim + 1 :])
                if (
                    tensor.stride() != strides
                    or list(tensor.shape) != shape_i
                    or tensor._data.untyped_storage().data_ptr() != data_ptr
                    or tensor.storage_offset() != offset_size
                ):
4636
4637
4638
                    noop_ok = False
                    break
            if noop_ok:
4639
4640
4641
                ret = torch.Tensor().to(
                    device=grad_outputs[0].device, dtype=grad_outputs[0]._data.dtype
                )
4642
4643
                new_shape = list(shape)
                new_shape[split_dim] = sum(split_sizes)
4644
4645
4646
4647
4648
                ret.set_(
                    grad_outputs[0]._data.untyped_storage(),
                    grad_outputs[0]._data.storage_offset(),
                    new_shape,
                    strides,
4649
4650
4651
4652
                )
                return Float8Tensor.make_like(grad_outputs[0], data=ret), None, None

            grad_outputs_data = [x._data for x in grad_outputs]
4653
4654
4655
4656
4657
4658
4659
            return (
                Float8Tensor.make_like(
                    grad_outputs[0], data=torch.cat(grad_outputs_data, dim=split_dim)
                ),
                None,
                None,
            )
4660
4661
        noop_ok = True
        strides = grad_outputs[0].stride()
4662
        data_ptr = grad_outputs[0].untyped_storage().data_ptr()
cyanguwa's avatar
cyanguwa committed
4663
        shape = list(grad_outputs[0].shape)
4664
        for i, tensor in enumerate(grad_outputs):
cyanguwa's avatar
cyanguwa committed
4665
4666
            shape_i = shape
            shape_i[split_dim] = split_sizes[i]
4667
4668
4669
4670
4671
4672
4673
            offset_size = sum(split_sizes[:i]) * np.prod(shape[split_dim + 1 :])
            if (
                tensor.stride() != strides
                or list(tensor.shape) != shape_i
                or tensor.untyped_storage().data_ptr() != data_ptr
                or tensor.storage_offset() != offset_size
            ):
4674
4675
4676
                noop_ok = False
                break
        if noop_ok:
4677
            ret = torch.Tensor().to(device=grad_outputs[0].device, dtype=grad_outputs[0].dtype)
4678
            new_shape = list(shape)
cyanguwa's avatar
cyanguwa committed
4679
            new_shape[split_dim] = sum(split_sizes)
4680
4681
4682
4683
4684
            ret.set_(
                grad_outputs[0].untyped_storage(),
                grad_outputs[0].storage_offset(),
                new_shape,
                strides,
4685
            )
cyanguwa's avatar
cyanguwa committed
4686
            return ret, None, None
4687

4688
        return torch.cat(grad_outputs, dim=split_dim), None, None
4689
4690
4691
4692
4693
4694
4695
4696
4697


class UnfusedDotProductAttention(torch.nn.Module):
    """Parallel attention w/o QKV and Proj Gemms
    BMM1 -> softmax + dropout -> BMM2
    """

    def __init__(
        self,
4698
        softmax_scale: float,
4699
        attention_type: str = "self",
4700
4701
4702
4703
4704
4705
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        layer_number: Optional[int] = None,
    ) -> None:
        super().__init__()

4706
        self.softmax_scale = softmax_scale
4707
        self.attention_type = attention_type
4708
4709
4710
        self.attention_dropout_ctx = attention_dropout_ctx
        self.layer_number = layer_number

4711
        self.scale_mask_softmax = FusedScaleMaskSoftmax(attention_mask_func)
4712
4713
4714
4715
4716
4717

        # Dropout. Note that for a single iteration, this layer will generate
        # different outputs on different number of parallel partitions but
        # on average it should not be partition dependent.
        self.attention_dropout = torch.nn.Dropout(attention_dropout)

4718
4719
        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
4720
4721
            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None
        )
4722

4723
4724
4725
4726
4727
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
4728
        qkv_layout: str = "sbh3d",
4729
4730
        cu_seqlens_q: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
4731
        attn_mask_type: str = "causal",
4732
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4733
4734
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
4735
        alibi_slopes: Optional[torch.Tensor] = None,
4736
    ) -> torch.Tensor:
4737
        """Unfused attention fprop"""
4738
4739
4740
4741
4742
        assert (
            qkv_layout in QKVLayouts
        ), f"UnfusedDotProductAttention does not support qkv_layout = {qkv_layout}!"
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
        if qkv_format == "bshd":
4743
            # convert to sbhd and use sbhd implementation for now
4744
4745
4746
            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
4747
4748
4749
4750
4751
4752
4753
4754
4755
4756
4757
4758
4759
4760
4761
4762
4763
4764
4765
4766
4767
4768
4769
4770
4771
4772
4773
4774
4775
4776
4777
4778
4779
4780
4781
4782
4783
4784
4785
4786
4787
4788
4789
4790
4791
4792
4793
4794
4795
4796
4797
4798
        batch_size, max_seqlen_q, max_seqlen_kv = (
            query_layer.shape[1],
            query_layer.shape[0],
            key_layer.shape[0],
        )
        if "padding" in attn_mask_type:
            if self.attention_type == "self":
                assert attention_mask.shape == (
                    batch_size,
                    1,
                    1,
                    max_seqlen_q,
                ), "attention_mask should be a single tensor with [b, 1, 1, sq] shape!"
                attention_mask = torch.logical_or(
                    attention_mask.squeeze(1).unsqueeze(3), attention_mask
                )
            else:
                assert (
                    len(attention_mask) == 2
                    and attention_mask[0].shape == (batch_size, 1, 1, max_seqlen_q)
                    and attention_mask[1].shape == (batch_size, 1, 1, max_seqlen_kv)
                ), (
                    "attention_mask should be a tuple of two tensors with shapes "
                    "[b, 1, 1, sq] and [b, 1, 1, skv]!"
                )
                attention_mask = torch.logical_or(
                    attention_mask[0].squeeze(1).unsqueeze(3), attention_mask[1]
                )
            mask = attention_mask.squeeze(1).logical_not()
            actual_seqlens_q = mask[:, :, 0].sum(dim=1)
            actual_seqlens_kv = mask[:, 0, :].sum(dim=1)
            mask = torch.arange(max_seqlen_q, dtype=torch.int32, device="cuda").view(
                1, 1, max_seqlen_q, 1
            ) - torch.arange(max_seqlen_kv, dtype=torch.int32, device="cuda").view(
                1, 1, 1, max_seqlen_kv
            )
            if attn_mask_type == "padding_causal":
                attention_mask = torch.logical_or(
                    torch.where(mask.view(1, 1, max_seqlen_q, max_seqlen_kv) < 0, 1, 0),
                    attention_mask,
                )
            if attn_mask_type == "padding_causal_bottom_right":
                attention_mask = torch.logical_or(
                    torch.where(
                        mask.expand(batch_size, 1, max_seqlen_q, max_seqlen_kv)
                        + (actual_seqlens_kv - actual_seqlens_q).view(batch_size, 1, 1, 1)
                        < 0,
                        1,
                        0,
                    ),
                    attention_mask,
                )
4799

4800
        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]
4801
        apply_qk_layer_scaling = self.apply_qk_layer_scaling and key_layer.dtype == torch.float16
4802
4803
4804
4805
4806
4807
4808
4809
4810

        # [b, np, sq, sk]
        output_size = (
            query_layer.size(1),
            query_layer.size(2),
            query_layer.size(0),
            key_layer.size(0),
        )

4811
        if key_layer.shape[2] != query_layer.shape[2]:
4812
4813
4814
            assert (
                query_layer.shape[2] % key_layer.shape[2] == 0
            ), "The number of attention heads must be divisible by the number of GQA groups!"
4815
            key_layer = key_layer.repeat_interleave(
4816
4817
                int(query_layer.shape[2] / key_layer.shape[2]), dim=2
            )
4818
            value_layer = value_layer.repeat_interleave(
4819
4820
                int(query_layer.shape[2] / value_layer.shape[2]), dim=2
            )
4821

4822
        # [sq, b, np, hn] -> [sq, b * np, hn]
4823
        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
4824
4825
4826
4827
        # [sk, b, np, hn] -> [sk, b * np, hn]
        key_layer = key_layer.reshape(output_size[3], output_size[0] * output_size[1], -1)

        # preallocting result tensor: [b * np, sq, sk]
4828
4829
        # WAR to set dtype to FP32 as ONNX lacks BF16 support for ConstantOfShape operator
        is_bf16 = query_layer.dtype == torch.bfloat16
4830
4831
4832
4833
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
4834
            dtype=torch.float32 if is_in_onnx_export_mode() and is_bf16 else query_layer.dtype,
4835
4836
4837
            device=torch.cuda.current_device(),
        )

4838
4839
4840
        if is_in_onnx_export_mode() and is_bf16:
            matmul_result = matmul_result.bfloat16()

4841
        scale = self.softmax_scale
4842
        if apply_qk_layer_scaling:
4843
            scale /= self.layer_number
4844
4845

        # Raw attention scores. [b * np, sq, sk]
4846
4847
4848
4849
4850
4851
        if core_attention_bias_type == "no_bias":
            matmul_result = torch.baddbmm(
                matmul_result,
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
4852
                alpha=scale,
4853
            ).view(*output_size)
4854
4855
4856
4857
4858
4859
4860

        elif core_attention_bias_type == "pre_scale_bias":
            assert core_attention_bias is not None, "core_attention_bias should not be None!"
            matmul_result = torch.bmm(
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
            )
4861
            matmul_result = matmul_result.view(*output_size) + core_attention_bias
4862
            matmul_result *= scale
4863

4864
4865
4866
4867
        elif core_attention_bias_type in ["post_scale_bias", "alibi"]:
            if core_attention_bias_type == "post_scale_bias":
                assert core_attention_bias is not None, "core_attention_bias should not be None!"
            if core_attention_bias_type == "alibi":
4868
                _, core_attention_bias = get_alibi(
4869
4870
4871
                    output_size[1],
                    output_size[2],
                    output_size[3],
4872
4873
                    actual_seqlens_q=actual_seqlens_q if "padding" in attn_mask_type else None,
                    actual_seqlens_kv=actual_seqlens_kv if "padding" in attn_mask_type else None,
4874
4875
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
4876
                )
4877
4878
4879
4880
4881
            matmul_result = torch.baddbmm(
                matmul_result,
                query_layer.transpose(0, 1),  # [b * np, sq, hn]
                key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
                beta=0.0,
4882
                alpha=scale,
4883
            )
4884
4885
            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
4886
            )
4887
4888
4889

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
4890
        attention_probs = self.scale_mask_softmax(
4891
            matmul_result, attention_mask, attn_mask_type, softmax_scale
4892
        )
4893

4894
4895
4896
4897
4898
        # mask out the pad positions in softmax results, mostly for the rows (pad tokens from q)
        # the columns (pad tokens from k) are already zeroed out during softmax
        if "padding" in attn_mask_type:
            attention_probs = attention_probs.masked_fill(attention_mask, 0)

4899
4900
4901
4902
4903
4904
4905
4906
4907
4908
4909
4910
4911
4912
4913
        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        with self.attention_dropout_ctx():
            attention_probs = self.attention_dropout(attention_probs)

        # value_layer -> context layer.
        # [sk, b, np, hn] --> [b, np, sq, hn]
        output_size = (
            value_layer.size(1),
            value_layer.size(2),
            query_layer.size(0),
            value_layer.size(3),
        )

        # change view [sk, b * np, hn]
4914
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
4915
4916

        # change view [b * np, sq, sk]
4917
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
4918
4919
4920
4921
4922
4923
4924

        # matmul: [b * np, sq, hn]
        context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))

        # change view [b, np, sq, hn]
        context_layer = context_layer.view(*output_size)

4925
        if qkv_format == "sbhd":
4926
4927
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
4928

4929
4930
4931
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

4932
        if qkv_format == "bshd":
4933
4934
4935
4936
4937
            # [b, np, sq, hn] --> [b, sq, np, hn]
            context_layer = context_layer.permute(0, 2, 1, 3).contiguous()

            # [b, sq, np, hn] --> [b, sq, hp]
            context_layer = context_layer.view(batch_size, seqlen, -1)
4938
4939
4940
4941
4942
4943

        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
4944
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
4945
4946

    @staticmethod
4947
4948
4949
4950
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
4951
        value_layer: torch.Tensor,
4952
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
4953
        # pylint: disable=missing-function-docstring
4954
4955
4956
4957
4958
4959
4960
4961
4962
4963
4964
        # All inputs received are non-contiguous tensors.
        # The `query_layer` tensor is used to access the
        # full memory region of the QKV tensor.
        qkv = tex.fa_prepare_fwd(query_layer)
        q, k, v = split_tensor_along_dim(qkv, 0, 3)
        query_layer = torch.squeeze(q, 0)
        key_layer = torch.squeeze(k, 0)
        value_layer = torch.squeeze(v, 0)
        return query_layer, key_layer, value_layer

    @staticmethod
4965
4966
4967
4968
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
4969
        dv: torch.Tensor,
4970
    ) -> Tuple[Union[torch.Tensor, None], ...]:
4971
        # pylint: disable=missing-function-docstring
4972
4973
4974
4975
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

4976

4977
def get_qkv_layout(
4978
4979
4980
4981
4982
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    qkv_format: str = "sbhd",
) -> str:
4983
    """Get qkv layout.
4984

4985
4986
4987
4988
4989
4990
4991
4992
4993
4994
4995
    Parameters
    ----------
    q: torch.Tensor
        Query tensor.
    k: torch.Tensor
        Key tensor.
    v: torch.Tensor
        Value tensor.
    qkv_format: str, default = `sbhd`
        Dimension format for `q`, `k` and `v`, {`sbhd`, `bshd`, `thd`}. `s` stands for
        the sequence length dimension, `b` batch size, `h` the number of attention heads,
4996
        `d` head size, and `t` the total number of tokens in a batch, i.e.
4997
4998
4999
5000
5001
5002
5003
5004
5005
5006
5007
5008
5009
5010
5011
        `t = sum(s_i) for i = 0...b-1`.

    Returns
    ----------
    qkv_layout: str
       Memory layout of `q`, `k` and `v`. Each `qkv_format` can be mapped to one of five
       memory layouts. For example, `sb3hd` means `q`, `k`, `v` are created as one chunk
       of memory and that they are interleaved in the `2`nd dimension. `sbhd_sbh2d` means
       `q` and `kv` are created in two chunks and that `q` itself is contiguous and `k`, `v`
       are interleaved with each other in the `3`rd dimension, `k = kv[:,:,:,0,:]` and
       `v = kv[:,:,:,1,:]`.
       Mapping:
       `sbhd`: {`sb3hd`, `sbh3d`, `sbhd_sb2hd`, `sbhd_sbh2d`, `sbhd_sbhd_sbhd`}
       `bshd`: {`bs3hd`, `bsh3d`, `bshd_bs2hd`, `bshd_bsh2d`, `bshd_bshd_bshd`}
       `thd` : {`t3hd`, `th3d`, `thd_t2hd`, `thd_th2d`, `thd_thd_thd`}
5012
5013
5014
5015
5016
5017
5018
5019
5020
    q: torch.Tensor
        Query tensor. It may be different from input `q` as we try to fit tensors to
        a supported layout.
    k: torch.Tensor
        Key tensor. It may be different from input `k` as we try to fit tensors to
        a supported layout.
    v: torch.Tensor
        Value tensor. It may be different from input `v` as we try to fit tensors to
        a supported layout.
5021
    """
5022

5023
5024
    check_last_dim_contiguous = all(x.stride(-1) == 1 for x in [q, k, v])
    assert check_last_dim_contiguous, "q, k and v must have stride 1 in their last dimension!"
5025

5026
    def run_iteratively(q, k, v):
5027
        # check data pointers
5028
5029
        data_ptr = q.untyped_storage().data_ptr()
        check_ptrs_qkv = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k, v])
5030
        check_ptrs_qk = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k])
5031
5032
5033
        data_ptr = k.untyped_storage().data_ptr()
        check_ptrs_kv = all(x.untyped_storage().data_ptr() == data_ptr for x in [k, v])

5034
5035
5036
5037
5038
5039
5040
        # check tensor shapes
        shape = q.shape
        check_shapes_qkv = all(shape == x.shape for x in [q, k, v])
        shape = k.shape
        check_shapes_kv = shape[:-1] == v.shape[:-1]

        # check tensor strides
5041
5042
        stride = q.stride()
        check_strides_qkv = all(stride == x.stride() for x in [q, k, v])
5043
5044
        check_strides_kv = tuple(sk / k.shape[-1] for sk in k.stride()[:-1]) == tuple(
            sv / v.shape[-1] for sv in v.stride()[:-1]
5045
        )
5046

5047
5048
5049
5050
5051
5052
        # check tensor offsets for h3d and 3hd layouts
        prod_h_d = q.shape[-1] * q.shape[-2]
        check_3hd_offsets = all(x.storage_offset() == i * prod_h_d for i, x in enumerate([q, k, v]))
        check_h3d_offsets = all(
            x.storage_offset() == i * q.shape[-1] for i, x in enumerate([q, k, v])
        )
5053

5054
5055
5056
5057
5058
5059
        # check tensor offsets for hd_h2d and hd_2hd layouts
        prod_all_dims = [np.prod(x.shape) for x in [q, k]]
        offset = prod_all_dims[0] if check_ptrs_qkv else 0
        prod_h_d = k.shape[-1] * k.shape[-2]
        check_2hd_offsets = all(
            x.storage_offset() == (offset + i * prod_h_d) for i, x in enumerate([k, v])
5060
        )
5061
5062
        check_h2d_offsets = all(
            x.storage_offset() == (offset + i * k.shape[-1]) for i, x in enumerate([k, v])
5063
        )
5064

5065
5066
5067
5068
5069
5070
5071
5072
5073
5074
        # check tensor offsets for hd_hd_hd layouts
        check_hd_offsets_qkv = (
            all(x.storage_offset() == sum(prod_all_dims[:i]) for i, x in enumerate([q, k, v]))
            if check_ptrs_qkv
            else all(x.storage_offset() == 0 for i, x in enumerate([q, k, v]))
        )
        check_hd_offsets_qk = (
            all(x.storage_offset() == sum(prod_all_dims[:i]) for i, x in enumerate([q, k]))
            if not check_ptrs_qkv and check_ptrs_qk
            else all(x.storage_offset() == 0 for i, x in enumerate([q, k]))
5075
        )
5076
5077
5078
5079
        check_hd_offsets_kv = (
            all(x.storage_offset() == sum(prod_all_dims[1 : i + 1]) for i, x in enumerate([k, v]))
            if not check_ptrs_qkv and check_ptrs_kv
            else all(x.storage_offset() == 0 for i, x in enumerate([k, v]))
5080
        )
5081

5082
        if check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_3hd_offsets:
5083
            # sb3hd, bs3hd, t3hd
5084
            # one chunk of memory, qkv, with q, k, v interleaved at dim=-3 in qkv
5085
            qkv_layout = qkv_format[:-2] + "3" + qkv_format[-2:]
5086
        elif check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_h3d_offsets:
5087
            # sbh3d, bsh3d, th3d
5088
            # one chunk of memory, qkv, with q, k, v interleaved at dim=-2 in qkv
5089
            qkv_layout = qkv_format[:-1] + "3" + qkv_format[-1:]
5090
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_2hd_offsets:
5091
            # sbhd_sb2hd, bshd_bs2hd, thd_t2hd
5092
5093
5094
            # two chunks of memory, q and kv, with k, v interleaved at dim=-3 in kv
            # q and kv may be disjoint or consecutive in memory, and when consecutive, they may
            # have the same data pointer, i.e. check_ptrs_qkv=True
5095
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
5096
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_h2d_offsets:
5097
            # sbhd_sbh2d, bshd_bsh2d, thd_th2d
5098
5099
5100
            # two chunks of memory, q and kv, with k, v interleaved at dim=-2 in kv
            # q and kv may be disjoint or consecutive in memory, and when consecutive, they may
            # have the same data pointer, i.e. check_ptrs_qkv=True
5101
            qkv_layout = qkv_format + "_" + qkv_format[:-1] + "2" + qkv_format[-1:]
5102
5103
5104
5105
5106
        elif (
            check_strides_kv
            and check_shapes_kv
            and (check_hd_offsets_qkv or check_hd_offsets_kv or check_hd_offsets_qk)
        ):
5107
            # sbhd_sbhd_sbhd, bshd_bshd_bshd, thd_thd_thd
5108
5109
5110
            # three chunks of memory, q, k and v, which may be disjoint or consecutive, and
            # when consecutive, they may have the same data pointer, i.e. check_ptrs_qkv=True or
            # check_ptrs_qk=True or check_ptrs_kv=True
5111
            qkv_layout = "_".join(list([qkv_format]) * 3)
5112
        else:
5113
            qkv_layout = "not_supported"
5114
5115
5116
5117

        return qkv_layout

    qkv_layout = run_iteratively(q, k, v)
5118
    if qkv_layout == "not_supported":
5119
5120
5121
        # force q,k,v to be contiguous and run get_layout again
        q, k, v = [x.contiguous() for x in [q, k, v]]
        qkv_layout = run_iteratively(q, k, v)
5122
    if qkv_layout == "not_supported":
5123
        raise RuntimeError("The provided qkv memory layout is not supported!")
5124

5125
    return qkv_layout, q, k, v
5126

5127

5128
def check_set_window_size(
5129
5130
5131
    attn_mask_type: str,
    window_size: Tuple[int, int] = None,
):
5132
5133
5134
5135
5136
5137
5138
5139
    """Check if sliding window size is compliant with attention mask type.
    If not, set it to the appropriate size.

         attn_mask_type                              |   window_size
    -------------------------------------------------------------------------
    no_mask, padding, arbitrary                      | (-1, -1) or (>=0, >=0)
    causal, padding_causal                           | (-1,  0) or (>=0, 0)
    causal_bottom_right, padding_causal_bottom_right | (-1,  0) or (>=0, 0)
5140
    """
5141
    orig_window_size = window_size
5142
    if "causal" in attn_mask_type:
5143
        if orig_window_size is None:
5144
            window_size = (-1, 0)
5145
5146
5147
        elif orig_window_size == (-1, -1) or (
            orig_window_size[0] >= 0 and orig_window_size[1] != 0
        ):
5148
5149
5150
5151
            window_size = (orig_window_size[0], 0)
            warnings.warn(
                "window_size should be (-1, 0) or (>=0, 0) for attn_mask_type=" + attn_mask_type
            )
5152
        elif orig_window_size != (-1, 0) and (orig_window_size[0] < 0 or orig_window_size[1] != 0):
5153
5154
5155
5156
            assert False, (
                "window_size should be (-1, 0) or (>=0, 0) for attn_mask_type=" + attn_mask_type
            )
    elif attn_mask_type in ["no_mask", "padding", "arbitrary"]:
5157
5158
5159
        if orig_window_size is None:
            window_size = (-1, -1)
        elif orig_window_size == (-1, 0):
5160
            window_size = (-1, -1)
5161
5162
5163
            warnings.warn(
                "window_size should be (-1, -1) or (>=0, >=0) for attn_mask_type=" + attn_mask_type
            )
5164
        elif orig_window_size != (-1, -1) and (orig_window_size[0] < 0 or orig_window_size[1] < 0):
5165
5166
5167
5168
5169
            assert False, (
                "window_size should be (-1, -1) or (>=0, >=0) for attn_mask_type=" + attn_mask_type
            )
    else:
        assert False, "Invalid attn_mask_type: " + attn_mask_type
5170
    return window_size
5171

5172

5173
class FlashAttention(torch.nn.Module):
5174
    """Dot product attention, using HazyResearch flash-attn package:
5175
    https://github.com/Dao-AILab/flash-attention
5176
5177
5178
5179
    """

    def __init__(
        self,
5180
        softmax_scale: float,
5181
5182
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
5183
5184
        attention_type: str = "self",
        layer_number: Optional[int] = None,
5185
        deterministic: bool = False,
5186
5187
5188
    ) -> None:
        super().__init__()

5189
5190
5191
5192
5193
5194
5195
        if _flash_attn_is_installed:
            assert (
                _flash_attn_version >= _flash_attn_version_required
            ), f"FlashAttention minimum version {_flash_attn_version_required} is required."
            assert (
                _flash_attn_version <= _flash_attn_max_version
            ), f"FlashAttention maximum version {_flash_attn_max_version} is supported."
5196

5197
        self.softmax_scale = softmax_scale
5198
5199
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
5200
5201
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
5202
        self.deterministic = deterministic
5203
5204
5205
5206
        self.logger = logging.getLogger("FlashAttention")
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
5207
5208
5209
5210
5211
5212

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5213
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5214
5215
5216
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5217
5218
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5219
        attn_mask_type: str = "causal",
5220
        window_size: Optional[Tuple[int, int]] = None,
5221
        alibi_slopes: Optional[torch.Tensor] = None,
5222
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5223
        cp_global_ranks: List[int] = None,
5224
        cp_stream: torch.cuda.Stream = None,
5225
        cp_comm_type: str = "p2p",
5226
5227
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
5228
5229
5230
    ) -> torch.Tensor:
        """flash-attn fprop"""

5231
5232
5233
5234
        assert all(
            x.dtype in [torch.float16, torch.bfloat16] or isinstance(x, Float8Tensor)
            for x in [query_layer, key_layer, value_layer]
        ), "FlashAttention only supports FP16 and BF16 data types, or Float8Tensors."
5235
5236
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
5237
        ), "FlashAttention currently only supports CUDA tensors."
5238
5239
        assert (
            qkv_layout in QKVLayouts
5240
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
5241

5242
5243
5244
5245
5246
5247
        cp_size = 1
        if isinstance(cp_group, dist_group_type):
            cp_size = get_distributed_world_size(cp_group)
        elif isinstance(cp_group, list):
            for group in cp_group:
                cp_size *= get_distributed_world_size(group)
5248
        context_parallel = cp_size > 1
5249

5250
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
5251

5252
5253
5254
5255
5256
5257
5258
5259
5260
5261
5262
5263
5264
        if all(not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]):
            if qkv_format == "sbhd":
                # For now just 128, will make it more general in the future
                if (
                    query_layer.shape[-1] == 128
                    and query_layer.shape[0] * query_layer.shape[1] >= 512
                    and qkv_layout == "sbh3d"
                ):
                    query_layer, key_layer, value_layer = _PrepareQKVForFA.apply(
                        query_layer, key_layer, value_layer
                    )
                else:
                    query_layer, key_layer, value_layer = [
5265
                        x.transpose(0, 1) for x in (query_layer, key_layer, value_layer)
5266
                    ]
5267
            if context_parallel:
5268
                query_layer, key_layer, value_layer = [
5269
5270
5271
5272
5273
                    x.contiguous() for x in (query_layer, key_layer, value_layer)
                ]
        else:
            if qkv_format == "sbhd":
                query_layer._data, key_layer._data, value_layer._data = [
5274
                    x.transpose(0, 1)
5275
5276
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
5277
5278
5279
5280
                query_layer, key_layer, value_layer = [
                    Float8Tensor.make_like(x, data=x._data)
                    for x in (query_layer, key_layer, value_layer)
                ]
5281
            if context_parallel:
5282
5283
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
5284
                ]
5285

5286
        batch_size = query_layer.shape[0]
5287

5288
        if qkv_format in ["sbhd", "bshd"]:
5289
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
5290
5291
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
5292
5293
5294

            if "padding" in attn_mask_type:
                assert not context_parallel, "Padding mask not supported with context parallelism!"
5295
5296
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
5297
                    x.reshape(x.shape[0] * x.shape[1], *x.shape[2:])
5298
5299
5300
5301
5302
5303
5304
                    for x in [query_layer, key_layer, value_layer]
                ]

                if self.attention_type == "self":
                    assert (
                        max_seqlen_q == max_seqlen_kv
                    ), "Maximum sequence length for Q and KV should be the same."
5305
                    if cu_seqlens_q is None:
5306
5307
5308
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
5309
5310
5311
5312
5313
5314
                        cu_seqlens_q, indices_q = get_cu_seqlens_and_indices(attention_mask)
                    else:
                        indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                    cu_seqlens_kv = cu_seqlens_q
                    query_layer, key_layer, value_layer = PackTensors.apply(
                        indices_q, query_layer, key_layer, value_layer
5315
5316
                    )
                else:
5317
                    if cu_seqlens_q is None or cu_seqlens_kv is None:
5318
5319
5320
5321
5322
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
                        cu_seqlens_q, indices_q = get_cu_seqlens_and_indices(attention_mask[0])
                        cu_seqlens_kv, indices_kv = get_cu_seqlens_and_indices(attention_mask[1])
5323
5324
5325
5326
                    else:
                        indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                        indices_kv = get_indices(max_seqlen_kv, cu_seqlens_kv)
                    query_layer = PackTensors.apply(indices_q, query_layer)
5327
                    key_layer, value_layer = PackTensors.apply(indices_kv, key_layer, value_layer)
5328
            else:
5329
5330
5331
5332
5333
5334
5335
5336
5337
5338
5339
5340
5341
                # Cumulative sequence lengths for unpadded data
                if cu_seqlens_q is None:
                    cu_seqlens_q = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
                    cu_seqlens_kv = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
5342
5343
5344
5345
        elif qkv_format == "thd":
            assert (
                cu_seqlens_q is not None and cu_seqlens_kv is not None
            ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
5346
5347
5348
5349
5350
5351
            if max_seqlen_q is None:
                seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                max_seqlen_q = seqlens_q.max().item()
            if max_seqlen_kv is None:
                seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                max_seqlen_kv = seqlens_kv.max().item()
5352

5353
5354
5355
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
5356
5357
5358
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
5359
            with self.attention_dropout_ctx():
5360
                output = attn_forward_func_with_cp(
5361
5362
5363
5364
5365
5366
5367
5368
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
5369
5370
                    cu_seqlens_q,
                    cu_seqlens_kv,
5371
                    self.attention_dropout if self.training else 0.0,
5372
5373
5374
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
5375
                    cp_comm_type,
5376
                    softmax_scale=self.softmax_scale,
5377
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
5378
                    attn_mask_type=attn_mask_type,
5379
                    deterministic=self.deterministic,
5380
                    window_size=window_size,
5381
5382
                )
        else:
5383
5384

            from .cpu_offload import CPUOffloadEnabled
5385

5386
5387
5388
5389
5390
5391
            if CPUOffloadEnabled:
                tensor_list = [query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_kv]
                for tensor in tensor_list:
                    if tensor is not None:
                        tensor.activation_offloading = True

5392
            with self.attention_dropout_ctx():
5393
                fa_optional_forward_kwargs = {}
5394
5395
                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
5396
5397
5398
5399
                if _flash_attn_2_4_plus:
                    fa_optional_forward_kwargs["alibi_slopes"] = alibi_slopes
                if _flash_attn_2_4_1_plus:
                    fa_optional_forward_kwargs["deterministic"] = self.deterministic
5400
5401
5402
5403
                fa_optional_forward_args_thd = []
                if qkv_format in ["bshd", "sbhd"] and "padding" not in attn_mask_type:
                    func = flash_attn_func if not _use_flash_attn_3 else flash_attn_func_v3
                else:
5404
5405
                    if _flash_attn_2_5_7_plus:
                        fa_optional_forward_kwargs["block_table"] = None
5406
5407
5408
5409
5410
5411
5412
5413
5414
5415
                    func = (
                        flash_attn_varlen_func
                        if not _use_flash_attn_3
                        else flash_attn_varlen_func_v3
                    )
                    fa_optional_forward_args_thd.append(cu_seqlens_q)
                    fa_optional_forward_args_thd.append(cu_seqlens_kv)
                    fa_optional_forward_args_thd.append(max_seqlen_q)
                    fa_optional_forward_args_thd.append(max_seqlen_kv)
                if _use_flash_attn_3:
5416
5417
5418
                    fa_3_optional_forward_kwargs = {}
                    fa_3_optional_forward_kwargs["window_size"] = window_size
                    fa_3_optional_forward_kwargs["deterministic"] = self.deterministic
5419
                    activation_dtype = query_layer.dtype
5420
5421
5422
                    if fp8:
                        fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
                        torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)
5423
5424
5425
5426
5427
5428
5429
5430
5431
5432
5433

                        def convert_to_torch_float8(tensor, dtype):
                            out = torch.Tensor().to(device=tensor.device, dtype=dtype)
                            out.set_(
                                tensor._data.untyped_storage(),
                                tensor._data.storage_offset(),
                                tensor._data.shape,
                                tensor._data.stride(),
                            )
                            return out

5434
5435
5436
5437
5438
5439
5440
5441
                        if fp8_meta["recipe"].fp8_mha:
                            assert all(
                                isinstance(x, Float8Tensor)
                                for x in [query_layer, key_layer, value_layer]
                            ), "q/k/v must be Float8Tensors for FP8 MHA."
                            fp8_meta["scaling_fwd"].scale_inv[META_QKV] = query_layer._scale_inv
                        else:
                            query_layer, key_layer, value_layer = (
5442
5443
                                Float8Tensor.to_float8(x, fp8_dtype=fp8_dtype_forward)
                                for x in [query_layer, key_layer, value_layer]
5444
                            )
5445
5446
5447
5448
5449
5450
5451
5452
5453
5454
5455
5456
5457
5458
5459
5460
5461
5462
5463
5464
5465
                        fa_3_optional_forward_kwargs["descale_q"] = query_layer._scale_inv
                        fa_3_optional_forward_kwargs["descale_k"] = key_layer._scale_inv
                        fa_3_optional_forward_kwargs["descale_v"] = value_layer._scale_inv
                        query_layer, key_layer, value_layer = (
                            convert_to_torch_float8(x, torch_dtype)
                            for x in [query_layer, key_layer, value_layer]
                        )
                    try:
                        output, _ = func(
                            query_layer,
                            key_layer,
                            value_layer,
                            *fa_optional_forward_args_thd,
                            softmax_scale=self.softmax_scale,
                            causal="causal" in attn_mask_type,
                            **fa_3_optional_forward_kwargs,
                        )
                    except TypeError as e:
                        if _flash_attn_3_0_0_beta:
                            e.args = (
                                e.args[0]
5466
                                + ". Please update your flash-attn v3 (beta) installation as it "
5467
5468
5469
5470
5471
                                + "may have added more supported arguments to its API. \n"
                                + _flash_attn_3_installation_steps,
                            ) + e.args[1:]
                        raise

5472
5473
5474
5475
5476
5477
5478
5479
5480
5481
5482
5483
5484
5485
5486
5487
5488
5489
5490
5491
5492
5493
5494
5495
5496
5497
                    if fp8 and fp8_meta["recipe"].fp8_mha:
                        output = cast_to_fp8(
                            output,
                            fp8_meta["scaling_fwd"],
                            META_O,
                            fp8_dtype_forward,
                        )
                        output = Float8Tensor(
                            data=output,
                            fp8_meta=fp8_meta,
                            fp8_meta_forward=True,
                            fp8_meta_index=META_O,
                            fp8_dtype=fp8_dtype_forward,
                            dtype=activation_dtype,
                        )
                else:
                    output = func(
                        query_layer,
                        key_layer,
                        value_layer,
                        *fa_optional_forward_args_thd,
                        self.attention_dropout if self.training else 0.0,
                        softmax_scale=self.softmax_scale,
                        causal="causal" in attn_mask_type,
                        **fa_optional_forward_kwargs,
                    )
5498

5499
        if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
5500
            output = UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
5501

5502
        if qkv_format == "sbhd":
5503
            # (bs)hd -> bs(hd) -> sb(hd)
5504
            if fp8 and fp8_meta["recipe"].fp8_mha:
5505
5506
5507
5508
5509
5510
                output = Float8Tensor.make_like(
                    output,
                    data=output._data.reshape(batch_size, max_seqlen_q // cp_size, -1)
                    .transpose(0, 1)
                    .contiguous(),
                )
5511
            else:
5512
                output = output.view(batch_size, max_seqlen_q // cp_size, -1).transpose(0, 1)
5513
        elif qkv_format == "bshd":
5514
            # (bs)hd -> bs(hd)
5515
            output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
5516
        elif qkv_format == "thd":
5517
            # thd -> t(hd)
5518
            output = output.reshape(output.shape[0], -1)
5519

5520
        return output.contiguous()
5521

5522

5523
def _combine_tensors(
5524
5525
5526
    tensors: List[torch.Tensor],
    dim: int,
) -> torch.Tensor:
5527
5528
5529
5530
5531
5532
    """Combine tensors along a particular dimension"""

    num_tensors = len(tensors)
    new_shape = list(tensors[0].shape)
    new_shape.insert(dim, num_tensors)
    new_stride = list(tensors[0].stride())
5533
    new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
5534
    if isinstance(tensors[0], Float8Tensor):
5535
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
5536
5537
5538
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
5539
5540
5541
5542
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor)
5543
    else:
5544
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
5545
        combined_tensor.set_(
5546
5547
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
        )
5548
5549

    return combined_tensor
5550

5551

5552
5553
5554
5555
class FusedAttnFunc_qkvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed QKV input"""

    @staticmethod
5556
5557
5558
5559
5560
    def forward(
        ctx,
        is_training,
        max_seqlen,
        cu_seqlens,
5561
        cu_seqlens_padded,
5562
5563
5564
5565
5566
5567
5568
5569
5570
        qkv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
5571
        window_size,
5572
5573
5574
5575
5576
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
5577
        deterministic,
5578
    ):
5579
        # pylint: disable=missing-function-docstring
5580
5581
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
5582
        if fp8:
5583
5584
            is_input_fp8 = isinstance(qkv, Float8Tensor)
            if is_input_fp8:
5585
5586
5587
5588
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = qkv._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            # 1: qkv packed, 2: kv packed, 3: qkv separate
5589
            qkv_group = len(qkv_layout.split("_"))
5590
5591
5592
5593
            assert (
                qkv_group == 1
            ), f"qkv layout should conform to 3hd or h3d, e.g. sb3hd, but found {qkv_layout}."
            if is_input_fp8:
5594
5595
5596
                qkv_fp8 = qkv._data
            else:
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
5597
5598
5599
                qkv_fp8 = cast_to_fp8(
                    qkv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(qkv.shape)
5600
            out_fp8, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
5601
5602
5603
5604
5605
5606
5607
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
5608
                cu_seqlens_padded,
5609
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_qkv
                META_QKV,  # d_scale_qkv_offset
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_s
                META_S,  # d_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_s
                META_S,  # q_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_o
                META_O,  # q_scale_o_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_s
                META_S,  # amax_s_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_o
                META_O,  # amax_o_offset
5621
5622
5623
5624
5625
5626
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5627
                window_size,
5628
5629
                rng_gen,
            )
5630
            if is_output_fp8:
5631
5632
                out_ret = Float8Tensor(
                    data=out_fp8,
5633
5634
5635
5636
5637
5638
5639
5640
5641
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=qkv.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
5642
5643
5644
5645
5646
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
5647
            out_save = out_ret
5648
5649
5650
5651
5652
5653
5654
5655
5656
5657
5658
5659
5660
5661
5662
5663
5664
5665
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                if is_input_fp8:
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                    qkv = cast_from_fp8(
                        qkv_c._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[qkv.dtype],
                    ).view(qkv.shape)
                if is_output_fp8:
                    out_save = cast_from_fp8(
                        out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                        fp8_meta["scaling_fwd"],
                        META_O,
                        fp8_dtype_forward,
                        qkv_dtype,
                    ).view(out_fp8.shape)
5666
5667
5668
            fp8_tensors = (
                qkv_fp8,
                out_fp8,
5669
                fp8_meta["scaling_fwd"].scale.clone(),
5670
5671
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
5672
5673
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
5674
5675
5676
5677
5678
5679
5680
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
5681
                cu_seqlens_padded,
5682
5683
5684
5685
5686
5687
5688
5689
5690
5691
5692
5693
                None,  # d_scale_qkv
                0,  # d_scale_qkv_offset
                None,  # d_scale_s
                0,  # d_scale_s_offset
                None,  # q_scale_s
                0,  # q_scale_s_offset
                None,  # q_scale_o
                0,  # q_scale_o_offset
                None,  # amax_s
                0,  # amax_s_offset
                None,  # amax_o
                0,  # amax_o_offset
5694
5695
5696
5697
5698
5699
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5700
                window_size,
5701
5702
                rng_gen,
            )
5703
5704
5705
5706
            fp8_tensors = (None, None, None, None)
            out_save = out_ret

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
5707
5708
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
5709
        qkvo_tensors = (qkv, out_save) if not ctx.fp8 else (None, None)
5710
        ctx.save_for_backward(
5711
            *qkvo_tensors, cu_seqlens, cu_seqlens_padded, *fp8_tensors, *aux_ctx_tensors
5712
        )
5713
        ctx.fp8_meta = fp8_meta
5714
5715
5716
5717
5718
5719
5720
5721
        ctx.max_seqlen = max_seqlen
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
5722
        ctx.window_size = window_size
5723
        ctx.fused_attention_backend = (
5724
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
5725
        )
5726
        ctx.use_FAv2_bwd = use_FAv2_bwd
5727
        ctx.deterministic = deterministic
5728

5729
        return out_ret
5730
5731
5732

    @staticmethod
    def backward(ctx, d_out):
5733
        # pylint: disable=missing-function-docstring
5734
        if ctx.is_output_fp8:
5735
5736
5737
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
5738
5739
5740
            d_out_f8tensor = d_out
            d_out = d_out._data

5741
        d_out = d_out.contiguous()
5742
5743
5744
5745
        (
            qkv,
            out,
            cu_seqlens,
5746
            cu_seqlens_padded,
5747
5748
5749
5750
5751
5752
            qkv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
5753
        rest = [None]
5754
5755
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
5756
        if ctx.use_FAv2_bwd:
5757
            softmax_lse, rng_state = aux_ctx_tensors
5758
            dqkv = torch.empty_like(qkv)
5759
5760
5761
            d_out, q, k, v, out = [
                maybe_contiguous(x) for x in (d_out, qkv[:, 0], qkv[:, 1], qkv[:, 2], out)
            ]
5762
            flash_attn_cuda_bwd(
5763
5764
5765
5766
5767
5768
5769
5770
5771
5772
5773
5774
5775
5776
5777
5778
5779
5780
5781
                d_out,
                q,
                k,
                v,
                out,
                softmax_lse,
                dqkv[:, 0],
                dqkv[:, 1],
                dqkv[:, 2],
                cu_seqlens,
                cu_seqlens,
                ctx.max_seqlen,
                ctx.max_seqlen,
                ctx.dropout_p,
                ctx.attn_scale,
                False,
                "causal" in ctx.attn_mask_type,
                None,
                rng_state,
5782
            )
5783
            dqkv = dqkv[..., : d_out.shape[-1]]
5784
        else:
5785
5786
            with torch.cuda.nvtx.range("_FusedAttn_qkvpacked"):
                if ctx.fp8:
5787
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
5788
                    fp8_dtype_backward = get_fp8_te_dtype(
5789
5790
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
5791
                    if ctx.is_output_fp8:
5792
                        d_out_fp8 = d_out
5793
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
5794
5795
5796
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
5797
5798
5799
5800
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
5801
                    dqkv_fp8, *rest = fused_attn_bwd_qkvpacked(
5802
5803
5804
5805
5806
5807
5808
5809
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
5810
                        ctx.fused_attention_backend,
5811
                        cu_seqlens_padded,
5812
5813
5814
5815
5816
5817
5818
5819
5820
5821
5822
5823
5824
5825
5826
5827
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
5828
5829
                        ctx.window_size,
                        ctx.deterministic,
5830
                    )
5831
                    if ctx.is_input_fp8:
5832
5833
                        dqkv = Float8Tensor(
                            data=dqkv_fp8,
5834
5835
5836
5837
5838
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5839
                        )
5840
                    else:
5841
5842
5843
5844
5845
5846
5847
5848
5849
5850
                        dqkv_c_fp8 = dqkv_fp8.view(
                            -1, dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1]
                        )
                        dqkv = cast_from_fp8(
                            dqkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dqkv_fp8.shape)
5851
5852
5853
5854
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(qkv.dtype)
                    dqkv, *rest = fused_attn_bwd_qkvpacked(
5855
5856
5857
5858
5859
5860
5861
5862
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
5863
                        ctx.fused_attention_backend,
5864
                        cu_seqlens_padded,
5865
5866
5867
5868
5869
5870
5871
5872
5873
5874
5875
5876
5877
5878
5879
5880
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
5881
5882
                        ctx.window_size,
                        ctx.deterministic,
5883
                    )
5884

5885
5886
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
5887
5888
5889
5890
5891
5892
5893
5894
5895
5896
5897
5898
5899
5900
5901
5902
5903
5904
5905
5906
5907
            return (
                None,
                None,
                None,
                None,
                dqkv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
5908
5909
                None,
                None,
5910
            )
5911
        # else, return (dqkv, dbias)
5912
5913
5914
5915
5916
5917
5918
5919
5920
5921
5922
5923
5924
5925
5926
5927
5928
5929
5930
5931
5932
        return (
            None,
            None,
            None,
            None,
            dqkv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
5933
5934
            None,
            None,
5935
        )
5936

5937

5938
5939
5940
5941
class FusedAttnFunc_kvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed KV input"""

    @staticmethod
5942
5943
5944
5945
5946
5947
5948
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
5949
5950
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
5951
5952
5953
5954
5955
5956
5957
5958
5959
5960
        q,
        kv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
5961
        window_size,
5962
5963
5964
5965
5966
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
5967
        deterministic,
5968
    ):
5969
        # pylint: disable=missing-function-docstring
5970
5971
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
5972
        if fp8:
5973
5974
5975
            assert isinstance(kv, q.__class__), "q and kv must have the same type."
            is_input_fp8 = isinstance(q, Float8Tensor)
            if is_input_fp8:
5976
5977
5978
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
5979
            if is_input_fp8:
5980
5981
5982
                q_fp8, kv_fp8 = q._data, kv._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
5983
5984
                qkv_group = len(qkv_layout.split("_"))
                assert qkv_group == 2, (
5985
5986
                    "qkv layout should conform to hd_2hd or hd_h2d, e.g. sbhd_sb2hd, "
                    f"but found {qkv_layout}."
5987
5988
5989
5990
                )
                q_fp8 = cast_to_fp8(q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward).view(
                    q.shape
                )
5991
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
5992
5993
5994
                kv_fp8 = cast_to_fp8(
                    kv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(kv.shape)
5995
            out_fp8, aux_ctx_tensors = fused_attn_fwd_kvpacked(
5996
5997
5998
5999
6000
6001
6002
6003
6004
6005
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                kv_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
6006
6007
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6008
6009
6010
6011
6012
6013
6014
6015
6016
6017
6018
6019
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_qkv
                META_QKV,  # d_scale_qkv_offset
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_s
                META_S,  # d_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_s
                META_S,  # q_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_o
                META_O,  # q_scale_o_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_s
                META_S,  # amax_s_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_o
                META_O,  # amax_o_offset
6020
6021
6022
6023
6024
6025
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6026
                window_size,
6027
6028
                rng_gen,
            )
6029
            if is_output_fp8:
6030
6031
                out_ret = Float8Tensor(
                    data=out_fp8,
6032
6033
6034
6035
6036
6037
6038
6039
6040
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
6041
6042
6043
6044
6045
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
6046
            out_save = out_ret
6047
6048
6049
6050
6051
6052
6053
6054
6055
6056
6057
6058
6059
6060
6061
6062
6063
6064
6065
6066
6067
6068
6069
6070
6071
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                if is_input_fp8:
                    q = cast_from_fp8(
                        q._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[q.dtype],
                    ).view(q.shape)
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                    kv = cast_from_fp8(
                        kv_c._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[kv.dtype],
                    ).view(kv.shape)
                if is_output_fp8:
                    out_save = cast_from_fp8(
                        out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                        fp8_meta["scaling_fwd"],
                        META_O,
                        fp8_dtype_forward,
                        qkv_dtype,
                    ).view(out_fp8.shape)
6072
6073
6074
6075
            fp8_tensors = (
                q_fp8,
                kv_fp8,
                out_fp8,
6076
                fp8_meta["scaling_fwd"].scale.clone(),
6077
6078
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
6079
6080
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_kvpacked(
6081
6082
6083
6084
6085
6086
6087
6088
6089
6090
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                kv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
6091
6092
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6093
6094
6095
6096
6097
6098
6099
6100
6101
6102
6103
6104
                None,  # d_scale_qkv
                0,  # d_scale_qkv_offset
                None,  # d_scale_s
                0,  # d_scale_s_offset
                None,  # q_scale_s
                0,  # q_scale_s_offset
                None,  # q_scale_o
                0,  # q_scale_o_offset
                None,  # amax_s
                0,  # amax_s_offset
                None,  # amax_o
                0,  # amax_o_offset
6105
6106
6107
6108
6109
6110
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6111
                window_size,
6112
6113
                rng_gen,
            )
6114
6115
6116
6117
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None)

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
6118
6119
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
6120
        qkvo_tensors = (q, kv, out_save) if not ctx.fp8 else (None, None, None)
6121
6122
6123
6124
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
6125
6126
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6127
6128
6129
            *fp8_tensors,
            *aux_ctx_tensors,
        )
6130
        ctx.fp8_meta = fp8_meta
6131
6132
6133
6134
6135
6136
6137
6138
6139
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
6140
        ctx.window_size = window_size
6141
        ctx.fused_attention_backend = (
6142
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
6143
        )
6144
        ctx.use_FAv2_bwd = use_FAv2_bwd
6145
        ctx.deterministic = deterministic
6146

6147
        return out_ret
6148
6149
6150

    @staticmethod
    def backward(ctx, d_out):
6151
        # pylint: disable=missing-function-docstring
6152
        if ctx.is_output_fp8:
6153
6154
6155
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
6156
6157
6158
            d_out_f8tensor = d_out
            d_out = d_out._data

6159
        d_out = d_out.contiguous()
6160
6161
6162
6163
6164
6165
        (
            q,
            kv,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
6166
6167
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6168
6169
6170
6171
6172
6173
6174
            q_fp8,
            kv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
6175
        rest = [None]
6176
6177
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
6178
        if ctx.use_FAv2_bwd:
6179
            softmax_lse, rng_state = aux_ctx_tensors
6180
6181
            dq = torch.empty_like(q)
            dkv = torch.empty_like(kv)
6182
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, kv[:, 0], kv[:, 1], out)]
6183
            flash_attn_cuda_bwd(
6184
6185
6186
6187
6188
6189
6190
6191
6192
6193
6194
6195
6196
6197
6198
6199
6200
6201
6202
                d_out,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dkv[:, 0],
                dkv[:, 1],
                cu_seqlens_q,
                cu_seqlens_kv,
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                ctx.dropout_p,
                ctx.attn_scale,
                False,
                "causal" in ctx.attn_mask_type,
                None,
                rng_state,
6203
            )
6204
6205
            dq = dq[..., : d_out.shape[-1]]
            dkv = dkv[..., : d_out.shape[-1]]
6206
        else:
6207
6208
            with torch.cuda.nvtx.range("_FusedAttn_kvpacked"):
                if ctx.fp8:
6209
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
6210
                    fp8_dtype_backward = get_fp8_te_dtype(
6211
6212
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
6213
                    if ctx.is_output_fp8:
6214
                        d_out_fp8 = d_out
6215
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
6216
6217
6218
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
6219
6220
6221
6222
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
6223
                    dq_fp8, dkv_fp8, *rest = fused_attn_bwd_kvpacked(
6224
6225
6226
6227
6228
6229
6230
6231
6232
6233
6234
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q_fp8,
                        kv_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
6235
                        ctx.fused_attention_backend,
6236
6237
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6238
6239
6240
6241
6242
6243
6244
6245
6246
6247
6248
6249
6250
6251
6252
6253
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6254
6255
                        ctx.window_size,
                        ctx.deterministic,
6256
                    )
6257
                    if ctx.is_input_fp8:
6258
6259
                        dq = Float8Tensor(
                            data=dq_fp8,
6260
6261
6262
6263
6264
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6265
6266
6267
                        )
                        dkv = Float8Tensor(
                            data=dkv_fp8,
6268
6269
6270
6271
6272
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6273
                        )
6274
6275
6276
                    else:
                        dq = cast_from_fp8(
                            dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
6277
6278
6279
6280
6281
6282
6283
6284
6285
6286
6287
6288
6289
6290
6291
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dq_fp8.shape)
                        dkv_c_fp8 = dkv_fp8.view(
                            -1, dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1]
                        )
                        dkv = cast_from_fp8(
                            dkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"],
                            META_DQKV,
                            fp8_dtype_backward,
                            ctx.qkv_dtype,
                        ).view(dkv_fp8.shape)
6292
6293
6294
6295
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dkv, *rest = fused_attn_bwd_kvpacked(
6296
6297
6298
6299
6300
6301
6302
6303
6304
6305
6306
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        kv,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
6307
                        ctx.fused_attention_backend,
6308
6309
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6310
6311
6312
6313
6314
6315
6316
6317
6318
6319
6320
6321
6322
6323
6324
6325
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6326
6327
                        ctx.window_size,
                        ctx.deterministic,
6328
                    )
6329

6330
6331
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
6332
6333
6334
6335
6336
6337
6338
6339
6340
6341
6342
6343
6344
6345
6346
6347
6348
6349
6350
6351
6352
6353
6354
6355
6356
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                dq,
                dkv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
6357
6358
                None,
                None,
6359
            )
6360
        # else, return (dqkv, dbias)
6361
6362
6363
6364
6365
6366
6367
6368
6369
6370
6371
6372
6373
6374
6375
6376
6377
6378
6379
6380
6381
6382
6383
6384
6385
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            dq,
            dkv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
6386
6387
            None,
            None,
6388
6389
        )

6390

6391
6392
6393
6394
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
6395
6396
6397
6398
6399
6400
6401
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
6402
6403
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
6404
6405
6406
6407
6408
6409
6410
6411
6412
6413
6414
        q,
        k,
        v,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
6415
        window_size,
6416
6417
6418
6419
6420
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
6421
        deterministic,
6422
    ):
6423
        # pylint: disable=missing-function-docstring
6424
6425
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
6426
6427
6428
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
6429
6430
6431
6432
6433
            assert isinstance(k, q.__class__) and isinstance(
                v, q.__class__
            ), "q, k, and v must have the same type."
            is_input_fp8 = isinstance(q, Float8Tensor)
            if is_input_fp8:
6434
6435
6436
6437
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                q_fp8, k_fp8, v_fp8 = q._data, k._data, v._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
6438
                qkv_group = len(qkv_layout.split("_"))
6439
                if qkv_group == 1:
6440
6441
                    dim = qkv_layout.find("3")
                    qkv = _combine_tensors([q, k, v], dim)
6442
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
6443
6444
6445
6446
                    qkv_fp8 = cast_to_fp8(
                        qkv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(qkv.shape)
                    q_fp8, k_fp8, v_fp8 = _SplitAlongDim.apply(qkv_fp8, dim, [1, 1, 1])
6447
6448
                    q_fp8, k_fp8, v_fp8 = [x.squeeze(dim) for x in [q_fp8, k_fp8, v_fp8]]
                if qkv_group == 2:
6449
6450
6451
6452
6453
                    q_fp8 = cast_to_fp8(
                        q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(q.shape)
                    dim = qkv_layout.split("_")[1].find("2")
                    kv = _combine_tensors([k, v], dim)
6454
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
6455
6456
6457
6458
                    kv_fp8 = cast_to_fp8(
                        kv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(kv.shape)
                    k_fp8, v_fp8 = _SplitAlongDim.apply(kv_fp8, dim, [1, 1])
6459
6460
                    k_fp8, v_fp8 = [x.squeeze(dim) for x in [k_fp8, v_fp8]]
                if qkv_group == 3:
6461
6462
6463
6464
6465
6466
6467
6468
6469
                    q_fp8 = cast_to_fp8(
                        q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(q.shape)
                    k_fp8 = cast_to_fp8(
                        k, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(k.shape)
                    v_fp8 = cast_to_fp8(
                        v, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                    ).view(v.shape)
6470
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
6471
6472
6473
6474
6475
6476
6477
6478
6479
6480
6481
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                k_fp8,
                v_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
6482
6483
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6484
6485
6486
6487
6488
6489
6490
6491
6492
6493
6494
6495
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_qkv
                META_QKV,  # d_scale_qkv_offset
                fp8_meta["scaling_fwd"].scale_inv,  # d_scale_s
                META_S,  # d_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_s
                META_S,  # q_scale_s_offset
                fp8_meta["scaling_fwd"].scale,  # q_scale_o
                META_O,  # q_scale_o_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_s
                META_S,  # amax_s_offset
                fp8_meta["scaling_fwd"].amax_history,  # amax_o
                META_O,  # amax_o_offset
6496
6497
6498
6499
6500
6501
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6502
                window_size,
6503
6504
                rng_gen,
            )
6505
            if is_output_fp8:
6506
6507
                out_ret = Float8Tensor(
                    data=out_fp8,
6508
6509
6510
6511
6512
6513
6514
6515
6516
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
6517
6518
6519
6520
6521
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
6522
6523
            out_save = out_ret

6524
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
6525
                # 1: qkv packed, 2: kv packed, 3: qkv separate
6526
6527
6528
6529
6530
6531
6532
6533
6534
6535
6536
6537
6538
6539
6540
6541
6542
6543
6544
6545
6546
6547
6548
6549
6550
6551
6552
6553
6554
6555
6556
6557
6558
6559
6560
6561
6562
6563
6564
6565
6566
6567
6568
6569
6570
6571
6572
6573
6574
6575
6576
6577
6578
6579
6580
6581
6582
6583
6584
6585
                if is_input_fp8:
                    qkv_group = len(qkv_layout.split("_"))
                    if qkv_group == 1:
                        dim = qkv_layout.find("3")
                        qkv = _combine_tensors([q, k, v], dim)
                        qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                        qkv_no_fp8 = cast_from_fp8(
                            qkv_c._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[qkv.dtype],
                        ).view(qkv.shape)
                        q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1])
                        q, k, v = [x.squeeze(dim) for x in [q, k, v]]
                    if qkv_group == 2:
                        q = cast_from_fp8(
                            q._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[q.dtype],
                        ).view(q.shape)
                        dim = qkv_layout.split("_")[1].find("2")
                        kv = _combine_tensors([k, v], dim)
                        kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                        kv_no_fp8 = cast_from_fp8(
                            kv_c._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[kv.dtype],
                        ).view(kv.shape)
                        k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1])
                        k, v = [x.squeeze(dim) for x in [k, v]]
                    if qkv_group == 3:
                        q = cast_from_fp8(
                            q._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[q.dtype],
                        ).view(q.shape)
                        k = cast_from_fp8(
                            k._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[k.dtype],
                        ).view(k.shape)
                        v = cast_from_fp8(
                            v._data,
                            fp8_meta["scaling_fwd"],
                            META_QKV,
                            fp8_dtype_forward,
                            TE_DType[v.dtype],
                        ).view(v.shape)
                if is_output_fp8:
                    out_save = cast_from_fp8(
                        out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
6586
                        fp8_meta["scaling_fwd"],
6587
                        META_O,
6588
                        fp8_dtype_forward,
6589
6590
                        qkv_dtype,
                    ).view(out_fp8.shape)
6591
6592
6593
6594
6595
6596

            fp8_tensors = (
                q_fp8,
                k_fp8,
                v_fp8,
                out_fp8,
6597
                fp8_meta["scaling_fwd"].scale.clone(),
6598
6599
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
6600
6601
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd(
6602
6603
6604
6605
6606
6607
6608
6609
6610
6611
6612
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
6613
6614
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6615
6616
6617
6618
6619
6620
6621
6622
6623
6624
6625
6626
                None,  # d_scale_qkv
                0,  # d_scale_qkv_offset
                None,  # d_scale_s
                0,  # d_scale_s_offset
                None,  # q_scale_s
                0,  # q_scale_s_offset
                None,  # q_scale_o
                0,  # q_scale_o_offset
                None,  # amax_s
                0,  # amax_s_offset
                None,  # amax_o
                0,  # amax_o_offset
6627
6628
6629
6630
6631
6632
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6633
                window_size,
6634
6635
                rng_gen,
            )
6636
6637
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None, None)
6638

6639
6640
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))

6641
        from .cpu_offload import CPUOffloadEnabled
6642

6643
        if CPUOffloadEnabled:
6644
6645
6646
6647
6648
6649
6650
            if ctx.fp8:
                tensor_list = fp8_tensors
            else:
                tensor_list = [q, k, v, out_save]

            tensor_list.extend(aux_ctx_tensors)

6651
            qkv_layout = "sbhd_sbhd_sbhd"
6652
6653
6654
6655
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

6656
6657
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
6658
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
6659
6660
6661
6662
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
6663
6664
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6665
6666
6667
            *fp8_tensors,
            *aux_ctx_tensors,
        )
6668
        ctx.fp8_meta = fp8_meta
6669
6670
6671
6672
6673
6674
6675
6676
6677
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
6678
        ctx.window_size = window_size
6679
        ctx.fused_attention_backend = (
6680
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
6681
        )
6682
        ctx.use_FAv2_bwd = use_FAv2_bwd
6683
        ctx.deterministic = deterministic
6684

6685
        return out_ret
6686
6687
6688

    @staticmethod
    def backward(ctx, d_out):
6689
        # pylint: disable=missing-function-docstring
6690
        if ctx.is_output_fp8:
6691
6692
6693
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
6694
6695
6696
            d_out_f8tensor = d_out
            d_out = d_out._data

6697
        d_out = d_out.contiguous()
6698
6699
6700
6701
6702
6703
6704
        (
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
6705
6706
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6707
6708
6709
6710
6711
6712
6713
6714
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
6715
6716
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
6717
        rest = [None]
6718
        if ctx.use_FAv2_bwd:
6719
            softmax_lse, rng_state = aux_ctx_tensors
6720
6721
6722
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
6723
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
6724
            flash_attn_cuda_bwd(
6725
6726
6727
6728
6729
6730
6731
6732
6733
6734
6735
6736
6737
6738
6739
6740
6741
6742
6743
                d_out,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dk,
                dv,
                cu_seqlens_q,
                cu_seqlens_kv,
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                ctx.dropout_p,
                ctx.attn_scale,
                False,
                "causal" in ctx.attn_mask_type,
                None,
                rng_state,
6744
            )
6745
6746
6747
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
6748
        else:
6749
6750
6751
6752
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
6753
6754
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
6755
                    if ctx.is_output_fp8:
6756
                        d_out_fp8 = d_out
6757
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
6758
6759
6760
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
6761
6762
6763
6764
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
6765
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
6766
6767
6768
6769
6770
6771
6772
6773
6774
6775
6776
6777
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q_fp8,
                        k_fp8,
                        v_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
6778
                        ctx.fused_attention_backend,
6779
6780
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6781
6782
6783
6784
6785
6786
6787
6788
6789
6790
6791
6792
6793
6794
6795
6796
                        fwd_scale_invs[META_QKV],  # d_scale_qkv,
                        fwd_scale_invs[META_S],  # d_scale_s,
                        fwd_scale_invs[META_O],  # d_scale_o,
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO],  # d_scale_do
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DP],  # d_scale_dp
                        fwd_scales[META_S],  # q_scale_s
                        ctx.fp8_meta["scaling_bwd"].scale[META_DP],  # q_scale_dp
                        ctx.fp8_meta["scaling_bwd"].scale[META_DQKV],  # q_scale_dqkv
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DP],  # amax_dp
                        ctx.fp8_meta["scaling_bwd"].amax_history[0][META_DQKV],  # amax_dqkv
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6797
6798
                        ctx.window_size,
                        ctx.deterministic,
6799
                    )
6800

6801
                    if ctx.is_input_fp8:
6802
6803
                        dq = Float8Tensor(
                            data=dq_fp8,
6804
6805
6806
6807
6808
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6809
6810
6811
                        )
                        dk = Float8Tensor(
                            data=dk_fp8,
6812
6813
6814
6815
6816
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6817
6818
6819
                        )
                        dv = Float8Tensor(
                            data=dv_fp8,
6820
6821
6822
6823
6824
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6825
                        )
6826
                    else:
6827
                        qkv_group = len(ctx.qkv_layout.split("_"))
6828
                        if qkv_group == 1:
6829
6830
6831
6832
6833
6834
6835
6836
6837
6838
6839
6840
6841
                            dim = ctx.qkv_layout.find("3")
                            dqkv_fp8 = _combine_tensors([dq_fp8, dk_fp8, dv_fp8], dim)
                            dqkv_c_fp8 = dqkv_fp8.view(
                                -1, dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1]
                            )
                            dqkv = cast_from_fp8(
                                dqkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dqkv_fp8.shape)
                            dq, dk, dv = _SplitAlongDim.apply(dqkv, dim, [1, 1, 1])
6842
6843
6844
6845
                            dq, dk, dv = [x.squeeze(dim) for x in [dq, dk, dv]]
                        if qkv_group == 2:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
6846
6847
6848
6849
6850
6851
6852
6853
6854
6855
6856
6857
6858
6859
6860
6861
6862
6863
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dq_fp8.shape)
                            dim = ctx.qkv_layout.split("_")[1].find("2")
                            dkv_fp8 = _combine_tensors([dk_fp8, dv_fp8], dim)
                            dkv_c_fp8 = dkv_fp8.view(
                                -1, dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1]
                            )
                            dkv = cast_from_fp8(
                                dkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dkv_fp8.shape)
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1, 1])
6864
6865
6866
6867
                            dk, dv = [x.squeeze(dim) for x in [dk, dv]]
                        if qkv_group == 3:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
6868
6869
6870
6871
6872
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dq_fp8.shape)
6873
6874
                            dk = cast_from_fp8(
                                dk_fp8.view(-1, dk_fp8.shape[-2] * dk_fp8.shape[-1]),
6875
6876
6877
6878
6879
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dk_fp8.shape)
6880
6881
                            dv = cast_from_fp8(
                                dv_fp8.view(-1, dv_fp8.shape[-2] * dv_fp8.shape[-1]),
6882
6883
6884
6885
6886
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dv_fp8.shape)
6887
6888
6889
6890
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dk, dv, *rest = fused_attn_bwd(
6891
6892
6893
6894
6895
6896
6897
6898
6899
6900
6901
6902
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        k,
                        v,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
6903
                        ctx.fused_attention_backend,
6904
6905
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6906
6907
6908
6909
6910
6911
6912
6913
6914
6915
6916
6917
6918
6919
6920
6921
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
6922
6923
                        ctx.window_size,
                        ctx.deterministic,
6924
                    )
6925

6926
6927
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
6928
6929
6930
6931
6932
6933
6934
6935
6936
6937
6938
6939
6940
6941
6942
6943
6944
6945
6946
6947
6948
6949
6950
6951
6952
6953
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                dq,
                dk,
                dv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
6954
6955
                None,
                None,
6956
            )
6957
        # else, return (dqkv, dbias)
6958
6959
6960
6961
6962
6963
6964
6965
6966
6967
6968
6969
6970
6971
6972
6973
6974
6975
6976
6977
6978
6979
6980
6981
6982
6983
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            dq,
            dk,
            dv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
6984
6985
            None,
            None,
6986
        )
6987

6988

6989
class FusedAttention(torch.nn.Module):
6990
6991
6992
6993
6994
6995
6996
6997
6998
    """Dot product attention, with multiple backends:

    1. FusedAttnBackend["F16_max512_seqlen"]
       cuDNN based fused attention for FP16/BF16 and <=512 sequence length.
    2. FusedAttnBackend["F16_arbitrary_seqlen"]
       cuDNN based fused attention for FP16/BF16 and any sequence length.

    Support matrix:

6999
7000
7001
7002
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
7003
    | attn_type     | self/cross              | self/cross                     |
7004
    | qkv_layout    |                         |                                |
7005
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
7006
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
7007
7008
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
7009
7010
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
7011
    | dropout       | yes                     | yes                            |
7012
7013
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
7014
    | output dtype  | fp16/bf16               | fp16/bf16                      |
7015
7016
7017
7018
    """

    def __init__(
        self,
7019
        softmax_scale: float,
7020
7021
7022
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
7023
7024
        layer_number: Optional[int] = None,
        deterministic: bool = False,
7025
7026
7027
    ) -> None:
        super().__init__()

7028
        self.softmax_scale = softmax_scale
7029
7030
7031
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
7032
7033
7034
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
7035
        self.layer_number = 1 if layer_number is None else layer_number
7036
        self.deterministic = deterministic
7037

7038
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
7039
7040
            """
            Temporarily remove fused_attention._extra_state as a missing key
7041
            or an unexpected key when loading Transformer Engine checkpoints.
7042
7043
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
7044
            phased out in Transformer Engine 2.0.
7045
7046
            """
            for key in incompatible_keys.missing_keys:
7047
                if "fused_attention._extra_state" in key:
7048
                    incompatible_keys.missing_keys.remove(key)
7049
7050
7051
7052
7053
7054
7055
            for key in incompatible_keys.unexpected_keys:
                if "fused_attention._extra_state" in key:
                    incompatible_keys.unexpected_keys.remove(key)
                    warnings.warn(
                        "fused_attention._extra_state is not loaded from checkpoint. Please map "
                        "FusedAttention's _extra_state to DotProductAttention's _extra_state."
                    )
7056

7057
7058
        self.register_load_state_dict_post_hook(remove_extra_states_check)

7059
    @no_torch_dynamo()
7060
7061
7062
7063
7064
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
7065
7066
7067
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
7068
7069
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
7070
7071
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
7072
        attn_mask_type: str = "causal",
7073
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7074
        window_size: Optional[Tuple[int, int]] = None,
7075
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
7076
7077
7078
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
7079
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
7080
7081
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
7082
        cp_comm_type: str = "p2p",
7083
7084
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
7085
7086
    ) -> torch.Tensor:
        """fused attention fprop"""
7087
7088
7089
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
7090
7091
7092
7093
        assert all(
            x.dtype in [torch.float16, torch.bfloat16] or isinstance(x, Float8Tensor)
            for x in [query_layer, key_layer, value_layer]
        ), "FusedAttention only supports FP16 and BF16 data types, or Float8Tensors."
7094
7095
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
7096
        ), "FusedAttention only supports CUDA tensors."
7097
7098
        assert (
            qkv_layout in QKVLayouts
7099
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
7100

7101
7102
7103
7104
7105
7106
        cp_size = 1
        if isinstance(cp_group, dist_group_type):
            cp_size = get_distributed_world_size(cp_group)
        elif isinstance(cp_group, list):
            for group in cp_group:
                cp_size *= get_distributed_world_size(group)
7107
        context_parallel = cp_size > 1
7108

7109
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
7110

7111
7112
        if qkv_format in ["sbhd", "bshd"]:
            if qkv_format == "sbhd":
7113
                batch_size, max_seqlen_q, max_seqlen_kv = (
7114
7115
7116
7117
7118
                    query_layer.shape[1],
                    query_layer.shape[0],
                    key_layer.shape[0],
                )
            if qkv_format == "bshd":
7119
                batch_size, max_seqlen_q, max_seqlen_kv = (
7120
7121
7122
7123
                    query_layer.shape[0],
                    query_layer.shape[1],
                    key_layer.shape[1],
                )
7124
7125
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
7126
            if "padding" in attn_mask_type:
7127
7128
                assert not context_parallel, "Padding mask not supported with context parallelism!"

7129
7130
7131
7132
7133
                if cu_seqlens_q is None or cu_seqlens_kv is None:
                    if attention_mask is None:
                        raise RuntimeError(
                            "Please provide attention_mask or cu_seqlens for padding!"
                        )
7134
                    if self.attention_type == "self":
7135
7136
                        cu_seqlens_q = get_cu_seqlens(attention_mask)
                        cu_seqlens_kv = cu_seqlens_q
7137
                    else:
7138
7139
                        cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
7140
            else:
7141
7142
7143
7144
7145
7146
7147
7148
7149
7150
7151
7152
                if cu_seqlens_q is None:
                    cu_seqlens_q = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
                    cu_seqlens_kv = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
7153
7154
7155
        if qkv_format == "thd":
            assert (
                max_seqlen_q is not None
7156
7157
7158
                and max_seqlen_kv is not None
                and cu_seqlens_q is not None
                and cu_seqlens_kv is not None
7159
            ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
7160
7161
7162
7163

        if cu_seqlens_q_padded is None or cu_seqlens_kv_padded is None:
            cu_seqlens_q_padded = cu_seqlens_q
            cu_seqlens_kv_padded = cu_seqlens_kv
7164
7165
7166

        qkv_dtype = TE_DType[query_layer.dtype]

7167
7168
7169
7170
7171
        use_FAv2_bwd = (
            self.use_FAv2_bwd
            and (core_attention_bias_type == "no_bias")
            and (fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen)
        )
7172

7173
7174
7175
7176
7177
7178
7179
7180
7181
7182
7183
        if fp8:
            assert fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_FP8, (
                f"cuDNN attention sub-backend {int(tex.NVTE_Fused_Attn_Backend.NVTE_FP8)}"
                " is required for FP8 attention!"
            )
            assert fp8_meta is not None, "FP8 metadata fp8_meta is required for FP8 attention!"
            assert not context_parallel or fp8_meta["recipe"].reduce_amax, (
                "Amax reduction across TP+CP group is necessary when using context parallelism with"
                " FP8!"
            )

7184
        if context_parallel:
7185
            assert (
7186
7187
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
7188
7189
7190
7191
7192
7193
7194
            ), f"{fused_attention_backend} does not work with context parallelism!"
            assert core_attention_bias_type not in [
                "alibi"
            ], f"{core_attention_bias_type} is not supported with context parallelism!"
            query_layer, key_layer, value_layer = [
                x.contiguous() for x in (query_layer, key_layer, value_layer)
            ]
7195
7196
7197
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
7198
7199
7200
7201
7202
7203
7204
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
7205
7206
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
7207
                    self.attention_dropout if self.training else 0.0,
7208
7209
7210
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
7211
                    cp_comm_type,
7212
                    softmax_scale=self.softmax_scale,
7213
                    qkv_format=qkv_format,
7214
                    attn_mask_type=attn_mask_type,
7215
7216
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
7217
                    deterministic=self.deterministic,
7218
                    use_fused_attention=True,
7219
                    window_size=window_size,
7220
7221
                    fp8=fp8,
                    fp8_meta=fp8_meta,
7222
7223
                )
        else:
7224
7225
7226
7227
7228
7229
7230
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
7231
7232
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
7233
7234
7235
7236
7237
7238
7239
7240
7241
7242
7243
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_dtype,
                    core_attention_bias,
                    self.softmax_scale,
                    self.attention_dropout if self.training else 0.0,
                    fast_zero_fill,
                    qkv_layout,
                    core_attention_bias_type,
                    attn_mask_type,
7244
                    window_size,
7245
7246
7247
7248
7249
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
7250
                    self.deterministic,
7251
                )
7252

7253
7254
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
7255
7256


7257
class DotProductAttention(TransformerEngineBaseModule):
7258
7259
7260
7261
7262
7263
    """Allows the model to jointly attend to information from different
    representation subspaces as described in the paper:
    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.

    .. note::

7264
        Argument :attr:`attention_mask` in the `forward` call is only used when
7265
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
7266
7267
7268

    .. warning::

7269
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
7270
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
7271
7272
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
7273

7274
7275
7276
7277
7278
7279
7280
    .. note::

        Transformer Engine stores the FP8 metadata under a `._extra_state` key when checkpointing.
        As the FP8 attention support expands from one backend to multiple backends, the location
        of that key has also shifted (see `FP8 checkpoint compatibility <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/faq.html#fp8-checkpoint-compatibility>`_).


7281
7282
7283
7284
    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
7285
7286
7287
    kv_channels : Union[int, Tuple[int, int]]
                the head size in key and value tensors. If the same, :attr:`kv_channels` can be
                an integer; if not, :attr:`kv_channels` should be a tuple of two integers.
7288
7289
7290
7291
7292
7293
7294
7295
    num_gqa_groups : Optional[int] = None
                    number of GQA groups in the transformer layer.
                    Grouped Query Attention is described in
                    `this paper <https://arxiv.org/pdf/2305.13245.pdf>`_.
                    This only affects the keys and values, not the queries.
                    GQA-1 is equivalent to Multi-Query Attention
                    (`MQA <https://arxiv.org/pdf/1911.02150.pdf>`_), while GQA-H
                    is equivalent to MHA, i.e. `num_gqa_groups = num_attention_heads`.
7296
7297
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
7298
    attn_mask_type: str, default = `causal`
7299
                   type of attention mask passed into softmax operation, options are "`no_mask`",
7300
7301
7302
7303
7304
7305
7306
7307
7308
                   "`padding`", "`causal`", "`padding,causal`", "`causal,padding`",
                   "`padding_causal`", "`causal_bottom_right`", "`padding_causal_bottom_right`", and
                   "`arbitrary`", where "`padding,causal`", "`causal,padding`" and "`padding_causal`"
                   are equivalent. This arg can be overridden by :attr:`attn_mask_type` in the
                   `forward` method. It is useful for cases involving compilation/tracing, e.g.
                   ONNX export, and the forward arg is useful for dynamically changing mask types,
                   e.g. a different mask for training and inference.
                   1. For "`no_mask`", no attention mask is applied.
                   2. For "`causal`", "`causal_bottom_right`", or the causal mask in
7309
                   "`padding_causal`" and "`padding_causal_bottom_right`", Transformer Engine
7310
7311
7312
7313
7314
7315
7316
7317
7318
7319
7320
7321
7322
7323
                   calculates and applies an upper triangular mask to the softmax input.
                   No user input is needed. Causal masks without the "`bottom_right`" appendix align
                   the diagonal line to the top left corner of the softmax matrix. With
                   "`bottom_right`", the causal mask is aligned to the bottom right corner, which is
                   often used in inference/KV caching.
                   3. For "`padding`", or the padding mask in "`padding_causal`" and
                   "`padding_causal_bottom_right`", users need to provide the locations of padded
                   tokens, either via :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv` (both in shape
                   [batch_size + 1]), or via :attr:`attention_mask` (one tensor for self-attention
                   in shape [batch_size, 1, 1, max_seqlen_q], or two tensors in a tuple for
                   cross-attention in shapes [batch_size, 1, 1, max_seqlen_q] and
                   [batch_size, 1, 1, max_seqlen_kv]).
                   4. For "`arbitrary`", users need to provide a mask that is broadcastable to
                   the shape of softmax input [batch_size, num_heads, max_seqlen_q, max_seqlen_kv].
7324
7325
7326
7327
    window_size: Optional[Tuple[int, int]], default = `None`
                sliding window size for local attention, where query at position i attends to keys
                in [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q
                + window_size[1]] inclusive. Special cases (-1, -1) and (-1, 0) mean no sliding
7328
7329
7330
                window and causal mask specifically. Both `causal` and `causal_bottom_right` masks
                map to `window_size = (-1, 0)` and Transformer Engine distinguishes them based on
                `attn_mask_type`. Similar to :attr:`attn_mask_type`, `window_size` can
7331
                be overridden by :attr:`window_size` in `forward` as well.
7332
7333
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
7334
7335
7336
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
7337
7338
7339
    qkv_format: str, default = `sbhd`
               dimension format for `query_layer`, `key_layer` and `value_layer`,
               {`sbhd`, `bshd`, `thd`}. `s` stands for the sequence length, `b` batch size,
7340
               `h` the number of heads, `d` head size, and `t` the total number of tokens
7341
7342
7343
7344
7345
               in a batch, with `t = sum(s_i), for i = 0...b-1`. `sbhd` and `bshd` formats
               are used for when sequences in a batch are of equal length or padded to
               equal length, and the `thd` format is used for when sequences in a batch
               have different lengths. Please note that these formats do not reflect how
               tensors `query_layer`, `key_layer`, `value_layer` are laid out in memory.
7346
               For that, please use `get_qkv_layout` to gain the layout information.
7347
7348
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
7349
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
7350
7351
7352
7353
7354
7355
7356
7357
7358

    Parallelism parameters
    ----------------------
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_size : int, default = 1
             tensor parallel world size.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
7359
    cp_group : Union[ProcessGroup, List[ProcessGroup]], default = `None`
7360
              context parallel process group.
7361
7362
7363
              ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
              List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
              and cp_group[1] are for a2a and p2p communications respectively.
7364
7365
7366
7367
7368
7369
7370
    cp_global_ranks : list of global rank IDs, default = `None`
                     global rank IDs of GPUs that are in cp_group.
    cp_stream : CUDA stream, default = `None`
               context parallelism splits flash attention into multiple steps for
               compute and communication overlapping. To address the wave quantization
               issue of each split step, we add an additional CUDA stream so that we
               can overlap two flash attention kernels.
7371
    cp_comm_type : str, default = `p2p`
7372
                  inter-gpu communication type for context parallelism.
7373
                  Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
7374
7375
7376
7377
7378
7379
                  "p2p": Exchange KV chunks with P2P communications in ring topology.
                         P2P is async and can be overlapped with attention compute.
                  "all_gather": All-gather to get full sequence of KV before attention.
                                The all-gather is not async, and cannot be overlapped.
                  "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                         group, and gather to get full sequence of QKV.
7380
7381
7382
                  "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                  across each CP sub-group (e.g., via NVLink), then exchanging KV with
                  p2p between sub-groups (e.g., via IBLink).
7383
7384
7385
7386
7387
    """

    def __init__(
        self,
        num_attention_heads: int,
7388
        kv_channels: Union[int, Tuple[int, int]],
7389
        num_gqa_groups: Optional[int] = None,
7390
        attention_dropout: float = 0.0,
7391
        qkv_format: str = "sbhd",
7392
        attn_mask_type: str = "causal",
7393
        window_size: Optional[Tuple[int, int]] = None,
7394
7395
7396
7397
7398
        sequence_parallel: bool = False,
        tp_size: int = 1,
        get_rng_state_tracker: Optional[Callable] = None,
        tp_group: Optional[dist_group_type] = None,
        layer_number: Optional[int] = None,
7399
        attention_type: str = "self",
7400
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
7401
        cp_global_ranks: List[int] = None,
7402
        cp_stream: torch.cuda.Stream = None,
7403
        cp_comm_type: str = "p2p",
7404
        softmax_scale: Optional[float] = None,
7405
7406
7407
    ) -> None:
        super().__init__()

7408
        self.logger = logging.getLogger("DotProductAttention")
7409
7410
7411
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
7412
        self.qkv_format = qkv_format
7413
        attn_mask_type = attn_mask_type.replace(",", "_")
7414
7415
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
7416
        self.attn_mask_type = attn_mask_type
7417
        self.window_size = check_set_window_size(attn_mask_type, window_size)
7418
7419
7420
7421
7422
7423
7424
        if tp_group is None:
            self.tp_size = tp_size
            if tp_size == 1:
                self.set_tensor_parallel_group(tp_group)
        else:
            self.tp_size = get_distributed_world_size(tp_group)
            self.set_tensor_parallel_group(tp_group)
7425
        self.get_rng_state_tracker = get_rng_state_tracker
7426
        self.num_attention_heads = num_attention_heads
7427
        self.layer_number = 1 if layer_number is None else layer_number
7428
7429
7430
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
7431
        self.cp_comm_type = cp_comm_type
7432

7433
7434
7435
7436
7437
7438
        self.hidden_size_per_attention_head_k = (
            kv_channels if isinstance(kv_channels, int) else kv_channels[0]
        )
        self.hidden_size_per_attention_head_v = (
            kv_channels if isinstance(kv_channels, int) else kv_channels[1]
        )
7439

7440
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
7441
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
7442

7443
7444
7445
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
7446

7447
        self.rng_states_tracker = None
7448
7449
7450
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
7451
7452
7453
            self.rng_states_tracker = get_rng_state_tracker()
            set_all_rng_states(self.rng_states_tracker.get_states())
            attention_dropout_ctx = self.rng_states_tracker.fork
7454

7455
        if softmax_scale is None:
7456
7457
7458
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
7459

7460
7461
7462
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
7463
        )
7464
7465
7466
7467
7468
7469
7470
7471
7472
7473
7474
7475
7476
7477
7478
7479
7480
7481
7482
        # To use the workspace optimization path for determinism, please
        # set NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT=1 for cuDNN >=8.9.5 and <9.0.0,
        # and set NVTE_ALLOW_NONDETERMINISTIC_ALGO=0 for cuDNN >=9.0.0.
        cudnn_version = get_cudnn_version()
        if (8, 9, 5) <= cudnn_version < (9, 0, 0):
            if self.deterministic:
                os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] = "1"

            # CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT
            # - unset:       enables workspace optimization when required workspace is <= 256MB
            #                or when bias gradient needs to be computed
            # - n:           enables workspace optimization when required workspace is <= n bytes
            # - -1:          enables workspace optimization always
            # - 0:           disables workspace optimization always
            if "NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT" in os.environ:
                if os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] == "0":
                    os.environ["CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT"] = "0"
                if os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] == "1":
                    os.environ["CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT"] = "-1"
7483

7484
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
7485
7486
7487
7488

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

7489
7490
7491
7492
7493
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

7494
7495
7496
7497
7498
7499
7500
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
7501

7502
        # Instantiating three types since use of flash-attn and FusedAttention
7503
        # might be ruled out due to forward inputs.
7504
7505
7506
7507
7508
7509
7510
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
7511

7512
        self.unfused_attention = UnfusedDotProductAttention(
7513
7514
7515
7516
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
7517
        )
7518

7519
7520
7521
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
7522
7523
            when loading older Transformer Engine checkpoints. Will phase out
            this hook in Transformer Engine 2.0.
7524
7525
7526
7527
7528
7529
7530
            """
            for key in incompatible_keys.missing_keys:
                if "core_attention._extra_state" in key:
                    incompatible_keys.missing_keys.remove(key)

        self.register_load_state_dict_post_hook(remove_extra_states_check)

7531
7532
7533
7534
7535
7536
7537
7538
7539
7540
7541
7542
7543
7544
7545
7546
7547
7548
7549
7550
7551
7552
    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        """
        This function helps to load Transformer Engine 1.6 and 1.7 checkpoints, where FP8 attention
        metadata is stored under the `core_attention.fused_attention._extra_state` key and not the
        `core_attention._extra_state` key. Please see `FP8 checkpoint compatibility
        <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/faq.html#fp8-checkpoint-compatibility>`_ for more details.
        """
        fused_attn_key = False
        dot_product_attn_key = False
        for k in state_dict.keys():
            if "core_attention.fused_attention._extra_state" in k:
                fused_attn_key = True
            if "core_attention._extra_state" in k:
                dot_product_attn_key = True
        if fused_attn_key and not dot_product_attn_key:
            prefix = prefix + "fused_attention."
        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

7553
7554
7555
7556
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
7557
        **forward_kwargs: Dict[str, Any],
7558
7559
7560
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

7561
7562
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
7563
7564
7565

        hidden_states = checkpoint(
            custom_forward,
7566
7567
7568
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
7569
            *forward_args,
7570
            **forward_kwargs,
7571
7572
7573
7574
        )

        return hidden_states

7575
7576
    def set_context_parallel_group(
        self,
7577
        cp_group: Union[dist_group_type, List[dist_group_type], None],
7578
7579
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
7580
        cp_comm_type: str = "p2p",
7581
    ) -> None:
7582
7583
7584
7585
7586
7587
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
7588
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
7589
                  context parallel process group.
7590
7591
7592
                  ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
                  List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
                  and cp_group[1] are for a2a and p2p communications respectively.
7593
7594
7595
7596
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
7597
        cp_comm_type : str, default = `p2p`
7598
                      inter-gpu communication type for context parallelism.
7599
                      Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
7600
7601
7602
7603
7604
7605
                      "p2p": Exchange KV chunks with P2P communications in ring topology.
                             P2P is async and can be overlapped with attention compute.
                      "all_gather": All-gather to get full sequence of KV before attention.
                                    The all-gather is not async, and cannot be overlapped.
                      "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                             group, and gather to get full sequence of QKV.
7606
7607
7608
                      "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                      across each CP sub-group (e.g., via NVLink), then exchanging KV with
                      p2p between sub-groups (e.g., via IBLink).
7609
        """
7610
7611
7612
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
7613
        self.cp_comm_type = cp_comm_type
7614

7615
    @no_torch_dynamo(recursive=False)
7616
7617
7618
7619
7620
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
7621
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7622
7623
7624
        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
7625
7626
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
7627
7628
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
7629
        attn_mask_type: Optional[str] = None,
7630
        window_size: Optional[Tuple[int, int]] = None,
7631
        checkpoint_core_attention: bool = False,
7632
7633
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
7634
        alibi_slopes: Optional[torch.Tensor] = None,
7635
        fast_zero_fill: bool = True,
7636
        inference_params: Optional[InferenceParams] = None,
7637
        is_first_microbatch: Optional[bool] = None,
7638
7639
7640
7641
7642
7643
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

7644
7645
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
7646

7647
7648
        .. note::

7649
7650
7651
7652
7653
7654
7655
7656
7657
7658
7659
7660
7661
            DotProductAttention supports three backends: 1) FlashAttention which calls
            HazyResearch/Dao-AILab's `flash-attn <https://arxiv.org/pdf/2305.13245.pdf>`_
            PyTorch API, 2) FusedAttention which has multiple fused attention implementations
            based on `cuDNN Graph API
            <https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html#op-fusion>`_
            (see :attr:`FusedAttention` for more details on FusedAttention backends), and 3)
            UnfusedDotProductAttention which is the native PyTorch implementation
            with fused scaled masked softmax.

        .. note::

            Users can use environment variables :attr:`NVTE_FLASH_ATTN`, :attr:`NVTE_FUSED_ATTN`,
            and :attr:`NVTE_FUSED_ATTN_BACKEND` to control which DotProductAttention backend,
7662
            and FusedAttention backend if applicable, to use. Transformer Engine prioritizes
7663
7664
7665
7666
            FlashAttention over FusedAttention and over UnfusedDotProductAttention.
            If FusedAttention is being used, users can also choose to switch to flash-attn's
            implementation for backward by setting :attr:`NVTE_FUSED_ATTN_USE_FAv2_BWD=1`
            (default: 0), because of the performance differences between various versions of
7667
7668
            flash-attn and FusedAttention. Further, :attr:`NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT`
            can be used to enable (:attr:`1`) or disable (:attr:`0`) the workspace related
7669
            optimizations in FusedAttention. When unset, Transformer Engine determines the code path
7670
7671
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
7672

7673
7674
7675
7676
7677
7678
7679
7680
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
7681
7682
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
7683
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
7684
7685
             a single tensor of [batch_size, 1, 1, seqlen_q] for self-attention, and a tuple of
             two tensors in shapes [batch_size, 1, 1, seqlen_q] and [batch_size, 1, 1, seqlen_kv]
7686
7687
7688
7689
             for cross-attention. For "`arbitrary`" mask, it should be in a shape broadcastable
             to [batch_size, num_heads, max_seqlen_q, max_seqlen_kv]. A `True` value means
             the corresponding position is masked out and a `False` means that position
             is allowed to participate in attention.
7690
7691
7692
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
7693
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
7694
7695
                   with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
7696
7697
7698
7699
7700
7701
7702
7703
7704
7705
7706
7707
                   Cumulative sum of sequence lengths (without offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_q_padded: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (with offset) in a batch for
                   `query_layer`, with shape [batch_size + 1] and dtype torch.int32.
                   When there is no padding between sequences in a batch,
                   `cu_seqlens_q_padded = cu_seqlens_q`.
        cu_seqlens_kv_padded: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (with offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
                   When there is no padding between sequences in a batch,
                   `cu_seqlens_kv_padded = cu_seqlens_kv`.
7708
7709
7710
7711
7712
7713
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
                      Calculated from `cu_seqlens_q` if not provided.
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
                       Calculated from `cu_seqlens_kv` if not provided.
7714
7715
7716
7717
7718
7719
7720
        attn_mask_type: {'no_mask', 'padding', 'causal', 'padding,causal', 'causal,padding',
                       'padding_causal', 'causal_bottom_right', 'padding_causal_bottom_right',
                       'arbitrary'}, default = `None`. Type of attention mask passed into
                       softmax operation. 'padding,causal', 'causal,padding' and 'padding_causal'
                       are equivalent. By default, causal masks are aligned to the top left corner
                       of the softmax matrix. When "`bottom_right`" is specified in the mask type,
                       causal masks are aligned to the bottom right corner.
7721
        window_size: Optional[Tuple[int, int]], default = `None`
7722
                    Sliding window size for local attention.
7723
7724
7725
7726
7727
        checkpoint_core_attention : bool, default = `False`
                                   If true, forward activations for attention are recomputed
                                   during the backward pass in order to save memory that would
                                   otherwise be occupied to store the forward activations until
                                   backprop.
7728
        core_attention_bias_type: str, default = `no_bias`
7729
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
7730
        core_attention_bias: Optional[torch.Tensor], default = `None`
7731
7732
                    Bias tensor for Q * K.T, shape [1, num_head, max_seqlen_q, max_seqlen_kv].
                    It should be 'None' for 'no_bias' and 'alibi' bias types.
7733
7734
7735
7736
        alibi_slopes: Optional[torch.Tensor], default = `None`
                     ALiBi slopes in FP32 and shape [nheads] or [batch_size, nheads].
                     It adds a bias of (-alibi_slope * (i + seqlen_k - seqlen_q - j))
                     to the attention score of query i and key j.
7737
        fast_zero_fill: bool, default = `True`
7738
                    Whether to use the fast path to set output tensors to 0 or not.
7739
7740
7741
7742
7743
7744
7745
7746
7747
7748
        inference_params: Optional[InferenceParams], default = `None`
            Optimizes execution performance during inference by caching Keys and Values of the
            current decoding iteration. These cached values are appended to the K and V values
            computed in previous iterations, eliminating the need to recalculate them for the
            entire sequence.
            Initialization of `inference_params` is required prior to use to ensure sufficient
            memory allocation.
            Adjustments of the sequence_len_offset should be done after a complete forward pass.
            If rotary positional embeddings (RoPE) are utilized, they must be prepared beforehand.
            Supports "sbhd" and "bshd" layouts, with the "sbhd" layout being more efficient.
7749
7750
7751
7752
7753
7754
7755
7756
7757
7758
7759
7760
7761
        is_first_microbatch : {True, False, None}, default = None
                             During training using either gradient accumulation or
                             pipeline parallelism a minibatch of data is further split
                             into microbatches. Between the microbatches of the same minibatch
                             the model weights are not updated. Setting this parameter indicates
                             whether the current microbatch is the first in a minibatch or not.
                             When set, this parameter enables additional optimizations:

                             * during FP8 training, it allows caching of the FP8 versions of
                               the weights
                             * it also allows skipping gradient accumulation during the
                               first microbatch (since it is the first gradient being
                               produced)
7762
        """
7763
7764
7765
7766
7767
7768
7769
7770
7771
7772
7773
        with self.prepare_forward(
            query_layer,
            is_first_microbatch,
            num_gemms=3,
            allow_non_contiguous=True,
        ) as query_layer:

            if self.fp8:
                if self.fp8_meta["recipe"].fp8_mha:
                    if not self.fp8_meta["recipe"].fp8_dpa:
                        self.fp8_meta["recipe"].fp8_dpa = True
7774
                        self.logger.warning(
7775
7776
7777
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
7778
7779
7780
7781
7782
7783
7784
7785
7786
7787
7788

            if self.fp8 and self.fp8_meta["recipe"].fp8_dpa:
                forward_dtype = get_fp8_te_dtype(self.fp8_meta["recipe"], fprop_tensor=True)
                backward_dtype = get_fp8_te_dtype(self.fp8_meta["recipe"], fprop_tensor=False)
                assert forward_dtype in [
                    tex.DType.kFloat8E4M3,
                    tex.DType.kFloat8E5M2,
                ] and backward_dtype in [
                    tex.DType.kFloat8E4M3,
                    tex.DType.kFloat8E5M2,
                ], """DotProductAttention only supports "E4M3" and "E5M2" FP8 data types."""
7789

7790
7791
7792
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
7793
7794
7795
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
7796
7797
7798
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
7799
7800
7801
7802
7803
7804
7805
7806
            assert (
                key_layer.shape[-1] == self.hidden_size_per_attention_head_k
            ), f"Keys have head_dim = {key_layer.shape[-1]}, "
            "but expected head_dim = {self.hidden_size_per_attention_head_k}!"
            assert (
                value_layer.shape[-1] == self.hidden_size_per_attention_head_v
            ), f"Values have head_dim = {value_layer.shape[-1]}, "
            "but expected head_dim = {self.hidden_size_per_attention_head_v}!"
7807

7808
7809
7810
7811
7812
7813
            if attn_mask_type is None:
                attn_mask_type = self.attn_mask_type
            else:
                attn_mask_type = attn_mask_type.replace(",", "_")
                if attn_mask_type == "causal_padding":
                    attn_mask_type = "padding_causal"
7814
            assert (
7815
7816
7817
7818
7819
7820
                attn_mask_type in AttnMaskTypes
            ), f"Attention mask type {attn_mask_type} is not supported!"
            if qkv_format == "thd":
                assert (
                    "padding" in attn_mask_type
                ), "Attention mask type must be padding or padding_causal for qkv_format=thd!"
7821

7822
7823
7824
7825
            if window_size is None:
                window_size = self.window_size
            window_size = check_set_window_size(attn_mask_type, window_size)

7826
7827
7828
7829
7830
7831
7832
            if self.rng_states_tracker is not None and is_graph_capturing():
                assert isinstance(
                    self.rng_states_tracker, CudaRNGStatesTracker
                ), "Unsupported RNG states tracker."
                assert (
                    graph_safe_rng_available()
                ), "Upgrade PyTorch version to get RNG manipulation support for cuda graph capture."
7833

7834
7835
            if qkv_format is None:
                qkv_format = self.qkv_format
7836

7837
7838
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"
7839

7840
7841
7842
7843
7844
                # convert causal to causal_bottom_right in inference when KV-caching is in use
                # so users can run with the same attn_mask_type for training and inference
                if attn_mask_type in ["causal", "padding_causal"]:
                    attn_mask_type = attn_mask_type + "_bottom_right"

7845
7846
7847
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
7848

7849
7850
7851
7852
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]
7853

7854
7855
7856
                batch_start = inference_params.batch_size_offset
                batch_end = batch_start + key_layer.size(1)
                assert batch_end <= inference_key_memory.size(1)
7857

7858
7859
7860
                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + key_layer.size(0)
                assert sequence_end <= inference_key_memory.size(0)
7861

7862
7863
7864
7865
7866
7867
7868
7869
7870
                # Copy keys and values into KV-cache
                inference_key_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = (
                    key_layer
                )
                inference_value_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = (
                    value_layer
                )
                key_layer = inference_key_memory[:sequence_end, batch_start:batch_end, ...]
                value_layer = inference_value_memory[:sequence_end, batch_start:batch_end, ...]
7871

7872
7873
7874
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
7875

7876
7877
                key_layer = key_layer.contiguous()
                value_layer = value_layer.contiguous()
7878
7879

            assert (
7880
7881
7882
7883
7884
7885
7886
7887
7888
7889
                key_layer.shape[-2] == self.num_gqa_groups_per_partition
                and value_layer.shape[-2] == self.num_gqa_groups_per_partition
            ), f"Keys and values must have num_gqa_group = {self.num_gqa_groups} heads!"
            assert qkv_format in [
                "sbhd",
                "bshd",
                "thd",
            ], "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"

            if qkv_format == "thd":
7890
                assert all(
7891
7892
7893
7894
7895
7896
7897
7898
7899
7900
7901
7902
7903
7904
                    len(x.shape) == 3 for x in (query_layer, key_layer, value_layer)
                ), "Queries, keys and values must be 3D tensors when qkv_format = thd!"
                assert (
                    cu_seqlens_q is not None and cu_seqlens_kv is not None
                ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
                assert (
                    cu_seqlens_q.shape == cu_seqlens_kv.shape
                    and len(cu_seqlens_q.shape) == 1
                    and len(cu_seqlens_kv.shape) == 1
                ), "cu_seqlens_q and cu_seqlens_q must both have shape [batch_size + 1]!"
                assert (
                    cu_seqlens_q.dtype == torch.int32 and cu_seqlens_kv.dtype == torch.int32
                ), "cu_seqlens_q and cu_seqlens_q must both be in dtype torch.int32!"
                if max_seqlen_q is None:
7905
7906
7907
7908
                    if cu_seqlens_q_padded is not None:
                        seqlens_q = cu_seqlens_q_padded[1:] - cu_seqlens_q_padded[:-1]
                    else:
                        seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
7909
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
7910
                if max_seqlen_kv is None:
7911
7912
7913
7914
                    if cu_seqlens_kv_padded is not None:
                        seqlens_kv = cu_seqlens_kv_padded[1:] - cu_seqlens_kv_padded[:-1]
                    else:
                        seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
7915
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
7916
                batch_size = len(cu_seqlens_q) - 1
7917

7918
7919
7920
7921
7922
7923
            cp_size = 1
            if isinstance(self.cp_group, dist_group_type):
                cp_size = get_distributed_world_size(self.cp_group)
            elif isinstance(self.cp_group, list):
                for group in self.cp_group:
                    cp_size *= get_distributed_world_size(group)
7924
7925
            context_parallel = cp_size > 1

7926
            if qkv_format in ["sbhd", "bshd"]:
7927
                assert all(
7928
7929
7930
7931
                    len(x.shape) == 4 for x in (query_layer, key_layer, value_layer)
                ), f"Queries, keys and values must be 4D tensors when qkv_format = {qkv_format}!"
                if qkv_format == "sbhd":
                    max_seqlen_q, max_seqlen_kv = (query_layer.shape[0], key_layer.shape[0])
7932
                    batch_size = query_layer.shape[1]
7933
                else:
7934
                    max_seqlen_q, max_seqlen_kv = (query_layer.shape[1], key_layer.shape[1])
7935
                    batch_size = query_layer.shape[0]
7936
7937
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
7938
7939
7940
7941
7942
7943
7944
7945
7946
7947
7948
7949
                if cu_seqlens_q is not None:
                    seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                    assert all(
                        seqlens_q <= max_seqlen_q
                    ), """Sequence lengths indicated by cu_seqlens_q must be no greater than
                        the sequence dimention in 'query_layer'!"""
                if cu_seqlens_kv is not None:
                    seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                    assert all(
                        seqlens_kv <= max_seqlen_kv
                    ), """Sequence lengths indicated by cu_seqlens_kv must be no greater than
                        the sequence dimention in 'key_layer' and 'value_layer'!"""
7950
7951
7952
7953
7954
                if cu_seqlens_q is None or cu_seqlens_kv is None:
                    if "padding" in attn_mask_type:
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
7955
                        if self.attention_type == "self":
7956
7957
7958
7959
7960
7961
7962
7963
7964
7965
7966
7967
7968
7969
7970
7971
                            cu_seqlens_q = get_cu_seqlens(attention_mask)
                            cu_seqlens_kv = cu_seqlens_q
                        else:
                            cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                            cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
                    else:
                        cu_seqlens_q = _get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
                        )
                        cu_seqlens_kv = _get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
                        )
7972

7973
7974
7975
7976
7977
            if (
                isinstance(query_layer, Float8Tensor)
                and isinstance(key_layer, Float8Tensor)
                and isinstance(value_layer, Float8Tensor)
            ):
7978
                qkv_layout, query_layer._data, key_layer._data, value_layer._data = get_qkv_layout(
7979
7980
7981
                    query_layer._data, key_layer._data, value_layer._data, qkv_format=qkv_format
                )
            else:
7982
                qkv_layout, query_layer, key_layer, value_layer = get_qkv_layout(
7983
7984
                    query_layer, key_layer, value_layer, qkv_format=qkv_format
                )
7985

7986
7987
7988
7989
7990
7991
7992
7993
            global _alibi_cache
            if alibi_slopes is not None:
                assert (
                    core_attention_bias_type == "alibi"
                ), "core_attention_bias_type must be alibi in order to use alibi_slopes!"
                if self.layer_number == 1:
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True
7994
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
7995
7996
7997
7998
7999
8000
8001
8002
            if core_attention_bias_type == "alibi":
                assert (
                    core_attention_bias is None
                ), "core_attention_bias must be None when core_attention_bias_type is alibi!"
                if (
                    _alibi_cache["_num_heads"] != query_layer.shape[-2]
                    or _alibi_cache["_max_seqlen_q"] != max_seqlen_q
                    or _alibi_cache["_max_seqlen_kv"] != max_seqlen_kv
8003
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
8004
8005
8006
8007
8008
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

8009
8010
            core_attention_bias_shape = None
            if core_attention_bias is not None:
8011
                if (
8012
8013
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
8014
                ):
8015
8016
8017
8018
8019
8020
8021
8022
8023
8024
8025
8026
8027
8028
8029
8030
8031
8032
8033
8034
8035
8036
8037
8038
                    core_attention_bias_shape = "bhss"
                elif (
                    core_attention_bias.shape[0] == 1
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
                ):
                    core_attention_bias_shape = "1hss"
                elif (
                    core_attention_bias.shape[0] == batch_size and core_attention_bias.shape[1] == 1
                ):
                    core_attention_bias_shape = "b1ss"
                elif core_attention_bias.shape[0] == 1 and core_attention_bias.shape[1] == 1:
                    core_attention_bias_shape = "11ss"
                else:
                    assert (
                        False
                    ), "core_attention_bias must be in one of {bhss, 1hss, b1ss, 11ss} shapes"

            pad_between_seqs = (
                cu_seqlens_q_padded is not None
                and not torch.equal(cu_seqlens_q_padded, cu_seqlens_q)
            ) or (
                cu_seqlens_kv_padded is not None
                and not torch.equal(cu_seqlens_kv_padded, cu_seqlens_kv)
            )
8039

8040
            attention_params = AttentionParams(
8041
8042
8043
8044
8045
8046
8047
8048
                qkv_type=type(query_layer),
                qkv_dtype=query_layer.dtype,
                qkv_layout=qkv_layout,
                batch_size=batch_size,
                num_heads=query_layer.shape[-2],
                num_gqa_groups=key_layer.shape[-2],
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
8049
8050
                head_dim_qk=query_layer.shape[-1],
                head_dim_v=value_layer.shape[-1],
8051
8052
8053
8054
8055
8056
8057
8058
8059
8060
8061
                attn_mask_type=attn_mask_type,
                window_size=window_size,
                alibi_slopes_shape=alibi_slopes.shape if alibi_slopes is not None else None,
                core_attention_bias_type=core_attention_bias_type,
                core_attention_bias_shape=core_attention_bias_shape,
                core_attention_bias_requires_grad=(
                    core_attention_bias.requires_grad if core_attention_bias is not None else False
                ),
                pad_between_seqs=pad_between_seqs,
                attention_dropout=self.attention_dropout,
                context_parallel=context_parallel,
8062
8063
                deterministic=self.deterministic,
                is_training=self.training,
8064
8065
8066
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
            )
8067
            global _attention_backends, _use_flash_attn_3
8068
8069
8070
8071
8072
8073
8074
            if (
                _attention_backends["attention_params"] is None
                or attention_params != _attention_backends["attention_params"]
            ):
                _attention_backends["attention_params"] = attention_params
                _attention_backends["backend_selection_requires_update"] = True
            if _attention_backends["backend_selection_requires_update"]:
8075
                _use_flash_attn_3 = _flash_attn_3_is_installed
8076
8077
8078
8079
8080
8081
8082
8083
                (
                    use_flash_attention,
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
                ) = get_attention_backend(attention_params)
                if use_flash_attention:
8084
8085
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
8086
                        _flash_attn_version if not _use_flash_attn_3 else _flash_attn_3_version,
8087
                    )
8088
8089
8090
8091
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
8092
                    )
8093
8094
8095
8096
8097
8098
8099
                elif use_unfused_attention:
                    self.logger.info("Running with UnfusedDotProductAttention backend")
            else:
                use_flash_attention = _attention_backends["use_flash_attention"]
                use_fused_attention = _attention_backends["use_fused_attention"]
                fused_attention_backend = _attention_backends["fused_attention_backend"]
                use_unfused_attention = _attention_backends["use_unfused_attention"]
8100

8101
8102
8103
8104
8105
8106
8107
8108
8109
8110
8111
8112
8113
8114
8115
8116
8117
8118
8119
8120
8121
8122
            if use_flash_attention:
                if core_attention_bias_type == "alibi":
                    alibi_slopes, _ = get_alibi(
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                    )
                return self.flash_attention(
                    query_layer,
                    key_layer,
                    value_layer,
                    attention_mask=attention_mask,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
                    attn_mask_type=attn_mask_type,
                    window_size=window_size,
                    alibi_slopes=alibi_slopes,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
8123
                    cp_comm_type=self.cp_comm_type,
8124
8125
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
8126
8127
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
8128
                )
8129

8130
            if use_fused_attention:
8131
8132
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
8133
8134
8135
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
8136
8137
8138
8139
8140
8141
8142
                    fu_core_attention_bias_type = "post_scale_bias"
                    _, fu_core_attention_bias = get_alibi(
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                        bias_dtype=query_layer.dtype,
8143
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
8144
                    )
8145
8146
8147
8148
8149
8150
8151
8152
8153
                if checkpoint_core_attention:
                    return self._checkpointed_attention_forward(
                        self.fused_attention,
                        query_layer,
                        key_layer,
                        value_layer,
                        qkv_layout=qkv_layout,
                        cu_seqlens_q=cu_seqlens_q,
                        cu_seqlens_kv=cu_seqlens_kv,
8154
8155
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
8156
8157
8158
8159
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
8160
                        window_size=window_size,
8161
8162
8163
8164
8165
8166
8167
                        fused_attention_backend=fused_attention_backend,
                        core_attention_bias_type=fu_core_attention_bias_type,
                        core_attention_bias=fu_core_attention_bias,
                        fast_zero_fill=fast_zero_fill,
                        cp_group=self.cp_group,
                        cp_global_ranks=self.cp_global_ranks,
                        cp_stream=self.cp_stream,
8168
                        cp_comm_type=self.cp_comm_type,
8169
8170
8171
8172
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
                    )
                return self.fused_attention(
8173
8174
8175
8176
8177
8178
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
8179
8180
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
8181
8182
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
8183
8184
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
8185
                    window_size=window_size,
8186
                    fused_attention_backend=fused_attention_backend,
8187
8188
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
8189
8190
8191
8192
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
8193
                    cp_comm_type=self.cp_comm_type,
8194
8195
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
8196
                )
8197

8198
            from .cpu_offload import CPUOffloadEnabled
8199

8200
8201
8202
8203
8204
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
8205

8206
            if use_unfused_attention:
8207
8208
8209
8210
8211
8212
                if window_size is not None and (
                    window_size[0] != -1 or window_size[1] not in [-1, 0]
                ):
                    attn_mask_type, attention_mask = get_swa_mask(
                        window_size, max_seqlen_q, max_seqlen_kv, attn_mask_type, attention_mask
                    )
8213
8214
8215
8216
8217
8218
8219
8220
8221
8222
8223
8224
8225
8226
8227
8228
                if checkpoint_core_attention:
                    return self._checkpointed_attention_forward(
                        self.unfused_attention,
                        query_layer,
                        key_layer,
                        value_layer,
                        qkv_layout=qkv_layout,
                        cu_seqlens_q=cu_seqlens_q,
                        cu_seqlens_kv=cu_seqlens_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
                        core_attention_bias_type=core_attention_bias_type,
                        core_attention_bias=core_attention_bias,
                        alibi_slopes=alibi_slopes,
                    )
                return self.unfused_attention(
8229
8230
8231
                    query_layer,
                    key_layer,
                    value_layer,
8232
8233
8234
8235
8236
8237
8238
8239
8240
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
                    core_attention_bias_type=core_attention_bias_type,
                    core_attention_bias=core_attention_bias,
                    alibi_slopes=alibi_slopes,
                )
8241

8242
            raise ValueError("No dot product attention support for the provided inputs!")
8243
8244


8245
8246
8247
8248
8249
8250
8251
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

8252
8253
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
8254

8255
8256
8257
8258
8259
8260
8261
8262
8263
8264
8265
8266
8267
8268
8269
8270
8271
8272
8273
8274
8275
8276
8277
8278
8279
    Parameters
    ----------
    hidden_size : int
                 size of each input sample.
    num_attention_heads : int
                         number of attention heads in the transformer layer.
    kv_channels: int, default = `None`
                number of key-value channels. defaults to
                :attr:`hidden_size` / :attr:`num_attention_heads` if `None`.
    attention_dropout: float, default = 0.1
                      dropout probability for the dropout op during multi-head attention.
    layernorm_epsilon : float, default = 1e-5
                       a value added to the denominator of layer normalization
                       for numerical stability.
    init_method : Callable, default = `None`
                 used for initializing weights of QKV and FC1 weights in the following way:
                 `init_method(weight)`. When set to `None`, defaults to
                 `torch.nn.init.normal_(mean=0.0, std=0.023)`.
    output_layer_init_method : Callable, default = `None`
                              used for initializing weights of PROJ and FC2 in the following way:
                              `output_layer_init_method(weight)`. When set to `None`, defaults to
                              `torch.nn.init.normal_(mean=0.0, std=0.023)`.
    layer_number: int, default = `None`
                 layer number of the current `TransformerLayer` when multiple such modules are
                 concatenated to form a transformer block.
8280
8281
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
8282
                   default = `causal`
8283
8284
8285
8286
8287
                   type of attention mask passed into softmax operation. Overridden by
                   :attr:`attn_mask_type` in the `forward` method. The forward
                   arg is useful for dynamically changing mask types, e.g. a different
                   mask for training and inference. The init arg is useful for cases
                   involving compilation/tracing, e.g. ONNX export.
8288
8289
8290
8291
    window_size: Optional[Tuple[int, int]], default = `None`
                sliding window size for local attention, where query at position i attends to keys
                in [i + seqlen_k - seqlen_q - window_size[0], i + seqlen_k - seqlen_q
                + window_size[1]] inclusive. Special cases (-1, -1) and (-1, 0) mean no sliding
8292
8293
8294
                window and causal mask specifically. Both `causal` and `causal_bottom_right` masks
                map to `window_size = (-1, 0)` and Transformer Engine distinguishes them based on
                `attn_mask_type`. Similar to :attr:`attn_mask_type`, `window_size` can
8295
                be overridden by :attr:`window_size` in `forward` as well.
8296
8297
8298
8299
8300
8301
8302
8303
8304
8305
8306
8307
8308
    num_gqa_groups : int, default = `None`
                         number of GQA groups in the transformer layer.
                         Grouped Query Attention is described in
                         `this paper <https://arxiv.org/pdf/2305.13245.pdf>`_.
                         This only affects the keys and values, not the querys.
                         GQA-1 is equivalent to Multi-Query Attention
                         (`MQA <https://arxiv.org/pdf/1911.02150.pdf>`_), while GQA-H
                         is equivalent to MHA, i.e. `num_gqa_groups = num_attention_heads`.
    return_layernorm_output : bool, default = `False`
                             if set to `True`, output of layernorm is returned from the forward
                             together with the output of the linear transformation.
                             Example use case: residual connection for transformer module is
                             taken post layernorm.
8309
8310
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
8311
8312
8313
8314
8315
8316
8317
8318
8319
8320
8321
8322
8323
8324
8325
8326
8327
8328
8329
8330
    attention_type: { 'self', 'cross' }, default = 'self'
                   type of attention applied.
    zero_centered_gamma : bool, default = 'False'
                         if set to 'True', gamma parameter in LayerNorm is initialized to 0 and
                         the LayerNorm formula changes to

                         .. math::
                            y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} *
                            (1 + \gamma) + \beta
    normalization : { 'LayerNorm', 'RMSNorm' }, default = 'LayerNorm'
                   type of normalization applied.
    qkv_weight_interleaved : bool, default = `True`
                            if set to `False`, the QKV weight is interpreted as a concatenation of
                            query, key, and value weights along the `0th` dimension. The default
                            interpretation is that the individual `q`, `k`, and `v` weights for each
                            attention head are interleaved. This parameter is set to `False` when
                            using :attr:`fuse_qkv_params=False`.
    bias : bool, default = `True`
          if set to `False`, the transformer layer will not learn any additive biases.
    device : Union[torch.device, str], default = "cuda"
8331
          The device on which the parameters of the model will be allocated. It is the user's
8332
8333
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
8334
8335
8336
8337
8338
8339
8340
    qkv_format: str, default = `sbhd`
            dimension format for `query_layer`, `key_layer` and `value_layer`,
            {`sbhd`, `bshd`}. `s` stands for the sequence length, `b` batch size,
            `h` the number of heads and `d` head size. `sbhd` and `bshd` formats
            are used for when sequences in a batch are of equal length or padded to
            equal length. Please note that these formats do not reflect how
            tensors `query_layer`, `key_layer`, `value_layer` are laid out in memory.
8341
            For that, please use `get_qkv_layout` to gain the layout information.
8342
8343
8344
8345
8346
8347
8348
8349
8350
8351
8352
8353
8354
8355
8356
8357
8358
8359
8360
8361
8362
8363
8364
8365
8366
8367
8368
8369
8370
8371
8372
8373
8374
8375
8376
8377
8378
8379
8380
8381

    Parallelism parameters
    ----------------------
    set_parallel_mode : bool, default = `False`
                      if set to `True`, QKV and FC1 layers are used as Column Parallel
                      whereas PROJ and FC2 is used as Row Parallel as described
                      `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
    tp_size : int, default = 1
             used as TP (tensor parallel) world size when TP groups are not formed during
             initialization. In this case, users must call the
             `set_tensor_parallel_group(tp_group)` method on the initialized module before the
             forward pass to supply the tensor parallel group needed for tensor and sequence
             parallel collectives.

    Optimization parameters
    -----------------------
    fuse_wgrad_accumulation : bool, default = 'False'
                             if set to `True`, enables fusing of creation and accumulation of
                             the weight gradient. When enabled, it is assumed that the weights
                             have an additional `main_grad` attribute (used instead of the
                             regular `grad`) which is a pre-allocated buffer of the correct
                             size to accumulate gradients in.
    params_dtype : torch.dtype, default = `torch.get_default_dtype()`
                  it controls the type used to allocate the initial parameters. Useful when
                  the model is trained with lower precision and the original FP32 parameters
                  would not fit in GPU memory.
    return_bias : bool, default = `False`
                 when set to `True`, this module will not apply the additive bias itself, but
                 instead return the bias value during the forward pass together with the
                 output of the linear transformation :math:`y = xA^T`. This is useful when
                 the bias addition can be fused to subsequent operations.
    fuse_qkv_params: bool, default = 'False'
                    if set to `True`, `TransformerLayer` module exposes a single fused
                    parameter for query-key-value. This enables optimizations such as QKV
                    fusion without concatentations/splits and also enables the argument
                    `fuse_wgrad_accumulation`.
8382
8383
8384
8385
8386
8387
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
8388
8389
8390
8391
8392
        kv_channels: Optional[int] = None,
        attention_dropout: float = 0.1,
        layernorm_epsilon: float = 1e-5,
        init_method: Optional[Callable] = None,
        output_layer_init_method: Optional[Callable] = None,
8393
        layer_number: Optional[int] = None,
8394
        attn_mask_type: str = "causal",
8395
        window_size: Optional[Tuple[int, int]] = None,
8396
8397
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
8398
        num_gqa_groups: Optional[int] = None,
8399
8400
8401
        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
8402
        params_dtype: Optional[torch.dtype] = None,
8403
        return_bias: bool = False,
8404
8405
8406
8407
8408
8409
8410
8411
8412
        return_layernorm_output: bool = False,
        input_layernorm: bool = False,
        attention_type: str = "self",
        set_parallel_mode: bool = False,
        fuse_qkv_params: bool = False,
        zero_centered_gamma: bool = False,
        qkv_weight_interleaved: bool = True,
        ub_bulk_wgrad: bool = False,
        ub_bulk_dgrad: bool = False,
Jaemin Choi's avatar
Jaemin Choi committed
8413
        ub_overlap_rs_dgrad: bool = False,
8414
8415
        ub_overlap_rs: bool = False,
        ub_overlap_ag: bool = False,
8416
        bias: bool = True,
8417
        normalization: str = "LayerNorm",
8418
        device: Union[torch.device, str] = "cuda",
8419
        qkv_format: str = "sbhd",
8420
8421
    ) -> None:
        super().__init__()
8422

8423
        self.qkv_format = qkv_format
8424
        self.attn_mask_type = attn_mask_type
8425
        self.window_size = check_set_window_size(attn_mask_type, window_size)
8426
        self.layer_number = layer_number
8427
8428
8429
8430
8431
        self.input_layernorm = input_layernorm
        self.attention_type = attention_type
        self.get_rng_state_tracker = get_rng_state_tracker
        self.tp_group = tp_group
        self.return_layernorm_output = return_layernorm_output
8432
        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
8433
        self.num_attention_heads = num_attention_heads
8434
8435
8436
8437
8438
8439
8440
8441
        self.return_bias = return_bias

        kv_channels = kv_channels if kv_channels else (hidden_size // num_attention_heads)

        if init_method is None:
            init_method = get_default_init_method()
        if output_layer_init_method is None:
            output_layer_init_method = get_default_init_method()
8442
8443
8444
8445
8446

        if not fuse_qkv_params:
            qkv_weight_interleaved = False
        self.qkv_weight_interleaved = qkv_weight_interleaved

8447
8448
8449
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
        if layer_number is not None:
            assert layer_number > 0, "layer_number must be a positive integer"
8450
8451
8452
8453
8454
8455

        tp_size = tp_size if tp_group is None else get_distributed_world_size(tp_group)
        self.tp_size = tp_size
        self.sequence_parallel = (tp_size > 1) and sequence_parallel

        self.num_attention_heads_per_partition = divide(num_attention_heads, tp_size)
8456
8457
8458
8459
8460
8461
8462
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
        assert (
            self.num_gqa_groups % tp_size == 0
        ), "The number of GQA groups must be divisible by tensor parallel size!"
8463
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)
8464
8465
8466
8467

        self.hidden_size_per_attention_head = kv_channels
        self.hidden_size_q = self.hidden_size_per_attention_head * num_attention_heads
        self.hidden_size_kv = self.hidden_size_per_attention_head * self.num_gqa_groups
8468
8469
8470
8471
8472
8473
8474

        common_gemm_kwargs = {
            "fuse_wgrad_accumulation": fuse_wgrad_accumulation,
            "tp_group": tp_group,
            "tp_size": tp_size,
            "get_rng_state_tracker": get_rng_state_tracker,
            "sequence_parallel": sequence_parallel,
8475
            "params_dtype": self.params_dtype,
8476
            "device": device,
8477
8478
8479
8480
        }

        qkv_parallel_mode = "column" if set_parallel_mode else None

cyanguwa's avatar
cyanguwa committed
8481
        if self.attention_type == "self":
8482
8483
            parameters_split = None
            if not fuse_qkv_params:
8484
8485
8486
8487
8488
8489
8490
                parameters_split = collections.OrderedDict(
                    [
                        ("query", self.hidden_size_q),
                        ("key", self.hidden_size_kv),
                        ("value", self.hidden_size_kv),
                    ]
                )
8491
8492
8493
            if self.input_layernorm:
                self.layernorm_qkv = LayerNormLinear(
                    hidden_size,
8494
                    self.hidden_size_q + 2 * self.hidden_size_kv,
8495
8496
8497
8498
8499
8500
                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    return_layernorm_output=return_layernorm_output,
cyanguwa's avatar
cyanguwa committed
8501
                    parameters_split=parameters_split,
8502
8503
8504
                    zero_centered_gamma=zero_centered_gamma,
                    ub_bulk_wgrad=ub_bulk_wgrad,
                    ub_bulk_dgrad=ub_bulk_dgrad,
Jaemin Choi's avatar
Jaemin Choi committed
8505
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
8506
                    ub_overlap_ag=ub_overlap_ag,
8507
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
8508
                    ub_name="qkv",
8509
8510
8511
8512
8513
                    **common_gemm_kwargs,
                )
            else:
                self.qkv = Linear(
                    hidden_size,
8514
                    self.hidden_size_q + 2 * self.hidden_size_kv,
8515
8516
8517
8518
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
cyanguwa's avatar
cyanguwa committed
8519
                    parameters_split=parameters_split,
8520
8521
                    **common_gemm_kwargs,
                )
cyanguwa's avatar
cyanguwa committed
8522
        elif self.attention_type == "cross":
8523
8524
8525
            if self.input_layernorm:
                self.layernorm_query = LayerNormLinear(
                    hidden_size,
8526
                    self.hidden_size_q,
8527
8528
8529
8530
8531
                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
8532
                    parameters_split=("query",) if not fuse_qkv_params else None,
8533
8534
8535
8536
                    return_layernorm_output=return_layernorm_output,
                    zero_centered_gamma=zero_centered_gamma,
                    ub_bulk_wgrad=ub_bulk_wgrad,
                    ub_bulk_dgrad=ub_bulk_dgrad,
Jaemin Choi's avatar
Jaemin Choi committed
8537
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
8538
                    ub_overlap_ag=ub_overlap_ag,
8539
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
8540
                    ub_name="qkv",
8541
8542
8543
8544
8545
                    **common_gemm_kwargs,
                )
            else:
                self.query_layer = Linear(
                    hidden_size,
8546
                    self.hidden_size_q,
8547
8548
8549
8550
8551
8552
8553
8554
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    **common_gemm_kwargs,
                )
            self.key_value = Linear(
                hidden_size,
8555
                2 * self.hidden_size_kv,
8556
8557
8558
8559
                init_method=init_method,
                bias=bias,
                return_bias=False,
                parallel_mode=qkv_parallel_mode,
8560
                parameters_split=("key", "value") if not fuse_qkv_params else None,
8561
8562
8563
8564
8565
8566
                **common_gemm_kwargs,
            )

        # Attention.
        self.core_attention = DotProductAttention(
            num_attention_heads,
8567
            self.hidden_size_per_attention_head,
8568
8569
            num_gqa_groups=self.num_gqa_groups,
            attention_dropout=attention_dropout,
8570
            qkv_format=self.qkv_format,
8571
8572
8573
8574
            tp_size=tp_size,
            get_rng_state_tracker=get_rng_state_tracker,
            sequence_parallel=sequence_parallel,
            tp_group=tp_group,
8575
            layer_number=self.layer_number,
8576
            attention_type=self.attention_type,
8577
8578
8579
8580
        )

        # Linear
        self.proj = Linear(
8581
            self.hidden_size_q,
8582
8583
8584
            hidden_size,
            init_method=output_layer_init_method,
            bias=bias,
8585
            return_bias=return_bias,
8586
            parallel_mode="row" if set_parallel_mode else None,
8587
8588
            ub_overlap_rs=ub_overlap_rs,
            ub_overlap_ag=ub_overlap_ag,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
8589
            ub_name="proj",
8590
8591
8592
8593
            **common_gemm_kwargs,
        )

    def _allocate_memory(
8594
        self, inference_max_sequence_len: int, batch_size: int, dtype: torch.dtype
8595
    ) -> torch.Tensor:
8596
        """Allocates memory for KV cache."""
8597
8598
8599
        return torch.empty(
            inference_max_sequence_len,
            batch_size,
8600
            self.num_gqa_groups_per_partition,
8601
            self.hidden_size_per_attention_head,
8602
            dtype=dtype,
8603
8604
8605
8606
            device=torch.cuda.current_device(),
        )

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
8607
8608
8609
8610
8611
8612
8613
8614
8615
        """
        Set the tensor parallel group for the given
        module before executing the forward pass.

        Parameters
        ----------
        tp_group : ProcessGroup, default = `None`
                  tensor parallel process group.
        """
8616
8617
        self.tp_group = tp_group

8618
    def set_context_parallel_group(
8619
        self,
8620
        cp_group: Union[dist_group_type, List[dist_group_type], None],
8621
        cp_global_ranks: List[int],
8622
        cp_stream: torch.cuda.Stream,
8623
        cp_comm_type: str = "p2p",
8624
    ) -> None:
8625
8626
8627
8628
8629
8630
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
8631
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
8632
                  context parallel process group.
8633
8634
8635
                  ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
                  List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
                  and cp_group[1] are for a2a and p2p communications respectively.
8636
8637
8638
8639
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
8640
        cp_comm_type : str, default = `p2p`
8641
                      inter-gpu communication type for context parallelism.
8642
                      Can be "p2p" or "all_gather" or "a2a", "a2a+p2p".
8643
8644
8645
8646
8647
8648
                      "p2p": Exchange KV chunks with P2P communications in ring topology.
                             P2P is async and can be overlapped with attention compute.
                      "all_gather": All-gather to get full sequence of KV before attention.
                                    The all-gather is not async, and cannot be overlapped.
                      "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                             group, and gather to get full sequence of QKV.
8649
8650
8651
                      "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                      across each CP sub-group (e.g., via NVLink), then exchanging KV with
                      p2p between sub-groups (e.g., via IBLink).
8652
        """
8653
8654
8655
8656
8657
        # Deep iterate but skip self to avoid infinite recursion.
        for index, child in enumerate(self.modules()):
            if index == 0:
                continue
            if hasattr(child, "set_context_parallel_group"):
8658
                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream, cp_comm_type)
8659

8660
8661
8662
    def forward(
        self,
        hidden_states: torch.Tensor,
8663
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
8664
        encoder_output: Optional[torch.Tensor] = None,
8665
        attn_mask_type: Optional[str] = None,
8666
        window_size: Optional[Tuple[int, int]] = None,
8667
8668
        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
8669
        inference_params: Optional[InferenceParams] = None,
8670
        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
8671
8672
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
8673
        alibi_slopes: Optional[torch.Tensor] = None,
8674
8675
8676
8677
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
8678
        fast_zero_fill: bool = True,
8679
    ) -> Tuple[Union[torch.Tensor, None], ...]:
8680
8681
8682
8683
8684
        """
        Forward propagation for MultiheadAttention layer.

        .. note::

8685
8686
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes `"padding"` or `"arbitrary"`.
8687
8688
8689
8690
8691

        Parameters
        ----------
        hidden_states : torch.Tensor
             Input tensor.
8692
8693
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
8694
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
8695
8696
             a single tensor of [batch_size, 1, 1, seqlen_q] for self-attention, and a tuple of
             two tensors in shapes [batch_size, 1, 1, seqlen_q] and [batch_size, 1, 1, seqlen_kv]
8697
8698
8699
8700
8701
8702
             for cross-attention. For "`arbitrary`" mask, it should be in a shape broadcastable to
             [batch_size, num_heads, max_seqlen_q, max_seqlen_kv]. A `True` value means
             the corresponding position is masked out and a `False` means that position
             is allowed to participate in attention.
        attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                       'padding_causal_bottom_right','arbitrary'},
8703
                       default = `None`
8704
8705
8706
8707
                       type of attention mask passed into softmax operation. By default,
                       causal masks are aligned to the top left corner of the softmax matrix.
                       When "`bottom_right`" is specified in the mask type, causal masks are
                       aligned to the bottom right corner.
8708
8709
        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
8710
8711
8712
8713
8714
8715
8716
8717
8718
8719
8720
8721
8722
8723
8724
8725
8726
8727
8728
8729
8730
8731
8732
8733
8734
        encoder_output : Optional[torch.Tensor], default = `None`
             Output of the encoder block to be fed into the decoder block if using
             `layer_type="decoder"`.
        is_first_microbatch : {True, False, None}, default = None
                             During training using either gradient accumulation or
                             pipeline parallelism a minibatch of data is further split
                             into microbatches. Between the microbatches of the same minibatch
                             the model weights are not updated. Setting this parameter indicates
                             whether the current microbatch is the first in a minibatch or not.
                             When set, this parameter enables additional optimizations:

                             * during FP8 training, it allows caching of the FP8 versions of
                               the weights
                             * it also allows skipping gradient accumulation during the
                               first microbatch (since it is the first gradient being
                               produced)
        checkpoint_core_attention: bool, default = `False`
                                  If true, forward activations for core attention are recomputed
                                  during the backward pass in order to save memory that would
                                  otherwise be occupied to store the forward activations until
                                  backprop.
        rotary_pos_emb: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], default = `None`
                       Embeddings for query and key tensors for applying rotary position
                       embedding. By default no input embedding is applied.
        core_attention_bias_type: str, default = `no_bias`
8735
                    Bias type, {`no_bias`, `pre_scale_bias`, 'post_scale_bias`, `alibi`}
8736
        core_attention_bias: Optional[torch.Tensor], default = `None`
8737
8738
                    Bias tensor for Q * K.T, shape [1, num_head, max_seqlen_q, max_seqlen_kv].
                    It should be 'None' for 'no_bias' and 'alibi' bias types.
8739
8740
8741
8742
        alibi_slopes: Optional[torch.Tensor], default = `None`
                     ALiBi slopes in FP32 and shape [nheads] or [batch_size, nheads].
                     It adds a bias of (-alibi_slope * (i + seqlen_k - seqlen_q - j))
                     to the attention score of query i and key j.
8743
8744
8745
8746
8747
8748
8749
8750
8751
8752
8753
8754
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
                   with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (without offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
                      Calculated from `cu_seqlens_q` if not provided.
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
                       Calculated from `cu_seqlens_kv` if not provided.
8755
8756
8757
        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
        """
8758
8759
        # hidden_states: [sq, b, h]

8760
        if attn_mask_type is None:
8761
            attn_mask_type = self.attn_mask_type
8762
8763
        if window_size is None:
            window_size = self.window_size
8764
        window_size = check_set_window_size(attn_mask_type, window_size)
8765

8766
        if "padding" in attn_mask_type and attention_mask is not None:
8767
8768
            for mask in attention_mask:
                assert mask.dtype == torch.bool, "Attention mask must be in boolean type!"
8769

8770
8771
8772
        assert (
            core_attention_bias_type in AttnBiasTypes
        ), f"core_attention_bias_type {core_attention_bias_type} is not supported!"
8773

8774
        # =================================================
8775
        # Pre-allocate memory for key-values for inference
8776
8777
8778
        # =================================================

        if inference_params and self.layer_number is not None:
8779
8780
8781
            assert (
                self.qkv_format != "thd"
            ), "qkv_format == thd is not supported for an inference with KV-cache!"
8782
            if self.layer_number not in inference_params.key_value_memory_dict:
8783
                inf_max_seq_len = inference_params.max_sequence_length
8784
8785
                inf_max_batch_size = inference_params.max_batch_size
                inference_key_memory = self._allocate_memory(
8786
                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
8787
8788
                )
                inference_value_memory = self._allocate_memory(
8789
                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
8790
8791
8792
8793
8794
8795
8796
8797
8798
8799
8800
                )
                inference_params.key_value_memory_dict[self.layer_number] = (
                    inference_key_memory,
                    inference_value_memory,
                )
            else:
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]

8801
        # ======================
8802
        # Query, Key, and Value
8803
        # ======================
8804

8805
8806
8807
8808
8809
        fp8_mha = (
            FP8GlobalStateManager.is_fp8_enabled()
            and FP8GlobalStateManager.get_fp8_recipe().fp8_mha
        )

8810
        layernorm_output = None
cyanguwa's avatar
cyanguwa committed
8811
8812
        if self.attention_type == "self":
            # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn]
8813
8814
8815
8816
            if self.input_layernorm:
                layernorm_qkv_outputs = self.layernorm_qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
8817
                    fp8_output=fp8_mha and rotary_pos_emb is None,
8818
8819
8820
8821
8822
8823
8824
8825
8826
                )
                if self.return_layernorm_output:
                    mixed_x_layer, layernorm_output = layernorm_qkv_outputs
                else:
                    mixed_x_layer = layernorm_qkv_outputs
            else:
                mixed_x_layer = self.qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
8827
                    fp8_output=fp8_mha and rotary_pos_emb is None,
8828
8829
                )

8830
8831
8832
            num_queries_per_key_value = (
                self.num_attention_heads_per_partition // self.num_gqa_groups_per_partition
            )
8833
            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
8834
                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, ng, (np/ng + 2), hn]
8835
                new_tensor_shape = mixed_x_layer.size()[:-1] + (
cyanguwa's avatar
cyanguwa committed
8836
8837
                    self.num_gqa_groups_per_partition,
                    (num_queries_per_key_value + 2),
8838
8839
8840
8841
                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2
cyanguwa's avatar
cyanguwa committed
8842
8843
8844
8845
8846
            else:
                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, (np/ng + 2), ng, hn]
                new_tensor_shape = mixed_x_layer.size()[:-1] + (
                    (num_queries_per_key_value + 2),
                    self.num_gqa_groups_per_partition,
8847
                    self.hidden_size_per_attention_head,
cyanguwa's avatar
cyanguwa committed
8848
8849
8850
                )
                # split along third last dimension
                split_dim = -3
8851
8852
8853

            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

cyanguwa's avatar
cyanguwa committed
8854
8855
8856
8857
8858
8859
8860
8861
8862
            # qkv_weight_interleaved:
            #  [sq, b, ng, (np/ng + 2), hn]
            #  --> [sq, b, ng, np/ng, hn], [sq, b, ng, 1, hn], [sq, b, ng, 1, hn]
            # not qkv_weight_interleaved:
            #  [sq, b, (np/ng + 2), ng, hn]
            #  --> [sq, b, np/ng, np, hn], [sq, b, 1, ng, hn], [sq, b, 1, ng, hn]
            if not is_in_onnx_export_mode():
                query_layer, key_layer, value_layer = _SplitAlongDim.apply(
                    mixed_x_layer, split_dim, (num_queries_per_key_value, 1, 1)
8863
                )
8864
            else:
cyanguwa's avatar
cyanguwa committed
8865
                query_layer, key_layer, value_layer = torch.split(
8866
8867
8868
8869
                    mixed_x_layer,
                    (num_queries_per_key_value, 1, 1),
                    dim=split_dim,
                )
cyanguwa's avatar
cyanguwa committed
8870

8871
8872
8873
8874
8875
8876
8877
8878
8879
8880
8881
8882
            if self.qkv_format == "thd":
                query_layer, key_layer, value_layer = (
                    x.reshape(x.size(0), -1, self.hidden_size_per_attention_head)
                    for x in (query_layer, key_layer, value_layer)
                )
            else:
                # query: -> [sq, b, np, hn]
                # key, value: -> [sq, b, ng, hn]
                query_layer, key_layer, value_layer = (
                    x.reshape(x.size(0), x.size(1), -1, self.hidden_size_per_attention_head)
                    for x in (query_layer, key_layer, value_layer)
                )
cyanguwa's avatar
cyanguwa committed
8883
8884
        elif self.attention_type == "cross":
            # Attention heads [sk, b, h] --> [sk, b, (ng * 2 * hn)]
8885
            mixed_kv_layer = self.key_value(
cyanguwa's avatar
cyanguwa committed
8886
                encoder_output,
8887
                is_first_microbatch=is_first_microbatch,
8888
                fp8_output=fp8_mha and rotary_pos_emb is None,
8889
8890
8891
            )

            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
8892
                # [sq, b, (ng * 2 * hn)] --> [sq, b, ng, 2 * hn]
8893
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
8894
                    self.num_gqa_groups_per_partition,
8895
8896
8897
8898
8899
                    2 * self.hidden_size_per_attention_head,
                )
                # split along last dimension
                split_dim = -1
            else:
cyanguwa's avatar
cyanguwa committed
8900
                # [sq, b, (ng * 2 * hn)] --> [sq, b, 2 * ng, hn]
8901
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
8902
                    2 * self.num_gqa_groups_per_partition,
8903
8904
8905
8906
8907
8908
8909
                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2

            mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)

cyanguwa's avatar
cyanguwa committed
8910
8911
8912
            # mixed_kv_layer --> 2 [sk, b, ng, hn]
            if not is_in_onnx_export_mode():
                key_layer, value_layer = _SplitAlongDim.apply(
8913
8914
8915
                    mixed_kv_layer,
                    split_dim,
                    mixed_kv_layer.shape[split_dim] // 2,
cyanguwa's avatar
cyanguwa committed
8916
                )
8917
            else:
cyanguwa's avatar
cyanguwa committed
8918
                key_layer, value_layer = torch.split(
8919
8920
8921
                    mixed_kv_layer,
                    mixed_kv_layer.shape[split_dim] // 2,
                    dim=split_dim,
cyanguwa's avatar
cyanguwa committed
8922
                )
8923
8924
8925
8926
8927
8928
8929
8930
8931
            key_layer, value_layer = (
                x.reshape(
                    x.size(0),
                    x.size(1),
                    -1,
                    self.hidden_size_per_attention_head,
                )
                for x in (key_layer, value_layer)
            )
8932
8933
8934
8935
8936
8937

            # Attention head [sq, b, h] --> [sq, b, hp]
            if self.input_layernorm:
                layernorm_query_outputs = self.layernorm_query(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
8938
                    fp8_output=fp8_mha and rotary_pos_emb is None,
8939
8940
8941
8942
8943
8944
8945
8946
8947
                )
                if self.return_layernorm_output:
                    query_layer, layernorm_output = layernorm_query_outputs
                else:
                    query_layer = layernorm_query_outputs
            else:
                query_layer = self.query_layer(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
8948
                    fp8_output=fp8_mha and rotary_pos_emb is None,
8949
8950
8951
8952
8953
8954
8955
8956
8957
                )

            # [sq, b, hp] --> [sq, b, np, hn]
            new_tensor_shape = query_layer.size()[:-1] + (
                self.num_attention_heads_per_partition,
                self.hidden_size_per_attention_head,
            )
            query_layer = query_layer.view(*new_tensor_shape)

8958
8959
8960
        # ======================================================
        # Apply relative positional encoding (rotary embedding)
        # ======================================================
8961

8962
        if rotary_pos_emb is not None:
8963
8964
8965
            assert not isinstance(query_layer, Float8Tensor) and not isinstance(
                key_layer, Float8Tensor
            ), "RoPE is not supported for Float8Tensors!"
8966
            # duplicate the pos_emb for self attention
8967
            if not isinstance(rotary_pos_emb, tuple):
8968
                rotary_pos_emb = (rotary_pos_emb,) * 2
8969
8970

            q_pos_emb, k_pos_emb = rotary_pos_emb
8971
8972
8973
8974
8975
8976
8977

            # adjust key and value for inference
            if inference_params is not None:
                if self.qkv_format == "sbhd":
                    sequence_length = key_layer.size(0)
                elif self.qkv_format == "bshd":
                    sequence_length = key_layer.size(1)
8978
8979
                else:
                    raise ValueError(f"QKV format {self.qkv_format} not supported for KV caching.")
8980
8981
8982
8983
8984
8985
8986

                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + sequence_length

                q_pos_emb = q_pos_emb[sequence_start:sequence_end, ...]
                k_pos_emb = k_pos_emb[sequence_start:sequence_end, ...]

8987
8988
            query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb, self.qkv_format, fused=True)
            key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb, self.qkv_format, fused=True)
8989

8990
8991
8992
8993
        # ===========================
        # Core attention computation
        # ===========================

8994
8995
8996
8997
        context_layer = self.core_attention(
            query_layer,
            key_layer,
            value_layer,
8998
            qkv_format=self.qkv_format,
8999
9000
9001
9002
            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_kv=cu_seqlens_kv,
            max_seqlen_q=max_seqlen_q,
            max_seqlen_kv=max_seqlen_kv,
9003
9004
            attention_mask=attention_mask,
            attn_mask_type=attn_mask_type,
9005
            window_size=window_size,
9006
9007
9008
            checkpoint_core_attention=checkpoint_core_attention,
            core_attention_bias_type=core_attention_bias_type,
            core_attention_bias=core_attention_bias,
9009
            alibi_slopes=alibi_slopes,
9010
            fast_zero_fill=fast_zero_fill,
9011
            inference_params=inference_params,
9012
9013
        )

9014
        # ===================
9015
        # Output. [sq, b, h]
9016
        # ===================
9017

9018
        projection_output = self.proj(
9019
9020
            context_layer,
            is_first_microbatch=is_first_microbatch,
9021
9022
        )

9023
9024
9025
9026
9027
9028
9029
9030
        if self.return_bias:
            attention_output, attention_bias = projection_output
        else:
            attention_output, attention_bias = projection_output, None

        outputs = (attention_output,)
        if self.return_bias:
            outputs += (attention_bias,)
9031
        if self.input_layernorm and self.return_layernorm_output:
9032
9033
            outputs += (layernorm_output,)
        return outputs if len(outputs) > 1 else outputs[0]