attention.py 400 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
2538

        seq_dim = None
2539
        if ctx.qkv_format in ["bshd", "sbhd"]:
2540
            seq_dim = ctx.qkv_format.index("s")
2541
2542
2543
            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
2544

2545
        if attn_biases[0] is not None:
2546
2547
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
2548
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
2549
2550
2551
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
2552
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
2553
2554
2555
            )
        else:
            attn_dbias = None
2556
            attn_dbias_ = None
2557

2558
2559
2560
2561
        softmax_lse_in_packed_format = not ctx.use_fused_attention and (
            _flash_attn_2_6_0_plus or _use_flash_attn_3
        )

2562
        if causal:
2563
            if ctx.qkv_format == "thd" or softmax_lse_in_packed_format:
2564
                softmax_lse_ = tex.thd_read_second_half_lse(
2565
                    softmax_lse, cu_seqlens_q_padded, softmax_lse_in_packed_format
2566
                )
2567
2568
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
2569
2570
2571
                softmax_lse_ = softmax_lse.view(
                    *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
                )
2572
2573
2574
2575
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
                if ctx.use_fused_attention:
                    # [b, np, sq//2] -> [b, np, sq//2, 1]
                    softmax_lse_.unsqueeze_(-1)
2576
2577
2578
        if ctx.use_fused_attention:
            # [b, np, sq] -> [b, np, sq, 1]
            softmax_lse.unsqueeze_(-1)
2579

2580
        dout_dtype = dout.dtype
2581
2582
2583
2584
2585
        fused_attn_backend = None
        fused_attn_qkv_dtype = None
        fused_attn_dqkv_dtype = None
        amax_per_step = None
        dout_fp8_dtype = None
2586
2587
        if ctx.fp8:
            if ctx.use_fused_attention:
2588
                fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
2589
                fp8_dtype_backward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=False)
2590
                fused_attn_qkv_dtype = fp8_dtype_forward
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
2618
                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:
2619
2620
2621
2622
2623
2624
2625
                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
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
            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]
2637
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
2638
2639
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
        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(
2656
2657
2658
2659
2660
2661
                    dout,
                    None,
                    None,
                    dout_fp8_dtype,
                    TE_DType[dout_dtype],
                    scale_inv=dout_scale_inv,  # pylint: disable=used-before-assignment
2662
2663
                )

2664
2665
2666
2667
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

2668
        flash_attn_bwd = None
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
        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
2681

2682
2683
2684
2685
2686
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

2687
2688
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
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
2717
            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
                )
2718

2719
            kv = p2p_comm_buffers[i % 2][0]
2720
            dk_, dv_ = None, None
2721
2722
2723
            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]
2724
            # In reversed order of fwd
2725
            if causal:
2726
                if i == (cp_size - 1):
2727
                    if ctx.use_fused_attention:
2728
2729
2730
                        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:])
2731
2732
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2733
2734
2735
2736
2737
2738
                            # [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:])
2739
2740
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2741
2742
2743
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2744
2745
                        elif ctx.qkv_format == "thd":
                            q_, kv_, out_, dout_ = q, kv, out, dout
2746
2747
2748
2749
2750
2751
2752
2753
                        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]]
2754
                        if attn_dbias is not None:
2755
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2756
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2757
                            ctx.max_seqlen_q,
2758
2759
2760
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2761
                            q_,
2762
2763
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2764
2765
                            out_,
                            dout_,
2766
2767
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2768
                            aux_ctx_tensors,
2769
                            fused_attn_backend,
2770
2771
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2772
2773
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2774
                            qkv_layout=qkv_layout,
2775
                            attn_mask_type=ctx.attn_mask_type,
2776
                            attn_bias_type=ctx.attn_bias_type,
2777
2778
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2779
2780
2781
2782
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2783
                        dq_ = torch.zeros_like(q_)
2784
2785
2786
2787
2788
2789
                        # [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:])
2790
2791
2792
2793
2794
                        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(
2795
2796
2797
2798
2799
2800
2801
2802
2803
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2804
2805
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2806
                            ctx.max_seqlen_q,
2807
                            ctx.max_seqlen_kv,
2808
2809
                            causal=True,
                            **fa_backward_kwargs,
2810
                        )
2811
                elif i >= (cp_size - rank - 1):
2812
                    if ctx.use_fused_attention:
2813
2814
2815
                        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:])
2816
2817
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                            kv_ = kv[:, 0, ...].contiguous()
2818
2819
2820
2821
2822
2823
                            # [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:])
2824
2825
                            # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                            kv_ = kv[0].contiguous()
2826
2827
2828
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2829
2830
2831
                        elif ctx.qkv_format == "thd":
                            q_, out_, dout_ = q, out, dout
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2832
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2833
2834
2835
2836
2837
2838
2839
2840
                        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]]
2841
                        if attn_dbias is not None:
2842
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2843
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2844
                            ctx.max_seqlen_q,
2845
2846
2847
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2848
                            q_,
2849
2850
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2851
2852
                            out_,
                            dout_,
2853
2854
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2855
                            aux_ctx_tensors,
2856
                            fused_attn_backend,
2857
2858
2859
2860
                            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
                            ),
2861
2862
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2863
                            qkv_layout=qkv_layout,
2864
                            attn_mask_type="padding" if padding else "no_mask",
2865
                            attn_bias_type=ctx.attn_bias_type,
2866
2867
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2868
2869
2870
2871
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2872
                        dq_ = torch.zeros_like(q_)
2873
2874
                        if ctx.qkv_format == "thd":
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2875
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2876
2877
2878
                        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:])
2879
2880
2881
2882
                        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:])
2883
2884
2885
2886
2887
                        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(
2888
2889
2890
2891
2892
2893
2894
2895
2896
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2897
2898
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2899
                            ctx.max_seqlen_q,
2900
                            ctx.max_seqlen_kv // 2,
2901
2902
                            causal=False,
                            **fa_backward_kwargs,
2903
2904
2905
                        )
                else:
                    if ctx.use_fused_attention:
2906
2907
2908
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous()
2909
2910
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2911
2912
2913
2914
2915
2916
                            # [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()
2917
2918
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2919
2920
2921
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            out_ = out[1].contiguous()
                            dout_ = dout[1].contiguous()
2922
2923
                        elif ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2924
2925
2926
                            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)
2927
                            kv_ = kv
2928
2929
2930
2931
2932
2933
2934
2935
                        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]]
2936
                        if attn_dbias is not None:
2937
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2938
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2939
                            ctx.max_seqlen_q // 2,
2940
2941
2942
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2943
                            q_,
2944
2945
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2946
2947
                            out_,
                            dout_,
2948
2949
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2950
                            aux_ctx_tensors,
2951
                            fused_attn_backend,
2952
2953
2954
2955
                            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,
2956
2957
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2958
                            qkv_layout=qkv_layout,
2959
                            attn_mask_type="padding" if padding else "no_mask",
2960
                            attn_bias_type=ctx.attn_bias_type,
2961
2962
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2963
2964
                        )
                    else:
2965
2966
                        if ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2967
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q_padded, 1)
2968
2969
2970
                        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:])
2971
                        dq_ = torch.zeros_like(q_)
2972
2973
2974
                        # [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_)
2975
                        if ctx.qkv_format == "thd":
2976
2977
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q_padded, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q_padded, 1)
2978
2979
2980
2981
                        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:])
2982
2983
2984
2985
2986
                        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(
2987
2988
2989
2990
2991
2992
2993
2994
2995
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse_,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2996
2997
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2998
                            ctx.max_seqlen_q // 2,
2999
                            ctx.max_seqlen_kv,
3000
3001
                            causal=False,
                            **fa_backward_kwargs,
3002
3003
3004
                        )
            else:
                if ctx.use_fused_attention:
3005
3006
3007
3008
                    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]]
3009
                    if attn_dbias is not None:
3010
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
3011
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
3012
                        ctx.max_seqlen_q,
3013
3014
3015
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
3016
                        q,
3017
3018
                        kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0],
                        kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1],
3019
3020
                        out,
                        dout,
3021
3022
                        fused_attn_qkv_dtype,
                        fused_attn_dqkv_dtype,
3023
                        aux_ctx_tensors,
3024
                        fused_attn_backend,
3025
3026
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
3027
3028
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
3029
                        qkv_layout=qkv_layout,
3030
                        attn_mask_type=ctx.attn_mask_type,
3031
                        attn_bias_type=ctx.attn_bias_type,
3032
3033
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
3034
3035
3036
                    )
                else:
                    # [b, sq, np, hn] -> [b*sq, np, hn]
3037
                    q_ = q.view(-1, *q.shape[-2:])
3038
                    dq_ = torch.zeros_like(q_)
3039
                    # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
3040
3041
                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
3042
                    # [b, sq, np, hn] -> [b*sq, np, hn]
3043
3044
                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
3045
3046
3047
3048
3049
                    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(
3050
3051
3052
3053
3054
3055
3056
3057
3058
                        dout_,
                        q_,
                        kv_[0],
                        kv_[1],
                        out_,
                        softmax_lse,
                        dq_,
                        dkv_[0],
                        dkv_[1],
3059
3060
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
3061
                        ctx.max_seqlen_q,
3062
                        ctx.max_seqlen_kv,
3063
3064
                        causal=False,
                        **fa_backward_kwargs,
3065
3066
                    )

3067
3068
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
3069
            if i >= (cp_size - rank - 1) or not causal:
3070
3071
3072
3073
                # [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:
3074
3075
3076
3077
3078
3079
                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:])
3080

3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
            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:
3092
                if i > (cp_size - rank - 1):
3093
                    dq.add_(dq_)
3094
3095
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
3096
3097
                        dq.copy_(dq_)
                    else:
3098
3099
3100
3101
3102
3103
                        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])
3104
                        elif ctx.qkv_format == "thd":
3105
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
3106
                elif i > 0:
3107
3108
3109
3110
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
3111
                    elif ctx.qkv_format == "thd":
3112
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
3113
                else:
3114
3115
3116
3117
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
3118
                    elif ctx.qkv_format == "thd":
3119
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
3120
3121
3122
3123
3124
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
3125

3126
            if attn_dbias is not None:
3127
                idx = (rank + i + 1) % cp_size
3128
                if i == (cp_size - 1) or not causal:
3129
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
3130
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
3131
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
3132
3133
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
3134
3135
3136
3137
                    # [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)]
3138
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
3139
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
3140
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
3141

3142
3143
3144
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
3145

3146
3147
3148
3149
3150
3151
3152
            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]
3153
            if ctx.use_fused_attention:
3154
3155
3156
                dkv_ = torch.cat(
                    (dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0
                )  # pylint: disable=used-before-assignment
3157
3158
3159
3160
                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:])
3161
            if causal and i >= (cp_size - rank - 1) and i != (cp_size - 1):
3162
3163
3164
3165
3166
3167
                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:])
3168
3169
3170
3171
            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)
3172

3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
            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:
3184
                if i == (cp_size - 1):
3185
                    if rank == 0:
3186
3187
3188
3189
3190
3191
                        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, ...])
3192
                        elif ctx.qkv_format == "thd":
3193
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
3194
3195
                    else:
                        dkv.add_(dkv_)
3196
3197
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
3198
3199
3200
3201
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
3202
                        elif ctx.qkv_format == "thd":
3203
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
3204
                    else:
3205
3206
3207
3208
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
3209
                        elif ctx.qkv_format == "thd":
3210
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
3211
3212
3213
3214
3215
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
3216
3217
3218
3219
3220
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
        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]]

3241
        if causal:
3242
3243
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
3244
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
3245
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
3246
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
3247
3248
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
3249
                dq = dq.view(-1, *dq.shape[-3:])
3250
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
3251
3252
3253
3254
3255
3256
3257
3258
3259
                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_
3260

3261
3262
3263
3264
3265
        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]
            ]
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
        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:
3285
3286
3287
3288
3289
3290
3291
3292
3293
            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,
                )
3294
                for x in [dq, dk, dv]
3295
3296
            ]

3297
3298
3299
3300
        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)

3301
3302
3303
        return (
            None,
            dq,
3304
3305
            dk,
            dv,
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
3317
            attn_dbias,
3318
3319
3320
3321
3322
            None,
            None,
            None,
            None,
            None,
3323
3324
            None,
            None,
3325
        )
3326
3327


3328
3329
def get_kv_seq_info_after_all_gather(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size, causal
3330
):
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
    """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)
3353
3354
3355
3356


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
3357
3358
    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>`_.
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
    """

    @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,
3381
3382
        cp_group,
        cp_stream,
3383
    ):
3384
        # pylint: disable=missing-function-docstring
3385
3386
3387
3388
3389
3390
3391
3392
        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
3393
        assert not padding, f"{attn_mask_type} mask type is not supported!"
3394
3395
3396
3397
3398
3399
3400
        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!"
3401

3402
        flash_attn_fwd = None
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
        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
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428

        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)

3429
3430
3431
3432
        # [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]]
3433

3434
        # [s, b, np, hn] -> [cp, s, b, np, hn]
3435
3436
        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
3437
3438

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
3439
3440
        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:])
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
        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]
3451
3452

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
3453
3454
3455
        kv_seq_range_per_step = [None, None]
        window_size_per_step = [None, None]
        cu_seqlens_kv_per_step = [None, None]
3456
3457
3458
3459
3460
3461
3462
3463
        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]):
3464
3465
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
3466
3467
3468
3469
3470
3471
3472
3473
3474
                    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,
3475
                        )
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
                    )
                    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_]]
3488
3489
3490
3491
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
3492
                            max_seqlen_kv_,
3493
                            cu_seqlens_q,
3494
                            cu_seqlens_kv_per_step[i],
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
                            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,
3507
3508
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
                            window_size=window_size_per_step[i],
3509
3510
3511
                        )
                    else:
                        q_, k_, v_ = [x.view(-1, *x.shape[-2:]) for x in [q_, k_, v_]]
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
                        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,
3523
                        )
3524
3525
3526
3527
                        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]
3528
3529
3530
3531

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
3532
                        out[:, i - 1].copy_(out_per_step[i - 1].view(out[:, i - 1].shape))
3533
                    elif qkv_format == "sbhd":
3534
                        out[i - 1].copy_(out_per_step[i - 1].view(out[i - 1].shape))
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551

        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,
3552
            *cu_seqlens_kv_per_step,
3553
3554
3555
3556
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
3557
3558
        ctx.kv_seq_range_per_step = kv_seq_range_per_step
        ctx.window_size_per_step = window_size_per_step
3559
3560
3561
3562
3563
3564
3565
        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
3566
        ctx.attn_mask_type = attn_mask_type
3567
3568
3569
3570
3571
3572
        ctx.deterministic = deterministic
        ctx.use_fused_attention = use_fused_attention
        return out

    @staticmethod
    def backward(ctx, dout):
3573
        # pylint: disable=missing-function-docstring
3574
3575
3576
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

3577
3578
3579
3580
3581
3582
3583
        (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
3584

3585
        seq_dim = ctx.qkv_format.index("s")
3586
3587
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

3588
        dout = dout.view(q.shape)
3589
        dq = torch.empty_like(q)
3590
        dk = torch.zeros((k.shape[0] * cp_size, *k.shape[1:]), dtype=k.dtype, device=k.device)
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
        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()

3601
        # [s, b, np, hn] -> [cp, s, b, np, hn]
3602
3603
        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
3604
3605

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
3606
3607
        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:])
3608
3609
3610
3611
3612
3613
3614
        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())
3615
3616
3617

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

3618
        flash_attn_bwd = None
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
        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
3631
3632
3633
3634

        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]):
3635
3636
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
3637
3638
3639
3640
3641
3642
3643
3644
3645
                    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_]]
3646
                    out_ = out_per_step[i]
3647
                    dout_ = dout.select(seq_dim, i).contiguous().view(out_.shape)
3648
3649
3650
3651
                    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,
3652
                            max_seqlen_kv,
3653
                            cu_seqlens_q,
3654
                            cu_seqlens_kv_per_step[i],
3655
3656
3657
3658
3659
3660
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
                            TE_DType[q.dtype],
3661
                            TE_DType[dout.dtype],
3662
3663
3664
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
3665
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
3666
3667
3668
3669
3670
                            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,
3671
3672
                            window_size=window_size_per_step[i],
                            deterministic=ctx.deterministic,
3673
3674
                        )
                    else:
3675
                        batch_size = k_.shape[0]
3676
3677
3678
3679
                        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_]
                        ]
3680
3681
3682
                        if not _use_flash_attn_3:
                            fa_backward_kwargs["rng_state"] = rng_states[i]
                        flash_attn_bwd(
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
                            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,
3693
                            cu_seqlens_kv_per_step[i],
3694
                            ctx.max_seqlen_q,
3695
                            max_seqlen_kv,
3696
                            causal="causal" in ctx.attn_mask_type,
3697
                            window_size=window_size_per_step[i],
3698
                            **fa_backward_kwargs,
3699
                        )
3700
3701
3702
3703
3704
3705
3706
                        # [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]]
                        ]
3707
3708
3709
3710

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if ctx.qkv_format == "bshd":
3711
                        dq[:, i - 1].copy_(dq_per_step[i - 1])
3712
                    elif ctx.qkv_format == "sbhd":
3713
3714
3715
3716
3717
3718
                        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]]
                    ]
3719
3720
3721
                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
3722
3723
3724
3725
3726
3727
                    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])
3728
3729
3730
3731
3732
                    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)

3733
3734
3735
3736
3737
3738
3739
        # [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]
3740
3741
3742
3743
3744
        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)

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
3804
        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,
    ):
3805
        # pylint: disable=missing-function-docstring
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
        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!"
3822

3823
        flash_attn_fwd = None
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
        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
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852

        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!"

3853
3854
        fused_attn_backend = None
        fused_attn_qkv_dtype = None
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
3934
        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]]
3935
            fa_outputs = flash_attn_fwd(
3936
3937
3938
3939
3940
3941
3942
3943
                q,
                k,
                v,
                cu_seqlens_q,
                cu_seqlens_kv,
                max_seqlen_q,
                max_seqlen_kv,
                causal=causal,
3944
                **fa_forward_kwargs,
3945
            )
3946
3947
            out, softmax_lse = fa_outputs[4], fa_outputs[5]
            rng_state = fa_outputs[7] if not _use_flash_attn_3 else None
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
4047
            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):
4048
        # pylint: disable=missing-function-docstring
4049
4050
4051
4052
4053
4054
4055
4056
4057
4058
4059
4060
4061
        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")

4062
4063
4064
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
        fused_attn_qkv_dtype = None
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
4115
        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
        )

4116
        flash_attn_bwd = None
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
4127
4128
4129
4130
4131
        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
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
4162

        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]]
4163
4164
4165
            if not _use_flash_attn_3:
                fa_backward_kwargs["rng_state"] = rng_state
            flash_attn_bwd(
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
                dout,
                q,
                k,
                v,
                out,
                softmax_lse,
                dq,
                dk,
                dv,
                cu_seqlens_q,
                cu_seqlens_kv,
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
4179
4180
                causal=causal,
                **fa_backward_kwargs,
4181
4182
4183
4184
4185
4186
4187
4188
            )
            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
        )

4189
        if ctx.qkv_format == "bshd":
4190
            dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
4191
        elif ctx.qkv_format == "sbhd":
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
4217
            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]
                ]
4218
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4241
4242
4243
            None,
            None,
            None,
4244
4245
4246
        )


4247
def attn_forward_func_with_cp(
4248
4249
4250
4251
4252
    is_training,
    q,
    k,
    v,
    cu_seqlens_q,
4253
    cu_seqlens_kv,
4254
    max_seqlen_q,
4255
    max_seqlen_kv,
4256
4257
    cu_seqlens_q_padded,
    cu_seqlens_kv_padded,
4258
4259
4260
4261
    dropout_p,
    cp_group,
    cp_global_ranks,
    cp_stream,
4262
    cp_comm_type,
4263
4264
4265
4266
4267
4268
4269
    softmax_scale=None,
    qkv_format="bshd",
    attn_mask_type="causal",
    attn_bias_type="no_bias",
    attn_bias=None,
    deterministic=False,
    use_fused_attention=False,
4270
    window_size=None,
4271
4272
    fp8=False,
    fp8_meta=None,
4273
) -> torch.Tensor:
4274
4275
4276
4277
    """
    Attention implementation with context parallelism.
    """

4278
4279
4280
4281
4282
4283
4284
4285
4286
4287
4288
4289
4290
4291
4292
4293
    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}!"

4294
4295
4296
4297
4298
4299
4300
4301
4302
4303
4304
4305
4306
4307
4308
4309
4310
4311
4312
4313
    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!"""
    )
4314
4315
4316
    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!"
4317
4318
4319

    sliding_window_attn = (
        window_size is not None and window_size != (-1, 0) and window_size != (-1, -1)
4320
    )
4321
4322
4323
4324
4325
    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!"
4326

4327
4328
4329
4330
4331
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
4344
4345
4346
4347
    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,
    ]

4348
    if cp_comm_type in ["p2p", "a2a+p2p"]:
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
        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)
4359
4360
4361
    else:
        raise ValueError(f"Unsupported communication type: {cp_comm_type}!")

4362
4363
4364
    return out


4365
4366
4367
4368
class RotaryPositionEmbedding(torch.nn.Module):
    """
    Implements Rotary Position Embedding from https://arxiv.org/abs/2104.09864.
    """
4369

4370
4371
4372
    def __init__(
        self,
        dim: int,
4373
        rotary_percent: float = 1.0,
4374
4375
        seq_len_interpolation_factor: Optional[int] = None,
        pretrained_max_position_embeddings: Optional[int] = None,
4376
        rotary_base: float = 10000.0,
4377
4378
4379
4380
4381
4382
    ):
        """
        Parameters
        ----------
        dim: int
            rotary embedding dimension
4383
4384
        rotary_percent: float
            Percent of rotary dimension to use for rotary position embeddings.
4385
4386
4387
4388
4389
4390
4391
        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__()
4392
4393
        if rotary_percent < 1.0:
            dim = int(dim * rotary_percent)
4394
        self.seq_len_interpolation_factor = seq_len_interpolation_factor
4395
        self.rotary_base = rotary_base
4396
        inv_freq = 1.0 / (
4397
            self.rotary_base
4398
4399
4400
4401
4402
            ** (
                torch.arange(0, dim, 2, dtype=torch.float32, device=torch.cuda.current_device())
                / dim
            )
        )
4403
        self.register_buffer("inv_freq", inv_freq)
4404
4405
4406
4407
4408
4409
4410
4411
4412
4413
4414
4415
4416
        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
        """
4417
4418
4419
4420
        seq = (
            torch.arange(max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
            + offset
        )
4421

4422
4423
4424
4425
4426
4427
4428
4429
        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
            ):
4430
4431
4432
4433
4434
4435
                # 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

4436
        freqs = torch.einsum("i , j -> i j", seq, self.inv_freq)
4437
4438
4439
4440
4441
4442
        # 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))

4443
4444
4445
4446
4447
4448
4449
4450
4451
4452
4453
4454
4455
4456
4457
4458
4459

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,
4460
4461
        cp_size: int = 1,
        cp_rank: int = 0,
4462
    ) -> torch.Tensor:
4463
        # pylint: disable=missing-function-docstring
4464
4465
        if freqs.dtype != torch.float32:
            freqs = freqs.float()
4466
4467
4468
        if tensor_format == "sbhd":
            output = tex.fused_rope_forward(t, freqs, False)
        elif tensor_format == "bshd":
4469
            output = tex.fused_rope_forward(t.transpose(0, 1), freqs, True).transpose(0, 1)
4470
        elif tensor_format == "thd":
4471
            output = tex.fused_rope_thd_forward(t, cu_seqlens, freqs, cp_size, cp_rank)
4472
4473
4474
4475
        else:
            raise ValueError(f"Unsupported tensor_format: {tensor_format}.")
        ctx.save_for_backward(freqs, cu_seqlens)
        ctx.tensor_format = tensor_format
4476
4477
        ctx.cp_size = cp_size
        ctx.cp_rank = cp_rank
4478
4479
4480
4481

        return output

    @staticmethod
4482
    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
4483
        # pylint: disable=missing-function-docstring
4484
4485
4486
4487
4488
4489
4490
4491
        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":
4492
4493
4494
            grad_input = tex.fused_rope_thd_backward(
                grad_output, cu_seqlens, freqs, ctx.cp_size, ctx.cp_rank
            )
4495
4496
4497
        else:
            raise ValueError(f"Unsupported tensor_format: {ctx.tensor_format}.")

4498
        return grad_input, None, None, None, None, None
4499
4500


4501
4502
4503
4504
4505
4506
4507
4508
4509
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)


4510
def apply_rotary_pos_emb(
4511
4512
4513
4514
4515
    t: torch.Tensor,
    freqs: torch.Tensor,
    tensor_format: str = "sbhd",
    fused: bool = False,
    cu_seqlens: Union[torch.Tensor, None] = None,
4516
4517
    cp_size: int = 1,
    cp_rank: int = 0,
4518
) -> torch.Tensor:
4519
    """
4520
    Apply rotary positional embedding tensor to the input tensor.
4521

4522
4523
4524
    Parameters
    ----------
    t: torch.Tensor
4525
        Input tensor of shape `[s, b, h, d]`, `[b, s, h, d]` or `[t, h, d]`, on which
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
4537
        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'.
4538
4539
4540
4541
4542
        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.
4543
    """
4544
4545
4546
4547
    if fused:
        assert (
            tensor_format != "thd" or cu_seqlens is not None
        ), "cu_seqlens must not be None when tensor_format is 'thd'."
4548
        return FusedRoPEFunc.apply(t, freqs, tensor_format, cu_seqlens, cp_size, cp_rank)
4549
4550
4551
4552
4553
4554

    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}."
    )

4555
4556
4557
4558
4559
    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.
4560
4561
4562
    assert (
        cur_seq_len <= max_seq_len
    ), f"Rotary Embeddings only supported up to {max_seq_len} sequence length!"
4563
    freqs = freqs[:cur_seq_len]
4564
    if tensor_format == "bshd":
4565
4566
4567
4568
        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)
4569

4570
4571
4572
4573
4574
4575
    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
4576
    t = (t * cos_) + (_rotate_half(t) * sin_)
4577
4578
4579
    return torch.cat((t, t_pass), dim=-1)


cyanguwa's avatar
cyanguwa committed
4580
class _SplitAlongDim(torch.autograd.Function):
4581
4582
4583
    """"""

    @staticmethod
4584
4585
4586
4587
4588
    def forward(
        ctx,
        mixed_x_layer: torch.Tensor,
        split_dim: int,
        split_size_or_sections: Union[int, List[int], Tuple[int]],
4589
    ) -> Tuple[torch.Tensor, ...]:
4590
        # pylint: disable=missing-function-docstring
cyanguwa's avatar
cyanguwa committed
4591
4592
        ctx.split_dim = split_dim
        ctx.split_size_or_sections = split_size_or_sections
4593
        if isinstance(mixed_x_layer, Float8Tensor):
4594
4595
4596
4597
4598
4599
            return tuple(
                Float8Tensor.make_like(
                    mixed_x_layer,
                    data=x,
                )
                for x in torch.split(
4600
4601
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
4602
4603
4604
4605
                    dim=split_dim,
                )
            )
        return torch.split(mixed_x_layer, split_size_or_sections, dim=split_dim)
4606
4607

    @staticmethod
4608
    def backward(ctx, *grad_outputs):
4609
        # pylint: disable=missing-function-docstring
4610
4611
        assert len(grad_outputs) > 0, "No gradients received for backprop!"

cyanguwa's avatar
cyanguwa committed
4612
4613
        if isinstance(ctx.split_size_or_sections, (list, tuple)):
            split_sizes = ctx.split_size_or_sections
4614
4615
4616
            assert len(grad_outputs) == len(
                split_sizes
            ), "Unequal number of gradients vs split sections for backprop!"
cyanguwa's avatar
cyanguwa committed
4617
4618
4619
4620
4621
        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

4622
4623
4624
4625
4626
4627
4628
4629
        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]
4630
4631
4632
4633
4634
4635
4636
                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
                ):
4637
4638
4639
                    noop_ok = False
                    break
            if noop_ok:
4640
4641
4642
                ret = torch.Tensor().to(
                    device=grad_outputs[0].device, dtype=grad_outputs[0]._data.dtype
                )
4643
4644
                new_shape = list(shape)
                new_shape[split_dim] = sum(split_sizes)
4645
4646
4647
4648
4649
                ret.set_(
                    grad_outputs[0]._data.untyped_storage(),
                    grad_outputs[0]._data.storage_offset(),
                    new_shape,
                    strides,
4650
4651
4652
4653
                )
                return Float8Tensor.make_like(grad_outputs[0], data=ret), None, None

            grad_outputs_data = [x._data for x in grad_outputs]
4654
4655
4656
4657
4658
4659
4660
            return (
                Float8Tensor.make_like(
                    grad_outputs[0], data=torch.cat(grad_outputs_data, dim=split_dim)
                ),
                None,
                None,
            )
4661
4662
        noop_ok = True
        strides = grad_outputs[0].stride()
4663
        data_ptr = grad_outputs[0].untyped_storage().data_ptr()
cyanguwa's avatar
cyanguwa committed
4664
        shape = list(grad_outputs[0].shape)
4665
        for i, tensor in enumerate(grad_outputs):
cyanguwa's avatar
cyanguwa committed
4666
4667
            shape_i = shape
            shape_i[split_dim] = split_sizes[i]
4668
4669
4670
4671
4672
4673
4674
            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
            ):
4675
4676
4677
                noop_ok = False
                break
        if noop_ok:
4678
            ret = torch.Tensor().to(device=grad_outputs[0].device, dtype=grad_outputs[0].dtype)
4679
            new_shape = list(shape)
cyanguwa's avatar
cyanguwa committed
4680
            new_shape[split_dim] = sum(split_sizes)
4681
4682
4683
4684
4685
            ret.set_(
                grad_outputs[0].untyped_storage(),
                grad_outputs[0].storage_offset(),
                new_shape,
                strides,
4686
            )
cyanguwa's avatar
cyanguwa committed
4687
            return ret, None, None
4688

4689
        return torch.cat(grad_outputs, dim=split_dim), None, None
4690
4691
4692
4693
4694
4695
4696
4697
4698


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

    def __init__(
        self,
4699
        softmax_scale: float,
4700
        attention_type: str = "self",
4701
4702
4703
4704
4705
4706
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        layer_number: Optional[int] = None,
    ) -> None:
        super().__init__()

4707
        self.softmax_scale = softmax_scale
4708
        self.attention_type = attention_type
4709
4710
4711
        self.attention_dropout_ctx = attention_dropout_ctx
        self.layer_number = layer_number

4712
        self.scale_mask_softmax = FusedScaleMaskSoftmax(attention_mask_func)
4713
4714
4715
4716
4717
4718

        # 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)

4719
4720
        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
4721
4722
            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None
        )
4723

4724
4725
4726
4727
4728
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
4729
        qkv_layout: str = "sbh3d",
4730
4731
        cu_seqlens_q: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
4732
        attn_mask_type: str = "causal",
4733
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4734
4735
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
4736
        alibi_slopes: Optional[torch.Tensor] = None,
4737
    ) -> torch.Tensor:
4738
        """Unfused attention fprop"""
4739
4740
4741
4742
4743
        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":
4744
            # convert to sbhd and use sbhd implementation for now
4745
4746
4747
            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
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
4799
        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,
                )
4800

4801
        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]
4802
        apply_qk_layer_scaling = self.apply_qk_layer_scaling and key_layer.dtype == torch.float16
4803
4804
4805
4806
4807
4808
4809
4810
4811

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

4812
        if key_layer.shape[2] != query_layer.shape[2]:
4813
4814
4815
            assert (
                query_layer.shape[2] % key_layer.shape[2] == 0
            ), "The number of attention heads must be divisible by the number of GQA groups!"
4816
            key_layer = key_layer.repeat_interleave(
4817
4818
                int(query_layer.shape[2] / key_layer.shape[2]), dim=2
            )
4819
            value_layer = value_layer.repeat_interleave(
4820
4821
                int(query_layer.shape[2] / value_layer.shape[2]), dim=2
            )
4822

4823
        # [sq, b, np, hn] -> [sq, b * np, hn]
4824
        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
4825
4826
4827
4828
        # [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]
4829
4830
        # WAR to set dtype to FP32 as ONNX lacks BF16 support for ConstantOfShape operator
        is_bf16 = query_layer.dtype == torch.bfloat16
4831
4832
4833
4834
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
4835
            dtype=torch.float32 if is_in_onnx_export_mode() and is_bf16 else query_layer.dtype,
4836
4837
4838
            device=torch.cuda.current_device(),
        )

4839
4840
4841
        if is_in_onnx_export_mode() and is_bf16:
            matmul_result = matmul_result.bfloat16()

4842
        scale = self.softmax_scale
4843
        if apply_qk_layer_scaling:
4844
            scale /= self.layer_number
4845
4846

        # Raw attention scores. [b * np, sq, sk]
4847
4848
4849
4850
4851
4852
        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,
4853
                alpha=scale,
4854
            ).view(*output_size)
4855
4856
4857
4858
4859
4860
4861

        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]
            )
4862
            matmul_result = matmul_result.view(*output_size) + core_attention_bias
4863
            matmul_result *= scale
4864

4865
4866
4867
4868
        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":
4869
                _, core_attention_bias = get_alibi(
4870
4871
4872
                    output_size[1],
                    output_size[2],
                    output_size[3],
4873
4874
                    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,
4875
4876
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
4877
                )
4878
4879
4880
4881
4882
            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,
4883
                alpha=scale,
4884
            )
4885
4886
            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
4887
            )
4888
4889
4890

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
4891
        attention_probs = self.scale_mask_softmax(
4892
            matmul_result, attention_mask, attn_mask_type, softmax_scale
4893
        )
4894

4895
4896
4897
4898
4899
        # 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)

4900
4901
4902
4903
4904
4905
4906
4907
4908
4909
4910
4911
4912
4913
4914
        # 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]
4915
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
4916
4917

        # change view [b * np, sq, sk]
4918
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
4919
4920
4921
4922
4923
4924
4925

        # 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)

4926
        if qkv_format == "sbhd":
4927
4928
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
4929

4930
4931
4932
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

4933
        if qkv_format == "bshd":
4934
4935
4936
4937
4938
            # [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)
4939
4940
4941
4942
4943
4944

        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
4945
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
4946
4947

    @staticmethod
4948
4949
4950
4951
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
4952
        value_layer: torch.Tensor,
4953
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
4954
        # pylint: disable=missing-function-docstring
4955
4956
4957
4958
4959
4960
4961
4962
4963
4964
4965
        # 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
4966
4967
4968
4969
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
4970
        dv: torch.Tensor,
4971
    ) -> Tuple[Union[torch.Tensor, None], ...]:
4972
        # pylint: disable=missing-function-docstring
4973
4974
4975
4976
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

4977

4978
def get_qkv_layout(
4979
4980
4981
4982
4983
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    qkv_format: str = "sbhd",
) -> str:
4984
    """Get qkv layout.
4985

4986
4987
4988
4989
4990
4991
4992
4993
4994
4995
4996
    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,
4997
        `d` head size, and `t` the total number of tokens in a batch, i.e.
4998
4999
5000
5001
5002
5003
5004
5005
5006
5007
5008
5009
5010
5011
5012
        `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`}
5013
5014
5015
5016
5017
5018
5019
5020
5021
    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.
5022
    """
5023

5024
5025
    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!"
5026

5027
    def run_iteratively(q, k, v):
5028
        # check data pointers
5029
5030
        data_ptr = q.untyped_storage().data_ptr()
        check_ptrs_qkv = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k, v])
5031
        check_ptrs_qk = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k])
5032
5033
5034
        data_ptr = k.untyped_storage().data_ptr()
        check_ptrs_kv = all(x.untyped_storage().data_ptr() == data_ptr for x in [k, v])

5035
5036
5037
5038
5039
5040
5041
        # 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
5042
5043
        stride = q.stride()
        check_strides_qkv = all(stride == x.stride() for x in [q, k, v])
5044
5045
        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]
5046
        )
5047

5048
5049
5050
5051
5052
5053
        # 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])
        )
5054

5055
5056
5057
5058
5059
5060
        # 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])
5061
        )
5062
5063
        check_h2d_offsets = all(
            x.storage_offset() == (offset + i * k.shape[-1]) for i, x in enumerate([k, v])
5064
        )
5065

5066
5067
5068
5069
5070
5071
5072
5073
5074
5075
        # 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]))
5076
        )
5077
5078
5079
5080
        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]))
5081
        )
5082

5083
        if check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_3hd_offsets:
5084
            # sb3hd, bs3hd, t3hd
5085
            # one chunk of memory, qkv, with q, k, v interleaved at dim=-3 in qkv
5086
            qkv_layout = qkv_format[:-2] + "3" + qkv_format[-2:]
5087
        elif check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_h3d_offsets:
5088
            # sbh3d, bsh3d, th3d
5089
            # one chunk of memory, qkv, with q, k, v interleaved at dim=-2 in qkv
5090
            qkv_layout = qkv_format[:-1] + "3" + qkv_format[-1:]
5091
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_2hd_offsets:
5092
            # sbhd_sb2hd, bshd_bs2hd, thd_t2hd
5093
5094
5095
            # 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
5096
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
5097
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_h2d_offsets:
5098
            # sbhd_sbh2d, bshd_bsh2d, thd_th2d
5099
5100
5101
            # 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
5102
            qkv_layout = qkv_format + "_" + qkv_format[:-1] + "2" + qkv_format[-1:]
5103
5104
5105
5106
5107
        elif (
            check_strides_kv
            and check_shapes_kv
            and (check_hd_offsets_qkv or check_hd_offsets_kv or check_hd_offsets_qk)
        ):
5108
            # sbhd_sbhd_sbhd, bshd_bshd_bshd, thd_thd_thd
5109
5110
5111
            # 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
5112
            qkv_layout = "_".join(list([qkv_format]) * 3)
5113
        else:
5114
            qkv_layout = "not_supported"
5115
5116
5117
5118

        return qkv_layout

    qkv_layout = run_iteratively(q, k, v)
5119
    if qkv_layout == "not_supported":
5120
5121
5122
        # 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)
5123
    if qkv_layout == "not_supported":
5124
        raise RuntimeError("The provided qkv memory layout is not supported!")
5125

5126
    return qkv_layout, q, k, v
5127

5128

5129
def check_set_window_size(
5130
5131
5132
    attn_mask_type: str,
    window_size: Tuple[int, int] = None,
):
5133
5134
5135
5136
5137
5138
5139
5140
    """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)
5141
    """
5142
    orig_window_size = window_size
5143
    if "causal" in attn_mask_type:
5144
        if orig_window_size is None:
5145
            window_size = (-1, 0)
5146
5147
5148
        elif orig_window_size == (-1, -1) or (
            orig_window_size[0] >= 0 and orig_window_size[1] != 0
        ):
5149
5150
5151
5152
            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
            )
5153
        elif orig_window_size != (-1, 0) and (orig_window_size[0] < 0 or orig_window_size[1] != 0):
5154
5155
5156
5157
            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"]:
5158
5159
5160
        if orig_window_size is None:
            window_size = (-1, -1)
        elif orig_window_size == (-1, 0):
5161
            window_size = (-1, -1)
5162
5163
5164
            warnings.warn(
                "window_size should be (-1, -1) or (>=0, >=0) for attn_mask_type=" + attn_mask_type
            )
5165
        elif orig_window_size != (-1, -1) and (orig_window_size[0] < 0 or orig_window_size[1] < 0):
5166
5167
5168
5169
5170
            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
5171
    return window_size
5172

5173

5174
class FlashAttention(torch.nn.Module):
5175
    """Dot product attention, using HazyResearch flash-attn package:
5176
    https://github.com/Dao-AILab/flash-attention
5177
5178
5179
5180
    """

    def __init__(
        self,
5181
        softmax_scale: float,
5182
5183
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
5184
5185
        attention_type: str = "self",
        layer_number: Optional[int] = None,
5186
        deterministic: bool = False,
5187
5188
5189
    ) -> None:
        super().__init__()

5190
5191
5192
5193
5194
5195
5196
        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."
5197

5198
        self.softmax_scale = softmax_scale
5199
5200
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
5201
5202
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
5203
        self.deterministic = deterministic
5204
5205
5206
5207
        self.logger = logging.getLogger("FlashAttention")
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
5208
5209
5210
5211
5212
5213

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5214
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5215
5216
5217
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5218
5219
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5220
        attn_mask_type: str = "causal",
5221
        window_size: Optional[Tuple[int, int]] = None,
5222
        alibi_slopes: Optional[torch.Tensor] = None,
5223
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5224
        cp_global_ranks: List[int] = None,
5225
        cp_stream: torch.cuda.Stream = None,
5226
        cp_comm_type: str = "p2p",
5227
5228
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
5229
5230
5231
    ) -> torch.Tensor:
        """flash-attn fprop"""

5232
5233
5234
5235
        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."
5236
5237
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
5238
        ), "FlashAttention currently only supports CUDA tensors."
5239
5240
        assert (
            qkv_layout in QKVLayouts
5241
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
5242

5243
5244
5245
5246
5247
5248
        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)
5249
        context_parallel = cp_size > 1
5250

5251
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
5252

5253
5254
5255
5256
5257
5258
5259
5260
5261
5262
5263
5264
5265
        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 = [
5266
                        x.transpose(0, 1) for x in (query_layer, key_layer, value_layer)
5267
                    ]
5268
            if context_parallel:
5269
                query_layer, key_layer, value_layer = [
5270
5271
5272
5273
5274
                    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 = [
5275
                    x.transpose(0, 1)
5276
5277
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
5278
5279
5280
5281
                query_layer, key_layer, value_layer = [
                    Float8Tensor.make_like(x, data=x._data)
                    for x in (query_layer, key_layer, value_layer)
                ]
5282
            if context_parallel:
5283
5284
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
5285
                ]
5286

5287
        batch_size = query_layer.shape[0]
5288

5289
        if qkv_format in ["sbhd", "bshd"]:
5290
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
5291
5292
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
5293
5294
5295

            if "padding" in attn_mask_type:
                assert not context_parallel, "Padding mask not supported with context parallelism!"
5296
5297
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
5298
                    x.reshape(x.shape[0] * x.shape[1], *x.shape[2:])
5299
5300
5301
5302
5303
5304
5305
                    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."
5306
                    if cu_seqlens_q is None:
5307
5308
5309
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
5310
5311
5312
5313
5314
5315
                        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
5316
5317
                    )
                else:
5318
                    if cu_seqlens_q is None or cu_seqlens_kv is None:
5319
5320
5321
5322
5323
                        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])
5324
5325
5326
5327
                    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)
5328
                    key_layer, value_layer = PackTensors.apply(indices_kv, key_layer, value_layer)
5329
            else:
5330
5331
5332
5333
5334
5335
5336
5337
5338
5339
5340
5341
5342
                # 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,
                    )
5343
5344
5345
5346
        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!"
5347
5348
5349
5350
5351
5352
            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()
5353

5354
5355
5356
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
5357
5358
5359
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
5360
            with self.attention_dropout_ctx():
5361
                output = attn_forward_func_with_cp(
5362
5363
5364
5365
5366
5367
5368
5369
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
5370
5371
                    cu_seqlens_q,
                    cu_seqlens_kv,
5372
                    self.attention_dropout if self.training else 0.0,
5373
5374
5375
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
5376
                    cp_comm_type,
5377
                    softmax_scale=self.softmax_scale,
5378
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
5379
                    attn_mask_type=attn_mask_type,
5380
                    deterministic=self.deterministic,
5381
                    window_size=window_size,
5382
5383
                )
        else:
5384
5385

            from .cpu_offload import CPUOffloadEnabled
5386

5387
5388
5389
5390
5391
5392
            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

5393
            with self.attention_dropout_ctx():
5394
                fa_optional_forward_kwargs = {}
5395
5396
                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
5397
5398
5399
5400
                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
5401
5402
5403
5404
                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:
5405
5406
                    if _flash_attn_2_5_7_plus:
                        fa_optional_forward_kwargs["block_table"] = None
5407
5408
5409
5410
5411
5412
5413
5414
5415
5416
                    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:
5417
5418
5419
                    fa_3_optional_forward_kwargs = {}
                    fa_3_optional_forward_kwargs["window_size"] = window_size
                    fa_3_optional_forward_kwargs["deterministic"] = self.deterministic
5420
                    activation_dtype = query_layer.dtype
5421
5422
5423
                    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)
5424
5425
5426
5427
5428
5429
5430
5431
5432
5433
5434

                        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

5435
5436
5437
5438
5439
5440
5441
5442
                        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 = (
5443
5444
                                Float8Tensor.to_float8(x, fp8_dtype=fp8_dtype_forward)
                                for x in [query_layer, key_layer, value_layer]
5445
                            )
5446
5447
5448
5449
5450
5451
5452
5453
5454
5455
5456
5457
5458
5459
5460
5461
5462
5463
5464
5465
5466
                        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]
5467
                                + ". Please update your flash-attn v3 (beta) installation as it "
5468
5469
5470
5471
5472
                                + "may have added more supported arguments to its API. \n"
                                + _flash_attn_3_installation_steps,
                            ) + e.args[1:]
                        raise

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
5498
                    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,
                    )
5499

5500
        if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
5501
            output = UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
5502

5503
        if qkv_format == "sbhd":
5504
            # (bs)hd -> bs(hd) -> sb(hd)
5505
            if fp8 and fp8_meta["recipe"].fp8_mha:
5506
5507
5508
5509
5510
5511
                output = Float8Tensor.make_like(
                    output,
                    data=output._data.reshape(batch_size, max_seqlen_q // cp_size, -1)
                    .transpose(0, 1)
                    .contiguous(),
                )
5512
            else:
5513
                output = output.view(batch_size, max_seqlen_q // cp_size, -1).transpose(0, 1)
5514
        elif qkv_format == "bshd":
5515
            # (bs)hd -> bs(hd)
5516
            output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
5517
        elif qkv_format == "thd":
5518
            # thd -> t(hd)
5519
            output = output.reshape(output.shape[0], -1)
5520

5521
        return output.contiguous()
5522

5523

5524
def _combine_tensors(
5525
5526
5527
    tensors: List[torch.Tensor],
    dim: int,
) -> torch.Tensor:
5528
5529
5530
5531
5532
5533
    """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())
5534
    new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
5535
    if isinstance(tensors[0], Float8Tensor):
5536
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
5537
5538
5539
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
5540
5541
5542
5543
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor)
5544
    else:
5545
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
5546
        combined_tensor.set_(
5547
5548
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
        )
5549
5550

    return combined_tensor
5551

5552

5553
5554
5555
5556
class FusedAttnFunc_qkvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed QKV input"""

    @staticmethod
5557
5558
5559
5560
5561
    def forward(
        ctx,
        is_training,
        max_seqlen,
        cu_seqlens,
5562
        cu_seqlens_padded,
5563
5564
5565
5566
5567
5568
5569
5570
5571
        qkv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
5572
        window_size,
5573
5574
5575
5576
5577
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
5578
        deterministic,
5579
    ):
5580
        # pylint: disable=missing-function-docstring
5581
5582
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
5583
        if fp8:
5584
5585
            is_input_fp8 = isinstance(qkv, Float8Tensor)
            if is_input_fp8:
5586
5587
5588
5589
                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
5590
            qkv_group = len(qkv_layout.split("_"))
5591
5592
5593
5594
            assert (
                qkv_group == 1
            ), f"qkv layout should conform to 3hd or h3d, e.g. sb3hd, but found {qkv_layout}."
            if is_input_fp8:
5595
5596
5597
                qkv_fp8 = qkv._data
            else:
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
5598
5599
5600
                qkv_fp8 = cast_to_fp8(
                    qkv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(qkv.shape)
5601
            out_fp8, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
5602
5603
5604
5605
5606
5607
5608
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
5609
                cu_seqlens_padded,
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
5621
                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
5622
5623
5624
5625
5626
5627
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5628
                window_size,
5629
5630
                rng_gen,
            )
5631
            if is_output_fp8:
5632
5633
                out_ret = Float8Tensor(
                    data=out_fp8,
5634
5635
5636
5637
5638
5639
5640
5641
5642
                    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]),
5643
5644
5645
5646
5647
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
5648
            out_save = out_ret
5649
5650
5651
5652
5653
5654
5655
5656
5657
5658
5659
5660
5661
5662
5663
5664
5665
5666
            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)
5667
5668
5669
            fp8_tensors = (
                qkv_fp8,
                out_fp8,
5670
                fp8_meta["scaling_fwd"].scale.clone(),
5671
5672
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
5673
5674
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
5675
5676
5677
5678
5679
5680
5681
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
5682
                cu_seqlens_padded,
5683
5684
5685
5686
5687
5688
5689
5690
5691
5692
5693
5694
                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
5695
5696
5697
5698
5699
5700
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5701
                window_size,
5702
5703
                rng_gen,
            )
5704
5705
5706
5707
            fp8_tensors = (None, None, None, None)
            out_save = out_ret

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
5708
5709
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
5710
        qkvo_tensors = (qkv, out_save) if not ctx.fp8 else (None, None)
5711
        ctx.save_for_backward(
5712
            *qkvo_tensors, cu_seqlens, cu_seqlens_padded, *fp8_tensors, *aux_ctx_tensors
5713
        )
5714
        ctx.fp8_meta = fp8_meta
5715
5716
5717
5718
5719
5720
5721
5722
        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
5723
        ctx.window_size = window_size
5724
        ctx.fused_attention_backend = (
5725
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
5726
        )
5727
        ctx.use_FAv2_bwd = use_FAv2_bwd
5728
        ctx.deterministic = deterministic
5729

5730
        return out_ret
5731
5732
5733

    @staticmethod
    def backward(ctx, d_out):
5734
        # pylint: disable=missing-function-docstring
5735
        if ctx.is_output_fp8:
5736
5737
5738
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
5739
5740
5741
            d_out_f8tensor = d_out
            d_out = d_out._data

5742
        d_out = d_out.contiguous()
5743
5744
5745
5746
        (
            qkv,
            out,
            cu_seqlens,
5747
            cu_seqlens_padded,
5748
5749
5750
5751
5752
5753
            qkv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
5754
        rest = [None]
5755
5756
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
5757
        if ctx.use_FAv2_bwd:
5758
            softmax_lse, rng_state = aux_ctx_tensors
5759
            dqkv = torch.empty_like(qkv)
5760
5761
5762
            d_out, q, k, v, out = [
                maybe_contiguous(x) for x in (d_out, qkv[:, 0], qkv[:, 1], qkv[:, 2], out)
            ]
5763
            flash_attn_cuda_bwd(
5764
5765
5766
5767
5768
5769
5770
5771
5772
5773
5774
5775
5776
5777
5778
5779
5780
5781
5782
                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,
5783
            )
5784
            dqkv = dqkv[..., : d_out.shape[-1]]
5785
        else:
5786
5787
            with torch.cuda.nvtx.range("_FusedAttn_qkvpacked"):
                if ctx.fp8:
5788
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
5789
                    fp8_dtype_backward = get_fp8_te_dtype(
5790
5791
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
5792
                    if ctx.is_output_fp8:
5793
                        d_out_fp8 = d_out
5794
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
5795
5796
5797
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
5798
5799
5800
5801
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
5802
                    dqkv_fp8, *rest = fused_attn_bwd_qkvpacked(
5803
5804
5805
5806
5807
5808
5809
5810
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
5811
                        ctx.fused_attention_backend,
5812
                        cu_seqlens_padded,
5813
5814
5815
5816
5817
5818
5819
5820
5821
5822
5823
5824
5825
5826
5827
5828
                        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,
5829
5830
                        ctx.window_size,
                        ctx.deterministic,
5831
                    )
5832
                    if ctx.is_input_fp8:
5833
5834
                        dqkv = Float8Tensor(
                            data=dqkv_fp8,
5835
5836
5837
5838
5839
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5840
                        )
5841
                    else:
5842
5843
5844
5845
5846
5847
5848
5849
5850
5851
                        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)
5852
5853
5854
5855
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(qkv.dtype)
                    dqkv, *rest = fused_attn_bwd_qkvpacked(
5856
5857
5858
5859
5860
5861
5862
5863
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
5864
                        ctx.fused_attention_backend,
5865
                        cu_seqlens_padded,
5866
5867
5868
5869
5870
5871
5872
5873
5874
5875
5876
5877
5878
5879
5880
5881
                        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,
5882
5883
                        ctx.window_size,
                        ctx.deterministic,
5884
                    )
5885

5886
5887
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
5888
5889
5890
5891
5892
5893
5894
5895
5896
5897
5898
5899
5900
5901
5902
5903
5904
5905
5906
5907
5908
            return (
                None,
                None,
                None,
                None,
                dqkv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
5909
5910
                None,
                None,
5911
            )
5912
        # else, return (dqkv, dbias)
5913
5914
5915
5916
5917
5918
5919
5920
5921
5922
5923
5924
5925
5926
5927
5928
5929
5930
5931
5932
5933
        return (
            None,
            None,
            None,
            None,
            dqkv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
5934
5935
            None,
            None,
5936
        )
5937

5938

5939
5940
5941
5942
class FusedAttnFunc_kvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed KV input"""

    @staticmethod
5943
5944
5945
5946
5947
5948
5949
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
5950
5951
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
5952
5953
5954
5955
5956
5957
5958
5959
5960
5961
        q,
        kv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
5962
        window_size,
5963
5964
5965
5966
5967
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
5968
        deterministic,
5969
    ):
5970
        # pylint: disable=missing-function-docstring
5971
5972
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
5973
        if fp8:
5974
5975
5976
            assert isinstance(kv, q.__class__), "q and kv must have the same type."
            is_input_fp8 = isinstance(q, Float8Tensor)
            if is_input_fp8:
5977
5978
5979
                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)
5980
            if is_input_fp8:
5981
5982
5983
                q_fp8, kv_fp8 = q._data, kv._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
5984
5985
                qkv_group = len(qkv_layout.split("_"))
                assert qkv_group == 2, (
5986
5987
                    "qkv layout should conform to hd_2hd or hd_h2d, e.g. sbhd_sb2hd, "
                    f"but found {qkv_layout}."
5988
5989
5990
5991
                )
                q_fp8 = cast_to_fp8(q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward).view(
                    q.shape
                )
5992
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
5993
5994
5995
                kv_fp8 = cast_to_fp8(
                    kv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(kv.shape)
5996
            out_fp8, aux_ctx_tensors = fused_attn_fwd_kvpacked(
5997
5998
5999
6000
6001
6002
6003
6004
6005
6006
                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,
6007
6008
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6009
6010
6011
6012
6013
6014
6015
6016
6017
6018
6019
6020
                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
6021
6022
6023
6024
6025
6026
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6027
                window_size,
6028
6029
                rng_gen,
            )
6030
            if is_output_fp8:
6031
6032
                out_ret = Float8Tensor(
                    data=out_fp8,
6033
6034
6035
6036
6037
6038
6039
6040
6041
                    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]),
6042
6043
6044
6045
6046
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
6047
            out_save = out_ret
6048
6049
6050
6051
6052
6053
6054
6055
6056
6057
6058
6059
6060
6061
6062
6063
6064
6065
6066
6067
6068
6069
6070
6071
6072
            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)
6073
6074
6075
6076
            fp8_tensors = (
                q_fp8,
                kv_fp8,
                out_fp8,
6077
                fp8_meta["scaling_fwd"].scale.clone(),
6078
6079
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
6080
6081
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_kvpacked(
6082
6083
6084
6085
6086
6087
6088
6089
6090
6091
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                kv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
6092
6093
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6094
6095
6096
6097
6098
6099
6100
6101
6102
6103
6104
6105
                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
6106
6107
6108
6109
6110
6111
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6112
                window_size,
6113
6114
                rng_gen,
            )
6115
6116
6117
6118
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None)

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
6119
6120
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
6121
        qkvo_tensors = (q, kv, out_save) if not ctx.fp8 else (None, None, None)
6122
6123
6124
6125
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
6126
6127
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6128
6129
6130
            *fp8_tensors,
            *aux_ctx_tensors,
        )
6131
        ctx.fp8_meta = fp8_meta
6132
6133
6134
6135
6136
6137
6138
6139
6140
        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
6141
        ctx.window_size = window_size
6142
        ctx.fused_attention_backend = (
6143
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
6144
        )
6145
        ctx.use_FAv2_bwd = use_FAv2_bwd
6146
        ctx.deterministic = deterministic
6147

6148
        return out_ret
6149
6150
6151

    @staticmethod
    def backward(ctx, d_out):
6152
        # pylint: disable=missing-function-docstring
6153
        if ctx.is_output_fp8:
6154
6155
6156
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
6157
6158
6159
            d_out_f8tensor = d_out
            d_out = d_out._data

6160
        d_out = d_out.contiguous()
6161
6162
6163
6164
6165
6166
        (
            q,
            kv,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
6167
6168
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6169
6170
6171
6172
6173
6174
6175
            q_fp8,
            kv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
6176
        rest = [None]
6177
6178
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
6179
        if ctx.use_FAv2_bwd:
6180
            softmax_lse, rng_state = aux_ctx_tensors
6181
6182
            dq = torch.empty_like(q)
            dkv = torch.empty_like(kv)
6183
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, kv[:, 0], kv[:, 1], out)]
6184
            flash_attn_cuda_bwd(
6185
6186
6187
6188
6189
6190
6191
6192
6193
6194
6195
6196
6197
6198
6199
6200
6201
6202
6203
                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,
6204
            )
6205
6206
            dq = dq[..., : d_out.shape[-1]]
            dkv = dkv[..., : d_out.shape[-1]]
6207
        else:
6208
6209
            with torch.cuda.nvtx.range("_FusedAttn_kvpacked"):
                if ctx.fp8:
6210
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
6211
                    fp8_dtype_backward = get_fp8_te_dtype(
6212
6213
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
6214
                    if ctx.is_output_fp8:
6215
                        d_out_fp8 = d_out
6216
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
6217
6218
6219
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
6220
6221
6222
6223
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
6224
                    dq_fp8, dkv_fp8, *rest = fused_attn_bwd_kvpacked(
6225
6226
6227
6228
6229
6230
6231
6232
6233
6234
6235
                        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,
6236
                        ctx.fused_attention_backend,
6237
6238
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6239
6240
6241
6242
6243
6244
6245
6246
6247
6248
6249
6250
6251
6252
6253
6254
                        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,
6255
6256
                        ctx.window_size,
                        ctx.deterministic,
6257
                    )
6258
                    if ctx.is_input_fp8:
6259
6260
                        dq = Float8Tensor(
                            data=dq_fp8,
6261
6262
6263
6264
6265
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6266
6267
6268
                        )
                        dkv = Float8Tensor(
                            data=dkv_fp8,
6269
6270
6271
6272
6273
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6274
                        )
6275
6276
6277
                    else:
                        dq = cast_from_fp8(
                            dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
6278
6279
6280
6281
6282
6283
6284
6285
6286
6287
6288
6289
6290
6291
6292
                            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)
6293
6294
6295
6296
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dkv, *rest = fused_attn_bwd_kvpacked(
6297
6298
6299
6300
6301
6302
6303
6304
6305
6306
6307
                        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,
6308
                        ctx.fused_attention_backend,
6309
6310
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6311
6312
6313
6314
6315
6316
6317
6318
6319
6320
6321
6322
6323
6324
6325
6326
                        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,
6327
6328
                        ctx.window_size,
                        ctx.deterministic,
6329
                    )
6330

6331
6332
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
6333
6334
6335
6336
6337
6338
6339
6340
6341
6342
6343
6344
6345
6346
6347
6348
6349
6350
6351
6352
6353
6354
6355
6356
6357
            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,
6358
6359
                None,
                None,
6360
            )
6361
        # else, return (dqkv, dbias)
6362
6363
6364
6365
6366
6367
6368
6369
6370
6371
6372
6373
6374
6375
6376
6377
6378
6379
6380
6381
6382
6383
6384
6385
6386
        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,
6387
6388
            None,
            None,
6389
6390
        )

6391

6392
6393
6394
6395
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
6396
6397
6398
6399
6400
6401
6402
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
6403
6404
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
6405
6406
6407
6408
6409
6410
6411
6412
6413
6414
6415
        q,
        k,
        v,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
6416
        window_size,
6417
6418
6419
6420
6421
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
6422
        deterministic,
6423
    ):
6424
        # pylint: disable=missing-function-docstring
6425
6426
        is_input_fp8 = False
        is_output_fp8 = fp8_meta["recipe"].fp8_mha
6427
6428
6429
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
6430
6431
6432
6433
6434
            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:
6435
6436
6437
6438
                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
6439
                qkv_group = len(qkv_layout.split("_"))
6440
                if qkv_group == 1:
6441
6442
                    dim = qkv_layout.find("3")
                    qkv = _combine_tensors([q, k, v], dim)
6443
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
6444
6445
6446
6447
                    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])
6448
6449
                    q_fp8, k_fp8, v_fp8 = [x.squeeze(dim) for x in [q_fp8, k_fp8, v_fp8]]
                if qkv_group == 2:
6450
6451
6452
6453
6454
                    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)
6455
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
6456
6457
6458
6459
                    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])
6460
6461
                    k_fp8, v_fp8 = [x.squeeze(dim) for x in [k_fp8, v_fp8]]
                if qkv_group == 3:
6462
6463
6464
6465
6466
6467
6468
6469
6470
                    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)
6471
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
6472
6473
6474
6475
6476
6477
6478
6479
6480
6481
6482
                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,
6483
6484
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6485
6486
6487
6488
6489
6490
6491
6492
6493
6494
6495
6496
                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
6497
6498
6499
6500
6501
6502
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6503
                window_size,
6504
6505
                rng_gen,
            )
6506
            if is_output_fp8:
6507
6508
                out_ret = Float8Tensor(
                    data=out_fp8,
6509
6510
6511
6512
6513
6514
6515
6516
6517
                    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]),
6518
6519
6520
6521
6522
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
6523
6524
            out_save = out_ret

6525
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
6526
                # 1: qkv packed, 2: kv packed, 3: qkv separate
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
6586
                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]),
6587
                        fp8_meta["scaling_fwd"],
6588
                        META_O,
6589
                        fp8_dtype_forward,
6590
6591
                        qkv_dtype,
                    ).view(out_fp8.shape)
6592
6593
6594
6595
6596
6597

            fp8_tensors = (
                q_fp8,
                k_fp8,
                v_fp8,
                out_fp8,
6598
                fp8_meta["scaling_fwd"].scale.clone(),
6599
6600
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
6601
6602
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd(
6603
6604
6605
6606
6607
6608
6609
6610
6611
6612
6613
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
6614
6615
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
6616
6617
6618
6619
6620
6621
6622
6623
6624
6625
6626
6627
                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
6628
6629
6630
6631
6632
6633
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
6634
                window_size,
6635
6636
                rng_gen,
            )
6637
6638
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None, None)
6639

6640
6641
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))

6642
        from .cpu_offload import CPUOffloadEnabled
6643

6644
        if CPUOffloadEnabled:
6645
6646
6647
6648
6649
6650
6651
            if ctx.fp8:
                tensor_list = fp8_tensors
            else:
                tensor_list = [q, k, v, out_save]

            tensor_list.extend(aux_ctx_tensors)

6652
            qkv_layout = "sbhd_sbhd_sbhd"
6653
6654
6655
6656
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

6657
6658
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
6659
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
6660
6661
6662
6663
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
6664
6665
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6666
6667
6668
            *fp8_tensors,
            *aux_ctx_tensors,
        )
6669
        ctx.fp8_meta = fp8_meta
6670
6671
6672
6673
6674
6675
6676
6677
6678
        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
6679
        ctx.window_size = window_size
6680
        ctx.fused_attention_backend = (
6681
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
6682
        )
6683
        ctx.use_FAv2_bwd = use_FAv2_bwd
6684
        ctx.deterministic = deterministic
6685

6686
        return out_ret
6687
6688
6689

    @staticmethod
    def backward(ctx, d_out):
6690
        # pylint: disable=missing-function-docstring
6691
        if ctx.is_output_fp8:
6692
6693
6694
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
6695
6696
6697
            d_out_f8tensor = d_out
            d_out = d_out._data

6698
        d_out = d_out.contiguous()
6699
6700
6701
6702
6703
6704
6705
        (
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
6706
6707
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
6708
6709
6710
6711
6712
6713
6714
6715
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
6716
6717
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
6718
        rest = [None]
6719
        if ctx.use_FAv2_bwd:
6720
            softmax_lse, rng_state = aux_ctx_tensors
6721
6722
6723
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
6724
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
6725
            flash_attn_cuda_bwd(
6726
6727
6728
6729
6730
6731
6732
6733
6734
6735
6736
6737
6738
6739
6740
6741
6742
6743
6744
                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,
6745
            )
6746
6747
6748
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
6749
        else:
6750
6751
6752
6753
            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(
6754
6755
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
6756
                    if ctx.is_output_fp8:
6757
                        d_out_fp8 = d_out
6758
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
6759
6760
6761
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
6762
6763
6764
6765
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
6766
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
6767
6768
6769
6770
6771
6772
6773
6774
6775
6776
6777
6778
                        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,
6779
                        ctx.fused_attention_backend,
6780
6781
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6782
6783
6784
6785
6786
6787
6788
6789
6790
6791
6792
6793
6794
6795
6796
6797
                        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,
6798
6799
                        ctx.window_size,
                        ctx.deterministic,
6800
                    )
6801

6802
                    if ctx.is_input_fp8:
6803
6804
                        dq = Float8Tensor(
                            data=dq_fp8,
6805
6806
6807
6808
6809
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6810
6811
6812
                        )
                        dk = Float8Tensor(
                            data=dk_fp8,
6813
6814
6815
6816
6817
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6818
6819
6820
                        )
                        dv = Float8Tensor(
                            data=dv_fp8,
6821
6822
6823
6824
6825
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
6826
                        )
6827
                    else:
6828
                        qkv_group = len(ctx.qkv_layout.split("_"))
6829
                        if qkv_group == 1:
6830
6831
6832
6833
6834
6835
6836
6837
6838
6839
6840
6841
6842
                            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])
6843
6844
6845
6846
                            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]),
6847
6848
6849
6850
6851
6852
6853
6854
6855
6856
6857
6858
6859
6860
6861
6862
6863
6864
                                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])
6865
6866
6867
6868
                            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]),
6869
6870
6871
6872
6873
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dq_fp8.shape)
6874
6875
                            dk = cast_from_fp8(
                                dk_fp8.view(-1, dk_fp8.shape[-2] * dk_fp8.shape[-1]),
6876
6877
6878
6879
6880
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dk_fp8.shape)
6881
6882
                            dv = cast_from_fp8(
                                dv_fp8.view(-1, dv_fp8.shape[-2] * dv_fp8.shape[-1]),
6883
6884
6885
6886
6887
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dv_fp8.shape)
6888
6889
6890
6891
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dk, dv, *rest = fused_attn_bwd(
6892
6893
6894
6895
6896
6897
6898
6899
6900
6901
6902
6903
                        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,
6904
                        ctx.fused_attention_backend,
6905
6906
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
6907
6908
6909
6910
6911
6912
6913
6914
6915
6916
6917
6918
6919
6920
6921
6922
                        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,
6923
6924
                        ctx.window_size,
                        ctx.deterministic,
6925
                    )
6926

6927
6928
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
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
6954
            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,
6955
6956
                None,
                None,
6957
            )
6958
        # else, return (dqkv, dbias)
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
6984
        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,
6985
6986
            None,
            None,
6987
        )
6988

6989

6990
class FusedAttention(torch.nn.Module):
6991
6992
6993
6994
6995
6996
6997
6998
6999
    """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:

7000
7001
7002
7003
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
7004
    | attn_type     | self/cross              | self/cross                     |
7005
    | qkv_layout    |                         |                                |
7006
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
7007
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
7008
7009
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
7010
7011
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
7012
    | dropout       | yes                     | yes                            |
7013
7014
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
7015
    | output dtype  | fp16/bf16               | fp16/bf16                      |
7016
7017
7018
7019
    """

    def __init__(
        self,
7020
        softmax_scale: float,
7021
7022
7023
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
7024
7025
        layer_number: Optional[int] = None,
        deterministic: bool = False,
7026
7027
7028
    ) -> None:
        super().__init__()

7029
        self.softmax_scale = softmax_scale
7030
7031
7032
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
7033
7034
7035
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
7036
        self.layer_number = 1 if layer_number is None else layer_number
7037
        self.deterministic = deterministic
7038

7039
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
7040
7041
            """
            Temporarily remove fused_attention._extra_state as a missing key
7042
            or an unexpected key when loading Transformer Engine checkpoints.
7043
7044
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
7045
            phased out in Transformer Engine 2.0.
7046
7047
            """
            for key in incompatible_keys.missing_keys:
7048
                if "fused_attention._extra_state" in key:
7049
                    incompatible_keys.missing_keys.remove(key)
7050
7051
7052
7053
7054
7055
7056
            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."
                    )
7057

7058
7059
        self.register_load_state_dict_post_hook(remove_extra_states_check)

7060
    @no_torch_dynamo()
7061
7062
7063
7064
7065
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
7066
7067
7068
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
7069
7070
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
7071
7072
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
7073
        attn_mask_type: str = "causal",
7074
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7075
        window_size: Optional[Tuple[int, int]] = None,
7076
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
7077
7078
7079
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
7080
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
7081
7082
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
7083
        cp_comm_type: str = "p2p",
7084
7085
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
7086
7087
    ) -> torch.Tensor:
        """fused attention fprop"""
7088
7089
7090
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
7091
7092
7093
7094
        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."
7095
7096
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
7097
        ), "FusedAttention only supports CUDA tensors."
7098
7099
        assert (
            qkv_layout in QKVLayouts
7100
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
7101

7102
7103
7104
7105
7106
7107
        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)
7108
        context_parallel = cp_size > 1
7109

7110
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
7111

7112
7113
        if qkv_format in ["sbhd", "bshd"]:
            if qkv_format == "sbhd":
7114
                batch_size, max_seqlen_q, max_seqlen_kv = (
7115
7116
7117
7118
7119
                    query_layer.shape[1],
                    query_layer.shape[0],
                    key_layer.shape[0],
                )
            if qkv_format == "bshd":
7120
                batch_size, max_seqlen_q, max_seqlen_kv = (
7121
7122
7123
7124
                    query_layer.shape[0],
                    query_layer.shape[1],
                    key_layer.shape[1],
                )
7125
7126
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
7127
            if "padding" in attn_mask_type:
7128
7129
                assert not context_parallel, "Padding mask not supported with context parallelism!"

7130
7131
7132
7133
7134
                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!"
                        )
7135
                    if self.attention_type == "self":
7136
7137
                        cu_seqlens_q = get_cu_seqlens(attention_mask)
                        cu_seqlens_kv = cu_seqlens_q
7138
                    else:
7139
7140
                        cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
7141
            else:
7142
7143
7144
7145
7146
7147
7148
7149
7150
7151
7152
7153
                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,
                    )
7154
7155
7156
        if qkv_format == "thd":
            assert (
                max_seqlen_q is not None
7157
7158
7159
                and max_seqlen_kv is not None
                and cu_seqlens_q is not None
                and cu_seqlens_kv is not None
7160
            ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
7161
7162
7163
7164

        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
7165
7166
7167

        qkv_dtype = TE_DType[query_layer.dtype]

7168
7169
7170
7171
7172
        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)
        )
7173

7174
7175
7176
7177
7178
7179
7180
7181
7182
7183
7184
        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!"
            )

7185
        if context_parallel:
7186
            assert (
7187
7188
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
7189
7190
7191
7192
7193
7194
7195
            ), 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)
            ]
7196
7197
7198
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
7199
7200
7201
7202
7203
7204
7205
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
7206
7207
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
7208
                    self.attention_dropout if self.training else 0.0,
7209
7210
7211
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
7212
                    cp_comm_type,
7213
                    softmax_scale=self.softmax_scale,
7214
                    qkv_format=qkv_format,
7215
                    attn_mask_type=attn_mask_type,
7216
7217
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
7218
                    deterministic=self.deterministic,
7219
                    use_fused_attention=True,
7220
                    window_size=window_size,
7221
7222
                    fp8=fp8,
                    fp8_meta=fp8_meta,
7223
7224
                )
        else:
7225
7226
7227
7228
7229
7230
7231
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
7232
7233
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
7234
7235
7236
7237
7238
7239
7240
7241
7242
7243
7244
                    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,
7245
                    window_size,
7246
7247
7248
7249
7250
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
7251
                    self.deterministic,
7252
                )
7253

7254
7255
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
7256
7257


7258
class DotProductAttention(TransformerEngineBaseModule):
7259
7260
7261
7262
7263
7264
    """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::

7265
        Argument :attr:`attention_mask` in the `forward` call is only used when
7266
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
7267
7268
7269

    .. warning::

7270
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
7271
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
7272
7273
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
7274

7275
7276
7277
7278
7279
7280
7281
    .. 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>`_).


7282
7283
7284
7285
    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
7286
7287
7288
    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.
7289
7290
7291
7292
7293
7294
7295
7296
    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`.
7297
7298
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
7299
    attn_mask_type: str, default = `causal`
7300
                   type of attention mask passed into softmax operation, options are "`no_mask`",
7301
7302
7303
7304
7305
7306
7307
7308
7309
                   "`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
7310
                   "`padding_causal`" and "`padding_causal_bottom_right`", Transformer Engine
7311
7312
7313
7314
7315
7316
7317
7318
7319
7320
7321
7322
7323
7324
                   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].
7325
7326
7327
7328
    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
7329
7330
7331
                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
7332
                be overridden by :attr:`window_size` in `forward` as well.
7333
7334
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
7335
7336
7337
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
7338
7339
7340
    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,
7341
               `h` the number of heads, `d` head size, and `t` the total number of tokens
7342
7343
7344
7345
7346
               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.
7347
               For that, please use `get_qkv_layout` to gain the layout information.
7348
7349
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
7350
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
7351
7352
7353
7354
7355
7356
7357
7358
7359

    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.
7360
    cp_group : Union[ProcessGroup, List[ProcessGroup]], default = `None`
7361
              context parallel process group.
7362
7363
7364
              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.
7365
7366
7367
7368
7369
7370
7371
    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.
7372
    cp_comm_type : str, default = `p2p`
7373
                  inter-gpu communication type for context parallelism.
7374
                  Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
7375
7376
7377
7378
7379
7380
                  "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.
7381
7382
7383
                  "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).
7384
7385
7386
7387
7388
    """

    def __init__(
        self,
        num_attention_heads: int,
7389
        kv_channels: Union[int, Tuple[int, int]],
7390
        num_gqa_groups: Optional[int] = None,
7391
        attention_dropout: float = 0.0,
7392
        qkv_format: str = "sbhd",
7393
        attn_mask_type: str = "causal",
7394
        window_size: Optional[Tuple[int, int]] = None,
7395
7396
7397
7398
7399
        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,
7400
        attention_type: str = "self",
7401
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
7402
        cp_global_ranks: List[int] = None,
7403
        cp_stream: torch.cuda.Stream = None,
7404
        cp_comm_type: str = "p2p",
7405
        softmax_scale: Optional[float] = None,
7406
7407
7408
    ) -> None:
        super().__init__()

7409
        self.logger = logging.getLogger("DotProductAttention")
7410
7411
7412
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
7413
        self.qkv_format = qkv_format
7414
        attn_mask_type = attn_mask_type.replace(",", "_")
7415
7416
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
7417
        self.attn_mask_type = attn_mask_type
7418
        self.window_size = check_set_window_size(attn_mask_type, window_size)
7419
7420
7421
7422
7423
7424
7425
        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)
7426
        self.get_rng_state_tracker = get_rng_state_tracker
7427
        self.num_attention_heads = num_attention_heads
7428
        self.layer_number = 1 if layer_number is None else layer_number
7429
7430
7431
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
7432
        self.cp_comm_type = cp_comm_type
7433

7434
7435
7436
7437
7438
7439
        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]
        )
7440

7441
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
7442
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
7443

7444
7445
7446
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
7447

7448
        self.rng_states_tracker = None
7449
7450
7451
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
7452
7453
7454
            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
7455

7456
        if softmax_scale is None:
7457
7458
7459
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
7460

7461
7462
7463
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
7464
        )
7465
7466
7467
7468
7469
7470
7471
7472
7473
7474
7475
7476
7477
7478
7479
7480
7481
7482
7483
        # 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"
7484

7485
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
7486
7487
7488
7489

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

7490
7491
7492
7493
7494
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

7495
7496
7497
7498
7499
7500
7501
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
7502

7503
        # Instantiating three types since use of flash-attn and FusedAttention
7504
        # might be ruled out due to forward inputs.
7505
7506
7507
7508
7509
7510
7511
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
7512

7513
        self.unfused_attention = UnfusedDotProductAttention(
7514
7515
7516
7517
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
7518
        )
7519

7520
7521
7522
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
7523
7524
            when loading older Transformer Engine checkpoints. Will phase out
            this hook in Transformer Engine 2.0.
7525
7526
7527
7528
7529
7530
7531
            """
            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)

7532
7533
7534
7535
7536
7537
7538
7539
7540
7541
7542
7543
7544
7545
7546
7547
7548
7549
7550
7551
7552
7553
    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
        )

7554
7555
7556
7557
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
7558
        **forward_kwargs: Dict[str, Any],
7559
7560
7561
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

7562
7563
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
7564
7565
7566

        hidden_states = checkpoint(
            custom_forward,
7567
7568
7569
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
7570
            *forward_args,
7571
            **forward_kwargs,
7572
7573
7574
7575
        )

        return hidden_states

7576
7577
    def set_context_parallel_group(
        self,
7578
        cp_group: Union[dist_group_type, List[dist_group_type], None],
7579
7580
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
7581
        cp_comm_type: str = "p2p",
7582
    ) -> None:
7583
7584
7585
7586
7587
7588
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
7589
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
7590
                  context parallel process group.
7591
7592
7593
                  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.
7594
7595
7596
7597
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
7598
        cp_comm_type : str, default = `p2p`
7599
                      inter-gpu communication type for context parallelism.
7600
                      Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
7601
7602
7603
7604
7605
7606
                      "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.
7607
7608
7609
                      "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).
7610
        """
7611
7612
7613
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
7614
        self.cp_comm_type = cp_comm_type
7615

7616
    @no_torch_dynamo(recursive=False)
7617
7618
7619
7620
7621
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
7622
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7623
7624
7625
        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
7626
7627
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
7628
7629
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
7630
        attn_mask_type: Optional[str] = None,
7631
        window_size: Optional[Tuple[int, int]] = None,
7632
        checkpoint_core_attention: bool = False,
7633
7634
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
7635
        alibi_slopes: Optional[torch.Tensor] = None,
7636
        fast_zero_fill: bool = True,
7637
        inference_params: Optional[InferenceParams] = None,
7638
        is_first_microbatch: Optional[bool] = None,
7639
7640
7641
7642
7643
7644
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

7645
7646
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
7647

7648
7649
        .. note::

7650
7651
7652
7653
7654
7655
7656
7657
7658
7659
7660
7661
7662
            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,
7663
            and FusedAttention backend if applicable, to use. Transformer Engine prioritizes
7664
7665
7666
7667
            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
7668
7669
            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
7670
            optimizations in FusedAttention. When unset, Transformer Engine determines the code path
7671
7672
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
7673

7674
7675
7676
7677
7678
7679
7680
7681
7682
7683
7684
7685
7686
7687
7688
7689
7690
7691
7692
7693
7694
7695
7696
7697
7698
7699
7700
7701
7702
7703
7704
7705
7706
7707
7708
7709
7710
7711
7712
7713
7714
7715
7716
7717
7718
7719
7720
7721
7722
7723
7724
7725
7726
7727
        .. note::
            .. _cu_seqlens note:

            When training data has variable sequence lengths, users have two options.

            1. Manipulate the data and pad all sequences to the same length. Use
               :attr:`qkv_format` = {"bshd", "sbhd"} and
               :attr:`attn_mask_type` = {"padding", "padding_causal", "padding_causal_bottom_right"}.
               Pass in :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv`, or :attr:`attention_mask`
               (which will be converted to :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv`), to provide
               the real sequence length information. For example, a batch of 3 sequences
               [a a a b b c c c c] can be padded to [a a a PAD b b PAD PAD c c c c], and the cumulative
               sequence length tensors would be
               :attr:`cu_seqlens_q` = :attr:`cu_seqlens_kv` = [0, 3, 5, 9] for self-attention.

            2. Do not perform padding on training data. Use :attr:`qkv_format` = "thd" and
               :attr:`attn_mask_type` = {"padding", "padding_causal", "padding_causal_bottom_right"}.
               Pass in :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv`, or :attr:`attention_mask`,
               as in option 1. For example, a batch of 3 sequences [a a a b b c c c c] can be processed
               without any padding, and the sequence length tensors would be
               :attr:`cu_seqlens_q` = :attr:`cu_seqlens_kv` = [0, 3, 5, 9] for self-attention.

               In certain use cases, a varying number of identifier tokens are inserted between
               sequences. These tokens do not participate in the attention calculation.
               :attr:`cu_seqlens_q_padded` and :attr:`cu_seqlens_kv_padded` must be specified
               in such cases to correctly identify the start and end of each sequence in a batch.
               For example, a batch of 3 sequences [a a a 1 b b 2 2 c c c c 3] would have
               :attr:`cu_seqlens_q` = :attr:`cu_seqlens_kv` = [0, 3, 5, 9], and
               :attr:`cu_seqlens_q_padded` = :attr:`cu_seqlens_kv_padded` = [0, 4, 8, 13]
               for self-attention.

        .. note::
            .. _max_seqlen note:

            When :attr:`qkv_format` = {"bshd", "sbhd"}, sequences are of equal length in a batch.
            :attr:`max_seqlen_q` and :attr:`max_seqlen_kv` should be the same as the "s" dimension of
            :attr:`query_layer` and :attr:`key_layer` tensors. When unset, Transformer Engine will
            infer them as such.

            When :attr:`qkv_format` = "thd", sequences have varying lengths. :attr:`max_seqlen_q` and
            :attr:`max_seqlen_kv` should be the maximum query and key/value sequence length in a batch.
            When unset, Transformer Engine deduces them from :attr:`cu_seqlens_q` and :attr:`cu_seqlens_kv`.
            This deduction costs a small kernel and some CPU-GPU synchronization, and to avoid this
            overhead, users are recommended to obtain the maximum sequence lengths from the data loaders
            and pass them in.

            - As the maximum sequence lengths, batch size, and number of tokens change from batch to batch,
              dynamic shapes need to be supported for tensor construction. FlashAttention and
              UnfusedDotProductAttention naturally do so, while FusedAttention requires parameters to be static
              to create graphs before performance heuristics analysis. To reduce the number of graphs created
              per run, Transformer Engine 1.13+ quantizes relevant parameters: for cuDNN < 9.6, {batch size,
              :attr:`max_seqlen_q`, :attr:`max_seqlen_kv`}, and for cuDNN >= 9.6, {"t" dimension of
              :attr:`query_layer`, "t" dimension of :attr:`key_layer`}.

7728
7729
7730
7731
7732
7733
7734
7735
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
7736
7737
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
7738
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
7739
7740
             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]
7741
7742
7743
7744
             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.
7745
7746
7747
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
7748
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
7749
                   with shape [batch_size + 1] and dtype torch.int32.
7750
                   See :ref:`note<cu_seqlens note>` for more details.
7751
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
7752
7753
                   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.
7754
                   See :ref:`note<cu_seqlens note>` for more details.
7755
7756
7757
7758
7759
        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`.
7760
                   See :ref:`note<cu_seqlens note>` for more details.
7761
7762
7763
7764
7765
        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`.
7766
                   See :ref:`note<cu_seqlens note>` for more details.
7767
7768
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
7769
                      See :ref:`note<max_seqlen note>` for more details.
7770
7771
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
7772
                       See :ref:`note<max_seqlen note>` for more details.
7773
7774
7775
7776
7777
7778
7779
        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.
7780
        window_size: Optional[Tuple[int, int]], default = `None`
7781
                    Sliding window size for local attention.
7782
7783
7784
7785
7786
        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.
7787
        core_attention_bias_type: str, default = `no_bias`
7788
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
7789
        core_attention_bias: Optional[torch.Tensor], default = `None`
7790
7791
                    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.
7792
7793
7794
7795
        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.
7796
        fast_zero_fill: bool, default = `True`
7797
                    Whether to use the fast path to set output tensors to 0 or not.
7798
7799
7800
7801
7802
7803
7804
7805
7806
7807
        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.
7808
7809
7810
7811
7812
7813
7814
7815
7816
7817
7818
7819
7820
        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)
7821
        """
7822
7823
7824
7825
7826
7827
7828
7829
7830
7831
7832
        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
7833
                        self.logger.warning(
7834
7835
7836
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
7837
7838
7839
7840
7841
7842
7843
7844
7845
7846
7847

            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."""
7848

7849
7850
7851
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
7852
7853
7854
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
7855
7856
7857
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
7858
7859
7860
7861
7862
7863
7864
7865
            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}!"
7866

7867
7868
7869
7870
7871
7872
            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"
7873
            assert (
7874
7875
7876
7877
7878
7879
                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!"
7880

7881
7882
7883
7884
            if window_size is None:
                window_size = self.window_size
            window_size = check_set_window_size(attn_mask_type, window_size)

7885
7886
7887
7888
7889
7890
7891
            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."
7892

7893
7894
            if qkv_format is None:
                qkv_format = self.qkv_format
7895

7896
7897
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"
7898

7899
7900
7901
7902
7903
                # 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"

7904
7905
7906
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
7907

7908
7909
7910
7911
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]
7912

7913
7914
7915
                batch_start = inference_params.batch_size_offset
                batch_end = batch_start + key_layer.size(1)
                assert batch_end <= inference_key_memory.size(1)
7916

7917
7918
7919
                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + key_layer.size(0)
                assert sequence_end <= inference_key_memory.size(0)
7920

7921
7922
7923
7924
7925
7926
7927
7928
7929
                # 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, ...]
7930

7931
7932
7933
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
7934

7935
7936
                key_layer = key_layer.contiguous()
                value_layer = value_layer.contiguous()
7937
7938

            assert (
7939
7940
7941
7942
7943
7944
7945
7946
7947
7948
                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":
7949
                assert all(
7950
7951
7952
7953
7954
7955
7956
7957
7958
7959
7960
7961
7962
                    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!"
7963
                batch_size = len(cu_seqlens_q) - 1
7964
                if max_seqlen_q is None:
7965
7966
7967
7968
                    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]
7969
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
7970
                if max_seqlen_kv is None:
7971
7972
7973
7974
                    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]
7975
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
7976

7977
7978
7979
7980
7981
7982
            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)
7983
7984
            context_parallel = cp_size > 1

7985
            if qkv_format in ["sbhd", "bshd"]:
7986
                assert all(
7987
7988
7989
                    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":
7990
7991
                    max_seqlen_q = query_layer.shape[0] if max_seqlen_q is None else max_seqlen_q
                    max_seqlen_kv = key_layer.shape[0] if max_seqlen_kv is None else max_seqlen_kv
7992
                    batch_size = query_layer.shape[1]
7993
                else:
7994
7995
                    max_seqlen_q = query_layer.shape[1] if max_seqlen_q is None else max_seqlen_q
                    max_seqlen_kv = key_layer.shape[1] if max_seqlen_kv is None else max_seqlen_kv
7996
                    batch_size = query_layer.shape[0]
7997
7998
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
7999
8000
8001
8002
8003
                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
8004
                        the sequence dimension in 'query_layer'!"""
8005
8006
8007
8008
8009
                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
8010
                        the sequence dimension in 'key_layer' and 'value_layer'!"""
8011
8012
8013
8014
8015
                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!"
8016
                        if self.attention_type == "self":
8017
8018
8019
8020
8021
8022
8023
8024
8025
8026
8027
8028
8029
8030
8031
8032
                            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,
                        )
8033

8034
8035
8036
8037
8038
            if (
                isinstance(query_layer, Float8Tensor)
                and isinstance(key_layer, Float8Tensor)
                and isinstance(value_layer, Float8Tensor)
            ):
8039
                qkv_layout, query_layer._data, key_layer._data, value_layer._data = get_qkv_layout(
8040
8041
8042
                    query_layer._data, key_layer._data, value_layer._data, qkv_format=qkv_format
                )
            else:
8043
                qkv_layout, query_layer, key_layer, value_layer = get_qkv_layout(
8044
8045
                    query_layer, key_layer, value_layer, qkv_format=qkv_format
                )
8046

8047
8048
8049
8050
8051
8052
8053
8054
            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
8055
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
8056
8057
8058
8059
8060
8061
8062
8063
            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
8064
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
8065
8066
8067
8068
8069
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

8070
8071
            core_attention_bias_shape = None
            if core_attention_bias is not None:
8072
                if (
8073
8074
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
8075
                ):
8076
8077
8078
8079
8080
8081
8082
8083
8084
8085
8086
8087
8088
8089
8090
8091
8092
8093
8094
8095
8096
8097
8098
8099
                    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)
            )
8100

8101
            attention_params = AttentionParams(
8102
8103
8104
8105
8106
8107
8108
8109
                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,
8110
8111
                head_dim_qk=query_layer.shape[-1],
                head_dim_v=value_layer.shape[-1],
8112
8113
8114
8115
8116
8117
8118
8119
8120
8121
8122
                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,
8123
8124
                deterministic=self.deterministic,
                is_training=self.training,
8125
8126
8127
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
            )
8128
            global _attention_backends, _use_flash_attn_3
8129
8130
8131
8132
8133
8134
8135
            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"]:
8136
                _use_flash_attn_3 = _flash_attn_3_is_installed
8137
8138
8139
8140
8141
8142
8143
8144
                (
                    use_flash_attention,
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
                ) = get_attention_backend(attention_params)
                if use_flash_attention:
8145
8146
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
8147
                        _flash_attn_version if not _use_flash_attn_3 else _flash_attn_3_version,
8148
                    )
8149
8150
8151
8152
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
8153
                    )
8154
8155
8156
8157
8158
8159
8160
                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"]
8161

8162
8163
8164
8165
8166
8167
8168
8169
8170
8171
8172
8173
8174
8175
8176
8177
8178
8179
8180
8181
8182
8183
            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,
8184
                    cp_comm_type=self.cp_comm_type,
8185
8186
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
8187
8188
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
8189
                )
8190

8191
            if use_fused_attention:
8192
8193
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
8194
8195
8196
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
8197
8198
8199
8200
8201
8202
8203
                    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,
8204
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
8205
                    )
8206
8207
8208
8209
8210
8211
8212
8213
8214
                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,
8215
8216
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
8217
8218
8219
8220
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
8221
                        window_size=window_size,
8222
8223
8224
8225
8226
8227
8228
                        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,
8229
                        cp_comm_type=self.cp_comm_type,
8230
8231
8232
8233
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
                    )
                return self.fused_attention(
8234
8235
8236
8237
8238
8239
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
8240
8241
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
8242
8243
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
8244
8245
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
8246
                    window_size=window_size,
8247
                    fused_attention_backend=fused_attention_backend,
8248
8249
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
8250
8251
8252
8253
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
8254
                    cp_comm_type=self.cp_comm_type,
8255
8256
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
8257
                )
8258

8259
            from .cpu_offload import CPUOffloadEnabled
8260

8261
8262
8263
8264
8265
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
8266

8267
            if use_unfused_attention:
8268
8269
8270
8271
8272
8273
                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
                    )
8274
8275
8276
8277
8278
8279
8280
8281
8282
8283
8284
8285
8286
8287
8288
8289
                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(
8290
8291
8292
                    query_layer,
                    key_layer,
                    value_layer,
8293
8294
8295
8296
8297
8298
8299
8300
8301
                    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,
                )
8302

8303
            raise ValueError("No dot product attention support for the provided inputs!")
8304
8305


8306
8307
8308
8309
8310
8311
8312
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

8313
8314
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
8315

8316
8317
8318
8319
8320
8321
8322
8323
8324
8325
8326
8327
8328
8329
8330
8331
8332
8333
8334
8335
8336
8337
8338
8339
8340
    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.
8341
8342
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
8343
                   default = `causal`
8344
8345
8346
8347
8348
                   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.
8349
8350
8351
8352
    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
8353
8354
8355
                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
8356
                be overridden by :attr:`window_size` in `forward` as well.
8357
8358
8359
8360
8361
8362
8363
8364
8365
8366
8367
8368
8369
    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.
8370
8371
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
8372
8373
8374
8375
8376
8377
8378
8379
8380
8381
8382
8383
8384
8385
8386
8387
8388
8389
8390
8391
    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"
8392
          The device on which the parameters of the model will be allocated. It is the user's
8393
8394
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
8395
8396
8397
8398
8399
8400
8401
    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.
8402
            For that, please use `get_qkv_layout` to gain the layout information.
8403
8404
8405
8406
8407
8408
8409
8410
8411
8412
8413
8414
8415
8416
8417
8418
8419
8420
8421
8422
8423
8424
8425
8426
8427
8428
8429
8430
8431
8432
8433
8434
8435
8436
8437
8438
8439
8440
8441
8442

    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`.
8443
8444
8445
8446
8447
8448
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
8449
8450
8451
8452
8453
        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,
8454
        layer_number: Optional[int] = None,
8455
        attn_mask_type: str = "causal",
8456
        window_size: Optional[Tuple[int, int]] = None,
8457
8458
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
8459
        num_gqa_groups: Optional[int] = None,
8460
8461
8462
        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
8463
        params_dtype: Optional[torch.dtype] = None,
8464
        return_bias: bool = False,
8465
8466
8467
8468
8469
8470
8471
8472
8473
        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
8474
        ub_overlap_rs_dgrad: bool = False,
8475
8476
        ub_overlap_rs: bool = False,
        ub_overlap_ag: bool = False,
8477
        bias: bool = True,
8478
        normalization: str = "LayerNorm",
8479
        device: Union[torch.device, str] = "cuda",
8480
        qkv_format: str = "sbhd",
8481
8482
    ) -> None:
        super().__init__()
8483

8484
        self.qkv_format = qkv_format
8485
        self.attn_mask_type = attn_mask_type
8486
        self.window_size = check_set_window_size(attn_mask_type, window_size)
8487
        self.layer_number = layer_number
8488
8489
8490
8491
8492
        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
8493
        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
8494
        self.num_attention_heads = num_attention_heads
8495
8496
8497
8498
8499
8500
8501
8502
        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()
8503
8504
8505
8506
8507

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

8508
8509
8510
        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"
8511
8512
8513
8514
8515
8516

        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)
8517
8518
8519
8520
8521
8522
8523
        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!"
8524
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)
8525
8526
8527
8528

        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
8529
8530
8531
8532
8533
8534
8535

        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,
8536
            "params_dtype": self.params_dtype,
8537
            "device": device,
8538
8539
8540
8541
        }

        qkv_parallel_mode = "column" if set_parallel_mode else None

cyanguwa's avatar
cyanguwa committed
8542
        if self.attention_type == "self":
8543
8544
            parameters_split = None
            if not fuse_qkv_params:
8545
8546
8547
8548
8549
8550
8551
                parameters_split = collections.OrderedDict(
                    [
                        ("query", self.hidden_size_q),
                        ("key", self.hidden_size_kv),
                        ("value", self.hidden_size_kv),
                    ]
                )
8552
8553
8554
            if self.input_layernorm:
                self.layernorm_qkv = LayerNormLinear(
                    hidden_size,
8555
                    self.hidden_size_q + 2 * self.hidden_size_kv,
8556
8557
8558
8559
8560
8561
                    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
8562
                    parameters_split=parameters_split,
8563
8564
8565
                    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
8566
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
8567
                    ub_overlap_ag=ub_overlap_ag,
8568
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
8569
                    ub_name="qkv",
8570
8571
8572
8573
8574
                    **common_gemm_kwargs,
                )
            else:
                self.qkv = Linear(
                    hidden_size,
8575
                    self.hidden_size_q + 2 * self.hidden_size_kv,
8576
8577
8578
8579
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
cyanguwa's avatar
cyanguwa committed
8580
                    parameters_split=parameters_split,
8581
8582
                    **common_gemm_kwargs,
                )
cyanguwa's avatar
cyanguwa committed
8583
        elif self.attention_type == "cross":
8584
8585
8586
            if self.input_layernorm:
                self.layernorm_query = LayerNormLinear(
                    hidden_size,
8587
                    self.hidden_size_q,
8588
8589
8590
8591
8592
                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
8593
                    parameters_split=("query",) if not fuse_qkv_params else None,
8594
8595
8596
8597
                    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
8598
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
8599
                    ub_overlap_ag=ub_overlap_ag,
8600
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
8601
                    ub_name="qkv",
8602
8603
8604
8605
8606
                    **common_gemm_kwargs,
                )
            else:
                self.query_layer = Linear(
                    hidden_size,
8607
                    self.hidden_size_q,
8608
8609
8610
8611
8612
8613
8614
8615
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    **common_gemm_kwargs,
                )
            self.key_value = Linear(
                hidden_size,
8616
                2 * self.hidden_size_kv,
8617
8618
8619
8620
                init_method=init_method,
                bias=bias,
                return_bias=False,
                parallel_mode=qkv_parallel_mode,
8621
                parameters_split=("key", "value") if not fuse_qkv_params else None,
8622
8623
8624
8625
8626
8627
                **common_gemm_kwargs,
            )

        # Attention.
        self.core_attention = DotProductAttention(
            num_attention_heads,
8628
            self.hidden_size_per_attention_head,
8629
8630
            num_gqa_groups=self.num_gqa_groups,
            attention_dropout=attention_dropout,
8631
            qkv_format=self.qkv_format,
8632
8633
8634
8635
            tp_size=tp_size,
            get_rng_state_tracker=get_rng_state_tracker,
            sequence_parallel=sequence_parallel,
            tp_group=tp_group,
8636
            layer_number=self.layer_number,
8637
            attention_type=self.attention_type,
8638
8639
8640
8641
        )

        # Linear
        self.proj = Linear(
8642
            self.hidden_size_q,
8643
8644
8645
            hidden_size,
            init_method=output_layer_init_method,
            bias=bias,
8646
            return_bias=return_bias,
8647
            parallel_mode="row" if set_parallel_mode else None,
8648
8649
            ub_overlap_rs=ub_overlap_rs,
            ub_overlap_ag=ub_overlap_ag,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
8650
            ub_name="proj",
8651
8652
8653
8654
            **common_gemm_kwargs,
        )

    def _allocate_memory(
8655
        self, inference_max_sequence_len: int, batch_size: int, dtype: torch.dtype
8656
    ) -> torch.Tensor:
8657
        """Allocates memory for KV cache."""
8658
8659
8660
        return torch.empty(
            inference_max_sequence_len,
            batch_size,
8661
            self.num_gqa_groups_per_partition,
8662
            self.hidden_size_per_attention_head,
8663
            dtype=dtype,
8664
8665
8666
8667
            device=torch.cuda.current_device(),
        )

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
8668
8669
8670
8671
8672
8673
8674
8675
8676
        """
        Set the tensor parallel group for the given
        module before executing the forward pass.

        Parameters
        ----------
        tp_group : ProcessGroup, default = `None`
                  tensor parallel process group.
        """
8677
8678
        self.tp_group = tp_group

8679
    def set_context_parallel_group(
8680
        self,
8681
        cp_group: Union[dist_group_type, List[dist_group_type], None],
8682
        cp_global_ranks: List[int],
8683
        cp_stream: torch.cuda.Stream,
8684
        cp_comm_type: str = "p2p",
8685
    ) -> None:
8686
8687
8688
8689
8690
8691
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
8692
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
8693
                  context parallel process group.
8694
8695
8696
                  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.
8697
8698
8699
8700
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
8701
        cp_comm_type : str, default = `p2p`
8702
                      inter-gpu communication type for context parallelism.
8703
                      Can be "p2p" or "all_gather" or "a2a", "a2a+p2p".
8704
8705
8706
8707
8708
8709
                      "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.
8710
8711
8712
                      "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).
8713
        """
8714
8715
8716
8717
8718
        # 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"):
8719
                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream, cp_comm_type)
8720

8721
8722
8723
    def forward(
        self,
        hidden_states: torch.Tensor,
8724
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
8725
        encoder_output: Optional[torch.Tensor] = None,
8726
        attn_mask_type: Optional[str] = None,
8727
        window_size: Optional[Tuple[int, int]] = None,
8728
8729
        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
8730
        inference_params: Optional[InferenceParams] = None,
8731
        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
8732
8733
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
8734
        alibi_slopes: Optional[torch.Tensor] = None,
8735
8736
8737
8738
        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,
8739
        fast_zero_fill: bool = True,
8740
    ) -> Tuple[Union[torch.Tensor, None], ...]:
8741
8742
8743
8744
8745
        """
        Forward propagation for MultiheadAttention layer.

        .. note::

8746
8747
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes `"padding"` or `"arbitrary"`.
8748
8749
8750
8751
8752

        Parameters
        ----------
        hidden_states : torch.Tensor
             Input tensor.
8753
8754
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
8755
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
8756
8757
             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]
8758
8759
8760
8761
8762
8763
             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'},
8764
                       default = `None`
8765
8766
8767
8768
                       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.
8769
8770
        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
8771
8772
8773
8774
8775
8776
8777
8778
8779
8780
8781
8782
8783
8784
8785
8786
8787
8788
8789
8790
8791
8792
8793
8794
8795
        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`
8796
                    Bias type, {`no_bias`, `pre_scale_bias`, 'post_scale_bias`, `alibi`}
8797
        core_attention_bias: Optional[torch.Tensor], default = `None`
8798
8799
                    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.
8800
8801
8802
8803
        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.
8804
8805
8806
8807
8808
8809
8810
8811
8812
8813
8814
8815
        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.
8816
8817
8818
        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
        """
8819
8820
        # hidden_states: [sq, b, h]

8821
        if attn_mask_type is None:
8822
            attn_mask_type = self.attn_mask_type
8823
8824
        if window_size is None:
            window_size = self.window_size
8825
        window_size = check_set_window_size(attn_mask_type, window_size)
8826

8827
        if "padding" in attn_mask_type and attention_mask is not None:
8828
8829
            for mask in attention_mask:
                assert mask.dtype == torch.bool, "Attention mask must be in boolean type!"
8830

8831
8832
8833
        assert (
            core_attention_bias_type in AttnBiasTypes
        ), f"core_attention_bias_type {core_attention_bias_type} is not supported!"
8834

8835
        # =================================================
8836
        # Pre-allocate memory for key-values for inference
8837
8838
8839
        # =================================================

        if inference_params and self.layer_number is not None:
8840
8841
8842
            assert (
                self.qkv_format != "thd"
            ), "qkv_format == thd is not supported for an inference with KV-cache!"
8843
            if self.layer_number not in inference_params.key_value_memory_dict:
8844
                inf_max_seq_len = inference_params.max_sequence_length
8845
8846
                inf_max_batch_size = inference_params.max_batch_size
                inference_key_memory = self._allocate_memory(
8847
                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
8848
8849
                )
                inference_value_memory = self._allocate_memory(
8850
                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
8851
8852
8853
8854
8855
8856
8857
8858
8859
8860
8861
                )
                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]

8862
        # ======================
8863
        # Query, Key, and Value
8864
        # ======================
8865

8866
8867
8868
8869
8870
        fp8_mha = (
            FP8GlobalStateManager.is_fp8_enabled()
            and FP8GlobalStateManager.get_fp8_recipe().fp8_mha
        )

8871
        layernorm_output = None
cyanguwa's avatar
cyanguwa committed
8872
8873
        if self.attention_type == "self":
            # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn]
8874
8875
8876
8877
            if self.input_layernorm:
                layernorm_qkv_outputs = self.layernorm_qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
8878
                    fp8_output=fp8_mha and rotary_pos_emb is None,
8879
8880
8881
8882
8883
8884
8885
8886
8887
                )
                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,
8888
                    fp8_output=fp8_mha and rotary_pos_emb is None,
8889
8890
                )

8891
8892
8893
            num_queries_per_key_value = (
                self.num_attention_heads_per_partition // self.num_gqa_groups_per_partition
            )
8894
            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
8895
                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, ng, (np/ng + 2), hn]
8896
                new_tensor_shape = mixed_x_layer.size()[:-1] + (
cyanguwa's avatar
cyanguwa committed
8897
8898
                    self.num_gqa_groups_per_partition,
                    (num_queries_per_key_value + 2),
8899
8900
8901
8902
                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2
cyanguwa's avatar
cyanguwa committed
8903
8904
8905
8906
8907
            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,
8908
                    self.hidden_size_per_attention_head,
cyanguwa's avatar
cyanguwa committed
8909
8910
8911
                )
                # split along third last dimension
                split_dim = -3
8912
8913
8914

            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

cyanguwa's avatar
cyanguwa committed
8915
8916
8917
8918
8919
8920
8921
8922
8923
            # 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)
8924
                )
8925
            else:
cyanguwa's avatar
cyanguwa committed
8926
                query_layer, key_layer, value_layer = torch.split(
8927
8928
8929
8930
                    mixed_x_layer,
                    (num_queries_per_key_value, 1, 1),
                    dim=split_dim,
                )
cyanguwa's avatar
cyanguwa committed
8931

8932
8933
8934
8935
8936
8937
8938
8939
8940
8941
8942
8943
            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
8944
8945
        elif self.attention_type == "cross":
            # Attention heads [sk, b, h] --> [sk, b, (ng * 2 * hn)]
8946
            mixed_kv_layer = self.key_value(
cyanguwa's avatar
cyanguwa committed
8947
                encoder_output,
8948
                is_first_microbatch=is_first_microbatch,
8949
                fp8_output=fp8_mha and rotary_pos_emb is None,
8950
8951
8952
            )

            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
8953
                # [sq, b, (ng * 2 * hn)] --> [sq, b, ng, 2 * hn]
8954
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
8955
                    self.num_gqa_groups_per_partition,
8956
8957
8958
8959
8960
                    2 * self.hidden_size_per_attention_head,
                )
                # split along last dimension
                split_dim = -1
            else:
cyanguwa's avatar
cyanguwa committed
8961
                # [sq, b, (ng * 2 * hn)] --> [sq, b, 2 * ng, hn]
8962
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
8963
                    2 * self.num_gqa_groups_per_partition,
8964
8965
8966
8967
8968
8969
8970
                    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
8971
8972
8973
            # mixed_kv_layer --> 2 [sk, b, ng, hn]
            if not is_in_onnx_export_mode():
                key_layer, value_layer = _SplitAlongDim.apply(
8974
8975
8976
                    mixed_kv_layer,
                    split_dim,
                    mixed_kv_layer.shape[split_dim] // 2,
cyanguwa's avatar
cyanguwa committed
8977
                )
8978
            else:
cyanguwa's avatar
cyanguwa committed
8979
                key_layer, value_layer = torch.split(
8980
8981
8982
                    mixed_kv_layer,
                    mixed_kv_layer.shape[split_dim] // 2,
                    dim=split_dim,
cyanguwa's avatar
cyanguwa committed
8983
                )
8984
8985
8986
8987
8988
8989
8990
8991
8992
            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)
            )
8993
8994
8995
8996
8997
8998

            # 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,
8999
                    fp8_output=fp8_mha and rotary_pos_emb is None,
9000
9001
9002
9003
9004
9005
9006
9007
9008
                )
                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,
9009
                    fp8_output=fp8_mha and rotary_pos_emb is None,
9010
9011
9012
9013
9014
9015
9016
9017
9018
                )

            # [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)

9019
9020
9021
        # ======================================================
        # Apply relative positional encoding (rotary embedding)
        # ======================================================
9022

9023
        if rotary_pos_emb is not None:
9024
9025
9026
            assert not isinstance(query_layer, Float8Tensor) and not isinstance(
                key_layer, Float8Tensor
            ), "RoPE is not supported for Float8Tensors!"
9027
            # duplicate the pos_emb for self attention
9028
            if not isinstance(rotary_pos_emb, tuple):
9029
                rotary_pos_emb = (rotary_pos_emb,) * 2
9030
9031

            q_pos_emb, k_pos_emb = rotary_pos_emb
9032
9033
9034
9035
9036
9037
9038

            # 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)
9039
9040
                else:
                    raise ValueError(f"QKV format {self.qkv_format} not supported for KV caching.")
9041
9042
9043
9044
9045
9046
9047

                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, ...]

9048
9049
            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)
9050

9051
9052
9053
9054
        # ===========================
        # Core attention computation
        # ===========================

9055
9056
9057
9058
        context_layer = self.core_attention(
            query_layer,
            key_layer,
            value_layer,
9059
            qkv_format=self.qkv_format,
9060
9061
9062
9063
            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_kv=cu_seqlens_kv,
            max_seqlen_q=max_seqlen_q,
            max_seqlen_kv=max_seqlen_kv,
9064
9065
            attention_mask=attention_mask,
            attn_mask_type=attn_mask_type,
9066
            window_size=window_size,
9067
9068
9069
            checkpoint_core_attention=checkpoint_core_attention,
            core_attention_bias_type=core_attention_bias_type,
            core_attention_bias=core_attention_bias,
9070
            alibi_slopes=alibi_slopes,
9071
            fast_zero_fill=fast_zero_fill,
9072
            inference_params=inference_params,
9073
9074
        )

9075
        # ===================
9076
        # Output. [sq, b, h]
9077
        # ===================
9078

9079
        projection_output = self.proj(
9080
9081
            context_layer,
            is_first_microbatch=is_first_microbatch,
9082
9083
        )

9084
9085
9086
9087
9088
9089
9090
9091
        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,)
9092
        if self.input_layernorm and self.return_layernorm_output:
9093
9094
            outputs += (layernorm_output,)
        return outputs if len(outputs) > 1 else outputs[0]