attention.py 346 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

16
from dataclasses import dataclass, fields
cyanguwa's avatar
cyanguwa committed
17
import numpy as np
18
from packaging.version import Version as PkgVersion
19
20

import torch
21
import torch.nn.functional as F
22

23
import transformer_engine_torch as tex
24
25
import transformer_engine as te
from transformer_engine.pytorch.utils import get_cudnn_version
26
27
28
29
from transformer_engine.pytorch.cpp_extensions import (
    cast_to_fp8,
    cast_from_fp8,
)
30
31
32
33
34
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,
35
36
    fused_attn_fwd,
    fused_attn_bwd,
37
38
39
40
41
    QKVLayout,
    AttnBiasType,
    AttnMaskType,
    FusedAttnBackend,
)
42
from transformer_engine.pytorch.fp8 import get_fp8_te_dtype, get_fp8_torch_dtype
43
from transformer_engine.pytorch.float8_tensor import Float8Tensor
44
from transformer_engine.pytorch.module import LayerNormLinear, Linear
45
from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
46
47
48
49
50
from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
51
    get_default_init_method,
52
53
54
55
)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
56
    AttnBiasTypes,
57
    QKVLayouts,
58
    dist_group_type,
59
    TE_DType,
60
61
62
63
)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
64
    get_distributed_rank,
65
    checkpoint,
66
67
68
    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
69
70
    gather_along_first_dim,
    reduce_scatter_along_first_dim,
71
72
)
from transformer_engine.pytorch.export import is_in_onnx_export_mode
73
from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
74
75
from transformer_engine.pytorch.graph import is_graph_capturing

76

77
78
79
_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"))
80
81
_flash_attn_version = PkgVersion(get_pkg_version("flash-attn"))
_flash_attn_version_required = PkgVersion("2.0.6")
82
_flash_attn_max_version = PkgVersion("2.6.3")
83
_flash_attn_2_plus = _flash_attn_version >= PkgVersion("2")
84
85
86
87
_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")
88
_flash_attn_2_5_7_plus = _flash_attn_version >= PkgVersion("2.5.7")
89
90
91
92
93
94
_flash_attn_3_plus = False
_use_flash_attn_3 = False
try:
    _flash_attn_v3_version = PkgVersion(get_pkg_version("flashattn-hopper"))
    _flash_attn_3_plus = _flash_attn_v3_version >= PkgVersion("2.6.1")
except PackageNotFoundError:
95
96
97
98
99
100
101
102
    if get_device_compute_capability() == (9, 0) and _NVTE_FLASH_ATTN:
        warnings.warn(
            "To use flash-attn v3, please use the following commands to install: \n"
            """(1) pip install "git+https://github.com/Dao-AILab/flash-attention.git#egg=flashattn-hopper&subdirectory=hopper" \n"""
            """(2) python_path=`python -c "import site; print(site.getsitepackages()[0])"` \n"""
            """(3) mkdir -p $python_path/flashattn_hopper \n"""
            """(4) wget -P $python_path/flashattn_hopper https://raw.githubusercontent.com/Dao-AILab/flash-attention/main/hopper/flash_attn_interface.py"""
        )
103
104
105
106
107
108
109
110
111
112
113
114
115
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,
    )
    from flashattn_hopper.flash_attn_interface import (  # pylint: disable=unused-import
        _flash_attn_forward as _flash_attn_forward_v3,
    )
    from flashattn_hopper.flash_attn_interface import (  # pylint: disable=unused-import
        _flash_attn_backward as _flash_attn_backward_v3,
    )

    _use_flash_attn_3 = True
116

117
if _flash_attn_version >= _flash_attn_version_required:
118
    from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_varlen_func
119
120
121
    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward as _flash_attn_forward
    from flash_attn.flash_attn_interface import _flash_attn_varlen_backward as _flash_attn_backward
    from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd
122

123
META_QKV = tex.FP8FwdTensors.GEMM1_OUTPUT
124
META_DQKV = tex.FP8BwdTensors.GRAD_OUTPUT1
125
126
127
128
META_O = tex.FP8FwdTensors.GEMM2_INPUT
META_DO = tex.FP8BwdTensors.GRAD_INPUT2
META_S = tex.FP8FwdTensors.GEMM3_OUTPUT
META_DP = tex.FP8BwdTensors.GRAD_INPUT3
129
130
131
# repurpose some unused amax history buffers for partial results of CP fwd and bwd
META_O_CP = tex.FP8FwdTensors.GEMM2_OUTPUT
META_DQKV_CP = tex.FP8BwdTensors.GRAD_INPUT1
132

133
# NVTE_DEBUG = 0/1 # disables/enables debug mode, default = 0
134
_NVTE_DEBUG = int(os.getenv("NVTE_DEBUG", "0"))
135
136
# 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"))
137
138
139
140
141
142
_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)
143

144
145
146
147
148
149
150
_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,
151
}
152
153


154
155
@dataclass(eq=True)
class AttentionParams:
156
    """
157
    Attention parameters used to determine which backend to be used.
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176

    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.
177
178
179
180
    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.
181
182
183
    attn_mask_type: str, default = `no_mask`
        Attention mask type, {`no_mask`, `padding`, `causal`, `padding_causal`,
        `causal_bottom_right`, `padding_causal_bottom_right`, `arbitrary`}
184
    window_size: Tuple[int, int], default = None
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
        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.
203
204
    is_training: bool, default = `True`
        Whether in training mode (`True`) or inference mode (`False`)
205
206
207
208
    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`.
209
210
211
212
213
214
215
216
217
218
    """

    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
219
220
    head_dim_qk: int = 64
    head_dim_v: int = 64
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
    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"]


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`.
260
261
262
263
264
265
266

    Returns
    ----------
    use_flash_attention: bool
        Whether the `FlashAttention` backend has been selected.
    use_fused_attention: bool
        Whether the `FusedAttention` backend has been selected.
267
268
    fused_attention_backend: tex.NVTE_Fused_Attn_Backend
        If `use_fused_attention = True`, one of `FusedAttention` three sub-backends, else `None`.
269
270
271
272
273
274
    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].
    """
275
276
277
278
279
280
281
282
    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
283
284
    head_dim_qk = attention_params.head_dim_qk
    head_dim_v = attention_params.head_dim_v
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
    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
300
    logger = logging.getLogger("DotProductAttention")
301
302
303
    logger.setLevel(_log_level)
    if not logger.hasHandlers():
        logger.addHandler(_stream_handler)
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
    device_compute_capability = get_device_compute_capability()
    cudnn_version = get_cudnn_version()
    run_config = {
        "transformer_engine_version": te.__version__,
        "compute_capability": "sm"
        + str(
            (lambda x, y: x * 10 + y)(device_compute_capability[0], device_compute_capability[1])
        ),
        "flash_attn_version": _flash_attn_version,
        "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)
322
323

    # Filter: Environment variables
324
325
326
327
328
329
330
    global _NVTE_FLASH_ATTN, _NVTE_FUSED_ATTN, _NVTE_UNFUSED_ATTN
    _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"))
    use_flash_attention = _NVTE_FLASH_ATTN
    use_fused_attention = _NVTE_FUSED_ATTN
    use_unfused_attention = _NVTE_UNFUSED_ATTN
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
    if not use_flash_attention:
        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():
        if use_flash_attention:
            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
348
    global _flash_attn_3_plus, _use_flash_attn_3
349
350
351
352
353
354
355
    if device_compute_capability < (8, 0):
        if use_flash_attention:
            logger.debug("Disabling FlashAttention as it requires compute capability sm80+")
            use_flash_attention = False
        if use_fused_attention:
            logger.debug("Disabling FusedAttention as it requires compute capability sm80+")
            use_fused_attention = False
356
357
358
359
    if device_compute_capability < (9, 0):
        if use_flash_attention and _flash_attn_3_plus:
            logger.debug("Disabling FlashAttention 3 as it requires compute capability sm90+")
            _use_flash_attn_3 = False
360
361

    # Filter: Data type
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
    if qkv_dtype not in [torch.bfloat16, torch.float16] or qkv_type not in [
        torch.Tensor,
        Float8Tensor,
    ]:
        if use_flash_attention:
            logger.debug(
                "Disabling FlashAttention due to unsupported QKV data type. "
                "Supported: qkv_dtype = {torch.bfloat16, torch.float16}. "
                "Found: qkv_dtype = %s.",
                qkv_dtype,
            )
            use_flash_attention = False
        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
382
383
384

    # Filter: Execution type
    if fp8 and fp8_meta["recipe"].fp8_dpa:
385
386
387
388
389
390
391
        if use_flash_attention and not _use_flash_attn_3:
            logger.debug("Disabling FlashAttention as FlashAttention 2 does not support FP8")
            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"
            )
392
393
394
395
396
397
            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
398
399
400
    if use_flash_attention and head_dim_qk != head_dim_v:
        logger.debug("Disabling FlashAttention as it does not support MLA.")
        use_flash_attention = False
401
    if use_flash_attention and (
402
403
404
        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)))
405
406
    ):
        logger.debug(
407
408
409
410
411
412
            "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,
413
414
415
            ".".join([str(i) for i in device_compute_capability]),
        )
        use_flash_attention = False
416
417
418
419
420
421
422
    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
423
424
425
426
427
428
429
430
431
432
433
434
435
436

    # 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:
            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]"
            )
            use_flash_attention = False

437
438
439
440
441
442
    # Filter: Dropout
    if attention_dropout != 0.0 and use_flash_attention:
        if _flash_attn_3_plus and _use_flash_attn_3:
            logger.debug("Disabling FlashAttention 3 for dropout")
            _use_flash_attn_3 = False

443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
    # 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:
461
462
463
464
465
466
467
468
        if _flash_attn_3_plus and _use_flash_attn_3:
            logger.debug("Disabling FlashAttention 3 for context parallelism")
            _use_flash_attn_3 = False
        if fp8 and fp8_meta["recipe"].fp8_dpa:
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with FP8"
            )
            use_flash_attention = False
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
        if "bottom_right" in attn_mask_type:
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with"
                " causal_bottom_right masking"
            )
            use_flash_attention = False
        elif "causal" in attn_mask_type and max_seqlen_q != max_seqlen_kv:
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with causal"
                " masking for cross-attention"
            )
            use_flash_attention = False
        elif core_attention_bias_type not in ["no_bias", "post_scale_bias"]:
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with bias type"
                " of %s",
                core_attention_bias_type,
            )
            use_flash_attention = False
        elif qkv_format == "thd" and core_attention_bias_type != "no_bias":
            logger.debug(
                "Disabling FlashAttention as it does not support context parallelism with attention"
                " bias for THD format"
            )
            use_flash_attention = False
494

495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
    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

527
    # Filter: Attention mask
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
    # 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]                      |
547
548
549
550
551
552
553
    if attn_mask_type == "arbitrary":
        if use_flash_attention:
            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
554
555
556
557
558
559
560
561
562
563
564
565
    if (
        use_flash_attention
        and _flash_attn_3_plus
        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
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
    if (
        use_flash_attention
        and _flash_attn_2_1_plus
        and attn_mask_type in ["causal", "padding_causal"]
        and max_seqlen_q != max_seqlen_kv
    ):
        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 (
        use_flash_attention
        and not _flash_attn_2_1_plus
        and attn_mask_type in ["causal_bottom_right", "padding_causal_bottom_right"]
        and max_seqlen_q != max_seqlen_kv
    ):
        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

    # Filter: Sliding window attention
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
    #    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 context_parallel:
                logger.debug(
                    "Disabling FusedAttention as it does not support sliding window attention "
                    "with context parallelism"
                )
                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
639
640
641
642
643
644
645
646
647
        if (
            use_flash_attention
            and (window_size[0] != -1 or window_size[1] not in [-1, 0])
            and _flash_attn_3_plus
        ):
            logger.debug(
                "Disabling FlashAttention 3 as it does not support sliding window attention"
            )
            _use_flash_attn_3 = False
648
649
650
        if (
            use_flash_attention
            and (window_size[0] != -1 or window_size[1] not in [-1, 0])
651
            and not _flash_attn_2_3_plus
652
        ):
653
            logger.debug(
654
                "Disabling FlashAttention as sliding window attention requires flash-attn 2.3+"
655
656
657
658
            )
            use_flash_attention = False

    # Filter: Attention bias
659
660
661
662
663
664
665
666
    #    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
667
668
669
670
671
672
673
674
    if use_flash_attention and core_attention_bias_type == "alibi":
        if _flash_attn_3_plus and _use_flash_attn_3:
            logger.debug("Disabling FlashAttention 3 for ALiBi")
            _use_flash_attn_3 = False
            if not _flash_attn_2_4_plus:
                logger.debug("Disabling FlashAttention for ALiBi")
                use_flash_attention = False

675
676
677
678
679
680
681
682
683
684
685
686
687
    if use_flash_attention and (
        core_attention_bias_type not in ["no_bias", "alibi"]
        or core_attention_bias_shape is not None
    ):
        logger.debug("Disabling FlashAttention for pre/post_scale_bias")
        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"
688
        and (alibi_slopes_shape is not None or max_seqlen_q != max_seqlen_kv)
689
690
691
    ):
        fu_core_attention_bias_type = "post_scale_bias"
        fu_core_attention_bias_requires_grad = False
692
693
694
695
696
        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 (
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
723
724
725
726
727
728
729
730
731
732
733
734
735
            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,
736
737
            head_dim_qk,
            head_dim_v,
738
739
            window_size[0],
            window_size[1],
740
        )
741
        if fused_attention_backend == FusedAttnBackend["No_Backend"]:
742
743
            logger.debug("Disabling FusedAttention as no backend supports the provided input")
            use_fused_attention = False
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
            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"]
761
762
763
764
765
766
767
768
            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
769
            fused_attention_backend = None
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789

    # 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
    if use_flash_attention and deterministic and not _flash_attn_2_4_1_plus:
        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
790
791
792
793
794
795
796
797
798
799
800
    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)
801
            )
802
803
804
        ):
            logger.debug("Disabling FusedAttention for determinism reasons")
            use_fused_attention = False
805
806
807

    # All available backends
    available_backends = [use_flash_attention, use_fused_attention, use_unfused_attention]
808
809
810
811
812
813
814
815
816
817
818
819
    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]),
    )
820
821
822
823
824
825
826
827
828
829
830
831
832
833

    # 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

834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
    # Select FusedAttention for FP8
    # FA3 uses default scaling factors (i.e. 1) in FP8 execution, while FusedAttention takes
    # scaling factors from `fp8_meta` and offers more accurate quantization/de-quantization
    if (
        use_flash_attention
        and use_fused_attention
        and fused_attention_backend == FusedAttnBackend["FP8"]
        and _use_flash_attn_3
    ):
        logger.debug(
            "Disabling FlashAttention 3 to give FusedAttention preference as FusedAttention "
            "supports more accurate scaling factors in FP8 execution"
        )
        use_flash_attention = False

849
850
851
852
853
854
    # Selected backend
    if use_flash_attention:
        use_fused_attention = False
        use_unfused_attention = False
    elif use_fused_attention:
        use_unfused_attention = False
855
    selected_backend = "NoBackend"
856
857
858
859
860
861
    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"
862
    logger.debug("Selected backend = %s", selected_backend)
863

864
865
866
867
868
869
    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
870
871
872
873

    return (
        use_flash_attention,
        use_fused_attention,
874
        fused_attention_backend,
875
876
877
878
879
        use_unfused_attention,
        available_backends,
    )


880
class InferenceParams:  # pylint: disable=too-few-public-methods
881
882
    """
    Inference parameters that are passed to the main model in order
883
    to efficiently calculate and store the context during inference.
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923

    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,
            )
924

925

926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
@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


984
985
986
987
988
@torch.no_grad()
def get_alibi(
    num_heads: int,
    max_seqlen_q: int,
    max_seqlen_kv: int,
989
990
    actual_seqlens_q: Optional[torch.Tensor] = None,
    actual_seqlens_kv: Optional[torch.Tensor] = None,
991
992
    alibi_slopes: Optional[torch.Tensor] = None,
    bias_dtype: Optional[torch.dtype] = None,
993
    bottom_right_alignment: bool = True,
994
) -> Tuple[torch.Tensor, torch.Tensor]:
995
    """
996
997
998
999
1000
1001
1002
1003
    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.
1004
1005
1006
1007
    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].
1008
1009
1010
1011
    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.
1012
1013
1014
    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`).
1015

1016
1017
1018
1019
1020
    Returns
    ----------
    alibi_slopes: torch.Tensor
        ALiBi slopes in FP32 and shape [num_heads] or [batch_size, num_heads].
    alibi_bias: torch.Tensor
1021
1022
1023
1024
1025
1026
        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`.
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
    """
    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])
        if _alibi_cache["_alibi_slopes"].dim() == 2:
            slopes_shape = torch.Size([*_alibi_cache["_alibi_slopes"].shape[:], 1, 1])
1052
        bias = torch.arange(max_seqlen_q, dtype=torch.int32, device="cuda").view(
1053
            1, 1, max_seqlen_q, 1
1054
1055
        ) - torch.arange(max_seqlen_kv, dtype=torch.int32, device="cuda").view(
            1, 1, 1, max_seqlen_kv
1056
        )
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
        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!"
1069
1070
1071
        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
1072
        _alibi_cache["_bottom_right_alignment"] = bottom_right_alignment
1073
1074
1075
1076
1077
        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"]
1078
1079
1080
1081
1082
1083
1084
1085
1086


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)
1087
    reduced_mask = mask.logical_not().sum(dim=1)
1088
1089
1090
1091
1092
1093
    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

1094

1095
1096
1097
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
1098
1099
1100
    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.
1101
1102
1103
1104
    """
    mask = mask.squeeze(1).squeeze(1)
    bs, seqlen = mask.shape

1105
    reduced_mask = mask.logical_not().sum(dim=1)
1106
1107
1108
1109
1110
    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)
1111
    indices = mask.logical_not().nonzero()
1112
1113
1114
1115
    indices = indices.unsqueeze(-1)

    num_nonzeros = indices.shape[0]
    pad_amount = bs * seqlen - num_nonzeros
1116
1117
1118
    indices = F.pad(
        input=indices, pad=(0, 0, 0, 0, 0, pad_amount), mode="constant", value=float(bs * seqlen)
    )
1119
1120
1121
1122

    return cu_seqlens, indices


1123
1124
1125
1126
1127
1128
1129
1130
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]
1131
1132
    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")
1133
1134
1135

    num_nonzeros = indices.shape[0]
    pad_amount = bs * max_seqlen - num_nonzeros
1136
1137
1138
1139
1140
1141
    indices = F.pad(
        input=indices,
        pad=(0, 0, 0, 0, 0, pad_amount),
        mode="constant",
        value=float(bs * max_seqlen),
    )
1142
1143
1144

    return indices

1145

1146
_cu_seqlens_cache = {}
1147
1148


1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
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.

    """
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
    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)]
1169
1170


1171
@torch.compile
1172
1173
1174
1175
1176
1177
1178
1179
def pack_tensor(
    indices: torch.Tensor,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Packs the given tensor using the `indices`.
    """
    padding_indice = torch.zeros(
1180
1181
        1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device
    )
1182
    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
1183
1184
1185
1186
1187
1188
1189
1190
    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)
1191
1192
1193
    return packed


1194
@torch.compile
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
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


1208
@torch.compile
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
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


1224
@torch.compile
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
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(
1235
1236
        dim0 + 1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device
    )
1237
1238
1239
1240
1241
1242
    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, :, :]
1243
1244
1245
    return unpacked


1246
@torch.compile
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
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


1261
@torch.compile
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
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.
    """
1282

1283
1284
    @staticmethod
    def forward(
1285
        ctx, indices: torch.Tensor, *tensors: Tuple[torch.Tensor, ...]
1286
1287
    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
        assert 1 <= len(tensors) <= 3, f"Packing {len(tensors)} tensors not supported."
1288
        ctx.save_for_backward(indices)
1289
1290
1291
1292
1293
1294
1295
1296
1297
        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, ...]):
1298
        (indices,) = ctx.saved_tensors
1299
        if len(grad_outputs) == 1:
1300
            return None, unpack_tensor(indices, ctx.dim0, *grad_outputs)
1301
        if len(grad_outputs) == 2:
1302
1303
            return None, *unpack_2_tensors(indices, ctx.dim0, *grad_outputs)
        return None, *unpack_3_tensors(indices, ctx.dim0, *grad_outputs)
1304
1305
1306
1307
1308
1309


class UnpackTensor(torch.autograd.Function):
    """
    Autograd function to unpack a tensor.
    """
1310

1311
1312
1313
1314
1315
1316
1317
    @staticmethod
    def forward(
        ctx,
        indices: torch.Tensor,
        dim0: int,
        tensor: torch.Tensor,
    ) -> torch.Tensor:
1318
        ctx.save_for_backward(indices)
1319
1320
1321
1322
        return unpack_tensor(indices, dim0, tensor)

    @staticmethod
    def backward(ctx, grad_output):
1323
1324
        (indices,) = ctx.saved_tensors
        return None, None, pack_tensor(indices, grad_output)
1325
1326


1327
1328
1329
def flash_attn_p2p_communicate(
    rank, send_tensor, send_dst, recv_tensor, recv_src, cp_group, batch_p2p_comm
):
1330
    """Point-to-point communications of KV and dKV in Attention with context parallelism"""
1331
1332
1333
1334
    send_recv_ops = []

    if batch_p2p_comm:
        if rank % 2 == 0:
1335
1336
1337
1338
1339
1340
            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
            )
1341
1342
1343
            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
1344
1345
1346
1347
1348
1349
            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
            )
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
            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


1369
@jit_fuser
1370
def flash_attn_fwd_out_correction(out, out_per_step, seq_dim, softmax_lse, softmax_lse_per_step):
1371
    """Merge partial outputs of each step in Attention with context parallelism"""
1372
    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse).movedim(2, seq_dim)
1373
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
1374
    out_corrected = out_per_step * softmax_lse_corrected_exp
1375
1376
1377
    out.add_(out_corrected)


1378
@jit_fuser
1379
def flash_attn_fwd_softmax_lse_correction(softmax_lse, softmax_lse_per_step):
1380
    """Merge softmax stats of each step in Attention with context parallelism"""
1381
1382
1383
1384
    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)
1385
1386


1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
@jit_fuser
def get_cu_seqlens_on_cp_rank(
    cu_seqlens, cu_seqlens_padded_on_cp_rank, cp_size, cp_rank, first_half, second_half
):
    """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


1408
class AttnFuncWithCPAndKVP2P(torch.autograd.Function):
1409
    """
1410
1411
1412
    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.
1413
1414
1415
    """

    @staticmethod
1416
1417
1418
1419
1420
1421
1422
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
1423
        cu_seqlens_kv,
1424
        max_seqlen_q,
1425
        max_seqlen_kv,
1426
1427
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
        dropout_p,
        cp_group,
        cp_global_ranks,
        cp_stream,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
1439
1440
        fp8,
        fp8_meta,
1441
    ):
1442
1443
1444
1445
1446
1447
        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)
        send_dst = cp_global_ranks[(rank + 1) % cp_size]
1448
        recv_src = cp_global_ranks[(rank - 1) % cp_size]
1449
1450
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

1451
1452
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
1453

1454
        if qkv_format in ["bshd", "sbhd"]:
1455
            seq_dim = qkv_format.index("s")
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
            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)]
1468

1469
1470
1471
        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!"
1472
        if causal:
1473
1474
            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
1475
                q, k, v = [x.view(x.shape[0], 2, x.shape[1] // 2, *x.shape[2:]) for x in [q, k, v]]
1476
1477
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
1478
                q, k, v = [x.view(2, x.shape[0] // 2, *x.shape[1:]) for x in [q, k, v]]
1479
1480
1481
        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]]
1482
        if attn_bias is not None:
1483
            assert len(attn_bias.shape) == 4, (
1484
1485
1486
                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
1487
1488
1489
            assert (
                attn_bias.shape[-2] % 2 == 0 and attn_bias.shape[-1] % (2 * cp_size) == 0
            ), "Sequence length does not meet divisible requirements!"
1490
            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
1491
1492
1493
1494
1495
1496
            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),
1497
1498
            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
1499
1500
            attn_bias = attn_bias.view(
                *attn_bias.shape[:-1], 2 * cp_size, attn_bias.shape[-1] // (2 * cp_size)
1501
            )
1502
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
1503
1504
        fa_optional_forward_kwargs = {}
        if _flash_attn_2_3_plus:
1505
            fa_optional_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
1506
1507
        if _flash_attn_2_4_plus:
            fa_optional_forward_kwargs["alibi_slopes"] = None
1508
1509
        if _flash_attn_2_5_7_plus:
            fa_optional_forward_kwargs["block_table"] = None
1510

1511
1512
1513
        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
1514
        attn_bias_inputs = [None, None]
1515
1516
1517
1518
        # 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)]
1519
        attn_biases = [None for _ in range(cp_size)]
1520
1521
1522
1523
1524
1525

        # 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()

1526
1527
1528
1529
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
        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
                    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[META_QKV]
                fp8_meta_kwargs["d_scale_s"] = fp8_meta["scaling_fwd"].scale_inv[META_S]
                fp8_meta_kwargs["q_scale_s"] = fp8_meta["scaling_fwd"].scale[META_S]
                fp8_meta_kwargs["q_scale_o"] = fp8_meta["scaling_fwd"].scale[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"]

1563
        p2p_comm_buffers = [None for _ in range(cp_size)]
1564
1565
1566
1567
        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)
1568
1569
        send_recv_reqs = [[], []]

1570
        for i in range(cp_size + 1):
1571
            if i < cp_size:
1572
                with torch.cuda.stream(flash_attn_streams[i % 2]):
1573
                    # wait until KV is received
1574
                    for req in send_recv_reqs[(i + 1) % 2]:
1575
1576
                        req.wait()

1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
                    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,
                        )

1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
                    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:
                        fp8_meta_kwargs["amax_s"] = amax_per_step[0][i]
                        fp8_meta_kwargs["amax_o"] = amax_per_step[1][i]
1606
1607
                    if causal:
                        if i == 0:
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
                            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
1620
                            if use_fused_attention:
1621
1622
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
1623
                                    q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
1624
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
1625
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
1626
                                        k.shape[0], -1, 2, *k.shape[-2:]
1627
                                    )
1628
1629
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
1630
                                    q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
1631
1632
1633
1634
                                    # [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:]
                                    )
1635
                                elif qkv_format == "thd":
1636
                                    q_inputs[i % 2] = q
1637
1638
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1639
1640
1641
1642
1643
1644
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1645
                                    ).contiguous()
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
                                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,
1674
                                )
1675
1676
1677
1678
1679
                                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
1680
1681
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
1682
                                q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
1683
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
                                (
                                    _,
                                    _,
                                    _,
                                    _,
                                    out_per_step[i],
                                    softmax_lse_per_step[i],
                                    _,
                                    rng_states[i],
                                ) = _flash_attn_forward(
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
1698
1699
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1700
                                    max_seqlen_q,
1701
                                    max_seqlen_kv,
1702
1703
1704
1705
1706
                                    dropout_p,
                                    softmax_scale,
                                    causal=True,
                                    return_softmax=False,
                                    **fa_optional_forward_kwargs,
1707
                                )
1708
                        elif i <= rank:
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
                            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)
1726
                            if use_fused_attention:
1727
1728
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
1729
                                    q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
1730
1731
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2][:, 0, ...].contiguous()
1732
1733
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
1734
                                    q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
1735
1736
                                    # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                                    kv_inputs[i % 2] = kv_inputs[i % 2][0].contiguous()
1737
                                elif qkv_format == "thd":
1738
                                    q_inputs[i % 2] = q
1739
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
1740
                                    kv_inputs[i % 2] = tex.thd_read_half_tensor(
1741
                                        kv_inputs[i % 2], cu_seqlens_kv_padded, 0
1742
                                    )
1743
1744
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1745
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
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
                                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,
1778
                                )
1779
1780
1781
1782
1783
                                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
1784
1785
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
1786
                                q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
1787
1788
                                if qkv_format == "thd":
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
1789
                                    kv_inputs[i % 2] = tex.thd_read_half_tensor(
1790
                                        kv_inputs[i % 2], cu_seqlens_kv_padded, 0
1791
                                    )
1792
1793
                                else:
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
1794
                                    kv_inputs[i % 2] = kv_inputs[i % 2][:, :, 0, ...].contiguous()
1795
                                # [2, b, sk//2, np, hn] -> [2, b*sk//2, np, hn]
1796
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
1797
                                if _flash_attn_2_3_plus:
1798
                                    fa_optional_forward_kwargs["window_size"] = (-1, -1)
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
                                (
                                    _,
                                    _,
                                    _,
                                    _,
                                    out_per_step[i],
                                    softmax_lse_per_step[i],
                                    _,
                                    rng_states[i],
                                ) = _flash_attn_forward(
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
1812
1813
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1814
                                    max_seqlen_q,
1815
                                    max_seqlen_kv // 2,
1816
1817
1818
1819
1820
                                    dropout_p,
                                    softmax_scale,
                                    causal=False,
                                    return_softmax=False,
                                    **fa_optional_forward_kwargs,
1821
1822
                                )
                        else:
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
                            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
1840
                            if use_fused_attention:
1841
1842
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
1843
                                    q_inputs[i % 2] = q[:, 1, ...].contiguous()
1844
                                    # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
1845
                                    kv_inputs[i % 2] = kv_inputs[i % 2].view(
1846
                                        k.shape[0], -1, 2, *k.shape[-2:]
1847
                                    )
1848
1849
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
1850
                                    q_inputs[i % 2] = q[1].contiguous()
1851
1852
1853
1854
                                    # [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:]
                                    )
1855
1856
                                elif qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
1857
1858
1859
                                    q_inputs[i % 2] = tex.thd_read_half_tensor(
                                        q, cu_seqlens_q_padded, 1
                                    )
1860
1861
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1862
1863
1864
1865
1866
1867
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1868
                                    ).contiguous()
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
                                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,
1901
                                )
1902
1903
1904
1905
1906
                                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
1907
                            else:
1908
1909
                                if qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
1910
1911
1912
                                    q_inputs[i % 2] = tex.thd_read_half_tensor(
                                        q, cu_seqlens_q_padded, 1
                                    )
1913
1914
                                else:
                                    # [b, 2, sq//2, np, hn]->[b, sq//2, np, hn]->[b*sq//2, np, hn]
1915
                                    q_inputs[i % 2] = (
1916
                                        q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
1917
                                    )
1918
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
1919
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
1920
                                if _flash_attn_2_3_plus:
1921
                                    fa_optional_forward_kwargs["window_size"] = (-1, -1)
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
                                (
                                    _,
                                    _,
                                    _,
                                    _,
                                    out_per_step[i],
                                    softmax_lse_per_step[i],
                                    _,
                                    rng_states[i],
                                ) = _flash_attn_forward(
                                    q_inputs[i % 2],
                                    kv_inputs[i % 2][0],
                                    kv_inputs[i % 2][1],
1935
1936
                                    cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv_per_step[i],
1937
                                    max_seqlen_q // 2,
1938
                                    max_seqlen_kv,
1939
1940
1941
1942
1943
                                    dropout_p,
                                    softmax_scale,
                                    causal=False,
                                    return_softmax=False,
                                    **fa_optional_forward_kwargs,
1944
1945
                                )
                    else:
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
                        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
1963
                        if use_fused_attention:
1964
1965
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
1966
1967
1968
1969
1970
1971
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
1972
                                ).contiguous()
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
                            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,
2001
                            )
2002
2003
2004
2005
2006
                            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
2007
                        else:
2008
                            # [b, sq, np, hn] -> [b*sq, np, hn]
2009
                            q_inputs[i % 2] = q.view(-1, *q.shape[-2:])
2010
                            # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
                            kv_inputs[i % 2] = kv_inputs[i % 2].view(2, -1, *k.shape[-2:])
                            (
                                _,
                                _,
                                _,
                                _,
                                out_per_step[i],
                                softmax_lse_per_step[i],
                                _,
                                rng_states[i],
                            ) = _flash_attn_forward(
                                q_inputs[i % 2],
                                kv_inputs[i % 2][0],
                                kv_inputs[i % 2][1],
2025
2026
                                cu_seqlens_q_per_step[i],
                                cu_seqlens_kv_per_step[i],
2027
                                max_seqlen_q,
2028
                                max_seqlen_kv,
2029
2030
2031
2032
2033
                                dropout_p,
                                softmax_scale,
                                causal=False,
                                return_softmax=False,
                                **fa_optional_forward_kwargs,
2034
                            )
2035
2036
2037
2038

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
2039
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
2040

2041
2042
                if use_fused_attention:
                    # [b, np, sq, 1] -> [b, np, sq]
2043
                    softmax_lse_per_step[i - 1].squeeze_(-1)
2044

2045
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
2046
2047
2048
2049
2050
2051
2052
2053
                    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],
                        )
2054
                    if i == 1:
2055
                        out = torch.zeros_like(q if not fp8 else out_per_step[0]).view(q.shape)
2056
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
2057
                        if causal and qkv_format != "thd":
2058
2059
                            # [b, np, sq] -> [b, np, 2, sq//2]
                            softmax_lse_ = softmax_lse.view(
2060
                                *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
2061
                            )
2062
2063
2064
2065
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
2066
                    else:
2067
                        if qkv_format == "thd":
2068
                            tex.thd_second_half_lse_correction(
2069
2070
2071
2072
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
                                max_seqlen_q,
2073
                            )
2074
                        else:
2075
2076
2077
                            flash_attn_fwd_softmax_lse_correction(
                                softmax_lse_[..., 1, :], softmax_lse_per_step[i - 1]
                            )
2078
2079

                if i < cp_size:
2080
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
2081
2082
2083
2084
2085

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

        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
2086
2087
2088
2089
2090
2091
            if qkv_format == "bshd":
                out_per_step[i] = out_per_step[i].view(out.shape[0], -1, *out.shape[-2:])
                out_ = out[:, 1, ...]
            elif qkv_format == "sbhd":
                out_per_step[i] = out_per_step[i].view(-1, *out.shape[-3:])
                out_ = out[1]
2092

2093
            if i <= rank or not causal:
2094
                if qkv_format in ["bshd", "sbhd"]:
2095
2096
2097
2098
2099
2100
2101
                    flash_attn_fwd_out_correction(
                        out.view(*out_per_step[i].shape),
                        out_per_step[i],
                        seq_dim,
                        softmax_lse,
                        softmax_lse_per_step[i],
                    )
2102
                elif qkv_format == "thd":
2103
2104
2105
2106
2107
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2108
                        cu_seqlens_q_padded,
2109
2110
                        False,
                    )
2111
            else:
2112
                if qkv_format in ["bshd", "sbhd"]:
2113
2114
2115
2116
2117
2118
2119
                    flash_attn_fwd_out_correction(
                        out_,
                        out_per_step[i],
                        seq_dim,
                        softmax_lse_[..., 1, :],
                        softmax_lse_per_step[i],
                    )
2120
                elif qkv_format == "thd":
2121
2122
2123
2124
2125
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
2126
                        cu_seqlens_q_padded,
2127
2128
                        True,
                    )
2129
2130

        kv = p2p_comm_buffers[-1]
2131
        if use_fused_attention:
2132
2133
2134
2135
            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:])
2136
2137
        else:
            out = out.view(-1, *out.shape[-2:])
2138

2139
2140
2141
2142
2143
2144
2145
2146
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
        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]

        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:
            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:
            q_save, kv_save, out_save = q_f16, kv, out_f16
            fp8_fwd_scales, fp8_fwd_scale_invs = None, None

2179
        ctx.save_for_backward(
2180
2181
2182
            q_save,
            kv_save,
            out_save,
2183
            softmax_lse,
2184
2185
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
2186
2187
            fp8_fwd_scales,
            fp8_fwd_scale_invs,
2188
2189
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
2190
2191
            *rng_states,
            *attn_biases,
2192
        )
2193
2194
2195
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
        ctx.dropout_p = dropout_p
2196
        ctx.total_tokens_kv = total_tokens_kv
2197
        ctx.max_seqlen_q = max_seqlen_q
2198
        ctx.max_seqlen_kv = max_seqlen_kv
2199
        ctx.softmax_scale = softmax_scale
2200
        ctx.qkv_format = qkv_format
2201
        ctx.attn_mask_type = attn_mask_type
2202
2203
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
2204
        ctx.deterministic = deterministic
2205
        ctx.use_fused_attention = use_fused_attention
2206
2207
2208
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
        return out_ret
2209
2210
2211
2212
2213

    @staticmethod
    def backward(ctx, dout):
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
2214
        send_dst = ctx.cp_global_ranks[(rank - 1) % cp_size]
2215
2216
2217
        recv_src = ctx.cp_global_ranks[(rank + 1) % cp_size]
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

2218
        (q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded) = ctx.saved_tensors[:6]
2219
2220
2221
2222
2223
        (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]
2224

2225
2226
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
2227
2228
2229
2230
        if ctx.qkv_format in ["bshd", "sbhd"]:
            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
2231

2232
        if attn_biases[0] is not None:
2233
2234
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
2235
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
2236
2237
2238
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
2239
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
2240
2241
2242
2243
            )
        else:
            attn_dbias = None

2244
        if causal:
2245
            if ctx.qkv_format == "thd":
2246
2247
2248
                softmax_lse_ = tex.thd_read_second_half_lse(
                    softmax_lse, cu_seqlens_q_padded, ctx.max_seqlen_q
                )
2249
2250
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
2251
2252
2253
                softmax_lse_ = softmax_lse.view(
                    *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1] // 2
                )
2254
2255
2256
2257
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
                if ctx.use_fused_attention:
                    # [b, np, sq//2] -> [b, np, sq//2, 1]
                    softmax_lse_.unsqueeze_(-1)
2258
2259
2260
        if ctx.use_fused_attention:
            # [b, np, sq] -> [b, np, sq, 1]
            softmax_lse.unsqueeze_(-1)
2261
2262
2263

        if ctx.fp8:
            if ctx.use_fused_attention:
2264
                fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
2265
                fp8_dtype_backward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=False)
2266
                fused_attn_qkv_dtype = fp8_dtype_forward
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
                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)
                dout_dtype = dout.dtype
                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:
                q, kv, dout = [x.from_float8(x.dtype) for x in [q, kv, dout]]
            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]
2308
                fused_attn_dqkv_dtype = TE_DType[dout.dtype]
2309
2310
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

2311
2312
2313
2314
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

2315
2316
2317
2318
2319
2320
        fa_optional_backward_kwargs = {}
        if _flash_attn_2_4_plus:
            fa_optional_backward_kwargs["alibi_slopes"] = None
        if _flash_attn_2_4_1_plus:
            fa_optional_backward_kwargs["deterministic"] = ctx.deterministic

2321
2322
2323
2324
2325
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

2326
2327
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
            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
                )
2357

2358
            kv = p2p_comm_buffers[i % 2][0]
2359
2360
2361
            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]
2362
            # In reversed order of fwd
2363
            if causal:
2364
                if i == (cp_size - 1):
2365
                    if ctx.use_fused_attention:
2366
2367
2368
                        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:])
2369
2370
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2371
2372
2373
2374
2375
2376
                            # [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:])
2377
2378
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2379
2380
2381
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2382
2383
                        elif ctx.qkv_format == "thd":
                            q_, kv_, out_, dout_ = q, kv, out, dout
2384
2385
2386
2387
2388
2389
2390
2391
                        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]]
2392
                        if attn_dbias is not None:
2393
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2394
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2395
                            ctx.max_seqlen_q,
2396
2397
2398
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2399
                            q_,
2400
2401
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2402
2403
                            out_,
                            dout_,
2404
2405
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2406
                            aux_ctx_tensors,
2407
                            fused_attn_backend,
2408
2409
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2410
2411
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2412
                            qkv_layout=qkv_layout,
2413
                            attn_mask_type=ctx.attn_mask_type,
2414
                            attn_bias_type=ctx.attn_bias_type,
2415
2416
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2417
2418
2419
2420
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2421
                        dq_ = torch.zeros_like(q_)
2422
2423
2424
2425
2426
2427
2428
                        # [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:])
                        if _flash_attn_2_3_plus:
2429
                            fa_optional_backward_kwargs["window_size"] = (-1, 0)
2430
                        _flash_attn_backward(
2431
2432
2433
2434
2435
2436
2437
2438
2439
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2440
2441
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2442
                            ctx.max_seqlen_q,
2443
                            ctx.max_seqlen_kv,
2444
2445
2446
2447
2448
                            ctx.dropout_p,
                            ctx.softmax_scale,
                            True,
                            rng_state=rng_states[cp_size - i - 1],
                            **fa_optional_backward_kwargs,
2449
                        )
2450
                elif i >= (cp_size - rank - 1):
2451
                    if ctx.use_fused_attention:
2452
2453
2454
                        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:])
2455
2456
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                            kv_ = kv[:, 0, ...].contiguous()
2457
2458
2459
2460
2461
2462
                            # [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:])
2463
2464
                            # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                            kv_ = kv[0].contiguous()
2465
2466
2467
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
2468
2469
2470
                        elif ctx.qkv_format == "thd":
                            q_, out_, dout_ = q, out, dout
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2471
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2472
2473
2474
2475
2476
2477
2478
2479
                        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]]
2480
                        if attn_dbias is not None:
2481
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2482
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2483
                            ctx.max_seqlen_q,
2484
2485
2486
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2487
                            q_,
2488
2489
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2490
2491
                            out_,
                            dout_,
2492
2493
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2494
                            aux_ctx_tensors,
2495
                            fused_attn_backend,
2496
2497
2498
2499
                            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
                            ),
2500
2501
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2502
                            qkv_layout=qkv_layout,
2503
                            attn_mask_type="padding" if padding else "no_mask",
2504
                            attn_bias_type=ctx.attn_bias_type,
2505
2506
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2507
2508
2509
2510
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
2511
                        dq_ = torch.zeros_like(q_)
2512
2513
                        if ctx.qkv_format == "thd":
                            # [2, t, np, hn] -> [2, t/2, np, hn]
2514
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
2515
2516
2517
                        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:])
2518
2519
2520
2521
2522
                        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:])
                        if _flash_attn_2_3_plus:
2523
                            fa_optional_backward_kwargs["window_size"] = (-1, -1)
2524
                        _flash_attn_backward(
2525
2526
2527
2528
2529
2530
2531
2532
2533
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2534
2535
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2536
                            ctx.max_seqlen_q,
2537
                            ctx.max_seqlen_kv // 2,
2538
2539
2540
2541
2542
                            ctx.dropout_p,
                            ctx.softmax_scale,
                            False,
                            rng_state=rng_states[cp_size - i - 1],
                            **fa_optional_backward_kwargs,
2543
2544
2545
                        )
                else:
                    if ctx.use_fused_attention:
2546
2547
2548
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous()
2549
2550
                            # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
2551
2552
2553
2554
2555
2556
                            # [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()
2557
2558
                            # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                            kv_ = kv.view(-1, *kv.shape[-4:])
2559
2560
2561
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            out_ = out[1].contiguous()
                            dout_ = dout[1].contiguous()
2562
2563
                        elif ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2564
2565
2566
                            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)
2567
                            kv_ = kv
2568
2569
2570
2571
2572
2573
2574
2575
                        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]]
2576
                        if attn_dbias is not None:
2577
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2578
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2579
                            ctx.max_seqlen_q // 2,
2580
2581
2582
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2583
                            q_,
2584
2585
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2586
2587
                            out_,
                            dout_,
2588
2589
                            fused_attn_qkv_dtype,
                            fused_attn_dqkv_dtype,
2590
                            aux_ctx_tensors,
2591
                            fused_attn_backend,
2592
2593
2594
2595
                            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,
2596
2597
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2598
                            qkv_layout=qkv_layout,
2599
                            attn_mask_type="padding" if padding else "no_mask",
2600
                            attn_bias_type=ctx.attn_bias_type,
2601
2602
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2603
2604
                        )
                    else:
2605
2606
                        if ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
2607
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q_padded, 1)
2608
2609
2610
                        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:])
2611
                        dq_ = torch.zeros_like(q_)
2612
2613
2614
                        # [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_)
2615
                        if ctx.qkv_format == "thd":
2616
2617
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q_padded, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q_padded, 1)
2618
2619
2620
2621
                        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:])
2622
                        if _flash_attn_2_3_plus:
2623
                            fa_optional_backward_kwargs["window_size"] = (-1, -1)
2624
                        _flash_attn_backward(
2625
2626
2627
2628
2629
2630
2631
2632
2633
                            dout_,
                            q_,
                            kv_[0],
                            kv_[1],
                            out_,
                            softmax_lse_,
                            dq_,
                            dkv_[0],
                            dkv_[1],
2634
2635
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2636
                            ctx.max_seqlen_q // 2,
2637
                            ctx.max_seqlen_kv,
2638
2639
2640
2641
2642
                            ctx.dropout_p,
                            ctx.softmax_scale,
                            False,
                            rng_state=rng_states[cp_size - i - 1],
                            **fa_optional_backward_kwargs,
2643
2644
2645
                        )
            else:
                if ctx.use_fused_attention:
2646
2647
2648
2649
                    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]]
2650
                    if attn_dbias is not None:
2651
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2652
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2653
                        ctx.max_seqlen_q,
2654
2655
2656
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
2657
                        q,
2658
2659
                        kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0],
                        kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1],
2660
2661
                        out,
                        dout,
2662
2663
                        fused_attn_qkv_dtype,
                        fused_attn_dqkv_dtype,
2664
                        aux_ctx_tensors,
2665
                        fused_attn_backend,
2666
2667
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2668
2669
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
2670
                        qkv_layout=qkv_layout,
2671
                        attn_mask_type=ctx.attn_mask_type,
2672
                        attn_bias_type=ctx.attn_bias_type,
2673
2674
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
2675
2676
2677
                    )
                else:
                    # [b, sq, np, hn] -> [b*sq, np, hn]
2678
                    q_ = q.view(-1, *q.shape[-2:])
2679
                    dq_ = torch.zeros_like(q_)
2680
                    # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
2681
2682
                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
2683
                    # [b, sq, np, hn] -> [b*sq, np, hn]
2684
2685
                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
2686
                    if _flash_attn_2_3_plus:
2687
                        fa_optional_backward_kwargs["window_size"] = (-1, -1)
2688
                    _flash_attn_backward(
2689
2690
2691
2692
2693
2694
2695
2696
2697
                        dout_,
                        q_,
                        kv_[0],
                        kv_[1],
                        out_,
                        softmax_lse,
                        dq_,
                        dkv_[0],
                        dkv_[1],
2698
2699
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
2700
                        ctx.max_seqlen_q,
2701
                        ctx.max_seqlen_kv,
2702
2703
2704
                        ctx.dropout_p,
                        ctx.softmax_scale,
                        False,
2705
                        rng_state=rng_states[cp_size - i - 1],
2706
                        **fa_optional_backward_kwargs,
2707
2708
                    )

2709
2710
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
2711
            if i >= (cp_size - rank - 1) or not causal:
2712
2713
2714
2715
                # [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:
2716
2717
2718
2719
2720
2721
                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:])
2722

2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
            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:
2734
                if i > (cp_size - rank - 1):
2735
                    dq.add_(dq_)
2736
2737
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
2738
2739
                        dq.copy_(dq_)
                    else:
2740
2741
2742
2743
2744
2745
                        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])
2746
                        elif ctx.qkv_format == "thd":
2747
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
2748
                elif i > 0:
2749
2750
2751
2752
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
2753
                    elif ctx.qkv_format == "thd":
2754
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
2755
                else:
2756
2757
2758
2759
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
2760
                    elif ctx.qkv_format == "thd":
2761
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
2762
2763
2764
2765
2766
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
2767

2768
            if attn_dbias is not None:
2769
                idx = (rank + i + 1) % cp_size
2770
                if i == (cp_size - 1) or not causal:
2771
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
2772
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2773
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
2774
2775
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
2776
2777
2778
2779
                    # [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)]
2780
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2781
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
2782
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
2783

2784
2785
2786
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
2787

2788
2789
2790
2791
2792
2793
2794
            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]
2795
2796
            if ctx.use_fused_attention:
                dkv_ = torch.cat((dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0)
2797
2798
2799
2800
                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:])
2801
            if causal and i >= (cp_size - rank - 1) and i != (cp_size - 1):
2802
2803
2804
2805
2806
2807
                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:])
2808
2809
2810
2811
            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)
2812

2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
            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:
2824
                if i == (cp_size - 1):
2825
                    if rank == 0:
2826
2827
2828
2829
2830
2831
                        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, ...])
2832
                        elif ctx.qkv_format == "thd":
2833
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
2834
2835
                    else:
                        dkv.add_(dkv_)
2836
2837
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
2838
2839
2840
2841
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
2842
                        elif ctx.qkv_format == "thd":
2843
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
2844
                    else:
2845
2846
2847
2848
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
2849
                        elif ctx.qkv_format == "thd":
2850
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
2851
2852
2853
2854
2855
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
2856
2857
2858
2859
2860
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
        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]]

2881
        if causal:
2882
2883
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
2884
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
2885
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
2886
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
2887
2888
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
2889
                dq = dq.view(-1, *dq.shape[-3:])
2890
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
2891
2892
2893
2894
2895
2896
2897
2898
2899
                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_
2900

2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
        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]
            ]
            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,
                )
                for x in [dq, dkv[0], dkv[1]]
            ]
        else:
            dk, dv = dkv[0], dkv[1]

2920
2921
2922
2923
        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)

2924
2925
2926
        return (
            None,
            dq,
2927
2928
            dk,
            dv,
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            attn_dbias,
            None,
            None,
2946
2947
            None,
            None,
2948
        )
2949
2950


2951
@torch.compile
2952
def get_seq_chunk_ids_to_all_gathered_kv(
2953
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size_left, device
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
):
    """Compute sequence chunk ids to the all-gathered KV."""
    seq_end_idx = (local_chunk_id + 1) * max_seqlen_kv
    seq_start_idx = max(0, seq_end_idx - max_seqlen_q - window_size_left)
    seqlen = seq_end_idx - seq_start_idx
    num_chunks = (seqlen + max_seqlen_kv - 1) // max_seqlen_kv
    chunk_ids = torch.arange(
        local_chunk_id - num_chunks + 1,
        local_chunk_id + 1,
        dtype=torch.int32,
2964
        device=device,
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
    )
    chunk_ids_to_all_gathered_kv = torch.where(
        chunk_ids < cp_size, 2 * chunk_ids, 2 * (2 * cp_size - chunk_ids) - 1
    )
    return chunk_ids_to_all_gathered_kv


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
    Attention implementation with context parallelism.
    KV all-gather between CP ranks is exposed.
    """

    @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,
        cp_group,
        cp_stream,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
    ):
        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
        assert causal and not padding, f"{attn_mask_type} mask type is not supported!"
        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!"
        fa_optional_forward_kwargs = {}
        if _flash_attn_2_4_plus:
            fa_optional_forward_kwargs["alibi_slopes"] = None

        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_kv = cu_seqlens_kv // (2 * cp_size)
        cu_seqlens_q_padded = cu_seqlens_q_padded // (2 * cp_size)
        cu_seqlens_kv_padded = cu_seqlens_kv_padded // (2 * cp_size)

        if causal:
            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
                q = q.view(q.shape[0], 2, q.shape[1] // 2, *q.shape[2:])
                # [b, s, np, hn] -> [s, b, np, hn]
                k, v = [x.transpose(0, 1).contiguous() for x in [k, v]]
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
                q = q.view(2, q.shape[0] // 2, *q.shape[1:])

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

        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
        cp_stream.wait_stream(torch.cuda.current_stream())
        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:])

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
        chunk_ids_to_kv_ag_per_step = [None, None]
        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]):
                    chunk_ids_to_kv_ag = get_seq_chunk_ids_to_all_gathered_kv(
                        local_seq_chunk_ids[i],
                        cp_size,
                        max_seqlen_q,
                        max_seqlen_kv,
                        (
                            max_seqlen_kv * cp_size * 2
                            if (window_size is None or window_size[0] == -1)
                            else window_size[0]
                        ),
3078
                        k.device,
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
                    )
                    chunk_ids_to_kv_ag_per_step[i] = chunk_ids_to_kv_ag
                    num_kv_chunks = chunk_ids_to_kv_ag.numel()
                    if qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                        q_ = q[:, i].contiguous()
                        # [num_kv_chunks, sq//2, b, np, hn] -> [b, num_kv_chunks*sq//2, np, hn]
                        k_ = (
                            torch.index_select(k_ag, dim=0, index=chunk_ids_to_kv_ag)
                            .movedim(2, 0)
                            .contiguous()
                            .view(k.shape[1], -1, *k.shape[-2:])
                        )
                        v_ = (
                            torch.index_select(v_ag, dim=0, index=chunk_ids_to_kv_ag)
                            .movedim(2, 0)
                            .contiguous()
                            .view(v.shape[1], -1, *v.shape[-2:])
                        )
                    elif qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                        q_ = q[i].contiguous()
                        # [num_kv_chunks, sq//2, b, np, hn] -> [num_kv_chunks*sq//2, b, np, hn]
                        k_ = torch.index_select(k_ag, dim=0, index=chunk_ids_to_kv_ag).view(
                            -1, *k.shape[-3:]
                        )
                        v_ = torch.index_select(v_ag, dim=0, index=chunk_ids_to_kv_ag).view(
                            -1, *v.shape[-3:]
                        )
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
                            max_seqlen_kv * num_kv_chunks,
                            cu_seqlens_q,
                            cu_seqlens_kv * num_kv_chunks,
                            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,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded * num_kv_chunks,
                            window_size=window_size,
                        )
                    else:
                        q_, k_, v_ = [x.view(-1, *x.shape[-2:]) for x in [q_, k_, v_]]
                        _, _, _, _, out_per_step[i], softmax_lse_per_step[i], _, rng_states[i] = (
                            _flash_attn_forward(
                                q_,
                                k_,
                                v_,
                                cu_seqlens_q,
                                cu_seqlens_kv * num_kv_chunks,
                                max_seqlen_q,
                                max_seqlen_kv * num_kv_chunks,
                                dropout_p,
                                softmax_scale,
                                causal=True,
                                return_softmax=False,
                                window_size=window_size,
                                **fa_optional_forward_kwargs,
                            )
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
                        out[:, i - 1].copy_(out_per_step[i - 1].view_as(out[:, i - 1]))
                    elif qkv_format == "sbhd":
                        out[i - 1].copy_(out_per_step[i - 1].view_as(out[i - 1]))

        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_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            *chunk_ids_to_kv_ag_per_step,
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
        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.use_fused_attention = use_fused_attention
        ctx.window_size = window_size
        return out

    @staticmethod
    def backward(ctx, dout):
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

        (q, k, v, cu_seqlens_q, cu_seqlens_kv, cu_seqlens_q_padded, cu_seqlens_kv_padded) = (
            ctx.saved_tensors[:7]
        )
        chunk_ids_to_kv_ag_per_step = ctx.saved_tensors[7:9]
        out_per_step = ctx.saved_tensors[9:11]
        softmax_lse_per_step = ctx.saved_tensors[11:13]
        rng_states = ctx.saved_tensors[13:15]

        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

        dout = dout.view_as(q)
        dq = torch.empty_like(q)
        dk = torch.zeros(
            (2 * cp_size, k.shape[0] // 2, *k.shape[1:]), dtype=k.dtype, device=k.device
        )
        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()

        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
        ctx.cp_stream.wait_stream(torch.cuda.current_stream())
        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:])

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

        fa_optional_backward_kwargs = {}
        if _flash_attn_2_4_plus:
            fa_optional_backward_kwargs["alibi_slopes"] = None
        if _flash_attn_2_4_1_plus:
            fa_optional_backward_kwargs["deterministic"] = ctx.deterministic

        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]):
                    chunk_ids_to_kv_ag = chunk_ids_to_kv_ag_per_step[i]
                    num_kv_chunks = chunk_ids_to_kv_ag.numel()
                    out_ = out_per_step[i]
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                        q_ = q[:, i].contiguous()
                        # [num_kv_chunks, sq//2, b, np, hn] -> [b, num_kv_chunks*sq//2, np, hn]
                        k_ = (
                            torch.index_select(k_ag, dim=0, index=chunk_ids_to_kv_ag)
                            .movedim(2, 0)
                            .contiguous()
                            .view(k.shape[1], -1, *k.shape[-2:])
                        )
                        v_ = (
                            torch.index_select(v_ag, dim=0, index=chunk_ids_to_kv_ag)
                            .movedim(2, 0)
                            .contiguous()
                            .view(v.shape[1], -1, *v.shape[-2:])
                        )
                        dout_ = dout[:, i].contiguous().view_as(out_)
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                        q_ = q[i].contiguous()
                        # [num_kv_chunks, sq//2, b, np, hn] -> [num_kv_chunks*sq//2, b, np, hn]
                        k_ = torch.index_select(k_ag, dim=0, index=chunk_ids_to_kv_ag).view(
                            -1, *k.shape[-3:]
                        )
                        v_ = torch.index_select(v_ag, dim=0, index=chunk_ids_to_kv_ag).view(
                            -1, *v.shape[-3:]
                        )
                        dout_ = dout[i].contiguous().view_as(out_)
                    if ctx.use_fused_attention:
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
                        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,
                            ctx.max_seqlen_kv * num_kv_chunks,
                            cu_seqlens_q,
                            cu_seqlens_kv * num_kv_chunks,
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
                            TE_DType[q.dtype],
                            TE_DType[k.dtype],
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded * num_kv_chunks,
                            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,
                        )
                    else:
                        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_]
                        ]
                        _flash_attn_backward(
                            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,
                            cu_seqlens_kv * num_kv_chunks,
                            ctx.max_seqlen_q,
                            ctx.max_seqlen_kv * num_kv_chunks,
                            ctx.dropout_p,
                            ctx.softmax_scale,
                            True,
                            window_size=ctx.window_size,
                            rng_state=rng_states[i],
                            **fa_optional_backward_kwargs,
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    chunk_ids_to_kv_ag = chunk_ids_to_kv_ag_per_step[i - 1]
                    num_kv_chunks = chunk_ids_to_kv_ag.numel()
                    if ctx.qkv_format == "bshd":
                        dq[:, i - 1].copy_(dq_per_step[i - 1].view_as(dq[:, i - 1]))
                        dk_per_step[i - 1] = (
                            dk_per_step[i - 1]
                            .view(k.shape[1], num_kv_chunks, -1, *k.shape[-2:])
                            .movedim(0, 2)
                            .contiguous()
                        )
                        dv_per_step[i - 1] = (
                            dv_per_step[i - 1]
                            .view(v.shape[1], num_kv_chunks, -1, *v.shape[-2:])
                            .movedim(0, 2)
                            .contiguous()
                        )
                    elif ctx.qkv_format == "sbhd":
                        dq[i - 1].copy_(dq_per_step[i - 1].view_as(dq[i - 1]))
                        dk_per_step[i - 1] = dk_per_step[i - 1].view(
                            num_kv_chunks, -1, *k.shape[-3:]
                        )
                        dv_per_step[i - 1] = dv_per_step[i - 1].view(
                            num_kv_chunks, -1, *v.shape[-3:]
                        )

                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
                    dk.index_add_(0, chunk_ids_to_kv_ag, dk_per_step[i - 1])
                    dv.index_add_(0, chunk_ids_to_kv_ag, dv_per_step[i - 1])
                    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)

        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)

        if ctx.qkv_format == "bshd":
            dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
            dk = dk.transpose(0, 1).contiguous()
            dv = dv.transpose(0, 1).contiguous()
        elif ctx.qkv_format == "sbhd":
            dq = dq.view(-1, *dq.shape[-3:])

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


3401
def attn_forward_func_with_cp(
3402
3403
3404
3405
3406
    is_training,
    q,
    k,
    v,
    cu_seqlens_q,
3407
    cu_seqlens_kv,
3408
    max_seqlen_q,
3409
    max_seqlen_kv,
3410
3411
    cu_seqlens_q_padded,
    cu_seqlens_kv_padded,
3412
3413
3414
3415
    dropout_p,
    cp_group,
    cp_global_ranks,
    cp_stream,
3416
    cp_comm_type,
3417
3418
3419
3420
3421
3422
3423
    softmax_scale=None,
    qkv_format="bshd",
    attn_mask_type="causal",
    attn_bias_type="no_bias",
    attn_bias=None,
    deterministic=False,
    use_fused_attention=False,
3424
    window_size=None,
3425
3426
    fp8=False,
    fp8_meta=None,
3427
) -> torch.Tensor:
3428
3429
3430
3431
    """
    Attention implementation with context parallelism.
    """

3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
    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!"""
    )
3452
3453
3454
    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!"
3455
3456
3457

    sliding_window_attn = (
        window_size is not None and window_size != (-1, 0) and window_size != (-1, -1)
3458
    )
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506

    if sliding_window_attn or cp_comm_type == "all_gather":
        out = AttnFuncWithCPAndKVAllGather.apply(
            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,
            cp_group,
            cp_stream,
            softmax_scale,
            qkv_format,
            attn_mask_type,
            attn_bias_type,
            attn_bias,
            deterministic,
            use_fused_attention,
            window_size,
        )
    elif cp_comm_type == "p2p":
        out = AttnFuncWithCPAndKVP2P.apply(
            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,
            cp_group,
            cp_global_ranks,
            cp_stream,
            softmax_scale,
            qkv_format,
            attn_mask_type,
            attn_bias_type,
            attn_bias,
            deterministic,
            use_fused_attention,
3507
3508
            fp8,
            fp8_meta,
3509
3510
3511
3512
        )
    else:
        raise ValueError(f"Unsupported communication type: {cp_comm_type}!")

3513
3514
3515
    return out


3516
3517
3518
3519
class RotaryPositionEmbedding(torch.nn.Module):
    """
    Implements Rotary Position Embedding from https://arxiv.org/abs/2104.09864.
    """
3520

3521
3522
3523
    def __init__(
        self,
        dim: int,
3524
        rotary_percent: float = 1.0,
3525
3526
3527
3528
3529
3530
3531
3532
        seq_len_interpolation_factor: Optional[int] = None,
        pretrained_max_position_embeddings: Optional[int] = None,
    ):
        """
        Parameters
        ----------
        dim: int
            rotary embedding dimension
3533
3534
        rotary_percent: float
            Percent of rotary dimension to use for rotary position embeddings.
3535
3536
3537
3538
3539
3540
3541
        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__()
3542
3543
        if rotary_percent < 1.0:
            dim = int(dim * rotary_percent)
3544
        self.seq_len_interpolation_factor = seq_len_interpolation_factor
3545
3546
3547
3548
3549
3550
3551
        inv_freq = 1.0 / (
            10000
            ** (
                torch.arange(0, dim, 2, dtype=torch.float32, device=torch.cuda.current_device())
                / dim
            )
        )
3552
        self.register_buffer("inv_freq", inv_freq)
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
        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
        """
3566
3567
3568
3569
        seq = (
            torch.arange(max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
            + offset
        )
3570

3571
3572
3573
3574
3575
3576
3577
3578
        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
            ):
3579
3580
3581
3582
3583
3584
                # 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

3585
        freqs = torch.einsum("i , j -> i j", seq, self.inv_freq)
3586
3587
3588
3589
3590
3591
        # 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))

3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609

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,
    ) -> torch.Tensor:
3610
3611
        if freqs.dtype != torch.float32:
            freqs = freqs.float()
3612
3613
3614
        if tensor_format == "sbhd":
            output = tex.fused_rope_forward(t, freqs, False)
        elif tensor_format == "bshd":
3615
            output = tex.fused_rope_forward(t.transpose(0, 1), freqs, True).transpose(0, 1)
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
        elif tensor_format == "thd":
            output = tex.fused_rope_thd_forward(t, cu_seqlens, freqs)
        else:
            raise ValueError(f"Unsupported tensor_format: {tensor_format}.")
        ctx.save_for_backward(freqs, cu_seqlens)
        ctx.tensor_format = tensor_format

        return output

    @staticmethod
3626
    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
        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":
            grad_input = tex.fused_rope_thd_backward(grad_output, cu_seqlens, freqs)
        else:
            raise ValueError(f"Unsupported tensor_format: {ctx.tensor_format}.")

        return grad_input, None, None, None, None


3642
3643
3644
3645
3646
3647
3648
3649
3650
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)


3651
def apply_rotary_pos_emb(
3652
3653
3654
3655
3656
3657
    t: torch.Tensor,
    freqs: torch.Tensor,
    tensor_format: str = "sbhd",
    fused: bool = False,
    cu_seqlens: Union[torch.Tensor, None] = None,
) -> torch.Tensor:
3658
    """
3659
    Apply rotary positional embedding tensor to the input tensor.
3660

3661
3662
3663
    Parameters
    ----------
    t: torch.Tensor
3664
        Input tensor of shape `[s, b, h, d]`, `[b, s, h, d]` or `[t, h, d]`, on which
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
        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'.
3677
    """
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
    if fused:
        assert (
            tensor_format != "thd" or cu_seqlens is not None
        ), "cu_seqlens must not be None when tensor_format is 'thd'."
        return FusedRoPEFunc.apply(t, freqs, tensor_format, cu_seqlens)

    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}."
    )

3689
3690
3691
3692
3693
    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.
3694
3695
3696
    assert (
        cur_seq_len <= max_seq_len
    ), f"Rotary Embeddings only supported up to {max_seq_len} sequence length!"
3697
    freqs = freqs[:cur_seq_len]
3698
    if tensor_format == "bshd":
3699
3700
3701
3702
        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)
3703

3704
3705
3706
3707
3708
3709
    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
3710
    t = (t * cos_) + (_rotate_half(t) * sin_)
3711
3712
3713
    return torch.cat((t, t_pass), dim=-1)


cyanguwa's avatar
cyanguwa committed
3714
class _SplitAlongDim(torch.autograd.Function):
3715
3716
3717
    """"""

    @staticmethod
3718
3719
3720
3721
3722
    def forward(
        ctx,
        mixed_x_layer: torch.Tensor,
        split_dim: int,
        split_size_or_sections: Union[int, List[int], Tuple[int]],
3723
    ) -> Tuple[torch.Tensor, ...]:
cyanguwa's avatar
cyanguwa committed
3724
3725
        ctx.split_dim = split_dim
        ctx.split_size_or_sections = split_size_or_sections
3726
        if isinstance(mixed_x_layer, Float8Tensor):
3727
3728
3729
3730
3731
3732
            return tuple(
                Float8Tensor.make_like(
                    mixed_x_layer,
                    data=x,
                )
                for x in torch.split(
3733
3734
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
3735
3736
3737
3738
                    dim=split_dim,
                )
            )
        return torch.split(mixed_x_layer, split_size_or_sections, dim=split_dim)
3739
3740

    @staticmethod
3741
    def backward(ctx, *grad_outputs):
3742
3743
        assert len(grad_outputs) > 0, "No gradients received for backprop!"

cyanguwa's avatar
cyanguwa committed
3744
3745
        if isinstance(ctx.split_size_or_sections, (list, tuple)):
            split_sizes = ctx.split_size_or_sections
3746
3747
3748
            assert len(grad_outputs) == len(
                split_sizes
            ), "Unequal number of gradients vs split sections for backprop!"
cyanguwa's avatar
cyanguwa committed
3749
3750
3751
3752
3753
        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

3754
3755
3756
3757
3758
3759
3760
3761
        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]
3762
3763
3764
3765
3766
3767
3768
                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
                ):
3769
3770
3771
                    noop_ok = False
                    break
            if noop_ok:
3772
3773
3774
                ret = torch.Tensor().to(
                    device=grad_outputs[0].device, dtype=grad_outputs[0]._data.dtype
                )
3775
3776
                new_shape = list(shape)
                new_shape[split_dim] = sum(split_sizes)
3777
3778
3779
3780
3781
                ret.set_(
                    grad_outputs[0]._data.untyped_storage(),
                    grad_outputs[0]._data.storage_offset(),
                    new_shape,
                    strides,
3782
3783
3784
3785
                )
                return Float8Tensor.make_like(grad_outputs[0], data=ret), None, None

            grad_outputs_data = [x._data for x in grad_outputs]
3786
3787
3788
3789
3790
3791
3792
            return (
                Float8Tensor.make_like(
                    grad_outputs[0], data=torch.cat(grad_outputs_data, dim=split_dim)
                ),
                None,
                None,
            )
3793
3794
        noop_ok = True
        strides = grad_outputs[0].stride()
3795
        data_ptr = grad_outputs[0].untyped_storage().data_ptr()
cyanguwa's avatar
cyanguwa committed
3796
        shape = list(grad_outputs[0].shape)
3797
        for i, tensor in enumerate(grad_outputs):
cyanguwa's avatar
cyanguwa committed
3798
3799
            shape_i = shape
            shape_i[split_dim] = split_sizes[i]
3800
3801
3802
3803
3804
3805
3806
            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
            ):
3807
3808
3809
                noop_ok = False
                break
        if noop_ok:
3810
            ret = torch.Tensor().to(device=grad_outputs[0].device, dtype=grad_outputs[0].dtype)
3811
            new_shape = list(shape)
cyanguwa's avatar
cyanguwa committed
3812
            new_shape[split_dim] = sum(split_sizes)
3813
3814
3815
3816
3817
            ret.set_(
                grad_outputs[0].untyped_storage(),
                grad_outputs[0].storage_offset(),
                new_shape,
                strides,
3818
            )
cyanguwa's avatar
cyanguwa committed
3819
            return ret, None, None
3820

3821
        return torch.cat(grad_outputs, dim=split_dim), None, None
3822
3823
3824
3825
3826
3827
3828
3829
3830


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

    def __init__(
        self,
3831
        softmax_scale: float,
3832
        attention_type: str = "self",
3833
3834
3835
3836
3837
3838
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        layer_number: Optional[int] = None,
    ) -> None:
        super().__init__()

3839
        self.softmax_scale = softmax_scale
3840
        self.attention_type = attention_type
3841
3842
3843
        self.attention_dropout_ctx = attention_dropout_ctx
        self.layer_number = layer_number

3844
        self.scale_mask_softmax = FusedScaleMaskSoftmax(attention_mask_func)
3845
3846
3847
3848
3849
3850

        # 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)

3851
3852
        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
3853
3854
            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None
        )
3855

3856
3857
3858
3859
3860
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
3861
        qkv_layout: str = "sbh3d",
3862
3863
        cu_seqlens_q: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
3864
        attn_mask_type: str = "causal",
3865
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
3866
3867
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
3868
        alibi_slopes: Optional[torch.Tensor] = None,
3869
    ) -> torch.Tensor:
3870
        """Unfused attention fprop"""
3871
3872
3873
3874
3875
        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":
3876
            # convert to sbhd and use sbhd implementation for now
3877
3878
3879
            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
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
        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,
                )
3932

3933
        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]
3934
        apply_qk_layer_scaling = self.apply_qk_layer_scaling and key_layer.dtype == torch.float16
3935
3936
3937
3938
3939
3940
3941
3942
3943

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

3944
        if key_layer.shape[2] != query_layer.shape[2]:
3945
3946
3947
            assert (
                query_layer.shape[2] % key_layer.shape[2] == 0
            ), "The number of attention heads must be divisible by the number of GQA groups!"
3948
            key_layer = key_layer.repeat_interleave(
3949
3950
                int(query_layer.shape[2] / key_layer.shape[2]), dim=2
            )
3951
            value_layer = value_layer.repeat_interleave(
3952
3953
                int(query_layer.shape[2] / value_layer.shape[2]), dim=2
            )
3954

3955
        # [sq, b, np, hn] -> [sq, b * np, hn]
3956
        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
3957
3958
3959
3960
        # [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]
3961
3962
        # WAR to set dtype to FP32 as ONNX lacks BF16 support for ConstantOfShape operator
        is_bf16 = query_layer.dtype == torch.bfloat16
3963
3964
3965
3966
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
3967
            dtype=torch.float32 if is_in_onnx_export_mode() and is_bf16 else query_layer.dtype,
3968
3969
3970
            device=torch.cuda.current_device(),
        )

3971
3972
3973
        if is_in_onnx_export_mode() and is_bf16:
            matmul_result = matmul_result.bfloat16()

3974
        scale = self.softmax_scale
3975
        if apply_qk_layer_scaling:
3976
            scale /= self.layer_number
3977
3978

        # Raw attention scores. [b * np, sq, sk]
3979
3980
3981
3982
3983
3984
        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,
3985
                alpha=scale,
3986
            ).view(*output_size)
3987
3988
3989
3990
3991
3992
3993

        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]
            )
3994
            matmul_result = matmul_result.view(*output_size) + core_attention_bias
3995
            matmul_result *= scale
3996

3997
3998
3999
4000
        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":
4001
                _, core_attention_bias = get_alibi(
4002
4003
4004
                    output_size[1],
                    output_size[2],
                    output_size[3],
4005
4006
                    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,
4007
4008
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
4009
                )
4010
4011
4012
4013
4014
            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,
4015
                alpha=scale,
4016
            )
4017
4018
            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
4019
            )
4020
4021
4022

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
4023
        attention_probs = self.scale_mask_softmax(
4024
            matmul_result, attention_mask, attn_mask_type, softmax_scale
4025
        )
4026

4027
4028
4029
4030
4031
        # 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)

4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
        # 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]
4047
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
4048
4049

        # change view [b * np, sq, sk]
4050
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
4051
4052
4053
4054
4055
4056
4057

        # 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)

4058
        if qkv_format == "sbhd":
4059
4060
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
4061

4062
4063
4064
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

4065
        if qkv_format == "bshd":
4066
4067
4068
4069
4070
            # [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)
4071
4072
4073
4074
4075
4076

        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
4077
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
4078
4079

    @staticmethod
4080
4081
4082
4083
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
4084
        value_layer: torch.Tensor,
4085
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
4086
4087
4088
4089
4090
4091
4092
4093
4094
4095
4096
        # 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
4097
4098
4099
4100
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
4101
        dv: torch.Tensor,
4102
4103
4104
4105
4106
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

4107

4108
def get_qkv_layout(
4109
4110
4111
4112
4113
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    qkv_format: str = "sbhd",
) -> str:
4114
    """Get qkv layout.
4115

4116
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
    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,
4127
        `d` head size, and `t` the total number of tokens in a batch, i.e.
4128
4129
4130
4131
4132
4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
        `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`}
    """
4144

4145
4146
    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!"
4147

4148
4149
4150
4151
4152
4153
4154
4155
4156
    def run_iteratively(q, k, v):
        data_ptr = q.untyped_storage().data_ptr()
        check_ptrs_qkv = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k, v])
        data_ptr = k.untyped_storage().data_ptr()
        check_ptrs_kv = all(x.untyped_storage().data_ptr() == data_ptr for x in [k, v])

        stride = q.stride()
        check_strides_qkv = all(stride == x.stride() for x in [q, k, v])
        stride = k.stride()
4157
4158
4159
        check_strides_kv = torch.equal(
            torch.Tensor(stride[:-1]) / k.shape[-1], torch.Tensor(v.stride()[:-1]) / v.shape[-1]
        )
4160
4161
4162
4163

        shape = q.shape
        check_shapes_qkv = all(shape == x.shape for x in [q, k, v])
        shape = k.shape
4164
        check_shapes_kv = shape[:-1] == v.shape[:-1]
4165
4166

        last_dim_size = q.shape[-1]
4167
4168
4169
        check_last_dim_offsets_qkv = all(
            i * last_dim_size == x.storage_offset() for i, x in enumerate([q, k, v])
        )
4170
        last_dim_size = k.shape[-1]
4171
4172
4173
        check_last_dim_offsets_kv = all(
            i * last_dim_size == x.storage_offset() for i, x in enumerate([k, v])
        )
4174
4175

        last_two_dims_size = q.shape[-1] * q.shape[-2]
4176
4177
4178
        check_last_two_dims_offsets_qkv = all(
            i * last_two_dims_size == x.storage_offset() for i, x in enumerate([q, k, v])
        )
4179
        last_two_dims_size = k.shape[-1] * k.shape[-2]
4180
4181
4182
        check_last_two_dims_offsets_kv = all(
            i * last_two_dims_size == x.storage_offset() for i, x in enumerate([k, v])
        )
4183

4184
4185
4186
4187
        if (
            check_ptrs_qkv
            and check_strides_qkv
            and check_shapes_qkv
4188
            and check_last_two_dims_offsets_qkv
4189
4190
            and not check_last_dim_offsets_qkv
        ):
4191
            # sb3hd, bs3hd, t3hd
4192
4193
4194
4195
            qkv_layout = qkv_format[:-2] + "3" + qkv_format[-2:]
        elif (
            check_ptrs_qkv and check_strides_qkv and check_shapes_qkv and check_last_dim_offsets_qkv
        ):
4196
            # sbh3d, bsh3d, th3d
4197
4198
4199
4200
4201
            qkv_layout = qkv_format[:-1] + "3" + qkv_format[-1:]
        elif (
            check_ptrs_kv
            and check_strides_kv
            and check_shapes_kv
4202
            and check_last_two_dims_offsets_kv
4203
4204
            and not check_last_dim_offsets_kv
        ):
4205
            # sbhd_sb2hd, bshd_bs2hd, thd_t2hd
4206
4207
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
        elif check_ptrs_kv and check_strides_kv and check_shapes_kv and check_last_dim_offsets_kv:
4208
            # sbhd_sbh2d, bshd_bsh2d, thd_th2d
4209
            qkv_layout = qkv_format + "_" + qkv_format[:-1] + "2" + qkv_format[-1:]
4210
4211
        elif check_strides_kv and check_shapes_kv:
            # sbhd_sbhd_sbhd, bshd_bshd_bshd, thd_thd_thd
4212
            qkv_layout = "_".join(list([qkv_format]) * 3)
4213
        else:
4214
            qkv_layout = "not_supported"
4215
4216
4217
4218

        return qkv_layout

    qkv_layout = run_iteratively(q, k, v)
4219
    if qkv_layout == "not_supported":
4220
4221
4222
        # 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)
4223
    if qkv_layout == "not_supported":
4224
4225
        raise Exception("The provided qkv memory layout is not supported!")

4226
    return qkv_layout, q, k, v
4227

4228

4229
def check_set_window_size(
4230
4231
4232
    attn_mask_type: str,
    window_size: Tuple[int, int] = None,
):
4233
4234
4235
4236
4237
4238
4239
4240
    """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)
4241
    """
4242
    orig_window_size = window_size
4243
    if "causal" in attn_mask_type:
4244
        if orig_window_size is None:
4245
            window_size = (-1, 0)
4246
4247
4248
        elif orig_window_size == (-1, -1) or (
            orig_window_size[0] >= 0 and orig_window_size[1] != 0
        ):
4249
4250
4251
4252
            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
            )
4253
        elif orig_window_size != (-1, 0) and (orig_window_size[0] < 0 or orig_window_size[1] != 0):
4254
4255
4256
4257
            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"]:
4258
4259
4260
        if orig_window_size is None:
            window_size = (-1, -1)
        elif orig_window_size == (-1, 0):
4261
            window_size = (-1, -1)
4262
4263
4264
            warnings.warn(
                "window_size should be (-1, -1) or (>=0, >=0) for attn_mask_type=" + attn_mask_type
            )
4265
        elif orig_window_size != (-1, -1) and (orig_window_size[0] < 0 or orig_window_size[1] < 0):
4266
4267
4268
4269
4270
            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
4271
    return window_size
4272

4273

4274
class FlashAttention(torch.nn.Module):
4275
    """Dot product attention, using HazyResearch flash-attn package:
4276
    https://github.com/Dao-AILab/flash-attention
4277
4278
4279
4280
    """

    def __init__(
        self,
4281
        softmax_scale: float,
4282
4283
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
4284
4285
        attention_type: str = "self",
        layer_number: Optional[int] = None,
4286
        deterministic: bool = False,
4287
4288
4289
4290
4291
4292
    ) -> None:
        super().__init__()

        assert (
            _flash_attn_version >= _flash_attn_version_required
        ), f"FlashAttention minimum version {_flash_attn_version_required} is required."
4293
4294
4295
        assert (
            _flash_attn_version <= _flash_attn_max_version
        ), f"FlashAttention maximum version {_flash_attn_max_version} is supported."
4296

4297
        self.softmax_scale = softmax_scale
4298
4299
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
4300
4301
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
4302
        self.deterministic = deterministic
4303
4304
4305
4306
4307
4308

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
4309
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4310
4311
4312
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
4313
4314
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
4315
        attn_mask_type: str = "causal",
4316
        window_size: Optional[Tuple[int, int]] = None,
4317
        alibi_slopes: Optional[torch.Tensor] = None,
4318
        cp_group: Optional[dist_group_type] = None,
4319
        cp_global_ranks: List[int] = None,
4320
        cp_stream: torch.cuda.Stream = None,
4321
        cp_comm_type: str = "p2p",
4322
4323
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
4324
4325
4326
    ) -> torch.Tensor:
        """flash-attn fprop"""

4327
4328
4329
4330
        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."
4331
4332
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
4333
        ), "FlashAttention currently only supports CUDA tensors."
4334
4335
        assert (
            qkv_layout in QKVLayouts
4336
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
4337

4338
4339
        cp_size = 1 if cp_group is None else get_distributed_world_size(cp_group)
        context_parallel = cp_size > 1
4340

4341
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
4342

4343
4344
4345
4346
4347
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
4359
        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 = [
                        x.transpose(0, 1).contiguous()
                        for x in (query_layer, key_layer, value_layer)
                    ]
            elif qkv_format in ["bshd", "thd"]:
4360
                query_layer, key_layer, value_layer = [
4361
4362
4363
4364
4365
4366
4367
4368
4369
4370
4371
                    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 = [
                    x.transpose(0, 1).contiguous()
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
            elif qkv_format in ["bshd", "thd"]:
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
4372
                ]
4373

4374
        batch_size = query_layer.shape[0]
4375

4376
        if qkv_format in ["sbhd", "bshd"]:
4377
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
4378
4379
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
4380
4381
4382

            if "padding" in attn_mask_type:
                assert not context_parallel, "Padding mask not supported with context parallelism!"
4383
4384
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
4385
                    x.reshape(x.shape[0] * x.shape[1], *x.shape[2:])
4386
4387
4388
4389
4390
4391
4392
                    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."
4393
                    if cu_seqlens_q is None:
4394
4395
4396
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
4397
4398
4399
4400
4401
4402
                        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
4403
4404
                    )
                else:
4405
                    if cu_seqlens_q is None or cu_seqlens_kv is None:
4406
4407
4408
4409
4410
                        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])
4411
4412
4413
4414
                    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)
4415
                    key_layer, value_layer = PackTensors.apply(indices_kv, key_layer, value_layer)
4416
            else:
4417
4418
4419
4420
4421
4422
4423
4424
4425
4426
4427
4428
4429
                # 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,
                    )
4430
4431
4432
4433
        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!"
4434
4435
4436
4437
4438
4439
            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()
4440

4441
4442
4443
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
4444
4445
4446
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
4447
            with self.attention_dropout_ctx():
4448
                output = attn_forward_func_with_cp(
4449
4450
4451
4452
4453
4454
4455
4456
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
4457
4458
                    cu_seqlens_q,
                    cu_seqlens_kv,
4459
                    self.attention_dropout if self.training else 0.0,
4460
4461
4462
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
4463
                    cp_comm_type,
4464
                    softmax_scale=self.softmax_scale,
4465
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
4466
                    attn_mask_type=attn_mask_type,
4467
                    deterministic=self.deterministic,
4468
                    window_size=window_size,
4469
4470
                )
        else:
4471
4472

            from .cpu_offload import CPUOffloadEnabled
4473

4474
4475
4476
4477
4478
4479
            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

4480
            with self.attention_dropout_ctx():
4481
                fa_optional_forward_kwargs = {}
4482
4483
                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
4484
4485
4486
4487
                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
4488
4489
                if _flash_attn_2_5_7_plus:
                    fa_optional_forward_kwargs["block_table"] = None
4490
4491
4492
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
4506
4507
4508
4509
4510
4511
4512
4513
4514
4515
4516
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
4537
4538
4539
4540
4541
4542
4543
4544
4545
4546
4547
4548
4549
4550
4551
4552
4553
4554
4555
4556
                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:
                    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:
                    if fp8:
                        fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
                        activation_dtype = query_layer.dtype
                        torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)
                        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
                            query_layer, key_layer, value_layer = (
                                x.to(activation_dtype).to(torch_dtype)
                                for x in [query_layer, key_layer, value_layer]
                            )
                        else:
                            query_layer, key_layer, value_layer = (
                                x.to(torch_dtype) for x in [query_layer, key_layer, value_layer]
                            )
                    output, _ = func(
                        query_layer,
                        key_layer,
                        value_layer,
                        *fa_optional_forward_args_thd,
                        softmax_scale=self.softmax_scale,
                        causal="causal" in attn_mask_type,
                        deterministic=self.deterministic,
                    )
                    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,
                    )
4557

4558
        if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
4559
            output = UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
4560

4561
        if qkv_format == "sbhd":
4562
            # (bs)hd -> bs(hd) -> sb(hd)
4563
4564
4565
4566
4567
4568
4569
4570
4571
            if fp8 and fp8_meta["recipe"].fp8_mha:
                output.reshape(batch_size * max_seqlen_q // cp_size, -1).transpose_2d()
                output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
            else:
                output = (
                    output.view(batch_size, max_seqlen_q // cp_size, -1)
                    .transpose(0, 1)
                    .contiguous()
                )
4572
        elif qkv_format == "bshd":
4573
            # (bs)hd -> bs(hd)
4574
            output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
4575
        elif qkv_format == "thd":
4576
            # thd -> t(hd)
4577
            output = output.reshape(output.shape[0], -1)
4578
4579

        return output
4580

4581

4582
def _combine_tensors(
4583
4584
4585
    tensors: List[torch.Tensor],
    dim: int,
) -> torch.Tensor:
4586
4587
4588
4589
4590
4591
    """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())
4592
    new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
4593
    if isinstance(tensors[0], Float8Tensor):
4594
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
4595
4596
4597
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
4598
4599
4600
4601
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor)
4602
    else:
4603
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
4604
        combined_tensor.set_(
4605
4606
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
        )
4607
4608

    return combined_tensor
4609

4610

4611
4612
4613
4614
class FusedAttnFunc_qkvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed QKV input"""

    @staticmethod
4615
4616
4617
4618
4619
    def forward(
        ctx,
        is_training,
        max_seqlen,
        cu_seqlens,
4620
        cu_seqlens_padded,
4621
4622
4623
4624
4625
4626
4627
4628
4629
        qkv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
4630
        window_size,
4631
4632
4633
4634
4635
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
4636
        deterministic,
4637
    ):
4638
4639
        if fp8:
            if fp8_meta["recipe"].fp8_mha:
4640
                assert isinstance(qkv, Float8Tensor), "qkv must be Float8Tensors for FP8 MHA."
4641
4642
4643
4644
                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
4645
4646
4647
4648
4649
            qkv_group = len(qkv_layout.split("_"))
            assert qkv_group == 1, (
                "qkv layout should conform to 3hd or h3d, e.g. sb3hd,                 but found"
                f" {qkv_layout}."
            )
4650
4651
4652
4653
            if fp8_meta["recipe"].fp8_mha:
                qkv_fp8 = qkv._data
            else:
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
4654
4655
4656
                qkv_fp8 = cast_to_fp8(
                    qkv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(qkv.shape)
4657
            out_fp8, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
4658
4659
4660
4661
4662
4663
4664
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv_fp8,
                fp8_dtype_forward,
                fused_attention_backend,
                attn_bias,
4665
                cu_seqlens_padded,
4666
4667
4668
4669
4670
4671
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
4672
4673
4674
4675
4676
4677
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4678
                window_size,
4679
4680
                rng_gen,
            )
4681
            if fp8_meta["recipe"].fp8_mha:
4682
4683
                out_ret = Float8Tensor(
                    data=out_fp8,
4684
4685
4686
4687
4688
4689
4690
4691
4692
                    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]),
4693
4694
4695
4696
4697
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
4698
4699
4700
            out_save = out_ret
            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
4701
4702
                qkv = cast_from_fp8(
                    qkv_c._data,
4703
                    fp8_meta["scaling_fwd"],
4704
4705
4706
4707
                    META_QKV,
                    fp8_dtype_forward,
                    TE_DType[qkv.dtype],
                ).view(qkv.shape)
4708
4709
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
4710
4711
4712
4713
4714
4715
4716
4717
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
            fp8_tensors = (
                qkv_fp8,
                out_fp8,
4718
                fp8_meta["scaling_fwd"].scale.clone(),
4719
4720
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
4721
4722
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
4723
4724
4725
4726
4727
4728
4729
                is_training,
                max_seqlen,
                cu_seqlens,
                qkv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
4730
                cu_seqlens_padded,
4731
4732
4733
4734
4735
4736
4737
4738
4739
4740
4741
4742
                None,
                None,
                None,
                None,
                None,
                None,
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4743
                window_size,
4744
4745
                rng_gen,
            )
4746
4747
4748
4749
4750
            fp8_tensors = (None, None, None, None)
            out_save = out_ret

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (qkv, out_save) if not ctx.fp8 else (None, None)
4751
        ctx.save_for_backward(
4752
            *qkvo_tensors, cu_seqlens, cu_seqlens_padded, *fp8_tensors, *aux_ctx_tensors
4753
        )
4754
        ctx.fp8_meta = fp8_meta
4755
4756
4757
4758
4759
4760
4761
4762
        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
4763
        ctx.window_size = window_size
4764
        ctx.fused_attention_backend = (
4765
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
4766
        )
4767
        ctx.use_FAv2_bwd = use_FAv2_bwd
4768
        ctx.deterministic = deterministic
4769

4770
        return out_ret
4771
4772
4773

    @staticmethod
    def backward(ctx, d_out):
4774
        if ctx.fp8_meta["recipe"].fp8_mha:
4775
4776
4777
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
4778
4779
4780
            d_out_f8tensor = d_out
            d_out = d_out._data

4781
        d_out = d_out.contiguous()
4782
4783
4784
4785
        (
            qkv,
            out,
            cu_seqlens,
4786
            cu_seqlens_padded,
4787
4788
4789
4790
4791
4792
            qkv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
4793
4794
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
4795
        if ctx.use_FAv2_bwd:
4796
            softmax_lse, rng_state = aux_ctx_tensors
4797
4798
            dqkv = torch.empty_like(qkv)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
4799
4800
4801
            d_out, q, k, v, out = [
                maybe_contiguous(x) for x in (d_out, qkv[:, 0], qkv[:, 1], qkv[:, 2], out)
            ]
4802
            flash_attn_cuda_bwd(
4803
4804
4805
4806
4807
4808
4809
4810
4811
4812
4813
4814
4815
4816
4817
4818
4819
4820
4821
                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,
4822
            )
4823
            dqkv = dqkv[..., : d_out.shape[-1]]
4824
        else:
4825
4826
            with torch.cuda.nvtx.range("_FusedAttn_qkvpacked"):
                if ctx.fp8:
4827
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
4828
                    fp8_dtype_backward = get_fp8_te_dtype(
4829
4830
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
4831
4832
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
4833
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
4834
4835
4836
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
4837
4838
4839
4840
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
4841
                    dqkv_fp8, *rest = fused_attn_bwd_qkvpacked(
4842
4843
4844
4845
4846
4847
4848
4849
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv_fp8,
                        out_fp8,
                        d_out_fp8,
                        fp8_dtype_forward,
                        fp8_dtype_backward,
                        aux_ctx_tensors,
4850
                        ctx.fused_attention_backend,
4851
                        cu_seqlens_padded,
4852
4853
4854
4855
4856
4857
4858
4859
4860
4861
4862
4863
4864
4865
4866
4867
                        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,
4868
4869
                        ctx.window_size,
                        ctx.deterministic,
4870
                    )
4871
                    if ctx.fp8_meta["recipe"].fp8_mha:
4872
4873
                        dqkv = Float8Tensor(
                            data=dqkv_fp8,
4874
4875
4876
4877
4878
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
4879
                        )
4880
                    else:
4881
4882
4883
4884
4885
4886
4887
4888
4889
4890
                        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)
4891
4892
4893
4894
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(qkv.dtype)
                    dqkv, *rest = fused_attn_bwd_qkvpacked(
4895
4896
4897
4898
4899
4900
4901
4902
                        ctx.max_seqlen,
                        cu_seqlens,
                        qkv,
                        out,
                        d_out,
                        ctx.qkv_dtype,
                        ctx.qkv_dtype,
                        aux_ctx_tensors,
4903
                        ctx.fused_attention_backend,
4904
                        cu_seqlens_padded,
4905
4906
4907
4908
4909
4910
4911
4912
4913
4914
4915
4916
4917
4918
4919
4920
                        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,
4921
4922
                        ctx.window_size,
                        ctx.deterministic,
4923
                    )
4924

4925
4926
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
4927
4928
4929
4930
4931
4932
4933
4934
4935
4936
4937
4938
4939
4940
4941
4942
4943
4944
4945
4946
4947
            return (
                None,
                None,
                None,
                None,
                dqkv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
4948
4949
                None,
                None,
4950
            )
4951
        # else, return (dqkv, dbias)
4952
4953
4954
4955
4956
4957
4958
4959
4960
4961
4962
4963
4964
4965
4966
4967
4968
4969
4970
4971
4972
        return (
            None,
            None,
            None,
            None,
            dqkv,
            None,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4973
4974
            None,
            None,
4975
        )
4976

4977

4978
4979
4980
4981
class FusedAttnFunc_kvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed KV input"""

    @staticmethod
4982
4983
4984
4985
4986
4987
4988
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
4989
4990
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
4991
4992
4993
4994
4995
4996
4997
4998
4999
5000
        q,
        kv,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
5001
        window_size,
5002
5003
5004
5005
5006
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
5007
        deterministic,
5008
    ):
5009
5010
        if fp8:
            if fp8_meta["recipe"].fp8_mha:
5011
5012
5013
                assert isinstance(q, Float8Tensor) and isinstance(
                    kv, Float8Tensor
                ), "q/kv must be Float8Tensors for FP8 MHA."
5014
5015
5016
5017
5018
5019
5020
                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)
            if fp8_meta["recipe"].fp8_mha:
                q_fp8, kv_fp8 = q._data, kv._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
5021
5022
5023
5024
5025
5026
5027
5028
                qkv_group = len(qkv_layout.split("_"))
                assert qkv_group == 2, (
                    "qkv layout should conform to hd_2hd or hd_h2d, e.g. sbhd_sb2hd,              "
                    f"       but found {qkv_layout}."
                )
                q_fp8 = cast_to_fp8(q, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward).view(
                    q.shape
                )
5029
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
5030
5031
5032
                kv_fp8 = cast_to_fp8(
                    kv_c, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward
                ).view(kv.shape)
5033
            out_fp8, aux_ctx_tensors = fused_attn_fwd_kvpacked(
5034
5035
5036
5037
5038
5039
5040
5041
5042
5043
                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,
5044
5045
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
5046
5047
5048
5049
5050
5051
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
5052
5053
5054
5055
5056
5057
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5058
                window_size,
5059
5060
                rng_gen,
            )
5061
            if fp8_meta["recipe"].fp8_mha:
5062
5063
                out_ret = Float8Tensor(
                    data=out_fp8,
5064
5065
5066
5067
5068
5069
5070
5071
5072
                    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]),
5073
5074
5075
5076
5077
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
5078
5079
            out_save = out_ret
            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
5080
5081
5082
                q = cast_from_fp8(
                    q._data, fp8_meta["scaling_fwd"], META_QKV, fp8_dtype_forward, TE_DType[q.dtype]
                ).view(q.shape)
5083
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
5084
5085
                kv = cast_from_fp8(
                    kv_c._data,
5086
                    fp8_meta["scaling_fwd"],
5087
5088
5089
5090
                    META_QKV,
                    fp8_dtype_forward,
                    TE_DType[kv.dtype],
                ).view(kv.shape)
5091
5092
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
5093
5094
5095
5096
5097
5098
5099
5100
5101
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
            fp8_tensors = (
                q_fp8,
                kv_fp8,
                out_fp8,
5102
                fp8_meta["scaling_fwd"].scale.clone(),
5103
5104
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
5105
5106
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd_kvpacked(
5107
5108
5109
5110
5111
5112
5113
5114
5115
5116
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                kv,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
5117
5118
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
5119
5120
5121
5122
5123
5124
5125
5126
5127
5128
5129
5130
                None,
                None,
                None,
                None,
                None,
                None,
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5131
                window_size,
5132
5133
                rng_gen,
            )
5134
5135
5136
5137
5138
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None)

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (q, kv, out_save) if not ctx.fp8 else (None, None, None)
5139
5140
5141
5142
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
5143
5144
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
5145
5146
5147
            *fp8_tensors,
            *aux_ctx_tensors,
        )
5148
        ctx.fp8_meta = fp8_meta
5149
5150
5151
5152
5153
5154
5155
5156
5157
        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
5158
        ctx.window_size = window_size
5159
        ctx.fused_attention_backend = (
5160
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
5161
        )
5162
        ctx.use_FAv2_bwd = use_FAv2_bwd
5163
        ctx.deterministic = deterministic
5164

5165
        return out_ret
5166
5167
5168

    @staticmethod
    def backward(ctx, d_out):
5169
        if ctx.fp8_meta["recipe"].fp8_mha:
5170
5171
5172
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
5173
5174
5175
            d_out_f8tensor = d_out
            d_out = d_out._data

5176
        d_out = d_out.contiguous()
5177
5178
5179
5180
5181
5182
        (
            q,
            kv,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
5183
5184
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
5185
5186
5187
5188
5189
5190
5191
            q_fp8,
            kv_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
5192
5193
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
5194
        if ctx.use_FAv2_bwd:
5195
            softmax_lse, rng_state = aux_ctx_tensors
5196
5197
5198
            dq = torch.empty_like(q)
            dkv = torch.empty_like(kv)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
5199
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, kv[:, 0], kv[:, 1], out)]
5200
            flash_attn_cuda_bwd(
5201
5202
5203
5204
5205
5206
5207
5208
5209
5210
5211
5212
5213
5214
5215
5216
5217
5218
5219
                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,
5220
            )
5221
5222
            dq = dq[..., : d_out.shape[-1]]
            dkv = dkv[..., : d_out.shape[-1]]
5223
        else:
5224
5225
            with torch.cuda.nvtx.range("_FusedAttn_kvpacked"):
                if ctx.fp8:
5226
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
5227
                    fp8_dtype_backward = get_fp8_te_dtype(
5228
5229
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
5230
5231
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
5232
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
5233
5234
5235
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
5236
5237
5238
5239
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
5240
                    dq_fp8, dkv_fp8, *rest = fused_attn_bwd_kvpacked(
5241
5242
5243
5244
5245
5246
5247
5248
5249
5250
5251
                        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,
5252
                        ctx.fused_attention_backend,
5253
5254
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
5255
5256
5257
5258
5259
5260
5261
5262
5263
5264
5265
5266
5267
5268
5269
5270
                        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,
5271
5272
                        ctx.window_size,
                        ctx.deterministic,
5273
                    )
5274
                    if ctx.fp8_meta["recipe"].fp8_mha:
5275
5276
                        dq = Float8Tensor(
                            data=dq_fp8,
5277
5278
5279
5280
5281
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5282
5283
5284
                        )
                        dkv = Float8Tensor(
                            data=dkv_fp8,
5285
5286
5287
5288
5289
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5290
                        )
5291
5292
5293
                    else:
                        dq = cast_from_fp8(
                            dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
5294
5295
5296
5297
5298
5299
5300
5301
5302
5303
5304
5305
5306
5307
5308
                            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)
5309
5310
5311
5312
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dkv, *rest = fused_attn_bwd_kvpacked(
5313
5314
5315
5316
5317
5318
5319
5320
5321
5322
5323
                        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,
5324
                        ctx.fused_attention_backend,
5325
5326
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
5327
5328
5329
5330
5331
5332
5333
5334
5335
5336
5337
5338
5339
5340
5341
5342
                        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,
5343
5344
                        ctx.window_size,
                        ctx.deterministic,
5345
                    )
5346

5347
5348
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
5349
5350
5351
5352
5353
5354
5355
5356
5357
5358
5359
5360
5361
5362
5363
5364
5365
5366
5367
5368
5369
5370
5371
5372
5373
            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,
5374
5375
                None,
                None,
5376
            )
5377
        # else, return (dqkv, dbias)
5378
5379
5380
5381
5382
5383
5384
5385
5386
5387
5388
5389
5390
5391
5392
5393
5394
5395
5396
5397
5398
5399
5400
5401
5402
        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,
5403
5404
            None,
            None,
5405
5406
        )

5407

5408
5409
5410
5411
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
5412
5413
5414
5415
5416
5417
5418
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
5419
5420
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
5421
5422
5423
5424
5425
5426
5427
5428
5429
5430
5431
        q,
        k,
        v,
        qkv_dtype,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
5432
        window_size,
5433
5434
5435
5436
5437
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
5438
        deterministic,
5439
    ):
5440
5441
5442
5443
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if fp8_meta["recipe"].fp8_mha:
5444
5445
                assert (
                    isinstance(q, Float8Tensor)
5446
                    and isinstance(k, Float8Tensor)
5447
5448
                    and isinstance(v, Float8Tensor)
                ), "q/k/v must be Float8Tensors for FP8 MHA."
5449
5450
5451
5452
                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
5453
                qkv_group = len(qkv_layout.split("_"))
5454
                if qkv_group == 1:
5455
5456
                    dim = qkv_layout.find("3")
                    qkv = _combine_tensors([q, k, v], dim)
5457
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
5458
5459
5460
5461
                    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])
5462
5463
                    q_fp8, k_fp8, v_fp8 = [x.squeeze(dim) for x in [q_fp8, k_fp8, v_fp8]]
                if qkv_group == 2:
5464
5465
5466
5467
5468
                    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)
5469
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
5470
5471
5472
5473
                    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])
5474
5475
                    k_fp8, v_fp8 = [x.squeeze(dim) for x in [k_fp8, v_fp8]]
                if qkv_group == 3:
5476
5477
5478
5479
5480
5481
5482
5483
5484
                    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)
5485
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
5486
5487
5488
5489
5490
5491
5492
5493
5494
5495
5496
                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,
5497
5498
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
5499
5500
5501
5502
5503
5504
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
5505
5506
5507
5508
5509
5510
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5511
                window_size,
5512
5513
                rng_gen,
            )
5514
            if fp8_meta["recipe"].fp8_mha:
5515
5516
                out_ret = Float8Tensor(
                    data=out_fp8,
5517
5518
5519
5520
5521
5522
5523
5524
5525
                    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]),
5526
5527
5528
5529
5530
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)
5531
5532
5533
5534
            out_save = out_ret

            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                # 1: qkv packed, 2: kv packed, 3: qkv separate
5535
                qkv_group = len(qkv_layout.split("_"))
5536
                if qkv_group == 1:
5537
5538
                    dim = qkv_layout.find("3")
                    qkv = _combine_tensors([q, k, v], dim)
5539
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
5540
5541
                    qkv_no_fp8 = cast_from_fp8(
                        qkv_c._data,
5542
                        fp8_meta["scaling_fwd"],
5543
5544
5545
5546
5547
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[qkv.dtype],
                    ).view(qkv.shape)
                    q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1])
5548
5549
                    q, k, v = [x.squeeze(dim) for x in [q, k, v]]
                if qkv_group == 2:
5550
5551
                    q = cast_from_fp8(
                        q._data,
5552
                        fp8_meta["scaling_fwd"],
5553
5554
5555
5556
5557
5558
                        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)
5559
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
5560
5561
                    kv_no_fp8 = cast_from_fp8(
                        kv_c._data,
5562
                        fp8_meta["scaling_fwd"],
5563
5564
5565
5566
5567
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[kv.dtype],
                    ).view(kv.shape)
                    k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1])
5568
5569
                    k, v = [x.squeeze(dim) for x in [k, v]]
                if qkv_group == 3:
5570
5571
                    q = cast_from_fp8(
                        q._data,
5572
                        fp8_meta["scaling_fwd"],
5573
5574
5575
5576
5577
5578
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[q.dtype],
                    ).view(q.shape)
                    k = cast_from_fp8(
                        k._data,
5579
                        fp8_meta["scaling_fwd"],
5580
5581
5582
5583
5584
5585
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[k.dtype],
                    ).view(k.shape)
                    v = cast_from_fp8(
                        v._data,
5586
                        fp8_meta["scaling_fwd"],
5587
5588
5589
5590
                        META_QKV,
                        fp8_dtype_forward,
                        TE_DType[v.dtype],
                    ).view(v.shape)
5591
5592
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
5593
5594
5595
5596
5597
5598
5599
5600
5601
5602
5603
                    fp8_meta["scaling_fwd"],
                    META_O,
                    fp8_dtype_forward,
                    qkv_dtype,
                ).view(out_fp8.shape)

            fp8_tensors = (
                q_fp8,
                k_fp8,
                v_fp8,
                out_fp8,
5604
                fp8_meta["scaling_fwd"].scale.clone(),
5605
5606
                fp8_meta["scaling_fwd"].scale_inv.clone(),
            )
5607
5608
        else:
            out_ret, aux_ctx_tensors = fused_attn_fwd(
5609
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
                qkv_dtype,
                fused_attention_backend,
                attn_bias,
5620
5621
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
5622
5623
5624
5625
5626
5627
5628
5629
5630
5631
5632
5633
                None,
                None,
                None,
                None,
                None,
                None,
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
5634
                window_size,
5635
5636
                rng_gen,
            )
5637
5638
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None, None)
5639

5640
        from .cpu_offload import CPUOffloadEnabled
5641

5642
        if CPUOffloadEnabled:
5643
            tensor_list = [q, k, v, out_save, cu_seqlens_q, cu_seqlens_kv]
5644
            qkv_layout = "sbhd_sbhd_sbhd"
5645
5646
5647
5648
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

5649
5650
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
5651
5652
5653
5654
        ctx.save_for_backward(
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
5655
5656
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
5657
5658
5659
            *fp8_tensors,
            *aux_ctx_tensors,
        )
5660
        ctx.fp8_meta = fp8_meta
5661
5662
5663
5664
5665
5666
5667
5668
5669
        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
5670
        ctx.window_size = window_size
5671
        ctx.fused_attention_backend = (
5672
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
5673
        )
5674
        ctx.use_FAv2_bwd = use_FAv2_bwd
5675
        ctx.deterministic = deterministic
5676

5677
        return out_ret
5678
5679
5680

    @staticmethod
    def backward(ctx, d_out):
5681
        if ctx.fp8_meta["recipe"].fp8_mha:
5682
5683
5684
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
5685
5686
5687
            d_out_f8tensor = d_out
            d_out = d_out._data

5688
        d_out = d_out.contiguous()
5689
5690
5691
5692
5693
5694
5695
        (
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
5696
5697
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
5698
5699
5700
5701
5702
5703
5704
5705
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
            fwd_scales,
            fwd_scale_invs,
            *aux_ctx_tensors,
        ) = ctx.saved_tensors
5706
5707
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
5708
        if ctx.use_FAv2_bwd:
5709
            softmax_lse, rng_state = aux_ctx_tensors
5710
5711
5712
5713
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
5714
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
5715
            flash_attn_cuda_bwd(
5716
5717
5718
5719
5720
5721
5722
5723
5724
5725
5726
5727
5728
5729
5730
5731
5732
5733
5734
                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,
5735
            )
5736
5737
5738
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
5739
        else:
5740
5741
5742
5743
            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(
5744
5745
                        ctx.fp8_meta["recipe"], fprop_tensor=False
                    )
5746
5747
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
5748
                        ctx.fp8_meta["scaling_bwd"].scale_inv[META_DO] = d_out_f8tensor._scale_inv
5749
5750
5751
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
5752
5753
5754
5755
                            ctx.fp8_meta["scaling_bwd"],
                            META_DO,
                            fp8_dtype_backward,
                        ).view(d_out.shape)
5756
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
5757
5758
5759
5760
5761
5762
5763
5764
5765
5766
5767
5768
                        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,
5769
                        ctx.fused_attention_backend,
5770
5771
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
5772
5773
5774
5775
5776
5777
5778
5779
5780
5781
5782
5783
5784
5785
5786
5787
                        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,
5788
5789
                        ctx.window_size,
                        ctx.deterministic,
5790
                    )
5791

5792
                    if ctx.fp8_meta["recipe"].fp8_mha:
5793
5794
                        dq = Float8Tensor(
                            data=dq_fp8,
5795
5796
5797
5798
5799
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5800
5801
5802
                        )
                        dk = Float8Tensor(
                            data=dk_fp8,
5803
5804
5805
5806
5807
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5808
5809
5810
                        )
                        dv = Float8Tensor(
                            data=dv_fp8,
5811
5812
5813
5814
5815
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
5816
                        )
5817
                    else:
5818
                        qkv_group = len(ctx.qkv_layout.split("_"))
5819
                        if qkv_group == 1:
5820
5821
5822
5823
5824
5825
5826
5827
5828
5829
5830
5831
5832
                            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])
5833
5834
5835
5836
                            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]),
5837
5838
5839
5840
5841
5842
5843
5844
5845
5846
5847
5848
5849
5850
5851
5852
5853
5854
                                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])
5855
5856
5857
5858
                            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]),
5859
5860
5861
5862
5863
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dq_fp8.shape)
5864
5865
                            dk = cast_from_fp8(
                                dk_fp8.view(-1, dk_fp8.shape[-2] * dk_fp8.shape[-1]),
5866
5867
5868
5869
5870
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dk_fp8.shape)
5871
5872
                            dv = cast_from_fp8(
                                dv_fp8.view(-1, dv_fp8.shape[-2] * dv_fp8.shape[-1]),
5873
5874
5875
5876
5877
                                ctx.fp8_meta["scaling_bwd"],
                                META_DQKV,
                                fp8_dtype_backward,
                                ctx.qkv_dtype,
                            ).view(dv_fp8.shape)
5878
5879
5880
5881
                else:
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dk, dv, *rest = fused_attn_bwd(
5882
5883
5884
5885
5886
5887
5888
5889
5890
5891
5892
5893
                        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,
5894
                        ctx.fused_attention_backend,
5895
5896
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
5897
5898
5899
5900
5901
5902
5903
5904
5905
5906
5907
5908
5909
5910
5911
5912
                        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,
5913
5914
                        ctx.window_size,
                        ctx.deterministic,
5915
                    )
5916

5917
5918
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
5919
5920
5921
5922
5923
5924
5925
5926
5927
5928
5929
5930
5931
5932
5933
5934
5935
5936
5937
5938
5939
5940
5941
5942
5943
5944
            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,
5945
5946
                None,
                None,
5947
            )
5948
        # else, return (dqkv, dbias)
5949
5950
5951
5952
5953
5954
5955
5956
5957
5958
5959
5960
5961
5962
5963
5964
5965
5966
5967
5968
5969
5970
5971
5972
5973
5974
        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,
5975
5976
            None,
            None,
5977
        )
5978

5979

5980
class FusedAttention(torch.nn.Module):
5981
5982
5983
5984
5985
5986
5987
5988
5989
    """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:

5990
5991
5992
5993
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
5994
    | attn_type     | self/cross              | self/cross                     |
5995
    | qkv_layout    |                         |                                |
5996
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
5997
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
5998
5999
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
6000
6001
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
6002
    | dropout       | yes                     | yes                            |
6003
6004
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
6005
    | output dtype  | fp16/bf16               | fp16/bf16                      |
6006
6007
6008
6009
    """

    def __init__(
        self,
6010
        softmax_scale: float,
6011
6012
6013
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
6014
6015
        layer_number: Optional[int] = None,
        deterministic: bool = False,
6016
6017
6018
    ) -> None:
        super().__init__()

6019
        self.softmax_scale = softmax_scale
6020
6021
6022
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
6023
6024
6025
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
6026
        self.layer_number = 1 if layer_number is None else layer_number
6027
        self.deterministic = deterministic
6028

6029
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
6030
6031
            """
            Temporarily remove fused_attention._extra_state as a missing key
6032
6033
6034
6035
            or an unexpected key when loading TransformerEngine checkpoints.
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
            phased out in TransformerEngine 2.0.
6036
6037
            """
            for key in incompatible_keys.missing_keys:
6038
                if "fused_attention._extra_state" in key:
6039
                    incompatible_keys.missing_keys.remove(key)
6040
6041
6042
6043
6044
6045
6046
            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."
                    )
6047

6048
6049
        self.register_load_state_dict_post_hook(remove_extra_states_check)

6050
    @no_torch_dynamo()
6051
6052
6053
6054
6055
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
6056
6057
6058
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
6059
6060
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
6061
6062
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
6063
        attn_mask_type: str = "causal",
6064
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
6065
        window_size: Optional[Tuple[int, int]] = None,
6066
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
6067
6068
6069
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
6070
6071
6072
        cp_group: Optional[dist_group_type] = None,
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
6073
        cp_comm_type: str = "p2p",
6074
6075
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
6076
6077
    ) -> torch.Tensor:
        """fused attention fprop"""
6078
6079
6080
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
6081
6082
6083
6084
        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."
6085
6086
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
6087
        ), "FusedAttention only supports CUDA tensors."
6088
6089
        assert (
            qkv_layout in QKVLayouts
6090
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
6091

6092
6093
        cp_size = 1 if cp_group is None else get_distributed_world_size(cp_group)
        context_parallel = cp_size > 1
6094

6095
        qkv_format = "".join([i for i in qkv_layout.split("_")[0] if i.isalpha()])
6096

6097
6098
        if qkv_format in ["sbhd", "bshd"]:
            if qkv_format == "sbhd":
6099
                batch_size, max_seqlen_q, max_seqlen_kv = (
6100
6101
6102
6103
6104
                    query_layer.shape[1],
                    query_layer.shape[0],
                    key_layer.shape[0],
                )
            if qkv_format == "bshd":
6105
                batch_size, max_seqlen_q, max_seqlen_kv = (
6106
6107
6108
6109
                    query_layer.shape[0],
                    query_layer.shape[1],
                    key_layer.shape[1],
                )
6110
6111
            max_seqlen_q *= cp_size
            max_seqlen_kv *= cp_size
6112
            if "padding" in attn_mask_type:
6113
6114
                assert not context_parallel, "Padding mask not supported with context parallelism!"

6115
6116
6117
6118
6119
                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!"
                        )
6120
                    if self.attention_type == "self":
6121
6122
                        cu_seqlens_q = get_cu_seqlens(attention_mask)
                        cu_seqlens_kv = cu_seqlens_q
6123
                    else:
6124
6125
                        cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
6126
            else:
6127
6128
6129
6130
6131
6132
6133
6134
6135
6136
6137
6138
                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,
                    )
6139
6140
6141
        if qkv_format == "thd":
            assert (
                max_seqlen_q is not None
6142
6143
6144
                and max_seqlen_kv is not None
                and cu_seqlens_q is not None
                and cu_seqlens_kv is not None
6145
            ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
6146
6147
6148
6149

        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
6150
6151
6152

        qkv_dtype = TE_DType[query_layer.dtype]

6153
6154
6155
6156
6157
        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)
        )
6158

6159
6160
6161
6162
6163
6164
6165
6166
6167
6168
6169
        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!"
            )

6170
        if context_parallel:
6171
            assert (
6172
6173
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
6174
6175
6176
6177
6178
6179
6180
            ), 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)
            ]
6181
6182
6183
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
6184
6185
6186
6187
6188
6189
6190
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
6191
6192
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
6193
                    self.attention_dropout if self.training else 0.0,
6194
6195
6196
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
6197
                    cp_comm_type,
6198
                    softmax_scale=self.softmax_scale,
6199
                    qkv_format=qkv_format,
6200
                    attn_mask_type=attn_mask_type,
6201
6202
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
6203
                    deterministic=self.deterministic,
6204
                    use_fused_attention=True,
6205
                    window_size=window_size,
6206
6207
                    fp8=fp8,
                    fp8_meta=fp8_meta,
6208
6209
                )
        else:
6210
6211
6212
6213
6214
6215
6216
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
6217
6218
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
6219
6220
6221
6222
6223
6224
6225
6226
6227
6228
6229
                    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,
6230
                    window_size,
6231
6232
6233
6234
6235
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
6236
                    self.deterministic,
6237
                )
6238

6239
6240
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
6241
6242


6243
class DotProductAttention(TransformerEngineBaseModule):
6244
6245
6246
6247
6248
6249
    """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::

6250
        Argument :attr:`attention_mask` in the `forward` call is only used when
6251
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
6252
6253
6254

    .. warning::

6255
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
6256
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
6257
6258
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
6259
6260
6261
6262
6263

    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
6264
6265
6266
    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.
6267
6268
6269
6270
6271
6272
6273
6274
    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`.
6275
6276
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
6277
    attn_mask_type: str, default = `causal`
6278
                   type of attention mask passed into softmax operation, options are "`no_mask`",
6279
6280
6281
6282
6283
6284
6285
6286
6287
6288
6289
6290
6291
6292
6293
6294
6295
6296
6297
6298
6299
6300
6301
6302
                   "`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
                   "`padding_causal`" and "`padding_causal_bottom_right`", TransformerEngine
                   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].
6303
6304
6305
6306
    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
6307
6308
6309
                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
6310
                be overridden by :attr:`window_size` in `forward` as well.
6311
6312
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
6313
6314
6315
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
6316
6317
6318
    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,
6319
               `h` the number of heads, `d` head size, and `t` the total number of tokens
6320
6321
6322
6323
6324
               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.
6325
               For that, please use `get_qkv_layout` to gain the layout information.
6326
6327
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
6328
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
6329
6330
6331
6332
6333
6334
6335
6336
6337

    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.
6338
6339
6340
6341
6342
6343
6344
6345
6346
    cp_group : ProcessGroup, default = `None`
              context parallel process group.
    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.
6347
6348
6349
    cp_comm_type : str
                  inter-gpu communication type for context parallelism.
                  Can be "p2p" or "all_gather".
6350
6351
6352
6353
6354
    """

    def __init__(
        self,
        num_attention_heads: int,
6355
        kv_channels: Union[int, Tuple[int, int]],
6356
        num_gqa_groups: Optional[int] = None,
6357
        attention_dropout: float = 0.0,
6358
        qkv_format: str = "sbhd",
6359
        attn_mask_type: str = "causal",
6360
        window_size: Optional[Tuple[int, int]] = None,
6361
6362
6363
6364
6365
        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,
6366
        attention_type: str = "self",
6367
        cp_group: Optional[dist_group_type] = None,
6368
        cp_global_ranks: List[int] = None,
6369
        cp_stream: torch.cuda.Stream = None,
6370
        cp_comm_type: str = "p2p",
6371
        softmax_scale: Optional[float] = None,
6372
6373
6374
    ) -> None:
        super().__init__()

6375
        self.logger = logging.getLogger("DotProductAttention")
6376
6377
6378
        self.logger.setLevel(_log_level)
        if not self.logger.hasHandlers():
            self.logger.addHandler(_stream_handler)
6379
        self.qkv_format = qkv_format
6380
        attn_mask_type = attn_mask_type.replace(",", "_")
6381
6382
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
6383
        self.attn_mask_type = attn_mask_type
6384
        self.window_size = check_set_window_size(attn_mask_type, window_size)
6385
6386
6387
6388
6389
6390
6391
        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)
6392
        self.get_rng_state_tracker = get_rng_state_tracker
6393
        self.num_attention_heads = num_attention_heads
6394
        self.layer_number = 1 if layer_number is None else layer_number
6395
6396
6397
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
6398
        self.cp_comm_type = cp_comm_type
6399

6400
6401
6402
6403
6404
6405
        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]
        )
6406

6407
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
6408
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
6409

6410
6411
6412
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
6413

6414
        self.rng_states_tracker = None
6415
6416
6417
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
6418
6419
6420
            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
6421

6422
        if softmax_scale is None:
6423
6424
6425
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
6426

6427
6428
6429
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
6430
        )
6431
6432
6433
6434
6435
6436
6437
6438
6439
6440
6441
6442
6443
6444
6445
6446
6447
6448
6449
        # 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"
6450

6451
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
6452
6453
6454
6455

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

6456
6457
6458
6459
6460
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

6461
6462
6463
6464
6465
6466
6467
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
6468

6469
        # Instantiating three types since use of flash-attn and FusedAttention
6470
        # might be ruled out due to forward inputs.
6471
6472
6473
6474
6475
6476
6477
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
6478

6479
        self.unfused_attention = UnfusedDotProductAttention(
6480
6481
6482
6483
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
6484
        )
6485

6486
6487
6488
6489
6490
6491
6492
6493
6494
6495
6496
6497
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
            when loading older TransformerEngine checkpoints. Will phase out
            this hook in TransformerEngine 2.0.
            """
            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)

6498
6499
6500
6501
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
6502
        **forward_kwargs: Dict[str, Any],
6503
6504
6505
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

6506
6507
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
6508
6509
6510

        hidden_states = checkpoint(
            custom_forward,
6511
6512
6513
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
6514
            *forward_args,
6515
            **forward_kwargs,
6516
6517
6518
6519
        )

        return hidden_states

6520
6521
6522
6523
6524
    def set_context_parallel_group(
        self,
        cp_group: Union[dist_group_type, None],
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
6525
        cp_comm_type: str = "p2p",
6526
    ) -> None:
6527
6528
6529
6530
6531
6532
6533
6534
6535
6536
6537
6538
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
        cp_group : ProcessGroup
                  context parallel process group.
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
6539
6540
6541
        cp_comm_type : str
                      inter-gpu communication type for context parallelism.
                      Can be "p2p" or "all_gather".
6542
        """
6543
6544
6545
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
6546
        self.cp_comm_type = cp_comm_type
6547

6548
    @no_torch_dynamo(recursive=False)
6549
6550
6551
6552
6553
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
6554
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
6555
6556
6557
        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
6558
6559
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
6560
6561
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
6562
        attn_mask_type: Optional[str] = None,
6563
        window_size: Optional[Tuple[int, int]] = None,
6564
        checkpoint_core_attention: bool = False,
6565
6566
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
6567
        alibi_slopes: Optional[torch.Tensor] = None,
6568
        fast_zero_fill: bool = True,
6569
        inference_params: Optional[InferenceParams] = None,
6570
        is_first_microbatch: Optional[bool] = None,
6571
6572
6573
6574
6575
6576
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

6577
6578
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
6579

6580
6581
        .. note::

6582
6583
6584
6585
6586
6587
6588
6589
6590
6591
6592
6593
6594
6595
6596
6597
6598
6599
            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,
            and FusedAttention backend if applicable, to use. TransformerEngine prioritizes
            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
6600
6601
6602
6603
6604
            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
            optimizations in FusedAttention. When unset, TransformerEngine determines the code path
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
6605

6606
6607
6608
6609
6610
6611
6612
6613
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
6614
6615
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
6616
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
6617
6618
             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]
6619
6620
6621
6622
             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.
6623
6624
6625
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
6626
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
6627
6628
                   with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
6629
6630
6631
6632
6633
6634
6635
6636
6637
6638
6639
6640
                   Cumulative sum of sequence lengths (without offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
        cu_seqlens_q_padded: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (with offset) in a batch for
                   `query_layer`, with shape [batch_size + 1] and dtype torch.int32.
                   When there is no padding between sequences in a batch,
                   `cu_seqlens_q_padded = cu_seqlens_q`.
        cu_seqlens_kv_padded: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths (with offset) in a batch for `key_layer`
                   and `value_layer`, with shape [batch_size + 1] and dtype torch.int32.
                   When there is no padding between sequences in a batch,
                   `cu_seqlens_kv_padded = cu_seqlens_kv`.
6641
6642
6643
6644
6645
6646
        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.
6647
6648
6649
6650
6651
6652
6653
        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.
6654
        window_size: Optional[Tuple[int, int]], default = `None`
6655
                    Sliding window size for local attention.
6656
6657
6658
6659
6660
        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.
6661
        core_attention_bias_type: str, default = `no_bias`
6662
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
6663
        core_attention_bias: Optional[torch.Tensor], default = `None`
6664
6665
                    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.
6666
6667
6668
6669
        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.
6670
        fast_zero_fill: bool, default = `True`
6671
                    Whether to use the fast path to set output tensors to 0 or not.
6672
6673
6674
6675
6676
6677
6678
6679
6680
6681
        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.
6682
6683
6684
6685
6686
6687
6688
6689
6690
6691
6692
6693
6694
        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)
6695
        """
6696
6697
6698
6699
6700
6701
6702
6703
6704
6705
6706
        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
6707
                        self.logger.warning(
6708
6709
6710
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
6711
6712
6713
6714
6715
6716
6717
6718
6719
6720
6721

            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."""
6722

6723
6724
6725
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
6726
6727
6728
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
6729
6730
6731
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
6732
6733
6734
6735
6736
6737
6738
6739
            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}!"
6740

6741
6742
6743
6744
6745
6746
            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"
6747
            assert (
6748
6749
6750
6751
6752
6753
                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!"
6754

6755
6756
6757
6758
            if window_size is None:
                window_size = self.window_size
            window_size = check_set_window_size(attn_mask_type, window_size)

6759
6760
6761
6762
6763
6764
6765
            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."
6766

6767
6768
            if qkv_format is None:
                qkv_format = self.qkv_format
6769

6770
6771
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"
6772

6773
6774
6775
6776
6777
                # 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"

6778
6779
6780
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
6781

6782
6783
6784
6785
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]
6786

6787
6788
6789
                batch_start = inference_params.batch_size_offset
                batch_end = batch_start + key_layer.size(1)
                assert batch_end <= inference_key_memory.size(1)
6790

6791
6792
6793
                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + key_layer.size(0)
                assert sequence_end <= inference_key_memory.size(0)
6794

6795
6796
6797
6798
6799
6800
6801
6802
6803
                # 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, ...]
6804

6805
6806
6807
                if qkv_format == "bshd":
                    key_layer = key_layer.transpose(0, 1)
                    value_layer = value_layer.transpose(0, 1)
6808

6809
6810
                key_layer = key_layer.contiguous()
                value_layer = value_layer.contiguous()
6811
6812

            assert (
6813
6814
6815
6816
6817
6818
6819
6820
6821
6822
                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":
6823
                assert all(
6824
6825
6826
6827
6828
6829
6830
6831
6832
6833
6834
6835
6836
6837
                    len(x.shape) == 3 for x in (query_layer, key_layer, value_layer)
                ), "Queries, keys and values must be 3D tensors when qkv_format = thd!"
                assert (
                    cu_seqlens_q is not None and cu_seqlens_kv is not None
                ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
                assert (
                    cu_seqlens_q.shape == cu_seqlens_kv.shape
                    and len(cu_seqlens_q.shape) == 1
                    and len(cu_seqlens_kv.shape) == 1
                ), "cu_seqlens_q and cu_seqlens_q must both have shape [batch_size + 1]!"
                assert (
                    cu_seqlens_q.dtype == torch.int32 and cu_seqlens_kv.dtype == torch.int32
                ), "cu_seqlens_q and cu_seqlens_q must both be in dtype torch.int32!"
                if max_seqlen_q is None:
6838
6839
6840
6841
                    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]
6842
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
6843
                if max_seqlen_kv is None:
6844
6845
6846
6847
                    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]
6848
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
6849
                batch_size = len(cu_seqlens_q) - 1
6850

6851
6852
6853
            cp_size = 1 if self.cp_group is None else get_distributed_world_size(self.cp_group)
            context_parallel = cp_size > 1

6854
            if qkv_format in ["sbhd", "bshd"]:
6855
                assert all(
6856
6857
6858
6859
                    len(x.shape) == 4 for x in (query_layer, key_layer, value_layer)
                ), f"Queries, keys and values must be 4D tensors when qkv_format = {qkv_format}!"
                if qkv_format == "sbhd":
                    max_seqlen_q, max_seqlen_kv = (query_layer.shape[0], key_layer.shape[0])
6860
                    batch_size = query_layer.shape[1]
6861
6862
                if qkv_format == "bshd":
                    max_seqlen_q, max_seqlen_kv = (query_layer.shape[1], key_layer.shape[1])
6863
                    batch_size = query_layer.shape[0]
6864
6865
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
6866
6867
6868
6869
6870
6871
6872
6873
6874
6875
6876
6877
                if cu_seqlens_q is not None:
                    seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                    assert all(
                        seqlens_q <= max_seqlen_q
                    ), """Sequence lengths indicated by cu_seqlens_q must be no greater than
                        the sequence dimention in 'query_layer'!"""
                if cu_seqlens_kv is not None:
                    seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                    assert all(
                        seqlens_kv <= max_seqlen_kv
                    ), """Sequence lengths indicated by cu_seqlens_kv must be no greater than
                        the sequence dimention in 'key_layer' and 'value_layer'!"""
6878
6879
6880
6881
6882
                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!"
6883
                        if self.attention_type == "self":
6884
6885
6886
6887
6888
6889
6890
6891
6892
6893
6894
6895
6896
6897
6898
6899
                            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,
                        )
6900

6901
6902
6903
6904
6905
            if (
                isinstance(query_layer, Float8Tensor)
                and isinstance(key_layer, Float8Tensor)
                and isinstance(value_layer, Float8Tensor)
            ):
6906
                qkv_layout, query_layer._data, key_layer._data, value_layer._data = get_qkv_layout(
6907
6908
6909
                    query_layer._data, key_layer._data, value_layer._data, qkv_format=qkv_format
                )
            else:
6910
                qkv_layout, query_layer, key_layer, value_layer = get_qkv_layout(
6911
6912
                    query_layer, key_layer, value_layer, qkv_format=qkv_format
                )
6913

6914
6915
6916
6917
6918
6919
6920
6921
            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
6922
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
6923
6924
6925
6926
6927
6928
6929
6930
            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
6931
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
6932
6933
6934
6935
6936
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

6937
6938
            core_attention_bias_shape = None
            if core_attention_bias is not None:
6939
                if (
6940
6941
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
6942
                ):
6943
6944
6945
6946
6947
6948
6949
6950
6951
6952
6953
6954
6955
6956
6957
6958
6959
6960
6961
6962
6963
6964
6965
6966
                    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)
            )
6967

6968
            attention_params = AttentionParams(
6969
6970
6971
6972
6973
6974
6975
6976
                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,
6977
6978
                head_dim_qk=query_layer.shape[-1],
                head_dim_v=value_layer.shape[-1],
6979
6980
6981
6982
6983
6984
6985
6986
6987
6988
6989
                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,
6990
6991
                deterministic=self.deterministic,
                is_training=self.training,
6992
6993
6994
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
            )
6995
            global _attention_backends, _flash_attn_3_plus, _use_flash_attn_3
6996
6997
6998
6999
7000
7001
7002
            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"]:
7003
                _use_flash_attn_3 = _flash_attn_3_plus
7004
7005
7006
7007
7008
7009
7010
7011
                (
                    use_flash_attention,
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
                ) = get_attention_backend(attention_params)
                if use_flash_attention:
7012
7013
7014
7015
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
                        _flash_attn_version if not _use_flash_attn_3 else _flash_attn_v3_version,
                    )
7016
7017
7018
7019
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
7020
                    )
7021
7022
7023
7024
7025
7026
7027
                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"]
7028

7029
7030
7031
7032
7033
7034
7035
7036
7037
7038
7039
7040
7041
7042
7043
7044
7045
7046
7047
7048
7049
7050
            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,
7051
                    cp_comm_type=self.cp_comm_type,
7052
7053
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
7054
7055
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
7056
                )
7057

7058
            if use_fused_attention:
7059
7060
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
7061
7062
7063
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
7064
7065
7066
7067
7068
7069
7070
                    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,
7071
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
7072
                    )
7073
7074
7075
7076
7077
7078
7079
7080
7081
                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,
7082
7083
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
7084
7085
7086
7087
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
7088
                        window_size=window_size,
7089
7090
7091
7092
7093
7094
7095
                        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,
7096
                        cp_comm_type=self.cp_comm_type,
7097
7098
7099
7100
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
                    )
                return self.fused_attention(
7101
7102
7103
7104
7105
7106
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
7107
7108
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
7109
7110
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
7111
7112
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
7113
                    window_size=window_size,
7114
                    fused_attention_backend=fused_attention_backend,
7115
7116
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
7117
7118
7119
7120
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
7121
                    cp_comm_type=self.cp_comm_type,
7122
7123
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
7124
                )
7125

7126
            from .cpu_offload import CPUOffloadEnabled
7127

7128
7129
7130
7131
7132
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
7133

7134
            if use_unfused_attention:
7135
7136
7137
7138
7139
7140
                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
                    )
7141
7142
7143
7144
7145
7146
7147
7148
7149
7150
7151
7152
7153
7154
7155
7156
                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(
7157
7158
7159
                    query_layer,
                    key_layer,
                    value_layer,
7160
7161
7162
7163
7164
7165
7166
7167
7168
                    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,
                )
7169

7170
            raise Exception("No dot product attention support for the provided inputs!")
7171
7172


7173
7174
7175
7176
7177
7178
7179
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

7180
7181
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
7182

7183
7184
7185
7186
7187
7188
7189
7190
7191
7192
7193
7194
7195
7196
7197
7198
7199
7200
7201
7202
7203
7204
7205
7206
7207
    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.
7208
7209
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
7210
                   default = `causal`
7211
7212
7213
7214
7215
                   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.
7216
7217
7218
7219
    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
7220
7221
7222
                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
7223
                be overridden by :attr:`window_size` in `forward` as well.
7224
7225
7226
7227
7228
7229
7230
7231
7232
7233
7234
7235
7236
    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.
7237
7238
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
7239
7240
7241
7242
7243
7244
7245
7246
7247
7248
7249
7250
7251
7252
7253
7254
7255
7256
7257
7258
7259
7260
7261
    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"
          The device on which the parameters of the model will allocated. It is the user's
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
7262
7263
7264
7265
7266
7267
7268
    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.
7269
            For that, please use `get_qkv_layout` to gain the layout information.
7270
7271
7272
7273
7274
7275
7276
7277
7278
7279
7280
7281
7282
7283
7284
7285
7286
7287
7288
7289
7290
7291
7292
7293
7294
7295
7296
7297
7298
7299
7300
7301
7302
7303
7304
7305
7306
7307
7308
7309

    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`.
7310
7311
7312
7313
7314
7315
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
7316
7317
7318
7319
7320
        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,
7321
        layer_number: Optional[int] = None,
7322
        attn_mask_type: str = "causal",
7323
        window_size: Optional[Tuple[int, int]] = None,
7324
7325
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
7326
        num_gqa_groups: Optional[int] = None,
7327
7328
7329
        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
7330
        params_dtype: Optional[torch.dtype] = None,
7331
        return_bias: bool = False,
7332
7333
7334
7335
7336
7337
7338
7339
7340
        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
7341
        ub_overlap_rs_dgrad: bool = False,
7342
7343
        ub_overlap_rs: bool = False,
        ub_overlap_ag: bool = False,
7344
        bias: bool = True,
7345
        normalization: str = "LayerNorm",
7346
        device: Union[torch.device, str] = "cuda",
7347
        qkv_format: str = "sbhd",
7348
7349
    ) -> None:
        super().__init__()
7350

7351
        self.qkv_format = qkv_format
7352
        self.attn_mask_type = attn_mask_type
7353
        self.window_size = check_set_window_size(attn_mask_type, window_size)
7354
        self.layer_number = layer_number
7355
7356
7357
7358
7359
        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
7360
        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
7361
        self.num_attention_heads = num_attention_heads
7362
7363
7364
7365
7366
7367
7368
7369
        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()
7370
7371
7372
7373
7374

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

7375
7376
7377
        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"
7378
7379
7380
7381
7382
7383

        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)
7384
7385
7386
7387
7388
7389
7390
        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!"
7391
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)
7392
7393
7394
7395

        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
7396
7397
7398
7399
7400
7401
7402

        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,
7403
            "params_dtype": self.params_dtype,
7404
            "device": device,
7405
7406
7407
7408
        }

        qkv_parallel_mode = "column" if set_parallel_mode else None

cyanguwa's avatar
cyanguwa committed
7409
        if self.attention_type == "self":
7410
7411
            parameters_split = None
            if not fuse_qkv_params:
7412
7413
7414
7415
7416
7417
7418
                parameters_split = collections.OrderedDict(
                    [
                        ("query", self.hidden_size_q),
                        ("key", self.hidden_size_kv),
                        ("value", self.hidden_size_kv),
                    ]
                )
7419
7420
7421
            if self.input_layernorm:
                self.layernorm_qkv = LayerNormLinear(
                    hidden_size,
7422
                    self.hidden_size_q + 2 * self.hidden_size_kv,
7423
7424
7425
7426
7427
7428
                    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
7429
                    parameters_split=parameters_split,
7430
7431
7432
                    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
7433
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
7434
                    ub_overlap_ag=ub_overlap_ag,
7435
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
7436
                    ub_name="qkv",
7437
7438
7439
7440
7441
                    **common_gemm_kwargs,
                )
            else:
                self.qkv = Linear(
                    hidden_size,
7442
                    self.hidden_size_q + 2 * self.hidden_size_kv,
7443
7444
7445
7446
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
cyanguwa's avatar
cyanguwa committed
7447
                    parameters_split=parameters_split,
7448
7449
                    **common_gemm_kwargs,
                )
cyanguwa's avatar
cyanguwa committed
7450
        elif self.attention_type == "cross":
7451
7452
7453
            if self.input_layernorm:
                self.layernorm_query = LayerNormLinear(
                    hidden_size,
7454
                    self.hidden_size_q,
7455
7456
7457
7458
7459
                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
7460
                    parameters_split=("query",) if not fuse_qkv_params else None,
7461
7462
7463
7464
                    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
7465
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
7466
                    ub_overlap_ag=ub_overlap_ag,
7467
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
7468
                    ub_name="qkv",
7469
7470
7471
7472
7473
                    **common_gemm_kwargs,
                )
            else:
                self.query_layer = Linear(
                    hidden_size,
7474
                    self.hidden_size_q,
7475
7476
7477
7478
7479
7480
7481
7482
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    **common_gemm_kwargs,
                )
            self.key_value = Linear(
                hidden_size,
7483
                2 * self.hidden_size_kv,
7484
7485
7486
7487
                init_method=init_method,
                bias=bias,
                return_bias=False,
                parallel_mode=qkv_parallel_mode,
7488
                parameters_split=("key", "value") if not fuse_qkv_params else None,
7489
7490
7491
7492
7493
7494
                **common_gemm_kwargs,
            )

        # Attention.
        self.core_attention = DotProductAttention(
            num_attention_heads,
7495
            self.hidden_size_per_attention_head,
7496
7497
            num_gqa_groups=self.num_gqa_groups,
            attention_dropout=attention_dropout,
7498
            qkv_format=self.qkv_format,
7499
7500
7501
7502
            tp_size=tp_size,
            get_rng_state_tracker=get_rng_state_tracker,
            sequence_parallel=sequence_parallel,
            tp_group=tp_group,
7503
            layer_number=self.layer_number,
7504
            attention_type=self.attention_type,
7505
7506
7507
7508
        )

        # Linear
        self.proj = Linear(
7509
            self.hidden_size_q,
7510
7511
7512
            hidden_size,
            init_method=output_layer_init_method,
            bias=bias,
7513
            return_bias=return_bias,
7514
            parallel_mode="row" if set_parallel_mode else None,
7515
7516
            ub_overlap_rs=ub_overlap_rs,
            ub_overlap_ag=ub_overlap_ag,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
7517
            ub_name="proj",
7518
7519
7520
7521
            **common_gemm_kwargs,
        )

    def _allocate_memory(
7522
        self, inference_max_sequence_len: int, batch_size: int, dtype: torch.dtype
7523
7524
7525
7526
    ) -> torch.Tensor:
        return torch.empty(
            inference_max_sequence_len,
            batch_size,
7527
            self.num_gqa_groups_per_partition,
7528
            self.hidden_size_per_attention_head,
7529
            dtype=dtype,
7530
7531
7532
7533
            device=torch.cuda.current_device(),
        )

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
7534
7535
7536
7537
7538
7539
7540
7541
7542
        """
        Set the tensor parallel group for the given
        module before executing the forward pass.

        Parameters
        ----------
        tp_group : ProcessGroup, default = `None`
                  tensor parallel process group.
        """
7543
7544
        self.tp_group = tp_group

7545
    def set_context_parallel_group(
7546
7547
        self,
        cp_group: Union[dist_group_type, None],
7548
        cp_global_ranks: List[int],
7549
        cp_stream: torch.cuda.Stream,
7550
        cp_comm_type: str = "p2p",
7551
    ) -> None:
7552
7553
7554
7555
7556
7557
7558
7559
7560
7561
7562
7563
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
        cp_group : ProcessGroup
                  context parallel process group.
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
7564
7565
7566
        cp_comm_type : str
                      inter-gpu communication type for context parallelism.
                      Can be "p2p" or "all_gather".
7567
        """
7568
7569
7570
7571
7572
        # 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"):
7573
                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream, cp_comm_type)
7574

7575
7576
7577
    def forward(
        self,
        hidden_states: torch.Tensor,
7578
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7579
        encoder_output: Optional[torch.Tensor] = None,
7580
        attn_mask_type: Optional[str] = None,
7581
        window_size: Optional[Tuple[int, int]] = None,
7582
7583
        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
7584
        inference_params: Optional[InferenceParams] = None,
7585
        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
7586
7587
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
7588
        alibi_slopes: Optional[torch.Tensor] = None,
7589
7590
7591
7592
        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,
7593
        fast_zero_fill: bool = True,
7594
    ) -> Tuple[Union[torch.Tensor, None], ...]:
7595
7596
7597
7598
7599
        """
        Forward propagation for MultiheadAttention layer.

        .. note::

7600
7601
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes `"padding"` or `"arbitrary"`.
7602
7603
7604
7605
7606

        Parameters
        ----------
        hidden_states : torch.Tensor
             Input tensor.
7607
7608
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
7609
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
7610
7611
             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]
7612
7613
7614
7615
7616
7617
             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'},
7618
                       default = `None`
7619
7620
7621
7622
                       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.
7623
7624
        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
7625
7626
7627
7628
7629
7630
7631
7632
7633
7634
7635
7636
7637
7638
7639
7640
7641
7642
7643
7644
7645
7646
7647
7648
7649
        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`
7650
                    Bias type, {`no_bias`, `pre_scale_bias`, 'post_scale_bias`, `alibi`}
7651
        core_attention_bias: Optional[torch.Tensor], default = `None`
7652
7653
                    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.
7654
7655
7656
7657
        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.
7658
7659
7660
7661
7662
7663
7664
7665
7666
7667
7668
7669
        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.
7670
7671
7672
        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
        """
7673
7674
        # hidden_states: [sq, b, h]

7675
        if attn_mask_type is None:
7676
            attn_mask_type = self.attn_mask_type
7677
7678
        if window_size is None:
            window_size = self.window_size
7679
        window_size = check_set_window_size(attn_mask_type, window_size)
7680

7681
        if "padding" in attn_mask_type and attention_mask is not None:
7682
            for i, _ in enumerate(attention_mask):
7683
7684
7685
                assert (
                    attention_mask[i].dtype == torch.bool
                ), "Attention mask must be in boolean type!"
7686

7687
7688
7689
        assert (
            core_attention_bias_type in AttnBiasTypes
        ), f"core_attention_bias_type {core_attention_bias_type} is not supported!"
7690

7691
        # =================================================
7692
        # Pre-allocate memory for key-values for inference
7693
7694
7695
        # =================================================

        if inference_params and self.layer_number is not None:
7696
7697
7698
            assert (
                self.qkv_format != "thd"
            ), "qkv_format == thd is not supported for an inference with KV-cache!"
7699
            if self.layer_number not in inference_params.key_value_memory_dict:
7700
                inf_max_seq_len = inference_params.max_sequence_length
7701
7702
                inf_max_batch_size = inference_params.max_batch_size
                inference_key_memory = self._allocate_memory(
7703
                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
7704
7705
                )
                inference_value_memory = self._allocate_memory(
7706
                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
7707
7708
7709
7710
7711
7712
7713
7714
7715
7716
7717
                )
                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]

7718
        # ======================
7719
        # Query, Key, and Value
7720
        # ======================
7721

cyanguwa's avatar
cyanguwa committed
7722
7723
        if self.attention_type == "self":
            # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn]
7724
7725
7726
7727
7728
7729
7730
7731
7732
7733
7734
7735
7736
            if self.input_layernorm:
                layernorm_qkv_outputs = self.layernorm_qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
                )
                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,
7737
                    is_first_module_in_mha=True,  # specific to FP8 MHA
7738
7739
                )

7740
7741
7742
            num_queries_per_key_value = (
                self.num_attention_heads_per_partition // self.num_gqa_groups_per_partition
            )
7743
            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
7744
                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, ng, (np/ng + 2), hn]
7745
                new_tensor_shape = mixed_x_layer.size()[:-1] + (
cyanguwa's avatar
cyanguwa committed
7746
7747
                    self.num_gqa_groups_per_partition,
                    (num_queries_per_key_value + 2),
7748
7749
7750
7751
                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2
cyanguwa's avatar
cyanguwa committed
7752
7753
7754
7755
7756
            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,
7757
                    self.hidden_size_per_attention_head,
cyanguwa's avatar
cyanguwa committed
7758
7759
7760
                )
                # split along third last dimension
                split_dim = -3
7761
7762
7763

            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

cyanguwa's avatar
cyanguwa committed
7764
7765
7766
7767
7768
7769
7770
7771
7772
            # 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)
7773
                )
7774
            else:
cyanguwa's avatar
cyanguwa committed
7775
                query_layer, key_layer, value_layer = torch.split(
7776
7777
7778
7779
                    mixed_x_layer,
                    (num_queries_per_key_value, 1, 1),
                    dim=split_dim,
                )
cyanguwa's avatar
cyanguwa committed
7780

7781
7782
7783
7784
7785
7786
7787
7788
7789
7790
7791
7792
            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
7793
7794
        elif self.attention_type == "cross":
            # Attention heads [sk, b, h] --> [sk, b, (ng * 2 * hn)]
7795
            mixed_kv_layer = self.key_value(
cyanguwa's avatar
cyanguwa committed
7796
                encoder_output,
7797
                is_first_microbatch=is_first_microbatch,
7798
                is_first_module_in_mha=True,  # specific to FP8 MHA
7799
7800
7801
            )

            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
7802
                # [sq, b, (ng * 2 * hn)] --> [sq, b, ng, 2 * hn]
7803
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
7804
                    self.num_gqa_groups_per_partition,
7805
7806
7807
7808
7809
                    2 * self.hidden_size_per_attention_head,
                )
                # split along last dimension
                split_dim = -1
            else:
cyanguwa's avatar
cyanguwa committed
7810
                # [sq, b, (ng * 2 * hn)] --> [sq, b, 2 * ng, hn]
7811
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
7812
                    2 * self.num_gqa_groups_per_partition,
7813
7814
7815
7816
7817
7818
7819
                    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
7820
7821
7822
            # mixed_kv_layer --> 2 [sk, b, ng, hn]
            if not is_in_onnx_export_mode():
                key_layer, value_layer = _SplitAlongDim.apply(
7823
7824
7825
                    mixed_kv_layer,
                    split_dim,
                    mixed_kv_layer.shape[split_dim] // 2,
cyanguwa's avatar
cyanguwa committed
7826
                )
7827
            else:
cyanguwa's avatar
cyanguwa committed
7828
                key_layer, value_layer = torch.split(
7829
7830
7831
                    mixed_kv_layer,
                    mixed_kv_layer.shape[split_dim] // 2,
                    dim=split_dim,
cyanguwa's avatar
cyanguwa committed
7832
                )
7833
7834
7835
7836
7837
7838
7839
7840
7841
            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)
            )
7842
7843
7844
7845
7846
7847
7848
7849
7850
7851
7852
7853
7854
7855
7856

            # 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,
                )
                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,
7857
                    is_first_module_in_mha=True,  # specific to FP8 MHA
7858
7859
7860
7861
7862
7863
7864
7865
7866
                )

            # [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)

7867
7868
7869
        # ======================================================
        # Apply relative positional encoding (rotary embedding)
        # ======================================================
7870

7871
        if rotary_pos_emb is not None:
7872
7873
7874
            assert not isinstance(query_layer, Float8Tensor) and not isinstance(
                key_layer, Float8Tensor
            ), "RoPE is not supported for Float8Tensors!"
7875
            # duplicate the pos_emb for self attention
7876
            if not isinstance(rotary_pos_emb, tuple):
7877
                rotary_pos_emb = (rotary_pos_emb,) * 2
7878
7879

            q_pos_emb, k_pos_emb = rotary_pos_emb
7880
7881
7882
7883
7884
7885
7886
7887
7888
7889
7890
7891
7892
7893

            # 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)

                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, ...]

7894
7895
            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)
7896

7897
7898
7899
7900
        # ===========================
        # Core attention computation
        # ===========================

7901
7902
7903
7904
        context_layer = self.core_attention(
            query_layer,
            key_layer,
            value_layer,
7905
            qkv_format=self.qkv_format,
7906
7907
7908
7909
            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_kv=cu_seqlens_kv,
            max_seqlen_q=max_seqlen_q,
            max_seqlen_kv=max_seqlen_kv,
7910
7911
            attention_mask=attention_mask,
            attn_mask_type=attn_mask_type,
7912
            window_size=window_size,
7913
7914
7915
            checkpoint_core_attention=checkpoint_core_attention,
            core_attention_bias_type=core_attention_bias_type,
            core_attention_bias=core_attention_bias,
7916
            alibi_slopes=alibi_slopes,
7917
            fast_zero_fill=fast_zero_fill,
7918
            inference_params=inference_params,
7919
7920
        )

7921
        # ===================
7922
        # Output. [sq, b, h]
7923
        # ===================
7924

7925
        projection_output = self.proj(
7926
7927
            context_layer,
            is_first_microbatch=is_first_microbatch,
7928
7929
        )

7930
7931
7932
7933
7934
7935
7936
7937
        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,)
7938
        if self.input_layernorm and self.return_layernorm_output:
7939
7940
            outputs += (layernorm_output,)
        return outputs if len(outputs) > 1 else outputs[0]